Benign lymph node enlargement can mislead surgeons into overstaging colorectal cancer (CRC), causing unnecessarily extended lymphadenectomy. This study aimed to develop and validate a machine learning (ML) classifier utilizing multi-phase CT (MPCT) radiomics for accurate evaluation of the pre-treatment status of enlarged tumor-draining lymph nodes (TDLNs; defined as long-axis diameter ≥ 10 mm). This study included 430 pathologically confirmed CRC patients who underwent radical resection, stratified into a development cohort (n = 319; January 2015-December 2019, retrospectively enrolled) and test cohort (n = 111; January 2020-May 2023, prospectively enrolled). Radiomics features were extracted from multi-regional lesions (tumor and enlarged TDLNs) on MPCT. Following rigorous feature selection, optimal features were employed to train multiple ML classifiers. The top-performing classifier based on area under receiver operating characteristic curves (AUROCs) was validated. Ultimately, 15 classifiers based on features from multi-regional lesions were constructed (Tumor_{N, A}, _V; Ln_N, _A, _V; Ln, lymph node; _N, non-contrast phase; _A, arterial phase; _V, venous phase). Among all classifiers, the enlarged TDLNs fusion MPCT classifier (Ln_NAV) demonstrated the highest predictive efficacy, with AUROCs and AUPRCs of 0.820 and 0.883, respectively. When pre-treatment clinical variables were integrated (Clinical_Ln_NAV), the model's efficacy improved, with AUROCs of 0.839, AUPRCs of 0.903, accuracy of 76.6%, sensitivity of 67.7%, and specificity of 89.1%. The classifier Clinical_Ln_NAV demonstrated well performance in evaluating pre-treatment status of enlarged TDLNs. This tool may support clinicians in developing individualized treatment plans for CRC patients, helping to avoid inappropriate treatment. Question There are currently no effective non-invasive tools to assess the status of enlarged tumor-draining lymph nodes in colorectal cancer prior to treatment. Findings Pre-treatment multi-phase CT radiomics, combined with clinical variables, effectively assessed the status of enlarged tumor-draining lymph nodes, achieving a specificity of 89.1%. Clinical relevance statement The multi-phase CT-based classifier may assist clinicians in developing individualized treatment plans for colorectal cancer patients, potentially helping to avoid inappropriate preoperative adjuvant therapy and unnecessary extended lymphadenectomy.

Uterine sarcoma is a rare disease whose association with body composition parameters is poorly understood. This study explored the impact of body composition parameters on overall survival with uterine sarcoma. This multicenter study included 52 patients with uterine sarcomas treated at three Japanese hospitals between 2007 and 2023. A semi-automatic segmentation program based on deep learning analyzed transaxial CT images at the L3 vertebral level, calculating body composition parameters as follows: area indices (areas divided by height squared) of skeletal muscle, visceral and subcutaneous adipose tissue (SMI, VATI, and SATI, respectively); skeletal muscle density; and the visceral-to-subcutaneous fat area ratio (VSR). The optimal cutoff values for each parameter were calculated using maximally selected rank statistics with several p value approximations. The effects of body composition parameters and clinical data on overall survival (OS) and cancer-specific survival (CSS) were analyzed. Univariate Cox proportional hazards regression analysis revealed that advanced stage (III-IV) and high VSR were unfavorable prognostic factors for both OS and CSS. Multivariate Cox proportional hazard regression analysis revealed that advanced stage (III-IV) (hazard ratios (HRs), 4.67 for OS and 4.36 for CSS, p < 0.01), and high VSR (HRs, 9.36 for OS and 8.22 for CSS, p < 0.001) were poor prognostic factors for both OS and CSS. Added values were observed when the VSR was incorporated into the OS and the CSS prediction models. Increased VSR and tumor stage are significant predictors of poor overall survival in patients with uterine sarcoma.

