Usefulness of texture features of apparent diffusion coefficient maps in predicting chemoradiotherapy response in muscle-invasive bladder cancer

Progress of Multiparameter Magnetic Resonance Imaging in Bladder Cancer: A Comprehensive Literature Review

Magnetic resonance imaging (MRI) has been proven to be an indispensable imaging method in bladder cancer, and it can accurately identify muscular invasion of bladder cancer. Multiparameter MRI is a promising tool widely used for preoperative staging evaluation of bladder cancer. Vesical Imaging-Reporting and Data System (VI-RADS) scoring has proven to be a reliable tool for local staging of bladder cancer with high accuracy in preoperative staging, but VI-RADS still faces challenges and needs further improvement. Artificial intelligence (AI) holds great promise in improving the accuracy of diagnosis and predicting the prognosis of bladder cancer. Automated machine learning techniques based on radiomics features derived from MRI have been utilized in bladder cancer diagnosis and have demonstrated promising potential for practical implementation. Future work should focus on conducting more prospective, multicenter studies to validate the additional value of quantitative studies and optimize prediction models by combining other biomarkers, such as urine and serum biomarkers. This review assesses the value of multiparameter MRI in the accurate evaluation of muscular invasion of bladder cancer, as well as the current status and progress of its application in the evaluation of efficacy and prognosis.

Multiparametric MRI in Era of Artificial Intelligence for Bladder Cancer Therapies

This review focuses on the principles, applications, and performance of mpMRI for bladder imaging. Quantitative imaging biomarkers (QIBs) derived from mpMRI are increasingly used in oncological applications, including tumor staging, prognosis, and assessment of treatment response. To standardize mpMRI acquisition and interpretation, an expert panel developed the Vesical Imaging-Reporting and Data System (VI-RADS). Many studies confirm the standardization and high degree of inter-reader agreement to discriminate muscle invasiveness in bladder cancer, supporting VI-RADS implementation in routine clinical practice. The standard MRI sequences for VI-RADS scoring are anatomical imaging, including T2w images, and physiological imaging with diffusion-weighted MRI (DW-MRI) and dynamic contrast-enhanced MRI (DCE-MRI). Physiological QIBs derived from analysis of DW- and DCE-MRI data and radiomic image features extracted from mpMRI images play an important role in bladder cancer. The current development of AI tools for analyzing mpMRI data and their potential impact on bladder imaging are surveyed. AI architectures are often implemented based on convolutional neural networks (CNNs), focusing on narrow/specific tasks. The application of AI can substantially impact bladder imaging clinical workflows; for example, manual tumor segmentation, which demands high time commitment and has inter-reader variability, can be replaced by an autosegmentation tool. The use of mpMRI and AI is projected to drive the field toward the personalized management of bladder cancer patients.

The assessment of pathological response to neoadjuvant chemotherapy in muscle-invasive bladder cancer patients with DCE-MRI and DWI: a systematic review and meta-analysis

OBJECTIVE

The purpose of this meta-analysis was to determine the value of dynamic contrast-enhanced-MRI (DCE-MRI) and diffusion-weighted imaging (DWI) in evaluating the pathological response of muscle invasive bladder cancer (MIBC) to neoadjuvant chemotherapy (NAC), and further indirectly compare the diagnostic performance of DCE-MRI and DWI.

METHODS

Literatures associated to DCE-MRI and DWI in the evaluation of pathological response of MIBC to NAC were searched from PubMed, Cochrane Library, web of science, and EMBASE databases. The quality assessment of diagnostic accuracy studies 2 tool was used to assess the quality of studies. Pooled sensitivity (SE), specificity (SP), positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and the area under the receiver operating characteristic curves (AUC) with their 95% confidence intervals (CIs) were calculated to evaluate the diagnostic performance of DCE-MRI and DWI in predicting the pathological response to NAC in patients with MIBC.

