Search Results (1,800)

Background and Objectives: Non-urothelial bladder tumors and secondary bladder involvement from extravesical primaries are uncommon but clinically challenging. We compared clinicopathologic patterns between primary non-urothelial tumors and secondaries, and explored correlates of adverse pathologic features to inform diagnostic triage and surgical planning. Methods: We performed a single-center retrospective cohort (2014–2024) of consecutive bladder lesions meeting WHO 2022 criteria and AJCC 8th staging. Eligible cases were primary non-urothelial malignancies (squamous cell carcinoma (SCC), adenocarcinoma (ADK), small-cell/neuroendocrine (NEC), sarcomatoid) or secondary bladder involvement (colorectal, prostate, cervix, ovary, uterus, breast). Outcomes included advanced pT (≥pT3), lympho–vascular invasion (LVI), perineural invasion (PNI), nodal metastasis, margin status, and composite adverse events. Results: Of 235 analyzable cases, 59 were primary and 176 were secondary. Age and sex distributions were similar. Secondaries had a higher adverse burden: advanced pT 56.8% vs. 23.7%, LVI 47.2% vs. 27.1%, PNI 40.3% vs. 22.0%, node-positive 11.9% vs. 0%, and any adverse 65.3% vs. 33.9% (all significant). Histology composition differed (p < 10⁻⁶): secondaries were ADK-dominant (59.1%), whereas primaries were enriched for SCC (38.5%), sarcomatoid (28.8%), and NEC (21.2%). Among secondaries, prostate origin showed the most ominous profile (advanced pT 97.5%, PNI 77.5%, positive margins 64.7%); colorectal cases combined high advanced pT (70.2%) with lower margin positivity (27.6%). Adverse-feature count correlated with pT (ρ = 0.586). Conclusions: Secondary bladder involvement carries substantially higher adverse-pathology rates than primary non-urothelial tumors, with origin-specific risk gradients (prostate > colorectal ≳ cervix). Rigorous origin adjudication and a margin-focused, anatomy-adapted surgical strategy may improve outcomes; prospective outcome-linked validation is warranted. Full article

Background/Objectives: Despite the common use of invasive diagnostics and treatment of coronary artery disease (CAD), there are still doubts concerning the disease management method of choice in the population of very old patients. Our goal was to assess the patient profile, feasibility of coronary angiography (CAG), effectiveness (successful relieving of the coronary artery’s narrowing or occlusion) of percutaneous coronary intervention (PCI) and safety (mortality and other complications) of both procedures in nonagenarians. Methods: The database of the Dr. E. Warmiński Clinical Hospital of the Bydgoszcz University of Technology was searched for patients aged 90 years and older who underwent CAG and PCI between 2013 and 2023. We retrospectively analysed the case reports of these patients, including reason for hospital admission, course of hospitalisation, procedure data, and complications. Results: A total of 150 nonagenarians meeting the criteria were found, with a mean age of 92 years and 63% being female. A total of 110 patients (73%) were admitted on the basis of acute coronary syndrome (ACS). Upon CAG, 108 patients had obstructive coronary artery disease confirmed, 90% of whom had multivessel disease. In 96 out of 108 of these patients (that is, 89%), PCI was performed successfully in 89 (93%) procedures. Transradial access was used in 112 patients (75%). According to the diagnosis, PCI was performed in all cases (100%) of STEMI patients, in 80% cases of non-ST elevation acute coronary syndrome (NSTE-ACS) patients, and in 27% cases of stable CAD patients. Median time of hospitalisation was 6.5 days (IQR 4–10). In the course of hospitalisation, mortality was 8.7% (13 out of 150), although two cases were non-cardiological in nature. In the PCI group, mortality was 11.5% (11 out of 96); all 11 were treated due to ACS (no deaths in patients with stable ACS). In the STEMI subgroup, mortality was much higher at 33% (4 out of 12, with all 4 admitted with cardiogenic shock). Accordingly, in the NSTEMI group, mortality was 8.97%. Other complications in the PCI group were perforation of coronary artery in 1 case, access site complications in the case of transfemoral access in 10 patients, bleeding requiring transfusion in 2 patients, and contrast-induced nephropathy (CIN) in 4 patients. Conclusions: This analysis demonstrates that the CAG and PCI procedures are feasible and effective in nonagenarians, and the risk of complications is not as great as it was heretofore believed. Full article

