Search Results (435)

Head and neck cancer surgery has evolved from radical organ-sacrificing procedures to function-preserving approaches integrated within multidisciplinary frameworks. This comprehensive literature review, concentrating on studies from the past five years while incorporating relevant publications from the last three decades and landmark historical papers, examines the evolving role of surgery emphasizing diagnostic methodologies including comprehensive genomic profiling, validated imaging biomarkers, and their clinical integration for treatment selection and response prediction. Modern surgical practice demonstrates a paradigm shift toward precision medicine through validated diagnostic technologies. Comprehensive genomic profiling identifies clinically actionable alterations in over 90% of head and neck squamous cell carcinomas, with tumor mutational burden serving as a validated predictive biomarker for immunotherapy response. Programmed death-ligand 1 (PD-L1) combined positive score functions as a validated diagnostic biomarker for immunotherapy efficacy, demonstrating significant clinical benefit in biomarker-selected populations. Radiomics-based predictive models utilizing machine learning algorithms achieve diagnostic accuracies exceeding 85% for treatment response prediction when validated across independent cohorts. Quantitative ultrasound spectroscopy combined with magnetic resonance imaging radiomics demonstrates high sensitivity and specificity for radiation response prediction. Habitat imaging techniques characterizing tumor microenvironmental heterogeneity predict pathologic complete response to neoadjuvant chemoimmunotherapy with area under the curve values approaching 0.90 in validation studies. Integration of these diagnostic methodologies enables response-adaptive treatment strategies, with neoadjuvant chemotherapy facilitating mandibular preservation and adjuvant therapy omission in over half of human papillomavirus (HPV)-associated cases following surgical downstaging. Clinical validation of these diagnostic platforms enables accurate treatment response prediction and informed surgical decision-making, though standardization across institutions and demonstration of survival benefits through prospective trials remain essential for broader implementation. Full article

Background: Women’s health has historically served as an incubator for major medical innovations yet often faces relative neglect in sustained funding and implementation. The rise of artificial intelligence (AI) and machine learning (ML) presents both opportunities and risks for diagnostics in obstetrics and gynecology (OB/GYN). Methods: A narrative review (January 2018–August 2025) integrating peer-reviewed literature and clinical exemplars was conducted. OB/GYN relevance, clinical validation/scale, near-term outcome impact, and domain diversity were prioritized in selection. Results: We highlight ten promising AI applications across imaging, laboratory diagnostics, patient monitoring/digital biomarkers, and decision support, including AI-enhanced fetal ultrasound, cervical screening, preeclampsia prediction with cell-free RNA, noninvasive endometriosis testing, remote maternal–fetal monitoring, and reinforcement-learning decision support in gynecologic oncology. Conclusions: AI shows transformative potential for women’s health diagnostics but requires attention to bias, privacy, regulatory evolution, reimbursement, and workflow integration. Equity-focused development and diverse datasets are essential to ensure benefits accrue broadly. Full article

The widespread fragmentation of patient information across heterogeneous systems and the lack of standardized integration mechanisms hinder efficient and comprehensive medical diagnostics. To address these limitations, this work presents an architecture framework designed to support physicians in the diagnostic process by integrating clinical and socio-health information (patient medical histories), structured documents extracted from Health Information System (HIS), and data automatically extracted from diagnostic images using Artificial Intelligence (AI) techniques. The proposed architecture is made by several modules, in particular a Decision Support System (DSS) that enables risk assessment related to specific patient’s clinical conditions. In addition, the clinical information retrieved is aggregated, standardized, and transmitted to external systems for follow up. Standardization and data interoperability are ensured through the adoption of the international HL7 Fast Healthcare Interoperability Resources (FHIR) standard, which facilitates seamless connection with HIS. An Android application has been developed to communicate with different HISs in order to: (i) retrieve information, (ii) aggregate clinical data, (iii) calculate patient risk scores using AI algorithms, (iv) display results to healthcare professionals, and (v) generate and share relevant clinical information with external systems in a standardized format. To demonstrate architecture’s applicability, a case study on breast cancer diagnosis is presented. In this context, an AI-based Risk Assessment module was developed using the Breast Ultrasound Images Dataset (BUSI), which includes benign, malignant, and normal cases. Machine Learning algorithms were applied to perform the classification task. Model performance was evaluated using a 4-fold cross-validation strategy to ensure robustness and generalizability. The best results were achieved using the Multilayer Perceptron method, with a competitive F1-score of 0.97. Full article