RESULTS

There were 11 studies involved, 6 of which only underwent DCE- MRI examination, 4 of which only underwent DWI examination, and 1 of which underwent both DCE- MRI and DWI examination. The pooled SE, SP, PLR, NLR, DOR of DCE-MRI were 0.88 (95% CI: 0.78-0.93), 0.88 (95% CI: 0.67-0.96), 7.4 (95% CI: 2.3-24.2), 0.14 (95% CI: 0.07-0.27), and 53 (95% CI: 10-288), respectively. The pooled SE, SP, PLR, NLR, DOR of DWI were 0.83 (95% CI: 0.75-0.88), 0.88 (95% CI: 0.81-0.93), 7.1 (95% CI: 4.3-11.7), 0.20 (95% CI: 0.14-0.28), and 36 (95% CI:18-73), respectively. The AUCs of SROC curve for DCE-MRI and DWI were 0.93 (95% CI: 0.91-0.95) and 0.92 (95% CI: 0.89-0.94), respectively. There were no significant differences between DWI and DCE-MRI for SE, SP, and AUC.

CONCLUSION

This meta-analysis demonstrated high diagnostic performance of both DCE-MRI and DWI in predicting the pathological response to NAC in MIBC. DWI might be a potential substitute for DCE-MRI, with no significant difference in diagnostic performance between the two. However, caution should be taken when applying our results, as our results were based on indirect comparison.

ADVANCES IN KNOWLEDGE

No previous studies have comprehensively analysed the value of DCE-MRI and DWI in evaluating the pathological response to NAC in MIBC. According to the current study, both DCE-MRI and DWI yielded high diagnostic performance, with the AUCs of 0.93 and 0.92, respectively. Indirect comparison no significant difference in the diagnostic performanceof DCE-MRI and DWI.

Novel utility of Vesical Imaging-Reporting and Data System in multimodal treatment for muscle-invasive bladder cancer

OBJECTIVES

To examine the clinical significance of the Vesical Imaging-Reporting and Data System (VI-RADS) in predicting outcome of multimodal treatment (MMT) in muscle-invasive bladder cancer (MIBC) patients.

METHODS

We reviewed 78 pathologically proven MIBC patients who underwent MMT including transurethral resection and chemoradiotherapy, followed by partial or radical cystectomy. Treatment response was assessed through histologic evaluation of cystectomy specimens. Two radiologists categorized the index lesions of pretherapeutic MRI according to the 5-point VI-RADS score. The associations of VI-RADS score with the therapeutic effect of MMT were analyzed. The diagnostic performance of VI-RADS scores with a cut-off VI-RADS scores  ≤ 2 or ≤ 3 for predicting pathologic complete response to MMT (MMT-CR) was evaluated.

RESULTS

MMT-CR was achieved in 2 (100%) of VI-RADS score 1 (n = 2), 16 (84%) of score 2 (n = 19), 12 (86%) of score 3 (n = 14), 7 (64%) of score 4 (n = 11), and 14 (44%) of score 5 (n = 32). VI-RADS score was inversely associated with the incidence of MMT-CR (p = 0.00049). The cut-off VI-RADS score  ≤ 2 and  ≤ 3 could predict the favorable therapeutic outcome of MMT with high specificity (0.89 with 95% confidence interval [CI]: 0.71-0.98 and 0.82 with 95% CI: 0.62-0.94, respectively) and high positive predictive value (0.86 with 95% CI: 0.64-0.97 and 0.86 with 95% CI: 0.70-0.95, respectively).

CONCLUSION

VI-RADS score may serve as an imaging marker in MIBC patients for predicting the therapeutic outcome of MMT.

CLINICAL RELEVANCE STATEMENT

Muscle-invasive bladder cancer patients with a lower Vesical Imaging-Reporting and Data System score can be a good candidate for bladder-sparing treatment incorporating multimodal treatment.

KEY POINTS

• Vesical Imaging-Reporting and Data System (VI-RADS) score was potentially valuable for classifying pathologic tumor response in patients with muscle-invasive bladder cancer. • The likelihood of achieving complete response of multimodal treatment (MMT) decreased with increasing VI-RADS score. • VI-RADS score could serve as an imaging marker that optimizes patient selection for MMT.

