多中心评估磁共振成像上直肠癌淋巴结诊断的弱监督深度学习模型

IF 8.1 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Radiology-Artificial Intelligence Pub Date : 2024-03-01 DOI:10.1148/ryai.230152
Wei Xia, Dandan Li, Wenguang He, Perry J Pickhardt, Junming Jian, Rui Zhang, Junjie Zhang, Ruirui Song, Tong Tong, Xiaotang Yang, Xin Gao, Yanfen Cui
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The performances in binary and ternary N staging were evaluated using area under the receiver operating characteristic curve (AUC) and the concordance index (C index), respectively. Results A total of 1014 patients (median age, 62 years; IQR, 54-68 years; 590 male) were analyzed, including the training cohort (<i>n</i> = 589) and internal test cohort (<i>n</i> = 146) from center 1 and two external test cohorts (cohort 1: 117; cohort 2: 162) from centers 2 and 3. The WISDOM model yielded an overall AUC of 0.81 and C index of 0.765, significantly outperforming junior radiologists (AUC = 0.69, <i>P</i> < .001; C index = 0.689, <i>P</i> < .001) and performing comparably with senior radiologists (AUC = 0.79, <i>P</i> = .21; C index = 0.788, <i>P</i> = .22). Moreover, the model significantly improved the performance of junior radiologists (AUC = 0.80, <i>P</i> < .001; C index = 0.798, <i>P</i> < .001) and senior radiologists (AUC = 0.88, <i>P</i> < .001; C index = 0.869, <i>P</i> < .001). 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引用次数: 0

摘要

"刚刚接受 "的论文经过同行评审,已被接受在《放射学》上发表:人工智能》上发表。这篇文章在以最终版本发表之前,还将经过校对、排版和校对审核。请注意,在制作最终校对稿的过程中,可能会发现一些错误,从而影响文章内容。目的 开发一种弱监督模型开发框架(WISDOM)来构建直肠癌(RC)患者的淋巴结(LN)诊断模型,该模型使用术前 MRI 数据和术后患者水平的病理信息。材料与方法 在这项回顾性研究中,根据 2016 年 1 月至 2017 年 11 月期间的 RC 患者数据,利用 MRI(T2 加权和弥散加权成像)和患者层面的病理信息(术后确诊的转移性 LN 和切除的 LN 数量)构建了 WISDOM 模型。研究了该模型在协助放射科医生方面的增量价值。分别使用接收者操作曲线下面积(AUC)和一致性指数(C-index)评估了二元N分期和三元N分期的性能。结果 共分析了 1014 例患者(中位年龄 62 岁;IQR 54-68 岁;男性 590 例),包括第一中心的训练队列(n = 589)和内部测试队列(n = 146),以及第二和第三中心的两个外部测试队列(队列 1:n = 117;队列 2:n = 162)。WISDOM 模型的总体 AUC 为 0.81,C-index 为 0.765,明显优于初级放射科医生(AUC = 0.69,P < .001;C-index = 0.689,P < .001),与高级放射科医生的表现相当(AUC = 0.79,P = .21;C-index = 0.788,P = .22)。此外,该模型还大大提高了初级放射科医生(AUC = 0.80,P < .001;C-index = 0.798,P < .001)和高级放射科医生(AUC = 0.88,P < .001;C-index = 0.869,P < .001)的工作绩效。结论 本研究证明了 WISDOM 作为使用常规直肠 MRI 数据诊断 LN 的有用方法的潜力。在模型的帮助下,放射科医生的工作效率得到了提高,这凸显了 WISDOM 在临床实践中的潜在作用。以 CC BY 4.0 许可发布。
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Multicenter Evaluation of a Weakly Supervised Deep Learning Model for Lymph Node Diagnosis in Rectal Cancer at MRI.

Purpose To develop a Weakly supervISed model DevelOpment fraMework (WISDOM) model to construct a lymph node (LN) diagnosis model for patients with rectal cancer (RC) that uses preoperative MRI data coupled with postoperative patient-level pathologic information. Materials and Methods In this retrospective study, the WISDOM model was built using MRI (T2-weighted and diffusion-weighted imaging) and patient-level pathologic information (the number of postoperatively confirmed metastatic LNs and resected LNs) based on the data of patients with RC between January 2016 and November 2017. The incremental value of the model in assisting radiologists was investigated. The performances in binary and ternary N staging were evaluated using area under the receiver operating characteristic curve (AUC) and the concordance index (C index), respectively. Results A total of 1014 patients (median age, 62 years; IQR, 54-68 years; 590 male) were analyzed, including the training cohort (n = 589) and internal test cohort (n = 146) from center 1 and two external test cohorts (cohort 1: 117; cohort 2: 162) from centers 2 and 3. The WISDOM model yielded an overall AUC of 0.81 and C index of 0.765, significantly outperforming junior radiologists (AUC = 0.69, P < .001; C index = 0.689, P < .001) and performing comparably with senior radiologists (AUC = 0.79, P = .21; C index = 0.788, P = .22). Moreover, the model significantly improved the performance of junior radiologists (AUC = 0.80, P < .001; C index = 0.798, P < .001) and senior radiologists (AUC = 0.88, P < .001; C index = 0.869, P < .001). Conclusion This study demonstrates the potential of WISDOM as a useful LN diagnosis method using routine rectal MRI data. The improved radiologist performance observed with model assistance highlights the potential clinical utility of WISDOM in practice. Keywords: MR Imaging, Abdomen/GI, Rectum, Computer Applications-Detection/Diagnosis Supplemental material is available for this article. Published under a CC BY 4.0 license.

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来源期刊
CiteScore
16.20
自引率
1.00%
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期刊介绍: Radiology: Artificial Intelligence is a bi-monthly publication that focuses on the emerging applications of machine learning and artificial intelligence in the field of imaging across various disciplines. This journal is available online and accepts multiple manuscript types, including Original Research, Technical Developments, Data Resources, Review articles, Editorials, Letters to the Editor and Replies, Special Reports, and AI in Brief.
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