Magnetic resonance imaging before breast cancer surgery: results of an observational multicenter international prospective analysis (MIPA)

OBJECTIVES

Preoperative breast magnetic resonance imaging (MRI) can inform surgical planning but might cause overtreatment by increasing the mastectomy rate. The Multicenter International Prospective Analysis (MIPA) study investigated this controversial issue.

METHODS

This observational study enrolled women aged 18-80 years with biopsy-proven breast cancer, who underwent MRI in addition to conventional imaging (mammography and/or breast ultrasonography) or conventional imaging alone before surgery as routine practice at 27 centers. Exclusion criteria included planned neoadjuvant therapy, pregnancy, personal history of any cancer, and distant metastases.

RESULTS

Of 5896 analyzed patients, 2763 (46.9%) had conventional imaging only (noMRI group), and 3133 (53.1%) underwent MRI that was performed for diagnosis, screening, or unknown purposes in 692/3133 women (22.1%), with preoperative intent in 2441/3133 women (77.9%, MRI group). Patients in the MRI group were younger, had denser breasts, more cancers ≥ 20 mm, and a higher rate of invasive lobular histology than patients who underwent conventional imaging alone (p < 0.001 for all comparisons). Mastectomy was planned based on conventional imaging in 22.4% (MRI group) versus 14.4% (noMRI group) (p < 0.001). The additional planned mastectomy rate in the MRI group was 11.3%. The overall performed first- plus second-line mastectomy rate was 36.3% (MRI group) versus 18.0% (noMRI group) (p < 0.001). In women receiving conserving surgery, MRI group had a significantly lower reoperation rate (8.5% versus 11.7%, p < 0.001).

CONCLUSIONS

Clinicians requested breast MRI for women with a higher a priori probability of receiving mastectomy. MRI was associated with 11.3% more mastectomies, and with 3.2% fewer reoperations in the breast conservation subgroup.

KEY POINTS

• In 19% of patients of the MIPA study, breast MRI was performed for screening or diagnostic purposes. • The current patient selection to preoperative breast MRI implies an 11% increase in mastectomies, counterbalanced by a 3% reduction of the reoperation rate. • Data from the MIPA study can support discussion in tumor boards when preoperative MRI is under consideration and should be shared with patients to achieve informed decision-making.

The What, the Why, and the How of Liver Ablations: A Practical Guide for the Medical Oncologist

Interventional oncology plays a major role within modern oncological patient management. Image-guided thermal ablation has been recognized as a successful local therapeutic option in patients with primary and secondary malignant liver diseases, as also recalled by the recent European Society of Medical Oncology (ESMO) guidelines on colorectal metastases. As image-guided treatments may be as effective as surgery in selected patients with liver lesions, the clinical oncologist should be familiar with the indications, risks, and technical aspects of liver ablation in order to provide their patients with the best outcomes. This article provides a broad overview of the most commonly used ablation techniques and highlights the most relevant technical aspects such as the ideal setting in the operating theatre; which image-guided methods are available, including the growing application of fusion imaging; or contrast-enhanced ultrasound for guiding/monitoring the procedure. A further aim is to expand the knowledge among medical oncologists about liver ablation procedures and to provide insights into the future perspectives of percutaneous minimally invasive procedures in the liver.

MRI versus Mammography plus Ultrasound in Women at Intermediate Breast Cancer Risk: Study Design and Protocol of the MRIB Multicenter, Randomized, Controlled Trial

In women at high/intermediate lifetime risk of breast cancer (BC-LTR), contrast-enhanced magnetic resonance imaging (MRI) added to mammography ± ultrasound (MX ± US) increases sensitivity but decreases specificity. Screening with MRI alone is an alternative and potentially more cost-effective strategy. Here, we describe the study protocol and the characteristics of enrolled patients for MRIB feasibility, multicenter, randomized, controlled trial, which aims to compare MRI alone versus MX+US in women at intermediate breast cancer risk (aged 40-59, with a 15-30% BC-LTR and/or extremely dense breasts). Two screening rounds per woman were planned in ten centers experienced in MRI screening, the primary endpoint being the rate of cancers detected in the 2 arms after 5 years of follow-up. From July 2013 to November 2015, 1254 women (mean age 47 years) were enrolled: 624 were assigned to MX+US and 630 to MRI. Most of them were aged below 50 (72%) and premenopausal (45%), and 52% used oral contraceptives. Among postmenopausal women, 15% had used hormone replacement therapy. Breast and/or ovarian cancer in mothers and/or sisters were reported by 37% of enrolled women, 79% had extremely dense breasts, and 41% had a 15-30% BC-LTR. The distribution of the major determinants of breast cancer risk profiles (breast density and family history of breast and ovarian cancer) of enrolled women varied across centers.

