Diagnostics

15 pages, 851 KB

Open AccessArticle

A Composite Risk Score Based on VI-RADS, Tumor Contact Length, and CYFRA 21-1 for Prognostic Stratification in Bladder Cancer

by Shunsuke Ikuma, Jun Akatsuka, Godai Kaneko, Hayato Takeda, Yuki Endo, Go Kimura and Yukihiro Kondo

Diagnostics 2025, 15(23), 2968; https://doi.org/10.3390/diagnostics15232968 (registering DOI) - 22 Nov 2025

Abstract

Background/Objectives: The Vesical Imaging-Reporting and Data System (VI-RADS) provides high diagnostic accuracy for muscle-invasive bladder cancer; however, its prognostic value remains limited. We propose serum cytokeratin 19 fragment (CYFRA 21-1) and tumor contact length (TCL) as complementary prognostic factors. We aimed to [...] Read more.

Background/Objectives: The Vesical Imaging-Reporting and Data System (VI-RADS) provides high diagnostic accuracy for muscle-invasive bladder cancer; however, its prognostic value remains limited. We propose serum cytokeratin 19 fragment (CYFRA 21-1) and tumor contact length (TCL) as complementary prognostic factors. We aimed to construct a composite risk score integrating VI-RADS, CYFRA 21-1, and TCL for prognostic stratification. Methods: We retrospectively analyzed data from 101 patients with bladder cancer (BC) who underwent transurethral resection of bladder tumor (TURBT), magnetic resonance imaging, and postoperative serum CYFRA 21-1 measurement. For each factor, cut-off values were determined using receiver operating characteristic (ROC) analysis; meeting each threshold contributed one point (score range, 0–3). Overall survival (OS) was assessed using Kaplan–Meier and Cox regression analyses. Results: ROC analysis identified cut-offs of VI-RADS ≥ 3 (area under the curve [AUC] 0.779), TCL ≥ 40 mm (AUC 0.817), and CYFRA 21-1 ≥ 2.1 ng/mL (AUC 0.875). Based on these, patients were stratified into low- (0–1, n = 81), intermediate- (2, n = 12), and high-risk (3, n = 8) groups with 3-year OS rates of 95.1%, 75.0%, and 25.0%, respectively (p < 0.001). In univariate Cox regression, all factors significantly predicted poor OS: VI-RADS ≥ 3 (hazard ratio [HR], 6.51; p = 0.015), TCL ≥ 40 mm (HR, 8.36; p < 0.001), and CYFRA 21-1 ≥ 2.1 ng/mL (HR, 14.02; p < 0.001). In multivariate analysis, only CYFRA 21-1 remained independently significant (HR, 11.80; p < 0.001). Conclusions: A composite risk score combining VI-RADS, TCL, and CYFRA 21-1 effectively stratified patients with BC into distinct groups using minimally invasive, peri-TURBT assessments. Prospective multicenter validation is warranted. Full article

(This article belongs to the Section Clinical Diagnosis and Prognosis)

16 pages, 2159 KB

Open AccessArticle

Multimodal Deep Learning-Based Classification of Breast Non-Mass Lesions Using Gray Scale and Color Doppler Ultrasound

by Tianjiao Wang, Qingli Zhu, Tianxiang Yu, Denis Leonov, Xinran Shi, Zhuhuang Zhou, Ke Lv, Mengsu Xiao and Jianchu Li

Diagnostics 2025, 15(23), 2967; https://doi.org/10.3390/diagnostics15232967 (registering DOI) - 22 Nov 2025

Abstract

Objectives: To propose a multimodal deep learning method for the classification of benign and malignant breast non-mass lesions (NMLs) using grayscale and color Doppler ultrasound and to compare the performance of multi-modality and single-modality breast ultrasound (BUS) models. Methods: This retrospective study collected [...] Read more.

Objectives: To propose a multimodal deep learning method for the classification of benign and malignant breast non-mass lesions (NMLs) using grayscale and color Doppler ultrasound and to compare the performance of multi-modality and single-modality breast ultrasound (BUS) models. Methods: This retrospective study collected 248 pathologically confirmed NMLs from 241 female patients comprising grayscale and color Doppler BUS images from March 2018 to November 2024. Three types of convolutional neural networks (CNNs), including ResNet50, ResNet18, and VGG16, were evaluated as single-modality (grayscale or color Doppler) models via five-fold cross-validations. The optimal model for each single-modality approach was chosen as the backbone network for multimodal deep learning. Features extracted from grayscale and color Doppler BUS images were then concatenated to predict the probabilities of benignity and malignancy. The diagnostic efficacy of the multi-modality BUS models was comparatively evaluated against single-modality counterparts. Results: The single-modality VGG16 models outperformed the other two CNN types for both grayscale and color Doppler BUS using five-fold cross-validations. Additionally, single-modality grayscale models outperformed single-modality color Doppler models. With a mean accuracy of 91.54%, sensitivity of 94.15%, specificity of 87.30%, F1 score of 0.93, and area under the receiver operating characteristic curve (AUC) of 0.96, the multimodal VGG16 models performed better than single-modality counterparts. Conclusions: VGG 16-based multimodal ultrasound deep learning showed excellent diagnostic efficacy in distinguishing between benign and malignant NMLs, indicating therapeutic potential to help radiologists assess NMLs. Full article

(This article belongs to the Section Machine Learning and Artificial Intelligence in Diagnostics)

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13 pages, 900 KB

Open AccessReview

Osteoporosis and Fracture Risk in Ovarian Cancer: Beyond the Oncologic Burden

by Mariagrazia Irene Mineo, Giorgio Arnaldi and Valentina Guarnotta

Diagnostics 2025, 15(23), 2966; https://doi.org/10.3390/diagnostics15232966 (registering DOI) - 22 Nov 2025

Abstract

Background/Objectives: Osteoporosis is a prevalent condition characterized by reduced bone mass and microarchitectural deterioration, resulting in increased fracture risk. Ovarian cancer represents a paradigmatic model of cancer-related bone loss, owing to the combined effects of abrupt surgical menopause, chemotherapy, and tumor-driven pro-resorptive [...] Read more.