MRI-Based Radiomics in Bladder Cancer: A Systematic Review and Radiomics Quality Score Assessment

(1): Background: With the recent introduction of vesical imaging reporting and data system (VI-RADS), magnetic resonance imaging (MRI) has become the main imaging method used for the preoperative local staging of bladder cancer (BCa). However, the VI-RADS score is subject to interobserver variability and cannot provide information about tumor cellularity. These limitations may be overcome by using a quantitative approach, such as the new emerging domain of radiomics. (2) Aim: To systematically review published studies on the use of MRI-based radiomics in bladder cancer. (3) Materials and Methods: We performed literature research using the PubMed MEDLINE, Scopus, and Web of Science databases using PRISMA principles. A total of 1092 papers that addressed the use of radiomics for BC staging, grading, and treatment response were retrieved using the keywords "bladder cancer", "magnetic resonance imaging", "radiomics", and "textural analysis". (4) Results: 26 papers met the eligibility criteria and were included in the final review. The principal applications of radiomics were preoperative tumor staging (n = 13), preoperative prediction of tumor grade or molecular correlates (n = 9), and prediction of prognosis/response to neoadjuvant therapy (n = 4). Most of the developed radiomics models included second-order features mainly derived from filtered images. These models were validated in 16 studies. The average radiomics quality score was 11.7, ranging between 8.33% and 52.77%. (5) Conclusions: MRI-based radiomics holds promise as a quantitative imaging biomarker of BCa characterization and prognosis. However, there is still need for improving the standardization of image preprocessing, feature extraction, and external validation before applying radiomics models in the clinical setting.

Preoperative assessment of high-grade endometrial cancer using a radiomic signature and clinical indicators

Aim: To develop and validate a radiomics-based combined model (Model^RC) to predict the pathological grade of endometrial cancer. Methods: A total of 403 endometrial cancer patients from two independent centers were enrolled as training, internal validation and external validation sets. Radiomic features were extracted from T2-weighted images, apparent diffusion coefficient map and contrast-enhanced 3D volumetric interpolated breath-hold examination images. Results: Compared with the clinical model and radiomics model, Model^RC showed superior performance; the areas under the receiver operating characteristic curves were 0.920 (95% CI: 0.864-0.962), 0.882 (95% CI: 0.779-0.955) and 0.881 (95% CI: 0.815-0.939) for the training, internal validation and external validation sets, respectively. Conclusion: Model^RC, which incorporated clinical and radiomic features, exhibited excellent performance in the prediction of high-grade endometrial cancer.

Collinearity and Dimensionality Reduction in Radiomics: Effect of Preprocessing Parameters in Hypertrophic Cardiomyopathy Magnetic Resonance T1 and T2 Mapping

Radiomics and artificial intelligence have the potential to become a valuable tool in clinical applications. Frequently, radiomic analyses through machine learning methods present issues caused by high dimensionality and multicollinearity, and redundant radiomic features are usually removed based on correlation analysis. We assessed the effect of preprocessing-in terms of voxel size resampling, discretization, and filtering-on correlation-based dimensionality reduction in radiomic features from cardiac T1 and T2 maps of patients with hypertrophic cardiomyopathy. For different combinations of preprocessing parameters, we performed a dimensionality reduction of radiomic features based on either Pearson's or Spearman's correlation coefficient, followed by the computation of the stability index. With varying resampling voxel size and discretization bin width, for both T1 and T2 maps, Pearson's and Spearman's dimensionality reduction produced a slightly different percentage of remaining radiomic features, with a relatively high stability index. For different filters, the remaining features' stability was instead relatively low. Overall, the percentage of eliminated radiomic features through correlation-based dimensionality reduction was more dependent on resampling voxel size and discretization bin width for textural features than for shape or first-order features. Notably, correlation-based dimensionality reduction was less sensitive to preprocessing when considering radiomic features from T2 compared with T1 maps.

Correlation between ADC Histogram-Derived Metrics and the Time to Metastases in Resectable Pancreatic Adenocarcinoma

Background: A non-invasive method to improve the prognostic stratification would be clinically beneficial in patients with resectable pancreatic adenocarcinoma (PDAC). The aim of this study was to correlate conventional magnetic resonance (MR) features and the metrics derived from the histogram analysis of apparent diffusion coefficient (ADC) maps, with the risk and the time to metastases (TTM) after surgery in patients with PDAC. Methods: pre-operative MR examinations of 120 patients were retrospectively analyzed. Patients were grouped according to the presence (M+) or absence (M−) of metastases during follow-up. Conventional MR features and histogram-derived metrics were compared between M+ and M− patients using the Fisher’s or Mann−Whitney tests; receiver operating characteristic (ROC) curves were constructed for the features that showed a significant difference between groups. A Cox regression analysis was performed to identify the features with a significant effect on the TTM, and Kaplan−Meier curves were constructed for significant features. Results: 68.3% patients developed metastases over a mean follow-up time of 29 months (range, 3−54 months). ADC skewness and kurtosis were significantly higher in M+ than in M− patients (p < 0.001). Skewness had a significant effect on the risk of metastases (hazard ratio—HR = 5.22, p < 0.001). Patients with an ADC skewness ≥0.23 had a significantly shorter TTM than those with a skewness <0.22 (11.7 vs. 30.8 months, p < 0.001). Conclusions: pre-operative histogram analysis of ADC maps provides parameters correlated to the metastatic potential of PDAC. Higher ADC skewness seems to be associated with a significantly shorter TTM in patients with resectable PDAC.

