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.

摘要。