Background/Objectives: Osteoporosis is a prevalent condition characterized by reduced bone mass and microarchitectural deterioration, resulting in increased fracture risk. Ovarian cancer represents a paradigmatic model of cancer-related bone loss, owing to the combined effects of abrupt surgical menopause, chemotherapy, and tumor-driven pro-resorptive mechanisms. Methods: We conducted a narrative review of the literature on skeletal health in ovarian cancer. We synthesized current evidence on the pathogenesis, diagnostic strategies, and management of bone loss in women with ovarian cancer, with the aim of providing a disease-specific framework for clinical practice. Results: Available evidence highlights a multifactorial “triple hit” to bone health in ovarian cancer: accelerated estrogen deficiency following bilateral salpingo-oophorectomy, direct tumor-derived stimulation of osteoclast activity, and chemotherapy-induced skeletal toxicity. Despite the high incidence of bone loss and fractures, systematic bone health assessment is rarely integrated into oncological care. Dual-energy X-ray absorptiometry (DXA) remains the cornerstone diagnostic tool, complemented by vertebral morphometry and fracture risk algorithms. Antiresorptive therapies, particularly bisphosphonates and denosumab, together with calcium, vitamin D, exercise, and fall-prevention strategies, have demonstrated efficacy in reducing fracture risk, although disease-specific guidelines are still lacking. Conclusions: Fracture prevention in ovarian cancer survivors is often overlooked despite its significant impact on morbidity and quality of life. Integrating bone health assessment and early antiresorptive therapy into care pathways is warranted, and future studies should develop tailored guidelines to make bone protection a key element of survivorship care. Full article

(This article belongs to the Special Issue Diagnosis and Prognosis of Gynecological and Obstetric Diseases)

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8 pages, 495 KB

Open AccessArticle

Can the Control of Chronic Spontaneous Urticaria Symptoms Depend on the Stress-Coping Styles?

by Marzena Pluta-Kubicz, Edyta Jura-Szołtys, Radosław Gawlik, Magdalena Feusette, Robert Okuniewicz and Zenon Brzoza

Diagnostics 2025, 15(23), 2965; https://doi.org/10.3390/diagnostics15232965 (registering DOI) - 22 Nov 2025

Abstract

Background: The symptoms of chronic spontaneous urticaria can be exacerbated or even induced by psychological stress. Assessing the severity of symptoms using the recommended Urticaria Control Test is an important diagnostic step before deciding on the type of pharmacological treatment to be used. [...] Read more.

Background: The symptoms of chronic spontaneous urticaria can be exacerbated or even induced by psychological stress. Assessing the severity of symptoms using the recommended Urticaria Control Test is an important diagnostic step before deciding on the type of pharmacological treatment to be used. Due to the possibility of urticaria symptoms affecting patient’s emotional condition, the authors attempted to analyze if the way of coping with stress has an impact on urticaria symptom control as assessed with this questionnaire. Methods: The study included 61 (37 female; 60,6%;) patients with symptoms of chronic spontaneous urticaria without other coexisting diseases. All patients were treated with antihistamines. In the analyzed group of patients, the Urticaria Control Test and the Polish version of Endler and Parker’s Coping Inventory for Stressful Situations questionnaire were conducted. Results: The average score on the Urticaria Control Test in the analyzed group was 8.5 (±3.9) points. In our group, the most common coping style was a mixed style based on emotions and avoidance—32 (53%) respondents. Next, 11 (18%) patients reported an emotion-based style. A task-oriented style of coping with stress was observed in 8 (13%) respondents. In the study group, we found no statistical significance in the correlation between the UCT results and the patient’s coping style. Conclusions: Emotions play a significant role as a stress-coping style in chronic spontaneous urticaria patients. The lack of relation found between the Urticaria Control Test result and the Coping Inventory for Stressful Situations questionnaire confirms the objective usefulness of the Urticaria Control Test in assessing the control of spontaneous urticaria. Full article

(This article belongs to the Special Issue Novel Advances in Allergy Diagnosis)

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25 pages, 3074 KB

Open AccessArticle

Prediction of Neonatal Length of Stay in High-Risk Pregnancies Using Regression-Based Machine Learning on Computerized Cardiotocography Data

by Bianca Mihaela Danciu, Maria-Elisabeta Șișială, Andreea-Ioana Dumitru, Anca Angela Simionescu and Bogdan Sebacher

Diagnostics 2025, 15(23), 2964; https://doi.org/10.3390/diagnostics15232964 (registering DOI) - 22 Nov 2025

Abstract

Background/Objectives: The management of high-risk pregnancies remains a major clinical challenge, particularly regarding the optimal timing of delivery, which has significant implications for both perinatal outcomes and healthcare costs. In this context, computerized cardiotocography (cCTG) offers an objective, non-invasive and cost-effective method [...] Read more.

Background/Objectives: The management of high-risk pregnancies remains a major clinical challenge, particularly regarding the optimal timing of delivery, which has significant implications for both perinatal outcomes and healthcare costs. In this context, computerized cardiotocography (cCTG) offers an objective, non-invasive and cost-effective method for fetal surveillance, providing quantitative measures of heart rate dynamics that reflect autonomic regulation and oxygenation status. This study aimed to develop and validate regression-based machine learning models capable of predicting the duration of neonatal hospitalization—an objective and quantifiable indicator of neonatal well-being—using cCTG parameters obtained outside of labor, binary clinical variables describing the presence or absence of pregnancy pathologies, and gestational age at monitoring and at delivery. Methods: A total of 694 singleton high-risk pregnancies complicated by gestational diabetes, preexisting diabetes, intrahepatic cholestasis of pregnancy, pregnancy-induced or preexisting hypertension, or fetal growth restriction were enrolled. Twenty clinically relevant features derived from cCTG recordings and perinatal data were used to train and evaluate four regression algorithms: Random Forest, CatBoost, XGBoost, and LightGBM against a linear regression model with Ridge regularization serving as a benchmark. Results: Random Forest achieved the highest generalization performance (test R² = 0.8226; RMSE = 3.41 days; MAE = 2.02 days), outperforming CatBoost (R² = 0.7059), XGBoost (R² = 0.6911), LightGBM (R² = 0.6851) and the linear regression benchmark with Ridge regularization (R² = 0.5699) while showing a consistent train–validation–test profile (0.9428 → 0.8042 → 0.8226). The error magnitude (≈2 days on average) is clinically interpretable for neonatal resource planning, supporting the model’s practical utility. These findings justify selecting Random Forest as the final predictor and its integration into a clinician-facing application for real-time length-of-stay estimation. Conclusions: Machine learning models integrating cCTG features with maternal clinical factors can accurately predict neonatal hospitalization duration in pregnancies complicated by maternal or fetal disease. This approach provides a clinically interpretable and non-invasive decision support tool that may enhance delivery planning, optimize neonatal resource allocation, and improve perinatal care outcomes. Full article

(This article belongs to the Special Issue Artificial Intelligence in Clinical Decision Support—2nd Edition)

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16 pages, 3440 KB

Open AccessArticle

Multimodal-Imaging-Based Interpretable Deep Learning Framework for Distinguishing Brucella from Tuberculosis Spondylitis: A Dual-Center Study

by Mayidili Nijiati, Mei Zhang, Chencui Huang, Xinyue Chou, Lingyan Shen, Haiting Ma, Zhenwei Ren, Maimaitishawutiaji Maimaiti, Yi You, Xiaoguang Zou and Yunling Wang

Diagnostics 2025, 15(23), 2963; https://doi.org/10.3390/diagnostics15232963 (registering DOI) - 22 Nov 2025

Abstract

Objectives: Brucella spondylitis (BS) and tuberculosis spondylitis (TS) are two causes of infection that share overlapping clinical and imaging features, complicating diagnoses. Early differentiation is critical, as treatment regimens differ significantly. This study aims to develop a deep learning framework using multimodal computed [...] Read more.

