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.

Magnetic resonance imaging characteristics of chronic prostatitis in patients under the age of 50: is it more than the eye can see?

BACKGROUND: The magnetic resonance (MRI) diagnosis of chronic prostatitis (CP) is insufficiently evaluated. PURPOSE: To evaluate the MRI appearance of CP in young patients by comparing it to individuals with non-prostatic related pathology. MATERIAL AND METHODS: The study included 47 patients with prostatitis-like symptoms evaluated by urologists and referred to pelvic MRI examination (mean age=40.23±7 years; age range=23-49 years) and 93 age-matched individuals with non-prostatic related pathology (mean age=37.5±7 years; age range=21-49 years). All MRI examinations were performed on a 1.5-T machine using a prostate-specific protocol for the prostatitis group and different protocols that included high-resolution small field of view T2-weighted (T2WI) and diffusion-weighted imaging (DWI), for the control group, depending on the clinical indication. RESULTS: Four different T2WI intensity patterns were observed: hyperintense homogenous; slightly to moderate homogenous hypointense; inhomogeneous; and marked hypointense. We found statistically significant differences between the two analyzed groups regarding mean ADC values (P<0.001), distribution of T2WI intensity patterns (P<0.0001), and the presence of dilated venous plexus (P=0.0007). No differences were found regarding prostate volume (P=0.15). In multivariate analysis, all four analyzed imaging parameters were independent predictors of chronic prostatitis (R2=0.67; P<0.0001). Considered together, an age >28 years, an inhomogeneous or marked hypointense T2WI intensity pattern (types 3 and 4), an ADC value ≤1250, and the presence of dilated venous plexus are able to predict CP with an AUC of 93% (sensitivity=85.1%, specificity=88.4%). CONCLUSION: MR parameters like T2WI intensity patterns, ADC values, and venous plexus appearance are promising non-invasive tools in the challenging environment of CP diagnosis.

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.

Radiomic Analysis of MRI Images is Instrumental to the Stratification of Ovarian Cysts.

The imaging diagnosis of malignant ovarian cysts relies on their morphological features, which are not always specific to malignancy. The histological analysis of these cysts shows specific fluid characteristics, which cannot be assessed by conventional imaging techniques. This study investigates whether the texture-based radiomics analysis (TA) of magnetic resonance (MRI) images of the fluid content within ovarian cysts can function as a noninvasive tool in differentiating between benign and malignant lesions. Twenty-eight patients with benign (n = 15) and malignant (n = 13) ovarian cysts who underwent MRI examinations were retrospectively included. TA of the fluid component was undertaken on an axial T2-weighted sequence. A comparison of resulted parameters between benign and malignant groups was undertaken using univariate, multivariate, multiple regression, and receiver operating characteristics analyses, with the calculation of the area under the curve (AUC). The standard deviation of pixel intensity was identified as an independent predictor of malignant cysts (AUC = 0.738; sensitivity, 61.54%; specificity, 86.67%). The prediction model was able to identify malignant lesions with 84.62% sensitivity and 80% specificity (AUC = 0.841). TA of the fluid contained within the ovarian cysts can differentiate between malignant and benign lesions and potentially act as a noninvasive tool augmenting the imaging diagnosis of ovarian cystic lesions.

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.

Impact of Strain Elastography on BI-RADS classification in small invasive lobular carcinoma.

AIMS: The purpose of this study was to determine the impact of strain elastography (SE) on the Breast Imaging Reporting Data System (BI-RADS) classification depending on invasive lobular carcinoma (ILC) lesion size. MATERIALS AND METHODS: We performed a retrospective analysis on a sample of 152 female subjects examined between January 2010 - January 2017. SE was performed on all patients and ILC was subsequently diagnosed by surgical or ultrasound-guided biopsy. BI-RADS 1, 2, 6 and Tsukuba BGR cases were omitted. BI-RADS scores were recorded before and after the use of SE. The differences between scores were compared to the ILC tumor size using nonparametric tests and logistic binary regression. We controlled for age, focality, clinical assessment, heredo-collateral antecedents, B-mode and Doppler ultrasound examination. An ROC curve was used to identify the optimal cut-off point for size in relationship to BI-RADS classificationdifference using Youden's index. RESULTS: The histological subtypes of ILC lesions (n=180) included in the sample were luminal A (70%, n=126), luminal B (27.78%, n=50), triple negative (1.67%, n=3) and HER2+ (0.56%, n=1). The BI-RADS classification was higher when SE was performed (Z=- 6.629, p<0.000). The ROC curve identified a cut-off point of 13 mm for size in relationship to BI-RADS classification difference (J=0.670, p<0.000). Small ILC tumors were 17.92% more likely to influence BI-RADS classification (p<0.000). CONCLUSIONS: SE offers enhanced BI-RADS classification in small ILC tumors (<13 mm). Sonoelastography brings added value to B-mode breast ultrasound as an adjacent to mammography in breast cancer screening.

