Role of ultrasound-guided vacuum-assisted breast biopsy in the management of radiologic-pathologic discordance: a retrospective single-centre study.

PURPOSE: To evaluate the efficacy of US-guided vacuum-assisted biopsy (US-VAB) in radiologic-pathologic (rad-path) discordance in women with suspicious breast lesions. METHODS: Two thousand three hundred and sixty patients with 2385 BI-RADS category 4 and 5 lesions underwent percutaneous US-guided CNB. Thirty-six lesions were classified as discordant benign and underwent second-line US-VAB. A 14-gauge needle was utilized for CNB and 10-gauge for US-VAB. Final pathology was the reference standard for women who underwent surgery, imaging follow-up in other cases. Rates of malignancy for US-VAB and subsequent surgery were evaluated. Lesions with upgrade and no upgrade to second-line VAB were compared in terms of patient's age, lesion type and characteristics, size and BI-RADS category. Positive predictive value (PPV), negative predictive value (NPV) for BI-RADS categories and diagnostic performance for second-line US-VAB were calculated. p value < 0.05 was considered statistically significant (t-test, Mann-Whitney, χ2). RESULTS: US-VAB identified 10 B2, 9 B3 and 17 B5 lesions with upgrade to malignancy of 47.2% (17/36). There were 8 invasive no special type, 7 ductal in situ, 1 invasive lobular carcinoma and 1 angiosarcoma, and their distribution among BI-RADS categories was: 2/2 in BI-RADS 5 (100%), 12/18 in BI-RADS 4C (67%) and 3/16 in BI-RADS 4B lesions (19%) (p = 0.006). Of the remaining 19 lesions, 6 underwent surgery and 2 were upgraded to ductal carcinoma in situ; 13 underwent radiological follow-up and one resulted malignant. False-negative rate for US-VAB was 15.8% (3/19) with final upgrade to malignancy of 55% (20/36). The univariate analysis revealed mass shape (p = 0.008) and BI-RADS categories (p = 0.006) to be associated with upgrade to malignancy. Sensitivity, specificity, PPV, NPV and accuracy for US-VAB were 85, 100, 100, 84 and 92%, respectively. CONCLUSIONS: US-VAB identified almost 50% of cancers missed by CNB, avoiding surgical biopsies and validating as an effective mini-invasive approach in rad-path discordance.

The gut-brain axis: Correlation of choroid plexus volume and permeability with inflammatory biomarkers in Crohn's disease.

BACKGROUND: The dysregulation of the gut-brain axis in chronic inflammatory bowel diseases can cause neuro-psychological disturbances, but the underlying mechanisms are still not fully understood. The choroid plexus (CP) maintains brain homeostasis and nourishment through the secretion and clearance of cerebrospinal fluid. Recent research has demonstrated the existence of a CP vascular barrier in mice which is modulated during intestinal inflammation. This study investigates possible correlations between CP modifications and inflammatory activity in patients with Crohn's disease (CD). METHODS: In this prospective study, 17 patients with CD underwent concomitant abdominal and brain 3 T MRI. The volume and permeability of CP were compared with levels of C-reactive protein (CRP), fecal calprotectin (FC), sMARIA and SES-CD scores. RESULTS: The CP volume was negatively correlated with CRP levels (R = -0.643, p-value = 0.024) and FC (R = -0.571, p-value = 0.050). DCE metrics normalized by CP volume were positively correlated with CRP (K-trans: R = 0.587, p-value = 0.045; Vp: R = 0.706, p-value = 0.010; T1: R = 0.699, p-value = 0.011), and FC (Vp: R = 0.606, p-value = 0.037). CONCLUSIONS: Inflammatory activity in patients with CD is associated with changes in CP volume and permeability, thus supporting the hypothesis that intestinal inflammation could affect the brain through the modulation of CP vascular barrier also in humans.

CT-based radiomics can identify physiological modifications of bone structure related to subjects' age and sex.

PURPOSE: Radiomics of vertebral bone structure is a promising technique for identification of osteoporosis. We aimed at assessing the accuracy of machine learning in identifying physiological changes related to subjects' sex and age through analysis of radiomics features from CT images of lumbar vertebrae, and define its generalizability across different scanners. MATERIALS AND METHODS: We annotated spherical volumes-of-interest (VOIs) in the center of the vertebral body for each lumbar vertebra in 233 subjects who had undergone lumbar CT for back pain on 3 different scanners, and we evaluated radiomics features from each VOI. Subjects with history of bone metabolism disorders, cancer, and vertebral fractures were excluded. We performed machine learning classification and regression models to identify subjects' sex and age respectively, and we computed a voting model which combined predictions. RESULTS: The model was trained on 173 subjects and tested on an internal validation dataset of 60. Radiomics was able to identify subjects' sex within single CT scanner (ROC AUC: up to 0.9714), with lower performance on the combined dataset of the 3 scanners (ROC AUC: 0.5545). Higher consistency among different scanners was found in identification of subjects' age (R2 0.568 on all scanners, MAD 7.232 years), with highest results on a single CT scanner (R2 0.667, MAD 3.296 years). CONCLUSION: Radiomics features are able to extract biometric data from lumbar trabecular bone, and determine bone modifications related to subjects' sex and age with great accuracy. However, acquisition from different CT scanners reduces the accuracy of the analysis.

Editorial comment: Radiogenomics of glioblastoma: shifting the focus from tumor cells to immune microenvironment.

KEY POINTS: • The ICI score derived from gene expression profile of immune cells infiltrating GBM correlates with overall survival and is an effective prognostic biomarker.• In this study, the authors developed a radiomics-based machine learning model able to identify gene expression profiles of GBM intratumoral stromal and immune cells and predict the ICI score on the preoperative MRI scans with high accuracy.• Radiogenomics could potentially be applied in primary brain tumors to noninvasively assess the specific tumor immune characteristics, predict patients' prognosis and identify those patients with higher probability to respond to immunotherapy.