Multi-modal machine learning to guide adjuvant therapy in surgically resectable colorectal cancer

多模式机器学习指导可手术切除结直肠癌的辅助治疗

基本信息

批准号：
10588103
负责人：
Jeanne Shen
金额：
$ 62.96万
依托单位：
STANFORD UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-05-02 至 2028-04-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10588103
关键词：
Address Adjuvant Chemotherapy Adjuvant Therapy Architecture Area Biological Markers Cancer Etiology Catalogs Cessation of life Clinical Clinical Data Clinical Medicine Collaborations Colorectal Cancer Companions Computerized Medical Record Data Data Set Deposition Development Diagnosis Diagnostic Disease Disease-Free Survival Ensure Evaluation Excision Future Health Care Costs Human Image Institution Laboratories Machine Learning Magnetic Resonance Imaging Malignant Neoplasms Methods Modality Modeling Morbidity - disease rate Oncologist Oncology Operative Surgical Procedures Outcome Pathologic Pathologist Pathology Patient Triage Patients Performance Pharmaceutical Preparations Physicians Precision therapeutics Radiology Specialty Records Recurrence Research Research Personnel Resectable Resources Risk Secure Stratification Structure Testing Time Training Treatment-related toxicity Validation X-Ray Computed Tomography anticancer research biomedical data science cancer biomarkers cancer surgery cancer type chemotherapy cohort colon cancer patients computer science cost cost efficient data de-identification data fusion data modeling data sharing deep learning deep learning model electronic structure empowerment follow-up high risk imaging study improved machine learning model mortality multimodal data multimodality novel online repository overtreatment pathology imaging precision medicine primary outcome prognostic prognostic assays prognostication radiological imaging radiologist risk stratification standard of care statistics tool treatment planning tumor unnecessary treatment whole slide imaging

项目摘要

Project Summary / Abstract Colorectal cancer (CRC) is the third most commonly diagnosed malignancy and the second leading cause of cancer death worldwide. There is an unmet need for accurate, cost-efficient, and broadly accessible risk- stratification tools to identify patients at increased risk of recurrence , who are most likely to benefit from adjuvant therapy. Current standard-of-care risk stratification approaches are inadequate. Every CRC surgical candidate undergoes pathologic and radiologic evaluation of their tumor; these two modalities represent a rich, readily accessible and, thus far, underutilized resource for developing new risk-stratification tools. Deep learning (DL) has demonstrated great potential for augmenting physicians on an increasing range of diagnostic and prognostic tasks in pathology, radiology, and clinical medicine. We hypothesize that applying integrated DL-based analysis to multimodal (pathologic, radiologic, and electronic medical record (EMR)) data will yield greatly improved stratification of CRC patients for adjuvant treatment planning. We propose to build the first comprehensive, publicly-available, expert-annotated multimodal CRC dataset for deep learning, containing annotated CRC pathology whole-slide images (WSI), preoperative CT and MRI images, and structured clinical EMR data. Using this dataset, we will develop both single and multi-modality DL models for risk stratification of surgically-resectable (Stage I-III) CRC patients.To test our hypothesis, we will compare the performance of multi-modality models with that of single-modality models and existing methods of stratification. This project benefits from the complementary expertise and resources of a unique interdisciplinary team spanning the fields of machine learning, pathology, radiology, and oncology.

项目概要/摘要结直肠癌 (CRC) 是第三大最常见的恶性肿瘤，也是导致结直肠癌的第二大原因。全球癌症死亡人数。对准确、具有成本效益且可广泛获取的风险的需求尚未得到满足分层工具来识别复发风险增加的患者，他们最有可能从中受益辅助治疗。目前的护理标准风险分层方法还不够。每一次结直肠癌手术候选人对其肿瘤进行病理学和放射学评估；这两种方式代表了一种丰富的、用于开发新的风险分层工具的资源很容易获得，但迄今为止尚未得到充分利用。深的学习（DL）已显示出巨大的潜力，可以增强医生的诊断范围病理学、放射学和临床医学中的预后任务。我们假设应用集成对多模态（病理、放射学和电子病历 (EMR)）数据进行基于深度学习的分析将产生大大改善了 CRC 患者辅助治疗计划的分层。我们建议建造第一个用于深度学习的全面、公开、专家注释的多模态 CRC 数据集，包含带注释的 CRC 病理全切片图像 (WSI)、术前 CT 和 MRI 图像以及结构化临床电子病历数据。使用该数据集，我们将开发单模态和多模态深度学习模型，用于风险分层可手术切除（I-III 期）CRC 患者。为了检验我们的假设，我们将比较多模态模型与单模态模型和现有的分层方法。这个项目受益于跨领域的独特跨学科团队的互补专业知识和资源机器学习、病理学、放射学和肿瘤学。