基于变换器的深度学习模型,利用新辅助化疗前的 CT 图像早期预测局部晚期胃癌的淋巴结转移。

IF 9.6 1区 医学 Q1 MEDICINE, GENERAL & INTERNAL
EClinicalMedicine Pub Date : 2024-08-30 eCollection Date: 2024-09-01 DOI:10.1016/j.eclinm.2024.102805
Yunlin Zheng, Bingjiang Qiu, Shunli Liu, Ruirui Song, Xianqi Yang, Lei Wu, Zhihong Chen, Abudouresuli Tuersun, Xiaotang Yang, Wei Wang, Zaiyi Liu
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引用次数: 0

摘要

背景:新辅助化疗(NAC)后淋巴结状态的早期预测有助于及时优化治疗策略。本研究旨在利用基线计算机断层扫描图像开发和验证一种深度学习网络(DLN),以预测局部晚期胃癌(LAGC)患者新辅助化疗后的淋巴结转移(LNM):2013年1月至2023年3月期间,三家医院共回顾性招募了1205名LAGC患者,组成了一个训练队列、一个内部验证队列和两个外部验证队列。利用三维肿瘤图像开发了基于变压器的 DLN,用于预测 NAC 后的 LNM。通过多变量逻辑回归分析建立了一个临床模型,作为后续比较的基线。通过判别、校准和临床适用性评估了模型的性能。此外,还进行了 Kaplan-Meier 生存分析,以评估两个随访中心的 LAGC 患者的总生存率(OS):在训练组和验证组中,DLN的表现优于临床模型,在预测LNM方面表现强劲,曲线下面积(AUC)分别为0.804(95%置信区间[CI],0.752-0.849)、0.748(95% CI,0.660-0.830)、0.788(95% CI,0.735-0.835)和0.766(95% CI,0.717-0.814)。决策曲线分析表明,DLN 的临床净获益率很高。此外,DLN 与 LAGC 患者的 OS 显著相关[中心 1:危险比 (HR),1.789,P 解释:基于变压器的 DLN 可以早期有效地预测接受 NAC 治疗的 LAGC 患者的 LNM 和生存结果,有望指导个体化治疗。未来有必要开展前瞻性多中心研究,进一步验证我们的模型:国家自然科学基金(编号:82373432、82171923、82202142)、中国博士后科学基金项目(编号:2022M720857)、国家自然科学基金区域创新与发展联合基金(编号:U22A20345)、国家杰出青年科学基金(编号:81925023)、广东省杰出青年科学基金(编号:81925023)。81925023)、广东省医学影像人工智能分析与应用重点实验室(编号:2022B1212010011)、高水平医院建设项目(编号:DFJHBF202105)、广东省自然科学基金杰出青年学者项目(编号:2024B1515020091)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A transformer-based deep learning model for early prediction of lymph node metastasis in locally advanced gastric cancer after neoadjuvant chemotherapy using pretreatment CT images.

Background: Early prediction of lymph node status after neoadjuvant chemotherapy (NAC) facilitates promptly optimization of treatment strategies. This study aimed to develop and validate a deep learning network (DLN) using baseline computed tomography images to predict lymph node metastasis (LNM) after NAC in patients with locally advanced gastric cancer (LAGC).

Methods: A total of 1205 LAGC patients were retrospectively recruited from three hospitals between January 2013 and March 2023, constituting a training cohort, an internal validation cohort, and two external validation cohorts. A transformer-based DLN was developed using 3D tumor images to predict LNM after NAC. A clinical model was constructed through multivariate logistic regression analysis as a baseline for subsequent comparisons. The performance of the models was evaluated through discrimination, calibration, and clinical applicability. Furthermore, Kaplan-Meier survival analysis was conducted to assess overall survival (OS) of LAGC patients at two follow-up centers.

Findings: The DLN outperformed the clinical model and demonstrated a robust performance for predicting LNM in the training and validation cohorts, with areas under the curve (AUCs) of 0.804 (95% confidence interval [CI], 0.752-0.849), 0.748 (95% CI, 0.660-0.830), 0.788 (95% CI, 0.735-0.835), and 0.766 (95% CI, 0.717-0.814), respectively. Decision curve analysis exhibited a high net clinical benefit of the DLN. Moreover, the DLN was significantly associated with the OS of LAGC patients [Center 1: hazard ratio (HR), 1.789, P < 0.001; Center 2:HR, 1.776, P = 0.013].

Interpretation: The transformer-based DLN provides early and effective prediction of LNM and survival outcomes in LAGC patients receiving NAC, with promise to guide individualized therapy. Future prospective multicenter studies are warranted to further validate our model.

Funding: National Natural Science Foundation of China (NO. 82373432, 82171923, 82202142), Project Funded by China Postdoctoral Science Foundation (NO. 2022M720857), Regional Innovation and Development Joint Fund of National Natural Science Foundation of China (NO. U22A20345), National Science Fund for Distinguished Young Scholars of China (NO. 81925023), Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application (NO. 2022B1212010011), High-level Hospital Construction Project (NO. DFJHBF202105), Natural Science Foundation of Guangdong Province for Distinguished Young Scholars (NO. 2024B1515020091).

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来源期刊
EClinicalMedicine
EClinicalMedicine Medicine-Medicine (all)
CiteScore
18.90
自引率
1.30%
发文量
506
审稿时长
22 days
期刊介绍: eClinicalMedicine is a gold open-access clinical journal designed to support frontline health professionals in addressing the complex and rapid health transitions affecting societies globally. The journal aims to assist practitioners in overcoming healthcare challenges across diverse communities, spanning diagnosis, treatment, prevention, and health promotion. Integrating disciplines from various specialties and life stages, it seeks to enhance health systems as fundamental institutions within societies. With a forward-thinking approach, eClinicalMedicine aims to redefine the future of healthcare.
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