A Quantitative Risk Model for Predicting Outcome and Identifying Structural Biomarkers of Treatment Targets in Oral Cancer on a Large Multi-Center Patient Cohort

用于预测大型多中心患者队列口腔癌治疗目标的结果和识别结构生物标志物的定量风险模型

• 批准号: 9974099
• 负责人: Scott Doyle
• 金额: $ 38.29万
• 依托单位: STATE UNIVERSITY OF NEW YORK AT BUFFALO
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-04-23 至 2025-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/9974099
• 关键词: Active Learning; Address; Aftercare; Aggressive behavior; Algorithms; Architecture; Artificial Intelligence; Biological Markers; Blinded; Cancer Patient; Cessation of life; Clinical; Clinical Trials; Collection; Combined Modality Therapy; Communities; Companions; Consensus; Country; Data; Databases; Disease; Elements; Engineering; Evaluation; Excision; Foundations; Future; Goals; Head and Neck Surgery; Head and neck structure; Histologic; Image; Immune response; Link; Machine Learning; Malignant Neoplasms; Manuals; Medical Economics; Methods; Microscope; Modeling; Operative Surgical Procedures; Oral Stage; Outcome; Pathologist; Pathology; Pathology Report; Patients; Pattern; Performance; Play; Postoperative Period; Productivity; Quality of life; Radiation therapy; Randomized; Recurrence; Reporting; Reproducibility; Research; Resources; Risk; Salvage Therapy; Screening procedure; Semantics; Site; Slide; Specimen; Standardization; Structure; Surgical Pathology; System; Testing; Time; Tissues; Training; Validation; Work; Workload; analysis pipeline; angiogenesis; base; cancer recurrence; cancer type; clinical application; clinical practice; cohort; computational pipelines; deep learning; design; digital; digital pathology; expectation; experience; experimental study; feature extraction; high risk; imaging biomarker; improved; improved outcome; innovation; international center; malignant mouth neoplasm; novel strategies; outcome forecast; outcome prediction; pathology imaging; patient oriented; predictive modeling; pressure; prognostic; prognostic value; quality assurance; quantitative imaging; screening; segmentation algorithm; tool; treatment planning; tumor; uptake

Post-resection prognostication for oral cavity cancers (OCC) is qualitative and potentially ambiguous. A significant subset (25-37%) of Stage I/II patients still develop local recurrence after treatment with surgery alone. The long-term goal of this proposal will be to create a Quantitative Risk Model (QRM) using machine learning and artificial intelligence to predict recurrence risk for Stage I/II patients using image-based biomarkers of aggression. The objective is to develop and validate state-of-the-art systems for biomarker imaging, quantification, and modeling to accurately predict risk of recurrence in cancer patients based on image analytics. The central hypothesis is that a quantitative, artificial intelligence approach to pathology will result in significantly greater prognostic value compared with manual microscope-based analysis. The rationale for this work is that tumor aggression can be predicted from patterns present in pathology images, given the existence of histological risk models that have been clinically validated in the past; however, these risk models are not in widespread use because they are less accurate, robust, and transportable to the larger community of pathologists. This proposal will test the central hypothesis through three specific aims: (1) Develop an analysis pipeline that can accurately predict recurrence risk for Stage I/II OCC patients and identify treatment targets (e.g. adaptive local immune response and angiogenesis); (2) Demonstrate robust performance across a multi-site data cohort collected from seven national and international centers; and (3) Distil the results of QRM analysis to synoptic pathology reporting, demonstrating the ability of QRM to interface with standard clinical reporting tools. The innovation for addressing these aims comes from a unique application of active learning for training artificial intelligence to recognize tissue structures, new features for quantifying tissue architecture based on the interface between tumor and host, and a novel approach for large cross-site validation. Moreover, this proposal develops a unique mapping between computational pathology and commonly-used synoptic reporting variables, enabling rapid uptake of this work into existing clinical workflows. This research is significant because it provides personalized outcome predictions for a niche group of undertreated patients with limited options and can serve as the foundation for designing future clinical trials through identification of treatment targets. Multi-site training and evaluation, combined with AI-to-report mapping, will be broadly applicable to a large group of computational approaches, bridging the gap between engineering research labs and clinical application. The expected outcome of this work is a trained model for predicting Stage I/II OCC recurrence, identification of treatment targets, and mapping to synoptic reports, as well as a broadly-applicable workflow for the broader computational pathology community. This project will have a large positive impact on patients and surgical pathologists by enabling rapid, accurate prognosis and directed treatment plans in an easy-to-use pipeline that integrates seamlessly into existing clinical workflows.

口腔癌(OCC)切除后的预后是定性的，而且可能是不明确的。一个 I/II期患者中有相当一部分(25%-37%)在单纯手术治疗后仍会出现局部复发。 该提案的长期目标将是使用机器学习创建量化风险模型(QRM 和人工智能使用基于图像的生物标记物预测I/II期患者的复发风险 攻击性。目标是开发和验证用于生物标记物成像的最先进的系统， 量化和建模，以基于图像分析准确预测癌症患者的复发风险。 中心假设是，定量的、人工智能的病理学方法将导致显著的 与基于显微镜的手动分析相比，预后价值更高。这项工作的基本原理是 考虑到组织学的存在，肿瘤的侵袭性可以从病理图像中出现的模式中预测出来 过去已被临床验证的风险模型；然而，这些风险模型并未得到广泛应用 因为它们不那么准确，不那么健壮，也不太容易被更大的病理学家社区所接受。这项建议 我将通过三个具体目标来检验中心假设：(1)开发一种能够准确地 预测I/II期OCC患者的复发风险并确定治疗目标(例如适应性局部免疫 响应和血管生成)；(2)在从以下来源收集的多站点数据队列中显示出强劲的表现 7个国家和国际中心；(3)将QRM分析结果提炼成天气病理学 报告，展示了QRM与标准临床报告工具对接的能力。的创新 解决这些目标来自于将主动学习用于培训人工智能的独特应用 识别组织结构，基于组织结构之间的界面来量化组织结构的新特征 肿瘤和宿主，以及一种用于大型跨站点验证的新方法。此外，这项建议还开发了一种独特的 计算病理学和常用天气预报变量之间的映射，使快速 将这项工作纳入现有的临床工作流程。这项研究具有重要意义，因为它提供了个性化的 对选择有限的未得到充分治疗的患者的小众群体的结果预测可以作为 通过确定治疗靶点，为设计未来的临床试验奠定基础。多地点培训和 评估与人工智能到报告的映射相结合，将广泛适用于一大组计算 方法，在工程研究实验室和临床应用之间架起一座桥梁。预期的结果 这项工作的一个训练模型，用于预测I/II期OCC的复发，识别治疗目标，以及 映射到天气报告，以及更广泛的计算病理学的广泛适用的工作流 社区。该项目将对患者和外科病理学家产生巨大的积极影响，使 在易于使用的管道中提供准确的预测和定向的治疗计划，该管道可无缝集成到 现有的临床工作流程。