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: 10416206
• 负责人: Anant Madabhushi
• 金额: $ 60.3万
• 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/10416206
• 关键词: Accounting; Acute; Adjuvant; 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; Disease; Estrogen receptor positive; Gene Expression Profiling; Genomics; Head 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; Neck Cancer; 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; Resources; Risk; Role; Shapes; Slide; South Asian; Southwest Oncology Group; Stains; Testing; Therapeutic; Tissue Stains; Tissues; Universities; Validation; Visual; Woman; base; behavioral outcome; cancer diagnosis; caucasian American; chemotherapy; companion diagnostics; cost; 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; 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.

摘要：认识到许多癌症的过度诊断会导致过度治疗 化学疗法或放射疗法提升，人们对预后和需要的需求越来越多 预测性刺激识别那些可以从治疗中受益的癌症患者。尽管存在基于多基因表达的测试，例如Oncotype DX和DETIPHER，以识别早期乳房和 可以避免调整疗法并因此减轻副作用和并发症的前列腺癌患者 这些测试的价格（$ 3K-4K/患者）使它们超出了大多数低收入和中等收入患者的范围 国家（LMIC）。具有讽刺意味 印度，可以使用诸如放射和化学疗法之类的治疗资源，因此需要 明智地向那些从中获得最大受益的患者进行管理。 具有计算机视觉和模式识别工具的精致数字可致病分析已经 显示出有关苏木精和曙红的肿瘤行为和患者结局的“解锁”下视属性 （H＆E）单独使用的幻灯片。 Case Western Reserve University（CWRU）的Madabhushi团队已广泛 显示了这些方法的潜力，可以预测乳房，头部和 颈部，肺和前列腺癌。 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）表明，先进的致病分析 能够识别南方之间不同的乳腺癌的独特预后形态特征 亚洲（SA）和高加索美国（CA）女性1。此外，CWRU组还表明了数字病原体 基于的图像分类器可以显着改善甚至超过预后和预测性 乳房（ONCOTYPE DX）和前列腺癌（DECIPHER）的昂贵基因表达评估的性能2。 以CWRU和TMC 3之间的强大额外合作为基础 基于图像的预后和基于预测的测定法，我们建议优化和验证AI支持AI的数字 病理平台（适应）用于多癌诊断，预后和治疗益处的预测。 适应将涉及在SA​​的背景下优化CWRU组先前开发的图像测定 癌症患者。此外，通过将AI-PARTHomic工具与PathPresenter集成，这是一个广泛使用的数字 病理图像分析平台，适应将具有用于预后和预测工具的全球足迹。 具体而言，适应将得到验证，以预测可调化学和辐射疗法的结果和益处 在雌激素受体阳性（ER+）乳腺癌（BC）和三阴性乳腺癌（TNBC）的背景下， 通过许多临床试验数据集，TMC的口腔鳞状细胞癌（OC-SCC）和前列腺癌 在美国（Swog S8814，RTOG 0920，0521）和TMC（Arest，Pop-Rt）。成功的项目完成 将为印度癌症患者建立适应性的精密药物（APM）溶液。