This collection contains CT scans and segmentations from subjects from the training set of the 2019 Kidney and Kidney Tumor Segmentation Challenge (KiTS19). The challenge aimed to accelerate progress in automatic 3D semantic segmentation by releasing a dataset of CT scans for 210 patients with manual semantic segmentations of the kidneys and tumors in the corticomedullary phase.

The imaging was collected during routine care of patients who were treated by either partial or radical nephrectomy at the University of Minnesota Medical Center. Many of the CT scans were acquired at referring institutions and are therefore heterogeneous in terms of scanner manufacturers and acquisition protocols. Semantic segmentations were performed by students under the supervision of an experienced urologic cancer surgeon.

Protocol

Please refer to the data descriptor manuscript for a comprehensive account of the data collection and annotation process - arXiv:1904.00445. The Clinical Trial Time Point is calculated from Day of Surgery.

Dataset

This dataset was created by user

Released under CC0: Public Domain

Contents

The 2021 Kidney and Kidney Tumor Segmentation challenge (abbreviated KiTS21) is a competition in which teams compete to develop the best system for automatic semantic segmentation of renal tumors and surrounding anatomy. The 2021 Kidney and Kidney Tumor Segmentation Challenge The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: Results of the KiTS19 Challenge

3D-Unet reference models trained on KiTS19 dataset for MLPerf Inference. See MLPerf Inference GitHub repository for instructions about how to run these models. Link: https://github.com/mlcommons/inference/tree/r1.1/medical_imaging/3d-unet-kits19

IntroductionThis study aimed to construct a radiomics-based machine learning (ML) model for differentiation between non-clear cell and clear cell renal cell carcinomas (ccRCC) that is robust against institutional imaging protocols and scanners.Materials and methodsPreoperative unenhanced (UN), corticomedullary (CM), and excretory (EX) phase CT scans from 209 patients diagnosed with RCCs were retrospectively collected. After the three-dimensional segmentation, 107 radiomics features (RFs) were extracted from the tumor volumes in each contrast phase. For the ML analysis, the cases were randomly split into training and test sets with a 3:1 ratio. Highly correlated RFs were filtered out based on Pearson’s correlation coefficient (r > 0.95). Intraclass correlation coefficient analysis was used to select RFs with excellent reproducibility (ICC ≥ 0.90). The most predictive RFs were selected by the least absolute shrinkage and selection operator (LASSO). A support vector machine algorithm-based binary classifier (SVC) was constructed to predict tumor types and its performance was evaluated based-on receiver operating characteristic curve (ROC) analysis. The “Kidney Tumor Segmentation 2019” (KiTS19) publicly available dataset was used during external validation of the model. The performance of the SVC was also compared with an expert radiologist’s.ResultsThe training set consisted of 121 ccRCCs and 38 non-ccRCCs, while the independent internal test set contained 40 ccRCCs and 13 non-ccRCCs. For external validation, 50 ccRCCs and 23 non-ccRCCs were identified from the KiTS19 dataset with the available UN, CM, and EX phase CTs. After filtering out the highly correlated and poorly reproducible features, the LASSO algorithm selected 10 CM phase RFs that were then used for model construction. During external validation, the SVC achieved an area under the ROC curve (AUC) value, accuracy, sensitivity, and specificity of 0.83, 0.78, 0.80, and 0.74, respectively. UN and/or EX phase RFs did not further increase the model’s performance. Meanwhile, in the same comparison, the expert radiologist achieved similar performance with an AUC of 0.77, an accuracy of 0.79, a sensitivity of 0.84, and a specificity of 0.69.ConclusionRadiomics analysis of CM phase CT scans combined with ML can achieve comparable performance with an expert radiologist in differentiating ccRCCs from non-ccRCCs.