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

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.

Artificial Intelligence Applications in the Treatment of Colorectal Cancer: A Narrative Review

“…The best RSF model achieved a C-index of 0.820 in prognosis prediction of cases having stage III colon cancer, which is essential for therapeutic strategy selection. Another research 89 proposed a fusion model based on radiomics and deep convolutional neural networks (DCNN)to evaluate the prognosis of stage II CRC patients. Radiomics and DCNN features were obtained from CT of the primary tumors and peripheral lymph nodes to construct the model.…”

Addressing the Contrast Media Recognition Challenge

“…The external dataset (n = 240) included randomly selected scans from 4 openly available datasets from The Cancer Imaging Archive 25 : ACRIN-NSCLC-FDG-PET, 26,27 LIDC-IDRI, 28,29 C4KC-KiTS-NBIA, 30,31 and StageII-Colorectal-CT. 32,33 In C4KC-KiTS-NBIA, at times multiple series with different IV contrast phases (noncontrast, arterial, and urographic) were available for the same study. Therefore, for this…”

CTContour: An open-source Python pipeline for automatic contouring and calculation of mean SSDE along the abdomino-pelvic region for CT images; validation on fifteen systems

“…This dataset was the Climb 4 Kidney Cancer Kidney Tumor Segmentation Challenge 2019 (C4KC-KiTS) available on The Cancer Imaging Archive, an online public domain database of studies [21][22][23]. Filtering this dataset for abdomen and pelvis images yielded 138 studies which had The second dataset was the Stage-II Colorectal-CT [21,24,25] dataset, also available from The Cancer Imaging Archive. This dataset provided contrast enhanced abdomino-pelvic images of 230 patients prior to surgery where imaging was carried out on four Siemens and Philips systems.…”