Biomarkers for Staging and Treatment Response Monitoring of Bladder Cancer

描述(申请人提供)：膀胱癌是一种常见的癌症，在男性和女性中都会导致相当大的发病率和死亡率。在美国，膀胱癌每年导致超过15,210人死亡。据估计，2013年将新增72,570例膀胱癌确诊病例。膀胱癌的正确分期对新辅助化疗的决策和减少治疗不足或过度治疗的风险至关重要。在早期阶段对新辅助治疗的反应进行可靠的评估对于识别无反应的肿瘤并为患者提供替代治疗的机会至关重要。MRI和CT是各种膀胱癌治疗前分期或治疗反应监测的重要手段。CT是测量原发灶大体肿瘤体积(GTV)的一种有效的无创性检查方法，MRI的应用日益增多。GTV已被用作预测膀胱肿瘤治疗结果的生物标志物。其他病理信息和诊断测试(双指甲评估、膀胱镜检查)结果和免疫组织化学生物标志物也有助于分期和治疗反应监测。该项目的目标是开发有效的决策支持工具，将基于图像的和非基于图像的生物标记物结合在一起，以帮助放射科医生和肿瘤学家评估癌症分期和治疗后的变化。我们将(1)开发用于多模式(MM)图像上膀胱GTV估计的定量图像分析工具(QIBC)；(2)开发计算机决策支持系统(CDSS-S)以辅助临床医生进行癌症分期；(3)开发计算机决策支持系统(CDSS-T)以帮助临床医生评估新辅助治疗后肿瘤特性的变化；(4)评估QIBC和CDSS-T在估计GTV和治疗反应方面对临床医生间变异性和效率的影响；以及(5)评估CDSS-S和CDSS-T作为试点临床研究的决策支持工具。我们推测，使用QIBC、CDSS-S和CDSS-T可以提高临床医生在MM影像检查中估计膀胱GTV、评估膀胱癌分期和治疗反应的准确性、一致性和效率。为了验证我们的假设，我们将执行以下具体任务：(1)收集膀胱癌多模式MR、CT检查的数据库，用于开发、训练和测试QIBC和CDSS算法；(2)开发先进的计算机视觉技术，以定量估计膀胱GTV和图像特征；(3)利用机器学习技术开发预测模型，将基于MM图像的、病理和免疫组织化学的生物标记物结合用于癌症分期和确定无反应者；(4)通过观察者研究比较有无QIBC和CDSS-T在临床医生估计GTV和治疗反应方面的临床间变异性和效率；以及(5)在先导性临床研究中评价CDSS-S和CDSS-T作为决策支持工具的价值。

DESCRIPTION (provided by applicant): Bladder cancer is a common type of cancer that can cause substantial morbidity and mortality among both men and women. Bladder cancer causes over 15,210 deaths per year in the United States. It is estimated that 72,570 new bladder cancer cases will be diagnosed in 2013. Correct staging of the bladder cancer is crucial for the decision of neoadjuvant chemotherapy and minimizing the risk of under-treatment or over-treatment. A reliable assessment of the response to neoadjuvant therapy at an early stage is vital for identifying tumors that do not respond and allowing the patient a chance of alternative treatment. MRI and CT are important methods for pre-treatment staging or treatment response monitoring for a variety of bladder cancers. CT is an effective non-invasive modality for measuring primary site gross tumor volume (GTV) and the addition of MRI is on the rise. GTV has been used as a biomarker for predicting treatment outcome of bladder tumors. Other pathological information and diagnostic test (bimanual evaluation, cystoscopy) results and immunohistochemical biomarkers are also useful for staging and treatment response monitoring. The goal of this project is to develop effective decision support tools that merge image-based and non-image-based biomarkers to assist radiologists and oncologists in assessment of cancer stage and change as a result of treatment. We will (1) develop a quantitative image analysis tool (QIBC) for bladder GTV estimation on multi-modality (MM) images, (2) develop a computer decision support system (CDSS-S) to assist clinicians in cancer staging, (3) develop a computer decision support system (CDSS-T) to assist clinicians in evaluation of the change in the tumor characteristics as a result of neoadjuvant treatment, (4) evaluate the effects of QIBC and CDSS-T on inter-clinician variability and efficiency in estimation of GTV and treatment response, and (5) evaluate CDSS-S and CDSS-T as decision support tools in pilot clinical studies. We hypothesize that the use of QIBC, CDSS-S and CDSS-T can improve the clinicians' accuracy, consistency and efficiency in bladder GTV estimation on MM imaging exams, the assessment of bladder cancer stage and response to treatment. To test our hypothesis, we will perform the following specific tasks: (1) to collect a database of multi-modality MR, CT exams of bladder cancers for development, training and testing of the QIBC and CDSS algorithms; (2) to develop advanced computer vision techniques to quantitatively estimate bladder GTV and image characteristics; (3) to develop predictive models using machine learning techniques to combine MM image-based, pathological and immunohistochemical biomarkers for cancer staging and determination of non-responders; (4) to compare the inter-clinician variability and efficiency in clinicians' estimation of GTV and treatment response with and without the proposed QIBC and CDSS-T by observer studies; and (5) to evaluate the CDSS-S and CDSS-T as decision support tools in pilot clinical studies.