Diagnostics

Background: Robot-assisted radical prostatectomy is the standard minimally invasive treatment for localized prostate cancer. Conventional multiport robotic surgery is widely applied yet associated with more abdominal trauma and postoperative discomfort. Single-port robotic surgery can further minimize surgical injury. The domestic KangDuo robotic system has been gradually used in urological oncology, but high-level prospective evidence comparing single-port and multiport extraperitoneal radical prostatectomy remains scarce. This trial aims to compare the efficacy and safety of these two surgical approaches. Methods: This is a single-center, prospective, 1:1 randomized, non-inferiority trial. Eligible patients diagnosed with cT1c-cT2cN0M0 localized prostate cancer will be randomized to receive either single-port (experimental group) or multiport (control group) extraperitoneal robotic radical prostatectomy using the KangDuo system. All participants will receive standardized enhanced recovery after surgery (ERAS) protocols. The primary endpoint is postoperative positive surgical margin rate. Perioperative parameters, postoperative recovery, medium-term functional recovery and oncological outcomes will be assessed comprehensively. Expected results: We hypothesize that the single-port approach is non-inferior to the multiport approach in terms of oncological radical effect. The single-port technique is expected to provide better minimally invasive benefits, with comparable perioperative safety, urinary/sexual function recovery and mid-term oncological outcomes between groups. Conclusions: This randomized trial will provide high-quality clinical evidence for the application of domestic single-port robotic surgery. The findings will facilitate individualized surgical selection for patients with localized prostate cancer. Trial Registration: Chinese Clinical Trial Registry, ChiCTR2600125669 (http://www.chictr.org.cn/showproj.aspx?proj=2600125669).

Background: Self-supervised learning may improve segmentation when labelled data are limited, but its benefit may depend on model and initialisation strategy. This study evaluated in-domain masked-reconstruction pretraining across limited annotation budgets. Methods: The International Skin Imaging Collaboration (ISIC) 2016 development set was partitioned into 765 images for self-supervised pretraining, 94 for fine-tuning and 41 for validation. Five nested annotation budgets (12-94 images) and five seeds were evaluated on the 379-image test set. A custom U-Net compared random and in-domain self-supervised initialisation; a ResNet-34 U-Net compared random, ImageNet, in-domain self-supervised and sequential ImageNet-to-self-supervised initialisation. Models trained with 94 labelled images were evaluated without adaptation on ISIC 2017. A separate experiment examined restoration-based pretraining. Results: In the custom U-Net, self-supervised pretraining achieved higher mean Dice than random initialisation at all five budgets, including 0.664 versus 0.615 with 12 labels and 0.811 versus 0.768 with 94 labels. Seed-level 95% confidence intervals for paired differences excluded zero at 47 and 94 labels but not at the other budgets. With 47 labels, the self-supervised model achieved mean Dice of 0.789, compared with 0.768 for random initialisation with 94 labels. In ResNet-34, ImageNet initialisation achieved higher mean Dice than in-domain self-supervised pretraining at all five budgets, and adding self-supervised pretraining after ImageNet reduced mean Dice at four of five budgets. All six 94-label configurations achieved lower mean Dice on ISIC 2017 than ISIC 2016. In the restoration experiment, mean Dice was 0.686 with random initialisation and 0.638-0.642 following pretraining. Conclusions: In-domain masked-reconstruction pretraining improved mean Dice relative to random initialisation in the custom U-Net, but not consistently in the ResNet-34 U-Net, where ImageNet initialisation performed better at every budget. Adding self-supervised pretraining after ImageNet provided no consistent gain. These findings are specific to the dermoscopic lesion-segmentation experiments and do not establish melanoma-specific diagnostic or clinical performance.

