RCMIX model based on pre-treatment MRI imaging predicts T-downstage in MRI-cT4 stage rectal cancer.
Authors
Affiliations (7)
Affiliations (7)
- Department of General Surgery (Colorectal surgery), The Sixth Affiliated Hospital, Sun Yat-sen University, Guangdong, China; Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangdong, China; Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangdong, China.
- Department of Colorectal Surgery, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangdong, China.
- Division of Abdominal Tumor Multimodality Treatment, Cancer Center, West China Hospital, Sichuan University, Sichuan, China; Department of Radiation Oncology, Cancer Center, West China Hospital, Sichuan University, Sichuan, China.
- Department of Information Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangdong, China.
- Department of Colorectal Surgery, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangdong, China. Electronic address: [email protected].
- Department of General Surgery (Colorectal surgery), The Sixth Affiliated Hospital, Sun Yat-sen University, Guangdong, China; Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangdong, China; Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangdong, China. Electronic address: [email protected].
- Department of General Surgery (Colorectal surgery), The Sixth Affiliated Hospital, Sun Yat-sen University, Guangdong, China; Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangdong, China; Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangdong, China. Electronic address: [email protected].
Abstract
Neoadjuvant therapy (NAT) is the standard treatment strategy for MRI-defined cT4 rectal cancer. Predicting tumor regression can guide the resection plane to some extent. Here, we covered pre-treatment MRI imaging of 363 cT4 rectal cancer patients receiving NAT and radical surgery from three hospitals: Center 1 (n = 205), Center 2 (n = 109) and Center 3 (n = 52). We propose a machine learning model named RCMIX, which incorporates a multilayer perceptron algorithm based on 19 pre-treatment MRI radiomic features and 2 clinical features in cT4 rectal cancer patients receiving NAT. The model was trained on 205 cases of cT4 rectal cancer patients, achieving an AUC of 0.903 (95% confidence interval, 0.861-0.944) in predicting T-downstage. It also achieved AUC of 0.787 (0.699-0.874) and 0.773 (0.646-0.901) in two independent test cohorts, respectively. cT4 rectal cancer patients who were predicted as Well T-downstage by the RCMIX model had significantly better disease-free survival than those predicted as Poor T-downstage. Our study suggests that the RCMIX model demonstrates satisfactory performance in predicting T-downstage by NAT for cT4 rectal cancer patients, which may provide critical insights to improve surgical strategies.