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.

Contrast-enhanced Mammography versus Contrast-enhanced Breast MRI: A Systematic Review and Meta-Analysis.

Background Contrast-enhanced mammography (CEM) is a more accessible alternative to contrast-enhanced MRI (CE-MRI) in breast imaging, but a summary comparison of published studies is lacking. Purpose To directly compare the performance of CEM and CE-MRI regarding sensitivity, specificity, and negative predictive value in detecting breast cancer, involving all publicly available studies in the English language. Materials and Methods Two readers extracted characteristics of studies investigating the comparative diagnostic performance of CEM and CE-MRI in detecting breast cancer. Studies published until April 2021 were eligible. Sensitivity, specificity, negative predictive value, and positive and negative likelihood ratios were calculated using bivariate random effects models. A Fagan nomogram was used to identify the maximum pretest probability at which posttest probabilities of a negative CEM or CE-MRI examination were in line with the 2% malignancy rate benchmark for downgrading a Breast Imaging Reporting and Data System (BI-RADS) category 4 to a BI-RADS category 3 result. I 2 statistics, Deeks funnel plot asymmetry test for publication bias, and meta-regression were used. Results Seven studies investigating 1137 lesions (654 malignant, 483 benign) with an average cancer prevalence of 65.3% (range: 47.3%-82.2%) were included. No publication bias was found (P = .57). While the positive likelihood ratio was equal at a value of 3.1 for CE-MRI and 3.6 for CEM, the negative likelihood ratio of CE-MRI (0.04) was lower than that with CEM (0.12). CE-MRI had higher sensitivity for breast cancer than CEM (97% [95% CI: 86, 99] vs 91% [95% CI: 77, 97], respectively; P < .001) but lower specificity (69% [95% CI: 46, 85] vs 74% [95% CI: 52, 89]; P = .09). A Fagan nomogram demonstrated that the maximum pretest probability at which both tests could rule out breast cancer was 33% for CE-MRI and 14% for CEM. Furthermore, iodine concentration was positively associated with CEM sensitivity and negatively associated with its specificity (P = .04 and P < .001, respectively). Conclusion Contrast-enhanced MRI had superior sensitivity and negative likelihood ratios with higher pretest probabilities to rule out malignancy compared with contrast-enhanced mammography. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Mann and Veldhuis in this issue.

Contrast-Enhanced Mammography Versus MRI in the Evaluation of Neoadjuvant Therapy Response in Patients With Breast Cancer: A Prospective Study.

BACKGROUND. Contrast-enhanced mammography (CEM) is rapidly expanding as a credible alternative to MRI in various clinical settings. OBJECTIVE. The purpose of this study was to compare CEM and MRI for neoadjuvant therapy (NAT) response assessment in patients with breast cancer. METHODS. This prospective study included 51 patients (mean age, 46 ± 11 [SD] years) with biopsy-proven breast cancer who were candidates for NAT from May 2015 to April 2018. Patients underwent both CEM and MRI before, during, and after NAT (pre-NAT, mid-NAT, and post-NAT, respectively). Post-NAT CEM included a 6-minute delayed acquisition. One breast radiologist with experience in CEM reviewed CEM examinations; one breast radiologist with experience in MRI reviewed MRI examinations. The radiologists assessed for the presence of an enhancing lesion; if an enhancing lesion was detected, its size was measured. RECIST version 1.1 response assessment categories were derived. Pathologic complete response (pCR) was defined as absence of both invasive cancer and ductal carcinoma in situ (DCIS). RESULTS. Of 51 patients, 16 achieved pCR. CEM yielded systematically lower size measurements compared with MRI (mean difference, -0.2 mm for pre-NAT, -0.7 mm for mid-NAT, and -0.3 mm for post-NAT). All post-NAT imaging tests yielded systematically larger size measurements compared with pathology (mean difference, 0.8 mm for CEM, 1.2 mm for MRI, and 1.9 mm for delayed CEM). Of 12 patients with residual DCIS, an enhancing lesion was detected in seven on post-NAT CEM, eight on post-NAT MRI, and nine on post-NAT delayed CEM. Agreement of RECIST response categories between CEM and MRI, expressed as kappa coefficient, was 0.791 at mid-NAT and 0.871 at post-NAT. For detecting pCR by post-NAT imaging, sensitivity and specificity were 81% and 83% for CEM, 100% and 86% for MRI, and 81% and 89% for delayed CEM. Sensitivity was significantly higher for MRI than CEM (p = .001) and delayed CEM (p = .002); remaining comparisons were not significant (p > .05). CONCLUSION. After NAT for breast cancer, CEM and MRI yielded comparable assessments of lesion size (both slightly overestimated vs pathology) and RECIST categories and showed no significant difference in specificity for pCR. MRI had higher sensitivity for pCR. Delayed CEM acquisition may help detect residual DCIS. CLINICAL IMPACT. Although MRI remains the preferred test for NAT response monitoring, the findings support CEM as a useful alternative when MRI is contraindicated or not tolerated.

