Annals of Coloproctology

Reviews

Colorectal cancer

Performance reporting design in artificial intelligence studies using image-based TNM staging and prognostic parameters in rectal cancer: a systematic review: Minsung Kim, Taeyong Park, Bo Young Oh, Min Jeong Kim, Bum-Joo Cho, Il Tae Son; Ann Coloproctol. 2024;40(1):13-26. Published online February 28, 2024; DOI: https://doi.org/10.3393/ac.2023.00892.0127

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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.

Citations

Citations to this article as recorded by

Enhancing the role of MRI in rectal cancer: advances from staging to prognosis prediction
Xiaoling Gong, Zheng Ye, Yu Shen, Bin Song
European Radiology.2025;[Epub] CrossRef
L’intelligence artificielle pourrait-elle aider le chirurgien digestif dans la prise en charge du cancer du rectum ?
Arnaud Alves, Karem Slim
Journal de Chirurgie Viscérale.2024; 161(4): 253. CrossRef
Can artificial intelligence help a digestive surgeon in management of rectal cancer?
Arnaud Alves, Karem Slim
Journal of Visceral Surgery.2024; 161(4): 231. CrossRef
Artificial intelligence for the colorectal surgeon in 2024 – A narrative review of Prevalence, Policies, and (needed) Protections
Kurt S. Schultz, Michelle L. Hughes, Warqaa M. Akram, Anne K. Mongiu
Seminars in Colon and Rectal Surgery.2024; 35(3): 101037. CrossRef
Artificial Intelligence in Coloproctology: A Review of Emerging Technologies and Clinical Applications
Joana Mota, Maria João Almeida, Miguel Martins, Francisco Mendes, Pedro Cardoso, João Afonso, Tiago Ribeiro, João Ferreira, Filipa Fonseca, Manuel Limbert, Susana Lopes, Guilherme Macedo, Fernando Castro Poças, Miguel Mascarenhas
Journal of Clinical Medicine.2024; 13(19): 5842. CrossRef

Benign proctology

A systematic review of translation and experimental studies on internal anal sphincter for fecal incontinence: Minsung Kim, Bo-Young Oh, Ji-Seon Lee, Dogeon Yoon, Wook Chun, Il Tae Son; Ann Coloproctol. 2022;38(3):183-196. Published online June 9, 2022; DOI: https://doi.org/10.3393/ac.2022.00276.0039

4,513 View
154 Download
5 Web of Science
5 Citations

Abstract PDF

The complexity in the molecular mechanism of the internal anal sphincter (IAS) limits preclinical or clinical outcomes of fecal incontinence (FI) treatment. So far, there are no systematic reviews of IAS translation and experimental studies that have been reported. This systematic review aims to provide a comprehensive understanding of IAS critical role in FI. Previous studies revealed the key pathway for basal tone and relaxation of IAS in different properties as follows; calcium, Rho-associated, coiled-coil containing serine/threonine kinase, aging-associated IAS dysfunction, oxidative stress, renin-angiotensin-aldosterone, cyclooxygenase, and inhibitory neurotransmitters. Previous studies have reported improved functional outcomes of cellular treatment for regeneration of dysfunctional IAS, using various stem cells, but did not demonstrate the interrelationship between those results and basal tone or relaxation-related molecular pathway of IAS. Furthermore, these results have lower specificity for IAS-incontinence due to the included external anal sphincter or nerve injury regardless of the cell type. An acellular approach using bioengineered IAS showed a physiologic response of basal tone and relaxation response similar to human IAS. However, in both cellular and acellular approaches, the lack of human IAS data still hampers clinical application. Therefore, the IAS regeneration presents more challenges and warrants more advances.

Citations

Citations to this article as recorded by

A single-center retrospective analysis of endorectal advancement flaps used for the treatment of simple rectovaginal fistulas
Xuexiao Li, Wanjin Shao, Guidong Sun
Scandinavian Journal of Gastroenterology.2025; 60(4): 307. CrossRef
Tissue engineering and regenerative medicine approaches in colorectal surgery
Bigyan B. Mainali, James J. Yoo, Mitchell R. Ladd
Annals of Coloproctology.2024; 40(4): 336. CrossRef
3D spheroids versus 2D-cultured human adipose stem cells to generate smooth muscle cells in an internal anal sphincter-targeting cryoinjured mouse model
Iltae Son, Minsung Kim, Ji-Seon Lee, Dogeon Yoon, You-Rin Kim, Ji Hye Park, Bo-Young Oh, Wook Chun, Sung-Bum Kang
Stem Cell Research & Therapy.2024;[Epub] CrossRef
Differentiation of Adipose-Derived Stem Cells into Smooth Muscle Cells in an Internal Anal Sphincter-Targeting Anal Incontinence Rat Model
Minsung Kim, Bo-Young Oh, Ji-Seon Lee, Dogeon Yoon, You-Rin Kim, Wook Chun, Jong Wan Kim, Il Tae Son
Journal of Clinical Medicine.2023; 12(4): 1632. CrossRef
Improving Efficiency and Accuracy in English Translation Learning: Investigating a Semantic Analysis Correction Algorithm
Lingmei Cao, Junru Fu
Applied Artificial Intelligence.2023;[Epub] CrossRef

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