HER2 expression is crucial for the application of HER2-targeted antibody-drug conjugates. This study aims to construct a predictive model by integrating multiparametric magnetic resonance imaging (mpMRI) based multimodal radiomics and the Vesical Imaging-Reporting and Data System (VI-RADS) score for noninvasive identification of HER2 status in bladder urothelial carcinoma (BUC). A total of 197 patients were retrospectively enrolled and randomly divided into a training cohort (n = 145) and a testing cohort (n = 52). The multimodal radiomics features were derived from mpMRI, which were also utilized for VI-RADS score evaluation. LASSO algorithm and six machine learning methods were applied for radiomics feature screening and model construction. The optimal radiomics model was selected to integrate with VI-RADS score to predict HER2 status, which was determined by immunohistochemistry. The performance of predictive model was evaluated by receiver operating characteristic curve with area under the curve (AUC). Among the enrolled patients, 110 (55.8%) patients were demonstrated with HER2-positive and 87 (44.2%) patients were HER2-negative. Eight features were selected to establish radiomics signature. The optimal radiomics signature achieved the AUC values of 0.841 (95% CI 0.779-0.904) in the training cohort and 0.794 (95%CI 0.650-0.938) in the testing cohort, respectively. The KNN model was selected to evaluate the significance of radiomics signature and VI-RADS score, which were integrated as a predictive nomogram. The AUC values for the nomogram in the training and testing cohorts were 0.889 (95%CI 0.840-0.938) and 0.826 (95%CI 0.702-0.950), respectively. Our study indicated the predictive model based on the integration of mpMRI-based radiomics and VI-RADS score could accurately predict HER2 status in BUC. The model might aid clinicians in tailoring individualized therapeutic strategies.

Definitive chemoradiation is the primary treatment for locally advanced head and neck carcinoma (LAHNSCC). Optimising outcome predictions requires validated biomarkers, since TNM8 and HPV could have limitations. Radiomics may enhance risk stratification. This single-centre observational study collected clinical data and baseline CT scans from 171 LAHNSCC patients treated with chemoradiation. The dataset was divided into training (80%) and test (20%) sets, with a 5-fold cross-validation on the training set. Researchers extracted 108 radiomics features from each primary tumour and applied survival analysis and classification models to predict progression-free survival (PFS) and 5-year progression, respectively. Performance was evaluated using inverse probability of censoring weights and c-index for the PFS model and AUC, sensitivity, specificity, and accuracy for the 5-year progression model. Feature importance was measured by the SHapley Additive exPlanations (SHAP) method and patient stratification was assessed through Kaplan-Meier curves. The final dataset included 171 LAHNSCC patients, with 53% experiencing disease progression at 5 years. The random survival forest model best predicted PFS, with an AUC of 0.64 and CI of 0.66 on the test set, highlighting 4 radiomics features and TNM8 as significant contributors. It successfully stratified patients into low and high-risk groups (log-rank p < 0.005). The extreme gradient boosting model most effectively predicted a 5-year progression, incorporating 12 radiomics features and four clinical variables, achieving an AUC of 0.74, sensitivity of 0.53, specificity of 0.81, and accuracy of 0.66 on the test set. The combined clinical-radiomics model improved the standard TNM8 and clinical variables in predicting 5-year progression though further validation is necessary. Question There is an unmet need for non-invasive biomarkers to guide treatment in locally advanced head and neck cancer. Findings Clinical data (TNM8 staging, primary tumour site, age, and smoking) plus radiomics improved 5-year progression prediction compared with the clinical comprehensive model or TNM staging alone. Clinical relevance SHAP simplifies complex machine learning radiomics models for clinicians by using easy-to-understand graphical representations, promoting explainability.