CT-derived epicardial adipose tissue density: Systematic review and meta-analysis

PURPOSE

The aim of our work was to systematically review and meta-analyze epicardial adipose tissue (EAT) density values reported in literature, assessing potential correlations of EAT density with segmentation thresholds and other technical and clinical variables.

METHOD

A systematic search was performed, aiming for papers reporting global EAT density values in Hounsfield Units (HU) in patients undergoing chest CT for any clinical indication. After screening titles, abstract and full text of each retrieved work, studies reporting mean and standard deviation for EAT density were ultimately included. Technical, clinical and EAT data were extracted, and divided into subgroups according to clinical conditions of reported subjects. Pooled density analyses were performed both overall and for subgroups according to clinical conditions. Metaregression analyses were done to appraise the impact of clinical and technical variables on EAT volume.

RESULTS

Out of 152 initially retrieved works, 13 were ultimately included, totaling for 7683 subjects. EAT density showed an overall pooled value of -85.86 HU (95% confidence interval [95% CI] -91.84, -79.89 HU), being -86.40 HU (95% CI -112.69, -60.12 HU) in healthy subjects and -80.71 HU (95% CI -87.43, -73.99 HU) in patients with coronary artery disease. EAT volume and lower and higher segmentation thresholds were found to be significantly correlated with EAT density (p = 0.044, p < 0.001 and p< 0.001 respectively).

CONCLUSIONS

Patients with coronary artery disease appear to present with higher EAT density values, while the correlations observed at metaregression highlight the need for well-established, shared thresholds for EAT segmentation.

Mammography self-evaluation online test for screening readers: an Italian Society of Medical Radiology (SIRM) initiative

OBJECTIVES

To report and analyse the characteristics and performance of the first cohort of Italian radiologists completing the national mammography self-evaluation online test established by the Italian Society of Medical Radiology (SIRM).

METHODS

A specifically-built dataset of 132 mammograms (24 with screen-detected cancers and 108 negative cases) was preliminarily tested on 48 radiologists to define pass thresholds (62% sensitivity and 86% specificity) and subsequently made available online to SIRM members during a 13-month timeframe between 2018 and 2019. Associations between participants' characteristics, pass rates, and diagnostic accuracy were then investigated with descriptive statistics and univariate and multivariable regression analyses.

RESULTS

A total of 342 radiologists completed the test, 151/342 (44.2%) with success. All individual variables, except gender, showed a significant correlation with pass rates and diagnostic sensitivity, confirmed by univariate logistic regression, while only involvement in organised screening programs and number of mammograms read per year showed a positive association with specificity at univariate logistic regression. In the multivariable regression analysis, fewer variables remained significant: > 3000 mammograms read per year for success rate; female gender, public practice setting, and higher experience self-judgement for sensitivity; no variables were significantly associated with specificity.

CONCLUSIONS

This national self-evaluation test effectively differentiated multiple aspects of mammographic reading experience, but specific breast imaging experience was shown not to strictly guarantee good diagnostic accuracy. Due to its easy use and the validity of obtained results, this test could be extended to all Italian breast radiologists, regardless of their experience, also as a Breast Unit accreditation criterion.

KEY POINTS

• This self-evaluation test was found to be able to differentiate various degrees of mammographic interpretation experience. • Breast cancer screening readers should undergo a self-assessment test, since experience parameters alone do not guarantee diagnostic ability.

A critical appraisal of the quality of guidelines for radiation protection in interventional radiology using the AGREE II tool: A EuroAIM initiative

PURPOSE

To systematically review and assess the methodological quality of guidelines for radiation protection in interventional radiology.