Neoadjuvant therapy (NAT) is the standard treatment strategy for MRI-defined cT4 rectal cancer. Predicting tumor regression can guide the resection plane to some extent. Here, we covered pre-treatment MRI imaging of 363 cT4 rectal cancer patients receiving NAT and radical surgery from three hospitals: Center 1 (n = 205), Center 2 (n = 109) and Center 3 (n = 52). We propose a machine learning model named RCMIX, which incorporates a multilayer perceptron algorithm based on 19 pre-treatment MRI radiomic features and 2 clinical features in cT4 rectal cancer patients receiving NAT. The model was trained on 205 cases of cT4 rectal cancer patients, achieving an AUC of 0.903 (95% confidence interval, 0.861-0.944) in predicting T-downstage. It also achieved AUC of 0.787 (0.699-0.874) and 0.773 (0.646-0.901) in two independent test cohorts, respectively. cT4 rectal cancer patients who were predicted as Well T-downstage by the RCMIX model had significantly better disease-free survival than those predicted as Poor T-downstage. Our study suggests that the RCMIX model demonstrates satisfactory performance in predicting T-downstage by NAT for cT4 rectal cancer patients, which may provide critical insights to improve surgical strategies.

BackgroundHepatic steatosis (HS) is a common cardiometabolic risk factor frequently present but under- diagnosed in patients with suspected or known coronary artery disease. We used artificial intelligence (AI) to automatically quantify hepatic tissue measures for identifying HS from CT attenuation correction (CTAC) scans during myocardial perfusion imaging (MPI) and evaluate their added prognostic value for all-cause mortality prediction. MethodsThis study included 27039 consecutive patients [57% male] with MPI scans from nine sites. We used an AI model to segment liver and spleen on low dose CTAC scans and quantify the liver measures, and the difference of liver minus spleen (LmS) measures. HS was defined as mean liver attenuation < 40 Hounsfield units (HU) or LmS attenuation < -10 HU. Additionally, we used seven sites to develop an AI liver risk index (LIRI) for comprehensive hepatic assessment by integrating the hepatic measures and two external sites to validate its improved prognostic value and generalizability for all-cause mortality prediction over HS. FindingsMedian (interquartile range [IQR]) age was 67 [58, 75] years and body mass index (BMI) was 29.5 [25.5, 34.7] kg/m2, with diabetes in 8950 (33%) patients. The algorithm identified HS in 6579 (24%) patients. During median [IQR] follow-up of 3.58 [1.86, 5.15] years, 4836 (18%) patients died. HS was associated with increased mortality risk overall (adjusted hazard ratio (HR): 1.14 [1.05, 1.24], p=0.0016) and in subpopulations. LIRI provided higher prognostic value than HS after adjustments overall (adjusted HR 1.5 [1.32, 1.69], p<0.0001 vs HR 1.16 [1.02, 1.31], p=0.0204) and in subpopulations. InterpretationsAI-based hepatic measures automatically identify HS from CTAC scans in patients undergoing MPI without additional radiation dose or physician interaction. Integrated liver assessment combining multiple hepatic imaging measures improved risk stratification for all-cause mortality. FundingNational Heart, Lung, and Blood Institute/National Institutes of Health. Research in context Evidence before this studyExisting studies show that fully automated hepatic quantification analysis from chest computed tomography (CT) scans is feasible. While hepatic measures show significant potential for improving risk stratification and patient management, CT attenuation correction (CTAC) scans from patients undergoing myocardial perfusion imaging (MPI) have rarely been utilized for concurrent and automated volumetric hepatic analysis beyond its current utilization for attenuation correction and coronary artery calcium burden assessment. We conducted a literature review on PubMed and Google Scholar on April 1st, 2025, using the following keywords: ("liver" OR "hepatic") AND ("quantification" OR "measure") AND ("risk stratification" OR "survival analysis" OR "prognosis" OR "prognostic prediction") AND ("CT" OR "computed tomography"). Previous studies have established approaches for the identification of hepatic steatosis (HS) and its prognostic value in various small- scale cohorts using either invasive biopsy or non-invasive imaging approaches. However, CT-based non- invasive imaging, existing research predominantly focuses on manual region-of-interest (ROI)-based hepatic quantification from selected CT slices or on identifying hepatic steatosis without comprehensive prognostic assessment in large-scale and multi-site cohorts, which hinders the association evaluation of hepatic steatosis for risk stratification in clinical routine with less precise estimates, weak statistical reliability, and limited subgroup analysis to assess bias effects. No existing studies investigated the prognostic value of hepatic steatosis measured in consecutive patients undergoing MPI. These patients usually present with multiple cardiovascular risk factors such as hypertension, dyslipidemia, diabetes and family history of coronary disease. Whether hepatic measures could provide added prognostic value over existing cardiometabolic