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.

Keywords: Deep learning radiomics; Distant metastasis; Locally advanced rectal cancer; Magnetic resonance imaging; Neoadjuvant chemoradiotherapy.

Fig. 1

ROC curves of prognostic performance with different deep learning models of the (a) primary cohort (n = 162) and (b) external validation cohort (n = 62). ROC receiver operating characteristic; AUC area under receiver operating characteristic curve; T2W T2-weighted; ADC apparent diffusion coefficient.

Fig. 2

Predictive performance of DLRS for DMFS. (a) and (b) are time-dependent ROC curves for one year, two years and three years of the primary cohort (n = 170) and external validation cohort (n = 65). (c) and (d) are K-M curves for stratifying high- and low-risk patients of DM of the primary cohort (P < 0·0001, log-rank test) and external validation cohort (P < 0·0001, log-rank test). The numbers of patients at risk for each time step are shown in the bottom. DLRS deep learning risk signature; DMFS distant metastasis free survival; ROC receiver operating characteristic; AUC area under receiver operating characteristic curve.

Fig. 3

Integrated nomogram and evaluation of the nomogram in multi centers. (a) is a nomogram for individual prediction of DMFS combined with deep MRI information and clinicopathological factors. (b) and (c) are the decision curves of integrated nomogram/clinical model of the primary cohort (n = 170) and external validation cohort (n = 65). (d) and (e) are the plots of true- and false-positive rates of the primary cohort and external validation cohort, as functions of the risk threshold for integrated nomogram. (f) and (g) are clinical impact curves for 1000 random patients based on the integrated nomogram of the primary cohort and external validation cohort. 95% confidence intervals constructed via bootstrapping is displayed on both sides of the ROC components plot or clinical impact plot. ROC receiver operating characteristic; DMFS distant metastasis free survival; MRI magnetic resonance imaging.

Fig. 4

Nomogram-based K-M curves of patients with different responses to nCRT. (a) and (b) are the K-M DMFS curves for pCR (P = 0•0059, log-rank test) and non-pCR (P < 0•0001, log-rank test) patient subgroup. (c) and (d) are the K-M DMFS curves for downstaging (ypT0-2N0) (P < 0•0001, log-rank test) and non-downstaging (P < 0•0001, log-rank test) patient subgroup. The numbers of patients at risk for each time step are shown in the bottom. nCRT neoadjuvant chemoradiotherapy; DMFS distant metastasis free survival; pCR pathologic complete response.