Background: Conventional performance metrics do not indicate whether artificial intelligence (AI)-platforms and radiologists are right or wrong on the same examinations, nor whether their non-agreement is more often beneficial or harmful. Objective: To develop, illustrate, and apply directional metrics quantifying non-agreement between radiologists and AI-platforms against a common reference standard, and to adapt a fragility-based framework for quantifying the statistical robustness of these metrics. Methods: We defined Positive Non-Agreement (PNA, AI-platform correct, radiologist incorrect), Negative Non-Agreement (NNA, radiologist correct, AI-platform incorrect), and Net Non-Agreement (NENA, their difference). Metrics were illustrated in three hypothetical scenarios of 1,000 examinations each and applied to three previously published AI-radiologist studies on pulmonary embolism and mammography. Bootstrap confidence intervals were calculated, and a reclassification fragility index (FI) quantified the minimum case reclassification needed to remove statistical support for NENA's direction. Results: In illustration, NENA was positive in two scenarios and negative in one. In the three published studies, NENA ranged from -4.18% to +10.74% and decreased consistently with increasing radiologist experience (junior residents, +6.27%; consultants, +1.45%), a gradient driven primarily by declining PNA with modest increase in NNA. The reclassification FI was defined for all three illustrative scenarios (range, 0.10%-1.70%) and for six of the seven comparisons from these studies (range, 0.34%-4.31%), but not for the weakest comparison, whose confidence interval for NENA included zero. Conclusions: PNA, NNA, and NENA provide a directional, case-level complement to conventional performance metrics for AI-radiologist non-agreement. The accompanying reclassification FI offers a statistically grounded, generalizable method for quantifying their robustness.

Introduction: Lichen sclerosus (LS) is a chronic inflammatory dermatosis predominantly affecting the anogenital region of both women and men. Sexual dysfunction remains among the most debilitating and overlooked consequences of the disease, as available patient-reported outcome measures are either restricted to one sex or capture sexual health only partially. The aim of the present work is to validate the Qualisex questionnaire (in its the Italian version) in patients with LS and to evaluate its psychometric properties, factor structure, convergent, discriminant validity, and sex symmetry. Methods: A cross-sectional study was conducted on 211 patients (167 women, 44 men; mean age 52.9 years) with a confirmed diagnosis of LS. Participants completed the Italian Qualisex, Skindex-29, DASS-21, and sexual-health-specific scales, the Female Sexual Function Index (FSFI) for women, and the International Index of Erectile Function (IIEF) for men. Construct validity was assessed via Principal Component Analysis (PCA), and reliability via Cronbach's alpha. Convergent and discriminant validities were evaluated against standard psychometric batteries. Results: Item 2 was removed due to low communality (0.167). PCA on the remaining 9 items revealed a two-factor structure accounting for 72.5% of total variance: Component 1 (Sexual Functioning and Satisfaction, 59.89%) and Component 2 (Partner Devaluation, 12.66%). Internal consistency was high (alpha= 0.911 overall; alpha= 0.841 in women; alpha= 0.899 in men). Qualisex scores correlated strongly with Skindex-29 Social Functioning (r = 0.582) and Emotions (r = 0.531) subscales, as well as FSFI (r = -0.391) and IIEF (alpha= -0.566). Discriminant validity was supported by low-to-moderate correlations with DASS-21 domains (r < 0.38). While FSFI and IIEF showed significant negative correlations with age (p = 0.002), Qualisex remained age-independent across sexes (p > 0.05). Conclusion. The 9-item Italian Qualisex is a reliable, sex-symmetrical, and age-independent patient-reported outcome measure for assessing sexual health impairment in LS, successfully isolating disease-specific distress from physiological aging and general distress.

