Affiliations: [a] Department of Radiology, Affiliated Hospital of Jiangnan University, Wuxi, Jiangsu, China
| [b] Wuxi Research Institute, Fudan University, Wuxi, Jiangsu, China
| [c] Department of radiology, The First People’s Hospital of Yuhang District, Hangzhou, Zhejiang Province, China
| [d] Department of Pathology, Affiliated Hospital of Jiangnan University, 200 Huihe Road, Wuxi, Jiangsu, China. | [e] GE Healthcare, Shanghai, China
| [f] Department of Gastrointestinal Surgery, Affiliated Hospital of Jiangnan University, Wuxi, Jiangsu, China.
Correspondence:
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Corresponding authors: Bo-Jian Fei, Department of Gastrointestinal Surgery, Affiliated Hospital of Jiangnan University, Wuxi, Jiangsu 214000, China. Tel.: +86 188 6153 0306; E-mail: wx4yfbj@163.com and Shu-dong Hu, Department of Radiology, Affiliated Hospital of Jiangnan University, Wuxi, Jiangsu 214000, China. Tel.: +86 139 6174 9591; E-mail: hsd2001054@s163.com.
Note: [1] Note: Yu-Xi Ge and Wen-Bo Xu are co-first authors.
Abstract: OBJECTIVES:This study aims to evaluate diagnostic performance of radiomic analysis using computed tomography (CT) to identify lymphovascular invasion (LVI) in patients diagnosed with rectal cancer and assess diagnostic performance of different lesion segmentations. METHODS:The study is applied to 169 pre-treatment CT images and the clinical features of patients with rectal cancer. Radiomic features are extracted from two different volumes of interest (VOIs) namely, gross tumor volume and peri-tumor tissue volume. The maximum relevance and the minimum redundancy, and the least absolute shrinkage selection operator based logistic regression analyses are performed to select the optimal feature subset on the training cohort. Then, Rad and Rad-clinical combined models for LVI prediction are built and compared. Finally, the models are externally validated. RESULTS:Eighty-three patients had positive LVI on pathology, while 86 had negative LVI. An optimal multi-mode radiology nomogram for LVI estimation is established. The area under the receiver operating characteristic curves of the Rad and Rad-clinical combined model in the peri-tumor VOI group are significantly higher than those in the tumor VOI group (Rad: peri-tumor vs. tumor: 0.85 vs. 0.68; Rad-clinical: peri-tumor vs. tumor: 0.90 vs 0.82) in the validation cohort. Decision curve analysis shows that the peri-tumor-based Rad-clinical combined model has the best performance in identifying LVI than other models. CONCLUSIONS:CT radiomics model based on peri-tumor volumes improves prediction performance of LVI in rectal cancer compared with the model based on tumor volumes.