Objectives: Brucella spondylitis (BS) and tuberculosis spondylitis (TS) are two causes of infection that share overlapping clinical and imaging features, complicating diagnoses. Early differentiation is critical, as treatment regimens differ significantly. This study aims to develop a deep learning framework using multimodal computed tomography (CT) and magnetic resonance imaging (MRI) data to accurately distinguish between these two conditions, improving diagnostic accuracy and patient outcomes. Methods: In this study, imaging data were acquired from two centers using different MRI and CT protocols. Sagittal T1-weighted (T1WI) and T2-weighted imaging (T2WI), fat-suppression sequences (T2WI FSE), and sagittal CT data were collected. Image preprocessing included region of interest (ROI) segmentation, and normalization and augmentation techniques were used. A deep learning model, based on pre-trained GoogleNet architectures, was trained and evaluated against human radiologists using metrics including accuracy, sensitivity, and AUC to assess diagnostic performance. Results: In this study, the GoogleNet deep learning model outperformed other architectures in classifying TS and BS, achieving AUCs of 95.97%, 91.24%, and 81.25% across training, test, and external validation datasets, respectively. In contrast, ResNet, DenseNet, and EfficientNet models showed lower AUC values. GoogleNet also demonstrated high accuracy (90.77% training, 83.04% test) and 90.91% sensitivity and 61.11% specificity in external validation. When compared to three radiologists, GoogleNet outperformed in diagnostic accuracy and speed, achieving an AUC of 88.01% and processing cases in 0.001 min. These findings highlight the potential of AI to enhance diagnostic performance and efficiency. Lastly, the explanation provided by the Grad-Cam model precisely localized major lesions. Conclusions: This multimodal-imaging-based deep learning model could well differentiate TS and BS. Deep learning does not need manual feature extraction, selection, or model development, and has great potential in daily clinical practice. Full article

(This article belongs to the Special Issue AI-Driven Precision Medicine: Innovations in Diagnosis, Prognosis, and Management Response)

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15 pages, 1077 KB

Open AccessArticle

Clinicopathologic Determinants of Lymph Node Count and Prognostic Significance of Metastatic Lymph Node Ratio in Colorectal Cancer

by Fatma Yildirim, Murat Sezak, Osman Bozbiyik, Pinar Gursoy and Basak Doganavsargil

Diagnostics 2025, 15(23), 2962; https://doi.org/10.3390/diagnostics15232962 (registering DOI) - 22 Nov 2025

Abstract

Background/Objectives: Accurate lymph node (LN) evaluation is crucial to predicting outcomes in colorectal cancer (CRC). Higher lymph node counts (LNCs) improve prognosis, whereas increased metastatic involvement worsens survival. This study aimed to identify factors associated with higher LNCs and evaluate the prognostic [...] Read more.

Background/Objectives: Accurate lymph node (LN) evaluation is crucial to predicting outcomes in colorectal cancer (CRC). Higher lymph node counts (LNCs) improve prognosis, whereas increased metastatic involvement worsens survival. This study aimed to identify factors associated with higher LNCs and evaluate the prognostic value of the metastatic lymph node ratio (MLNR). Methods: A retrospective analysis was performed on 989 CRC resections. Patients were stratified into four MLNR categories—MLNR0 (no metastasis), MLNR1 (<0.20), MLNR2 (0.20–0.50), and MLNR3 (>0.50)—and into two LNC groups—lower LNC (<12) and higher LNC (≥12). Results: The median LN count was 14 (range: 5–198). Lower LNCs occurred in 346 cases (35.0%), predominantly in the left colon. Higher LNCs were significantly associated with younger age (p < 0.001), larger tumor size (p < 0.001), higher pN stage (p < 0.001), right-sided location (p = 0.003), Crohn’s-like lymphocytic response (p = 0.006), and the absence of satellite nodules (p = 0.016). There were 86 pT4 and 178 pN2 tumors. Overall survival was 50.6%, with the 1-, 3-, and 5-year rates being 0.891, 0.721, and 0.612, respectively. Survival was higher in patients with higher LNCs (53.5% vs. 45.1%, p < 0.001). Survival rates by MLNR were 61.2% (MLNR0), 47.7% (MLNR1), 34.0% (MLNR2), and 26.4% (MLNR3). Mortality strongly correlated with MLNR (p < 0.001), and life expectancy decreased as MLNR increased (p < 0.01). Conclusions: MLNR provides superior prognostic information compared to pN status, even in patients with suboptimal lymph node retrieval (LNC < 12). As an independent survival predictor, MLNR may be integrated into staging systems and guide therapeutic strategies, highlighting its clinical utility in both standard and “gray zone” CRC cases. Full article

(This article belongs to the Special Issue Hot Topics in Modern and Personalized Pathology)

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12 pages, 1618 KB

Open AccessArticle

Functional and Anatomical Micro-Structural Recovery of Idiopathic Macular Holes Following the Inverted Internal Limiting Membrane Flap Technique: A Long-Term Study

by Kai-Ling Peng, Ya-Hsin Kung and Tsung-Tien Wu

Diagnostics 2025, 15(23), 2961; https://doi.org/10.3390/diagnostics15232961 (registering DOI) - 22 Nov 2025

Abstract

Background: Idiopathic macular holes (MHs) are typically treated with pars plana vitrectomy and internal limiting membrane (ILM) peeling. The inverted ILM flap (ILMF) technique has emerged for MHs, but long-term outcome data remain inadequately established. This study evaluates the long-term functional and [...] Read more.