Purpose To evaluate the external transportability of source-derived probability calibration for chest radiograph deep learning classifiers under distribution shift and determine its dependence on sample size and disease prevalence. Materials and Methods This retrospective secondary computational analysis of public, de-identified radiographic datasets involved no participant contact or new data collection. Cohort sizes were prespecified by availability without prospective power calculation: 106,686 NIH ChestX-ray14 images (29,323 patients; 1992-2015) for development and internal testing, 20,000 CheXpert images (20,000 patients; October 2002-July 2017), and 14,998 VinBigData images (2018-2020) for external testing. ResNet-18, DenseNet-121, and DeiT-Tiny classifiers for cardiomegaly and pleural effusion were trained across four patient fractions and three seeds (36 fits). Calibrators were fitted exclusively on NIH data. Twelve primary external Brier-score contrasts (temperature-scaled versus raw probabilities) were evaluated using paired cluster-bootstrapping with Holm adjustment. Results Temperature scaling reduced Brier scores in nine of 12 primary contrasts but worsened pleural effusion calibration on VinBigData across all architectures (Brier increases: ResNet-18, 0.001460 [95% CI: 0.001347, 0.001568]; DenseNet-121, 0.001193 [95% CI: 0.001109, 0.001274]; DeiT-Tiny, 0.000498 [95% CI: 0.000417, 0.000577]; all adjusted P=.006). Standardizing class weights to NIH prevalence reversed all CheXpert improvements. Although full versus 10% training improved external AUROC across all comparisons, DeiT-Tiny CheXpert pleural effusion Brier score worsened by 0.007039 despite a 0.032466 AUROC gain. At 80% selective image retention, calibrated ensembles retained only 12.3%-17.2% of VinBigData positive pleural effusion examinations. Conclusion Source-fitted calibration is not uniformly transportable across health systems, and discrimination gains do not guarantee calibrated probabilities. External evaluation must simultaneously assess probability reliability, prevalence shifts, and retained-case composition.

Deep learning (DL) applied to haematoxylin and eosin (H&E)-stained pathology images can predict microsatellite instability (MSI) in gastric cancer (GC). We evaluated its diagnostic accuracy and discriminative performance. PubMed, Embase, Cochrane Library and Web of Science were searched for studies applying DL to H&E images for MSI prediction in GC. The protocol was prospectively registered in PROSPERO (CRD420261503140). Risk of bias was assessed with QUADAS-2. Pooled sensitivity and specificity were estimated with a bivariate random-effects model, with subgroup analyses by validation strategy and data source; slide-level areas under the curve (AUCs) were pooled after logit transformation. Prespecified scenario analyses assessed robustness. Ten studies were included; seven independent datasets from six studies entered the primary synthesis. Pooled sensitivity, specificity and summary receiver operating characteristic AUC were 0.78 (95% CI 0.63-0.88), 0.85 (95% CI 0.77-0.90) and 0.89 (95% CI 0.86-0.91); nine studies contributed to the secondary AUC synthesis (pooled AUC 0.85, 95% CI 0.80-0.89). Hospital-derived cohorts showed numerically higher sensitivity (0.86) and diagnostic odds ratio (39.8) than TCGA-derived cohorts (0.71 and 11.5), but subgroup differences were limited by the small number of studies and substantial heterogeneity. DL models show good diagnostic accuracy for MSI prediction in GC, but clinical utility still requires confirmation in standardised multicentre, independent external and prospective studies.

Background: The C-reactive protein-albumin-lymphocyte (CALLY) index integrates systemic inflammation, nutritional status, and immune function. We evaluated its association with survival in primary lung malignancies. Methods: Embase, PubMed, Scopus, and Web of Science were searched from inception to 18 September 2026. Risk of bias was assessed using the Quality In Prognosis Studies tool. Hazard ratios (HRs) were synthesized using random-effects models with restricted maximum likelihood estimation and Hartung-Knapp adjustment. Sensitivity analyses, subgroup analyses, and meta-regression examined robustness and heterogeneity. Results: Eleven studies contributed 13 cohorts to the overall survival (OS) analysis and 8 cohorts from seven studies to a combined recurrence/progression-free survival (RFS/PFS) analysis. Lower CALLY was associated with poorer OS (HR 1.78, 95% CI 1.49-2.12; I2=71.6%) and poorer RFS/PFS (HR 1.60, 95% CI 1.40-1.84; I2=0%). Associations persisted in leave-one-out analyses. No statistically significant subgroup differences were detected. Funnel-plot asymmetry suggested small-study effects for OS and RFS/PFS. Seven studies had moderate and four had high overall risk of bias. The certainty of evidence was rated as low for both OS and RFS/PFS, primarily due to risk of bias, heterogeneity, and endpoint indirectness. Conclusion: Lower CALLY was associated with adverse survival outcomes in the current synthesis. Methodological limitations and variation in biomarker measurement and endpoint definitions restrict interpretation. Prospective validation using standardized measurements and clinically coherent outcomes is needed to establish incremental prognostic value and clinical utility. Keywords: CALLY index; lung cancer; prognosis; overall survival; systematic review; meta-analysis

