Development and Validation of Prognostic Radiomic Markers of Response and Recurrence for Patients with Colorectal Liver Metastases

结直肠肝转移患者反应和复发的预后放射学标志物的开发和验证

• 批准号: 10684268
• 负责人: Yun Shin Chun
• 金额: $ 65.92万
• 依托单位: SLOAN-KETTERING INST CAN RESEARCH
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-03-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10684268
• 关键词: Affect; Algorithms; Biometry; Cancer Center; Cancer Etiology; Cessation of life; Clinical; Clinical Trials; Collaborations; Colorectal; Colorectal Cancer; Computer software; Data; Data Set; Decision Making; Development; Diagnostic; Disease; Disease Progression; Ensure; Excision; Eye; Goals; Hepatic; Human; Image; Image Analysis; Infusion procedures; Institution; Link; Malignant Neoplasms; Malignant neoplasm of liver; Manufacturer; Medical Oncology; Medical center; Metastatic Neoplasm to the Liver; Methods; Modeling; Network-based; Operative Surgical Procedures; Outcome; Pathology; Pathway interactions; Patient Selection; Patients; Pattern; Performance; Phase; Phase I/II Trial; Prediction of Response to Therapy; Prognostic Marker; Prospective Studies; Publications; Radiology Specialty; Recurrence; Regional Chemotherapy; Reproducibility; Research; Resolution; Scanning; Series; Specificity; Standardization; System; Technology; Testing; The Cancer Imaging Archive; Therapeutic; Training; United States; University of Texas M D Anderson Cancer Center; Update; Validation; Venous; Western World; X-Ray Computed Tomography; biomarker validation; cancer care; cancer diagnosis; chemotherapy; clinical care; colon cancer patients; comparative; contrast enhanced computed tomography; convolutional neural network; cost; design; efficacy trial; experience; high risk; imaging biomarker; imaging modality; improved; indexing; individualized medicine; innovation; mortality; novel; novel marker; personalized cancer therapy; personalized medicine; phantom model; precision medicine; predicting response; prediction algorithm; prevent; prognostic; programs; prospective; quantitative imaging; radiomics; reconstruction; response; response biomarker; specific biomarkers; tool

SUMMARY Colorectal cancer is the second leading cause of cancer-related mortality in the United States. More than 50% of patients with colorectal cancer will develop liver metastases in their lifetime with a dismal <10% surviving past three years. A major therapeutic problem in this disease is that no markers prognostic of hepatic recurrence or predictive of response prior to treatment are known. The goal of this research is to fill this gap by providing non-invasive and objective prognostic quantitative imaging markers for personalized treatment of colorectal liver metastases (CRLM). Our single-institution data support that quantitative imaging features extracted from routine CT scans predict volumetric response to systemic and regional chemotherapy and identify patients at high risk of hepatic recurrence and poor survival. Progress in developing these novel markers is limited by a lack of optimization, standardization, and validation, all critical barriers to clinical use. The objectives of this application are to develop and validate robust imaging features by standardizing image acquisition, to improve automated tools for clinical trial use, and to validate the predictive power of imaging features with external data. We have partnered with University of Texas MD Anderson Cancer Center, Rensselaer Polytechnic Institute, and GE Research, facilitating the widespread integration of the proposed technology into medical centers worldwide. Our central hypothesis is that quantitative CT-based imaging features provide novel and robust indices for predicting response, hepatic recurrence, and survival in CRLM patients. Specifically, we will (1) validate predictive and prognostic imaging features with external data, (2) prospectively assess repeatability and reproducibility of contrast-enhanced CT imaging features, and (3) develop an integrated rawdiomics pipeline by fully utilizing sinogram data. We have assembled a critical mass of experts in surgery, medical oncology, pathology, radiology, biostatistics, and image analysis. Combined with the largest clinical experience in CRLM in the western world, this application is a unique and unrivaled opportunity to define radiomics of CRLM. Integration into existing clinical workflows means that small medical centers without highly specialized radiology groups would benefit from predictive algorithms developed at two high-volume centers via a low-cost software update. Successful completion of our aims will provide validated prognostic imaging markers with a pathway to routine clinical use, which are of paramount importance to improving patient survival of this deadly disease.

