Organ preservation for early rectal cancer using preoperative chemoradiotherapy

Son, Gyung Mo

doi:10.3393/ac.2023.00409.0058

Cited by 2 publications

(1 citation statement)

References 12 publications

(13 reference statements)

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“…Recently, minimally invasive surgical techniques, such as robotic surgery and transanal surgery, have been increasingly performed [ 8 , 9 ]. In patients with advanced rectal cancer, if there is a good response to treatment after preoperative chemoradiotherapy (CRT), local excision or a nonoperative management, also known as the “watch-and-wait” strategy, may be attempted [ 10 , 11 ]. It is important to select the appropriate candidate for tailored treatment in rectal cancer, which requires accurate diagnosis and assessment of response to preoperative treatment [ 12 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

Performance reporting design in artificial intelligence studies using image-based TNM staging and prognostic parameters in rectal cancer: a systematic review

Kim,

Park,

et al. 2024

Ann Coloproctol

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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.

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