Purpose: Posterior pharyngeal wall (PPW) thickening is a common structural manifestation of treatment-related internal lymphedema in patients with head and neck cancer and can be evaluated using lateral videofluoroscopic images acquired during modified barium swallow (MBS) studies. However, no standardized method currently exists for quantitative measurement. Methods: This two-phase methodological study first conducted a scoping review to characterize existing approaches to PPW thickness measurement and identify the principal factors contributing to variation in PPW thickness measurement. Eight studies were identified with variation observed across these measurement factors. An empirical reliability study was then performed using 107 MBS studies from 93 Head and Neck Cancer patients. Two independent raters annotated 10 anatomical landmarks on pre-swallow and post-swallow resting frames, and a computer-assisted algorithm quantitatively generated seven candidate PPW thickness measurements based on different vertebral reference points and measurement directions. Reliability was assessed intraclass correlation coefficients (ICCs). Results: Inter-rater reliability was highest for the measurement originating from the midpoint of C3 and extending perpendicular to the anterior C3 surface (ICC, 0.78; 95% CI, 0.72-0.83). Among the normalization references, C2-C4 vertebral length demonstrated greater reliability than C3 vertebral length, with an ICC of 0.88 (95% CI, 0.85-0.91). No significant differences were observed between pre-swallow and post-swallow PPW thickness measurements across the evaluated approaches. Conclusions: These findings provide a methodological foundation for more standardized and reliable quantification of PPW thickness, with potential to expand the role of MBS from the functional evaluation of swallowing to include quantitative characterization of pharyngeal structural abnormalities.

Objective: Current guidelines recommend different adjuvant treatment strategies for women with endometrioid early stage endometrial cancer exhibiting a p53 abnormal pattern. We aimed to evaluate recurrence patterns and explore the clinical implications for adjuvant therapy in this population. Materials and methods: This multicenter retrospective study included 86 patients with endometrioid, p53 abnormal, FIGO stage I, II endometrial cancer treated between 2013 and 2023 at six hospitals in the Republic of Korea. Clinicopathologic data were collected through medical record review. Progression free survival (PFS) was estimated using the Kaplan Meier method, and prognostic factors were analyzed using Cox proportional hazards regression models. Results: The 12, 24, and 36 month PFS rates were 91%, 78%, and 76%, respectively. Among the 16 patients who experienced recurrence, 12 (75%) had distant metastases. Adjuvant chemotherapy showed a trend toward improved PFS compared with adjuvant radiation therapy with or without chemotherapy (p=0.10). In multivariable analysis, the p53 overexpression pattern was independently associated with worse PFS compared with the p53 null pattern (HR 8.59, 95% CI 1.61 to 45.79, p=0.01). Conclusion: Given the poor PFS, the predominance of distant recurrence and the observed trend toward improved PFS with chemotherapy, adjuvant chemotherapy may be considered in women with endometrioid early stage endometrial cancer exhibiting a p53 abnormal pattern. Further studies are warranted to clarify the prognostic impact of p53 abnormality subtypes.

