Computerized histologic image predictor of cancer outcome

摘要：越来越需要预测性和预测性分析来区分更多和更少 癌症侵袭性表型：A)癌症发病率显著增加；B) 早期诊断。尤其是预测性分析，将使侵袭性较弱的患者得以幸免。 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分析 (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。 在肿瘤之间，甚至在同一肿瘤内，表明推荐治疗的方法 患者基于许多细胞的平均分子信号过于简单化。 有趣的是，对于一些癌症，肿瘤级别(评估的组织的形态外观 病理学家的定性或半定量)被发现与疾病高度相关 结果。然而，病理分级往往受到观察者间显着差异的影响。的数字化 组织学样本或整个玻片成像有助于定量评估疾病。 发展和预测结果，同时也促进了远程病理学的采用。最近，一项研究 研究小组(包括我们自己)已经开始表明，计算机提取的肿瘤形态测量 (例如，从常规H&E染色的癌组织图像中捕获核取向、纹理、形状、结构) 可以预测疾病的侵袭性和治疗结果。通过计算询问整个肿瘤 景观及其最具侵入性的元素来自标准的H&E幻灯片，这些方法可以实现更多 准确捕捉肿瘤异质性、疾病风险，从而获得最合适的治疗策略。 这种学术和产业合作的目标是开发和验证计算机化的组织学 基于图像的预测指标(芯片)用于识别雌激素受体阳性(ER+)的早期乳腺癌 患者是单独激素治疗的候选对象，哪些妇女是辅助治疗的候选对象 化疗是基于对活检和手术标本的病理切片进行分析。Inspirata Inc.，一家癌症诊断公司，最近获得了许多基于组织形态计量学的 Madabhushi集团的技术将带来质量管理系统和生产软件 标准，以帮助创建芯片检测的商业化前配套诊断测试。此外，Inspirata Inc.将为该检测在美国和国外的成功翻译建立一个完整的监管途径。 最后，芯片分析的预商业化原型将使用相同的 战略和数据队列作为ODx。与ODx等分子分析方法相比，我们的方法有几个优点 因为它(1)可以询问病理图像的整个范围，从而能够更准确地捕获 肿瘤异质性和疾病风险，(2)非破坏性的病理工作流程，(3)非破坏性的 而且将大大(4)便宜(在低收入和中等收入国家至关重要)和(5)更快。

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.