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Optimizing Pancreatic Cancer Management with Next Generation Imaging and Liquid Biopsy

Optimizing Pancreatic Cancer Management with Next Generation Imaging and Liquid Biopsy
利用下一代成像和液体活检优化胰腺癌治疗
批准号:
10584523
负责人:
Eric Collisson
金额:
$59.83万
依托单位国家:
美国
项目类别:
财政年份:
2021
资助国家:
美国
项目状态:
未结题
起止时间:
2021-04-01 至 2026-03-31

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中文摘要
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英文摘要
PROJECT SUMMARY: Pancreatic ductal adenocarcinoma (PDA) will become the 2nd leading cause of cancer deaths in the United States by 2030. Most patients with PDA present with nonresectable/metastatic disease, and systemic chemotherapy is the anchoring treatment in these patients. Surgery is an option in the minority of patients (~30%) who present with localized disease, though most develop early recurrence after a highly morbid surgery due to occult metastases. Therefore, neoadjuvant therapy (NAT) is an emerging standard approach, but is only beneficial if the selected systemic therapy is effective. Indeed, for all patients with PDA, metastatic or otherwise, the duration of effective systemic therapy is the most important factor in their survival. There is a critical unmet need for accurate and timely assessment of treatment response in order to 1) get patients on effective systemic therapy as soon as possible and keep them on it as long as possible, 2) expeditiously discontinue toxic, costly and ineffective therapies, and 3) facilitate evidence-based personalized clinical decisions regarding curative- intent surgery. Current management of PDA relies principally on computed tomography (CT) and tumor markers (CA19-9). However, these tools are not sensitive enough and are too slow for adjudicating benefit in patients with rapidly lethal metastatic disease, and for identifying suitable candidates most likely to benefit from surgery after NAT. We now have cutting-edge tools for more precise quantification of disease burden at the molecular and metabolic levels. We have previously shown that mutant KRAS circulating tumor (ct)DNA can be detected with high sensitivity in PDA, tends to drop rapidly with effective therapy, and may be a more dynamic predictor of therapy response than CA19-9. We have also shown that metabolic imaging with hybrid integrated 18-fluoro- deoxyglucose (FDG) positron emission tomography/magnetic resonance imaging (PET/MRI) improves the detection of subtle metastases that are occult on CT, early response assessment in patients with nonresectable/metastatic PDA, and prediction of pathological response to NAT in patients who undergo resection. Building on these promising results, we hypothesize that the appropriate combination of KRAS ctDNA and PET/MRI biomarkers will enable timely assessment of the clinical utility of therapy in PDA patients. In Aim 1, we will define thresholds for early chemotherapy switch in unresectable/metastatic PDA using dynamic and quantitative changes in KRAS ctDNA and FDG PET/MRI biomarkers for use in future prospective trials. In Aim 2, we will construct, test and validate a model of surgical benefit or futility for patients with potentially resectable PDA using dynamic KRAS ctDNA and FDG PET/MRI response data. Our overarching goal is to integrate reliable biomarkers that can accurately guide therapy and enable precision medicine to improve outcomes of patients with this deadly disease.
期刊论文(9)
专著(0)
科研奖励(0)
会议论文
Assessing the robustness of a machine-learning model for early detection of pancreatic adenocarcinoma (PDA): evaluating resilience to variations in image acquisition and radiomics workflow using image perturbation methods.
评估用于早期检测胰腺腺癌 (PDA) 的机器学习模型的稳健性:使用图像扰动方法评估对图像采集和放射组学工作流程变化的恢复能力。
DOI: 10.1007/s00261-023-04127-1
发表时间: 2024
期刊: Abdominal radiology (New York)
影响因子: --
作者: [Mukherjee,Sovanlal, Korfiatis,Panagiotis, Patnam,NandakumarG, Trivedi,KamaxiH, Karbhari,Aashna, Suman,Garima, Fletcher,JoelG, Goenka,AjitH]
通讯作者: Goenka,AjitH
Radiomics for Detection of Pancreas Adenocarcinoma on CT Scans: Impact of Biliary Stents.
CT 扫描检测胰腺腺癌的放射组学:胆管支架的影响。
DOI: 10.1148/rycan.210081
发表时间: 2022
期刊: Radiology. Imaging cancer
影响因子: --
作者: [Suman,Garima, Patra,Anurima, Mukherjee,Sovanlal, Korffiatis,Panagiotis, Goenka,AjitH]
通讯作者: Goenka,AjitH
Radiomics-based machine learning (ML) classifier for detection of type 2 diabetes on standard-of-care abdomen CTs: a proof-of-concept study.
基于放射组学的机器学习 (ML) 分类器,用于在标准护理腹部 CT 上检测 2 型糖尿病:一项概念验证研究。
DOI: 10.1007/s00261-022-03668-1
发表时间: 2022
期刊: Abdominal radiology (New York)
影响因子: --
作者: [Wright,DarrylE, Mukherjee,Sovanlal, Patra,Anurima, Khasawneh,Hala, Korfiatis,Panagiotis, Suman,Garima, Chari,SureshT, Kudva,YogishC, Kline,TimothyL, Goenka,AjitH]
通讯作者: Goenka,AjitH
Optimizing Pancreatic Cancer Management with Next Generation Imaging and Liquid Biopsy
Understanding Efficacy and Fe(II)-Promoted Activation of 1,2,4-Trioxolanes in Cancer
Understanding Efficacy and Fe(II)-Promoted Activation of 1,2,4-Trioxolanes in Cancer
The structural and functional basis of MET exon 14 activation and acquired drug resistance
国内基金
海外基金
Journal of Integrative Plant Biology
  • 批准号:
    31024801
  • 项目类别:
    专项基金项目
  • 资助金额:
    24.0万元
  • 批准年份:
    2010
  • 负责人:
    贺萍
  • 依托单位: