Breast Implant Imaging Surveillance Practice: Survey of Breast Imaging Radiologists in the Society of Breast Imaging.

OBJECTIVE: The objectives of this Society of Breast Imaging (SBI)-member survey study were to assess the current imaging patterns for evaluation of symptomatic and asymptomatic breast implant integrity, including modalities used and imaging intervals. METHODS: A 12-question survey assessing the frequency of imaging modalities used to evaluate implant integrity, approximate number of breast implant integrity studies requested per month, intervals of integrity studies, and referring provider and radiology practice characteristics was distributed to members of the SBI. RESULTS: The survey response rate was 7.6% (143/1890). Of responding radiologists, 54.2% (77/142) were in private, 29.6% (42/142) in academic, and 16.2% (23/142) in hybrid practice. Among respondents, the most common initial examination for evaluating implant integrity was MRI without contrast at 53.1% (76/143), followed by handheld US at 46.9% (67/143). Of respondents using US, 67.4% (91/135) also evaluated the breast tissue for abnormalities. Among respondents, 34.1% (46/135) reported being very confident or confident in US for diagnosing implant rupture. There was a range of reported intervals for performing implant integrity studies: 39.1% (43/110) every 2-3 years, 26.4% (29/110) every 4-5 years, 15.5% (17/110) every 6-10 years, and 19.1% (21/110) every 10 years. CONCLUSION: For assessment of implant integrity, the majority of respondents (53.2%, 76/143) reported MRI as initial imaging test. US is less costly, but the minority of respondents (34.1%, 46/135) had confidence in US performance. Also, the minority of respondents (39.1%, 43/110) performed implant integrity evaluations every 2-3 years per the FDA recommendations for asymptomatic surveillance.

Profiles, diagnostic process, and patterns of care of patients with stage III non-small cell lung cancer: A French national study.

BACKGROUND: The management of stage III non-small-cell lung cancer (NSCLC) remains heterogeneous and complex, even after the approval of immune checkpoint inhibitors post-chemoradiotherapy (CRT). This observational study from France evaluated real-world practices in managing stage III NSCLC. METHODS: Between 2020 and 2022, we conducted a physician practice survey in 41 medical centers across France, and retrospectively analyzed aggregated information from 417 consecutive charts of patients with stage III NSCLC. We collected information on diagnostic and staging procedures, biomarker testing, surgical and non-surgical treatments, and follow-up. RESULTS: According to the physician survey, diagnostic workup of stage III NSCLC primarily relied on positron emission tomography/computed tomography and brain magnetic resonance imaging, performed for the majority of patients in 100 % and 78 % of centers, respectively. Of 417 patient charts, 414 were evaluable with 53 % of patients having stage IIIA disease, 37 % IIIB, and 10 % IIIC. The most common node involvement was N2 (59 %). Programmed death-ligand 1 testing was conducted for 98 % of patients. Invasive staging (mediastinoscopy or endobronchial ultrasound) was performed in 41 % of patients, of whom 83 % had N2 or N3 nodal involvement. Surgical resection was offered to 120 patients (29 %), with 85 % achieving R0 resection. In 292 charts of patients with unresectable stage III NSCLC, 190 patients (65 %) were offered CRT followed by consolidation immunotherapy. Within these patients, concurrent CRT was more frequently employed (52 %) than sequential CRT (13 %). CONCLUSIONS: Diagnostic procedures and treatment modalities in French medical centers generally align with clinical guidelines for stage III NSCLC, except for invasive staging that was less commonly performed than expected.

Patterns and Predictors of Referral for Screening Breast MRI: A Mixed-Methods Study.

