Columbia University Medical Center, New York
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Columbia University Medical Center, New York Quantitative Volume and Density Response Assessment: Sarcoma and HCC as a Model • To develop and deliver three robust computer-aided algorithms for segmentation of solid tumors in the lungs, liver and lymph nodes and necrosis in HCC • To provide insight into the magnitude of variability in measuring tumor volume (diameter as well) using computer-aided methods • A CT image dataset containing radiologists’ mark-up of tumor contours made from a subset of tumors in the lungs, liver and lymph nodes • New response metrics and criteria, based on CT imaging of changes in tumor volume and necrosis fraction, for better assessing response to novel therapies Specific Aims and Final Deliverables Objective A critical barrier to the development of new cancer therapies is the lack of evidence validating the imaging methods used for patient response assessment. Our project evaluates the ability of improved image analysis techniques, e.g., tumor volume and necrosis fraction, to assess response in cancers such as HCC and Sarcoma in which RECIST is known to correlate poorly with clinical outcome. Open source imaging platform with integrated tumor segmentation algorithms Segmentation algorithms developed for lesions in the lungs, liver and lymph nodes Lawrence H Schwartz, M.D. and Binsheng Zhao, D.Sc. Progress Summary – algorithm and platform development Progress Summary – variability study U01 / R01 Hub and Spoke Model Publicatio ns 1.Tan Y, Schwartz LH and Zhao B, Segmentation of lung tumors on CT scans using watershed and active contours. Med Phys. 2013, Apr; 40(4):043502. doi: 10.1118/1.4793409. 2.Zhao B, Lee S, Lee HJ, Tan Y, Qi J, Persigehl T, Tan T and Schwartz H, Variability in assessing treatment response: metastatic colorectal cancer as a paradigm. CCR (accepted). 3. Multiple abstracts resulted from this study have been published. Significant differences are marked in red Effects of CT slice thickness and reconstruction algorithm on volume measurement – a phantom study • Three radiologists participated; RECIST 1.0 used • A subset of 30 patients with metastatic cancer selected • The three segmentation algorithms used to assist in tumor measurement • Relative changes in total tumor volume at 6-wk from baseline calculated -17% 23% -24% 28% -18% 16% R1 / R2 R1 / R3 R2 / R3 Upper 95% limit Lower 95% limit Inter readers
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Quantitative Volume and Density Response Assessment: Sarcoma and HCC as a Model. Lawrence H Schwartz, M.D. and Binsheng Zhao, D.Sc. Columbia University Medical Center, New York. Objective. - PowerPoint PPT Presentation
Transcript of Columbia University Medical Center, New York
Columbia University Medical Center, New York
Quantitative Volume and Density Response Assessment: Sarcoma and HCC as a Model
• To develop and deliver three robust computer-aided algorithms for segmentation of solid tumors in the lungs, liver and lymph nodes and necrosis in HCC• To provide insight into the magnitude of variability in measuring tumor volume (diameter as well) using computer-aided methods• A CT image dataset containing radiologists’ mark-up of tumor contours made from a subset of tumors in the lungs, liver and lymph nodes• New response metrics and criteria, based on CT imaging of changes in tumor volume and necrosis fraction, for better assessing response to novel therapies
Specific Aims and Final Deliverables
ObjectiveA critical barrier to the development of new cancer therapies is the lack of evidence validating the imaging methods used for patient response assessment. Our project evaluates the ability of improved image analysis techniques, e.g., tumor volume and necrosis fraction, to assess response in cancers such as HCC and Sarcoma in which RECIST is known to correlate poorly with clinical outcome.
Open source imaging platform with integrated tumor segmentation algorithms
Segmentation algorithms developed for lesions in the lungs, liver and lymph nodes
Lawrence H Schwartz, M.D. and Binsheng Zhao, D.Sc.
Progress Summary – algorithm and platform development
Progress Summary – variability study
U01 / R01 Hub and Spoke Model
Publications1. Tan Y, Schwartz LH and Zhao B, Segmentation of lung tumors on CT scans using watershed and active contours. Med Phys. 2013,
Apr; 40(4):043502. doi: 10.1118/1.4793409.2. Zhao B, Lee S, Lee HJ, Tan Y, Qi J, Persigehl T, Tan T and Schwartz H, Variability in assessing treatment response: metastatic
colorectal cancer as a paradigm. CCR (accepted).3. Multiple abstracts resulted from this study have been published.
Significant differences are marked in red Effects of CT slice thickness and reconstruction algorithm on volume measurement – a phantom study
• Three radiologists participated; RECIST 1.0 used• A subset of 30 patients with metastatic cancer selected• The three segmentation algorithms used to assist in tumor measurement• Relative changes in total tumor volume at 6-wk from baseline calculated
-17% 23%-24% 28%-18% 16%
R1 / R2R1 / R3
R2 / R3
Upper 95% limit Lower 95% limit
Inter readers
