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Proceedings Paper

Radiomic biomarkers from PET/CT multi-modality fusion images for the prediction of immunotherapy response in advanced non-small cell lung cancer patients
Author(s): Wei Mu; Jin Qi; Hong Lu; Matthew Schabath; Yoganand Balagurunathan; Ilke Tunali; Robert James Gillies
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Paper Abstract

Purpose: Investigate the ability of using complementary information provided by the fusion of PET/CT images to predict immunotherapy response in non-small cell lung cancer (NSCLC) patients. Materials and methods: We collected 64 patients diagnosed with primary NSCLC treated with anti PD-1 checkpoint blockade. Using PET/CT images, fused images were created following multiple methodologies, resulting in up to 7 different images for the tumor region. Quantitative image features were extracted from the primary image (PET/CT) and the fused images, which included 195 from primary images and 1235 features from the fusion images. Three clinical characteristics were also analyzed. We then used support vector machine (SVM) classification models to identify discriminant features that predict immunotherapy response at baseline. Results: A SVM built with 87 fusion features and 13 primary PET/CT features on validation dataset had an accuracy and area under the ROC curve (AUROC) of 87.5% and 0.82, respectively, compared to a model built with 113 original PET/CT features on validation dataset 78.12% and 0.68. Conclusion: The fusion features shows better ability to predict immunotherapy response prediction compared to individual image features.

Paper Details

Date Published: 27 February 2018
PDF: 7 pages
Proc. SPIE 10575, Medical Imaging 2018: Computer-Aided Diagnosis, 105753S (27 February 2018); doi: 10.1117/12.2293376
Show Author Affiliations
Wei Mu, H. Lee Moffitt Cancer Ctr. & Research Institute (United States)
Jin Qi, H. Lee Moffitt Cancer Ctr. & Research Institute (United States)
Hong Lu, H. Lee Moffitt Cancer Ctr. & Research Institute (United States)
Matthew Schabath, H. Lee Moffitt Cancer Ctr. & Research Institute (United States)
Yoganand Balagurunathan, H. Lee Moffitt Cancer Ctr. & Research Institute (United States)
Ilke Tunali, H. Lee Moffitt Cancer Ctr. & Research Institute (United States)
Robert James Gillies, H. Lee Moffitt Cancer Ctr. & Research Institute (United States)


Published in SPIE Proceedings Vol. 10575:
Medical Imaging 2018: Computer-Aided Diagnosis
Nicholas Petrick; Kensaku Mori, Editor(s)

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