Introduction: Women with ≥20% lifetime breast cancer risk can receive supplemental breast cancer screening with MRI. We examined factors associated with recommendation for screening breast MRI among primary care providers (PCPs), gynecologists (GYNs), and radiologists. Methods: We conducted a sequential mixed-methods study. Quantitative: Participants (N = 72) reported recommendations for mammogram and breast MRI via clinical vignettes describing hypothetical patients with moderate, high, and very high breast cancer risk. Logistic regressions assessed the relationships of clinician-level factors (gender, specialty, years practicing) and practice-level factors (practice type, imaging facilities available) with screening recommendations. Qualitative: We interviewed a subset of survey participants (n = 17, 17/72 = 24%) regarding their decision-making about breast cancer screening recommendations. Interviews were audio-recorded, transcribed, and analyzed with directed content analysis. Results: Compared with PCPs, GYNs and radiologists were significantly more likely to recommend breast MRI for high-risk (ORs = 4.09 and 4.09, respectively) and very-high-risk patients (ORs = 8.56 and 18.33, respectively). Qualitative analysis identified two key phases along the clinical pathway for high-risk women. Phase 1 was "identifying high-risk women," which included three subthemes (systems for risk assessment, barriers to risk assessment, scope of practice issues). Phase 2 was "referral for screening," which included three subthemes (conflicting guidelines, scope of practice issues, legal implications). Frequency of themes differed between specialties, potentially explaining findings from the quantitative phase. Conclusions: There are significant differences between specialties in supplemental breast cancer screening recommendations. Multilevel interventions are needed to support identification and management of women with high breast cancer risk, particularly for PCPs.

Use of MRI in patients with severe diffuse traumatic brain injury: A matched National Trauma Data Bank analysis.

OBJECTIVE: Magnetic resonance imaging (MRI) is increasingly used to evaluate patients with diffuse traumatic brain injury (dTBI). However, the utility of early MRI is understudied. We hypothesize that early MRI patients will have increased length of stay but no changes in intracranial pressure (ICP) management or disposition. METHODS: The 2019 National Trauma Data Bank was queried for patients with dTBI and Glasgow Coma Scale score ≤8. Extra-axial and focal intra-axial hemorrhages were excluded. Clinical characteristics were controlled for. Patients with and without MRI were compared for ICP management, outcome, mortality, and disposition. A propensity score matching algorithm was used to create a 1:1 match cohort. RESULTS: In 2568 patients, MRI was less common in severe dTBI patients with clear reasons for poor examination, including bilaterally unreactive pupils or midline shift. After matching, 501 patients who underwent MRI within 1 week were compared with 501 patients without MRI. Magnetic resonance imaging patients had longer intensive care unit stays (11.6 ± 9.6 vs. 13.4 ± 9.5, p < 0.01; 95% confidence interval [95% CI], -3.03 to -0.66). There was no difference between groups in ICP monitor (23.6% vs. 27.3%; p = 0.17; 95% CI, -0.09 to 0.02) or ventriculostomy placement (13.6% vs. 13.2%, p = 0.85; 95% CI, -0.04 to 0.05) or in withdrawal of care (15.0% vs. 18.6%, p = 0.12; 95% CI, -0.08 to 0.01). MRI patients were more likely to be discharged to inpatient rehabilitation (42.9% vs. 33.5%; p < 0.01; 95% CI, 0.03-0.15) but not to home (9.4% vs. 9.0%; p = 0.83; 95% CI, -0.03 to 0.04). CONCLUSION: The decision to pursue early brain MRI may be driven by lack of obvious reasons for a patient's poor neurologic status. MRI patients had longer intensive care unit stays but no difference in rates of placement of ICP monitors or ventriculostomies or withdrawal of care. Further study is required to define the role of early MRI in dTBI patients. LEVEL OF EVIDENCE: Prognostic and Epidemiological; Level IV.

Brain Decoding Using fMRI Images for Multiple Subjects through Deep Learning.