Background/Objectives: Glypican-3 (GPC3) is a cell surface oncofetal protein that is highly expressed in hepatocellular carcinoma (HCC) but absent in normal liver tissue, making it an attractive target for molecularly targeted diagnosis and therapy. To support GPC3-targeted treatment strategies, there is a need for a non-invasive imaging tool capable of detecting GPC3-positive tumors. Methods: We conjugated a commercially available murine anti-GPC3 antibody (1G12), or a proprietary chimeric anti-GPC3 antibody (ET58) to the standard magnetic resonance imaging (MRI) contrast agent, gadolinium, via a DOTA chelator. The resulting probes, 1G12-DOTA-Gd or ET58-DOTA-Gd, respectively, were assessed for in vitro relaxivity and binding specificity to GPC3-positive HCC cells, as well as for in vivo imaging performance in mouse xenograft models bearing GPC3-positive or GPC3-negative HCC tumors. Conclusions: ET58-DOTA-Gd shows high specificity, imaging efficacy, and a favorable immunogenicity profile, thereby making it a promising candidate for clinical translation as a GPC3-targeted MRI probe. It holds potential as a non-invasive companion diagnostic for identifying GPC3-positive HCC patients who may benefit from GPC3-targeted therapies. Full article

Extracellular Vesicle-associated microRNAs (EV-miRNAs) are emerging as pivotal regulators of corneal health and disease, holding exceptional promise for transforming both diagnostics and therapeutics. These vesicles carry distinct miRNA signatures in biofluids such as tears, offering a powerful, non-invasive approach for early detection, risk stratification, and dynamic monitoring of corneal disorders. In addition, EV-miRNAs act as key mediators of critical biological processes, including inflammation, fibrosis, and tissue repair. Consequently, they represent attractive therapeutic targets; for example, engineered EVs loaded with miRNA mimics or inhibitors can precisely modulate these pathways to promote regeneration and suppress disease progression. Yet, despite this considerable promise, the translation of EV-miRNA research into clinical practice remains constrained by several challenges. Topmost among these are the lack of standardized EV isolation methods, variability in miRNA quantification, and the pressing need for regulatory frameworks tailored to the complexity of these biological therapeutics. Addressing these barriers is essential to ensure reproducibility, scalability, and safety in clinical applications. Accordingly, this review synthesizes current knowledge on EV-miRNA profiles in corneal diseases, critically evaluates their diagnostic and therapeutic potential, and highlights strategies to overcome existing technical and regulatory limitations. Ultimately, the successful integration of EV-miRNA-based approaches into personalized medicine frameworks could revolutionize the management of corneal diseases and substantially improve patient outcomes. Full article

Preimplantation genetic testing for monogenic disorders (PGT-M) is a powerful tool for identifying genetic disorders prior to gestation. For hemoglobinopathies like thalassemias and sickle cell disease, PGT-M offers a preventative strategy to ensure that only embryos deemed genetically healthy are transferred. A comprehensive review of 22 original articles explores and summarizes the existing evidence on PGT-M techniques in hemoglobinopathies. The review focuses on key aspects such as accuracy, benefits, and drawbacks related to various hemoglobinopathies. Given the limited quantity of DNA obtained from an embryo biopsy, whole genome amplification (WGA) is a critical step for amplifying the sample. One of the available methods of WGA, multiple displacement amplification (MDA) is one of the most widely adopted method with acceptable allele drop-out (ADO) rates for hemoglobinopathies compared with traditional methods. Dealing with ADO constitutes a primary technical obstacle in PGT-M. The failure to amplify one allele in single-cell analysis is a major factor limiting the overall diagnostic accuracy of the procedure. To mitigate this issue, PCR-based and next-generation sequencing (NGS)-based approaches are employed. These methods incorporate linkage analysis with genetic markers such as short tandem repeats (STRs) or single-nucleotide polymorphisms (SNPs) to reduce the risk of incorrect interpretations from ADO and enhance the proportion of conclusive results. A future direction for PGT-M that involves the development of non-invasive methods (niPGT) will be included and discussed. Full article

