Multimodal computational models to stratify ovarian cancer patients

对卵巢癌患者进行分层的多模态计算模型

• 批准号: 10348161
• 负责人: Kevin Michael Boehm
• 金额: $ 3.18万
• 依托单位: WEILL MEDICAL COLL OF CORNELL UNIV
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-02-01 至 2022-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10348161
• 关键词: Anatomy; Area; BRCA mutations; Biological Markers; Cancer Patient; Clinical; Collaborations; Computer Models; Cox Proportional Hazards Models; DNA Repair; Data; Data Set; Diagnostic radiologic examination; Disease; Disease Management; Eosine Yellowish; Future; Gene Targeting; Genomics; Glioma; Goals; Hematoxylin and Eosin Staining Method; High Performance Computing; Hospitals; Image; Infrastructure; Institution; Joints; Machine Learning; Malignant Female Reproductive System Neoplasm; Malignant neoplasm of lung; Malignant neoplasm of ovary; Measures; Memorial Sloan-Kettering Cancer Center; Mentorship; Methods; Microscopic; Modality; Modeling; Mutation; Neoplasm Metastasis; Outcome; Patients; Pattern; Performance; Physicians; Platinum; Point Mutation; Poly(ADP-ribose) Polymerases; Positioning Attribute; Primary Neoplasm; Process; Prognosis; Publishing; Research; Scanning; Scientist; Serous; Slide; Stratification; Structure; Survival Analysis; Survival Rate; Techniques; Testing; Training; Validation; Work; X-Ray Computed Tomography; anticancer research; base; cancer genome; cancer imaging; chemotherapy; clinical imaging; cohort; contrast enhanced computed tomography; cost; deep learning; deep learning model; genome sequencing; health care settings; histological image; homologous recombination; imaging modality; improved; indexing; inhibitor; innovation; interstitial; large datasets; machine learning model; malignant breast neoplasm; multimodality; mutational status; novel; patient prognosis; patient response; patient stratification; prognostic; prognostic signature; prospective; radiological imaging; radiologist; radiomics; response; standard of care; survival prediction; targeted sequencing; treatment response; tumor; whole genome; whole slide imaging

Project Summary/Abstract High-grade serous ovarian cancer (HGSC) is the most lethal gynecologic malignancy, with a five-year survival rate of less than 30% for metastatic disease. Our lab has identified mutational processes as predictors of survival and response to therapy, along with a working model to predict homologous recombination deficiency from hematoxylin and eosin (H&E) whole-slide images. Our collaborators in diagnostic radiology have discovered robust associations between BRCA mutational status and qualitative features on contrast-enhanced computed tomography (CE-CT). These two imaging modalities, however, have yet to be combined with genomic information to improve stratification of HGSC patients. Based on these preliminary data, I will test the hypothesis that combined mesoscopic information in CE-CT and microscopic information in H&E can be used to infer known mutational subtypes and also to identify novel patient strata. I have curated a cohort of 118 HGSC patients with matched targeted panel-based genome sequencing, scanned H&E whole-slide images, and segmented pre-treatment CE-CT images for this purpose. In Specific Aim 1, I will develop a machine learning model to integrate CE-CT and H&E imaging to predict mutational subtype from these ubiquitous imaging modalities. In Specific Aim 2, I will develop an end-to-end deep learning model to integrate the complementary information from CE-CT, H&E, and genome sequencing for survival analysis using a Cox Proportional Hazards model. I anticipate that this work will (1) identify refined stratification of HGSC patients using this multimodal prognostic signature and (2) develop a general-purpose machine learning model to integrate CE-CT, H&E, and genomic sequencing for cancer patient survival analysis. This research will be conducted at Memorial Sloan Kettering Cancer Center under the mentorship of Dr. Sohrab Shah. The training plan that Dr. Shah and I have developed will prepare me well for a future as a physician- scientist conducting machine learning research for cancer patient prognosis.

项目总结/摘要 高级别浆液性卵巢癌（HGSC）是最致命的妇科恶性肿瘤，生存期为5年 转移性疾病的发生率低于30%。我们的实验室已经确定了突变过程作为生存的预测因子 和对治疗的反应，沿着一个预测同源重组缺陷的工作模型， 苏木精和伊红（H&E）全载玻片图像。我们的放射诊断学合作者发现 BRCA突变状态与对比增强计算机图像上的定性特征之间的稳健关联 断层扫描（CE-CT）。然而，这两种成像方式还没有与基因组学相结合。 改善HGSC患者分层的信息。 基于这些初步的数据，我将测试的假设，结合中观信息在CE-CT和 H&E中的微观信息可用于推断已知的突变亚型， 地层我已经策划了一个118名HGSC患者的队列，这些患者具有匹配的基于靶向小组的基因组测序， 扫描的H&E全载玻片图像和用于此目的的分割的治疗前CE-CT图像。在特定 目的1，我将开发一个机器学习模型，将CE-CT和H&E成像结合起来预测突变， 这些无处不在的成像模式的亚型。在具体目标2中，我将开发一个端到端的深度学习 整合CE-CT、H&E和基因组测序的互补信息的生存模型 使用考克斯比例风险模型进行分析。我预计这项工作将（1）确定细化分层 的HGSC患者使用这种多模式预后签名和（2）开发一个通用的机器 学习模型，整合CE-CT、H&E和基因组测序，用于癌症患者生存分析。 这项研究将在Sohrab博士的指导下在纪念斯隆凯特琳癌症中心进行 Shah.沙阿医生和我制定的培训计划将为我将来成为一名医生做好准备- 科学家为癌症患者预后进行机器学习研究。