Substantial information related to human cerebral conditions can be decoded through various noninvasive evaluating techniques like fMRI. Exploration of the neuronal activity of the human brain can divulge the thoughts of a person like what the subject is perceiving, thinking, or visualizing. Furthermore, deep learning techniques can be used to decode the multifaceted patterns of the brain in response to external stimuli. Existing techniques are capable of exploring and classifying the thoughts of the human subject acquired by the fMRI imaging data. fMRI images are the volumetric imaging scans which are highly dimensional as well as require a lot of time for training when fed as an input in the deep learning network. However, the hassle for more efficient learning of highly dimensional high-level features in less training time and accurate interpretation of the brain voxels with less misclassification error is needed. In this research, we propose an improved CNN technique where features will be functionally aligned. The optimal features will be selected after dimensionality reduction. The highly dimensional feature vector will be transformed into low dimensional space for dimensionality reduction through autoadjusted weights and combination of best activation functions. Furthermore, we solve the problem of increased training time by using Swish activation function, making it denser and increasing efficiency of the model in less training time. Finally, the experimental results are evaluated and compared with other classifiers which demonstrated the supremacy of the proposed model in terms of accuracy.

Prognosis Evaluation of MRI Combined with Magnetic Resonance Myelography on Lumbar Disc Herniation after Transforaminal Endoscopic Discectomy.

OBJECTIVE: This study sets out to investigate the role of magnetic resonance imaging (MRI) combined with magnetic resonance myelography (MRM) in patients after percutaneous transforaminal endoscopic discectomy (PTED) and to evaluate its value in postoperative rehabilitation. METHODS: The clinical date of 96 patients with lumbar disc herniation (LDH) after PTED was retrospectively analyzed. The enrolled patients were divided into MRI group (n = 32) and MRI + MRM group (n = 64) according to whether MRM was performed. The nerve root sleeve (morphology, deformation) and dural indentation, intervertebral space height (ISH), intervertebral space angle (ISA), degree of pain (Visual Analogue Scale (VAS)), vertebral function (Japanese Orthopaedic Association (JOA)), and long-term recurrence were compared between the two groups. RESULTS: Compared with the MRI group, the MRI + MRM group better displayed nerve root morphology, sheath sleeve deformation, and dural indentation. Both MRI and MRI + MRM showed ISH and ISA changes well. Compared with the MRI group, the MRI + MRM group had a significantly lower VAS score for lumbar and leg pain, a significantly higher JOA score, and a significantly lower 2-year recurrence rate. CONCLUSION: MRM combined with MRI is more beneficial to improve the prognosis of LDH patients after PTED.

Machine Learning-Based MRI LAVA Dynamic Enhanced Scanning for the Diagnosis of Hilar Lesions.

OBJECTIVE: To explore the value of machine learning-based magnetic resonance imaging (MRI) liver acceleration volume acquisition (LAVA) dynamic enhanced scanning for diagnosing hilar lesions. METHODS: A total of 90 patients with hilar lesions and 130 patients without hilar lesions who underwent multiphase dynamic enhanced MRI LAVA were retrospectively selected as the study subjects. The 10-fold crossover method was used to establish the data set, 7/10 (154 cases) data were used to establish the training set, and 3/10 (66 cases) data were used to establish the validation set to verify the model. The region of interest was extracted from MRI images using radiomics, and the hilar lesion model was constructed based on a convolutional neural network. RESULTS: There were significant differences in respiration and pulse frequency between patients with hilar lesions and without hilar lesions (P <0.05). The subjective scores of the images in the first three phases of dynamic enhanced scanning in the training set were higher than those in the validation set (P < 0.05). There was no significant difference between the training and validation set in the last three phases of dynamic enhanced scanning. CONCLUSION: Machine learn-based MRI LAVA dynamic enhanced scanning for diagnosing hilar lesions has high diagnostic efficiency and can be used as an auxiliary diagnostic method.

Edge Constraint and Location Mapping for Liver Tumor Segmentation from Nonenhanced Images.

