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.

A systematic review of the clinical value and applications of three-dimensional virtual reconstructions in renal tumors.

Three-dimensional (3D) virtual reconstruction (VR) in the medical sciences has emerged as a novel, exciting and effective tool, with promising results for patients, trainees, and even experienced surgeons. The purpose of this review is to summarize the information on the clinical value and applications of 3D VR in renal tumors published in the last ten years. A literary search of PubMed-MEDLINE databases was performed to identify studies reporting the clinical application and usefulness of 3D VR models in renal tumors. Thirty-seven studies were found to meet the selection criteria and were included in the analysis. Most studies have provided a quantitative assessment focused on the accuracy of 3D VR models in replication of anatomy and renal tumor, on measuring 3D tumor volume and on the clinical value and utility of 3D VR in pre-surgical planning and simulation of renal procedures, with significant reductions of intraoperative complications. Fourteen studies provided a qualitative assessment of the usefulness of 3D VR models, with results showing an improved patient understanding of renal anatomy and pathology, improved undergraduate and postgraduate urology education. Moreover, 3D printing technology is a novel technique, and we are currently in the dynamic era, expanding into new surgical nephron-sparing procedures and the development of printed kidneys for transplantation.

The Utility of ADC First-Order Histogram Features for the Prediction of Metachronous Metastases in Rectal Cancer: A Preliminary Study.

This study aims the ability of first-order histogram-based features, derived from ADC maps, to predict the occurrence of metachronous metastases (MM) in rectal cancer. A total of 52 patients with pathologically confirmed rectal adenocarcinoma were retrospectively enrolled and divided into two groups: patients who developed metachronous metastases (n = 15) and patients without metachronous metastases (n = 37). We extracted 17 first-order (FO) histogram-based features from the pretreatment ADC maps. Student's t-test and Mann-Whitney U test were used for the association between each FO feature and presence of MM. Statistically significant features were combined into a model, using the binary regression logistic method. The receiver operating curve analysis was used to determine the diagnostic performance of the individual parameters and combined model. There were significant differences in ADC 90th percentile, interquartile range, entropy, uniformity, variance, mean absolute deviation, and robust mean absolute deviation in patients with MM, as compared to those without MM (p values between 0.002-0.01). The best diagnostic was achieved by the 90th percentile and uniformity, yielding an AUC of 0.74 [95% CI: 0.60-0.8]). The combined model reached an AUC of 0.8 [95% CI: 0.66-0.90]. Our observations point out that ADC first-order features may be useful for predicting metachronous metastases in rectal cancer.

Preoperative Predicting the WHO/ISUP Nuclear Grade of Clear Cell Renal Cell Carcinoma by Computed Tomography-Based Radiomics Features.

Nuclear grade is important for treatment selection and prognosis in patients with clear cell renal cell carcinoma (ccRCC). This study aimed to determine the ability of preoperative four-phase multiphasic multidetector computed tomography (MDCT)-based radiomics features to predict the WHO/ISUP nuclear grade. In all 102 patients with histologically confirmed ccRCC, the training set (n = 62) and validation set (n = 40) were randomly assigned. In both datasets, patients were categorized according to the WHO/ISUP grading system into low-grade ccRCC (grades 1 and 2) and high-grade ccRCC (grades 3 and 4). The feature selection process consisted of three steps, including least absolute shrinkage and selection operator (LASSO) regression analysis, and the radiomics scores were developed using 48 radiomics features (10 in the unenhanced phase, 17 in the corticomedullary (CM) phase, 14 in the nephrographic (NP) phase, and 7 in the excretory phase). The radiomics score (Rad-Score) derived from the CM phase achieved the best predictive ability, with a sensitivity, specificity, and an area under the curve (AUC) of 90.91%, 95.00%, and 0.97 in the training set. In the validation set, the Rad-Score derived from the NP phase achieved the best predictive ability, with a sensitivity, specificity, and an AUC of 72.73%, 85.30%, and 0.84. We constructed a complex model, adding the radiomics score for each of the phases to the clinicoradiological characteristics, and found significantly better performance in the discrimination of the nuclear grades of ccRCCs in all MDCT phases. The highest AUC of 0.99 (95% CI, 0.92-1.00, p < 0.0001) was demonstrated for the CM phase. Our results showed that the MDCT radiomics features may play a role as potential imaging biomarkers to preoperatively predict the WHO/ISUP grade of ccRCCs.

Differentiation of Clear Cell Renal Cell Carcinoma from other Renal Cell Carcinoma Subtypes and Benign Oncocytoma Using Quantitative MDCT Enhancement Parameters.