Advances in Diagnosis and Therapy for Bladder Cancer

Simple Summary

The clinical management of bladder cancer has been developing in the past decade, including diagnostic tools and treatment options. Both monotherapy and combination therapy have been undoubtedly upgraded. Multiple diagnostic techniques and therapeutic strategies have been developed to meet the urgent clinical needs, resulting in the emergence of various explorations for cancer diagnosis and therapy. In this review, we mainly focus on the advances in the diagnosis and treatment of bladder cancer.

Abstract

Bladder cancer (BCa) is one of the most common and expensive urinary system malignancies for its high recurrence and progression rate. In recent years, immense amounts of studies have been carried out to bring a more comprehensive cognition and numerous promising clinic approaches for BCa therapy. The development of innovative enhanced cystoscopy techniques (optical techniques, imaging systems) and tumor biomarkers-based non-invasive urine screening (DNA methylation-based urine test) would dramatically improve the accuracy of tumor detection, reducing the risk of recurrence and progression of BCa. Moreover, intravesical instillation and systemic therapeutic strategies (cocktail therapy, immunotherapy, vaccine therapy, targeted therapy) also provide plentiful measures to break the predicament of BCa. Several exploratory clinical studies, including novel surgical approaches, pharmaceutical compositions, and bladder preservation techniques, emerged continually, which are supposed to be promising candidates for BCa clinical treatment. Here, recent advances and prospects of diagnosis, intravesical or systemic treatment, and novel drug delivery systems for BCa therapy are reviewed in this paper.

Quantification of Coronary Artery Atherosclerotic Burden and Muscle Mass: Exploratory Comparison of Two Freely Available Software Programs

Coronary artery calcification and sarcopenia may have a relevant prognostic impact in oncological and non-oncological patients. The use of freeware software is promising for quantitative evaluation of these parameters after whole-body positron emission tomography (PET)/computed tomography (CT) and might be useful for one-stop shop risk stratification without additional radiation ionizing burden and further charges to health care costs. In this study, we compared two semiautomatic freeware software tools (Horos Medical Image software and LIFEx) for the assessment of coronary artery calcium (CAC) score and muscle mass in 40 patients undergoing whole-body PET/CT. The muscle areas obtained by the two software programs were comparable, showing high correlation with Lin’s concordance coefficient (0.9997; 95% confidence intervals: 0.9995–0.9999) and very good agreement with Bland–Altman analysis (mean difference = 0.41 cm2, lower limit = −1.06 cm2, upper limit = 1.89) was also found. For CAC score, Lin’s concordance correlation coefficient was 0.9976 (95% confidence intervals: 0.9965–0.9984) and in a Bland–Altman analysis an increasing mean difference from 8 to 78 by the mean values (intercept = −0.050; slope = 0.054; p < 0.001) was observed, with a slight overestimation of Horos CAC score as compared to LIFEx, likely due to a different calculation method of the CAC score, with the ROI being equal for the two software programs. Our results demonstrated that off-line analysis performed with freeware software may allow a comprehensive evaluation of the oncological patient, making available the evaluation of parameters, such as muscle mass and calcium score, that may be relevant for the staging and prognostic stratification of these patients, beside standard data obtained by PET/CT imaging. For this purpose, the Horos and LIFEx software seem to be interchangeable.