factors is unknown. Furthermore, despite the diverse hepatic measures on CT in existing literature, integrated AI-based assessment has not been investigated before though it may improve the risk stratification further over HS. Lastly, previous research relied on dedicated CT scans performed for screening purposes. CTAC scans obtained routinely with MPI had never been utilized for automated HS detection and prognostic evaluation, despite being readily available at no additional cost or radiation exposure. Added value of this studyIn this multi-center (nine sites) international (three countries) study of 27039 consecutive patients undergoing myocardial perfusion imaging (MPI) with PET or SPECT, we used an innovative artificial intelligence (AI)- based approach for automatically segmenting the entire liver and spleen volumes from low-dose ungated CT attenuation correction (CTAC) scans acquired during MPI, followed by the identification of hepatic steatosis. We evaluated the added prognostic value of several key hepatic metrics--liver measures (mean attenuation, coefficient of variation (CoV), entropy, and standard deviation), and similar measures for the difference of liver minus spleen (LmS)--derived from volumetric quantification of CTAC scans with adjustment for existing clinical and MPI variables. A HS imaging criterion (HSIC: a patient has moderate or severe hepatic steatosis if the mean liver attenuation is < 40 Hounsfield unit (HU) or the difference of liver mean attenuation and spleen mean attenuation is < -10 HU) was used to detect HS. These hepatic metrics were assessed for their ability to predict all-cause mortality in a large-scale and multi-center patient cohort. Additionally, we developed and validated an eXtreme Gradient Boosting decision tree model for integrated liver assessment and risk stratification by combining the hepatic metrics with the demographic variables to derive a liver risk index (LIRI). Our results demonstrated strong associations between the hepatic metrics and all-cause mortality, even after adjustment for clinical variables, myocardial perfusion, and atherosclerosis biomarkers. Our results revealed significant differences in the association of HS with mortality in different sex, age, and race subpopulations. Similar differences were also observed in various chronic disease subpopulations such as obese and diabetic subpopulations. These results highlighted the modifying effects of various patient characteristics, partially accounting for the inconsistent association observed in existing studies. Compared with individual hepatic measures, LIRI showed significant improvement compared to HSIC-based HS in mortality prediction in external testing. All these demonstrate the feasibility of HS detection and integrated liver assessment from cardiac low-dose CT scans from MPI, which is also expected to apply for generic chest CT scans which have coverage of liver and spleen while prior studies used dedicated abdominal CT scans for such purposes. Implications of all the available evidenceRoutine point-of-care analysis of hepatic quantification can be seamlessly integrated into all MPI using CTAC scans to noninvasively identify HS at no additional cost or radiation exposure. The automatically derived hepatic metrics enhance risk stratification by providing additional prognostic value beyond existing clinical and imaging factors, and the LIRI enables comprehensive assessment of liver and further improves risk stratification and patient management.

Prostate cancer (PCa) is one of the most common cancers among men, and artificial intelligence (AI) is emerging as a promising tool to enhance its diagnosis. This work proposes a classification approach for PCa cases using deep learning techniques. We conducted a comparison between unimodal models based either on biparametric magnetic resonance imaging (bpMRI) or clinical data (such as prostate-specific antigen levels, prostate volume, and age). We also introduced a bimodal model that simultaneously integrates imaging and clinical data to address the limitations of unimodal approaches. Furthermore, we propose a framework that not only detects the presence of PCa but also evaluates the uncertainty associated with the predictions. This approach makes it possible to identify highly confident predictions and distinguish them from those characterized by uncertainty, thereby enhancing the reliability and applicability of automated medical decisions in clinical practice. The results show that the bimodal model significantly improves performance, with an area under the curve (AUC) reaching 0.82±0.03, a sensitivity of 0.73±0.04, while maintaining high specificity. Uncertainty analysis revealed that the bimodal model produces more confident predictions, with an uncertainty accuracy of 0.85, surpassing the imaging-only model (which is 0.71). This increase in reliability is crucial in a clinical context, where precise and dependable diagnostic decisions are essential for patient care. The integration of clinical data with imaging data in a bimodal model not only improves diagnostic performance but also strengthens the reliability of predictions, making this approach particularly suitable for clinical use.