An approach to evaluate the quality of radiological reports in Head and Neck cancer loco-regional staging: experience of two Academic Hospitals.

OBJECTIVES: To evaluate the quality of the reports of loco-regional staging computed tomography (CT) or magnetic resonance imaging (MRI) in head and neck (H&N) cancer. METHODS: Consecutive reports of staging CT and MRI of all H&N cancer cases from 2018 to 2020 were collected. We created lists of quality indicators for tumor (T) for each district and for node (N). We marked these as 0 or 1 in the report calculating a report score (RS) and a maximum sum (MS) of each list. Two radiologists and two otolaryngologists in consensus classified reports as low quality (LQ) if the RS fell in the percentage range 0-59% of MS and as high quality (HQ) if it fell in the range 60-100%, annotating technique and district. We evaluated the distribution of reports in these categories. RESULTS: Two hundred thirty-seven reports (97 CT and 140 MRI) of 95 oral cavity, 52 laryngeal, 47 oropharyngeal, 19 hypo-pharyngeal, 14 parotid, and 10 nasopharyngeal cancers were included. Sixty-six percent of all the reports were LQ for T, 66% out of all the MRI reports, and 65% out of all CT reports were LQ. Eight-five percent of reports were HQ for N, 85% out of all the MRI reports, and 82% out of all CT reports were HQ. Reports of oral cavity, oro-nasopharynx, and parotid were LQ, respectively, in 76%, 73%, 100% and 92 out of cases. CONCLUSION: Reports of staging CT/MRI in H&N cancer were LQ for T description and HQ for N description.

Chest CT in patients with a moderate or high pretest probability of COVID-19 and negative swab.

OBJECTIVES: We aimed to assess the diagnostic performance of CT in patients with a negative first RT-PCR testing and to identify typical features of COVID-19 pneumonia that can guide diagnosis in this case. METHODS: Patients suspected of COVID-19 with a negative first RT-PCR testing were retrospectively revalued after undergoing CT. CT was reviewed by two radiologists and classified as suspected COVID-19 pneumonia, non-COVID-19 pneumonia or negative. The performance of both first RT-PCR result and CT was evaluated by using sensitivity (SE), specificity (SP), positive predictive value (PPV), negative predictive value (NPV) and area under the curve (AUC) and by using the second RT-PCR test as the reference standard. CT findings for confirmed COVID-19 positive or negative were compared by using the Pearson chi-squared test (P values < 0.05) RESULTS: Totally, 337 patients suspected of COVID-19 underwent CT and nasopharyngeal swabs in March 2020. Eighty-seven out of 337 patients had a negative first RT-PCR result; of these, 68 repeated RT-PCR testing and were included in the study. The first RT-PCR test showed SE 0, SP = 100%, PPV = NaN, NPV = 70%, AUC = 50%, and CT showed SE = 70% SP = 79%, PPV = 86%, NPV = 76%, AUC = 75%. The most relevant CT variables were ground glass opacity more than 50% and peripheral and/or perihilar distribution. DISCUSSION: Negative RT-PCR test but positive CT features should be highly suggestive of COVID-19 in a cluster or community transmission scenarios, and the second RT-PCR test should be promptly requested to confirm the final diagnosis.

Radiomics-based machine learning for the diagnosis of lymph node metastases in patients with head and neck cancer: Systematic review.

Machine learning (ML) is increasingly used to detect lymph node (LN) metastases in head and neck (H&N) carcinoma. We systematically reviewed the literature on radiomic-based ML for the detection of pathological LNs in H&N cancer. A systematic review was conducted in PubMed, EMBASE, and the Cochrane Library. Baseline study characteristics and methodological quality items (modeling, performance evaluation, clinical utility, and transparency items) were extracted and evaluated. The qualitative synthesis is presented using descriptive statistics. Seven studies were included in this study. Overall, the methodological quality items were generally favorable for modeling (57% of studies). The studies were mostly unsuccessful in terms of transparency (85.7%), evaluation of clinical utility (71.3%), and assessment of generalizability employing independent or external validation (72.5%). ML may be able to predict LN metastases in H&N cancer. Further studies are warranted to improve the generalizability assessment, clinical utility evaluation, and transparency items.

Pathologic response and residual tumor cellularity after neo-adjuvant chemotherapy predict prognosis in breast cancer patients.

