Deep learning radiomics-based prediction of distant metastasis in patients with locally advanced rectal cancer after neoadjuvant chemoradiotherapy: A multicentre study

Abstract

Background Accurate predictions of distant metastasis (DM) in locally advanced rectal cancer (LARC) patients receiving neoadjuvant chemoradiotherapy (nCRT) are helpful in developing appropriate treatment plans. This study aimed to perform DM prediction through deep learning radiomics. Methods We retrospectively sampled 235 patients receiving nCRT with the minimum 36 months’ postoperative follow-up from three hospitals. Through transfer learning, a deep learning radiomic signature (DLRS) based on multiparametric magnetic resonance imaging (MRI) was constructed. A nomogram was established integrating deep MRI information and clinicopathologic factors for better prediction. Harrell's concordance index (C-index) and time-dependent receiver operating characteristic (ROC) were used as performance metrics. Furthermore, the risk of DM in patients with different response to nCRT was evaluated with the nomogram. Findings DLRS performed well in DM prediction, with a C-index of 0·747 and an area under curve (AUC) at three years of 0·894 in the validation cohort. The performance of nomogram was better, with a C-index of 0·775. In addition, the nomogram could stratify patients with different responses to nCRT into high- and low-risk groups of DM ( P < 0·05). Interpretation MRI-based deep learning radiomics had potential in predicting the DM of LARC patients receiving nCRT and could help evaluate the risk of DM in patients who have different responses to nCRT. Funding The funding bodies that contributed to this study are listed in the Acknowledgements section.

Construction and Validation of New Nomograms to Predict Risk and Prognostic Factors of Breast Cancer Bone Metastasis in Asian Females: A Population-Based Retrospective Study

“… 11 Currently, nomograms are widely used in oncology and medical practice and have achieved good results. 12 , 13 There are many prediction models for the risk and prognosis of breast cancer bone metastasis, but these models were either not based on Asian females or did not exclude multiple organ metastases. 9 , 14 However, to our knowledge, there is currently no specific prediction model or nomogram for the risk of bone-only metastasis nor for the prognostic value of BCBM in Asian females.…”

Histopathologic Oral Cancer Prediction Using Oral Squamous Cell Carcinoma Biopsy Empowered with Transfer Learning

“…During the COVID phase, various researchers apply machine learning approaches to COVID patients to predict cancer [ 35 ] and get highly efficient results using different preprocessing techniques. Deep learning radionics-based detections [ 36 ] on cancerous patients give high feature results with the help of chemoradiotherapy.…”

MRI‐Based Artificial Intelligence in Rectal Cancer

“…Furthermore, Liu et al 50 proved an MRI‐based radiomic signature to be an independent factor for predicting DM in patients with LARC and found that a radiomic nomogram based on the radiomic signature and clinicopathologic factors performed better in predicting distant metastasis‐free survival (DMFS), with C‐indices of 0.848, 0.831, and 0.825 in the validation cohorts, respectively. In addition, a retrospective and multicenter study constructed a deep learning radiomics‐based model to predict the 3‐year DMFS in patients with LARC after receiving nCRT, with an AUC of 0.894 56 …”

“…In addition, a retrospective and multicenter study constructed a deep learning radiomics-based model to predict the 3-year DMFS in patients with LARC after receiving nCRT, with an AUC of 0.894. 56 In recent years, MRI-based AI has received increasing attention from scholars as a new biomarker with predictive prognostic ability. 57 In a retrospective study, scholars extracted multiple radiomics features from segmented tumor regions on axial three-dimensional (3D) LAVA multienhanced MR sequences and constructed a novel radiomic signature.…”