Background/Objectives: Liquid biopsy using cfDNA has emerged as a promising, minimally invasive alternative to traditional tissue biopsy for detecting cancer-associated mutations. However, the extremely low proportion of mutant DNA in cfDNA poses a major challenge for accurate detection, especially when using conventional sequencing methods. To address this limitation, we sought to develop a highly sensitive diagnostic strategy to selectively enrich rare mutant sequences and improve the detection of clinically important mutations in patients with NSCLC. Methods: We established a CRISPR/Cas12a-based diagnostic system designed to selectively cleave WT DNA, thereby increasing the relative abundance of mutant DNA in cfDNA samples. Following Cas12a-mediated WT cleavage, the remaining DNA was subjected to PCR amplification for mutation identification. The system was applied to plasma cfDNA from blood samples of 48 NSCLC patients to evaluate its ability to detect two major EGFR mutations: L858R and exon 19 deletion. Results: The CRISPR/Cas12a-based diagnostic system effectively identified low-frequency EGFR mutations in cfDNA. Specifically, all 7 L858R-positive samples and 6 out of 11 samples harboring exon 19 deletions—previously validated through tissue biopsy—were successfully detected. This demonstrated a high degree of concordance between our liquid biopsy approach and conventional diagnostic methods. Conclusions: Our findings highlight the potential of the CRISPR/Cas12a-based mutation enrichment system as a powerful tool for detecting rare oncogenic mutations in liquid biopsy samples. This technique enhances diagnostic sensitivity and could be broadly applicable for the non-invasive detection of various genetic alterations in cancer and other diseases. Full article

Background. Clinical assessment of Parkinson’s disease (PD) is limited by high subjectivity and inter-rater variability. Markerless video analysis, namely Computer Vision (CV), offers objective and scalable characterization of motor signs. We systematically reviewed CV technologies suited for PD diagnosis, symptom monitoring, and treatment management. Methods. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, we searched PubMed for articles published between 1 January 1984 and 9 May 2025. We used the following search strategy: (“Parkinson Disease” [MeSH Terms] OR “parkinson’s disease” OR “parkinson disease”) AND (“computer vision” OR “video analysis” OR “pose estimation” OR “OpenPose” OR “DeepLabCut” OR “OpenFace” OR “YOLO” OR “MediaPipe” OR “markerless motion capture” OR “skeleton tracking”). Results. Out of 154 identified studies, 45 met eligibility criteria and were synthesized. Gait was assessed in 42% of studies, followed by bradykinesia items (17.7%). OpenPose and custom CV solutions were each used in 36% of studies, followed by MediaPipe (16%), DeepLabCut (9%), YOLO (4%). Across aims, CV pipelines consistently showed diagnostic discrimination and severity tracking aligned with expert ratings. Conclusions. CV non-invasively quantifies PD motor impairment, holding potential for objective diagnosis, longitudinal monitoring, and therapy response. Guidelines for standardized video-recording protocols and software usage are needed for real-world applications. Full article

Cardiovascular diseases remain a leading cause of mortality worldwide, emphasizing the importance of early diagnosis. Heart sound analysis offers a non-invasive avenue for detecting cardiac abnormalities. This study systematically evaluates the effect of segmentation on phonocardiogram (PCG) classification performance. Unlike conventional fixed-window or HSMM-based methods, a data-adaptive segmentation approach combining Shannon energy and Otsu thresholding is proposed. After segmentation, features are extracted using Empirical Mode Decomposition (EMD) and Mel-Frequency Cepstral Coefficients (MFCCs), followed by classification with k-Nearest Neighbor (kNN), Support Vector Machine (SVM), and Random Forest (RF). Experiments on the PhysioNet/CinC 2016 and Pascal datasets revealed that segmentation markedly enhances classification accuracy. The optimal results were achieved using kNN with segmented EMD features, attaining 99.97% accuracy, 99.98% sensitivity, and 99.96% specificity; segmented MFCC features also provided high accuracy (99.37%). In contrast, non-segmented models yielded substantially lower performance. Principal Component Analysis (PCA) is applied for dimensionality reduction, preserving classification efficiency while minimizing computational cost. These findings demonstrate the critical importance of effective segmentation in heart sound classification and establish the proposed Shannon–Otsu-based method as a robust, interpretable, and resource-efficient tool for automated cardiac diagnostics. Using annotated PhysioNet recordings, segmentation achieved ~90% sensitivity for S1/S2 detection. A limitation is the absence of full segment annotations in the Pascal dataset, which prevents comprehensive timing-error evaluation. Full article

