The purpose of this study, performed between January 2014 and May 2019 across five different centers, was to construct an MRI radiomics model and help radiologists to improve the preoperative assessments of pelvic lymph node metastasis (PLNM) in endometrial cancer (EC). The authors were able to find that the MRI-based radiomics model could be used to assess the status of the pelvic lymph node and, moreover, help radiologists improve their performance in predicting PLNM in EC. Key points A total of 358 radiomics features were extracted. The 37 most important features were selected using the random forest classifier. The reclassification measures of discrimination confirmed that the radiomics-aided radiologists performed better than the radiologists alone, with an NRI of 1.26 and an IDI of 0.21 for radiologist 1 and an NRI of 1.37 and an IDI of 0.24 for radiologist 2. Article: Radiologists with MRI-based radiomics aids to predict the pelvic lymph node metastasis in endometrial cancer: a multicenter study Authors: Bi Cong Yan, Ying Li, Feng Hua Ma, Guo Fu Zhang, Feng Feng, Ming Hua Sun, Guang Wu Lin & Jin Wei Qiang
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
