Department of Gynecologic Oncology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China.
School of Biomedical Engineering, Southern Medical University, Guangzhou, China.
Lancet Digit Health. 2024 Mar;6(3):e176-e186. doi: 10.1016/S2589-7500(23)00245-5. Epub 2024 Jan 11.
Ovarian cancer is the most lethal gynecological malignancy. Timely diagnosis of ovarian cancer is difficult due to the lack of effective biomarkers. Laboratory tests are widely applied in clinical practice, and some have shown diagnostic and prognostic relevance to ovarian cancer. We aimed to systematically evaluate the value of routine laboratory tests on the prediction of ovarian cancer, and develop a robust and generalisable ensemble artificial intelligence (AI) model to assist in identifying patients with ovarian cancer.
In this multicentre, retrospective cohort study, we collected 98 laboratory tests and clinical features of women with or without ovarian cancer admitted to three hospitals in China during Jan 1, 2012 and April 4, 2021. A multi-criteria decision making-based classification fusion (MCF) risk prediction framework was used to make a model that combined estimations from 20 AI classification models to reach an integrated prediction tool developed for ovarian cancer diagnosis. It was evaluated on an internal validation set (3007 individuals) and two external validation sets (5641 and 2344 individuals). The primary outcome was the prediction accuracy of the model in identifying ovarian cancer.
Based on 52 features (51 laboratory tests and age), the MCF achieved an area under the receiver-operating characteristic curve (AUC) of 0·949 (95% CI 0·948-0·950) in the internal validation set, and AUCs of 0·882 (0·880-0·885) and 0·884 (0·882-0·887) in the two external validation sets. The model showed higher AUC and sensitivity compared with CA125 and HE4 in identifying ovarian cancer, especially in patients with early-stage ovarian cancer. The MCF also yielded acceptable prediction accuracy with the exclusion of highly ranked laboratory tests that indicate ovarian cancer, such as CA125 and other tumour markers, and outperformed state-of-the-art models in ovarian cancer prediction. The MCF was wrapped as an ovarian cancer prediction tool, and made publicly available to provide estimated probability of ovarian cancer with input laboratory test values.
The MCF model consistently achieved satisfactory performance in ovarian cancer prediction when using laboratory tests from the three validation sets. This model offers a low-cost, easily accessible, and accurate diagnostic tool for ovarian cancer. The included laboratory tests, not only CA125 which was the highest ranked laboratory test in importance of diagnostic assistance, contributed to the characterisation of patients with ovarian cancer.
Ministry of Science and Technology of China; National Natural Science Foundation of China; Natural Science Foundation of Guangdong Province, China; and Science and Technology Project of Guangzhou, China.
For the Chinese translation of the abstract see Supplementary Materials section.
卵巢癌是最致命的妇科恶性肿瘤。由于缺乏有效的生物标志物，卵巢癌的及时诊断较为困难。实验室检查在临床实践中得到广泛应用，其中一些检查已显示出对卵巢癌的诊断和预后相关性。我们旨在系统评估常规实验室检查对卵巢癌预测的价值，并开发一种稳健且可推广的集成人工智能（AI）模型，以协助识别卵巢癌患者。
在这项多中心、回顾性队列研究中，我们收集了 2012 年 1 月 1 日至 2021 年 4 月 4 日期间在中国三家医院就诊的有或无卵巢癌的女性的 98 项实验室检查和临床特征。使用基于多标准决策的分类融合（MCF）风险预测框架构建模型，该模型结合了 20 个 AI 分类模型的估计值，以达到用于卵巢癌诊断的综合预测工具。该模型在内部验证集（3007 人）和两个外部验证集（5641 人和 2344 人）上进行了评估。主要结局是模型在识别卵巢癌方面的预测准确性。
基于 52 个特征（51 项实验室检查和年龄），MCF 在内部验证集中的受试者工作特征曲线下面积（AUC）为 0.949（95%CI 0.948-0.950），在两个外部验证集中的 AUC 分别为 0.882（0.880-0.885）和 0.884（0.882-0.887）。该模型在识别卵巢癌方面的 AUC 和敏感性均高于 CA125 和 HE4，尤其是在早期卵巢癌患者中。MCF 在排除提示卵巢癌的高排名实验室检查（如 CA125 和其他肿瘤标志物）后，也能获得可接受的预测准确性，并且在卵巢癌预测方面优于最先进的模型。MCF 被包装为卵巢癌预测工具，并公开提供输入实验室检查值时卵巢癌的估计概率。
当使用三个验证集中的实验室检查时，MCF 模型在卵巢癌预测中始终表现出令人满意的性能。该模型为卵巢癌提供了一种低成本、易于获得且准确的诊断工具。所纳入的实验室检查，不仅包括 CA125，还包括在诊断辅助重要性中排名最高的实验室检查，有助于确定卵巢癌患者的特征。
中国科学技术部；国家自然科学基金委员会；中国广东省自然科学基金；中国广州市科技计划。
