The authors of this study investigated whether radiomics based on T2-weighted MRI was able to discriminate between benign and borderline epithelial ovarian tumors (EOTs) preoperatively. They were able to show that it can provide critical diagnostic information in the discrimination between benign and borderline EOTs, showing that there is potential to aid personalized treatment options. Key points T2-weighted MRI-based radiomics could preoperatively discriminate benign and borderline EOTs. Radiomics combined with clinical/radiological characteristics help differentiate benign and borderline EOTs. Different machine learning algorithms had different diagnostic performances. Article: T2-weighted MRI-based radiomics for discriminating between benign and borderline epithelial ovarian tumors: a multicenter study Authors: Mingxiang Wei, Yu Zhang, Genji Bai, Cong Ding, Haimin Xu, Yao Dai, Shuangqing Chen & Hong Wang
Impact of deep learning reconstruction on radiation dose reduction and cancer risk in CT examinations
Deep‑learning reconstruction (DLR) shifts CT image formation from a hardware‑limited process to a data‑driven one. In our real‑world cohort of >10,000 body scans, we observed a
28. April 2025
