The authors of this retrospective study aimed to develop and validate a CT-based radiomics model for preoperative prediction of spread through air space (STAS) in lung adenocarcinoma. They found that a CT-based radiomics model can preoperatively predict, with good diagnosis performance, STAS in lung adenocarcinoma. Key points CT-based radiomics and machine learning model can predict spread through air space (STAS) in lung adenocarcinoma with high accuracy. The random forest (RF) model achieved an AUC of 0.754 (a sensitivity of 0.880 and a specificity of 0.588) for predicting STAS. Article: CT-based radiomics and machine learning to predict spread through air space in lung adenocarcinoma Authors: Changsi Jiang, Yan Luo, Jialin Yuan, Shuyuan You, Zhiqiang Chen, Mingxiang Wu, Guangsuo Wang & Jingshan Gong
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