As there is no contrast enhancement, the liver tumor area in nonenhanced MRI exists with blurred edges and low contrast, which greatly affects the speed and accuracy of liver tumor diagnosis. As a result, precise segmentation of liver tumor from nonenhanced MRI has become an urgent and challenging task. In this paper, we propose an edge constraint and localization mapping segmentation model (ECLMS) to accurately segment liver tumor from nonenhanced MRI. It consists of two parts: localization network and dual-branch segmentation network. We build the localization network, which generates prior coarse masks to provide position mapping for the segmentation network. This part enhances the ability of the model to localize liver tumor in nonenhanced images. We design a dual-branch segmentation network, where the main decoding branch focuses on the feature representation in the core region of the tumor and the edge decoding branch concentrates on capturing the edge information of the tumor. To improve the ability of the model for capturing detailed features, sSE blocks and dense upward connections are introduced into it. We design the bottleneck multiscale module to construct multiscale feature representations using kernels of different sizes while integrating the location mapping of tumor. The ECLMS model is evaluated on a private nonenhanced MRI dataset that comprises 215 different subjects. The model achieves the best Dice coefficient, precision, and accuracy of 90.23%, 92.25%, and 92.39%, correspondingly. The effectiveness of our model is demonstrated by experiment results, and our model reaches superior results in the segmentation task of nonenhanced liver tumor compared to existing segmentation methods.

Establishment and Verification of Logistic Regression Model for Qualitative Diagnosis of Ovarian Cancer Based on MRI and Ultrasound Signs.

OBJECTIVE: To explore the establishment and verification of logistic regression model for qualitative diagnosis of ovarian cancer based on MRI and ultrasonic signs. METHOD: 207 patients with ovarian tumors in our hospital from April 2018 to April 2021 were selected, of which 138 were used as the training group for model creation and 69 as the validation group for model evaluation. The differences of MRI and ultrasound signs in patients with ovarian cancer and benign ovarian tumor in the training group were analyzed. The risk factors were screened by multifactor unconditional logistic regression analysis, and the regression equation was established. The self-verification was carried out by subject working characteristics (ROC), and the external verification was carried out by K-fold cross verification. RESULT: There was no significant difference in age, body mass index, menstruation, dysmenorrhea, times of pregnancy, cumulative menstrual years, and marital status between the two groups (P > 0.05). After logistic regression analysis, the diagnostic model of ovarian cancer was established: logit (P) = -1.153 + [MRI signs : morphology × 1.459 + boundary × 1.549 + reinforcement × 1.492 + tumor components × 1.553] + [ultrasonic signs : morphology × 1.594 + mainly real × 1.417 + separated form × 1.294 + large nipple × 1.271 + blood supply × 1.364]; self-verification: AUC of the model is 0.883, diagnostic sensitivity is 93.94%, and specificity is 80.95%; K-fold cross validation: the training accuracy was 0.904 ± 0.009 and the prediction accuracy was 0.881 ± 0.049. CONCLUSION: Irregular shape, unclear boundary, obvious enhancement in MRI signs, cystic or solid tumor components and irregular shape, solid-dominated shape, thick septate shape, large nipple, and abundant blood supply in ultrasound signs are independent risk factors for ovarian cancer. After verification, the diagnostic model has good accuracy and stability, which provides basis for clinical decision-making.

Interpretable Model Based on Pyramid Scene Parsing Features for Brain Tumor MRI Image Segmentation.

Due to the black box model nature of convolutional neural networks, computer-aided diagnosis methods based on depth learning are usually poorly interpretable. Therefore, the diagnosis results obtained by these unexplained methods are difficult to gain the trust of patients and doctors, which limits their application in the medical field. To solve this problem, an interpretable depth learning image segmentation framework is proposed in this paper for processing brain tumor magnetic resonance images. A gradient-based class activation mapping method is introduced into the segmentation model based on pyramid structure to visually explain it. The pyramid structure constructs global context information with features after multiple pooling layers to improve image segmentation performance. Therefore, class activation mapping is used to visualize the features concerned by each layer of pyramid structure and realize the interpretation of PSPNet. After training and testing the model on the public dataset BraTS2018, several sets of visualization results were obtained. By analyzing these visualization results, the effectiveness of pyramid structure in brain tumor segmentation task is proved, and some improvements are made to the structure of pyramid model based on the shortcomings of the model shown in the visualization results. In summary, the interpretable brain tumor image segmentation method proposed in this paper can well explain the role of pyramid structure in brain tumor image segmentation, which provides a certain idea for the application of interpretable method in brain tumor segmentation and has certain practical value for the evaluation and optimization of brain tumor segmentation model.

Artificial Intelligence Algorithm-Based MRI in Evaluating the Treatment Effect of Acute Cerebral Infarction.