MATERIALS AND METHODS

On April 15^th, 2021, a systematic search for guidelines on radiation protection in interventional radiology was performed using MEDLINE, EMBASE, National Guideline Clearinghouse, and National Institute for Health and Clinical Excellence databases. Among retrieved guidelines, we then excluded those not primarily focused on radiation protection or on interventional radiology. Authors' professional role and year of publication were recorded for each included guideline. Guideline quality evaluation was performed independently by three authors using the six-domain tool "AGREE II", with an overall guideline quality score divided into three classes: low (<60%), acceptable (60-80%), and good quality (>80%).

RESULTS

Our literature search identified 106 citations: after applying exclusion criteria, 11 guidelines published between 2009 and 2018 were included, most of their authors being interventional radiologists (168/224, 75%). Overall quality of included guidelines was acceptable (median 72%, interquartile range 64-83%), with only one guideline (9%) with overall low quality and four guidelines (36%) with overall good quality. Among AGREE II domains, "Scope and Purpose", "Clarity of Presentations", and "Editorial Independence" had the best results (87%, 76%, and 75% respectively), while "Applicability", "Rigor of Development", and "Stakeholder Involvement" the worst (46%, 49%, and 52% respectively).

CONCLUSION

Considering all guidelines, the overall methodological quality was acceptable with one third of them reaching the highest score class. The "Applicability" domain had the lowest median score, highlighting a practical implementation gap to be addressed by future guidelines.

Epicardial fat inflammation response to COVID-19 therapies

OBJECTIVE

Adipose tissue plays a role in the novel coronavirus disease 2019 (COVID-19). Epicardial adipose tissue (EAT), a unique visceral fat, presents with a high degree of inflammation in severe COVID-19. Whether and how adipose tissue may respond to the COVID-19 therapies is unknown.

METHODS

The difference in computed tomography-measured EAT and subcutaneous (SAT) attenuation, defined as mean attenuation expressed in Hounsfield units (HU), was retrospectively analyzed in 72 patients (mean [SD] age was 59.6 [12.4] years, 50 patients [69%] were men) at the hospital admission for COVID-19 and 99 days (interquartile range = 71-129) after discharge.

RESULTS

At the admission, EAT-HU was significantly correlated with blood glucose levels, interleukin 6, troponin T levels, and waist circumference. EAT-HU decreased from -87.21 (16.18) to -100.0 (11) (p < 0.001), whereas SAT-HU did not change (-110.21 [12.1] to -111.11 [27.82]; p = 0.78) after therapy. Changes in EAT-HU (expressed as ∆) significantly correlated with dexamethasone therapy (r = -0.46, p = 0.006) and when dexamethasone was combined with tocilizumab (r = -0.24, p = 0.04).

CONCLUSIONS

Dexamethasone therapy was associated with significant reduction of EAT inflammation in COVID-19 patients, whereas SAT showed no changes. Anti-inflammatory therapies targeting visceral fat may be helpful in COVID-19.

Axillary lymphadenopathy at the time of COVID-19 vaccination: ten recommendations from the European Society of Breast Imaging (EUSOBI)

Unilateral axillary lymphadenopathy is a frequent mild side effect of COVID-19 vaccination. European Society of Breast Imaging (EUSOBI) proposes ten recommendations to standardise its management and reduce unnecessary additional imaging and invasive procedures: (1) in patients with previous history of breast cancer, vaccination should be performed in the contralateral arm or in the thigh; (2) collect vaccination data for all patients referred to breast imaging services, including patients undergoing breast cancer staging and follow-up imaging examinations; (3) perform breast imaging examinations preferentially before vaccination or at least 12 weeks after the last vaccine dose; (4) in patients with newly diagnosed breast cancer, apply standard imaging protocols regardless of vaccination status; (5) in any case of symptomatic or imaging-detected axillary lymphadenopathy before vaccination or at least 12 weeks after, examine with appropriate imaging the contralateral axilla and both breasts to exclude malignancy; (6) in case of axillary lymphadenopathy contralateral to the vaccination side, perform standard work-up; (7) in patients without breast cancer history and no suspicious breast imaging findings, lymphadenopathy only ipsilateral to the vaccination side within 12 weeks after vaccination can be considered benign or probably-benign, depending on clinical context; (8) in patients without breast cancer history, post-vaccination lymphadenopathy coupled with suspicious breast finding requires standard work-up, including biopsy when appropriate; (9) in patients with breast cancer history, interpret and manage post-vaccination lymphadenopathy considering the timeframe from vaccination and overall nodal metastatic risk; (10) complex or unclear cases should be managed by the multidisciplinary team.

