Establishment and clinical application value of an automatic diagnosis platform for rectal cancer T-staging based on a deep neural network

Abstract

Background: Colorectal cancer is harmful to the patient's life. The treatment of patients is determined by accurate preoperative staging. Magnetic resonance imaging (MRI) played an important role in the preoperative examination of patients with rectal cancer, and artificial intelligence (AI) in the learning of images made significant achievements in recent years. Introducing AI into MRI recognition, a stable platform for image recognition and judgment can be established in a short period. This study aimed to establish an automatic diagnostic platform for predicting preoperative T staging of rectal cancer through a deep neural network. Methods: A total of 183 rectal cancer patients’ data were collected retrospectively as research objects. Faster region-based convolutional neural networks (Faster R-CNN) were used to build the platform. And the platform was evaluated according to the receiver operating characteristic (ROC) curve. Results: An automatic diagnosis platform for T staging of rectal cancer was established through the study of MRI. The areas under the ROC curve (AUC) were 0.99 in the horizontal plane, 0.97 in the sagittal plane, and 0.98 in the coronal plane. In the horizontal plane, the AUC of T1 stage was 1, AUC of T2 stage was 1, AUC of T3 stage was 1, AUC of T4 stage was 1. In the coronal plane, AUC of T1 stage was 0.96, AUC of T2 stage was 0.97, AUC of T3 stage was 0.97, AUC of T4 stage was 0.97. In the sagittal plane, AUC of T1 stage was 0.95, AUC of T2 stage was 0.99, AUC of T3 stage was 0.96, and AUC of T4 stage was 1.00. Conclusion: Faster R-CNN AI might be an effective and objective method to build the platform for predicting rectal cancer T-staging. Trial registration: chictr.org.cn: ChiCTR1900023575; http://www.chictr.org.cn/showproj.aspx?proj=39665 .

Role of artificial intelligence in risk prediction, prognostication, and therapy response assessment in colorectal cancer: current state and future directions

“…Using 290 MRI images from 133 patients, a CNN algorithm was developed to automatically segment and classify tumors as either T2 or T3 with an accuracy of 94%. More recently, Wu et al utilized faster regionbased CNNs to create an automatic diagnosis platform for T staging of rectal cancer via MRI (83). The study found AUC of 1 for T1-T4 stages in the horizontal plane and 0.96, 0.97, 0.97, and 0.97 for T1-T4, respectively.…”

“…More recently, Wu et al. utilized faster region-based CNNs to create an automatic diagnosis platform for T staging of rectal cancer via MRI ( 83 ). The study found AUC of 1 for T1-T4 stages in the horizontal plane and 0.96, 0.97, 0.97, and 0.97 for T1-T4, respectively.…”

MRI‐Based Artificial Intelligence in Rectal Cancer

“…Deep learning has also been applied in the T stage to realize more precise automatic T staging. Wu et al 16 used Faster R‐CNN to construct an automatic diagnosis platform based on MRI, with AUCs of 0.95–1.0 for the T1, T2, T3, and T4 stages in the horizontal, sagittal, and coronal planes.…”

Development of artificial intelligence technology in diagnosis, treatment, and prognosis of colorectal cancer

“…In 3D-T2 weighted MRI, the 3D full collaborative network architecture based on DL could segment CRC more reasonably and effectively than other techniques[ 35 ]. In the high-resolution MRI image of rectal cancer, the use of a faster region-based convolution NN (Faster R-CNN) had a high accuracy in evaluating tumor boundaries[ 36 , 37 ]. Circumferential resection margin is one of the key factors affecting the treatment decision of CRC patients.…”