TL;DR
The combination of radiomic and clinical features achieves excellent AUC performance and shows potential for the non-invasive prediction of ETF following radiotherapy in esophageal cancer patients.
A model integrating radiomics and clinical data may serve as a potentially effective tool for preoperative risk stratification of mediastinal tumors; this model shows great promise for optimizing individualized surgical planning.
The DLR model, leveraging automated CT lesion segmentation, effectively predicts post-COVID-19 pulmonary fibrosis, offering robust clinical utility and the nomogram integrating clinical and radiomic data enhances risk stratification but does not significantly improve upon the standalone DLR model.
PURPOSE Hemoptysis is a life-threatening complication in patients with lung malignancy. Although arterial embolization provides reliable immediate hemostasis, recurrence rates in the oncologic setting substantially exceed those observed in benign disease. Whether routine baseline clinical and tumor characteristics can...
Objective This study aimed to develop and validate a radiomics-based model using multiphase contrast-enhanced CT for the preoperative prediction of serosal invasion in gastric cancer, and to evaluate whether the integration of clinical and CT morphological features could further improve predictive performance. Methods...
Highlights • Combining traditional imaging and radiomics to diagnose small renal tumor nature.• T2WI, pseudocapsule, and Rad-score predict malignancy in SRMs independently.• This non-invsive tool may reduce unnecessry surgery and guide SRM managemen
The PRAD-Hybrid CNN (Prostate Adenocarcinoma Hybrid Convolutional Neural Network, PRADHC) model, a novel amalgamation of EfficientNet and Residual Blocks, which was developed and validated, improved the diagnostic accuracy inherent to the original EfficientNet by strategically increasing the number of Convolutional Neu...
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