Appropriate categorization based on magnetic resonance imaging (MRI) findings is important for managing intraductal papillary mucinous neoplasms (IPMNs). In this study, a large language model (LLM) that classifies IPMNs based on MRI findings was developed, and its performance was compared with that of less experienced human readers. The medical image management and processing systems of our hospital were searched to identify MRI reports of branch-duct IPMNs (BD-IPMNs). They were assigned to the training, validation, and testing datasets in chronological order. The model was trained on the training dataset, and the best-performing model on the validation dataset was evaluated on the test dataset. Furthermore, two radiology residents (Readers 1 and 2) and an intern (Reader 3) manually sorted the reports in the test dataset. The accuracy, sensitivity, and time required for categorizing were compared between the model and readers. The accuracy of the fine-tuned LLM for the test dataset was 0.966, which was comparable to that of Readers 1 and 2 (0.931-0.972) and significantly better than that of Reader 3 (0.907). The fine-tuned LLM had an area under the receiver operating characteristic curve of 0.982 for the classification of cyst diameter ≥ 10 mm, which was significantly superior to that of Reader 3 (0.944). Furthermore, the fine-tuned LLM (25 s) completed the test dataset faster than the readers (1,887-2,646 s). The fine-tuned LLM classified BD-IPMNs based on MRI findings with comparable performance to that of radiology residents and significantly reduced the time required.

Colonoscopy is a mainstay of colorectal cancer screening and has helped to lower cancer incidence and mortality. The resection of polyps during colonoscopy is critical for tissue diagnosis and prevention of colorectal cancer, albeit resulting in increased resource requirements and expense. Discarding resected benign polyps without sending for histopathological processing and confirmatory diagnosis, known as the resect and discard strategy, could enhance efficiency but is not commonly practiced due to endoscopists predominant preference for pathological confirmation. The inaccessibility of histopathology from unprocessed resected tissue hampers endoscopic decisions. We show that intraprocedural fibre-optic microscopy with ultraviolet-C surface excitation (FUSE) of polyps post-resection enables rapid diagnosis, potentially complementing endoscopic interpretation and incorporating pathologist oversight. In a clinical study of 28 patients, slide-free FUSE microscopy of freshly resected polyps yielded mucosal views that greatly magnified the surface patterns observed on endoscopy and revealed previously unavailable histopathological signatures. We term this new cross-specialty readout surface histology. In blinded interpretations of 42 polyps (19 training, 23 reading) by endoscopists and pathologists of varying experience, surface histology differentiated normal/benign, low-grade dysplasia, and high-grade dysplasia and cancer, with 100% performance in classifying high/low risk. This FUSE dataset was also successfully interpreted by foundation AI models pretrained on histopathology slides, illustrating a new potential for these models to not only expedite conventional pathology tasks but also autonomously provide instant expert feedback during procedures that typically lack pathologists. Surface histology readouts during colonoscopy promise to empower endoscopist decisions and broadly enhance confidence and participation in resect and discard. One Sentence SummaryRapid microscopy of resected polyps during colonoscopy yielded accurate diagnoses, promising to enhance colorectal screening.