Pancreatic ductal adenocarcinoma (PDAC) is the deadliest malignant tumor, with a grim 5-year overall survival rate of about 12%. As its incidence and mortality rates rise, it is likely to become the second-leading cause of cancer-related death. The radiological assessment determined the stage and management of PDAC. However, it is a highly heterogeneous disease with the complexity of the tumor microenvironment, and it is challenging to adequately reflect the biological aggressiveness and prognosis accurately through morphological evaluation alone. With the dramatic development of artificial intelligence (AI), multiparametric magnetic resonance imaging (mpMRI) using specific contrast media and special techniques can provide morphological and functional information with high image quality and become a powerful tool in quantifying intratumor characteristics. Besides, AI has been widespread in the field of medical imaging analysis. Radiomics is the high-throughput mining of quantitative image features from medical imaging that enables data to be extracted and applied for better decision support. Deep learning is a subset of artificial neural network algorithms that can automatically learn feature representations from data. AI-enabled imaging biomarkers of mpMRI have enormous promise to bridge the gap between medical imaging and personalized medicine and demonstrate huge advantages in predicting biological characteristics and the prognosis of PDAC. However, current AI-based models of PDAC operate mainly in the realm of a single modality with a relatively small sample size, and the technical reproducibility and biological interpretation present a barrage of new potential challenges. In the future, the integration of multi-omics data, such as radiomics and genomics, alongside the establishment of standardized analytical frameworks will provide opportunities to increase the robustness and interpretability of AI-enabled image biomarkers and bring these biomarkers closer to clinical practice. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 4.

Accurate preoperative evaluation of myometrial invasion (MI) is essential for treatment decisions in endometrial cancer (EC). However, the diagnostic accuracy of commonly utilized magnetic resonance imaging (MRI) techniques for this assessment exhibits considerable variability. This study aims to enhance preoperative discrimination of absence or presence of MI by developing and validating a multimodal deep learning radiomics (MDLR) model based on MRI. During March 2010 and February 2023, 1139 EC patients (age 54.771 ± 8.465 years; range 24-89 years) from five independent centers were enrolled retrospectively. We utilized ResNet18 to extract multi-scale deep learning features from T2-weighted imaging followed by feature selection via Mann-Whitney U test. Subsequently, a Deep Learning Signature (DLS) was formulated using Integrated Sparse Bayesian Extreme Learning Machine. Furthermore, we developed Clinical Model (CM) based on clinical characteristics and MDLR model by integrating clinical characteristics with DLS. The area under the curve (AUC) was used for evaluating diagnostic performance of the models. Decision curve analysis (DCA) and integrated discrimination index (IDI) were used to assess the clinical benefit and compare the predictive performance of models. The MDLR model comprised of age, histopathologic grade, subjective MR findings (TMD and Reading for MI status) and DLS demonstrated the best predictive performance. The AUC values for MDLR in training set, internal validation set, external validation set 1, and external validation set 2 were 0.899 (95% CI, 0.866-0.926), 0.874 (95% CI, 0.829-0.912), 0.862 (95% CI, 0.817-0.899) and 0.867 (95% CI, 0.806-0.914) respectively. The IDI and DCA showed higher diagnostic performance and clinical net benefits for the MDLR than for CM or DLS, which revealed MDLR may enhance decision-making support. The MDLR which incorporated clinical characteristics and DLS could improve preoperative accuracy in discriminating absence or presence of MI. This improvement may facilitate individualized treatment decision-making for EC.

