The goal of this research is to develop new response assessments for cancer treatment based on CT imaging of changes in tumor volume and necrosis fraction. Current RECIST criteria and cut-off values for response assessment are not evidence-based and may fail to detect the tumor changes associated with clinical response to targeted, non-cytotoxic treatments. This study will seek a proof of concept using two types of tumors in which RECIST is known to correlate poorly with tumor response to treatment and clinical outcome. HCC is one of the most common malignancies worldwide, and sarcomas, though rare, carry the same molecular alterations as many other heterogeneous cancers and are the classic cancer studied in drug discovery.
Aim 1 will demonstrate that assistance from new automated segmentation algorithms can reduce the variability in radiologists'measurement of lung, liver, and lymph tumors.
This aim will use images from 276 patients already collected by SARC 011, a large phase II multicenter clinical trial of sarcoma.
Aim 2 will correlate tumor volume and necrosis fraction with clinical outcome in SARC 011 and CALGB 80802, a phase trial of HCC with an estimated enrollment of 480 patients. The proposed research will first develop criteria based on quantitative biomarkers (tumor volume and necrosis fraction) and then compare the predictive value of these criteria to the current clinical standard using a concordance probability estimate.
Aim 3 will explore the correlation of these criteria to other biochemical markers, and use a concordance probability estimate to determine whether the combination of imaging biomarkers with biochemical markers affords superior prediction of patient survival as compared to either alone.
This aim will use data from three companion biology studies of SARC 011 and CALGB 80802. Development and validation of the new criteria will have substantial health significance because evidence that volume and necrosis changes are early biomarkers of response or progression will guide clinical trials and patient treatment. The new criteria will be widely applicable to clinical practice because CT is the most common imaging modality for cancer, the new algorithms run on popular imaging platforms, and this method will reduce the time required by radiologists.
;When new treatments for cancer are being tested, images of the patients'tumor are measured to determine whether the treatment is working. By developing a better way to measure tumor changes caused by treatment, this research will aid the discovery of cancer drugs and help match patients to the treatment that works best for them.
|Balagurunathan, Yoganand; Beers, Andrew; Kalpathy-Cramer, Jayashree et al. (2018) Semi-automated pulmonary nodule interval segmentation using the NLST data. Med Phys 45:1093-1107|
|Huang, Qiao; Lu, Lin; Dercle, Laurent et al. (2018) Interobserver variability in tumor contouring affects the use of radiomics to predict mutational status. J Med Imaging (Bellingham) 5:011005|
|Beichel, Reinhard R; Smith, Brian J; Bauer, Christian et al. (2017) Multi-site quality and variability analysis of 3D FDG PET segmentations based on phantom and clinical image data. Med Phys 44:479-496|
|Grossmann, Patrick; Narayan, Vivek; Chang, Ken et al. (2017) Quantitative imaging biomarkers for risk stratification of patients with recurrent glioblastoma treated with bevacizumab. Neuro Oncol 19:1688-1697|
|Kalpathy-Cramer, Jayashree; Mamomov, Artem; Zhao, Binsheng et al. (2016) Radiomics of Lung Nodules: A Multi-Institutional Study of Robustness and Agreement of Quantitative Imaging Features. Tomography 2:430-437|
|Koshkin, Vadim S; Bolejack, Vanessa; Schwartz, Lawrence H et al. (2016) Assessment of Imaging Modalities and Response Metrics in Ewing Sarcoma: Correlation With Survival. J Clin Oncol 34:3680-3685|
|Kalpathy-Cramer, Jayashree; Zhao, Binsheng; Goldgof, Dmitry et al. (2016) A Comparison of Lung Nodule Segmentation Algorithms: Methods and Results from a Multi-institutional Study. J Digit Imaging 29:476-87|
|Yankeelov, Thomas E; Mankoff, David A; Schwartz, Lawrence H et al. (2016) Quantitative Imaging in Cancer Clinical Trials. Clin Cancer Res 22:284-90|
|Aerts, Hugo J W L; Grossmann, Patrick; Tan, Yongqiang et al. (2016) Defining a Radiomic Response Phenotype: A Pilot Study using targeted therapy in NSCLC. Sci Rep 6:33860|
|Yan, Jiayong; Schwartz, Lawrence H; Zhao, Binsheng (2015) Semiautomatic segmentation of liver metastases on volumetric CT images. Med Phys 42:6283-93|
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