How can AI-enhanced radiomics improve ea... - tlooto, The Most Powerful AcademicGPT

The key to improving early detection of pancreatic and ovarian cancers—both notorious for silent progression and poor prognosis—lies in exploiting subtle imaging phenotypes and integrating them with other biomarkers through AI‐driven radiomics. Below, we outline how such an approach can be instantiated, drawing on existing successes in other organ systems and on principles validated in the literature. 1. Detection of Sub‐millimetre Imaging Phenotypes • In lung screening, a support-vector‐machine (SVM) coupled with LASSO selected just two CT radiomic features (lesion extent BBAP and texture SDIDM) to achieve an AUC of 0.89 for malignancy prediction—boosting accuracy 12.4% over Lung-RADS, which relies on size and morphological criteria alone [1]. • By analogy, pancreatic CT/MRI radiomics can be trained to identify minute textural or morphological perturbations in the parenchyma or within small cystic lesions (e.g., IPMN), which often precede overt mass formation. Deep‐learning frameworks