Background: Idiopathic macular holes (MHs) are typically treated with pars plana vitrectomy and internal limiting membrane (ILM) peeling. The inverted ILM flap (ILMF) technique has emerged for MHs, but long-term outcome data remain inadequately established. This study evaluates the long-term functional and anatomical outcomes of the ILMF in idiopathic MHs. Methods: We evaluated 71 consecutive eyes of patients with idiopathic MHs who underwent vitrectomy with the inverted ILMF. Follow-up duration was more than 12 months. Visual acuity was measured, and macular anatomy was monitored with optic coherence tomography (OCT). Long-term visual and anatomical outcomes were defined a priori and analyzed accordingly. Results: Final vision values showed significant improvement compared to preoperative ones, from 1.02 [Snellen Equivalent (SE), 19/200] ± 0.40 logarithm of the minimum angle of resolution (logMAR) to 0.47 (SE, 68/200) ± 0.39 logMAR (p < 0.001). The primary MH closure rates were 94.37% (67/71), while the secondary closure rate reached 97.18% (69/71). Factors associated with better final vision included smaller hole size, favorable hole stage, better preoperative vision, intact postoperative foveal microstructure and contour. The recovery of the external limiting membrane (ELM), inner and outer segment junction (IS/OS), and good foveal contour had improved to 73.4%, 40.3%, and 49.3% at one year and 80%, 71.4%, and 53.3% at three years postoperatively, respectively. Conclusions: In idiopathic MHs, the ILMF approach provides meaningful, long-term visual and microstructural recovery, especially with a favorable functional outcome and intact postoperative microstructure sustaining up to three years. Full article

(This article belongs to the Special Issue Diagnosis, Treatment and Management of Eye Diseases, Third Edition)

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20 pages, 4171 KB

Open AccessArticle

Characterization of Infarct Size and Remodeling Using CMR and PET in Mice Models of Reperfused and Non-Reperfused Myocardial Infarction

by Jose Gavara, Tamara Molina-Garcia, Mustafa Ezzeddin, Ana Diaz, Nerea Perez-Sole, Maria Ortega, Victor Marcos-Garces, Elena de Dios, Antoni Bayes-Genis, Amparo Ruiz-Sauri, Cesar Rios-Navarro and Vicente Bodi

Diagnostics 2025, 15(23), 2960; https://doi.org/10.3390/diagnostics15232960 (registering DOI) - 22 Nov 2025

Abstract

Background/Objectives: Unlike post-mortem histopathology, cardiovascular magnetic resonance (CMR) and positron emission tomography (PET) enable longitudinal assessment of structural, functional, and metabolic alterations in preclinical myocardial infarction models. This study aims to describe the temporal evolution of infarct size and systolic function by [...] Read more.

Background/Objectives: Unlike post-mortem histopathology, cardiovascular magnetic resonance (CMR) and positron emission tomography (PET) enable longitudinal assessment of structural, functional, and metabolic alterations in preclinical myocardial infarction models. This study aims to describe the temporal evolution of infarct size and systolic function by CMR and glucose consumption via PET, explore their differences in non-reperfused and reperfusion infarction models, and assess their capacity to predict histology-derived infarct size and systolic function at chronic phase CMR. Methods: Two murine models of myocardial infarction were generated using permanent (non-reperfused, n = 8) or transient (reperfused, n = 9) coronary occlusion. CMR and fluorine-18 2-fluoro-2-deoxyglucose PET imaging were performed at baseline and at 1, 7, and 21 days post-infarction to quantify infarct size, systolic function, and myocardial glucose metabolism. Infarct size was also assessed using Masson’s trichrome staining. Results: At 24 h post-infarction, CMR-derived infarction together with significant reduction in systolic function and glucose metabolism were already noted in both models. At 21-day CMR, however, reperfused mice showed lower infarct size and more preserved systolic function compared to their non-reperfused counterparts, while no differences in glucose metabolism were reported. Infarct size and systolic function at 1-day CMR and the number of segments with reduced glucose consumption at 1-day PET independently predicted histology-derived infarct size and long-term systolic function. Conclusions: Combined PET/CMR imaging enables non-invasive, sequential evaluation of infarct size, systolic function, and glucose metabolism in experimental myocardial infarction. This multimodality approach is well suited for assessing the efficacy of emerging therapies in preclinical research. Full article

(This article belongs to the Section Medical Imaging and Theranostics)

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23 pages, 2068 KB

Open AccessArticle

Semi-Quantitative ΔCt Thresholds for Bacteriuria and Pre-Analytic Drivers of PCR-Culture Discordance in Complicated UTI: An Analysis of NCT06996301

by Moustafa Kardjadj, Itoe P. Priestly, Roel Chavez, DeAndre Derrick and Thomas K. Huard

Diagnostics 2025, 15(23), 2959; https://doi.org/10.3390/diagnostics15232959 - 21 Nov 2025

Abstract

Background: Quantitative urine culture (CFU/mL) remains the reference standard for diagnosing urinary tract infections (UTIs) but is limited by delayed turnaround times and sensitivity to pre-analytic factors. Multiplex PCR panels offer rapid detection; however, standardized mappings between molecular signals and viable bacterial [...] Read more.

Background: Quantitative urine culture (CFU/mL) remains the reference standard for diagnosing urinary tract infections (UTIs) but is limited by delayed turnaround times and sensitivity to pre-analytic factors. Multiplex PCR panels offer rapid detection; however, standardized mappings between molecular signals and viable bacterial burdens are insufficiently defined. We used the multicenter NCT06996301 paired dataset to evaluate the analytical validity (AV), clinical validity (CV), and pre-analytic robustness of ΔCt (Ct_target − IC_Ct) as a semi-quantitative indicator of bacterial load. Methods: We analyzed 1027 paired PCR and quantitative urine culture specimens from six sites. The primary molecular predictor was ΔCt (Ct_target − IC_Ct). Species-level Spearman and Pearson correlations, species-specific linear mixed-effects calibration models (log₁₀CFU ~ ΔCt + (1|site)), and ROC analyses were performed for the taxa meeting pre-specified sample thresholds. A pooled multilevel model assessed the collection method and time-to-processing (hours) effects (log₁₀CFU ~ ΔCt × collection_method + ΔCt × time_to_processing_h + (1|site) + (1|run)). AV was assessed via reproducibility, internal control normalization, and site run variance. CV was assessed by ΔCt calibration and discrimination. Clinical utility (CU) was contextualized using outcomes from the parent randomized trial. Results: PCR positivity exceeded culture positivity across all sites (PCR ~82–88% vs. culture ~66–70%); this excess likely reflects a combination of molecular detection of non-viable DNA, detection of fastidious taxa less readily recovered by culture, and pre-analytic effects. For six common uropathogens (n = 90 pairs/species), ΔCt correlated strongly with log₁₀CFU (Spearman ρ = −0.64 to −0.75; Pearson r = −0.75 to −0.83). Species-specific mixed models yielded slopes of −0.746 to −0.922 log₁₀CFU per ΔCt unit (all p < 0.001), indicating that each 1 unit ΔCt change corresponds to a ~5.6–8.4-fold CFU difference. ROC AUCs for ΔCt discrimination were 0.78–0.84 (interpreted as good discrimination, i.e., ΔCt meaningfully improves the clinician’s probability estimate of a high CFU but does not perfectly classify every specimen). The collection method (catheter vs. clean-catch) did not materially modify the ΔCt→CFU relationship, whereas the processing delay was associated with reduced recovered CFU (~0.048 log₁₀CFU lost per hour) and a significant ΔCt × time interaction, consistent with time-dependent viability loss driving the PCR⁺/culture⁻ discordance. Conclusions: ΔCt from the DOC Lab UTM 2.0 panel shows a reproducible, analytically valid semi-quantitative measure of urinary bacterial load. Laboratories can derive assay- and workflow-specific ΔCt cut points for semi-quantitative reporting, but thresholds must be validated prospectively and paired with operational controls for specimen handling. Full article