Growth discordance in twin pregnancies is associated with increased perinatal morbidity and mortality, yet the patterns of discordance progression and the utility of Doppler assessments remain underinvestigated. The objective of this study was to conduct a longitudinal assessment of intertwin growth and Doppler discordance to identify possible distinct patterns and to investigate the predictive value of longitudinal discordance patterns for adverse perinatal outcomes in twin pregnancies. This retrospective cohort study included twin pregnancies followed and delivered at a tertiary hospital in London (United Kingdom) between 2010 and 2023. We included pregnancies with at least 3 ultrasound assessments after 18 weeks and delivery beyond 34 weeks' gestation. Monoamniotic twin pregnancies, pregnancies with twin-to-twin transfusion syndrome, genetic or structural abnormalities, or incomplete data were excluded. Data on chorionicity, biometry, Doppler indices, maternal characteristics and obstetrics, and neonatal outcomes were extracted from electronic records. Doppler assessment included velocimetry of the umbilical artery, middle cerebral artery, and cerebroplacental ratio. Intertwin growth discordance was calculated for each scan. The primary outcome was a composite of perinatal mortality and neonatal morbidity. Statistical analysis involved multilevel mixed effects regression models and unsupervised machine learning algorithms, specifically k-means clustering, to identify distinct patterns of intertwin discordance and their predictive value. Predictive models were compared using the area under the receiver operating characteristic curve, calibration intercept, and slope, validated with repeated cross-validation. Analyses were performed using R, with significance set at P<.05. Data from 823 twin pregnancies (647 dichorionic, 176 monochorionic) were analyzed. Five distinct patterns of intertwin growth discordance were identified using an unsupervised learning algorithm that clustered twin pairs based on the progression and patterns of discordance over gestation: low-stable (n=204, 24.8%), mild-decreasing (n=171, 20.8%), low-increasing (n=173, 21.0%), mild-increasing (n=189, 23.0%), and high-stable (n=86, 10.4%). In the high-stable cluster, the rates of perinatal morbidity (46.5%, 40/86) and mortality (9.3%, 8/86) were significantly higher compared to the low-stable (reference) cluster (P<.001). High-stable growth pattern was also associated with a significantly higher risk of composite adverse perinatal outcomes (odds ratio: 70.19, 95% confidence interval: 24.18-299.03, P<.001; adjusted odds ratio: 76.44, 95% confidence interval: 25.39-333.02, P<.001). The model integrating discordance pattern with cerebroplacental ratio discordance at the last ultrasound before delivery demonstrated superior predictive accuracy, evidenced by the highest area under the receiver operating characteristic curve of 0.802 (95% confidence interval: 0.712-0.892, P<.001), compared to only discordance patterns (area under the receiver operating characteristic curve: 0.785, 95% confidence interval: 0.697-0.873), intertwin weight discordance at the last ultrasound prior to delivery (area under the receiver operating characteristic curve: 0.677, 95% confidence interval: 0.545-0.809), combination of single measurements of estimated fetal weight and cardiopulmonary resuscitation discordance at the last ultrasound prior to delivery (area under the receiver operating characteristic curve: 0.702, 95% confidence interval: 0.586-0.818), and single measurement of cardiopulmonary resuscitation discordance only at the last ultrasound (area under the receiver operating characteristic curve: 0.633, 95% confidence interval: 0.515-0.751). Using an unsupervised machine learning algorithm, we identified 5 distinct trajectories of intertwin fetal growth discordance. Consistent high discordance is associated with increased rates of adverse perinatal outcomes, with a dose-response relationship. Moreover, a predictive model integrating discordance trajectory and cardiopulmonary resuscitation discordance at the last visit demonstrated superior predictive accuracy for the prediction of composite adverse perinatal outcomes, compared to either of these measurements alone or a single value of estimated fetal weight discordance at the last ultrasound prior to delivery.

Chronic liver disease (CLD) is a substantial cause of morbidity and mortality worldwide. Liver stiffness, as measured by MR elastography (MRE), is well-accepted as a surrogate marker of liver fibrosis. To develop and validate deep learning (DL) models for predicting MRE-derived liver stiffness using routine clinical non-contrast abdominal T1-weighted (T1w) and T2-weighted (T2w) data from multiple institutions/system manufacturers in pediatric and adult patients. We identified pediatric and adult patients with known or suspected CLD from four institutions, who underwent clinical MRI with MRE from 2011 to 2022. We used T1w and T2w data to train DL models for liver stiffness classification. Patients were categorized into two groups for binary classification using liver stiffness thresholds (≥ 2.5 kPa, ≥ 3.0 kPa, ≥ 3.5 kPa, ≥ 4 kPa, or ≥ 5 kPa), reflecting various degrees of liver stiffening. We identified 4695 MRI examinations from 4295 patients (mean ± SD age, 47.6 ± 18.7 years; 428 (10.0%) pediatric; 2159 males [50.2%]). With a primary liver stiffness threshold of 3.0 kPa, our model correctly classified patients into no/minimal (< 3.0 kPa) vs moderate/severe (≥ 3.0 kPa) liver stiffness with AUROCs of 0.83 (95% CI: 0.82, 0.84) in our internal multi-site cross-validation (CV) experiment, 0.82 (95% CI: 0.80, 0.84) in our temporal hold-out validation experiment, and 0.79 (95% CI: 0.75, 0.81) in our external leave-one-site-out CV experiment. The developed model is publicly available ( https://github.com/almahdir1/Multi-channel-DeepLiverNet2.0.git ). Our DL models exhibited reasonable diagnostic performance for categorical classification of liver stiffness on a large diverse dataset using T1w and T2w MRI data. Question Can DL models accurately predict liver stiffness using routine clinical biparametric MRI in pediatric and adult patients with CLD? Findings DeepLiverNet2.0 used biparametric MRI data to classify liver stiffness, achieving AUROCs of 0.83, 0.82, and 0.79 for multi-site CV, hold-out validation, and external CV. Clinical relevance Our DeepLiverNet2.0 AI model can categorically classify the severity of liver stiffening using anatomic biparametric MR images in children and young adults. Model refinements and incorporation of clinical features may decrease the need for MRE.

