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.

项目摘要/摘要 高级浆液卵巢癌（HGSC）是最致命的妇科恶性肿瘤，生存五年 转移性疾病的率小于30％。我们的实验室已将突变过程确定为生存的预测指标 以及对治疗的反应，以及一个工作模型，以预测来自 苏木精和曙红（H＆E）全扫描图像。我们在诊断放射学领域的合作者发现 BRCA突变状态与定性特征之间的牢固关联计算 断层扫描（CE-CT）。但是，这两种成像方式尚未与基因组结合 信息以改善HGSC患者的分层。 基于这些初步数据，我将检验以下假设，该假设合并了CE-CT和 H＆E中的微观信息可用于推断已知的突变亚型，并鉴定新的患者 地层。我已经策划了118名HGSC患者的队列，具有匹配的靶向图基因组测序， 为此，扫描的H＆E全扫描图像和分割的预处理CE-CT图像。具体 AIM 1，我将开发一个机器学习模型，以整合CE-CT和H＆E成像以预测突变 这些无处不在的成像方式的亚型。在特定目标2中，我将开发端到端的深度学习 模型以整合CE-CT，H＆E和基因组测序的互补信息以生存 使用COX比例危害模型进行分析。我预计这项工作将（1）确定精致的分层 使用此多模式预后签名和（2）开发通用机器的HGSC患者 学习模型以整合CE-CT，H＆E以及用于癌症患者生存分析的基因组测序。 这项研究将在Sohrab博士的指导下在纪念Sloan Kettering癌症中心进行。 莎阿。我和Shah博士制定的培训计划将为我做好准备，以使我成为医生的未来 - 科学家进行癌症患者预后的机器学习研究。