Background: To evaluate the predictive value of dual-energy computed tomography (DECT)-based quantitative parameters and radiomics models for the preoperative differentiation between benign and malignant thyroid nodules, and to compare their performance with radiologists’ interpretations. Methods: A retrospective analysis was conducted on 215 patients who underwent contrast-enhanced DECT of the thyroid, with pathological outcomes for thyroid nodules obtained. Patients were randomly assigned to training and testing groups in a 7:3 ratio. The images were evaluated by radiologists. Quantitative parameters derived from DECT were identified through univariate and multivariate logistic regression analyses to construct a DECT model. Radiomics features were extracted from the 40, 70, and 100 keV virtual monochromatic images, as well as iodine-based material-decomposition (IMD) images in the arterial phase (AP) and venous phase (VP) to develop radiomics models from both individual and combined images using a support vector machine (SVM), with the optimal performing model selected as the final radiomics model. Subsequently, a fusion model combining DECT parameters and the radiomics model was established. The diagnostic performances of the radiologist, DECT, radiomics and fusion models were evaluated using receiver operating characteristic (ROC) curves, and clinical usefulness was assessed through decision curve analysis (DCA). Results: The normalized iodine concentration (NIC) in DECT emerged as an independent factor for the preoperative differentiation between benign and malignant thyroid nodules. The multi-image radiomics model demonstrated excellent predictive performance in the test cohort, achieving an area under the curve (AUC) of 0.966 and was selected as the final model among the radiomics models. The ROC curves indicate that the radiomics model outperformed the radiologist model in predicting the thyroid nature in both the training (0.990 vs. 0.746) and test cohorts (0.966 vs. 0.697) (all p < 0.05). The diagnostic efficacy of the fusion model showed slight improvement over the radiomics model, although this difference was not statistically significant. Furthermore, the fusion model performed exceptionally well in DCA. Conclusions: The fusion model, which combined the NIC and the optimal multi-image radiomics model, demonstrates strong diagnostic capability in predicting benign and malignant thyroid nodules.

Accurately predicting muscle invasion status in bladder cancer (BCa) is essential for developing personalized treatment plans. This study integrates habita

The research aims to evaluate the effectiveness of a multi-dual-energy CT (DECT) image-based interpretable model that integrates habitat radiomics with a 3D Vision Transformer (ViT) deep learning (DL) for preoperatively predicting muscle invasion in bladder cancer (BCa). This retrospective study analyzed 200 BCa patients, who were divided into a training cohort (n=140) and a test cohort (n=60) in a 7:3 ratio. Univariate and multivariate analyses were performed on the DECT quantitative parameters to identify independent predictors, which were subsequently used to develop a DECT model. The K-means algorithm was employed to generate habitat sub-regions of BCa. Traditional radiomics (Rad) model, habitat model, ResNet 18 model, ViT model, and fusion models were constructed from the 40, 70, and 100 keV virtual monochromatic images (VMIs) in DECT. The evaluation of all models used the area under the receiver operating characteristic curve (AUC), calibration curve, decision curve analysis (DCA), net reclassification index (NRI), and integrated discrimination improvement (IDI). The SHAP method was employed to interpret the optimal model and visualize its decision-making process. The Habitat-ViT model demonstrated superior performance compared to other single models, achieving an AUC of 0.997 (95% CI 0.992, 1.000) in the training cohort and 0.892 (95% CI 0.814, 0.971) in the test cohort. The incorporation of DECT quantitative parameters did not improve the performance. DCA and calibration curve assessments indicated that the Habitat-ViT model provided a favorable net benefit and demonstrated strong calibration. Furthermore, SHAP clarified the decision-making processes underlying the model's predicted outcomes. Multi-DECT image-based interpretable model that integrates habitat radiomics with a ViT DL holds promise for predicting muscle invasion status in BCa, providing valuable insights for personalized treatment planning and prognostic assessment.

Ribeiro and colleagues offer timely evidence that large language models can make oncology imaging reports more accessible to radiologists and patient and public involvement representatives. Their findings also highlight an important distinction between a report that is easier to read and one that patients understand accurately. As the authors acknowledge, the patient-facing assessment included three representatives with previous experience in oncology imaging research and was restricted to reports that had received high factual-correctness scores from radiologists. This approach was suitable for comparing presentation preferences, although it provides limited evidence about comprehension in routine practice, particularly for lower-scoring or clinically ambiguous outputs. Further studies could examine unselected reports in patients with varied health and digital literacy, using outcomes such as comprehension, recognition of uncertainty, emotional response, and intended action. In future adequately powered studies, accounting for ratings clustered by reader and report may provide more precise estimates. Such evidence would help clarify the role of AI-generated explanations in patient-facing oncology care.