Development of a Joint Prediction Model Based on Both the Radiomics and Clinical Factors for Predicting the Tumor Response to Neoadjuvant Chemoradiotherapy in Patients with Locally Advanced Rectal Cancer

• Main Authors: Liu Y, Zhang FJ, Zhao XX, Yang Y, Liang CY, Feng LL, Wan XB, Ding Y, Zhang YW
• Format: Article
• Language: English
• Published: Dove Medical Press 2021-04-01
• Series: Cancer Management and Research
• Subjects: rectal cancer; neoadjuvant chemoradiotherapy; magnetic resonance imaging; tumor response.
• Online Access: https://www.dovepress.com/development-of-a-joint-prediction-model-based-on-both-the-radiomics-an-peer-reviewed-article-CMAR

Yang Liu,1,* Feng-Jiao Zhang,2,* Xi-Xi Zhao,3,* Yuan Yang,4 Chun-Yi Liang,3 Li-Li Feng,5 Xiang-Bo Wan,5 Yi Ding,1 Yao-Wei Zhang1 1Department of Radiation Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, 510515, People’s Republic of China; 2Shanghai Concord Medical Cancer Center, Shanghai, 200001, People’s Republic of China; 3Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, 510515, People’s Republic of China; 4Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, 510515, People’s Republic of China; 5Department of Radiation Oncology, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, 510655, People’s Republic of China*These authors contributed equally to this workCorrespondence: Yi Ding; Yao-Wei Zhang Tel [phone]Email [e-mail]; [e-mail]: Neoadjuvant chemoradiotherapy (nCRT) has become the standard treatment for locally advanced rectal cancer (LARC). However, the accuracy of traditional clinical indicators in predicting tumor response is poor. Recently, radiomics based on magnetic resonance imaging (MRI) has been regarded as a promising noninvasive assessment method. The present study was conducted to develop a model to predict the pathological response by analyzing the quantitative features of MRI and clinical risk factors, which might predict the therapeutic effects in patients with LARC as accurately as possible before treatment.Patients and Methods: A total of 82 patients with LARC were enrolled as the training cohort and internal validation cohort. The pre-CRT MRI after pretreatment was acquired to extract texture features, which was finally selected through the minimum redundancy maximum relevance (mRMR) algorithm. A support vector machine (SVM) was used as a classifier to classify different tumor responses. A joint radiomics model combined with clinical risk factors was then developed and evaluated by receiver operating characteristic (ROC) curves. External validation was performed with 107 patients from another center to evaluate the applicability of the model.Results: Twenty top image texture features were extracted from 6192 extracted-radiomic features. The radiomics model based on high-spatial-resolution T2-weighted imaging (HR-T2WI) and contrast-enhanced T1-weighted imaging (T1+C) demonstrated an area under the curve (AUC) of 0.8910 (0.8114– 0.9706) and 0.8938 (0.8084– 0.9792), respectively. The AUC value rose to 0.9371 (0.8751– 0.9997) and 0.9113 (0.8449– 0.9776), respectively, when the circumferential resection margin (CRM) and carbohydrate antigen 19-9 (CA19-9) levels were incorporated. Clinical usefulness was confirmed in an external validation cohort as well (AUC, 0.6413 and 0.6818).Conclusion: Our study indicated that the joint radiomics prediction model combined with clinical risk factors showed good predictive ability regarding the treatment response of tumors as accurately as possible before treatment.Keywords: rectal cancer, neoadjuvant chemoradiotherapy, magnetic resonance imaging, tumor response