Active surveillance (AS) is a suitable management option for newly-diagnosed prostate cancer (PCa), which usually presents low to intermediate clinical risk. Patients enrolled in AS have their tumor closely monitored via longitudinal multiparametric magnetic resonance imaging (mpMRI), serum prostate-specific antigen tests, and biopsies. Hence, the patient is prescribed treatment when these tests identify progression to higher-risk PCa. However, current AS protocols rely on detecting tumor progression through direct observation according to standardized monitoring strategies. This approach limits the design of patient-specific AS plans and may lead to the late detection and treatment of tumor progression. Here, we propose to address these issues by leveraging personalized computational predictions of PCa growth. Our forecasts are obtained with a spatiotemporal biomechanistic model informed by patient-specific longitudinal mpMRI data. Our results show that our predictive technology can represent and forecast the global tumor burden for individual patients, achieving concordance correlation coefficients ranging from 0.93 to 0.99 across our cohort (n=7). Additionally, we identify a model-based biomarker of higher-risk PCa: the mean proliferation activity of the tumor (p=0.041). Using logistic regression, we construct a PCa risk classifier based on this biomarker that achieves an area under the receiver operating characteristic curve of 0.83. We further show that coupling our tumor forecasts with this PCa risk classifier enables the early identification of PCa progression to higher-risk disease by more than one year. Thus, we posit that our predictive technology constitutes a promising clinical decision-making tool to design personalized AS plans for PCa patients.

翻译：主动监测（AS）是临床通常表现为低至中风险的新诊断前列腺癌（PCa）患者的一种合适管理策略。接受AS的患者通过纵向多参数磁共振成像（mpMRI）、血清前列腺特异性抗原检测和活检定期监测肿瘤。当这些检查识别出进展为高风险PCa时，患者将被建议接受治疗。然而，当前AS方案依赖于根据标准化监测策略通过直接观察来检测肿瘤进展。该方法限制了患者特异性AS方案的设计，可能导致对肿瘤进展的延迟识别与治疗。为此，我们提出通过利用患者个性化PCa生长的计算预测来解决上述问题。我们的预测基于一个由患者特异性纵向mpMRI数据驱动的时空生物力学模型。研究结果表明，我们的预测技术能够表征和预测个体患者的整体肿瘤负荷，在所研究队列（n=7）中实现了一致性相关系数介于0.93至0.99之间。此外，我们识别出一种基于模型的高风险PCa生物标志物：肿瘤的平均增殖活性（p=0.041）。利用逻辑回归，我们构建了基于该生物标志物的PCa风险分类器，其受试者工作特征曲线下面积达到0.83。进一步研究表明，将我们的肿瘤预测与这一PCa风险分类器相结合，能够将高风险PCa进展的识别时间提前一年以上。因此，我们认为该预测技术有望成为设计PCa患者个性化AS方案的临床决策支持工具。