The authors of this retrospective study aimed to evaluate the diagnostic performance of a radiomics model in order to classify hepatic cyst, hemangioma, and metastasis in patients who have been diagnosed with colorectal cancer (CRC) from portal-phase abdominopelvic CT images. The study found that, although inferior to radiologists, the radiomics model was able to achieve substantial diagnostic performance when differentiating hepatic lesions from portal-phase CT images of CRC patients. Key points Albeit inferior to the radiologists, the radiomics model could differentiate cyst, hemangioma, and metastasis with substantial diagnostic performance using portal-phase CT images of colorectal cancer patients. The radiomics model demonstrated limitations especially in classifying hemangiomas and subcentimeter liver lesions. Article: Radiomics analysis of contrast-enhanced CT for classification of hepatic focal lesions in colorectal cancer patients: its limitations compared to radiologists Authors: Heejin Bae, Hansang Lee, Sungwon Kim, Kyunghwa Han, Hyungjin Rhee, Dong-kyu Kim, Hyuk Kwon, Helen Hong & Joon Seok Lim
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