摘要 在美国，结直肠癌是癌症相关死亡的第二大原因。超过50% 的结直肠癌患者会在一生中发生肝转移，其中10%的患者存活率很低 过去三年。这种疾病的一个主要治疗问题是没有标志物来预测肝脏的预后。 在治疗前复发或预测反应是已知的。这项研究的目标是通过以下方式填补这一空白 为临床个体化治疗提供无创、客观的预后定量影像标志物 结直肠癌肝转移(CRLM)。我们的单一机构数据支持定量成像功能 从常规CT扫描中提取的信息可预测全身和区域化疗的体积反应 确定肝脏复发和存活率较低的高危患者。发展这些小说的进展 标记物的应用受到缺乏优化、标准化和验证的限制，这些都是临床使用的关键障碍。 该应用程序的目标是通过标准化图像来开发和验证健壮的成像功能 获取，以改进临床试验使用的自动化工具，并验证成像的预测能力 具有外部数据的要素。我们已经与德克萨斯大学MD安德森癌症中心合作， Rensselaer理工学院和GE Research，促进拟议的 技术应用于世界各地的医疗中心。我们的中心假设是基于CT的定量成像 这些特征为预测CRLM的反应、肝脏复发和生存提供了新的和可靠的指标 病人。具体地说，我们将(1)用外部数据验证预测和预后成像特征，(2) 前瞻性评估对比增强CT成像特征的重复性和再现性，以及(3) 充分利用正弦图数据，建立完整的原始组学流水线。我们已经聚集了一个临界点 由外科、内科肿瘤学、病理学、放射学、生物统计学和图像分析专家组成。与 CRLM在西方世界最大的临床经验，这种应用是独一无二的，无与伦比的 定义CRLM放射组学的机会。集成到现有的临床工作流程意味着小型医疗 没有高度专业化放射学小组的中心将受益于在两个月前开发的预测算法 通过低成本的软件更新实现高容量中心。成功完成我们的目标将提供经过验证的 具有常规临床使用途径的预后成像标记物，这对 提高这种致命疾病的患者存活率。

项目成果

Applying Natural Language Processing to Single-Report Prediction of Metastatic Disease Response Using the OR-RADS Lexicon.

• DOI: 10.3390/cancers15204909
• 发表时间: 2023-10-10
• 期刊: CANCERS
• 影响因子: 5.2
• 作者: Elbatarny, Lydia;Do, Richard K. G.;Gangai, Natalie;Ahmed, Firas;Chhabra, Shalini;Simpson, Amber L.
• 通讯作者: Simpson, Amber L.

Quantitative Computed Tomography Image Analysis to Predict Pancreatic Neuroendocrine Tumor Grade.

定量计算机断层扫描图像分析预测胰腺神经内分泌肿瘤等级。

• DOI: 10.1200/cci.20.00121
• 发表时间: 2021
• 期刊: JCO clinical cancer informatics
• 影响因子: 4.2
• 作者: Pulvirenti,Alessandra;Yamashita,Rikiya;Chakraborty,Jayasree;Horvat,Natally;Seier,Kenneth;McIntyre,CaitlinA;Lawrence,SharonA;Midya,Abhishek;Koszalka,MauraA;Gonen,Mithat;Klimstra,DavidS;Reidy,DianeL;Allen,PeterJ;Do,RichardK
• 通讯作者: Do,RichardK

The RSNA Cervical Spine Fracture CT Dataset.

RSNA 颈椎骨折 CT 数据集。

• DOI: 10.1148/ryai.230034
• 发表时间: 2023
• 期刊: Radiology. Artificial intelligence
• 作者: Lin,HuiMing;Colak,Errol;Richards,Tyler;Kitamura,FelipeC;Prevedello,LucianoM;Talbott,Jason;Ball,RobynL;Gumeler,Ekim;Yeom,KristenW;Hamghalam,Mohammad;Simpson,AmberL;Strika,Jasna;Bulja,Deniz;Angkurawaranon,Salita;Pérez-Lara,
• 通讯作者: Pérez-Lara,

Differences in Liver Parenchyma are Measurable with CT Radiomics at Initial Colon Resection in Patients that Develop Hepatic Metastases from Stage II/III Colon Cancer.

• DOI: 10.1245/s10434-020-09134-w
• 发表时间: 2021-04
• 期刊: Annals of surgical oncology
• 影响因子: 3.7
• 作者: Creasy JM;Cunanan KM;Chakraborty J;McAuliffe JC;Chou J;Gonen M;Kingham VS;Weiser MR;Balachandran VP;Drebin JA;Kingham TP;Jarnagin WR;D'Angelica MI;Do RKG;Simpson AL
• 通讯作者: Simpson AL

Radiomics in surgical oncology: applications and challenges.

• DOI: 10.1080/24699322.2021.1994014
• 发表时间: 2021-12
• 期刊: COMPUTER ASSISTED SURGERY
• 影响因子: 2.1
• 作者: Williams, Travis L.;Saadat, Lily V.;Gonen, Mithat;Wei, Alice;Do, Richard K. G.;Simpson, Amber L.
• 通讯作者: Simpson, Amber L.