(This article belongs to the Special Issue Advances in the Laboratory Diagnosis)

18 pages, 2110 KB

Open AccessArticle

Evaluation of the Performance of an Artificial Intelligence-Based Classification Model for Pediatric Maxillofacial Morphology

by Hiroki Sato, Akane Ueda, Camila Tussie, Sophie Kim, Yukinori Kuwajima, Emiko Kikuchi, Shigemi Nagai and Kazuro Satoh

Diagnostics 2025, 15(23), 2958; https://doi.org/10.3390/diagnostics15232958 - 21 Nov 2025

Abstract

Background/Objectives: Accurate assessment of craniofacial morphology is essential for orthodontic diagnosis and treatment planning. The Sassoni classification provides a useful framework for categorizing craniofacial morphology into nine groups but lacks standardized clinical criteria. This study developed an AI model to classify pediatric craniofacial [...] Read more.

Background/Objectives: Accurate assessment of craniofacial morphology is essential for orthodontic diagnosis and treatment planning. The Sassoni classification provides a useful framework for categorizing craniofacial morphology into nine groups but lacks standardized clinical criteria. This study developed an AI model to classify pediatric craniofacial morphology based on the Sassoni classification using lateral cephalometric radiographs and evaluated its agreement with classifications made by orthodontists. Methods: Data from 300 pediatric patients aged 6 to 10 years were analyzed. Nine cephalometric measurements and patient gender were used as input features. Three orthodontists classified morphology based on the Sassoni classification. Random forest (RF), logistic regression (LR), and support vector classification (SVC) models were trained and evaluated using 10-fold cross-validation. Results: The Random Forest (RF) model demonstrated the highest accuracy (RF: 0.907 ± 0.051, LR: 0.837 ± 0.057, SVC: 0.770 ± 0.055). It also outperformed the other two models in terms of F1 score, sensitivity, and positive predictive value, showing the best overall classification performance. The most influential feature was the ANB angle, while gender had minimal impact. Conclusions: The RF-based AI model demonstrated high accuracy in pediatric maxillofacial classification. Performance may be further improved with larger datasets and more balanced case distributions. Full article

(This article belongs to the Special Issue Advancing Clinical Diagnosis with Artificial Intelligence: Applications, Challenges, and Future Directions)

13 pages, 670 KB

Open AccessArticle

Recurrence, Readmission, and Key Mortality Predictors in Patients with Carbapenem-Resistant Enterobacterales Infections

by Bashayer Mohammed Alshehail, Marwan Jabr Alwezzeh, Hussain Humaid Almalki, Amani Alnimr, Haytham Wali, Zainab Al Jamea, Abdullatif S. Al Rashed, Mashael Alhajri, Hawra Abdulwahab Abdulaal, Lujain Ali Alanbari, Yazed S. Alsowaida, Abdullah Alamri, Sharifah Almuthen, Faten Azaiez, Saeed Alzahrani, Nawaf Zakari, Jaber Asiri, Wafa Alanazi, Mohanad Bakkar, Abdulaziz Alfifi, Omar Alzuwayed, Aiman El-Saed and Salma AlBahrani Show full author list Hide full author list

Diagnostics 2025, 15(23), 2957; https://doi.org/10.3390/diagnostics15232957 - 21 Nov 2025

Abstract

Background: Carbapenem-resistant Enterobacterales (CRE) are designated by the World Health Organization as critical-priority pathogens. While global outcomes are well documented, regional data from the Middle East remain limited. Methods: We performed a retrospective cohort study of adults with confirmed CRE infections admitted [...] Read more.

Background: Carbapenem-resistant Enterobacterales (CRE) are designated by the World Health Organization as critical-priority pathogens. While global outcomes are well documented, regional data from the Middle East remain limited. Methods: We performed a retrospective cohort study of adults with confirmed CRE infections admitted to King Fahad Hospital of the University, Saudi Arabia, between 2019 and 2024. Clinical, microbiological, and therapeutic data were analyzed. Primary outcomes were infection recurrence, recurrence-related readmissions, and all-cause mortality at 14, 30, and 90 days. Predictors were assessed using univariate tests and multivariate Cox regression. Results: Among 101 patients (mean age 65 years, 57% female), Klebsiella pneumoniae predominated (94%), with OXA-48 detected in 70%. Most infections were hospital-acquired (78%). Recurrence occurred in 16.8% of cases, with 12.9% requiring readmission. Mortality reached 22.8% at 14 days, 30.7% at 30 days, and 42.6% at 90 days. Diabetes mellitus predicted recurrence (p = 0.024). Independent predictors of 90-day mortality were pneumonia (HR 2.39, 95% CI 1.23–4.64), critical care admission (HR 6.24, 95% CI 2.44–15.97), and hypotension (HR 4.10, 95% CI 1.84–9.15). Elevated Pitt bacteremia and INCREMENT-CPE scores also stratified risk. Conclusions: CRE infections in Saudi Arabia impose a heavy clinical burden, with high recurrence, frequent readmissions, and late mortality. Identifying drivers of recurrence and mortality highlights opportunities for targeted risk stratification. Beyond treatment choices, these findings emphasize the need for proactive surveillance, integrated stewardship, and early recognition of high-risk patients. Region-specific evidence such as this is critical to shaping infection control policies and guiding future multicenter research into novel therapeutic approaches. Full article

(This article belongs to the Special Issue Recent Advances in Epidemiological Diagnostics: Detecting and Controlling Infectious Diseases)

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12 pages, 1132 KB

Open AccessReview

Pectus Excavatum—A Frequent but Often Neglected Entity in Sports Cardiology

by Łukasz Małek, Anna Lemańska and Mateusz Śpiewak

Diagnostics 2025, 15(23), 2956; https://doi.org/10.3390/diagnostics15232956 (registering DOI) - 21 Nov 2025

Abstract

Pectus excavatum (PE) is the most frequent chest wall deformity, representing 65–95% of all cases, with an estimated prevalence of up to 1 in 300 births. Despite its frequency, it remains underrecognized in sports cardiology. PE results from sternal depression and narrowing of [...] Read more.

