Rectal cancer: Toward fully automatic discrimination of T2 and T3 rectal cancers using deep convolutional neural network

Abstract

Preoperative chemoradiotherapy is known to reduce the local recurrence of locally advanced rectal cancer. However, the careful use of preoperative chemoradiotherapy is essential, because unnecessary over‐treatment can result in unintended complications. Therefore, a diagnostic system for distinguishing between T2 and T3 rectal cancers should be developed. According to the diagnostic criteria for rectal cancer, radiologists first identify the locations and the shapes of both the tumor and the rectum from a medical image and then diagnose the T2/T3 rectal cancer by determining whether the tumor passes through the rectal wall or not. We construct two distinct convolutional neural network models to achieve the automated segmentation of each rectum and tumor, respectively. Then, we construct another convolutional neural network model, which uses the output images of segmentation models as input and determines whether the tumor in the input magnetic resonance image is at the T2 stage or at the T3 stage. We evaluate the effectiveness of the proposed method based on 290 magnetic resonance images from 133 subjects. The proposed model demonstrates an accuracy of 94%.

Automatic T Staging Using Weakly Supervised Deep Learning for Nasopharyngeal Carcinoma on MR Images

“…Other studies of automated T staging in other cancers, such as non‐small‐cell lung cancer and rectal cancer, have been reported, and the results are satisfactory . The deep‐learning system developed by Choi et al for staging liver fibrosis outperformed radiologists .…”

“…Newly emerged deep‐learning‐based automatic methods have provided us with an opportunity for automatic staging . Many studies have reported the successful application of deep learning for some similar automated tasks, such as disease classification, pathological tumor classification, automated rectal cancer staging, and automated non‐small‐cell lung cancer staging …”

Artificial intelligence–based technology for semi-automated segmentation of rectal cancer using high-resolution MRI

“…Some recent studies have tried to estimate rectal cancer-related parameters on preoperative MR images using AI, and have shown that the accuracy was acceptable [22,[26][27][28]. However, these studies had several limitations: tumor tissue was not visualized on the MR image, the relationship of the tumor with the mesorectal fascia was difficult to assess, the results were not based on high-resolution MRI, or the ground-truth labels were not based on pathological assessment, the last issue being the one we consider to be most critical.…”

Applications of Artificial Intelligence in Screening, Diagnosis, Treatment, and Prognosis of Colorectal Cancer

“…The Faster R-CNN algorithm is very efficient and accurate in predicting lymph node metastases, which reduces the workload on the radiologist and minimizes differences between different diagnostic levels. Some other representative research about lymph node metastasis are listed in Table 2 [52][53][54][55]57,58,72].…”