P-331 Differentiating subcentimeter lung metastases in colorectal cancer patients by radiomics and deep learning approaches: A multicenter study
نویسندگان
چکیده
Preoperative evaluation of the indeterminate lung nodules is important for individual treatment colorectal cancer (CRC). We aim to develop and validate discriminative radiomics deep learning approaches differentiating subcentimenter metastases (LMs) in CRC patients. Models were developed a primary cohort included 1194 consecutive patients with initial subcentimeter size LMs on CT. Patients randomly assigned (7:3) training or internal validation cohorts (IVC). Machine (ML), (DL), integration DL features applied classify as benign lesions. Two independent external (EVC) consisted 101 (EVC1) 40 (EVC2) To verify generalizability smaller sizes, stepwise validations subgroups according nodule’s largest diameter (10, 9, 8, 7, 6, 5, ≤4 mm) conducted. The diagnostic accuracy by radiologists was 0.705 cohort. best ML based support vector machine (SVM) showed 0.981 area under curve (AUC) IVC, 0.961 0.996 AUC EVC1 EVC2. model 0.973 IVC 0.943 0.974 0.953 0.907 0.951 Stepwise demonstrated that LM decreasing, most stable LMs, ≤5 mm. Our findings CT could improve current LMs. Specially, novel This study provided an automatic noninvasive solution determining management CRC.
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ژورنال
عنوان ژورنال: Annals of Oncology
سال: 2023
ISSN: ['0923-7534', '1569-8041']
DOI: https://doi.org/10.1016/j.annonc.2023.04.387