INTRODUCTION: Residual tumor cellularity (RTC) and pathologic complete response (pCR) after neo-adjuvant chemotherapy (NAC) are prognostic factors associated with improved outcomes in breast cancer (BC). However, the majority of patients achieve partial pathologic response (pPR) and no clear correlation between RTC patterns and outcomes was described. Our aims were to define predictive factors for pCR and compare different outcomes of patients with pCR or pPR and with different RTC patterns. MATERIALS AND METHODS: Baseline and post-NAC demographics, clinicopathological characteristics, post-operative data, survival and recurrence status were recorded from our institutional database. A multivariable analysis was performed using a logistic regression model to identify independent predictors of pCR. Disease-free survival (DFS), distant disease-free survival (DDFS), and overall survival (OS) analyses were performed using the Kaplan-Meier method. RESULTS: Overall, of the 495 patients analyzed, 148 (29.9%) achieved pCR, 347 (70.1%) had pPR, and the median RTC was 40%. Multivariable analysis identified 3 independent factors predictive of pCR: tumor stage before NAC (cT1-2 84.5% versus cT3-4 15.5%), BC sub-type (HER2-positive 54.7% versus triple-negative 29.8% versus luminal-like 15.5%), and vascular invasion (absence 98.0% versus presence 2.0%). We found statistically significant longer DFS, DDFS, and OS in patients with pCR and with RTC <40%; no difference was observed in terms of OS between RTC <40% and RTC ≥40% groups. CONCLUSIONS: Tumor stage before NAC, BC sub-type, and vascular invasion are significant and independent factors associated with pCR. Patients with pCR and with RTC <40% have longer DFS, DDFS, and OS compared with patients with pPR.

Bronchoalveolar lavage in suspected COVID-19 cases with a negative nasopharyngeal swab: a retrospective cross-sectional study in a high-impact Northern Italy area.

COVID-19 diagnosis relies on molecular testing for SARS-CoV-2 via nasopharyngeal swab in the presence of suggestive clinical, radiological and laboratory findings. Since bronchoalveolar lavage liquid (BAL) collected during fibrobronchoscopy may increase test sensitivity compared to nasopharyngeal swabs, it was performed during the 2020 pandemic in clinically or radiologically suspected cases. Our aim was to determine whether clinical features, chest computed tomography (CT) findings or laboratory tests may predict patients testing positive for SARS-CoV-2 at BAL after a negative nasopharyngeal swab. We performed a retrospective cross-sectional study with multivariable analysis of suspected patients who were tested for SARS-CoV-2 at BAL after at least one negative nasopharyngeal swab. Univariable logistic regression for odds ratio and multivariate models was calculated to determine clinical, radiological and laboratory predictors. 32/198 (16%) patients had BAL positive for SARS-CoV-2, while 65/198 tested positive for other pathogens at BAL. Of the 32 patients positive for COVID, 4 had a coinfection at BAL, being thus positive both for COVID as well as for another pathogen while the remaining 105 patients were negative for COVID and other pathogens at BAL. COVID-19 patients had more often highly suggestive CT findings, higher number of involved lobes, more often ground glass opacity of more than 50% of lung parenchyma, and less frequently other radiologically suspected infections. At multivariate model, temperature also predicted BAL positivity. The procedure was well tolerated-with only one desaturation episode-while no healthcare worker was infected. In conclusion, when nasopharyngeal swabs are negative but there is clinical or imaging suspicion of COVID-19, BAL represents a complementary diagnostic tool, particularly in conjunction with suggestive/more extensive lung involvement at CT scan. The procedure did not carry increased risks for patients nor for operators, while allowing to free hospital resources, avoiding unnecessary isolations.

Volume-of-Interest Aware Deep Neural Networks for Rapid Chest CT-Based COVID-19 Patient Risk Assessment.

Since December 2019, the world has been devastated by the Coronavirus Disease 2019 (COVID-19) pandemic. Emergency Departments have been experiencing situations of urgency where clinical experts, without long experience and mature means in the fight against COVID-19, have to rapidly decide the most proper patient treatment. In this context, we introduce an artificially intelligent tool for effective and efficient Computed Tomography (CT)-based risk assessment to improve treatment and patient care. In this paper, we introduce a data-driven approach built on top of volume-of-interest aware deep neural networks for automatic COVID-19 patient risk assessment (discharged, hospitalized, intensive care unit) based on lung infection quantization through segmentation and, subsequently, CT classification. We tackle the high and varying dimensionality of the CT input by detecting and analyzing only a sub-volume of the CT, the Volume-of-Interest (VoI). Differently from recent strategies that consider infected CT slices without requiring any spatial coherency between them, or use the whole lung volume by applying abrupt and lossy volume down-sampling, we assess only the "most infected volume" composed of slices at its original spatial resolution. To achieve the above, we create, present and publish a new labeled and annotated CT dataset with 626 CT samples from COVID-19 patients. The comparison against such strategies proves the effectiveness of our VoI-based approach. We achieve remarkable performance on patient risk assessment evaluated on balanced data by reaching 88.88%, 89.77%, 94.73% and 88.88% accuracy, sensitivity, specificity and F1-score, respectively.