Background: Neoadjuvant chemotherapy (NACT) facilitates de-escalation of breast and axillary treatment in breast cancer, but pathological responses may differ between the primary tumour and axillary lymph nodes. Understanding differential response is crucial for de-escalation strategies. Methods: This single university hospital retrospective study analysed operable breast cancers (cT1-cT3, cN0-cN3) treated with NACT (January 2013-November 2020). Two-tailed Fishers exact test or the Chi-squared test (for 2x2 comparisons) analysed concordance between breast and axillary pathological responses; Cox proportional hazards regression ascertained predictors of distant recurrence and survival; and logistic regression ascertained predictors of complete axillary response. Results: Among 413 patients (cN0=229; cN+=184) with a median age of 51, pathological response concordance between the breast and axilla was 81% (both pCR, 22.2%; concurrent residual disease, 58.8%). The cN0 group had a significantly higher ypN0 rate in breast pCR versus non-pCR (97.1% vs 80.8%, p<0.001). cN+ had axillary pCR concordant with breast pCR, compared to only 16.5% with residual breast disease. Breast pCR was the most significant predictor of axillary pCR (OR=11.6, p<0.001). Axillary pCR was highest in ER-/HER2+(66.7%) and triple-negative(46.9%) subtypes. Residual axillary disease was independently associated with the worst breast cancer-specific (HR=7.13, p=0.006) and overall Survival (HR=3.32, p=0.029). Kaplan-Meier analyses showed a trend towards worse survival outcomes with axillary residual disease, even with a breast pCR. Conclusions: In this limited series, breast pCR predicts axillary pCR in cN0 and cN+, particularly in HER2-positives and triple-negatives, supporting axillary de-escalation strategies in selected patients stratified by careful assessment of disease at the end of NACT.

Introduction: Acute gastroenteritis (AGE) is a cause of childhood morbidity and may be associated with human adenovirus (HAdV) infection. This study aimed to investigate the occurrence of HAdV in fecal samples from children with AGE in Salvador, Bahia, Brazil, as well as to molecularly characterize the identified strains and evaluate their cytopathic effects in cell culture. Methods: A total of 131 samples were analyzed using the nested polymerase chain reaction (Nested-PCR) technique. Molecular characterization was performed through Sanger sequencing and phylogenetic analysis, while Vero cell cultures were used to investigate the occurrence of cytopathic effects. Results: Of the samples analyzed, 13 (9.9%) tested positive for HAdV, while 118 (90.1%) were negative. The highest detection frequency was observed in children aged three years or younger, particularly those under one year of age. Phylogenetic analysis identified variants related to HAdV-F41 and HAdV-B3, revealing genetic differences and distinct clustering patterns compared with reference sequences. In cell culture, cytopathic effects were observed four days after inoculation, although further analyses are required to confirm productive viral replication. Conclusion: These findings demonstrate the presence of HAdV in the samples studied and contribute to understanding its genetic diversity and circulation in Salvador, highlighting the importance of epidemiological surveillance and further studies on its role in the etiology of childhood AGE.

Purpose To assess whether follow-up MRI, biopsy status, and timing of cribriform disease detection predict active surveillance (AS) failure in a contemporary transatlantic cohort. Materials and Methods. Retrospective MRI-driven AS cohort of 619 men at two academic centers (UK/USA), 2012-2024. Primary outcome: AS failure (disease-specific mortality, metastasis, grade group [GG] [&ge;]4 progression, or post-treatment biochemical recurrence). Key exposures: final MRI and biopsy status, timing of cribriform [&ge;]GG2 detection. Cumulative incidence was derived; independent predictors used multivariable Fine-Gray models. Results. Over median follow-up of 6.4 years, 39/619 men (6%) experienced AS failure. Cribriform [&ge;]GG2 was detected in 82/619 (13%) on follow-up biopsy versus 24/619 (4%) at baseline, comparably in baseline GG1 (10%) and non-cribriform GG2 (9%), and only in MRI-visible tumors. Three-year AS failure after final biopsy was 31% in men with cribriform [&ge;]GG2 versus 0-2% with stable or non-cribriform disease; 44% of failures had stable MRI. On Fine-Gray analysis, cribriform [&ge;]GG2 was the only independent predictor of AS failure in re-biopsied men (SHR 30; 95% CI 5.2-167; P<0.001); no type of MRI progression category was independently predictive. Among 595 men cribriform-negative at baseline, 6-year AS failure after a second biopsy was 34% if cribriform [&ge;]GG2 was detected versus 7% if still cribriform-negative. Conclusions. Cribriform status at follow-up biopsy independently predicts AS failure, whereas MRI progression does not. The three-fold higher cribriform prevalence at follow-up likely reflects baseline sampling error. Because newly detected cribriform tumours were exclusively MRI-visible, early repeat biopsy of patients with MRI-visible disease may considerably improve risk stratification.