The study is aimed at exploring the application of artificial intelligence algorithm-based magnetic resonance imaging (MRI) in the diagnosis of acute cerebral infarction, expected to provide a reference for diagnosis and effect evaluation of acute cerebral infarction. In this study, 80 patients diagnosed with suspected acute cerebral infarction per Diagnostic Criteria for Cerebral Infarction were selected as the research subjects. MRI images were reconstructed by deep dictionary learning to improve their recognition ability. At the same time, the same diagnostic operation was performed by Computed Tomography (CT) images to compare with MRI. The results of the interalgorithm comparison showed the image reconstruction effect of the deep dictionary learning model is significantly better than SAE reconstruction, single-layer dictionary reconstruction model, and KAVD reconstruction. After comparison, the results of MRI based on artificial intelligence algorithm and CT evaluation were statistically significant (P < 0.05). In the lesion image, the diameter of MRI lesions (3.81 ± 0.77 cm) based on artificial intelligence algorithm and the diameter of lesions in CT (3.66 ± 1.65 cm) also had significant statistical significance (P < 0.05). The results showed that MRI based on deep learning was more sensitive than CT imaging for diagnosis and evaluation of patients with acute cerebral infarction, with only 1 case misdiagnosed. The rate of disease detection and lesion image quality had a higher improvement. The results can provide effective support for the clinical application of MRI based on artificial intelligence algorithm in the diagnosis of acute cerebral infarction.

Functional Magnetic Resonance Imaging Study of Electroacupuncture Stimulating Uterine Acupoints.

OBJECTIVE: Based on resting-state functional magnetic resonance imaging (rs-fMRI), to observe the changes of brain function of bilateral uterine points stimulated by electroacupuncture, so as to provide imaging basis for acupuncture in the treatment of gynecological and reproductive diseases. METHODS: 20 healthy female subjects were selected to stimulate bilateral uterine points (EX-CA1) by electroacupuncture. FMRI data before and after acupuncture were collected. The ReHo values before and after acupuncture were compared by using the analysis method of regional homogeneity (ReHo) of the whole brain, so as to explore the regulatory effect of acupuncture intervention on brain functional activities of healthy subjects. RESULTS: Compared with before acupuncture, the ReHo values of the left precuneus lobe, left central posterior gyrus, calcarine, left lingual gyrus, and cerebellum decreased significantly after acupuncture. CONCLUSION: Electroacupuncture at bilateral uterine points can induce functional activities in brain areas such as the precuneus, cerebellum, posterior central gyrus, talform sulcus, and lingual gyrus. The neural activities in these brain areas may be related to reproductive hormone level, emotional changes, somatic sensation, and visual information. It can clarify the neural mechanism of acupuncture at uterine points in the treatment of reproductive and gynecological diseases to a certain extent.

Intelligent Segmentation Medical Assistance System for MRI Images of Osteosarcoma in Developing Countries.

Osteosarcoma is the most common primary malignant bone tumor in children and adolescents. It has a high degree of malignancy and a poor prognosis in developing countries. The doctor manually explained that magnetic resonance imaging (MRI) suffers from subjectivity and fatigue limitations. In addition, the structure, shape, and position of osteosarcoma are complicated, and there is a lot of noise in MRI images. Directly inputting the original data set into the automatic segmentation system will bring noise and cause the model's segmentation accuracy to decrease. Therefore, this paper proposes an osteosarcoma MRI image segmentation system based on a deep convolution neural network, which solves the overfitting problem caused by noisy data and improves the generalization performance of the model. Firstly, we use Mean Teacher to optimize the data set. The noise data is put into the second round of training of the model to improve the robustness of the model. Then, we segment the image using a deep separable U-shaped network (SepUNet) and conditional random field (CRF). SepUnet can segment lesion regions of different sizes at multiple scales; CRF further optimizes the boundary. Finally, this article calculates the area of the tumor area, which provides a more intuitive reference for assisting doctors in diagnosis. More than 80000 MRI images of osteosarcoma from three hospitals in China were tested. The results show that the proposed method guarantees the balance of speed, accuracy, and cost under the premise of improving accuracy.

