An End-to-End Auto-Prediction Model Response to Neoadjuvant Chemoradiotherapy for Rectal Cancer Based on Multimodal Segmentation and Multipath Lightweight Convolution

Abstract

<h3>Purpose/Objective(s)</h3> Neoadjuvant chemoradiotherapy (nCRT) plus total mesorectal excision (TME) has become the standard treatment for patients with locally advanced rectal cancer (LARC). Therefore, accurate prediction of the tumor downstaging level and pathological complete remission (pCR) has signification impacts on the further treatment and surgical decisions. Currently, the judgment mainly relies on experience of clinical experts or clinical experts combined with radiomics semi-automatically. In this study, we developed an end-to-end fully automated model of lesion segmentation and tumor downstaging and pCR prediction without manual operation. By combining pre-segmentation and post-classification modules, the work purposes to realize automatic and integrated prediction of tumor downstaging and pCR. <h3>Materials/Methods</h3> We retrospectively enrolled 350 continuous patients with LARC who received standard nCRT. In the network design section, we proposed an integrated auto-prediction model including four-channel 3D nnUNet segmentation module and multipath fusion classification module via lightweight convolution. In the auto-segmentation module, 280 patients were used for training and 70 were split for validation. Each MRI image type corresponded to a 3D nnUNet channel. For every 3D nnUNet, the numbers of downsampling in the encoder side and the decoder side were designed to be 5. And each sampling layer was connected behind every two 3D convolution modules. The total training epoch was 300 and learning rate was set to 1e-3 with during training. In the prediction module, the segmentation results of the four channels were fused into the lightweight prediction network. Adam optimizer was applied (learning rate=7.0e-6, decay=1.0e-5) and the batch size was set to 25 with 500 epoch. <h3>Results</h3> Our experimental results showed relatively stable segmentation accuracy and prediction performance. For four types of MRI images (pre and post nCRT T2-weighted, DWI), the segmentation results are as follows: (1) pre-nCRT T2-weighted MRI: The average Dice coefficient was 0.81 (median 0.84, Maximum 0.93). (2): post-nCRT T2-weighted Mirtha average Dice coefficient was 0.60(median 0.66, Maximum 0.90). (3): pre-nCRT DWI (logb1000) Mirtha average Dice coefficient was 0.73(median 0.76, Maximum 0.88). (4): post-nCRT DWI (logb1000) MRI. The average Dice coefficient was 0.42(median 0.50, Maximum 0.77). In the prediction module, the training set accuracy was 0.99 and the accuracy was 0.73 validating on 90 samples. <h3>Conclusion</h3> The entire network can achieve rectal tumor segmentation in 3D volume level and classification of pCR and tumor downstaging degree by fusing the image information of T2-weighted MRI and diffusion-weighted MRI deeply. This is a potentially valuable method for the clinical application of neoadjuvant chemoradiotherapy prediction.