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: 10373021
• 负责人: Scott Doyle
• 金额: $ 37.62万
• 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/10373021
• 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; Prognosis; 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; artificial intelligence algorithm; 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 prediction; pathology imaging; patient oriented; predictive modeling; pressure; prognostic value; prognostication; quality assurance; quantitative imaging; screening; segmentation algorithm; tissue mapping; 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）切除术后的预后是定性的，并且可能含糊不清。一个