Radiomic Features Are Predictive of Response in Rectal Cancer Undergoing Therapy

Abstract

Background: Rectal cancer is a major mortality cause in the United States (US), and its treatment is based on individual risk factors for recurrence in each patient. In patients with rectal cancer, accurate assessment of response to chemoradiotherapy has increased in importance as the variety of treatment options has grown. In this scenario, a controversial non-operative approach may be considered in some patients for whom complete tumor regression is believed to have occurred. The recommended treatment for locally advanced rectal cancer (LARC, T3-4 ± N+) is total mesorectal excision (TME) after neoadjuvant chemoradiotherapy (nCRT). Magnetic resonance imaging (MRI) has become a standard technique for local staging of rectal cancer (tumor, lymph node, and circumferential resection margin [CRM] staging), in both the US and Europe, and it is getting widely used for restaging purposes.

Aim: In our study, we aimed to use an MRI radiomic model to identify features linked to the different responses of chemoradiotherapy of rectal cancer before surgery, and whether these features are helpful to understand the effectiveness of the treatments.

Methods: We retrospectively evaluated adult patients diagnosed with LARC who were subjected to at least 2 MRI examinations in 10-12 weeks at our hospital, before and after nCRT. The MRI acquisition protocol for the 2 exams included T2 sequence and apparent diffusion coefficient (ADC) map. The patients were divided into 2 groups according to the treatment response: complete or good responders (Group 1) and incomplete or poor responders (Group 2). MRI images were segmented, and quantitative features were extracted and compared between the two groups. Features that showed significant differences (SF) were then included in a LASSO regression method to build a radiomic-based predictive model.

Results: We included 38 patients (26 males and 12 females), who are classified from T2 and T4 stages in the rectal cancer TNM. After the nCRT, the patients were divided into Group 1 (13 patients), complete or good responders, and Group 2 (25 patients), incomplete or poor responders. Analysis at baseline generated the following significant features for the Mann-Whitney test (out of a total of 107) for each sequence. Also, the analysis at the end of the follow-up yielded a high number of significant features for the Mann-Whitney test (out of a total of 107) for each image. Features selected by the LASSO regression method for each image analyzed; ROC curves relative to each model are represented.

Conclusion: We developed an MRI-based radiomic model that is able to differentiate and predict between responders and non-responders who went through nCRT for rectal cancer. This approach might identify early lesions with high surgical potential from lesions potentially resolving after medical treatment.

Keywords: chemoradiotherapy; locally advanced rectal cancer; magnetic resonance imaging; pathological complete response; radiomic; rectal cancer.

Figure 1

ADC map pre-therapy (A), Axial MRI T2w pre-therapy (B), ADC map post-therapy (C) and Axial MRI T2w post-therapy (D). Images refer to rectal cancer (red arrows).

Figure 2

The image-processing software 3D Slicer was used to manually outline ROIs along the lesion margins on all the slices containing the tumor in the MRI T2w sequences (A,C,D) and ADC sequences, as shown in the figure. The volumetric 3D tumor reconstruction is shown in the upper right quadrant (B).

Figure 3

Boxplot of performance in prediction models (left) and ROC curves (with AUC) associated with prediction models (right); prediction models were calculated with the LASSO regression method on baseline images (T2 and ADC map).

Figure 4

Boxplot of performance in prediction models (left) and ROC curves (with AUC) associated with models (right); prediction models were calculated with the LASSO regression method on the follow-up images (T2 and ADC map) after nCRT.