用于预测不符合内镜治愈标准的早期胃癌淋巴结转移的机器学习模型。

IF 6 1区 医学 Q1 GASTROENTEROLOGY & HEPATOLOGY
Gastric Cancer Pub Date : 2024-09-01 Epub Date: 2024-05-25 DOI:10.1007/s10120-024-01511-8
Minoru Kato, Yoshito Hayashi, Ryotaro Uema, Takashi Kanesaka, Shinjiro Yamaguchi, Akira Maekawa, Takuya Yamada, Masashi Yamamoto, Shinji Kitamura, Takuya Inoue, Shunsuke Yamamoto, Takashi Kizu, Risato Takeda, Hideharu Ogiyama, Katsumi Yamamoto, Kenji Aoi, Koji Nagaike, Yasutaka Sasai, Satoshi Egawa, Haruki Akamatsu, Hiroyuki Ogawa, Masato Komori, Nishihara Akihiro, Takeo Yoshihara, Yoshiki Tsujii, Tetsuo Takehara
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引用次数: 0

摘要

背景我们开发了一种机器学习(ML)模型,用于预测不符合现有日本内镜治愈标准的早期胃癌(EGC)患者的淋巴结转移(LNM)风险,并将其与最常见的临床风险评分系统 eCura 系统的性能进行了比较:我们使用了来自21家医疗机构的4042名连续EGC患者的数据,这些患者在2010年至2021年间接受了内镜粘膜下剥离术(ESD)和/或手术。所有切除的EGC均经组织学证实不符合现行的日本内镜治愈标准。在所有患者中,3,506 人构成了开发基于神经网络的 ML 模型的训练队列,536 人构成了验证队列。以接收者操作特征曲线下面积(AUC)衡量,我们的 ML 模型的性能与 eCura 系统在验证队列中的性能进行了比较:训练队列和验证队列中的 LNM 率分别为 14%(503/3506)和 7%(39/536)。在验证队列中,ML 模型识别 LNM 患者的 AUC 为 0.83(95% 置信区间,0.76-0.89),而 eCura 系统识别 LNM 患者的 AUC 为 0.77(95% 置信区间,0.70-0.85)(P = 0.006,DeLong 检验):在预测不符合现有日本内镜治愈标准的EGC患者的LNM风险方面,我们的ML模型优于eCura系统。我们开发了一种基于神经网络的机器学习模型,可以预测不符合内镜下可治愈标准的早期胃癌患者的淋巴结转移风险。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

A machine learning model for predicting the lymph node metastasis of early gastric cancer not meeting the endoscopic curability criteria.

A machine learning model for predicting the lymph node metastasis of early gastric cancer not meeting the endoscopic curability criteria.

Background: We developed a machine learning (ML) model to predict the risk of lymph node metastasis (LNM) in patients with early gastric cancer (EGC) who did not meet the existing Japanese endoscopic curability criteria and compared its performance with that of the most common clinical risk scoring system, the eCura system.

Methods: We used data from 4,042 consecutive patients with EGC from 21 institutions who underwent endoscopic submucosal dissection (ESD) and/or surgery between 2010 and 2021. All resected EGCs were histologically confirmed not to satisfy the current Japanese endoscopic curability criteria. Of all patients, 3,506 constituted the training cohort to develop the neural network-based ML model, and 536 constituted the validation cohort. The performance of our ML model, as measured by the area under the receiver operating characteristic curve (AUC), was compared with that of the eCura system in the validation cohort.

Results: LNM rates were 14% (503/3,506) and 7% (39/536) in the training and validation cohorts, respectively. The ML model identified patients with LNM with an AUC of 0.83 (95% confidence interval, 0.76-0.89) in the validation cohort, while the eCura system identified patients with LNM with an AUC of 0.77 (95% confidence interval, 0.70-0.85) (P = 0.006, DeLong's test).

Conclusions: Our ML model performed better than the eCura system for predicting LNM risk in patients with EGC who did not meet the existing Japanese endoscopic curability criteria. We developed a neural network-based machine learning model that predicts the risk of lymph node metastasis in patients with early gastric cancer who did not meet the endoscopic curability criteria.

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来源期刊
Gastric Cancer
Gastric Cancer 医学-胃肠肝病学
CiteScore
14.70
自引率
2.70%
发文量
80
审稿时长
6-12 weeks
期刊介绍: Gastric Cancer is an esteemed global forum that focuses on various aspects of gastric cancer research, treatment, and biology worldwide. The journal promotes a diverse range of content, including original articles, case reports, short communications, and technical notes. It also welcomes Letters to the Editor discussing published articles or sharing viewpoints on gastric cancer topics. Review articles are predominantly sought after by the Editor, ensuring comprehensive coverage of the field. With a dedicated and knowledgeable editorial team, the journal is committed to providing exceptional support and ensuring high levels of author satisfaction. In fact, over 90% of published authors have expressed their intent to publish again in our esteemed journal.
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