Incidentally detected pulmonary nodules present a challenge in clinical routine with demand for reliable support systems for risk classification. We aimed to evaluate the performance of the lung-cancer-prediction-convolutional-neural-network (LCP-CNN), a deep learning-based approach, in comparison to multiparametric statistical methods (Brock model and Lung-RADS®) for risk classification of nodules in cohorts with different risk profiles and underlying pulmonary diseases. Retrospective analysis was conducted on non-contrast and contrast-enhanced CT scans containing pulmonary nodules measuring 5-30 mm. Ground truth was defined by histology or follow-up stability. The final analysis was performed on 297 patients with 422 eligible nodules, of which 105 nodules were malignant. Classification performance of the LCP-CNN, Brock model, and Lung-RADS® was evaluated in terms of diagnostic accuracy measurements including ROC-analysis for different subcohorts (total, screening, emphysema, and interstitial lung disease). LCP-CNN demonstrated superior performance compared to the Brock model in total and screening cohorts (AUC 0.92 (95% CI: 0.89-0.94) and 0.93 (95% CI: 0.89-0.96)). Superior sensitivity of LCP-CNN was demonstrated compared to the Brock model and Lung-RADS® in total, screening, and emphysema cohorts for a risk threshold of 5%. Superior sensitivity of LCP-CNN was also shown across all disease groups compared to the Brock model at a threshold of 65%, compared to Lung-RADS® sensitivity was better or equal. No significant differences in the performance of LCP-CNN were found between subcohorts. This study offers further evidence of the potential to integrate deep learning-based decision support systems into pulmonary nodule classification workflows, irrespective of the individual patient risk profile and underlying pulmonary disease. Question Is a deep-learning approach (LCP-CNN) superior to multiparametric models (Brock model, Lung-RADS®) in classifying pulmonary nodule risk across varied patient profiles? Findings LCP-CNN shows superior performance in risk classification of pulmonary nodules compared to multiparametric models with no significant impact on risk profiles and structural pulmonary diseases. Clinical relevance LCP-CNN offers efficiency and accuracy, addressing limitations of traditional models, such as variations in manual measurements or lack of patient data, while producing robust results. Such approaches may therefore impact clinical work by complementing or even replacing current approaches.

To establish morphological and radiomic models for early prediction of cognitive impairment associated with cerebrovascular disease (CI-CVD) in an elderly cohort based on cerebral magnetic resonance angiography (MRA). One-hundred four patients with CI-CVD and 107 control subjects were retrospectively recruited from the 14-year elderly MRA cohort, and 63 subjects were enrolled for external validation. Automated quantitative analysis was applied to analyse the morphological features, including the stenosis score, length, relative length, twisted angle, and maximum deviation of cerebral arteries. Clinical and morphological risk factors were screened using univariate logistic regression. Radiomic features were extracted via least absolute shrinkage and selection operator (LASSO) regression. The predictive models of CI-CVD were established in the training set and verified in the external testing set. A history of stroke was demonstrated to be a clinical risk factor (OR 2.796, 1.359-5.751). Stenosis ≥ 50% in the right middle cerebral artery (RMCA) and left posterior cerebral artery (LPCA), maximum deviation of the left internal carotid artery (LICA), and twisted angles of the right internal carotid artery (RICA) and LICA were identified as morphological risk factors, with ORs of 4.522 (1.237-16.523), 2.851 (1.438-5.652), 1.373 (1.136-1.661), 0.981 (0.966-0.997) and 0.976 (0.958-0.994), respectively. Overall, 33 radiomic features were screened as risk factors. The clinical-morphological-radiomic model demonstrated optimal performance, with an AUC of 0.883 (0.838-0.928) in the training set and 0.843 (0.743-0.943) in the external testing set. Radiomics features combined with morphological indicators of cerebral arteries were effective indicators for early signs of CI-CVD in elderly individuals. Question The relationship between morphological features of cerebral arteries and cognitive impairment associated with cerebrovascular disease (CI-CVD) deserves to be explored. Findings The multipredictor model combining with stroke history, vascular morphological indicators and radiomic features of cerebral arteries demonstrated optimal performance for the early warning of CI-CVD. Clinical relevance Stenosis percentage and tortuosity score of the cerebral arteries are important risk factors for cognitive impairment. The radiomic features combined with morphological quantification analysis based on cerebral MRA provide higher predictive performance of CI-CVD.