Background/Objectives: To evaluate the ability of radiomics analysis of QUS spectral parametric imaging to non-invasively differentiate intermediate-to-high-risk from low-risk Oncotype DX^TM Recurrence Score (ODXRS). Methods: This prospective study included 31 participants (21 intermediate-to-high-risk ODXRS (median age, 56 years [IQR: 49–68 years]) and 10 low-risk ODXRS (median age, 52 years [IQR: 48–58 years])) presenting with ER+ HER2− invasive breast masses acquired between September 2015 and August 2024. Quantitative ultrasound (QUS) spectroscopy produced five spectral maps, from which radiomics features (including statistical, texture, and morphological measures) were extracted from the tumor core and a 5 mm margin. The ground truth label was determined from thresholding the ODXRS. A multivariate predictive model was developed to differentiate intermediate-to-high-risk ODXRS from low-risk ODXRS, with performance assessed via nested leave-one-out cross-validation (LOOCV). Results: A nested leave-one-out cross-validation (LOOCV) analysis demonstrated the generalization performance of a four-feature model. The support vector machine (SVM-RBF) classifier achieved 86% recall, 100% specificity, 93% balanced accuracy, and an area under the receiver operating characteristic curve (AUROC) of 0.95 (CI = 0.88–1.00) in identifying intermediate-to-high-risk versus low-risk ODXRS. Conclusions: The preliminary results suggest the potential radiomics-based model of ODXRS in predicting the risks of recurrence. The results warrant further investigation on a larger cohort. This framework can be a useful surrogate for participants for whom ODX testing is neither affordable nor available. Full article

Objectives: Liver fibrosis staging and etiology diagnosis are critical for patient management, but non-invasive methods remain challenging. This study aims to evaluate the performance of radiomics models using 2D shear wave elastography (2D-SWE) of the liver and spleen for liver fibrosis staging and etiology differentiation, comparing them with serum biomarkers and conventional ultrasound. Methods: A retrospective analysis was conducted on 198 patients with liver fibrosis confirmed by biopsy. Radiomics features were extracted from the liver and spleen grayscale and 2D-SWE images. Machine learning (ML) and transfer learning (TL) models were established for fibrosis staging and etiology diagnosis. Model performance was evaluated according to receiver operating characteristic (ROC) curves. Results: For fibrosis staging, 2D-SWE-based models outperformed grayscale and serum biomarkers. The combined liver–spleen TL model achieved exceptional validation performance (AUCs 0.99 for S4, 0.98 for ≥S3, 1.00 for ≥S2). For etiology diagnosis, the liver 2D-SWE TL model and the combined liver–spleen TL model achieved AUCs of 0.97 and 0.94, respectively, significantly outperforming ML models in terms of AUC. Conclusions: Integrating liver and spleen 2D-SWE radiomics with TL significantly improves non-invasive liver fibrosis staging and etiology diagnosis, offering superior accuracy over conventional methods. This approach holds promise for clinical application, though further validation is needed. Full article