Introduction: Acute gastroenteritis (AGE) is a global public health problem and a major cause of mortality in low- and lower-middle-income countries. Human bocavirus (HBoV) is among the viral agents associated with this condition. Therefore, investigating the presence of HBoV in cases of AGE in Salvador, Bahia, as well as its prevalence and circulating strains, is important. Objective: To investigate the occurrence of HBoV infection during an AGE outbreak in Salvador. Methods: A total of 178 samples were analyzed for HBoV using nested polymerase chain reaction (nested PCR), and the amplification products were visualized by agarose gel electrophoresis. HBoV was detected in 5.7% of the samples analyzed. Conclusion: This study contributes to the understanding of HBoV epidemiology as a secondary agent associated with acute gastroenteritis during a norovirus outbreak in Salvador. Continued investigation and identification of the etiological agents involved in outbreaks are essential for evaluating the causes of AGE. These findings also highlight the need for broader diagnostic approaches and the importance of phylogenetic analysis for monitoring and controlling viral infections.

Introduction: Thyroid diseases are major health problems and among the most common endocrine disorders worldwide, including in Ethiopia. They are characterized by alterations in hormone secretion, enlargement of the thyroid gland, or both. Fine needle aspiration cytology is an important initial screening tool, and histopathologic examination is essential for the definitive diagnosis of thyroid disease. This study aimed to compare the diagnostic accuracy of fine needle aspiration cytology with histopathology in identifying thyroid diseases. Methods: A cross-sectional study was conducted at three health facilities in Hawassa City, Ethiopia, involving 304 patients who underwent both thyroid fine needle aspiration cytology and histopathologic examination of the same site between 30th October 2024 and 29th October 2025. FNAC results were classified using the Bethesda system, with histopathology as the gold standard. Data were entered into Kobo Toolbox, exported to SPSS version 27, and analyzed using cross-tabulation and chi-square ({chi}{superscript 2}) tests to determine diagnostic accuracy. Result: 304 patient records were included in the study. The fine needle aspiration cytology was determinate/diagnostic in 81.6% of cases. The study revealed the sensitivity and specificity of fine needle aspiration cytology to be 88.2% (CI 77.6-95.1%) and 94.4% (CI 90.6-97.1%), respectively. The false negative and false positive rates in this study were 2.4% and 4.4%, respectively. The positive predictive value was 80.3% (CI 68.7-89.3%), and the negative predictive value was 96.8% (CI 93.8-98.7%). The accuracy of fine needle aspiration cytology in differentiating benign from malignant thyroid lesions was 93.1%. Conclusion: FNAC demonstrated strong agreement with histopathology and remains a reliable, cost-effective first-line diagnostic tool for thyroid disease, particularly in resource-limited settings. Strengthening sample handling, ultrasound guidance, and adherence to Bethesda criteria may further improve diagnostic precision

Patients with implantable cardioverter-defibrillators (ICDs) often are excluded from receiving CMR scans at 3.0T due to ICD-induced image artifacts. This work aims to transfer sequence protocols established for CMR imaging in the presence of ICDs at 1.5T to a first human application at 3.0T. All measurements were performed on a clinical 3T MRI scanner. Phantom measurements were used to establish and optimize wideband PSIR LGE imaging at 3.0T. Volunteer measurements with externally placed ICDs were used for optimization of CINE imaging and wideband PSIR imaging. Thirdly, patients with implanted ICD were examined with the optimized protocols. Human measurements showed that SA stacks obtained with spoiled GRE after contrast agent showed a markedly lower artifact burden than the conventional approach. For scar imaging, a wideband PSIR with a bandwidth of 3kHz and a frequency offset of +750Hz allowed a sufficient suppression of relevant artifacts in the majority of scans. No strong correlation between the image quality and the distance between the cardiac silhouette and the device was detected. The presented work shows the feasibility of clinical cardiac MRI at 3.0T including GRE-CINE and wideband PSIR-LGE for patients with ICDs.

