Comparison of CT Texture Analysis Software Platforms in Renal Cell Carcinoma: Reproducibility of Numerical Values and Association With Histologic Subtype Across Platforms

Pancreatic Cystic Lesions: Next Generation of Radiologic Assessment

Pancreatic cystic lesions are frequently identified on cross-sectional imaging. As many of these are presumed branch-duct intraductal papillary mucinous neoplasms, these lesions generate much anxiety for the patients and clinicians, often necessitating long-term follow-up imaging and even unnecessary surgical resections. However, the incidence of pancreatic cancer is overall low for patients with incidental pancreatic cystic lesions. Radiomics and deep learning are advanced tools of imaging analysis that have attracted much attention in addressing this unmet need, however, current publications on this topic show limited success and large-scale research is needed.

Pilot Study for the Assessment of the Best Radiomic Features for Bosniak Cyst Classification Using Phantom and Radiologist Inter-Observer Selection

Since the Bosniak cysts classification is highly reader-dependent, automated tools based on radiomics could help in the diagnosis of the lesion. This study is an initial step in the search for radiomic features that may be good classifiers of benign-malignant Bosniak cysts in machine learning models. A CCR phantom was used through five CT scanners. Registration was performed with ARIA software, while Quibim Precision was used for feature extraction. R software was used for the statistical analysis. Robust radiomic features based on repeatability and reproducibility criteria were chosen. Excellent correlation criteria between different radiologists during lesion segmentation were imposed. With the selected features, their classification ability in benignity-malignity terms was assessed. From the phantom study, 25.3% of the features were robust. For the study of inter-observer correlation (ICC) in the segmentation of cystic masses, 82 subjects were prospectively selected, finding 48.4% of the features as excellent regarding concordance. Comparing both datasets, 12 features were established as repeatable, reproducible, and useful for the classification of Bosniak cysts and could serve as initial candidates for the elaboration of a classification model. With those features, the Linear Discriminant Analysis model classified the Bosniak cysts in terms of benignity or malignancy with 88.2% accuracy.

Differentiation of Clear Cell and Non-clear-cell Renal Cell Carcinoma through CT-based Radiomics Models and Nomogram

PURPOSE

The aim of the study was to investigate the feasibility of discriminating between clear-cell renal cell carcinoma (ccRCC) and non-clear-cell renal cell carcinoma (non-ccRCC) via radiomics models and nomogram.

METHODS

The retrospective study included 147 patients (ccRCC=100, non-ccRCC=47) who underwent enhanced CT before surgery. CT images of the corticomedullary phase (CMP) were collected and features from the images were extracted. The data were randomly grouped into training and validation sets according to 7:3, and then the training set was normalized to extract the normalization rule for the training set, and then the rule was applied to the validation set. First, the T-test, T'-test or Wilcoxon rank-sum test were executed in the training set data to keep the statistically different parameters, and then the optimal features were picked based on the least absolute shrinkage and selection operator (LASSO) algorithm. Five machine learning (ML) models were trained to differentiate ccRCC from noccRCC, rad+cli nomogram was constructed based on clinical factors and radscore (radiomics score), and the performance of the classifier was mainly measured by area under the curve (AUC), accuracy, sensitivity, specificity, and F1. Finally, the ROC curves and radar plots were plotted according to the five performance parameters.

RESULTS

1130 radiomics features were extracted, there were 736 radiomics features with statistical differences were obtained, and 4 features were finally selected after the LASSO algorithm. In the validation set of this study, three of the five ML models (logistic regression, random forest and support vector machine) had excellent performance (AUC 0.9-1.0) and two models (adaptive boosting and decision tree) had good performance (AUC 0.7-0.9), all with accuracy ≥ 0.800. The rad+cli nomogram performance was found excellent in both the training set (AUC = 0.982,0.963-1.000, accuracy=0.941) and the validation set (AUC = 0.949,0.885-1.000, accuracy=0.911). The random forest model with perfect performance (AUC = 1, accuracy=1) was found superior compared to the model performance in the training set. The rad+cli nomogram model prevailed in the comparison of the model's performance in the validation set.

CONCLUSION

The ML models and nomogram can be used to identify the relatively common pathological subtypes in clinic and provide some reference for clinicians.