Background and Objectives: Accurate evaluation of vulnerable carotid atherosclerotic plaques remains essential for preventing ischemic stroke. Conventional imaging modalities such as ultrasound and computed tomography angiography (CTA) have limited capacity to identify histopathological features of plaque instability, including fibrous cap rupture, lipid-rich necrotic core, and intraplaque hemorrhage. Artificial intelligence (AI) techniques—particularly deep learning (DL) and radiomics—have recently emerged as valuable adjuncts to standard imaging, achieving AUC values of 0.83–0.99 across modalities in identifying vulnerable plaques. This scoping review summarizes the available evidence on the application of AI in the detection and assessment of vulnerable carotid plaques. Methods: A systematic search of the English-language literature was conducted in MEDLINE, SCOPUS, and CENTRAL from 2000 to 30 June 2025, following the PRISMA-ScR framework. Eligible studies applied AI-based approaches (machine learning, deep learning, or radiomics) to evaluate carotid plaque vulnerability using ultrasound, CTA, or MRI. Extracted outcomes included diagnostic performance, correlation with histopathology or neurological events, and predictive modeling for stroke risk. Results: Of 201 records screened, 12 studies met inclusion criteria (ultrasound = 6; CTA = 4; high-resolution MRI = 2; publication years 2021–2025). All reported receiver operating characteristic area-under-the-curve (ROC-AUC) values for endpoints related to plaque vulnerability (symptomatic versus asymptomatic status, presence of intraplaque hemorrhage or lipid-rich necrotic core, fibrous-cap surrogates, and, less frequently, short-term cerebrovascular events). For ultrasound, contrast-enhanced videomics achieved an AUC of 0.87 (10 centers; n = 205), B-mode texture/radiomics reached 0.87 (n = 150), and segmentation-assisted models 0.827 (n = 202); other ultrasound models reported AUCs of 0.88–0.91. For CTA, a symptomatic-plaque machine-learning model yielded AUC 0.89 ( n = 106); a perivascular-adipose-tissue (PVAT) radiomics nomogram achieved AUC 0.836 on external validation; a histology-referenced pilot attained AUC 0.987; and one mild-stenosis TIA model reported ROC performance. For high-resolution MRI (HR-MRI), radiomics-based models showed AUC 0.835–0.864 in single-modality cohorts and up to 0.984 with multi-contrast inputs. Across modalities, AUC ranges were: ultrasound 0.827–0.91, CTA 0.836–0.987 (external 0.836), and HR-MRI 0.835–0.984. Only two out of twelve studies performed external validation; calibration and decision-curve analyses were rarely provided, and most cohorts were single-center, limiting generalizability. Conclusions: AI demonstrates strong potential as a complementary tool for evaluating carotid plaque vulnerability, with high diagnostic performance across imaging modalities. Reported AUCs ranged from 0.83 to 0.99 based primarily on internal or hold-out validation, representing the upper bound of theoretical rather than real-world performance. Nonetheless, large prospective multicenter studies with standardized protocols, histopathological correlation, and external validation are required before clinical integration into stroke prevention pathways. Full article

Background/Objectives: Chronic endometritis (CE) is a well-known risk factor for recurrent implantation failure. However, the traditional approach to CE diagnosis has several drawbacks. On the other hand, there is a lot of evidence that some clinical, instrumental, and/or laboratory parameters of patients are associated with CE. The aim of this study is to build a CE prediction model using machine learning tools based on low-invasive pathological features. Methods: The data of 108 women (44 with and 64 without CE) from a multicenter perspective cross-sectional study was included in this study. Basic characteristics, reproductive history, laboratory and ultrasound indicators, and immunohistochemistry results were collected. Binary feature selection was performed using forward stepwise selection with logistic regression as the evaluation criterion. For each feature configuration, a gradient-boosting model was trained on decision trees with a binary logistic loss function. The models were evaluated and compared on test data using standard metrics. Results: We built five comparable predictive models for CE. The models yielded the following AUCs (95% CI): Model 1 (seven indicators)—0.704 (0.5170, 0.8907), Model 2 (seven indicators)—0.673 (0.4716, 0.8745), Model 3 (nine indicators)—0.677 (0.4916, 0.8622), Model 4 (five indicators)—0.758 (0.5913, 0.9241), and Model 5 (five indicators)—0.769 (0.5913, 0.9241). Models 2 and 5 have the better recall and precision values, but the differences were not significant. SHAP values indicated that serum adiponectin level (Model 2) and SHBG (Model 5) had the greatest association with CE risks. Conclusions: Models 2 and 5 show the most promising potential for clinical application, as they demonstrate superior recall and precision metrics and require assessment of only 5–7 risk markers (with only a few being non-routine) for their implementation. Full article

