Predicting Metastatic Progression of High Risk Localized Prostate Cancer

预测高风险局限性前列腺癌的转移进展

• 批准号: 10684561
• 负责人: Isla Pearl Garraway
• 金额: --
• 依托单位: VA GREATER LOS ANGELES HEALTHCARE SYSTEM
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2027-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10684561
• 关键词: Algorithms; Artificial Intelligence; Biological Markers; Categories; Cessation of life; Clinic; Clinical; Clinical Data; Clinical Management; Clinical Trials; Collection; Complex; Computer Vision Systems; Data; Data Collection; Data Set; Data Sources; Databases; Development; Diagnosis; Diagnostic; Diagnostic Imaging; Digital Radiography; Digital biomarker; Disease; Disease remission; Extramural Activities; Eye; FOLH1 gene; Future; Genomics; Goals; Guidelines; Healthcare Systems; Hematoxylin and Eosin Staining Method; Histopathology; Human; Image; Individual; Information Systems; Infrastructure; Integrated Health Care Systems; Intervention; Knowledge; Link; Machine Learning; Magnetic Resonance Imaging; Malignant Neoplasms; Malignant neoplasm of prostate; Methods; Modeling; Molecular; National Comprehensive Cancer Network; Needle biopsy procedure; Neoplasm Metastasis; Operative Surgical Procedures; Outcome; Pathologic; Pathology; Patients; Pattern; Positron-Emission Tomography; Prostate; Protocols documentation; Radiation; Recurrence; Research Personnel; Risk; Science; Skin Carcinoma; Slide; Staging; Stains; Statistical Data Interpretation; Stratification; Structure; Testing; Tissues; Training; United States Department of Veterans Affairs; Validation; Veterans; Veterans Health Administration; Visual; artificial intelligence algorithm; biobank; cancer care; convolutional neural network; cost; data repository; data resource; data warehouse; digital imaging; digital pathology; digital repositories; feature selection; follow-up; health data; high risk; improved; improved outcome; machine learning model; military veteran; multidimensional data; multimodal data; multiple omics; novel; outcome prediction; precision oncology; predictive modeling; prognostic; prognostic model; prognostication; prostate biopsy; prostate cancer risk; radiological imaging; radiomics; repository; social health determinants; success; tool; transcriptomics; treatment strategy; tumor

ABSTRACT. Prostate cancer (CaP) is the most commonly diagnosed malignancy other than non-melanoma skin cancer amongst Veterans. Approximately 7% of US CaP cases are diagnosed and treated in the Veteran population. High risk (HR), localized CaP represents 20-25% of the approximately 250,000 new cases of CaP expected in the US in 2022. The outcomes of HR CaP are variable, with some patients remaining in remission and others suffering from metastatic progression and death. Our ability to discriminate between patients who will fare well following curative-intent treatment versus those destined for lethal metastatic progression remains poor. Our overall objective is to apply artificial intelligence (AI) algorithms to generate novel predictors of metastasis-free survival (MFS), the only validated surrogate for overall survival in localized CaP, from a large repository of digital pathology and radiographic images. We will then combine these AI-derived biomarkers with clinical-pathologic and social determinants of health (SDoH) variables collected from Veterans with HR CaP to develop and test multivariable prognostic models that improve our ability to predict MFS. AI, including computer vision and machine learning approaches, allows extraction of image patterns for sub- visual based characterization of CaP. Routine diagnostic prostate needle biopsy pathology slides that have been digitized as well as digital radiographic images (e.g. MRI) can be leveraged for machine learning derived from either (1) hand-crafted features (guided by existing domain knowledge) which are then used as the inputs to develop the machine-learning model based on the selected features, or (2) the raw data itself, which are used as inputs to develop the model through convolutional neural networks or other methods in an unsupervised manner. The former leverages existing domain knowledge and may require less input data, whereas the latter is not limited by prior knowledge, but requires more training data. We hypothesize that machine learning models based on multimodal data derived from MRI and digital pathology can be combined with clinic-pathologic and SDoH data to generate “super classifiers” that more accurately predict outcome without the need for costly tissue destructive methods. We propose to establish a collection of digital pathology and prostate MRI images along with clinic-pathologic and SDoH data from >5,000 Veterans with HR CaP who have been treated with curative intent and a minimum of 5 years of follow-up using our existing approved biorepository protocol. Subsequently, we will determine the most robust AI algorithm for each data source, and then test combinations of algorithms to generate a “superclassifier” that integrates AI-derived predictive models with standard clinico-pathologic and SDoH variables to predict MFS. Improved prognostication could illuminate strategies for treatment intensification or de- intensification that can be formally tested in future clinical trials. The substantial infrastructure and databases generated by this proposal as part of our repository will be accessible by intramural VA and extramural investigators for future approved studies.

抽象的。 前列腺癌 (CaP) 是除非黑色素瘤皮肤癌之外最常诊断的恶性肿瘤 在退伍军人中，大约 7% 的美国 CaP 病例是在退伍军人群体中诊断和治疗的。 高风险 (HR)、局部 CaP 占预计 2019 年约 250,000 例新发 CaP 病例的 20-25% 2022 年美国。HR CaP 的结果各不相同，一些患者仍处于缓解状态，另一些患者仍处于缓解状态 我们有能力区分患有转移进展和死亡的患者。 根治性治疗与致命性转移进展的治疗效果仍然较差。 总体目标是应用人工智能（AI）算法生成新的无转移预测因子 生存率 (MFS)，来自大型数字存储库的局部 CaP 总体生存率的唯一经过验证的替代指标 然后，我们将把这些人工智能衍生的生物标志物与临床病理结合起来。 从具有 HR CaP 的退伍军人那里收集用于开发和测试的健康社会决定因素 (SDoH) 变量 多变量预后模型可提高我们预测 MFS 的能力。 人工智能，包括计算机视觉和机器学习方法，允许提取图像模式以进行子 基于视觉的 CaP 常规诊断针活检病理切片。 数字化以及数字放射线图像（例如 MRI）可用于源自以下内容的机器学习： (1) 手工制作的特征（以现有领域知识为指导），然后将其用作 根据所选特征开发机器学习模型，或 (2) 使用原始数据本身 作为通过卷积神经网络或其他方法在无监督的情况下开发模型的输入 前者利用现有的领域知识，可能需要较少的输入数据，而后者。 不受先验知识的限制，但需要更多的训练数据，我们已经捕获了机器学习模型。 基于 MRI 和数字病理学的多模态数据可以与临床病理学和 SDoH 数据可生成“超级分类器”，无需昂贵的组织即可更准确地预测结果 破坏性方法。 我们建议建立数字病理学和前列腺 MRI 图像以及临床病理学图像的集合 以及来自超过 5,000 名患有 HR CapP 的退伍军人的 SDoH 数据，这些退伍军人已接受过治疗性治疗并至少接受过治疗 随后，我们将使用我们现有批准的生物样本库方案进行 5 年的随访。 针对每个数据源最强大的人工智能算法，然后测试算法组合以生成 “超级分类器”将人工智能衍生的预测模型与标准临床病理学和 SDoH 相结合 预测 MFS 的变量可以阐明强化或去治疗的策略。 可以在未来的临床试验中正式测试的强化。 由该提案生成的作为我们存储库一部分的 VA 校内和校外均可访问 未来批准研究的研究人员。