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Developing a PET Volumetric Staging System for NSCLC: a Complement to TNM Staging

Developing a PET Volumetric Staging System for NSCLC: a Complement to TNM Staging
开发 NSCLC 的 PET 容积分期系统:TNM 分期的补充
批准号:
8758482
负责人:
Yulei Jiang
金额:
$21.95万
依托单位:
依托单位国家:
美国
项目类别:
财政年份:
2014
资助国家:
美国
项目状态:
已结题
起止时间:
2014-09-22 至 2016-08-31

项目摘要

项目成果

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中文摘要
翻译
描述(由申请人提供):目前,非小细胞肺癌(NSCLC)的治疗和预后评估主要依赖于UICC/AJCC TNM(肿瘤、淋巴结和转移)分期系统。现代18F-FDG PET/CT在NSCLC患者的诊断、分期和再分期中发挥重要作用,并提供三维(3D)代谢和体积图像数据。然而,基于肿瘤手术可切除性的TNM分期系统没有纳入肿瘤体积数据的机制。最近,我们和其他研究小组发现,在调整TNM分期、肿瘤SUV和其他临床预后指标(包括年龄、性别、运动状态、治疗类型和肿瘤组织学)后,在手术和非手术治疗的NSCLC患者中,基线代谢肿瘤负担(MTB)是生存的预后指标,优于肿瘤标准化摄取值(SUV)测量。在PET/CT上测量MTB具有实用性和可重复性,观测者间变异率低,平均一致性相关系数大于0.94。参考临床PET/CT和基于我们回顾性研究的CT报告,每位手术患者的全身MTBs测量大约需要3.6分钟。然而,尽管有这些有希望的发现,目前还没有有效的方法将MTB测量纳入TNM分期系统或NSCLC患者的临床管理。为此,我们建议开发一种基于PET/ ct的体积预后(PVP)分期系统,该系统结合MTB测量和当前的TNM分期系统,以更好地预测非小细胞肺癌。我们提出了两个具体的目标:(1)通过开发两种不同的竞争数学模型来开发一种新的基于PET/ ct的非小细胞肺癌PVP分期系统;(2)使用来自不同机构的患者的新数据集来验证新的PVP分期系统。我们的长期目标是协同当前TNM分期系统和体积MTB测量的预后价值。期望在以下三个方面具有公共卫生意义:1)通过提供更准确的肿瘤分期和风险分层来改善非小细胞肺癌患者的治疗选择;2)通过提供更准确的预后评估来协助临床医生和患者;3)帮助更好地确定临床试验中的患者选择标准。该提案的创新之处包括:1)提出的新的基于PET/ ct的PVP分期系统具有创新性,可用于更准确的非小细胞肺癌预后;2)利用PET/CT的三维体积和代谢数据进行NSCLC分期是一种创新;3)采用PVP分期系统使非小细胞肺癌分期更加定量,具有创新性。
英文摘要
DESCRIPTION (provided by applicant): Currently, treatment and prognostic assessment for non-small cell lung cancer (NSCLC) depend primarily on the UICC/AJCC TNM (tumor, node, and metastasis) staging system. Modern 18F-FDG PET/CT plays an important role in the diagnosis, staging, and restaging of patients with NSCLC and provides three-dimensional (3D) metabolic and volumetric image data. However, the TNM staging system, based on surgical resectability of the tumor, has no mechanism to incorporate tumor volumetric data. Recently, we and other groups have found that baseline metabolic tumor burden (MTB) is a prognostic indicator of survival and is better than tumor standardized uptake value (SUV) measurement, in both surgically and non-surgically treated NSCLC patients, after adjusting for TNM stage, tumor SUV, and other clinical prognostic indicators including age, gender, performance status, treatment type, and tumor histology. Measurement of MTB on PET/CT is practical and reproducible with low inter-observer variability-with a mean concordance correlation coefficient greater than 0.94. It takes about 3.6 minutes to measure whole-body MTBs in each surgically treated patient with reference of clinical PET/CT and CT reports based our retrospective study. However, despite these promising findings, there is currently no effective method to incorporate MTB measurement into the TNM staging system or clinical management of NSCLC patients. To that end, we propose to develop a PET/CT-based volumetric prognostic (PVP) staging system that combines MTB measurements and the current TNM staging system for better prognostication in NSCLC. We propose two specific aims: (1) to develop a new PET/CT-based PVP staging system for NSCLC by developing two different competing mathematical models, and (2) to validate the novel PVP staging system with new datasets of patients from different institutions. Our long-term objective is to synergize the prognostic value of the current TNM staging system and volumetric MTB measurement. The public health significance is expected in the following three areas: 1) to improve patient treatment selection in NSCLC by providing more accurate tumor staging and risk- stratification, 2) to assist clinicians and patients by providing more accurate prognostic assessment, and 3) to help better define patient selection criteria in clinical trials. The innovations of the proposal include the following: 1) the proposed new PET/CT-based PVP staging system for more accurate prognostication in NSCLC is innovative; 2) making use of 3D volumetric and metabolic data from PET/CT for NSCLC staging is innovative; and 3) making NSCLC staging more quantitative with the PVP staging system is innovative.
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Developing a PET Volumetric Staging System for NSCLC: a Complement to TNM Staging
  • 批准号:
    8930933
  • 项目类别:
  • 资助金额:
    $17.08万
  • 财政年份:
    2014
  • 负责人:
    Yulei Jiang
  • 依托单位:
Computer-Aided Analysis of Histopathology Images of Prostate Cancer
  • 批准号:
    7258106
  • 项目类别:
  • 资助金额:
    $24.28万
  • 财政年份:
    2007
  • 负责人:
    Yulei Jiang
  • 依托单位:
Computer-Aided Analysis of Histopathology Images of Prostate Cancer
  • 批准号:
    7446099
  • 项目类别:
  • 资助金额:
    $18.73万
  • 财政年份:
    2007
  • 负责人:
    Yulei Jiang
  • 依托单位:
Neural Network Prediction of Prostate Cancer Progression
  • 批准号:
    6603799
  • 项目类别:
  • 资助金额:
    $14.75万
  • 财政年份:
    2002
  • 负责人:
    Yulei Jiang
  • 依托单位:
海外基金