Dataset Information

An artificial intelligence system for comprehensive pathologic outcome prediction in early gastric cancer through endoscopic image analysis (with video).

ABSTRACT:

Background

Accurate prediction of pathologic results for early gastric cancer (EGC) based on endoscopic findings is essential in deciding between endoscopic and surgical resection. This study aimed to develop an artificial intelligence (AI) model to assess comprehensive pathologic characteristics of EGC using white-light endoscopic images and videos.

Methods

To train the model, we retrospectively collected 4,336 images and prospectively included 153 videos from patients with EGC who underwent endoscopic or surgical resection. The performance of the model was tested and compared to that of 16 endoscopists (nine experts and seven novices) using a mutually exclusive set of 260 images and 10 videos. Finally, we conducted external validation using 436 images and 89 videos from another institution.

Results

After training, the model achieved predictive accuracies of 89.7% for undifferentiated histology, 88.0% for submucosal invasion, 87.9% for lymphovascular invasion (LVI), and 92.7% for lymph node metastasis (LNM), using endoscopic videos. The area under the curve values of the model were 0.992 for undifferentiated histology, 0.902 for submucosal invasion, 0.706 for LVI, and 0.680 for LNM in the test. In addition, the model showed significantly higher accuracy than the experts in predicting undifferentiated histology (92.7% vs. 71.6%), submucosal invasion (87.3% vs. 72.6%), and LNM (87.7% vs. 72.3%). The external validation showed accuracies of 75.6% and 71.9% for undifferentiated histology and submucosal invasion, respectively.

Conclusions

AI may assist endoscopists with high predictive performance for differentiation status and invasion depth of EGC. Further research is needed to improve the detection of LVI and LNM.

SUBMITTER: Lee S

PROVIDER: S-EPMC11335909 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature

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Publications

An artificial intelligence system for comprehensive pathologic outcome prediction in early gastric cancer through endoscopic image analysis (with video).

Lee Seunghan S Jeon Jiwoon J Park Jinbae J Chang Young Hoon YH Shin Cheol Min CM Oh Mi Jin MJ Kim Su Hyun SH Kang Seungkyung S Park Su Hee SH Kim Sang Gyun SG Lee Hyuk-Joon HJ Yang Han-Kwang HK Lee Hey Seung HS Cho Soo-Jeong SJ

Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association 20240702 5

<h4>Background</h4>Accurate prediction of pathologic results for early gastric cancer (EGC) based on endoscopic findings is essential in deciding between endoscopic and surgical resection. This study aimed to develop an artificial intelligence (AI) model to assess comprehensive pathologic characteristics of EGC using white-light endoscopic images and videos.<h4>Methods</h4>To train the model, we retrospectively collected 4,336 images and prospectively included 153 videos from patients with EGC w ...[more]

PMID: 38954175

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Dataset Information

An artificial intelligence system for comprehensive pathologic outcome prediction in early gastric cancer through endoscopic image analysis (with video).

Background

Methods

Results

Conclusions

Publications

An artificial intelligence system for comprehensive pathologic outcome prediction in early gastric cancer through endoscopic image analysis (with video).

Similar Datasets

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