Agreement between real-time elastography and delayed enhancement magnetic resonance enterography on quantifying bowel wall fibrosis in Crohn's disease.

BACKGROUND: the assessment of fibrosis in Crohn's disease (CD) bowel lesions helps to guide therapeutic decisions. Real-time elastography (RTE) and delayed-enhancement magnetic resonance enterography (DE-MRE) have demonstrated good accuracy in quantifying CD-related ileal fibrosis as compared with histological examination. To date no study has compared DE-MRE and RTE. AIMS: we aimed to evaluate the agreement between RTE and DE-MRE on quantifying CD-related ileal fibrosis. METHODS: consecutive patients with ileal or ileocolonic CD underwent RTE and DE-MRE. Ileal fibrosis was quantified by calculating the strain ratio (SR) at RTE and the 70s-7 min percentage of enhancement gain (%EG) of both mucosa and submucosa at DE-MRE. A SR ≥2 was applied to define severe fibrosis. Clinically relevant outcomes occurring at follow-up were recorded. RESULTS: 40 CD patients were enrolled. A significant linear correlation was observed between SR and submucosal %EG (r = 0.594, p < 0.001). Patients with severe fibrosis (SR ≥2) had significantly higher submucosal %EG values than patients with low/moderate fibrosis (median values 26.4% vs. 9.5%, p < 0.001). During a median 43.8-month follow-up relevant disease outcomes occurred more frequently in the severe-fibrosis group (75% vs. 36%, HR 5.4, 95% CI 1.2-24.6, p = 0.029). CONCLUSIONS: the study demonstrates an excellent agreement between RTE and DE-MRE in assessing ileal fibrosis in CD.

Imaging and pathologic features of non-calcified ductal carcinoma in situ: can sonography predict upgrade?

OBJECTIVE: The purpose of this study was to evaluate the imaging and pathologic features and upgrade rate of non-calcified ductal carcinoma in situ (NCDCIS). The study tested the hypothesis that lesions with sonographic findings have higher upgrade rate compared to lesions seen on mammography or MRI only. METHODS: This retrospective study included patients with ductal carcinoma in situ (DCIS) diagnosed by image-guided core breast biopsy from December 2009 to April 2018. Patients with microcalcifications on mammography or concurrent ipsilateral cancer on core biopsy were excluded. An upgrade was defined as surgical pathology showing microinvasive or invasive cancer. RESULTS: A total of 71 lesions constituted the study cohort. 62% of cases (44/71) had a mammographic finding, and 38% (27/71) of mammographically occult lesions had findings on either ultrasound, MRI, or both. Of the 67 cases that underwent sonography, a mass was noted in 56/67 (83.6%) cases and no sonographic correlate was identified in 11/67 (16.4%) cases. 21% (15/71) of lesions were upgraded on final surgical pathology. The upgrade rate of patients with sonographic correlate was 27% (15/56) vs with mammographic findings only was 0% (0/11). CONCLUSION: DCIS should be considered in the differential diagnosis of architectural distortion, asymmetries, focal asymmetries, and masses, even in the absence of microcalcifications. NCDCIS diagnosed by ultrasound may be an independent risk factor for upgrade. ADVANCES IN KNOWLEDGE: Radiologists must be aware of imaging features of DCIS and consider increased upgrade rate when NCDCIS is diagnosed by ultrasound.

Preoperative staging of ovarian cancer: comparison between ultrasound, CT and whole-body diffusion-weighted MRI (ISAAC study).

