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.

总结：越来越需要预测和预后分析来区分更多和更少的 由于A）癌症发病率的急剧增加和; B） 早期诊断特别是预测性检测将使侵袭性较低的疾病患者得以幸免 更积极的治疗。美国和欧洲的大多数预后测试都是基于基因表达测定 (e.g. 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，甚至在同一肿瘤内，这表明，推荐治疗方法， 基于许多细胞的“平均”分子信号来诊断患者是过于简单化的。 有趣的是，对于许多癌症，肿瘤分级（如评估的组织上的形态学外观）与肿瘤分级相关。 由病理学家定性或半定量）与疾病高度相关 结果。然而，病理分级往往遭受显着的观察者间的变异性。数字化 组织学样本或全载玻片成像有助于评估疾病的定量方法 进展和预测结果，同时也促进远程病理学的采用。最近，研究 研究小组（包括我们自己的）已经开始表明，计算机提取的肿瘤形态学测量结果 (e.g.捕获核取向、纹理、形状、结构）从常规H&E染色的癌组织图像 可以预测疾病的侵袭性和治疗结果。通过计算分析整个肿瘤 景观及其最具侵入性的元素，这些方法可以允许更多 准确捕获肿瘤异质性、疾病风险，从而获得最合适的治疗策略。 该学术-工业合作伙伴关系的目标是开发和验证计算机组织学 基于图像的预测（CHIP），以确定哪些早期，雌激素受体阳性（ER+）乳腺癌 患者是单独激素治疗的候选者，哪些女性是辅助治疗的候选者 化疗基于对活检和手术标本的病理切片的分析。Inspirata 股份有限公司、一家癌症诊断公司最近获得了一些基于组织形态学的许可， 来自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测定的商业化前伴随诊断测试。此外，Inspira Inc.将建立一个完整的监管途径，在美国和国外成功翻译的测定。 最后，CHIP测定的预商业化原型将使用相同的方法独立验证。 策略和数据队列作为ODx。我们的方法与ODx等分子测定相比具有几个优势 因为它（1）可以询问病理图像的整个范围， 肿瘤异质性和因此的疾病风险，（2）不破坏病理学工作流程，（3）不破坏 并且将大大（4）更便宜（在低收入到中等收入国家中至关重要）和（5）更快。