Clinical value of texture analysis in differentiation of urothelial carcinoma based on multiphase computed tomography images

Feasibility analysis of arterial CT radiomics model to predict the risk of local and metastatic recurrence after radical cystectomy for bladder cancer

PURPOSE

To construct a radiomics-clinical nomogram model for predicting the risk of local and metastatic recurrence within 3 years after radical cystectomy (RC) of bladder cancer (BCa) based on the radiomics features and important clinical risk factors for arterial computed tomography (CT) images and to evaluate its efficacy.

METHODS

Preoperative CT datasets of 134 BCa patients (24 recurrent) who underwent RC were collected and divided into training (n = 93) and validation sets (n = 41). Radiomics features were extracted from a 1.5 mm CT layer thickness image in the arterial phase. A radiomics score (Rad-Score) model was constructed using the feature dimension reduction method and a logistic regression model. Combined with important clinical factors, including gender, age, tumor size, tumor number and grade, pathologic T stage, lymph node stage and histology type of the archived lesion, and CT image signs, a radiomics-clinical nomogram was developed, and its performance was evaluated in the training and validation sets. Decision curve analyses (DCA) the potential clinical usefulness.

RESULTS

The radiomics model is finally linear combined by 8 features screened by LASSO regression, and after coefficient weighting, achieved good predictive results. The radiomics nomogram developed by combining two independent predictors, Rad-Score and pathologic T stage, was developed in the training set [AUC, 0.840; 95% confidence interval (CI) 0.743-0.937] and validation set (AUC, 0.883; 95% CI 0.777-0.989). The calibration curve showed good agreement between the predicted probability of the radiomics-clinical model and the actual recurrence rate within 3 years after RC for BCa. DCA show the clinical application value of the radiomics-clinical model.

CONCLUSION

The radiomics-clinical nomogram model constructed based on the radiomics features of arterial CT images and important clinical risk factors is potentially feasible for predicting the risk of recurrence within 3 years after RC for BCa.

Can Only the Shape Feature in Radiomics Help Machine Learning Show That Bladder Cancer Has Invaded Muscles?

OBJECTIVES

The presence of muscle invasion is an important factor in establishing a treatment strategy for bladder cancer (BCa). The aim of this study is to reveal the diagnostic performance of radiomic shape features in predicting muscle-invasive BCa.

METHODS

In this study, 60 patients with histologically proven BCa who underwent a preoperative MRI were retrospectively recruited. The whole tumor volume was segmented on apparent diffusion coefficient (ADC) maps and T2W images. Afterward, the shape features of the volume of interest were extracted using PyRadiomics. Machine learning classification was performed using statistically different shape features in MATLAB® (The MathWorks, Inc., Natick, Massachusetts, United States).

RESULTS

The findings revealed that 27 bladder cancer patients had muscle invasion, while 33 had superficial bladder cancer (53 men and seven women; mean age: 62±14). Surface area, volume, and relevant features were significantly greater in the invasive group than in the non-invasive group based on the ADC maps (P<0.05). Superficial bladder cancer had a more spherical form compared to invasive bladder cancer (P=0.05) with both imaging modalities. Flatness and elongation did not differ significantly between groups with either modality (P>0.05). Logistic regression had the highest accuracy of 83.3% (sensitivity 82.8%, specificity 84%) in assessing invasion based on the shape features of ADC maps, while K-nearest neighbors had the highest accuracy of 78.2% (sensitivity 79.1%, specificity 69.4%) in assessing invasion based on T2W images.

CONCLUSIONS

Shape features can be helpful in predicting muscle invasion in bladder cancer using machine learning methods.