OBJECTIVES: To compare the performance of transvaginal and transabdominal ultrasound with that of the first-line staging method (contrast-enhanced computed tomography (CT)) and a novel technique, whole-body magnetic resonance imaging with diffusion-weighted sequence (WB-DWI/MRI), in the assessment of peritoneal involvement (carcinomatosis), lymph-node staging and prediction of non-resectability in patients with suspected ovarian cancer. METHODS: Between March 2016 and October 2017, all consecutive patients with suspicion of ovarian cancer and surgery planned at a gynecological oncology center underwent preoperative staging and prediction of non-resectability with ultrasound, CT and WB-DWI/MRI. The evaluation followed a single, predefined protocol, assessing peritoneal spread at 19 sites and lymph-node metastasis at eight sites. The prediction of non-resectability was based on abdominal markers. Findings were compared to the reference standard (surgical findings and outcome and histopathological evaluation). RESULTS: Sixty-seven patients with confirmed ovarian cancer were analyzed. Among them, 51 (76%) had advanced-stage and 16 (24%) had early-stage ovarian cancer. Diagnostic laparoscopy only was performed in 16% (11/67) of the cases and laparotomy in 84% (56/67), with no residual disease at the end of surgery in 68% (38/56), residual disease ≤ 1 cm in 16% (9/56) and residual disease > 1 cm in 16% (9/56). Ultrasound and WB-DWI/MRI performed better than did CT in the assessment of overall peritoneal carcinomatosis (area under the receiver-operating-characteristics curve (AUC), 0.87, 0.86 and 0.77, respectively). Ultrasound was not inferior to CT (P = 0.002). For assessment of retroperitoneal lymph-node staging (AUC, 0.72-0.76) and prediction of non-resectability in the abdomen (AUC, 0.74-0.80), all three methods performed similarly. In general, ultrasound had higher or identical specificity to WB-DWI/MRI and CT at each of the 19 peritoneal sites evaluated, but lower or equal sensitivity in the abdomen. Compared with WB-DWI/MRI and CT, transvaginal ultrasound had higher accuracy (94% vs 91% and 85%, respectively) and sensitivity (94% vs 91% and 89%, respectively) in the detection of carcinomatosis in the pelvis. Better accuracy and sensitivity of ultrasound (93% and 100%) than WB-DWI/MRI (83% and 75%) and CT (84% and 88%) in the evaluation of deep rectosigmoid wall infiltration, in particular, supports the potential role of ultrasound in planning rectosigmoid resection. In contrast, for the bowel serosal and mesenterial assessment, abdominal ultrasound had the lowest accuracy (70%, 78% and 79%, respectively) and sensitivity (42%, 65% and 65%, respectively). CONCLUSIONS: This is the first prospective study to document that, in experienced hands, ultrasound may be an alternative to WB-DWI/MRI and CT in ovarian cancer staging, including peritoneal and lymph-node evaluation and prediction of non-resectability based on abdominal markers of non-resectability. © 2021 International Society of Ultrasound in Obstetrics and Gynecology.

The accuracy of three-dimensional ultrasonography in the diagnosis of Müllerian duct anomalies and its concordance with magnetic resonance imaging.

This retrospective study was performed to comparatively evaluate the diagnostic accuracies of three-dimensional ultrasonography (3D-US) and magnetic resonance imaging (MRI) for identification of Müllerian duct anomalies (MDAs). A total of 27 women with suspected MDAs underwent gynaecological examination, 2D-US, 3D-US and MRI, respectively. The MDAs were classified with respect to the European Society of Human Reproduction and Embryology-European Society for Gynaecological Endoscopy (ESHRE/ESGE) and American Society of Reproductive Medicine (ASRM) systems. Based on the ESHRE/ESGE classification, there was a discrepancy for only one patient between US and MRI. Thus, the concordance between US and MRI was 26/27 (96.3%). With respect to ASRM classification, there was a disagreement between MRI and 3D-US in three patients, thus the concordance between MRI and 3D-US was 24/27 (88.9%). To conclude, the 3D-US has a good level of agreement with MRI for recognition of MDAs.Impact StatementWhat is already known on this subject? Müllerian duct anomalies (MDAs) are relatively common malformations of the female genital tract and they may adversely affect the reproductive potential. The establishment of accurate and timely diagnosis of these malformations is critical to overcome clinical consequences of MDAs.What the results of this study add? The concordance between US and MRI for diagnosis of MDAs based on ESHRE-ESGE classification and ASRM were 96.3% and 88.9%, respectively. These results indicate that 3D US has a satisfactory level of diagnostic accuracy for MDAs and it can be used in conjunction with MRI. Minimisation of diagnostic errors is important to improve reproductive outcome and to avoid unnecessary surgical interventions.What the implications are of these findings for clinical practice and/or further research? Efforts must be spent to eliminate the discrepancies between the clinical and radiological diagnosis of MDAs. Further trials should be implemented for establishment and standardisation of radiological images for identification and classification of MDAs.