No instruments are available to predict preoperatively the risk of posthepatectomy liver failure (PHLF) in HCC patients. The aim was to predict the occurrence of PHLF preoperatively by radiomics and clinical data through machine-learning algorithms. Clinical data and 3-phases CT scans were retrospectively collected among 13 Italian centres between 2008 and 2022. Radiomics features were extracted in the non-tumoral liver area. Data were split between training(70 %) and test(30 %) sets. An oversampling was run(ADASYN) in the training set. Random-Forest(RF), extreme gradient boosting (XGB) and support vector machine (SVM) models were fitted to predict PHLF. Final evaluation of the metrics was run in the test set. The best models were included in an averaging ensemble model (AEM). Five-hundred consecutive preoperative CT scans were collected with the relative clinical data. Of them, 17 (3.4 %) experienced a PHLF. Two-hundred sixteen radiomics features per patient were extracted. PCA selected 19 dimensions explaining >75 % of the variance. Associated clinical variables were: size, macrovascular invasion, cirrhosis, major resection and MELD score. Data were split in training cohort (70 %, n = 351) and a test cohort (30 %, n = 149). The RF model obtained an AUC = 89.1 %(Spec. = 70.1 %, Sens. = 100 %, accuracy = 71.1 %, PPV = 10.4 %, NPV = 100 %). The XGB model showed an AUC = 89.4 %(Spec. = 100 %, Sens. = 20.0 %, Accuracy = 97.3 %, PPV = 20 %, NPV = 97.3 %). The AEM combined the XGB and RF model, obtaining an AUC = 90.1 %(Spec. = 89.5 %, Sens. = 80.0 %, accuracy = 89.2 %, PPV = 21.0 %, NPV = 99.2 %). The AEM obtained the best results in terms of discrimination and true positive identification. This could lead to better define patients fit or unfit for liver resection.

To evaluate the diagnostic ability and methodological quality of ML models in detecting Pancreatic Ductal Adenocarcinoma (PDAC) in Contrast CT images. Included studies assessed adults diagnosed with PDAC, confirmed by histopathology. Metrics of tests were interpreted by ML algorithms. Studies provided data on sensitivity and specificity. Studies that did not meet the inclusion criteria, segmentation-focused studies, multiple classifiers or non-diagnostic studies were excluded. PubMed, Cochrane Central Register of Controlled Trials, and Embase were searched without restrictions. Risk of bias was assessed using QUADAS-2, methodological quality was evaluated using Radiomics Quality Score (RQS) and a Checklist for AI in Medical Imaging (CLAIM). Bivariate random-effects models were used for meta-analysis of sensitivity and specificity, I² values and subgroup analysis used to assess heterogeneity. Nine studies were included and 12,788 participants were evaluated, of which 3,997 were included in the meta-analysis. AI models based on CT scans showed an accuracy of 88.7% (IC 95%, 87.7%-89.7%), sensitivity of 87.9% (95% CI, 82.9%-91.6%), and specificity of 92.2% (95% CI, 86.8%-95.5%). The average score of six radiomics studies was 17.83 RQS points. Nine ML methods had an average CLAIM score of 30.55 points. Our study is the first to quantitatively interpret various independent research, offering insights for clinical application. Despite favorable sensitivity and specificity results, the studies were of low quality, limiting definitive conclusions. Further research is necessary to validate these models before widespread adoption.