Quantitative MRI biomarker for treatment response assessment of multiple myeloma: robustness evaluation using independent test set of prospective cases

Chuan Zhou; Qian Dong; Heang-Ping Chan; Erica L. Campagnaro; Jun Wei; Lubomir M. Hadjiiski

doi:10.1117/12.2511898

13 March 2019 Quantitative MRI biomarker for treatment response assessment of multiple myeloma: robustness evaluation using independent test set of prospective cases

Chuan Zhou, Qian Dong, Heang-Ping Chan, Erica L. Campagnaro, Jun Wei, Lubomir M. Hadjiiski

Author Affiliations +

Proceedings Volume 10950, Medical Imaging 2019: Computer-Aided Diagnosis; 1095047 (2019) https://doi.org/10.1117/12.2511898
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

Abstract

We are developing quantitative radiomic biomarkers for treatment response assessment of multiple myeloma (MM) in MRI. In our previous study, we have developed a 3D dynamic intensity entropy transformation (DIET) method with MRI for deriving a radiomic biomarker for differentiating responders from non-responders in treatment of MM. DIET transforms MR signal voxel by voxel to a quantitative entropy enhancement value (qEEV), from which predictor variables are extracted and combined into a qEEV-based response index (qERI) for quantitative assessment of treatment response. We developed the qERI in a previous retrospective data set of 64 MRI cases. This study evaluated the performance of the qERI-based MRI biomarker using an independent test set of 15 MRI cases collected from an ongoing prospective study, in which 10 and 5 patients were clinically diagnosed as responders and non-responders. The results showed that the agreement between the qERI prediction and the clinical outcome reached 0.80 with a kappa value of 0.57. The area under the ROC curve (AUC) achieved 0.78.

Citation Download Citation

Chuan Zhou, Qian Dong, Heang-Ping Chan, Erica L. Campagnaro, Jun Wei, and Lubomir M. Hadjiiski "Quantitative MRI biomarker for treatment response assessment of multiple myeloma: robustness evaluation using independent test set of prospective cases", Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 1095047 (13 March 2019); https://doi.org/10.1117/12.2511898

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