Machine Learning to Predict In-Hospital Mortality in COVID-19 Patients Using Computed Tomography-Derived Pulmonary and Vascular Features

Pulmonary parenchymal and vascular damage are frequently reported in COVID-19 patients and can be assessed with unenhanced chest computed tomography (CT), widely used as a triaging exam. Integrating clinical data, chest CT features, and CT-derived vascular metrics, we aimed to build a predictive model of in-hospital mortality using univariate analysis (Mann-Whitney U test) and machine learning models (support vectors machines (SVM) and multilayer perceptrons (MLP)). Patients with RT-PCR-confirmed SARS-CoV-2 infection and unenhanced chest CT performed on emergency department admission were included after retrieving their outcome (discharge or death), with an 85/15% training/test dataset split. Out of 897 patients, the 229 (26%) patients who died during hospitalization had higher median pulmonary artery diameter (29.0 mm) than patients who survived (27.0 mm, p < 0.001) and higher median ascending aortic diameter (36.6 mm versus 34.0 mm, p < 0.001). SVM and MLP best models considered the same ten input features, yielding a 0.747 (precision 0.522, recall 0.800) and 0.844 (precision 0.680, recall 0.567) area under the curve, respectively. In this model integrating clinical and radiological data, pulmonary artery diameter was the third most important predictor after age and parenchymal involvement extent, contributing to reliable in-hospital mortality prediction, highlighting the value of vascular metrics in improving patient stratification.

Artificial Intelligence Applied to Chest X-ray for Differential Diagnosis of COVID-19 Pneumonia

We assessed the role of artificial intelligence applied to chest X-rays (CXRs) in supporting the diagnosis of COVID-19. We trained and cross-validated a model with an ensemble of 10 convolutional neural networks with CXRs of 98 COVID-19 patients, 88 community-acquired pneumonia (CAP) patients, and 98 subjects without either COVID-19 or CAP, collected in two Italian hospitals. The system was tested on two independent cohorts, namely, 148 patients (COVID-19, CAP, or negative) collected by one of the two hospitals (independent testing I) and 820 COVID-19 patients collected by a multicenter study (independent testing II). On the training and cross-validation dataset, sensitivity, specificity, and area under the curve (AUC) were 0.91, 0.87, and 0.93 for COVID-19 versus negative subjects, 0.85, 0.82, and 0.94 for COVID-19 versus CAP. On the independent testing I, sensitivity, specificity, and AUC were 0.98, 0.88, and 0.98 for COVID-19 versus negative subjects, 0.97, 0.96, and 0.98 for COVID-19 versus CAP. On the independent testing II, the system correctly diagnosed 652 COVID-19 patients versus negative subjects (0.80 sensitivity) and correctly differentiated 674 COVID-19 versus CAP patients (0.82 sensitivity). This system appears promising for the diagnosis and differential diagnosis of COVID-19, showing its potential as a second opinion tool in conditions of the variable prevalence of different types of infectious pneumonia.