Background/Objectives: Coronary artery disease (CAD) remains the leading global cause of mortality, underscoring the need for functional assessments that extend beyond anatomical evaluation. The Hyperemic Stenosis Resistance (HSR) index combines invasive pressure and flow parameters to assess stenosis severity but faces limitations due to methodological and standardization challenges. This study aimed to introduce and validate a novel non-invasive computational equivalent of HSR (cHSR), derived from coronary computed tomography angiography (CCTA), and to compare its diagnostic performance with fractional flow reserve derived from computational fluid dynamics (FFR_CFD) and quantitative flow ratio (QFR). Methods: A retrospective analysis was conducted on 64 patients (106 coronary lesions) with suspected chronic coronary syndrome (CCS) who underwent both CCTA and invasive coronary angiography (ICA). Computational simulations incorporated patient-specific boundary conditions based on CCTA-derived left ventricular and aortic flow data. Diagnostic accuracy for predicting revascularization was compared among cHSR, FFR_CFD, and QFR. Results: FFR_CFD showed a strong correlation with invasive FFR (r = 0.87, p < 0.0001). The cHSR index achieved the highest diagnostic accuracy (96.2%) at an optimal cut-off of 0.75 mmHg/cm·s⁻¹, outperforming both FFR_CFD and QFR. No significant correlation was found between cHSR and shear stress parameters, including the Oscillatory Shear Index (OSI) and Time-Averaged Wall Shear Stress (TAWSS), indicating complex hemodynamic interactions beyond simple flow–pressure relationships. Conclusions: The computational hyperemic stenosis resistance (cHSR) index represents a promising non-invasive tool for the functional assessment of CAD, demonstrating superior diagnostic performance compared with existing imaging-based indices. Prospective multicenter studies with larger populations are warranted to confirm its clinical applicability and prognostic value in chronic coronary syndrome management. Full article

Dyslexia is a neurodevelopmental disorder that impairs reading, affecting 5–17.5% of children and representing the most common learning disability. Individuals with dyslexia experience decoding, reading fluency, and comprehension difficulties, hindering vocabulary development and learning. Early and accurate identification is essential for targeted interventions. Traditional diagnostic methods rely on behavioral assessments and neuropsychological tests, which can be time-consuming and subjective. Recent studies suggest that physiological signals, such as electrooculography (EOG), can provide objective insights into reading-related cognitive and visual processes. Despite this potential, there is limited research on how typeface and font characteristics influence reading performance in dyslexic children using EOG measurements. To address this gap, we investigated the most suitable typefaces for Turkish-speaking children with dyslexia by analyzing EOG signals recorded during reading tasks. We developed a novel deep learning framework, DyslexiaNet, using scalogram images from horizontal and vertical EOG channels, and compared it with AlexNet, MobileNet, and ResNet. Reading performance indicators, including reading time, blink rate, regression rate, and EOG signal energy, were evaluated across multiple typefaces and font sizes. Results showed that typeface significantly affects reading efficiency in dyslexic children. The BonvenoCF font was associated with shorter reading times, fewer regressions, and lower cognitive load. DyslexiaNet achieved the highest classification accuracy (99.96% for horizontal channels) while requiring lower computational load than other networks. These findings demonstrate that EOG-based physiological measurements combined with deep learning offer a non-invasive, objective approach for dyslexia detection and personalized typeface selection. This method can provide practical guidance for designing educational materials and support clinicians in early diagnosis and individualized intervention strategies for children with dyslexia. Full article

Cardiovascular disease diagnosis heavily relies on accurate blood flow assessments, traditionally performed using invasive and often uncomfortable methods like catheterization. This research introduces PPG-Net 4, an innovative deep learning approach for non-invasive blood flow pattern classification using dual photoplethysmography (PPG) signals. By leveraging advanced machine learning techniques, the proposed method addresses critical limitations in current diagnostic technologies. The study employed a novel dual-sensor arrangement capturing PPG signals from two body locations, generating a comprehensive dataset from 75 participants. Advanced signal processing techniques, including mel spectrogram generation and mel-frequency cepstral coefficient extraction, enabled sophisticated feature representation. The deep learning model, PPG-Net 4, demonstrated good capability at classifying the following five distinct blood flow patterns: laminar, turbulent, stagnant, pulsatile, and oscillatory. The experimental results revealed strong classification performance, with F1-scores ranging from 0.86 to 0.92 across different flow patterns. The highest accuracy was observed for pulsatile flow (F1-score: 0.92), underscoring the model’s precision and reliability. This approach not only provides a non-invasive alternative to traditional diagnostic methods but also offers a potentially useful technique for early cardiovascular disease detection and continuous monitoring. Full article