The pomegranate, a widely consumed fruit, produces large quantities of waste, mainly from its peel. Pomegranate peels (PPs) contain high amounts of antioxidant compounds, such as polyphenols, flavonoids, and anthocyanins, which can be isolated from them and used for the benefit of humans and the environment. In the present work, a study of recovery of these compounds by ultrasound-assisted extraction (UAE) was carried out, whose parameters were optimized. The optimal results were a total polyphenol content of 195.55 mg gallic acid equivalents/g, total flavonoid content of 74.78 mg rutin equivalents/g, total anthocyanin content of 992.87 μg cyanidin 3-O-glucoside equivalents/g, and ascorbic acid content of 15.68 mg/g, while the antioxidant activity determined through ferric-reducing antioxidant power and DPPH assays was 2366.89 and 1755.17 μmol ascorbic acid equivalents/g, respectively. In parallel, an artificial intelligence (AI)-based framework was developed to model and predict antioxidant and phytochemical responses from UAE parameters. Six machine learning models were implemented on the experimental dataset, with the Random Forest (RF) regressor consistently achieving the best predictive accuracy. Partial dependence analysis revealed ethanol concentration as the dominant factor influencing outcomes, while ultrasonic power and extraction time exerted comparatively minor effects. Although dataset size limited model generalizability, the RF model reproduced experimental outcomes within experimental variability, underscoring its suitability for predictive extraction optimization. These findings demonstrate the complementary role of machine learning in accelerating antioxidant compound recovery research and its potential to guide future industrial-scale applications of AI-assisted extraction. Full article

Background and Objectives: Breast cancer invasiveness significantly impacts treatment strategies and prognosis. Combining ultrasound imaging modalities with serum biomarkers may improve diagnostic accuracy. We aimed to develop and validate a multimodal diagnostic model integrating ultrasound B-mode, Doppler imaging, and serum biomarkers for assessing breast cancer invasiveness. Materials and Methods: A multimodal diagnostic model was developed using ultrasound B-mode, Doppler imaging, and serum biomarkers from patients with invasive and non-invasive breast cancer. Machine learning algorithms, including Logistic Regression, Random Forest, and XGBoost, were applied to predict invasiveness, with performance evaluated using accuracy, precision, recall, F1-score, and AUC. Results: The multimodal model outperformed single-modality approaches, with XGBoost achieving the highest accuracy (88.90%) and AUC (0.930). The inclusion of specific serum biomarkers (e.g., CA125, CA15-3, CEA, and CA19-9) significantly enhanced diagnostic accuracy for breast cancer invasiveness. Conclusions: The proposed multimodal diagnostic model integrating ultrasound imaging and serum biomarkers provides a highly accurate and reliable method for assessing breast cancer invasiveness, offering potential to improve clinical decision-making and patient outcomes. Full article

Prostate cancer is one of the leading causes of cancer-related morbidity and mortality worldwide, and imaging plays a critical role in its detection, localization, staging, treatment, and management. The advent of artificial intelligence (AI) has introduced transformative possibilities in prostate imaging, offering enhanced accuracy, efficiency, and consistency. This review explores the integration of AI in prostate cancer diagnostics across key imaging modalities, including multiparametric MRI (mpMRI), PSMA PET/CT, and transrectal ultrasound (TRUS). Advanced AI technologies, such as machine learning, deep learning, and radiomics, are being applied for lesion detection, risk stratification, segmentation, biopsy targeting, and treatment planning. AI-augmented systems have demonstrated the ability to support PI-RADS scoring, automate prostate and tumor segmentation, guide targeted biopsies, and optimize radiation therapy. Despite promising performance, challenges persist regarding data heterogeneity, algorithm generalizability, ethical considerations, and clinical implementation. Looking ahead, multimodal AI models integrating imaging, genomics, and clinical data hold promise for advancing precision medicine in prostate cancer care and assisting clinicians, particularly in underserved regions with limited access to specialists. Continued multidisciplinary collaboration will be essential to translate these innovations into evidence-based practice. This article explores current AI applications and future directions that are transforming prostate imaging and patient care. Full article