CT-derived Chest Muscle Metrics for Outcome Prediction in Patients with COVID-19

Background Lower muscle mass is a known predictor of unfavorable outcomes, but its prognostic impact on patients with COVID-19 is unknown. Purpose To investigate the contribution of CT-derived muscle status in predicting clinical outcomes in patients with COVID-19. Materials and Methods Clinical or laboratory data and outcomes (intensive care unit [ICU] admission and death) were retrospectively retrieved for patients with reverse transcriptase polymerase chain reaction-confirmed SARS-CoV-2 infection, who underwent chest CT on admission in four hospitals in Northern Italy from February 21 to April 30, 2020. The extent and type of pulmonary involvement, mediastinal lymphadenopathy, and pleural effusion were assessed. Cross-sectional areas and attenuation by paravertebral muscles were measured on axial CT images at the T5 and T12 vertebral level. Multivariable linear and binary logistic regression, including calculation of odds ratios (ORs) with 95% CIs, were used to build four models to predict ICU admission and death, which were tested and compared by using receiver operating characteristic curve analysis. Results A total of 552 patients (364 men and 188 women; median age, 65 years [interquartile range, 54-75 years]) were included. In a CT-based model, lower-than-median T5 paravertebral muscle areas showed the highest ORs for ICU admission (OR, 4.8; 95% CI: 2.7, 8.5; P < .001) and death (OR, 2.3; 95% CI: 1.0, 2.9; P = .03). When clinical variables were included in the model, lower-than-median T5 paravertebral muscle areas still showed the highest ORs for both ICU admission (OR, 4.3; 95%: CI: 2.5, 7.7; P < .001) and death (OR, 2.3; 95% CI: 1.3, 3.7; P = .001). At receiver operating characteristic analysis, the CT-based model and the model including clinical variables showed the same area under the receiver operating characteristic curve (AUC) for ICU admission prediction (AUC, 0.83; P = .38) and were not different in terms of predicting death (AUC, 0.86 vs AUC, 0.87, respectively; P = .28). Conclusion In hospitalized patients with COVID-19, lower muscle mass on CT images was independently associated with intensive care unit admission and in-hospital mortality. © RSNA, 2021 Online supplemental material is available for this article.

Subcutaneous, Paracardiac, and Epicardial Fat CT Density Before/After Contrast Injection: Any Correlation with CAD?

Adipose tissue, in particular epicardial adipose tissue, has been identified as a potential biomarker of cardiovascular pathologies such as coronary artery disease (CAD) in the light of its metabolic activity and close anatomic and pathophysiologic relationship to the heart. Our purpose was to evaluate epicardial adipose tissue density at both unenhanced and contrast-enhanced computed tomography (CT), along with CT densities of paracardiac and subcutaneous adipose tissue, as well as the relations of such densities with CAD. We retrospectively reviewed patients who underwent cardiac CT at our institution for CAD assessment. We segmented regions of interest on epicardial, paracardiac, and subcutaneous adipose tissue on unenhanced and contrast-enhanced scans. A total of 480 patients were included, 164 of them presenting with CAD. Median epicardial adipose tissue density measured on contrast-enhanced scans (−81.5 HU; interquartile range −84.9 to −78.0) was higher than that measured on unenhanced scans (−73.4 HU; −76.9 to −69.4) (p < 0.001), whereas paracardiac and subcutaneous adipose tissue densities were not (p ≥ 0.055). Patients with or without CAD, did not show significant differences in density of epicardial, paracardiac, and subcutaneous adipose tissue either on unenhanced or contrast-enhanced scans (p ≥ 0.092). CAD patients may experience different phenomena (inflammation, fibrosis, increase in adipose depots) leading to rises or drops in epicardial adipose tissue density, resulting in variations that are difficult to detect.

Machine learning applied on chest x-ray can aid in the diagnosis of COVID-19: a first experience from Lombardy, Italy

BACKGROUND

We aimed to train and test a deep learning classifier to support the diagnosis of coronavirus disease 2019 (COVID-19) using chest x-ray (CXR) on a cohort of subjects from two hospitals in Lombardy, Italy.

METHODS

We used for training and validation an ensemble of ten convolutional neural networks (CNNs) with mainly bedside CXRs of 250 COVID-19 and 250 non-COVID-19 subjects from two hospitals (Centres 1 and 2). We then tested such system on bedside CXRs of an independent group of 110 patients (74 COVID-19, 36 non-COVID-19) from one of the two hospitals. A retrospective reading was performed by two radiologists in the absence of any clinical information, with the aim to differentiate COVID-19 from non-COVID-19 patients. Real-time polymerase chain reaction served as the reference standard.

RESULTS

At 10-fold cross-validation, our deep learning model classified COVID-19 and non-COVID-19 patients with 0.78 sensitivity (95% confidence interval [CI] 0.74-0.81), 0.82 specificity (95% CI 0.78-0.85), and 0.89 area under the curve (AUC) (95% CI 0.86-0.91). For the independent dataset, deep learning showed 0.80 sensitivity (95% CI 0.72-0.86) (59/74), 0.81 specificity (29/36) (95% CI 0.73-0.87), and 0.81 AUC (95% CI 0.73-0.87). