Pd08-04 virtual renal mass biopsy: predicting renal tumor histology on abdominal ct images using machine learning

You have accessJournal of UrologyCME1 Apr 2023PD08-04 VIRTUAL RENAL MASS BIOPSY: PREDICTING RENAL TUMOR HISTOLOGY ON ABDOMINAL CT IMAGES USING MACHINE LEARNING Abhinav Khanna, Vidit Sharma, Adriana Gregory, H. Chase Gottlich, Cole J. Cook, Jason Klug, Christine Lohse, Theodora Potretzke, Aaron Potretzke, Stephen A. Boorjian, R. Houston Thompson, Naoki Takahashi, Bradley Erickson, John Cheville, Timothy Kline, and Bradley Leibovich Abhinav KhannaAbhinav Khanna More articles by this author , Vidit SharmaVidit Sharma More articles by this author , Adriana GregoryAdriana Gregory More articles by this author , H. Chase GottlichH. Chase Gottlich More articles by this author , Cole J. CookCole J. Cook More articles by this author , Jason KlugJason Klug More articles by this author , Christine LohseChristine Lohse More articles by this author , Theodora PotretzkeTheodora Potretzke More articles by this author , Aaron PotretzkeAaron Potretzke More articles by this author , Stephen A. BoorjianStephen A. Boorjian More articles by this author , R. Houston ThompsonR. Houston Thompson More articles by this author , Naoki TakahashiNaoki Takahashi More articles by this author , Bradley EricksonBradley Erickson More articles by this author , John ChevilleJohn Cheville More articles by this author , Timothy KlineTimothy Kline More articles by this author , and Bradley LeibovichBradley Leibovich More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003239.04AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Up to 20% of renal tumors are benign and may not require treatment. However, benign versus malignant renal tumors cannot be distinguished using cross-sectional imaging. Renal mass biopsy is a possible solution, but biopsy is invasive and has notable non-diagnostic and false negative rates. As a result, many patients proceed directly to treatment, including some who undergo extirpative surgery for benign tumors. We aim to develop a radiomics and machine learning model for distinguishing between oncocytoma versus malignant renal neoplasms based on abdominal CT images. METHODS: Our institutional registry was queried for patients who underwent surgical treatment of renal tumors from 2000-2018. All surgical specimens underwent pathology re-review by an expert genitourinary pathologist. A total of 843 images from 609 patients (434 images of oncocytoma and 409 images of malignant renal tumor) were included. A previously developed artificial intelligence algorithm for segmentation of kidney, cyst, and tumor area was applied. Images were preprocessed by resampling via linear interpolation to 0.8 mm x 0.8 mm x 5 mm, window/level=440/40, and intensity normalized [0 255]. Features were extracted for the tumor region using PyRadiomics with a fixed bin width of 16. Both unsupervised (PCA, tSNE) and supervised (LR, SVM) machine learning approaches were explored. Data was split 80:20, and cross validation was used in the training/validation set parameters. The best performing model based on F1 macro was then calibrated using an isotonic calibration approach. RESULTS: Dimensionality reduction showed adequate class separation (Figure 1A). The top 10 radiomic features showed a mixture of first and second order features (Figure 1B). The final model reached an accuracy of 0.90 (Figure 1C). Of the 168 images in the testing set, only 2 benign cases were predicted to be malignant, and 15 malignant cases were predicted to be benign. The AUC for oncocytoma versus malignant histology prediction was 0.96 with CI [0.93 0.98] (Figure 1D). CONCLUSIONS: We developed a machine learning model for accurately distinguishing benign oncocytoma versus malignant renal tumors based on CT images alone. This may provide an opportunity for non-invasive risk stratification of solid renal neoplasms, which could potentially reduce overtreatment of renal tumors. Source of Funding: None © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e233 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Abhinav Khanna More articles by this author Vidit Sharma More articles by this author Adriana Gregory More articles by this author H. Chase Gottlich More articles by this author Cole J. Cook More articles by this author Jason Klug More articles by this author Christine Lohse More articles by this author Theodora Potretzke More articles by this author Aaron Potretzke More articles by this author Stephen A. Boorjian More articles by this author R. Houston Thompson More articles by this author Naoki Takahashi More articles by this author Bradley Erickson More articles by this author John Cheville More articles by this author Timothy Kline More articles by this author Bradley Leibovich More articles by this author Expand All Advertisement PDF downloadLoading ...