Siva P Raman1, Yifei Chen2, James L Schroeder2, Peng Huang3, Elliot K Fishman2. 1. Department of Radiology, JHOC 3251, Johns Hopkins University, 601 N. Caroline Street, Baltimore, MD 21287. Electronic address: sraman3@gmail.com. 2. Department of Radiology, JHOC 3251, Johns Hopkins University, 601 N. Caroline Street, Baltimore, MD 21287. 3. Biostatistics and Bioinformatics Division, Department of Oncology, Johns Hopkins University, Baltimore, Maryland.
Abstract
RATIONALE AND OBJECTIVES: Computed tomography texture analysis (CTTA) allows quantification of heterogeneity within a region of interest. This study investigates the possibility of distinguishing between several common renal masses using CTTA-derived parameters by developing and validating a predictive model. MATERIALS AND METHODS: CTTA software was used to analyze 20 clear cell renal cell carcinomas (RCCs), 20 papillary RCCs, 20 oncocytomas, and 20 renal cysts. Regions of interest were drawn around each mass on multiple slices in the arterial, venous, and delayed phases on renal mass protocol CT scans. Unfiltered images and spatial band-pass filtered images were analyzed to quantify heterogeneity. Random forest method was used to construct a predictive model to classify lesions using quantitative parameters. The model was externally validated on a separate set of 19 unknown cases. RESULTS: The random forest model correctly categorized oncocytomas in 89% of cases (sensitivity = 89%, specificity = 99%), clear cell RCCs in 91% of cases (sensitivity = 91%, specificity = 97%), cysts in 100% of cases (sensitivity = 100%, specificity = 100%), and papillary RCCs in 100% of cases (sensitivity = 100%, specificity = 98%). CONCLUSIONS: CTTA, in conjunction with random forest modeling, demonstrates promise as a tool to characterize lesions. Various renal masses were accurately classified using quantitative information derived from routine scans.
RATIONALE AND OBJECTIVES: Computed tomography texture analysis (CTTA) allows quantification of heterogeneity within a region of interest. This study investigates the possibility of distinguishing between several common renal masses using CTTA-derived parameters by developing and validating a predictive model. MATERIALS AND METHODS:CTTA software was used to analyze 20 clear cell renal cell carcinomas (RCCs), 20 papillary RCCs, 20 oncocytomas, and 20 renal cysts. Regions of interest were drawn around each mass on multiple slices in the arterial, venous, and delayed phases on renal mass protocol CT scans. Unfiltered images and spatial band-pass filtered images were analyzed to quantify heterogeneity. Random forest method was used to construct a predictive model to classify lesions using quantitative parameters. The model was externally validated on a separate set of 19 unknown cases. RESULTS: The random forest model correctly categorized oncocytomas in 89% of cases (sensitivity = 89%, specificity = 99%), clear cell RCCs in 91% of cases (sensitivity = 91%, specificity = 97%), cysts in 100% of cases (sensitivity = 100%, specificity = 100%), and papillary RCCs in 100% of cases (sensitivity = 100%, specificity = 98%). CONCLUSIONS:CTTA, in conjunction with random forest modeling, demonstrates promise as a tool to characterize lesions. Various renal masses were accurately classified using quantitative information derived from routine scans.
Authors: Ahmed Ba-Ssalamah; Dina Muin; Ruediger Schernthaner; Christiana Kulinna-Cosentini; Nina Bastati; Judith Stift; Richard Gore; Marius E Mayerhoefer Journal: Eur J Radiol Date: 2013-07-30 Impact factor: 3.528
Authors: Siva P Raman; Pamela T Johnson; Mohamad E Allaf; George Netto; Elliot K Fishman Journal: AJR Am J Roentgenol Date: 2013-12 Impact factor: 3.959
Authors: Konstantin Dmitriev; Arie E Kaufman; Ammar A Javed; Ralph H Hruban; Elliot K Fishman; Anne Marie Lennon; Joel H Saltz Journal: Med Image Comput Comput Assist Interv Date: 2017-09-04
Authors: Yulia Lakhman; Harini Veeraraghavan; Joshua Chaim; Diana Feier; Debra A Goldman; Chaya S Moskowitz; Stephanie Nougaret; Ramon E Sosa; Hebert Alberto Vargas; Robert A Soslow; Nadeem R Abu-Rustum; Hedvig Hricak; Evis Sala Journal: Eur Radiol Date: 2016-12-05 Impact factor: 5.315
Authors: Matthew S Davenport; Hersh Chandarana; Nicole E Curci; Ankur Doshi; Samuel D Kaffenberger; Ivan Pedrosa; Erick M Remer; Nicola Schieda; Atul B Shinagare; Andrew D Smith; Zhen J Wang; Shane A Wells; Stuart G Silverman Journal: Abdom Radiol (NY) Date: 2018-09
Authors: Kathleen Nguyen; Nicola Schieda; Nick James; Matthew D F McInnes; Mark Wu; Rebecca E Thornhill Journal: Eur Radiol Date: 2020-09-10 Impact factor: 5.315