Computerized histologic image predictor of cancer outcome

癌症结果的计算机组织学图像预测器

• 批准号: 9305968
• 负责人: MICHAEL D FELDMAN
• 金额: $ 62.5万
• 依托单位: CASE WESTERN RESERVE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-07-01 至 2021-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9305968
• 关键词: Adjuvant; Adjuvant Chemotherapy; Adoption; Age; Agreement; Algorithms; Appearance; Architecture; Behavior; Biological Assay; Biological Markers; Biopsy; Breast; Breast Cancer Patient; Cancer Diagnostics; Cells; Clinical; Clinical Trials; Computer Assisted; Computer Vision Systems; Computer software; Computers; Country; Cues; Data; Development; Diagnosis; Diagnostic tests; Disease; Disease Outcome; Disease Progression; Distant; Early Diagnosis; Eastern Cooperative Oncology Group; Elements; Epigenetic Process; Estrogen receptor positive; Europe; Excision; Exhibits; Gene Expression; Gene Expression Profiling; Gene Proteins; Genetic Heterogeneity; Genomics; Goals; Guidelines; Head Cancer; Health; Hematoxylin and Eosin Staining Method; Histologic; Histopathology; Image; Image Analysis; Incidence; Income; Industrialization; Interobserver Variability; Joints; Malignant Neoplasms; Malignant neoplasm of cervix uteri; Malignant neoplasm of prostate; Measurement; Molecular; Morphology; Mutation; National Surgical Adjuvant Breast and Bowel Project; Neck Cancer; Nuclear; Operative Surgical Procedures; Outcome; Pathologic; Pathologist; Pathology; Patients; Performance; Phenotype; Positive Lymph Node; Production; Randomized Clinical Trials; Reading; Recurrence; Regulatory Pathway; Research; Resources; Reverse Transcriptase Polymerase Chain Reaction; Risk; Running; Shapes; Signal Transduction; Slide; Specimen; Staining method; Stains; Sum; System; Tamoxifen; Techniques; Technology; Telepathology; Texture; Time; Tissue imaging; Tissues; Treatment outcome; Tumor Biology; Visual; Woman; base; behavioral response; cancer cell; cancer imaging; chemotherapy; cohort; companion diagnostics; computerized; digital; disorder risk; histological image; histological specimens; hormone therapy; industry partner; malignant breast neoplasm; neoplastic cell; outcome prediction; prognostic assays; prototype; response; translation assay; treatment response; treatment strategy; tumor; tumor heterogeneity

SUMMARY: There is an increased need for predictive and prognostic assays to distinguish more and less aggressive phenotypes of cancer due to A) dramatic increase in cancer incidence and; B) improvements in early diagnosis. Predictive assays in particular will allow for patients with less aggressive disease to be spared more aggressive treatment. Most prognostic tests in the US and Europe are based on gene expression assays (e.g. Oncotype DX (ODx)). Recent studies have shown extensive genetic heterogeneity among cancer cells between tumors and even within the same tumor, suggesting that approaches for recommending therapy for a patient based on the “average” molecular signal of many cells are overly simplistic. Interestingly, for a number of cancers, tumor grade (morphologic appearance on tissue as assessed qualitatively or semi-quantitatively by a pathologist) has been found to be highly correlated with disease outcome. However pathologic grade tends to suffer from significant inter-observer variability. Digitzation of histological samples, or whole slide imaging, facilitates a quantitative approach towards evaluating disease progression and predicting outcome, while also facilitating the adoption of telepathology. Recently, research groups (including our own) have begun to show that computer extracted measurements of tumor morphology (e.g. capturing nuclear orientation, texture, shape, architecture) from routine H&E stained cancer tissue images can predict disease aggressiveness and treatment outcome. By computationally interrogating the entire tumor landscape and its most invasive elements from a standard H&E slide, these approaches can allow for more accurate capture of tumor heterogeneity, disease risk and hence the most appropriate treatment strategy. The goal of this academic-industrial partnership is to develop and validate a computerized histologic image-based predictor (CHIP) to identify which early-stage, estrogen receptor positive (ER+) breast cancer patients are candidates for hormonal therapy alone and which women are candidates for adjuvant chemotherapy based off analysis of the pathology slides derived from biopsy and surgical specimens. Inspirata Inc., a cancer diagnostics company which has recently licensed a number of histomorphometry based technologies from the Madabhushi group, will bring quality management systems and production software standards to help create a pre-commercial companion diagnostic test of the CHIP assay. Additionally Inspirata Inc. will build a complete regulatory pathway for successful translation of the assay in the US and abroad. Finally, the pre-commercial prototype of the CHIP assay will be independently validated using the same strategy and data cohorts as ODx. Our approach has several advantages over molecular assays such as ODx in that it (1) can interrogate the entire expanse of the pathology image enabling a more accurate capture of tumor heterogeneity and hence disease risk, (2) is non-disruptive of pathology workflow, (3) non-destructive of tissue and would be substantially (4) cheaper (critical in low to middle income countries) and (5) faster.

摘要：对预测和预后分析的需求不断增加，以区分更多和更少的疾病。 癌症的侵袭性表型是由于 A) 癌症发病率急剧增加；B) 改善； 早期诊断尤其可以使患有较轻疾病的患者免受伤害。 美国和欧洲的大多数预后测试都是基于基因表达测定。 （例如 Oncotype DX (ODx)）。最近的研究表明癌细胞之间存在广泛的遗传异质性。 肿瘤之间甚至同一肿瘤内，这表明推荐治疗的方法 基于许多细胞的“平均”分子信号对患者的判断过于简单化。 统计上，对于许多癌症，肿瘤等级（评估的组织形态学外观） 由病理学家定性或半定量）已被发现与疾病高度相关 然而，病理分级会受到观察者之间的显着数字化趋势的影响。 组织学样本或全玻片成像有助于采用定量方法评估疾病 进展和预测结果，同时也促进了远程病理学最近的研究的采用。 研究小组（包括我们自己的小组）已经开始表明，计算机提取的肿瘤形态测量值 （例如，从常规 H&E 染色的癌症组织图像中捕获核方向、纹理、形状、结构） 可以通过计算询问整个肿瘤来预测疾病的侵袭性和治疗结果。 通过标准 H&E 幻灯片中的景观及其最具侵入性的元素，这些方法可以允许更多 准确捕捉肿瘤异质性、疾病风险，从而制定最合适的治疗策略。 这种学术-工业合作伙伴关系的目标是开发和验证计算机化组织学 基于图像的预测器 (CHIP) 可识别哪种早期雌激素受体阳性 (ER+) 乳腺癌 患者适合单独接受激素治疗，哪些女性适合接受辅助治疗 化疗基于对活检和手术标本的病理切片的分析。 Inc.，一家癌症诊断公司，最近获得了多项基于组织形态计量学的许可 Madabhushi 集团的技术将带来质量管理系统和生产软件 标准，以帮助创建 CHIP 检测的商业前伴随诊断测试。 Inc.将为该检测在美国和国外的成功转化建立完整的监管途径。 最后，CHIP 测定的预商业原型将使用相同的方法进行独立验证 与 ODx 等分子检测相比，我们的方法具有多个优势。 因为它 (1) 可以询问整个病理图像，从而能够更准确地捕获 肿瘤异质性和疾病风险，(2) 不破坏病理工作流程，(3) 不破坏病理学工作流程 组织，并且将大大 (4) 更便宜（对于中低收入国家至关重要）和 (5) 更快。