Background and objectives: The use of non-invasive techniques to predict the histological type of renal masses can avoid a renal mass biopsy, thus being of great clinical interest. The aim of our study was to assess if quantitative multiphasic multidetector computed tomography (MDCT) enhancement patterns of renal masses (malignant and benign) may be useful to enable lesion differentiation by their enhancement characteristics. Materials and Methods: A total of 154 renal tumors were retrospectively analyzed with a four-phase MDCT protocol. We studied attenuation values using the values within the most avidly enhancing portion of the tumor (2D analysis) and within the whole tumor volume (3D analysis). A region of interest (ROI) was also placed in the adjacent uninvolved renal cortex to calculate the relative tumor enhancement ratio. Results: Significant differences were noted in enhancement and de-enhancement (diminution of attenuation measurements between the postcontrast phases) values by histology. The highest areas under the receiver operating characteristic curves (AUCs) of 0.976 (95% CI: 0.924-0.995) and 0.827 (95% CI: 0.752-0.887), respectively, were demonstrated between clear cell renal cell carcinoma (ccRCC) and papillary RCC (pRCC)/oncocytoma. The 3D analysis allowed the differentiation of ccRCC from chromophobe RCC (chrRCC) with a AUC of 0.643 (95% CI: 0.555-0.724). Wash-out values proved useful only for discrimination between ccRCC and oncocytoma (43.34 vs 64.10, p < 0.001). However, the relative tumor enhancement ratio (corticomedullary (CM) and nephrographic phases) proved useful for discrimination between ccRCC, pRCC, and chrRCC, with the values from the CM phase having higher AUCs of 0.973 (95% CI: 0.929-0.993) and 0.799 (95% CI: 0.721-0.864), respectively. Conclusions: Our observations point out that imaging features may contribute to providing prognostic information helpful in the management strategy of renal masses.

Pre-Treatment T2-WI Based Radiomics Features for Prediction of Locally Advanced Rectal Cancer Non-Response to Neoadjuvant Chemoradiotherapy: A Preliminary Study.

Locally advanced rectal cancer (LARC) response to neoadjuvant chemoradiotherapy (nCRT) is very heterogeneous and up to 30% of patients are considered non-responders, presenting no tumor regression after nCRT. This study aimed to determine the ability of pre-treatment T2-weighted based radiomics features to predict LARC non-responders. A total of 67 LARC patients who underwent a pre-treatment MRI followed by nCRT and total mesorectal excision were assigned into training (n = 44) and validation (n = 23) groups. In both datasets, the patients were categorized according to the Ryan tumor regression grade (TRG) system into non-responders (TRG = 3) and responders (TRG 1 and 2). We extracted 960 radiomic features/patient from pre-treatment T2-weighted images. After a three-step feature selection process, including LASSO regression analysis, we built a radiomics score with seven radiomics features. This score was significantly higher among non-responders in both training and validation sets (p < 0.001 and p = 0.03) and it showed good predictive performance for LARC non-response, achieving an area under the curve (AUC) = 0.94 (95% CI: 0.82-0.99) in the training set and AUC = 0.80 (95% CI: 0.58-0.94) in the validation group. The multivariate analysis identified the radiomics score as an independent predictor for the tumor non-response (OR = 6.52, 95% CI: 1.87-22.72). Our results indicate that MRI radiomics features could be considered as potential imaging biomarkers for early prediction of LARC non-response to neoadjuvant treatment.

Association between cardiovascular risk factors and coronary artery disease assessed using CAD-RADS classification: a cross-sectional study in Romanian population.

OBJECTIVES: This study aimed to evaluate the association between cardiovascular risk factors and Coronary Artery Disease-Reporting and Data System (CAD-RADS) score in the Romanian population. CAD-RADS is a new, standardised method to assess coronary artery disease (CAD) using coronary CT angiography (CCTA). DESIGN: A cross-sectional observational, patient-based study. SETTING: Referred imaging centre for CAD in Transylvania, Romania. PARTICIPANTS: We retrospectively reviewed 674 patients who underwent CCTA between January 2017 and August 2018. The exclusion criteria included: previously known CAD, defined as prior myocardial infarction, percutaneous coronary intervention or coronary artery bypass graft surgery (n=91), cardiac CT for other than evaluation of possible CAD (n=85), significant arrhythmias compromising imaging quality (n=23). Finally, 475 patients fulfilled the inclusion criteria. METHODS: Demographical, clinical and CCTA characteristics of the patients were obtained. CAD was evaluated using CAD-RADS score. Obstructive CAD was defined as ≥50% stenosis of ≥1 coronary segment on CCTA. RESULTS: We evaluated the association between risk factors and CAD-RADS score in univariate and multivariable analysis. We divided the patients into two groups according to the CAD-RADS system: group 1: CAD-RADS score between 0 and 2 (stenosis <50%) and group 2: CAD-RADS score ≥3 (stenosis ≥50%). On univariate analysis, male gender, age, hypertension, dyslipidaemia, smoking and diabetes mellitus were positively associated with a CAD-RADS score ≥3. The multivariate analysis showed that male sex, age, dyslipidaemia, hypertension and smoking were independently associated with obstructive CAD. CONCLUSION: This study demonstrated a significant association between multiple cardiovascular risk factors and a higher coronary atherosclerotic burden assessed using CAD-RADS system in the Romanian population.