An AI-enabled Digital Pathology Platform for Multi-Cancer Diagnosis, Prognosis and Prediction of Therapeutic Benefit

基于人工智能的数字病理学平台，用于多种癌症的诊断、预后和治疗效果预测

• 批准号: 10698122
• 负责人: Anant Madabhushi
• 金额: $ 55.35万
• 依托单位: EMORY UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-05 至 2027-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10698122
• 关键词: Accounting; Acute; Adjuvant Chemotherapy; Adjuvant Radiotherapy; Adjuvant Therapy; Affect; Asian Americans; Biological Assay; Breast; Breast Cancer Patient; Cancer Center; Cancer Patient; Cell Nucleus; Chemotherapy and/or radiation; Clinical; Clinical Trials; Collaborations; Computer Vision Systems; Data Set; Diagnosis; Diagnostic tests; Early identification; Estrogen receptor positive; Gene Expression; Gene Expression Profiling; Genomics; Head and Neck Cancer; Health Services Accessibility; Hematoxylin and Eosin Staining Method; Image; Image Analysis; In complete remission; India; Individual; Malignant Neoplasms; Malignant Squamous Cell Neoplasm; Malignant neoplasm of lung; Malignant neoplasm of prostate; Molecular; Morphology; Neoadjuvant Therapy; Nomograms; Oral cavity; Outcome; Pathologic; Patient-Focused Outcomes; Patients; Pattern; Pattern Recognition; Pelvis; Performance; Physical shape; Population; Postoperative Period; Prevalence; Price; Prognosis; Prostate; Radiation; Radiation Therapy Oncology Group; Radiation therapy; Recurrence; Recurrent disease; Resources; Risk; Role; Shapes; Slide; South Asian; Southwest Oncology Group; Stains; Testing; Therapeutic; Tissue Stains; Tissues; Universities; Validation; Visual; Woman; behavioral outcome; cancer diagnosis; caucasian American; companion diagnostics; cost comparison; digital; digital imaging; digital pathology; high risk; high risk population; improved; innovation; low and middle-income countries; malignant breast neoplasm; malignant mouth neoplasm; men; mouth squamous cell carcinoma; oncotype; outcome prediction; overtreatment; precision medicine; predictive modeling; predictive test; predictive tools; prognostic; prognostic assays; prognostic model; prognostic tool; prognostication; prospective; prostate cancer risk; response; side effect; success; tool; treatment response; trial comparing; triple-negative invasive breast carcinoma; tumor behavior

SUMMARY: Recognizing that over-diagnosis of many cancers is leading to over-treatment with adjuvant chemotherapy or with radiation therapy boost, there is a growing appreciation for the need for prognostic and predictive assays to identify those cancer patients who can benefit from therapy de-intensification. While multi-gene-expression based tests such as Oncotype DX and Decipher exist for identifying early-stage breast and prostate cancer patients who could avoid adjuvant therapies and hence mitigate side-effects and complications, the price of these tests ($3K-4K/patient) puts them beyond the reach of most patients in low- and middle-income countries (LMICs). Ironically, the need for these prognostic and predictive tests is even more acute in LMICs like India, where access to treatment resources like radiation and chemotherapy are limited and hence need to be administered judiciously to those patients who stand to receive the most benefit from them. Sophisticated digital pathomic analysis with computer vision and pattern recognition tools has been shown to “unlock” sub-visual attributes about tumor behavior and patient outcomes from hematoxylin & eosin (H&E)-stained slides alone. The Madabhushi team at Case Western Reserve University (CWRU) has extensively shown the potential for these approaches for predicting outcome and therapeutic response for breast, head and neck, lung and prostate cancer. The Madabhushi team working with collaborators Dr. Parmar and Dr. Desai at the Tata Memorial Center (TMC), the largest cancer center in India, has shown that advanced pathomic analysis is able to identify unique prognostic morphologic signatures of breast cancer that are different between South Asian (SA) and Caucasian American (CA) women 1. In addition, the CWRU group has shown that digital pathomic based image classifiers can significantly improve and even outperform the prognostic and predictive performance of expensive gene-expression assays for breast (Oncotype Dx) and prostate cancer (Decipher) 2. Building on the strong extant collaboration between CWRU and TMC 3, and a strong track record in digital image based prognostic and predictive based assays, we propose to optimize and validate an AI-enabled Digital Pathology Platform (ADAPT) for Multi-Cancer Diagnosis, Prognosis and Prediction of Therapeutic Benefit. ADAPT will involve optimizing the previously developed image assays by the CWRU group in the context of SA cancer patients. Furthermore, by integrating the AI-pathomic tools with PathPresenter, a widely used digital pathology image analysis platform, ADAPT will have a global footprint for the prognostic and predictive tools. Specifically, ADAPT will be validated for predicting outcome and benefit of adjuvant chemo- and radiation therapy in the context of estrogen receptor positive (ER+) breast cancer (BC) and triple negative breast cancer (TNBC), oral cavity squamous cell carcinoma (OC-SCC) and prostate cancer at TMC via a number of clinical trial datasets in the US (SWOG S8814, RTOG 0920, 0521) and at TMC (AREST, POP-RT). Successful project completion will establish ADAPT as an Affordable Precision Medicine (APM) solution for Indian cancer patients.

