Artificial intelligence–assisted treatment strategy for T1 colorectal cancer after endoscopic resection

Ichimasa, Katsuro; Kudo, Shin‐ei; Lee, Jonathan Wei Jie; Nemoto, Tetsuo; Yeoh, Khay Guan

doi:10.1016/j.gie.2023.01.057

Cited by 8 publications

(6 citation statements)

References 26 publications

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Section: How Does Ai Improve Colon Polyp Detection and Characterization?mentioning

confidence: 99%

“…AI for polyp detection and characterization represents the most advanced tool in computer-aided endoscopy, and some of these techniques are already used in daily practice: (1) CADe for polyp detection and identification 7 ; (2) CADx for polyp characterization and classification (also called optical biopsy or histology prediction) 8 ; (3) CADx for the optical characterization of neoplasia in patients with ulcerative colitis (UC) 9 ; and (4) AI systems can help determine whether additional surgery is needed after endoscopic resection of T1 colorectal cancer by predicting lymph node metastasis. 10 Since 2022, Mori et al 11 have developed multiple algorithms designed on an extensive collection of routine colonoscopy videos featuring high-resolution imaging, magnification, and even endocystocopy. Minute and advanced lesions remain a big challenge, even when we have a chance to undergo high-quality colonoscopy screening.…”

Section: How Does Ai Improve Colon Polyp Detection and Characterization?mentioning

confidence: 99%

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As how artificial intelligence is revolutionizing endoscopy

Rey

2024

Clin Endosc

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With incessant advances in information technology and its implications in all domains of our lives, artificial intelligence (AI) has emerged as a requirement for improved machine performance. This brings forth the query of how this can benefit endoscopists and improve both diagnostic and therapeutic endoscopy in each part of the gastrointestinal tract. Additionally, it also raises the question of the recent benefits and clinical usefulness of this new technology in daily endoscopic practice. There are two main categories of AI systems: computer-assisted detection (CADe) for lesion detection and computer-assisted diagnosis (CADx) for optical biopsy and lesion characterization. Quality assurance is the next step in the complete monitoring of high-quality colonoscopies. In all cases, computer-aided endoscopy is used, as the overall results rely on the physician. Video capsule endoscopy is a unique example in which a computer operates a device, stores multiple images, and performs an accurate diagnosis. While there are many expectations, we need to standardize and assess various software packages. It is important for healthcare providers to support this new development and make its use an obligation in daily clinical practice. In summary, AI represents a breakthrough in digestive endoscopy. Screening for gastric and colonic cancer detection should be improved, particularly outside expert centers. Prospective and multicenter trials are mandatory before introducing new software into clinical practice.

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Section: How Does Ai Improve Colon Polyp Detection and Characterization?mentioning

confidence: 99%

Section: How Does Ai Improve Colon Polyp Detection and Characterization?mentioning

confidence: 99%

As how artificial intelligence is revolutionizing endoscopy

Rey

2024

Clin Endosc

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“…AI systems to help making decisions as to whether additional surgery is needed after an endoscopic resection of T1 CRCs or no, by predicting lymph node metastasis [5].…”

Section: Artificial Intelligence For Polyp Detection and Characteriza...mentioning

confidence: 99%

“…(3) CADx for optical characterization of neoplasia in patient with ulcerative colitis [4]. (4) AI systems to help making decisions as to whether additional surgery is needed after an endoscopic resection of T1 CRCs or no, by predicting lymph node metastasis [5].…”

Section: Andandmentioning

confidence: 99%

Artificial intelligence in digestive endoscopy: recent advances

Rey

2023

Current Opinion in Gastroenterology

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Purpose of review With the incessant advances in information technology and its implications in all domains of our life, artificial intelligence (AI) started to emerge as a need for better machine performance. How it can help endoscopists and what are the areas of interest in improving both diagnostic and therapeutic endoscopy in each part of the gastrointestinal (GI) tract. What are the recent benefits and clinical usefulness of this new technology in daily endoscopic practice. Recent Findings The two main AI systems categories are computer-assisted detection ‘CADe’ for lesion detection and computer-assisted diagnosis ‘CADx’ for optical biopsy and lesion characterization. Multiple softwares are now implemented in endoscopy practice. Other AI systems offer therapeutic assistance such as lesion delineation for complete endoscopic resection or prediction of possible lymphanode after endoscopic treatment. Quality assurance is the coming step with complete monitoring of high-quality colonoscopy. In all cases it is a computer-aid endoscopy as the overall result rely on the physician. Video capsule endoscopy is the unique example were the computer conduct the device, store multiple images, and perform accurate diagnosis. Summary AI is a breakthrough in digestive endoscopy. Screening gastric and colonic cancer detection should be improved especially outside of expert's centers. Prospective and multicenter trials are mandatory before introducing new software in clinical practice.

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“…Recent studies have attempted to solve these problems using artificial intelligence (AI) [23][24][25][26][27]. The two strategical approaches for AI-assisted assessment of the risk of LNM in T1 CRC were pathologist-dependent and independent [28,29]. The pathologistdependent strategy used text-based data, which included the histologic features obtained by a pathologist, such as depth of SM invasion, tumor differentiation, and LVI [23,25,30].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Lymph Node Metastasis in T1 Colorectal Cancer Using Artificial Intelligence with Hematoxylin and Eosin-Stained Whole-Slide-Images of Endoscopic and Surgical Resection Specimens

Song,

Kim,

Hong

et al. 2024

Cancers

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According to the current guidelines, additional surgery is performed for endoscopically resected specimens of early colorectal cancer (CRC) with a high risk of lymph node metastasis (LNM). However, the rate of LNM is 2.1–25.0% in cases treated endoscopically followed by surgery, indicating a high rate of unnecessary surgeries. Therefore, this study aimed to develop an artificial intelligence (AI) model using H&E-stained whole slide images (WSIs) without handcrafted features employing surgically and endoscopically resected specimens to predict LNM in T1 CRC. To validate with an independent cohort, we developed a model with four versions comprising various combinations of training and test sets using H&E-stained WSIs from endoscopically (400 patients) and surgically resected specimens (881 patients): Version 1, Train and Test: surgical specimens; Version 2, Train and Test: endoscopic and surgically resected specimens; Version 3, Train: endoscopic and surgical specimens and Test: surgical specimens; Version 4, Train: endoscopic and surgical specimens and Test: endoscopic specimens. The area under the curve (AUC) of the receiver operating characteristic curve was used to determine the accuracy of the AI model for predicting LNM with a 5-fold cross-validation in the training set. Our AI model with H&E-stained WSIs and without annotations showed good performance power with the validation of an independent cohort in a single center. The AUC of our model was 0.758–0.830 in the training set and 0.781–0.824 in the test set, higher than that of previous AI studies with only WSI. Moreover, the AI model with Version 4, which showed the highest sensitivity (92.9%), reduced unnecessary additional surgery by 14.2% more than using the current guidelines (68.3% vs. 82.5%). This revealed the feasibility of using an AI model with only H&E-stained WSIs to predict LNM in T1 CRC.

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Artificial intelligence–assisted treatment strategy for T1 colorectal cancer after endoscopic resection

Cited by 8 publications

References 26 publications

As how artificial intelligence is revolutionizing endoscopy

As how artificial intelligence is revolutionizing endoscopy

Artificial intelligence in digestive endoscopy: recent advances

Prediction of Lymph Node Metastasis in T1 Colorectal Cancer Using Artificial Intelligence with Hematoxylin and Eosin-Stained Whole-Slide-Images of Endoscopic and Surgical Resection Specimens

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