Purpose: To evaluate whether visually assessed high-quality prostate MRI, as defined by the Prostate Imaging Quality (PI-QUAL) scoring system, is sufficient to ensure consistent AI model performance, or whether hidden acquisition-related technical effects introduce variability that cannot be detected through visual inspection. Methods: This retrospective study included 568 bpMRI scans from seven institutions, classified using PI-QUAL v2 into high-quality (scores 2-3) and low-quality (score 1) groups. Quantitative image quality features were extracted using MRQy from T2w and ADC sequences. Three experiments were conducted on high-quality scans: (1) unsupervised clustering to identify hidden technical batch effects; (2) V-Net models (M) for prostate capsule segmentation on T2w using single-cluster and balanced multi-cluster training strategies; and (3) VoxelMorph-based deformable registration (M2) to align ADC maps to T2w images. Performance metrics included DSC, precision, recall, volume ratio, HD95, ASSD, and TRE. Kruskal-Wallis tests assessed cross-cluster variability, Two One-Sided Tests (TOST) evaluated performance equivalence, and bootstrap resampling quantified training efficiency. Results: Clustering identified four clusters among 277 high-quality bpMRI scans. Single-cluster models showed variable performance (DSC=0.41-0.85) with significant batch effects (p<0.001), even with 140 training samples. The balanced multi-cluster model (n=60) eliminated batch effects (p=0.075), achieved equivalent performance (p<0.001), and demonstrated 2.23-fold greater training efficiency despite using 2.3-fold fewer samples. Registration performance also varied significantly across clusters (TRE=0.86-5.47 mm, p<0.001). Conclusion: Acquisition-related batch effects significantly impact AI generalizability in prostate MRI, even among high-quality scans. Quantitative clustering enables detection of technical variations and supports diversity-aware training strategies that achieve equivalent performance with 2.3-fold fewer samples while eliminating batch effects.

Background. Real-world 12-lead electrocardiograms (ECGs) often have missing or corrupted leads. A deep-learning classifier can be prepared for this by random lead masking during training, or by injecting clinical knowledge, for example by pulling its attention towards the leads that guidelines use for each diagnosis. Whether this knowledge adds robustness beyond masking is unknown. Methods. Pre-registered comparison of three arms sharing one network (per-lead 1D-ResNet encoder and per-diagnosis attention over the 12 leads): standard training (M0); random whole-lead masking (M1); masking plus a Kullback-Leibler penalty pulling attention towards a fixed guideline-derived key-lead map (M2, guideline-based attention guidance). Six diagnoses; five seeds per arm. Development on PTB-XL (21,799 ECGs); single sealed evaluation on PTB-XL fold 10 (2,198 ECGs) and on CODE-15% (233,204 exams, external validation). Primary endpoint: AUROC difference M2 - M1, averaged over first-degree AV block, right and left bundle branch block and atrial fibrillation and over 1 to 6 lost (zeroed) leads, with a patient-level paired BCa bootstrap 95% confidence interval. Results. The guidance did not change robustness: {Delta}AUROC -0.0010 (95% CI -0.0033 to +0.0006), an interval entirely within the pre-specified +-0.02 margin; the result was unchanged with another bootstrap seed, other interval methods and leave-one-diagnosis-out analyses. Masking improved AUROC: M1 - M0 = +0.0134 (95% CI +0.0102 to +0.0172) when one key lead was lost, and the number of lost leads tolerated before AUROC fell by more than 0.02 rose from 7 (M0) to 10 (M1). M2 attention matched the map and M2 was non-inferior on intact ECGs, but its calibration slope was lower for all six diagnoses in both datasets. M2 lost slightly more when all key leads were lost together (-0.0067) and gained only when one to three leads remained (exploratory; AUROC 0.9408 vs 0.9210 with 11 leads lost). CODE-15% reproduced every pattern (primary -0.0007, 95% CI -0.0011 to -0.0003). Conclusions. Random lead masking suffices to make an ECG classifier robust to missing leads; adding guideline knowledge aligns attention with clinical criteria but adds no robustness and worsens calibration. Guideline-aligned attention should not be taken as evidence of robustness.