Objectives: Papillary thyroid carcinoma (PTC) frequently presents with cervical lymph node metastasis (CLNM), yet preoperative tools often encode BRAF V600E as a binary variable, potentially overlooking information contained in mutation abundance. We sought to quantify the dose–response relationship between BRAF V600E abundance and CLNM and to develop an interpretable model for preoperative risk stratification. Methods: We performed a single-center retrospective study of consecutive PTC patients who underwent preoperative BRAF V600E testing and surgery from 2019 to 2023. Patients were randomly split 70/30 into training and test sets. Candidate predictors included clinical and ultrasound features and BRAF V600E abundance. We used multivariable logistic regression and restricted cubic splines (RCS) to assess nonlinearity and compared six machine-learning algorithms (LR, KNN, SVM, XGB, LightGBM, and NN). Model performance was evaluated by F1, AUC, calibration, and decision-curve analyses; SHAP aided interpretation. Ethics approval: SYSKY-2024-169-01. Results: The cohort included 667 patients; CLNM occurred in 391 (58.6%). CLNM cases had higher BRAF abundance (median 23% vs. 17%) and characteristic clinical/sonographic differences. RCS revealed a nonlinear association between abundance and CLNM, with a steep risk rise of up to ~20.7% followed by a plateau. Among six algorithms, XGBoost showed the best validation performance (AUC 0.752; F1 0.73). SHAP indicated that maximum tumor diameter, BRAF abundance, age, and microcalcifications contributed most to predictions. Conclusions: Modeling BRAF V600E as a quantitative abundance—rather than a binary status—improves preoperative CLNM risk assessment in PTC. An interpretable XGBoost model integrating abundance with routine features demonstrates acceptable discrimination and potential clinical utility for individualized surgical planning and counseling. Full article

Background/Objectives: Breast cancer (BC) has emerged as a significant threat among female malignancies, resulting in approximately 670,000 fatalities. The capacity to identify BC has advanced over the past two decades because of deep learning (DL), machine learning (ML), and artificial intelligence. The early detection of BC is crucial; yet, conventional diagnostic techniques, including MRI, mammography, and biopsy, are costly, time-intensive, less sensitive, incorrect, and necessitate skilled physicians. This narrative review will examine six novel imaging approaches for BC diagnosis. Methods: Optical coherence tomography (OCT) surpasses existing approaches by providing non-invasive, high-resolution imaging. Raman Spectroscopy (RS) offers detailed chemical and structural insights into cancer tissue that traditional approaches cannot provide. Photoacoustic Imaging (PAI) provides superior optical contrast, exceptional ultrasonic resolution, and profound penetration and visualization capabilities. Hyperspectral Imaging (HSI) acquires spatial and spectral data, facilitating non-invasive tissue classification with superior accuracy compared to grayscale imaging. Contrast-Enhanced Spectral Mammography (CESM) utilizes contrast agents and dual energy to improve the visualization of blood vessels, enhance patient comfort, and surpass standard mammography in sensitivity. Multispectral Imaging (MSI) enhances tissue classification by employing many wavelength bands, resulting in high-dimensional images that surpass the ultrasound approach. The imaging techniques studied in this study are very useful for diagnosing tumors, staging them, and guiding surgery. They are not detrimental to morphological or immunohistochemical analysis, which is the gold standard for diagnosing breast cancer and determining molecular characteristics. Results: These imaging modalities provide enhanced sensitivity, specificity, and diagnostic accuracy. Notwithstanding their considerable potential, the majority of these procedures are not employed in standard clinical practices. Conclusions: Validations, standardization, and large-scale clinical trials are essential for the real-time application of these approaches. The analyzed studies demonstrated that the novel modalities displayed enhanced diagnostic efficacy, with reported sensitivities and specificities often exceeding those of traditional imaging methods. The results indicate that they may assist in early detection and surgical decision-making; however, for widespread adoption, they must be standardized, cost-reduced, and subjected to extensive clinical trials. This study offers a concise summary of each methodology, encompassing the methods and findings, while also addressing the many limits encountered in the imaging techniques and proposing solutions to mitigate these issues for future applications. Full article

