Computational Analysis of Routine Biopsies Improves Diagnosis and Prediction of Cardiac Allograft Vasculopathy.
Computational Analysis of Routine Biopsies Improves Diagnosis and Prediction of Cardiac Allograft Vasculopathy.
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DOI：
10.1161/circulationaha.121.058459
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发表时间：
2022-05-24
期刊：
影响因子：
37.8
通讯作者：
Margulies, Kenneth B.
中科院分区：
文献类型：
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作者：
Peyster, Eliot G.;Janowczyk, Andrew;Swamidoss, Abigail;Kethireddy, Samhith;Feldman, Michael D.;Margulies, Kenneth B.
关键词：
同种异体心脏移植血管病变（CAV）是心脏移植受者发病和死亡的主要原因。虽然CAV的临床危险因素已经确定，但目前还没有个性化的预后测试来确定患者发展为侵袭性CAV的高风险和低风险。本研究的目的是利用计算方法分析常规心内膜活检（EMB）的数字病理图像，以开发一种精确的医学工具，在临床表现明显之前预测CAV。来自宾夕法尼亚大学的302名移植受者在移植后1年的临床数据收集，包括53名“早期CAV”患者和249名“无CAV”对照组。这些数据被用于生成一个“临床模型”（ClinCAV-Pr），用于预测未来CAV的发展。从该队列中，收集n=183个存档的emb进行CD31和改良三色染色，然后进行数字扫描。其中包括移植后1年来自50名“早期CAV”患者和82名非CAV患者的EMBs，以及51名来自“疾病控制”患者的EMBs，这些患者在最终冠状动脉造影确认CAV时获得。利用从数字化EMBs中提取的生物学灵感，手工制作的特征，开发了用于区分非CAV与疾病对照的定量组织学模型（histav - dx），并用于预测移植后1年EMBs的未来CAV （histav - pr）。预测未来CAV的组织学和临床模型（分别为histav - pr和ClinCAV-Pr）的性能在保留验证集中进行比较，然后结合评估综合预测模型（iCAV-Pr）的附加预测价值。ClinCAV-Pr在独立测试集上的表现一般，受试者工作曲线下面积（AUROC）为0.70。诊断CAV的histav - dx模型的AUROC为0.91，而预测CAV的histav - pr模型的AUROC为0.80，表现良好。集成iCAV-Pr模型的预测性能优异，在hold -out测试集上AUROC为0.93。通过结合计算提取的组织学特征，大大提高了对未来CAV发展的预测。这些结果表明，在定期获得的活检组织中包含的形态学细节有可能提高心脏移植后患者治疗计划的准确性和个性化。
Cardiac allograft vasculopathy (CAV) is a leading cause of morbidity and mortality for heart transplant recipients. While clinical risk factors for CAV have been established, no personalized prognostic test exists to confidently identify patients at high vs. low risk of developing aggressive CAV. The aim of this investigation was to leverage computational methods for analyzing digital pathology images from routine endomyocardial biopsies (EMB) to develop a precision medicine tool for predicting CAV years before overt clinical presentation. Clinical data from 1-year post-transplant was collected on 302 transplant recipients from the University of Pennsylvania, including 53 ‘early CAV’ patients and 249 ‘no-CAV’ controls. This data was used to generate a ‘clinical model’ (ClinCAV-Pr) for predicting future CAV development. From this cohort, n=183 archived EMBs were collected for CD31 and modified trichrome staining and then digitally scanned. These included 1-year post-transplant EMBs from 50 ‘early CAV’ patients and 82 no-CAV patients, as well as 51 EMBs from ‘disease control’ patients obtained at the time of definitive coronary angiography confirming CAV. Using biologically-inspired, hand-crafted features extracted from digitized EMBs, quantitative histologic models for differentiating no-CAV from disease controls (HistoCAV-Dx), and for predicting future CAV from 1-year post-transplant EMBs were developed (HistoCAV-Pr). The performance of histologic and clinical models for predicting future CAV (i.e. HistoCAV-Pr and ClinCAV-Pr, respectively) were compared in a held-out validation set, before being combined to assess the added predictive value of an integrated predictive model (iCAV-Pr). ClinCAV-Pr achieved modest performance on the independent test set, with area under the receiver operating curve (AUROC) of 0.70. The HistoCAV-Dx model for diagnosing CAV achieved excellent discrimination, with an AUROC of 0.91, while HistoCAV-Pr model for predicting CAV achieved good performance with an AUROC of 0.80. The integrated iCAV-Pr model achieved excellent predictive performance, with an AUROC of 0.93 on the held-out test set. Prediction of future CAV development is greatly improved by incorporation of computationally extracted histologic features. These results suggest morphologic details contained within regularly obtained biopsy tissue have the potential to enhance precision and personalization of treatment plans for post-heart transplant patients.