Ann Coloproctol.
2024 Feb;40(1):13-26. 10.3393/ac.2023.00892.0127.
Performance reporting design in artificial intelligence studies using image-based TNM staging and prognostic parameters in rectal cancer: a systematic review
- Affiliations
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- 1Department of Surgery, Hallym University Sacred Heart Hospital, Hallym University College of Medicine, Anyang, Korea
- 2Medical Artificial Intelligence Center, Hallym University Medical Center, Anyang, Korea
- 3Department of Radiology, Hallym University Sacred Heart Hospital, Hallym University College of Medicine, Anyang, Korea
- KMID: 2552890
- DOI: http://doi.org/10.3393/ac.2023.00892.0127
Abstract
- Purpose
The integration of artificial intelligence (AI) and magnetic resonance imaging in rectal cancer has the potential to enhance diagnostic accuracy by identifying subtle patterns and aiding tumor delineation and lymph node assessment. According to our systematic review focusing on convolutional neural networks, AI-driven tumor staging and the prediction of treatment response facilitate tailored treatment strategies for patients with rectal cancer.
Methods
This paper summarizes the current landscape of AI in the imaging field of rectal cancer, emphasizing the performance reporting design based on the quality of the dataset, model performance, and external validation.
Results
AI-driven tumor segmentation has demonstrated promising results using various convolutional neural network models. AI-based predictions of staging and treatment response have exhibited potential as auxiliary tools for personalized treatment strategies. Some studies have indicated superior performance than conventional models in predicting microsatellite instability and KRAS status, offering noninvasive and cost-effective alternatives for identifying genetic mutations.
Conclusion
Image-based AI studies for rectal cancer have shown acceptable diagnostic performance but face several challenges, including limited dataset sizes with standardized data, the need for multicenter studies, and the absence of oncologic relevance and external validation for clinical implantation. Overcoming these pitfalls and hurdles is essential for the feasible integration of AI models in clinical settings for rectal cancer, warranting further research.
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