总结：认识到许多癌症的过度诊断导致了辅助治疗的过度治疗， 随着化疗或放射治疗加强，越来越多的人认识到需要预后和 预测性分析，以识别那些可以从治疗去强化中受益的癌症患者。虽然基于多基因表达的测试如Oncotype DX和Decipher用于识别早期乳腺癌， 前列腺癌患者可以避免辅助治疗，从而减轻副作用和并发症， 这些检查的价格（3000 - 4000美元/患者）使大多数中低收入患者无法负担 中低收入国家。具有讽刺意味的是，对这些预后和预测性测试的需求在LMIC中更为迫切， 印度，在那里获得放射和化疗等治疗资源是有限的，因此需要 明智地给予那些能够从中获得最大利益的患者。 利用计算机视觉和模式识别工具进行的复杂的数字病理组学分析已经被 显示从苏木精和伊红“解锁”关于肿瘤行为和患者结果的亚视觉属性 （H&E）染色的载玻片。凯斯西储大学（CWRU）的Madabhushi团队广泛地 显示了这些方法用于预测乳腺、头部和 颈部肺癌和前列腺癌Madabhushi团队与合作者Parmar博士和Desai博士合作， 印度最大的癌症中心塔塔纪念中心（TMC）已经表明，先进的病理组学分析 是能够识别乳腺癌的独特预后形态学标志，是不同的南方之间， 亚洲（SA）和高加索美国（CA）女性1。此外，CWRU小组已经表明，数字病理学 基于图像分类器可以显着改善，甚至优于预后和预测 用于乳腺癌（Oncotype Dx）和前列腺癌（Decipher）的昂贵基因表达测定的性能2. 基于CWRU和TMC 3之间现有的强大合作，以及在数字化方面的良好记录， 基于图像的预后和基于预测的分析，我们建议优化和验证支持AI的数字 病理学平台（ADAPT）用于多种癌症诊断、预后和治疗获益预测。 ADAPT将涉及在SA背景下优化CWRU组先前开发的图像分析 癌症患者。此外，通过将AI病理学工具与PathPresenter集成，PathPresenter是一个广泛使用的数字 作为病理图像分析平台，ADAPT将在全球范围内拥有预后和预测工具。 具体而言，将验证ADAPT用于预测辅助化疗和放疗的结局和获益 在雌激素受体阳性（ER+）乳腺癌（BC）和三阴性乳腺癌（TNBC）的情况下， 口腔鳞状细胞癌（OC-SCC）和前列腺癌在TMC通过一些临床试验数据集 在美国（SWOG S8814，RTOG 0920，0521）和TMC（AREST，POP-RT）。成功完成项目 ADAPT将成为印度癌症患者负担得起的精准医疗（APM）解决方案。