Computed Tomography Urography: State of the Art and Beyond

Computed Tomography Urography (CTU) is a multiphase CT examination optimized for imaging kidneys, ureters, and bladder, complemented by post-contrast excretory phase imaging. Different protocols are available for contrast administration and image acquisition and timing, with different strengths and limits, mainly related to kidney enhancement, ureters distension and opacification, and radiation exposure. The availability of new reconstruction algorithms, such as iterative and deep-learning-based reconstruction has dramatically improved the image quality and reducing radiation exposure at the same time. Dual-Energy Computed Tomography also has an important role in this type of examination, with the possibility of renal stone characterization, the availability of synthetic unenhanced phases to reduce radiation dose, and the availability of iodine maps for a better interpretation of renal masses. We also describe the new artificial intelligence applications for CTU, focusing on radiomics to predict tumor grading and patients' outcome for a personalized therapeutic approach. In this narrative review, we provide a comprehensive overview of CTU from the traditional to the newest acquisition techniques and reconstruction algorithms, and the possibility of advanced imaging interpretation to provide an up-to-date guide for radiologists who want to better comprehend this technique.

Bladder Urothelial Carcinoma: Machine Learning-based Computed Tomography Radiomics for Prediction of Histological Variant

RATIONALE AND OBJECTIVES

Histological variant (HV) of bladder urothelial carcinoma (UC) is a significant factor for therapy management. We aim to assess the predictive performance of machine learning (ML)-based Computed Tomography radiomics of UC for HV.

MATERIALS AND METHODS

Volume of interest of 37 bladder UC tumors, of which 21 were pure and 16 were HV, were manually segmented. The extracted first- and second-order texture features (n = 117) using 3-D Slicer radiomics were compared to the radical cystectomy histopathological results. ML algorithms were performed to determine the significant models using Python 2.3, Pycaret library. The sample size was increased to 74 by synthetic data generation, and three outliers from the training set were removed (training dataset; n = 52, test dataset; n = 19). The predictive performances of 15 ML algorithms were compared. Then, the best two models were evaluated on the test set and ensembled by Voting Classifier.

RESULTS

The ML algorithms demonstrated area under curve (AUC) and accuracy ranging 0.79-0.97 and 50%-90%, respectively on the train set. The best models were Gradient Boosting Classifier (AUC: 0.95, accuracy: 90%) and CatBoost Classifier (AUC: 0.97, accuracy: 85%). On the test set; the Voting Classifier of these two models demonstrated AUC, accuracy, recall, precision, and F1 scores as follows; 0.93, 79%, 86%, 67%, and 75%, respectively.

CONCLUSION

ML-based Computed Tomography radiomics of UC can predict HV, a prognostic factor that is indeterminable by qualitative radiological evaluation and can be missed in the preoperative histopathological specimens.

Utility of first order MRI-Texture analysis parameters in the prediction of histologic grade and muscle invasion in urinary bladder cancer: a preliminary study

OBJECTIVE

To explore the utility of first-order MRI-texture analysis (TA) parameters in predicting histologic grade and muscle invasion in urinary bladder cancer (UBC).

METHODS

After ethical clearance, 40 patients with UBC, who were imaged on a 3.0-Tesla scanner, were retrospectively included. Using the TexRAD^TM platform, two readers placed freehand ROI on the sections demonstrating the largest dimension of the tumor, evaluating only one tumor per patient. Interobserver reproducibility was assessed using the intraclass correlation coefficient (ICC). Mann-Whitney U test and ROC curve analysis were used to identify statistical significance and select parameters with high class separation capacity (AUC >0.8), respectively. Pearson's test was used to identify redundancy in the results.

RESULTS

All texture parameters showed excellent ICC. The best parameters in differentiating high and low-grade tumors were mean/ mean of positive pixels (MPP) at SSF 0 (AUC: 0.897) and kurtosis at SSF 5 (AUC: 0.828) on the ADC images. In differentiating muscle invasive from non-muscle invasive tumors, mean/ MPP at SSF 0 on the ADC images showed AUC >0.8; however, this finding resulted from the confounding effect of high-grade histology on the ADC values of muscle invasive tumors.

CONCLUSION

MRI-TA generated few parameters which were reproducible and useful in predicting histologic grade. No independent parameters predicted muscle invasion.

ADVANCES IN KNOWLEDGE

There is lacuna in the literature concerning the role of MRI-TA in the prediction of histologic grade and muscle invasion in UBC. Our study generated a few first-order parameters which were useful in predicting high-grade histology.