The Added Value of Color Parameters in Analyzing Elastographic Images of Ultrasound Detected Breast Focal Lesions.

AIMS: The purpose of the study was to determine if the color quantitative analysis obtained on elastographic images of breast lesions could improve the benign-malignant differentiation, and also to identify some of the circumstances which would benefit most from such an analysis. PATIENTS AND METHODS: The study design was a longitudinal prospective one, all data being acquired between May 2007 and September 2008. The US device used: Hitachi 8500 EUB machine with elastography option. For suspicious breast lesions histopathology was obtained by means of percutaneous biopsy or post-surgery. Studied color parameters (numeric values): average color (red, green, blue), color dispersion, average intensity, average hue, hue dispersion. Calculus modality: Image Processing Version 1.3, a program developed in collaboration with the Technical University of Cluj Napoca. RESULTS: Seventy-one (71) women were selected for the study. A hundred and six circumscribed breast lesions were detected by means of ultrasound in the studied group. Five color parameters were independently associated with the histological diagnosis (AvgBlue, AvgGreen and AvgRed; DispRed and DispIntensity) with AvgBlue parameter making the most important contribution (p<0.0001); the greater the values of AvgBlue (more than 92), the higher the chances of malignancy and the greater the values of AvgGreen (more than 88), the higher the chances for a benign lesion. CONCLUSION: High numeric values for Avg Blue (more than 92) would increase the probability of malignancy and thus recommend a more aggressive diagnostic management (biopsy), while high numeric values for AvgGreen (more than 88) would reassure the examiner to proceed conservatively with short interval or routine follow-ups.

Breast Diagnosis: Concordance Analysis Between the BI-RADS Classification and Tsukuba Sonoelastography Score.

AIMS: To establish the correlations between the ultrasound (US) BI-RADS classification and Tsukuba elastography score when assessing breast lesions. To determine which type of breast lesion (BI-RADS category) would benefit most from an elastographic assessment. PATIENTS AND METHODS: The investigated sample of imaging comprised a number of 129 images belonging to 92 subjects examined with a Hitachi 8500 US device. Each lesion was assessed according to the BI-RADS and Tsukuba elastography score. Histopathology was obtained by means of percutaneous biopsy or post-surgery. Fibroadenoma-like lesions unchanged over a period of 3 years were considered benign. RESULTS: The 1, 2 and BGR Tsukuba scores mostly correlated with BI-RADS II and III lesions such as cysts, hamartomas, lipomas, hematomas, non-palpable fibroadenomas. Palpable fibroadenomas initially included in BI-RADS IVa/b category, usually received benign elasticity scores (1 or 2), the exception being represented by a minority of cases of old, fibrotic or calcified lesions (elastic score 3 or 4). Non-specific BI-RADS IVa/b lesions, such as mastopathic nodules demonstrated rather soft, elastic properties on elastogram (score 1 or 2). The 4 and 5 Ueno-Itoh scores were predominantly correlated with BI-RADS IVc and V categories represented by high risk lesions (radial scar, papillomas, atypical epithelial ductal hyperplasia) and in situ or invasive carcinomas. CONCLUSIONS: Generally the BI-RADS classification correlates well with the Tsukuba elasticity score, the main exception being represented by fibrotic, calcified lesions which falsely appear more suspicious post-elastography. BI-RADS III and IV lesions would benefit most from an elastographic assessment, a low Tsukuba score allowing a less invasive approach, while a high score imposes histopathological evaluation.