دورية أكاديمية
Performance reporting design in artificial intelligence studies using image-based TNM staging and prognostic parameters in rectal cancer: a systematic review
| العنوان: | Performance reporting design in artificial intelligence studies using image-based TNM staging and prognostic parameters in rectal cancer: a systematic review |
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| المؤلفون: | Minsung Kim, Taeyong Park, Bo Young Oh, Min Jeong Kim, Bum-Joo Cho, Il Tae Son |
| المصدر: | Annals of Coloproctology, Vol 40, Iss 1, Pp 13-26 (2024) |
| بيانات النشر: | Korean Society of Coloproctology, 2024. |
| سنة النشر: | 2024 |
| المجموعة: | LCC:Diseases of the digestive system. Gastroenterology |
| مصطلحات موضوعية: | rectal neoplasms, artificial intelligence, convolutional neural network, Diseases of the digestive system. Gastroenterology, RC799-869 |
| الوصف: | 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. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2287-9714 2287-9722 |
| Relation: | http://coloproctol.org/upload/pdf/ac-2023-00892-0127.pdf; https://doaj.org/toc/2287-9714; https://doaj.org/toc/2287-9722 |
| DOI: | 10.3393/ac.2023.00892.0127 |
| URL الوصول: | https://doaj.org/article/157e67e2a3b048a5a0959292de30db5b |
| رقم الأكسشن: | edsdoj.157e67e2a3b048a5a0959292de30db5b |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 22879714 22879722 |
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| DOI: | 10.3393/ac.2023.00892.0127 |