Latent diffusion models (LDM) could alleviate data scarcity challenges affecting machine learning development for medical imaging. However, medical LDM training typically relies on performance- or scientific accessibility-limiting strategies including a reliance on short-prompt text encoders, the reuse of non-medical LDMs, or a requirement for fine-tuning with large data volumes. We propose a Class-Conditioned Efficient Large Language model Adapter (CCELLA) to address these limitations. CCELLA is a novel dual-head conditioning approach that simultaneously conditions the LDM U-Net with non-medical large language model-encoded text features through cross-attention and with pathology classification through the timestep embedding. We also propose a joint loss function and a data-efficient LDM training framework. In combination, these strategies enable pathology-conditioned LDM training for high-quality medical image synthesis given limited data volume and human data annotation, improving LDM performance and scientific accessibility. Our method achieves a 3D FID score of 0.025 on a size-limited prostate MRI dataset, significantly outperforming a recent foundation model with FID 0.071. When training a classifier for prostate cancer prediction, adding synthetic images generated by our method to the training dataset improves classifier accuracy from 69% to 74%. Training a classifier solely on our method's synthetic images achieved comparable performance to training on real images alone.

Renal cell carcinoma (RCC) represents the most prevalent malignant neoplasm of the kidney, with a rising global incidence. Tumor nuclear grade is a crucial prognostic factor, guiding treatment decisions, but current histopathological grading via biopsy is invasive and prone to sampling errors. This study aims to assess the diagnostic performance and quality of CT-based radiomics for preoperatively predicting RCC nuclear grade. A comprehensive search was conducted across PubMed, Scopus, Embase, and Web of Science to identify relevant studies up until 19 April 2025. Quality was assessed using the QUADAS-2 and METRICS tools. A bivariate random-effects meta-analysis was performed to evaluate model performance, including sensitivity, specificity, and Area Under the Curve (AUC). Results from separate validation cohorts were pooled, and clinical and combined models were analyzed separately in distinct analyses. A total of 26 studies comprising 1993 individuals in 10 external and 16 internal validation cohorts were included. Meta-analysis of radiomics models showed pooled AUC of 0.88, sensitivity of 0.78, and specificity of 0.82. Clinical and combined (clinical-radiomics) models showed AUCs of 0.73 and 0.86, respectively. QUADAS-2 revealed significant risk of bias in the Index Test and Flow and Timing domains. METRICS scores ranged from 49.7 to 88.4%, with an average of 66.65%, indicating overall good quality, though gaps in some aspects of study methodologies were identified. This study suggests that radiomics models show great potential and diagnostic accuracy for non-invasive preoperative nuclear grading of RCC. However, challenges related to generalizability and clinical applicability remain, as further research with standardized methodologies, external validation, and larger cohorts is needed to enhance their reliability and integration into routine clinical practice.

Fully automated, artificial intelligence (AI) -based software has recently become available for scalable body composition analysis. Prior to broad application in the clinical arena, validation studies are needed. Our goal was to compare the results of a fully automated, AI-based software with a semi-automatic software in a sample of hospitalized patients. A diverse group of patients with Coronovirus-2 (COVID-19) and evaluable computed tomography (CT) images were included in this retrospective cohort. Our goal was to compare multiple aspects of body composition procuring results from fully automated and semi-automated body composition software. Bland-Altman analyses and correlation coefficients were used to calculate average bias and trend of bias for skeletal muscle (SM), visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), intermuscular adipose tissue (IMAT), and total adipose tissue (TAT-the sum of SAT, VAT, and IMAT). A total of 141 patients (average (standard deviation (SD)) age of 58.2 (18.9), 61% male, and 31% White Non-Hispanic, 31% Black Non-Hispanic, and 33% Hispanic) contributed to the analysis. Average bias (mean ± SD) was small (in comparison to the SD) and negative for SM (-3.79 cm² ± 7.56 cm²) and SAT (-7.06 cm² ± 19.77 cm²), and small and positive for VAT (2.29 cm² ± 15.54 cm²). A large negative bias was observed for IMAT (-7.77 cm² ± 5.09 cm²), where fully automated software underestimated intramuscular tissue quantity relative to the semi-automated software. The discrepancy in IMAT calculation was not uniform across its range given a correlation coefficient of -0.625; as average IMAT increased, the bias (underestimation by fully automated software) was greater. When compared to a semi-automated software, a fully automated, AI-based software provides consistent findings for key CT body composition measures (SM, SAT, VAT, TAT). While our findings support good overall agreement as evidenced by small biases and limited outliers, additional studies are needed in other clinical populations to further support validity and advanced precision, especially in the context of body composition and malnutrition assessment.