Precision Identification of Locally Advanced Rectal Cancer in Denoised CT Scans Using EfficientNet and Voting System Algorithms

• Published in: Bioengineering (Basel) Vol. 11; no. 4; p. 399
• Main Authors: Lin, Chun-Yu, Wu, Jacky Chung-Hao, Kuan, Yen-Ming, Liu, Yi-Chun, Chang, Pi-Yi, Chen, Jun-Peng, Lu, Henry Horng-Shing, Lee, Oscar Kuang-Sheng
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 19-04-2024
• Subjects: Algorithms; Artificial intelligence; artificial intelligence (AI); Cancer; Cancer therapies; Chemotherapy; circumferential resection margin (CRM); Colorectal cancer; Computed tomography; computed tomography (CT); local advanced rectal cancer (LARC); Magnetic resonance imaging; medical image processing; Medical imaging; Model accuracy; Mortality; Patients; rectal cancer; Rectum; Surgery; Surgical outcomes; Test sets; Tumors; Voting systems; local advanced rectal cancer (LARC); computed tomography (CT); artificial intelligence (AI); circumferential resection margin (CRM); medical image processing; rectal cancer
• ISSN: 2306-5354; 2306-5354
• DOI: 10.3390/bioengineering11040399

Local advanced rectal cancer (LARC) poses significant treatment challenges due to its location and high recurrence rates. Accurate early detection is vital for treatment planning. With magnetic resonance imaging (MRI) being resource-intensive, this study explores using artificial intelligence (AI) to interpret computed tomography (CT) scans as an alternative, providing a quicker, more accessible diagnostic tool for LARC.
In this retrospective study, CT images of 1070 T3-4 rectal cancer patients from 2010 to 2022 were analyzed. AI models, trained on 739 cases, were validated using two test sets of 134 and 197 cases. By utilizing techniques such as nonlocal mean filtering, dynamic histogram equalization, and the EfficientNetB0 algorithm, we identified images featuring characteristics of a positive circumferential resection margin (CRM) for the diagnosis of locally advanced rectal cancer (LARC). Importantly, this study employs an innovative approach by using both hard and soft voting systems in the second stage to ascertain the LARC status of cases, thus emphasizing the novelty of the soft voting system for improved case identification accuracy. The local recurrence rates and overall survival of the cases predicted by our model were assessed to underscore its clinical value.
The AI model exhibited high accuracy in identifying CRM-positive images, achieving an area under the curve (AUC) of 0.89 in the first test set and 0.86 in the second. In a patient-based analysis, the model reached AUCs of 0.84 and 0.79 using a hard voting system. Employing a soft voting system, the model attained AUCs of 0.93 and 0.88, respectively. Notably, AI-identified LARC cases exhibited a significantly higher five-year local recurrence rate and displayed a trend towards increased mortality across various thresholds. Furthermore, the model's capability to predict adverse clinical outcomes was superior to those of traditional assessments.
AI can precisely identify CRM-positive LARC cases from CT images, signaling an increased local recurrence and mortality rate. Our study presents a swifter and more reliable method for detecting LARC compared to traditional CT or MRI techniques.