Pectus excavatum (PE) is the most frequent chest wall deformity, representing 65–95% of all cases, with an estimated prevalence of up to 1 in 300 births. Despite its frequency, it remains underrecognized in sports cardiology. PE results from sternal depression and narrowing of the anterior chest, which may lead to cardiac compression, impaired diastolic filling, and reduced stroke volume during exercise. Consequently, athletes with PE often present with cardiovascular symptoms such as exercise-induced dyspnoea, chest pain, palpitations, presyncope, or reduced physical fitness. Electrocardiographic changes, including right bundle branch block, axis deviation, atrial enlargement, T-wave inversion, QS complexes or Brugada phenocopies, are frequent and may mimic serious cardiovascular conditions, complicating pre-participation screening. Furthermore, PE is associated with potentially high-risk conditions including mitral valve prolapse, ventricular arrhythmias, and connective tissue disorders such as Marfan syndrome, which carry implications for sports eligibility and safety. Assessment of athletes with PE requires multimodal imaging (echocardiography, computed tomography, magnetic resonance), cardiopulmonary exercise testing, and exclusion of concomitant cardiovascular disease. Treatment strategies range from conservative approaches (physiotherapy, vacuum bell therapy) to surgical correction, most commonly with the Nuss procedure, which can improve cardiac function, exercise capacity, and quality of life. Management should involve shared decision making between clinicians, athletes, and families, weighing potential risks against athletic aspirations. Awareness of PE in sports cardiology is crucial, as it not only influences differential diagnosis and screening outcomes but also impacts career decisions and the psychological well-being of athletes. Full article

(This article belongs to the Special Issue Diagnostic Challenges in Sports Cardiology—2nd Edition)

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23 pages, 972 KB

Open AccessArticle

The Triage Role of Transabdominal Ultrasonography (TAUS) in the Diagnostic Management of Pancreatic and Distal Biliary Pathologies: A Comparative Efficacy Analysis with Endoscopic Ultrasonography (EUS)

by Serkan Yaraş, Osman Özdoğan and Orhan Sezgin

Diagnostics 2025, 15(23), 2955; https://doi.org/10.3390/diagnostics15232955 - 21 Nov 2025

Abstract

Background/Objectives: The diagnostic management of obstructive pancreatobiliary pathologies often leads to unnecessary invasive procedures and the overuse of costly imaging due to inherent diagnostic uncertainties. This dilemma highlights the need for a refined triaging strategy. This study aimed to compare the diagnostic [...] Read more.

Background/Objectives: The diagnostic management of obstructive pancreatobiliary pathologies often leads to unnecessary invasive procedures and the overuse of costly imaging due to inherent diagnostic uncertainties. This dilemma highlights the need for a refined triaging strategy. This study aimed to compare the diagnostic competence and triage potential of Transabdominal Ultrasonography (TAUS)—a cost-effective, first-line method—with the efficacy of the invasive method, Endoscopic Ultrasonography (EUS). Our objective was to identify specific TAUS findings that could render EUS redundant or serve as a clinical guide for referral to EUS. Methods: This prospective study included patients evaluated for suspected pancreatobiliary lesions (December 2024–September 2025). Final diagnoses (gold standard) were established using pathology, tumor board decisions, other imaging, or ≥6 months clinical follow-up. TAUS was performed by one operator blinded to clinical data. EUS was immediately performed by a different operator, blinded to TAUS results and all other clinical data. Data were grouped into normal findings, solid masses, cystic lesions, chronic pancreatitis, distal cholangiocarcinoma/ampullary tumors, and choledocholithiasis. Results: A total of 204 patients were included. TAUS sensitivity (76.5%) was significantly lower than EUS (94.6%) (p < 0.001), but both showed high specificity (TAUS: 82.9%; EUS: 88.24%). TAUS performance varied greatly by lesion type: high for solid lesions (81.8%) and chronic pancreatitis (88.9%), but markedly lower for distal common bile duct lesions/ampullary tumors (57.1%; p = 0.006). In univariate analysis, BMI (p < 0.001), lesion size (p = 0.002), MPD dilation (p = 0.001), and localization (p < 0.001) were associated with TAUS success. Lesion size (OR = 1.049, p = 0.029) was the independent predictor in the multivariate analysis. TAUS detected common bile duct dilation in obstructive cases at a high rate (95.9%) but had statistically significantly lower success in reaching a definitive diagnosis (63.3%; p < 0.001). Conclusions: While TAUS lacks the overall sensitivity of EUS, its robust detection performance for solid lesions and chronic pancreatitis suggests that it can reduce the need for further investigation in selected cases. The TAUS detection success, associated with factors like BMI and lesion size, combined with its high rate of common bile duct dilation detection, establishes a reliable triage guideline for referring patients to advanced diagnostic procedures, primarily EUS, to confirm the definitive etiology. Full article

(This article belongs to the Section Medical Imaging and Theranostics)

29 pages, 10408 KB

Open AccessArticle

A Robust Framework for Domain-Generalized Classification of Ovarian Cancer Histology Images

by Awais Ahmed and Xiaoyang Zeng

Diagnostics 2025, 15(23), 2954; https://doi.org/10.3390/diagnostics15232954 - 21 Nov 2025

Abstract

Background: In computational pathology (CP) analysis, computational efficiency and precise classification outcomes are paramount for robust and scalable solutions. Despite recent advancements in deep-learning frameworks for Whole-Slide Images (WSIs), the heterogeneity of WSIs across different domains poses considerable challenges for developing models with [...] Read more.

