基于深度学习的多模态数据整合提高了乳腺癌无病生存率预测能力。

IF 5.1 4区 医学 Q1 MEDICINE, RESEARCH & EXPERIMENTAL Precision Clinical Medicine Pub Date : 2024-05-29 eCollection Date: 2024-06-01 DOI:10.1093/pcmedi/pbae012
Zehua Wang, Ruichong Lin, Yanchun Li, Jin Zeng, Yongjian Chen, Wenhao Ouyang, Han Li, Xueyan Jia, Zijia Lai, Yunfang Yu, Herui Yao, Weifeng Su
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引用次数: 0

摘要

背景:乳腺癌的预后往往不佳,因此需要进行早期转移风险检测和准确的治疗预测。本研究旨在利用术前数据开发一种新型多模态深度学习模型,以预测无病生存期(DFS):我们回顾性地收集了来自癌症基因组图谱和中国一家独立机构的病理成像、分子和临床数据。我们开发了一种新型的基于深度学习的临床医学病理基因多模态(DeepClinMed-PGM)模型,将临床病理数据与分子洞察力相结合,用于预测无病生存期。患者包括训练队列(n = 741)、内部验证队列(n = 184)和外部测试队列(n = 95):结果:将多模态数据整合到DeepClinMed-PGM模型中能显著提高接收者工作特征曲线下面积(AUC)值。在训练队列中,1 年、3 年和 5 年 DFS 预测的 AUC 值分别增至 0.979、0.957 和 0.871,而在外部测试队列中,1 年、2 年和 3 年 DFS 预测的 AUC 值分别达到 0.851、0.878 和 0.938。DeepClinMed-PGM 强大的判别能力在包括训练队列在内的不同队列中始终如一地表现出来[危险比(HR)0.027,95% 置信区间(CI)0.0016-0.046,P P P 结论:这项研究介绍了一种乳腺癌预后的方法,它整合了成像、分子和临床数据,提高了预测的准确性,为个性化治疗策略带来了希望。
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Deep learning-based multi-modal data integration enhancing breast cancer disease-free survival prediction.

Background: The prognosis of breast cancer is often unfavorable, emphasizing the need for early metastasis risk detection and accurate treatment predictions. This study aimed to develop a novel multi-modal deep learning model using preoperative data to predict disease-free survival (DFS).

Methods: We retrospectively collected pathology imaging, molecular and clinical data from The Cancer Genome Atlas and one independent institution in China. We developed a novel Deep Learning Clinical Medicine Based Pathological Gene Multi-modal (DeepClinMed-PGM) model for DFS prediction, integrating clinicopathological data with molecular insights. The patients included the training cohort (n = 741), internal validation cohort (n = 184), and external testing cohort (n = 95).

Result: Integrating multi-modal data into the DeepClinMed-PGM model significantly improved area under the receiver operating characteristic curve (AUC) values. In the training cohort, AUC values for 1-, 3-, and 5-year DFS predictions increased to 0.979, 0.957, and 0.871, while in the external testing cohort, the values reached 0.851, 0.878, and 0.938 for 1-, 2-, and 3-year DFS predictions, respectively. The DeepClinMed-PGM's robust discriminative capabilities were consistently evident across various cohorts, including the training cohort [hazard ratio (HR) 0.027, 95% confidence interval (CI) 0.0016-0.046, P < 0.0001], the internal validation cohort (HR 0.117, 95% CI 0.041-0.334, P < 0.0001), and the external cohort (HR 0.061, 95% CI 0.017-0.218, P < 0.0001). Additionally, the DeepClinMed-PGM model demonstrated C-index values of 0.925, 0.823, and 0.864 within the three cohorts, respectively.

Conclusion: This study introduces an approach to breast cancer prognosis, integrating imaging and molecular and clinical data for enhanced predictive accuracy, offering promise for personalized treatment strategies.

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来源期刊
Precision Clinical Medicine
Precision Clinical Medicine MEDICINE, RESEARCH & EXPERIMENTAL-
CiteScore
10.80
自引率
0.00%
发文量
26
审稿时长
5 weeks
期刊介绍: Precision Clinical Medicine (PCM) is an international, peer-reviewed, open access journal that provides timely publication of original research articles, case reports, reviews, editorials, and perspectives across the spectrum of precision medicine. The journal's mission is to deliver new theories, methods, and evidence that enhance disease diagnosis, treatment, prevention, and prognosis, thereby establishing a vital communication platform for clinicians and researchers that has the potential to transform medical practice. PCM encompasses all facets of precision medicine, which involves personalized approaches to diagnosis, treatment, and prevention, tailored to individual patients or patient subgroups based on their unique genetic, phenotypic, or psychosocial profiles. The clinical conditions addressed by the journal include a wide range of areas such as cancer, infectious diseases, inherited diseases, complex diseases, and rare diseases.
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