The Impact of the COVID-19 Pandemic on the Workload, Case Mix and hospital Resources at a Tertiary Vascular Unit.

BACKGROUND: The aim of this study was to examine the COVID-19 pandemic and its associated impact on the provision of vascular services, and the pattern of presentation and practice in a tertiary referral vascular unit. METHODS: This is a retrospective observational study from a prospectively maintained data-base comparing two time frames, Period 1(15th March-30th May 2019-P1) and Period 2(15th March-30th May 2020-P2)All the patients who presented for a vascular review in the 2 timeframes were included. Metrics of service and patient care episodes were collected and compared including, the number of emergency referrals, patient encounters, consultations, emergency admissions and interventions. Impact on key hospital resources such as critical care and imaging facilities during the two time periods were also examined. RESULTS: There was an absolute reduction of 44% in the number of patients who required urgent or emergency treatment from P1 to P2 (141 vs 79). We noted a non-significant trend towards an increase in the proportion of patients presenting with Chronic Limb Threatening Ischaemia (CLTI) Rutherford 5&6 (P=0.09) as well as a reduction in the proportion of admissions related to Aortic Aneurysm (P=0.21). There was a significant absolute reduction of 77% in all vascular interventions from P1 to P2 with the greatest reductions noted in Carotid (P=0.02), Deep Venous (P=0.003) and Aortic interventions (P=0.016). The number of lower limb interventions also decreased though there was a significant increase as a relative proportion of all vascular interventions in P2 (P=0.001). There was an absolute reduction in the number of scans performed for vascular pathology; Duplex scans reduced by 86%(P<0.002), CT scans by 68%(P<0.003) and MRIs by 74%(P<0.009). CONCLUSION: We report a decrease in urgent and emergency vascular presentations, admissions and interventions. The reduction in patients presenting with lower limb pathology was not as significant as other vascular conditions, resulting in a significant rise in interventions for CLTI and DFI as a proportion of all vascular interventions. These observations will help guide the provision of vascular services during future pandemics.

Neural underpinnings of food choice and consumption in obesity.

BACKGROUND/OBJECTIVES: Obesity is associated with unhealthy food choices. Food selection is driven by the subjective valuation of available options, and the perceived and actual rewards accompanying consumption. These cognitive operations are mediated by brain regions including the ventromedial prefrontal cortex (vmPFC), dorsal anterior cingulate cortex (dACC), and ventral striatum (vStr). This study investigated the relationship between body mass index (BMI) and functional activations in the vmPFC, dACC, and vStr during food selection and consumption. SUBJECTS/METHODS: After overnight fasting, 26 individuals (BMI: 18-40 kg/m2) performed a food choice task while being scanned with functional magnetic resonance imaging (fMRI). Each trial involved selecting one beverage from a pair of presented options, followed by delivery of a 3 mL aliquot of the selected option using an MR-compatible gustometer. We also tracked subjective preference for each beverage throughout the experiment. RESULTS: During food choice, individuals with greater BMI had less activation in the dorsolateral prefrontal cortex when selecting a high-value option and less vmPFC activation upon its consumption. Independent of BMI, during food choice the dACC and anterior insula elicited higher activation when a less preferred beverage was selected. Activation of the dACC and a broader frontoparietal network was also observed when deciding between options more similar in value. During consumption, receipt of a more preferred beverage was associated with greater vmPFC response, and attenuation of the dACC. CONCLUSIONS: An individual's preference for a food option modulates the brain activity associated with choosing and consuming it. The relationship between food preference and underlying brain activity is altered in obesity, with reduced engagement of cognition-related regions when presented with a highly valued option, but a blunted response in reward-related regions upon consumption.