Apparent Diffusion Coefficient Map-Based Texture Analysis for the Differentiation of Chromophobe Renal Cell Carcinoma from Renal Oncocytoma

Preoperative imaging differentiation between ChRCC and RO is difficult with conventional subjective evaluation, and the development of quantitative analysis is a clinical challenge. Forty-nine patients underwent partial or radical nephrectomy preceded by MRI and followed by pathological diagnosis with ChRCC or RO (ChRCC: n = 41, RO: n = 8). The whole-lesion volume of interest was set on apparent diffusion coefficient (ADC) maps of 1.5T-MRI. The importance of selected texture features (TFs) was evaluated, and diagnostic models were created using random forest (RF) analysis. The Mean Decrease Gini as calculated through RF analysis was the highest for mean_ADC_value. ChRCC had a significantly lower mean_ADC_value than RO (1.26 vs. 1.79 × 10−3 mm2/s, p < 0.0001). Feature selection by the Boruta method identified the first-quartile ADC value and GLZLM_HGZE as important features. ROC curve analysis showed that there was no significant difference in the classification performances between the mean_ADC_value-only model and the Boruta model (AUC: 0.954 vs. 0.969, p = 0.236). The mean ADC value had good predictive ability for the distinction between ChRCC and RO, comparable to that of the combination of TFs optimized for the evaluated cohort. The mean ADC value may be useful in distinguishing between ChRCC and RO.

Breast Cancer Classification on Multiparametric MRI - Increased Performance of Boosting Ensemble Methods

Introduction: This study aims to assess the utility of Boosting ensemble classification methods for increasing the diagnostic performance of multiparametric Magnetic Resonance Imaging (mpMRI) radiomic models, in differentiating benign and malignant breast lesions. Methods: The dataset includes mpMR images of 140 female patients with mass-like breast lesions (70 benign and 70 malignant), consisting of Dynamic Contrast Enhanced (DCE) and T2-weighted sequences, and the Apparent Diffusion Coefficient (ADC) calculated from the Diffusion Weighted Imaging (DWI) sequence. Tumor masks were manually defined in all consecutive slices of the respective MRI volumes and 3D radiomic features were extracted with the Pyradiomics package. Feature dimensionality reduction was based on statistical tests and the Boruta wrapper. Hierarchical Clustering on Spearman's rank correlation coefficients between features and Random Forest classification for obtaining feature importance, were implemented for selecting the final feature subset. Adaptive Boosting (AdaBoost), Gradient Boosting (GB), Extreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM) classifiers, were trained and tested with bootstrap validation in differentiating breast lesions. A Support Vector Machine (SVM) classifier was also exploited for comparison. The Receiver Operator Characteristic (ROC) curves and DeLong's test were utilized to evaluate the classification performances. Results: The final feature subset consisted of 5 features derived from the lesion shape and the first order histogram of DCE and ADC images volumes. XGboost and LGBM achieved statistically significantly higher average classification performances [AUC = 0.95 and 0.94 respectively], followed by Adaboost [AUC = 0.90], GB [AUC = 0.89] and SVM [AUC = 0.88]. Conclusion: Overall, the integration of Ensemble Learning methods within mpMRI radiomic analysis can improve the performance of computer-assisted diagnosis of breast cancer lesions.

Application of CT-Based Radiomics in Discriminating Pancreatic Cystadenomas From Pancreatic Neuroendocrine Tumors Using Machine Learning Methods

Objectives

The purpose of this study aimed at investigating the reliability of radiomics features extracted from contrast-enhanced CT in differentiating pancreatic cystadenomas from pancreatic neuroendocrine tumors (PNETs) using machine-learning methods.

Methods

In this study, a total number of 120 patients, including 66 pancreatic cystadenomas patients and 54 PNETs patients were enrolled. Forty-eight radiomic features were extracted from contrast-enhanced CT images using LIFEx software. Five feature selection methods were adopted to determine the appropriate features for classifiers. Then, nine machine learning classifiers were employed to build predictive models. The performance of the forty-five models was evaluated with area under the curve (AUC), accuracy, sensitivity, specificity, and F1 score in the testing group.

Results

The predictive models exhibited reliable ability of differentiating pancreatic cystadenomas from PNETs when combined with suitable selection methods. A combination of DC as the selection method and RF as the classifier, as well as Xgboost+RF, demonstrated the best discriminative ability, with the highest AUC of 0.997 in the testing group.

Conclusions

Radiomics-based machine learning methods might be a noninvasive tool to assist in differentiating pancreatic cystadenomas and PNETs.