Background: This narrative review examines the role of artificial intelligence (AI) in bowel sound analysis for the diagnosis and management of inflammatory bowel disease (IBD). Inflammatory bowel disease (IBD), encompassing Crohn’s disease and ulcerative colitis, presents a significant clinical burden due to its unpredictable course, variable symptomatology, and reliance on invasive procedures for diagnosis and disease monitoring. Despite advances in imaging and biomarkers, tools such as colonoscopy and fecal calprotectin remain costly, uncomfortable, and impractical for frequent or real-time assessment. Meanwhile, bowel sounds—an overlooked physiologic signal—reflect underlying gastrointestinal motility and inflammation but have historically lacked objective quantification. With recent advances in artificial intelligence (AI) and acoustic signal processing, there is growing interest in leveraging bowel sound analysis as a novel, non-invasive biomarker for detecting IBD, monitoring disease activity, and predicting disease flares. This approach holds the promise of continuous, low-cost, and patient-friendly monitoring, which could transform IBD management. Objectives: This narrative review assesses the clinical utility, methodological rigor, and potential future integration of artificial intelligence (AI)-driven bowel sound analysis in inflammatory bowel disease (IBD), with a focus on its potential as a non-invasive biomarker for disease activity, flare prediction, and differential diagnosis. Methods: This manuscript reviews the potential of AI-powered bowel sound analysis as a non-invasive tool for diagnosing, monitoring, and managing inflammatory bowel disease (IBD), including Crohn’s disease and ulcerative colitis. Traditional diagnostic methods, such as colonoscopy and biomarkers, are often invasive, costly, and impractical for real-time monitoring. The manuscript explores bowel sounds, which reflect gastrointestinal motility and inflammation, as an alternative biomarker by utilizing AI techniques like convolutional neural networks (CNNs), transformers, and gradient boosting. We analyze data on acoustic signal acquisition (e.g., smart T-shirts, smartphones), signal processing methodologies (e.g., MFCCs, spectrograms, empirical mode decomposition), and validation metrics (e.g., accuracy, F1 scores, AUC). Studies were assessed for clinical relevance, methodological rigor, and translational potential. Results: Across studies enrolling 16–100 participants, AI models achieved diagnostic accuracies of 88–96%, with AUCs ≥ 0.83 and F1 scores ranging from 0.71 to 0.85 for differentiating IBD from healthy controls and IBS. Transformer-based approaches (e.g., HuBERT, Wav2Vec 2.0) consistently outperformed CNNs and tabular models, yielding F1 scores of 80–85%, while gradient boosting on wearable multi-microphone recordings demonstrated robustness to background noise. Distinct acoustic signatures were identified, including prolonged sound-to-sound intervals in Crohn’s disease (mean 1232 ms vs. 511 ms in IBS) and high-pitched tinkling in stricturing phenotypes. Despite promising performance, current models remain below established biomarkers such as fecal calprotectin (~90% sensitivity for active disease), and generalizability is limited by small, heterogeneous cohorts and the absence of prospective validation. Conclusions: AI-powered bowel sound analysis represents a promising, non-invasive tool for IBD monitoring. However, widespread clinical integration requires standardized data acquisition protocols, large multi-center datasets with clinical correlates, explainable AI frameworks, and ethical data governance. Future directions include wearable-enabled remote monitoring platforms and multi-modal decision support systems integrating bowel sounds with biomarker and symptom data. This manuscript emphasizes the need for large-scale, multi-center studies, the development of explainable AI frameworks, and the integration of these tools within clinical workflows. Future directions include remote monitoring using wearables and multi-modal systems that combine bowel sounds with biomarkers and patient symptoms, aiming to transform IBD care into a more personalized and proactive model. Full article