Objectives To evaluate discrepancies between radiologists and referring physicians in the interpretation of diagnostic probabilities conveyed by common Japanese expressions used in radiology reports. Materials and Methods Fifteen expressions commonly used to convey diagnostic probabilities in radiology reports were selected by five attending radiologists. An anonymous online survey was distributed to 26 radiologists and 33 referring physicians at a single institution. Participants assigned a diagnostic probability (from 0% to 100% in 10% increments) to each expression. Differences in median probabilities between radiologists and referring physicians were analyzed using the Mann-Whitney U test. Variability in interpretation was assessed using interquartile range (IQR), with IQR [&ge;] 30% defined as substantial variability. Results All radiologists (26/26) and 97% of referring physicians (32/33) completed the survey. Seven of the 15 expressions showed statistically significant differences in interpretation between radiologists and referring physicians: "c/w" (80% vs. 60%; p = 0.031), the Japanese equivalents of "present" (100% vs. 100%; p = 0.024), "suspect of" (80% vs. 70%; p = 0.007), "possibly" (50% vs. 70%; p < 0.001), "unlikely" (10% vs. 10%; p = 0.022), "cannot be ruled out" (25% vs. 40%; p < 0.001), and "not inconsistent with" (80% vs. 90%; p = 0.008). Substantial variability in interpretation (IQR [&ge;] 30%) was observed for four expressions: "cannot be ruled out" (IQR, 10%-40%), "s/o" (IQR, 60%-90%), "c/w" (IQR, 50%-90%), and "not inconsistent with" (IQR, 70%-100%). Conclusion This study revealed that nearly half of the Japanese expressions commonly used to convey diagnostic probabilities were interpreted differently by radiologists and referring physicians. These discrepancies highlight the need for a standardized lexicon to improve clarity and consistency in radiology reporting.

Cervical cytology screening relies on the visual assessment of nuclear and cellular morphology across thousands of cells per slide, a labor-intensive task that could benefit from quantitative computational support. Here we present CytoLens, a modular deep learning system that detects and counts nuclei, delineates nuclei and whole cells, measures the nuclear-to-cell area ratio (N:C), classifies cells from multifocal images and scores candidate abnormality. We trained and tested each module on a public dataset matched to its task, without pooling datasets. CytoLens detected nuclei with an F1 score of 0.931 (95% confidence interval (CI), 0.914-0.946) and counted 75 of 100 fields to within one nucleus. Point-prompted segmentation achieved mean Dice coefficients of 0.901 for nuclei and 0.847 for whole cells and, without retraining, transferred to a second dataset in which measured N:C increased with lesion grade in parallel with expert outlines. Multifocal hierarchical classification reached a mean accuracy of 0.947 across four Bethesda-aligned categories, and abnormality scores ranked annotated abnormal candidates with an area under the receiver operating characteristic curve (AUROC) of 0.865. These results support the evaluation of CytoLens on patient-linked clinical specimens.

Computed tomography (CT) interpretation requires recognition of the anatomical level of each slice. Classification may facilitate preprocessing and AI-assisted fracture diagnosis. We developed deep learning models to classify craniofacial CT slices into six anatomical categories. A total of 11,769 images were collected from a multicenter database involving 10 Japanese university hospitals and the public Head-Neck-Radiomics-HN1 dataset. Patients were divided into training (70%) and evaluation (30%) sets. Five CNN architectures were trained from scratch, and hard- and soft-voting ensembles were evaluated. Individual-model accuracy ranged from 94.72% to 96.09%; the hard-voting ensemble achieved 96.46% accuracy (Cohen's k = 0.954). Although performance differed among individual models (Cochran's Q = 99.29, p < 0.001), the ensemble did not significantly outperform EfficientNetV2B1 (McNemar's p = 0.163). Errors occurred mainly between adjacent anatomical regions. Deep learning can classify craniofacial CT slices accurately and may support automated dataset organization and AI-assisted fracture diagnosis. External validation is needed.