Background: In computational pathology (CP) analysis, computational efficiency and precise classification outcomes are paramount for robust and scalable solutions. Despite recent advancements in deep-learning frameworks for Whole-Slide Images (WSIs), the heterogeneity of WSIs across different domains poses considerable challenges for developing models with robust generalization capabilities. This study presents the WSI-P2P (Whole-Slide Imaging–Patch to Prediction), aimed at addressing these challenges. Methods: WSI-P2P leverages downscaled patch sampling and Multiple-Instance Learning (MIL) with transfer learning to optimize resource usage while maintaining a competitive performance. Within WSI-P2P, we introduce the K-TOP MIL aggregator, a variant of the MIL attention-based aggregator, which selectively processes the most informative K instances. The framework features an online, adaptive feature extractor that fine-tunes pre-trained models in an end-to-end manner, addressing multi-centered dataset variability. Results: WSI-P2P achieves state-of-the-art accuracy, demonstrating superior domain adaptability and computational efficiency. WSI-P2P is validated by employing several dataset splits and down-sampled patch variations, illustrating its potential as a scalable and reliable tool in clinical settings and large-scale histological studies. The framework achieved a maximum score of 95.89% AUROC and a test accuracy of 77.67% without attention, further improving to approximately 100% AUROC and a test accuracy of 95.72% recorded with the K-TOP MIL aggregator. Further, for intra-domain generalization experiments, WSI-P2P recorded a consistent performance across domains, validating its domain generalization capabilities. The K-TOP MIL aggregator also demonstrated

2.3 \times

computational efficiency as compared to base aggregators. Conclusions: The proposed framework outperforms traditional offline feature extraction methods, ensuring high discriminative ability even when exposed to data from diverse distributions. WSI-P2P demonstrates excellent performance between subtype classifications, positioning it as a reliable tool for large-scale histological studies. Full article

(This article belongs to the Special Issue Deep Learning in Medical and Biomedical Image Processing)

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14 pages, 1021 KB

Open AccessArticle

Preoperative Predictors of Subsequent Breast Cancer Events Detected on Abbreviated MRI in Patients with Early-Stage Breast Cancer

by Na Lae Eun, Ji Hyun Youk, Jeong-Ah Kim, Yoon Jin Cha, Soong June Bae, Sung Gwe Ahn, Joon Jeong, Hyejin Yang, Hye Sun Lee and Eun Ju Son

Diagnostics 2025, 15(23), 2953; https://doi.org/10.3390/diagnostics15232953 - 21 Nov 2025

Abstract

Background/Objectives: This study aimed to investigate the preoperative clinicopathologic and imaging features associated with subsequent breast cancer events detected on postoperative abbreviated MRI in early-stage breast cancer patients following breast and axillary surgery. Methods: A retrospective analysis was conducted on 1171 patients [...] Read more.

Background/Objectives: This study aimed to investigate the preoperative clinicopathologic and imaging features associated with subsequent breast cancer events detected on postoperative abbreviated MRI in early-stage breast cancer patients following breast and axillary surgery. Methods: A retrospective analysis was conducted on 1171 patients (median age, 53 years; range, 24–90 years) diagnosed with clinical stage I or II breast cancer between January 2013 and December 2017. Logistic regression analysis was used to evaluate preoperative imaging features—including breast density assessed on mammography and MRI descriptors—along with clinicopathologic characteristics, to identify factors independently associated with subsequent breast cancer events during abbreviated MRI screening. Results: Among the patients, 57 (4.9%) experienced subsequent breast cancer events at a median follow-up of 74 months. In the multivariable analysis, high nuclear grade (odds ratio [OR] = 2.821; 95% confidence interval [CI], 1.427–5.577; p = 0.003), dense breast tissue on mammography (OR = 4.680; 95% CI, 1.113–19.684; p = 0.035), and absence of heterogeneous internal enhancement on preoperative MRI (OR = 0.429; 95% CI, 0.206–0.891; p = 0.023) were independently associated with subsequent breast cancer events detected using an abbreviated breast MRI protocol. Age ≥ 40 years (OR = 0.448; 95% CI, 0.193–1.039; p = 0.061) and clinical T2 stage (OR = 1.744; 95% CI, 0.969–3.139; p = 0.064) showed borderline significance. Conclusions: High nuclear grade, dense breast tissue on mammography, and absence of heterogeneous internal enhancement on preoperative MRI were associated with an increased risk of subsequent breast cancer events in patients undergoing abbreviated MRI surveillance following surgery for early-stage breast cancer. Full article

(This article belongs to the Special Issue Recent Advances in Diagnosis and Management of Breast Cancer)

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15 pages, 1558 KB

Open AccessArticle

Quantitative CT Perfusion and Radiomics Reveal Complementary Markers of Treatment Response in HCC Patients Undergoing TACE

by Nicolas Fezoulidis, Jakob Slavicek, Julian-Niklas Nonninger, Klaus Hergan and Shahin Zandieh

Diagnostics 2025, 15(23), 2952; https://doi.org/10.3390/diagnostics15232952 - 21 Nov 2025

Abstract

Background: Hepatocellular carcinoma (HCC), the most prevalent primary malignancy of the liver, is commonly treated with transarterial chemoembolization (TACE), a locoregional therapy that combines targeted intra-arterial chemotherapy with selective embolization to induce tumor ischemia and necrosis. However, current methods for monitoring the [...] Read more.