Background/Objectives: Ischemia with non-obstructive coronary arteries (INOCA) remains an underdiagnosed and undertreated condition due to the extensive diagnostic testing required and heterogeneous pathophysiology of different endotypes, each of which require tailored treatment. This study aimed to explore the effect of intracoronary physiology testing-based endotype-specific medical therapy on quality of life in patients with INOCA. Methods: Intracoronary physiology testing was performed in patients presenting with cardiac symptoms, evidence of significant ischemia on non-invasive testing, and non-obstructive epicardial coronary arteries. Microvascular angina (MVA) was defined as coronary flow reserve ≤ 2.5 and an index of microvascular resistance ≥ 25. Vasospastic angina (VSA) was defined as a >90% vasoconstriction of an epicardial artery during acetylcholine provocation test in the presence of ischemic electrocardiogram changes and chest pain. Quality of life was evaluated using the Seattle Angina Questionnaire 7 (SAQ-7) before the start of new treatment and at the three months follow-up. Results: The total study population consisted of 35 patients (80% women), of whom MVA was observed in 19 (54.3%), VSA in 9 (25.7%), and the combination of MVA and VSA in 3 (8.6%) cases. Four patients (11.4%) had no pathology on intracoronary physiology testing detected. High rates of dyslipidemia (100%), arterial hypertension (85.7%), diabetes (17.1%), and depression and anxiety (34.3%) were documented. In the isolated MVA and VSA groups, adjustment of medical therapy resulted in an improvement in the SAQ-7 summary score at 3 months (p < 0.001 and p = 0.007, respectively). There was no change of SAQ-7 summary score in the mixed endotype group (p = 0.11). Conclusions: Adjustment of medical therapy according to intracoronary physiology testing-based phenotype resulted in improved quality of life as assessed by the SAQ-7. Our findings highlight the importance of invasive testing in patients with clinically suspected INOCA. Full article

Background/Objectives: Colorectal cancer (CRC) is a leading cause of cancer-related morbidity and mortality worldwide. This study evaluates the predictive value of Carcinoembryonic Antigen (CEA) in identifying CRC recurrence following surgical resection. Methods: This retrospective study was realized in the Oncology Institute in Cluj-Napoca and included 88 patients diagnosed with CRC. Clinical, demographic, and tumor-specific data were collected, including TNM staging, tumor histology. CEA levels were recorded before surgery. Receiver Operating Characteristic (ROC) analysis was performed to determine the diagnostic accuracy of CEA in predicting tumor relapse, and the sensitivity and specificity of various CEA cut-off values were assessed. Results: Most patients presented with advanced-stage tumors (T3/T4, 80.6%). CEA levels were significantly higher in patients with lymphatic and perineural invasion and in those with metastases (mean CEA: 45.0 ng/mL for M1 vs. 13.2 ng/mL for M0, p = 0.032). ROC analysis revealed an area under the curve (AUC) of 0.877 (95% CI: 0.763–0.949). A CEA cut-off value of 11.73 ng/mL yielded 100% sensitivity and 74.5% specificity for detecting recurrence; Conclusions: CEA is a valuable non-invasive biomarker for predicting CRC relapse, with high sensitivity and acceptable specificity. Regular CEA monitoring post-surgery can facilitate early detection of recurrence, improving prognosis. Full article

Background/Objectives: To evaluate whether incorporating both diffusion-weighted imaging (DWI) and perfusion-weighted imaging (PWI) in pituitary MRI examinations improves differential diagnosis by providing additional diagnostic value. Methods: A retrospective analysis was performed on 88 patients with histologically confirmed sellar or parasellar tumors who underwent 1.5T MRI with DWI and dynamic susceptibility contrast PWI (DSC-PWI) between October 2007 and April 2023. DWI parameters included minimum apparent diffusion coefficient (ADCmin) and relative ADCmin (rADCmin). PWI parameters included mean and maximum relative cerebral blood volume (rCBV, rCBVmax) and relative peak height (rPH, rPHmax), normalized to white matter. Tumor regions of interest were manually segmented, excluding calcified or hemorrhagic areas. Group comparisons and ROC analyses assessed diagnostic performance of individual and combined parameters. Results: Significant differences in diffusion and perfusion metrics were observed among the five tumor types. The combined analysis of DWI and PWI improved diagnostic accuracy in selected comparisons. The greatest benefit occurred in distinguishing meningiomas from solid non-functional pituitary adenomas (pituitary neuroendocrine tumors-PitNET), where the combination of ADCmin and rPHmax yielded an AUC of 0.818, sensitivity of 88%, and specificity of 76%, exceeding the performance of either parameter alone. In other comparisons, including meningiomas versus invasive PitNETs and adamantinomatous craniopharyngiomas, combined analysis did not substantially improve accuracy when single parameters, particularly rCBVmax (AUC = 0.995), already demonstrated excellent performance. Conclusions: Integration of DWI and PWI into pituitary MRI protocols enhances diagnostic performance in selected tumor groups. The additive value is context-dependent, supporting the tailored application of these sequences in the evaluation of sellar and parasellar tumors. Full article