Biomarkers for Staging and Treatment Response Monitoring of Bladder Cancer

用于膀胱癌分期和治疗反应监测的生物标志物

基本信息

项目摘要

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.
描述(由申请人提供):膀胱癌是一种常见的癌症类型,可导致男性和女性的大量发病率和死亡率。在美国,膀胱癌每年导致超过15,210人死亡。据估计,2013年将诊断出72,570例新的膀胱癌病例。膀胱癌的正确分期对于决定新辅助化疗和最大限度地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没有反应的肿瘤并允许患者有机会进行替代治疗至关重要。MRI和CT是各种膀胱癌治疗前分期或治疗反应监测的重要方法。CT是测量原发部位大体肿瘤体积(GTV)的有效非侵入性方式,MRI的增加正在增加。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(双手评估,膀胱镜检查)结果和免疫组化生物标志物也可用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,合并基于图像和非基于图像的生物标志物,以帮助放射科医生和肿瘤科医生评估癌症分期和治疗结果的变化。我们将(1)开发一种定量图像分析工具(QIBC),用于在多模态(MM)图像上估计膀胱GTV,(2)开发一种计算机决策支持系统(CDSS-S),以帮助临床医生进行癌症分期,(3)开发一种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导致的肿瘤特征变化,(4)评价QIBC和CDSS-T对GTV和治疗反应估计的临床医生间变异性和效率的影响;(5)评价CDSS-S和CDSS-T作为初步临床研究中的决策支持工具。我们假设使用QIBC、CDSS-S和CDSS-T可以提高临床医生在MM成像检查中估计膀胱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)收集膀胱癌的多模态MR、CT检查数据库,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)使用机器学习技术开发预测模型,以联合收割机组合基于MM图像的病理学和免疫组织化学生物标志物,用于癌症分期和确定无应答者;(4)通过观察者研究比较在有和没有拟议的QIBC和CDSS-T的情况下临床医生估计GTV和治疗反应的临床医生间变异性和效率;以及(5)评价CDSS-S和CDSS-T作为初步临床研究中的决策支持工具。

项目成果

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Lubomir M Hadjiyski其他文献

Lubomir M Hadjiyski的其他文献

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{{ truncateString('Lubomir M Hadjiyski', 18)}}的其他基金

Biomarkers for Staging and Treatment Response Monitoring of Bladder Cancer
用于膀胱癌分期和治疗反应监测的生物标志物
  • 批准号:
    8849399
  • 财政年份:
    2014
  • 资助金额:
    $ 50.12万
  • 项目类别:
Computer-Aided Detection of Urinary Tract Cancer on MDCT Urography
MDCT 尿路造影计算机辅助检测尿路癌
  • 批准号:
    8120679
  • 财政年份:
    2010
  • 资助金额:
    $ 50.12万
  • 项目类别:
Computer-Aided Detection of Urinary Tract Cancer on MDCT Urography
MDCT 尿路造影计算机辅助检测尿路癌
  • 批准号:
    8476785
  • 财政年份:
    2010
  • 资助金额:
    $ 50.12万
  • 项目类别:
Computer-Aided Detection of Urinary Tract Cancer on MDCT Urography
MDCT 尿路造影计算机辅助检测尿路癌
  • 批准号:
    7782907
  • 财政年份:
    2010
  • 资助金额:
    $ 50.12万
  • 项目类别:
Computer-Aided Detection of Urinary Tract Cancer on MDCT Urography
MDCT 尿路造影计算机辅助检测尿路癌
  • 批准号:
    8665805
  • 财政年份:
    2010
  • 资助金额:
    $ 50.12万
  • 项目类别:
Multimodality CAD system with image references for breast mass characterization
多模态 CAD 系统,具有用于乳腺质量表征的图像参考
  • 批准号:
    7677385
  • 财政年份:
    2006
  • 资助金额:
    $ 50.12万
  • 项目类别:

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