Special issue "The advance of solid tumor research in China": Prognosis prediction for stage II colorectal cancer by fusing computed tomography radiomics and deep‐learning features of primary lesions and peripheral lymph nodes. Issue 1 (13th May 2022)

• Record Type: Journal Article
• Authors: Li, Menglei; Gong, Jing; Bao, Yichao; Huang, Dan; Peng, Junjie; Tong, Tong
• Other Names: Qin Shuqui guestEditor.; Li Jin guestEditor.; Guo Jun guestEditor.; Ma Jun guestEditor.; Zhou Caicun guestEditor.
• Is Part Of: International journal of cancer. Volume 152:Issue 1(2023)
• Page Start: 31
• Page End: 41
• Publication Date: 2022-05-13
• Subjects: colorectal cancer -- deep‐learning -- lymph node -- prognostic evaluation -- radiomics; Cancer -- Periodicals; Cancer -- Prevention -- Periodicals; 616.994
• DOI: 10.1002/ijc.34053
• Languages: English
• ISSNs: 0020-7136

Abstract

Abstract: Currently, the prognosis assessment of stage II colorectal cancer (CRC) remains a difficult clinical problem; therefore, more accurate prognostic predictors must be developed. In our study, we developed a prognostic prediction model for stage II CRC by fusing radiomics and deep‐learning (DL) features of primary lesions and peripheral lymph nodes (LNs) in computed tomography (CT) scans. First, two CT radiomics models were built using primary lesion and LN image features. Subsequently, an information fusion method was used to build a fusion radiomics model by combining the tumor and LN image features. Furthermore, a transfer learning method was applied to build a deep convolutional neural network (CNN) model. Finally, the prediction scores generated by the radiomics and CNN models were fused to improve the prognosis prediction performance. The disease‐free survival (DFS) and overall survival (OS) prediction areas under the curves (AUCs) generated by the fusion model improved to 0.76 ± 0.08 and 0.91 ± 0.05, respectively. These were significantly higher than the AUCs generated by the models using the individual CT radiomics and deep image features. Applying the survival analysis method, the DFS and OS fusion models yielded concordance index (C‐index) values of 0.73 and 0.9, respectively. Hence, the combined model exhibited good predictive efficacy; therefore, it could be used for the accurate assessment of the prognosis of stage II CRC patients. Moreover, it could be used to screen out high‐risk patients with poor prognoses, and assist in the formulation of clinical treatment decisions in a timely manner to achieve precision medicine. Abstract : What's new? Prognostic indicators with high accuracy are needed to improve screening for stage II colorectal cancer (CRC) patients. In the present study, the authors developed a prognostic prediction model for stage II CRC based on the fusion of radiomics with deep‐learning features of primary lesions and peripheral lymph nodes (LNs) in CT scans. Compared to models focused on individual features, the fusion of tumor and LN features significantly improved prognostic performance. Gains were notable in area under the curve for disease‐free and overall survival. The novel model can potentially inform treatment decisions, advancing precision medicine for stage II CRC patients.