Background: Hepatocellular carcinoma (HCC), the most prevalent primary malignancy of the liver, is commonly treated with transarterial chemoembolization (TACE), a locoregional therapy that combines targeted intra-arterial chemotherapy with selective embolization to induce tumor ischemia and necrosis. However, current methods for monitoring the treatment response—such as the RECIST and mRECIST—often fail to detect early or subtle biological changes, such as tumor necrosis or microstructural remodeling, and therefore may underestimate the therapeutic effects, especially in cases with minimal or delayed tumor shrinkage. Thus, there is a critical need for quantitative imaging strategies that can improve early response assessment and guide more personalized treatment decision-making. The goal of this study was to assess the changes in computed tomography (CT) perfusion parameters and radiomic features in HCC before and after TACE and to evaluate the associations of these parameters/features with the tumor burden. Methods: In this retrospective, single-center study, 32 patients with histologically confirmed HCC underwent CT perfusion and radiomic analysis prior to and following TACE. Multiple quantitative perfusion parameters (arterial flow, perfusion flow, perfusion index) and radiomic features were extracted. Statistical comparisons were performed using the Wilcoxon signed-rank test and Spearman’s correlation. Radiomic feature extraction was performed in strict adherence to the Image Biomarker Standardization Initiative (IBSI) guidelines. Preprocessing steps included voxel resampling (1 × 1 × 1 mm), z-score normalization, and fixed bin-width discretization (bin width = 25). All tumor ROIs were manually segmented in consensus by two experienced radiologists to minimize inter-observer variability. Results: Arterial flow significantly decreased from a median of 56.5 to 47.7 mL/100 mL/min after TACE (p = 0.009), while nonsignificant increases in the perfusion flow (from 101.3 to 107.8 mL/100 mL/min, p = 0.44) and decreases in the perfusion index (from 38.6% to 35.7%, p = 0.25) were also observed. Perfusion flow was strongly and positively correlated with tumor size (ρ = 0.94, p < 0.001). Five radiomic texture feature values—especially those of ShortRunHighGrayLevelEmphasis (Δ = +2.11, p = 0.0001) and LargeAreaHighGrayLevelEmphasis (Δ = +75,706, p = 0.0006)—changed significantly after treatment. These radiomic feature value changes were more pronounced in tumors ≥50 mm in diameter. In addition, we performed a receiver operating characteristic (ROC) analysis of the two most discriminative radiomic features (SRHGLE and LAHGLE). We further developed a multivariable logistic regression model that achieved an AUC of 0.87, supporting the potential of these features as predictive biomarkers. Conclusions: CT perfusion and radiomics offer complementary insights into the treatment response of patients with HCC. While perfusion parameters reflect macroscopic vascular changes and are correlated with tumor burden, radiomic features can indicate microstructural changes after TACE. This combined imaging approach may improve early therapeutic assessment and support precision oncology strategies. Full article

(This article belongs to the Section Medical Imaging and Theranostics)

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18 pages, 3817 KB

Open AccessArticle

Improving Clinical Generalization of Pressure Ulcer Stage Classification Through Saliency-Guided Data Augmentation

by Jun-Woo Choi, Won Lo Rhee, Dong-Hun Han and Minsoo Kang

Diagnostics 2025, 15(23), 2951; https://doi.org/10.3390/diagnostics15232951 - 21 Nov 2025

Abstract

Background/Objectives: This study demonstrates improved generalization in pressure-ulcer stage classification. In medical imaging, training data are often scarce and disease specific. For skin conditions such as pressure ulcers, variation in camera to subject distance, resolution, illumination, and viewpoint across photographers reduces accuracy [...] Read more.

Background/Objectives: This study demonstrates improved generalization in pressure-ulcer stage classification. In medical imaging, training data are often scarce and disease specific. For skin conditions such as pressure ulcers, variation in camera to subject distance, resolution, illumination, and viewpoint across photographers reduces accuracy in clinical use. Methods: We developed a YOLOv7-based pressure ulcer stage classification model by employing a two-phase training strategy. Phase 1 was trained on the full dataset stratified by pressure-ulcer stage. Phase 2 was trained in saliency-guided images augmented with clinically plausible noise, including healing areas and white keratin. The added dataset comprised 296 images obtained by randomly sampling 30% from stages 1 through 3 of the full dataset. Results: The accuracy of the 38 newly acquired hospital images increased from 75% in Phase 1 to 89% in Phase 2. Five-fold cross-validation demonstrated stable performance (mAP@0.5: 86.20% ± 2.28%), confirming reproducibility. This exceeds by more than five percentage points the performance reported for pressure-ulcer staging models in prior studies conducted in clinical deployment settings. Conclusions: These findings suggest that curriculum learning combined with noise-enriched augmentation can improve generalization in clinical environments. Our results demonstrate that clinically informed data augmentation is a key factor in enhancing the model’s clinical generalization. Accordingly, the proposed approach provides a practical path to enhancing clinical usability in data-limited medical imaging. Full article

(This article belongs to the Section Machine Learning and Artificial Intelligence in Diagnostics)

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13 pages, 684 KB

Open AccessArticle

The Application of the NGS and MLPA Methods in the Molecular Diagnostics of Lynch Syndrome

by Ivana Rako, Ema Vinceljak, Marina Popovic and Tamara Zigman

Diagnostics 2025, 15(23), 2950; https://doi.org/10.3390/diagnostics15232950 - 21 Nov 2025

Abstract

Background: Lynch syndrome (LS) is a cancer-susceptibility syndrome associated with autosomal dominant predisposition to a spectrum of cancers, primarily of the colorectum and endometrium, which exhibit impaired DNA mismatch repair (MMR) activity. LS is caused by a hereditary (germline) pathogenic (PV) or [...] Read more.

Background: Lynch syndrome (LS) is a cancer-susceptibility syndrome associated with autosomal dominant predisposition to a spectrum of cancers, primarily of the colorectum and endometrium, which exhibit impaired DNA mismatch repair (MMR) activity. LS is caused by a hereditary (germline) pathogenic (PV) or likely pathogenic variant (LPV) in one of the mismatch repair (MMR) genes—MLH1, MSH2, MSH6, PMS2, or EPCAM. Although point mutations are the most common genetic changes in MMR genes, >20% are large genomic rearrangements. We hypothesized that a two-tier diagnostic strategy for Lynch syndrome (LS) using next generation sequencing (NGS) and multiplex ligation-dependent probe amplification (MLPA) can increase diagnostic yield of patients with Lynch syndrome. Methods: This study included 60 patients suspected of LS. After genetic counseling, they were referred to genetic testing. Genomic DNA was extracted from peripheral blood and sequenced using NGS multigene panel testing covering 113 cancer susceptibility genes, including MMR genes. Regarding limitations of NGS analysis, which cannot reliably detect genomic alterations larger than 50 base pairs in length, the MLPA method was used for NGS negative DNA samples in order to identify larger deletions and duplications, commonly referred to as copy number variations (CNVs). Results: Different PVs were detected by NGS in 10 patients and CNVs were detected by MLPA in 7 more patients: 3xMLH1 del ex9-15, 2xMSH2 del ex1 and upstream, 1xMSH2 del ex9, and 1xMSH2 del ex1. We did not detect LPVs or variants of uncertain significance (VUS). In our cohort, the addition of MLPA provided an incremental yield of seven pathogenic CNVs, representing an 11.6% absolute increase in diagnostic sensitivity (from 16.7% to 28.3%) over the NGS-alone workflow, with CNVs accounting for 41% of all pathogenic findings. Conclusions: Our results show that MLPA is a very useful method in molecular diagnostics of LS and its implementation in routine genetic testing in combination with NGS using multigene panel testing would benefit both patients and health care providers. Full article

(This article belongs to the Special Issue Exploring the Role of Diagnostic Biochemistry, 2nd Edition)

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