Background/Objectives: Preterm birth (PTB), defined as birth before 37 completed weeks of gestation, is a major global health challenge and a leading cause of neonatal mortality. PTB is broadly classified into spontaneous and medically indicated (iatrogenic), which have distinct etiologies. While prediction is key to improving outcomes, there is a lack of models that specifically differentiate between spontaneous and iatrogenic PTB subtypes. This study aimed to develop and validate predictive models for the prediction of spontaneous and iatrogenic PTB at <32, <34, and <37 weeks’ gestation using medical history and readily available second-trimester data. Methods: This was a retrospective cohort study on singleton pregnancies from a single tertiary institution (2012–2025). Predictor variables included maternal characteristics, obstetric history, and second-trimester ultrasound markers. Four algorithms, including multivariable Logistic Regression and three machine learning methods (Random Forest, XGBoost, and a Neural Network), were trained and evaluated on a held-out test set (20% of the data). Model performance was primarily assessed by the Area Under the Curve (AUC). Results: In total, 9805 singleton pregnancies were included. The models performed significantly better for iatrogenic PTB than for spontaneous PTB. For delivery <37 weeks, the highest AUC for iatrogenic PTB was 0.764 (Random Forest), while for spontaneous PTB it was 0.609 (Neural Network). Predictive accuracy improved for earlier gestations; for delivery <32 weeks, the best model for iatrogenic PTB achieved an AUC of 0.862 (Neural Network), and the best model for spontaneous PTB achieved an AUC of 0.749 (Random Forest). Model interpretation revealed that iatrogenic PTB was primarily driven by markers of placental dysfunction, such as estimated fetal weight by ultrasound scan and uterine artery pulsatility index, while spontaneous PTB was most associated with a history of PTB and a short cervical length. Conclusions: Models using routine mid-gestation data demonstrate effective prediction for iatrogenic PTB, with accuracy improving for earlier, more severe cases. In contrast, performance for spontaneous PTB was modest. Traditional Logistic Regression performed comparably to complex machine learning algorithms, highlighting that the clinical value is rooted in the subtype-specific modeling approach rather than in algorithmic complexity. Full article

Objective: This study aimed to evaluate the diagnostic value of logistic regression and nomogram models based on multimodal ultrasound in predicting sarcopenia in patients with peritoneal dialysis (PD). Methods: A total of 178 patients with PD admitted to our nephrology department between June 2024 and April 2025 were enrolled. According to the 2019 Asian Working Group for Sarcopenia (AWGS) diagnostic criteria, patients were categorized into sarcopenia and non-sarcopenia groups. Ultrasound examinations were used to measure the muscle thickness (MT), pinna angle (PA), fascicle length (FL), attenuation coefficient (Atten Coe), and echo intensity (EI) of the right gastrocnemius medial head. The clinical characteristics of the groups were compared using the Mann–Whitney U test. Binary logistic regression was used to identify sarcopenia risk factors to construct clinical prediction models and nomograms. Receiver operating characteristic (ROC) curves were used to assess the model accuracy and stability. Results: The sarcopenia group exhibited significantly lower MT, PA, and FL, but higher Atten Coe and EI than the non-sarcopenia group (all p < 0.05). A multimodal ultrasound logistic regression model was developed using machine learning—Logit(P) = −7.29 − 1.18 × MT − 0.074 × PA + 0.48 × FL + 0.52 × Atten Coe + 0.13 × EI (p < 0.05)—achieving an F1-score of 0.785. The area under the ROC curve (ROC-AUC) was 0.902, with an optimal cut-off value of 0.45 (sensitivity 77.3%, specificity 56.7%). Nomogram consistency analysis showed no statistical difference between the ultrasound diagnosis and the appendicular skeletal muscle index (ASMI) measured by bioelectrical impedance analysis (BIA) (Z = 0.415, p > 0.05). Conclusions: The multimodal ultrasound-based prediction model effectively assists clinicians in identifying patients with PD at a high risk of sarcopenia, enabling early intervention to improve clinical outcomes. Full article

Pancreaticobiliary endoscopy, encompassing endoscopic ultrasound (EUS), endoscopic retrograde cholangiopancreatography (ERCP), and digital single-operator cholangioscopy (DSOC), is essential for diagnosing and managing pancreatic and biliary diseases. However, these procedures are limited by operator dependency, variable diagnostic accuracy, and technical complexity. Artificial intelligence (AI), particularly through machine learning (ML) and deep learning (DL), has emerged as a promising tool to address these challenges. Early studies show that AI can enhance lesion detection, improve differentiation of pancreatic masses, classify cystic lesions, and aid in diagnosing malignant biliary strictures. AI has also been used to predict post-ERCP pancreatitis risk and reduce radiation exposure during ERCP. Despite this promise, current AI models are largely experimental—limited by small, single-center datasets, lack of external validation, and no FDA-approved systems for these indications. Major barriers include inconsistent data acquisition, limited interoperability across hardware platforms, and integration into real-time workflows. Future progress depends on multicenter data sharing, standardized imaging protocols, interpretable AI design, and regulatory pathways for model deployment and updates. AI can be developed as a valuable partner to endoscopists, enhancing diagnostic accuracy, reducing complications, and supporting more efficient, personalized care in pancreaticobiliary endoscopy. Full article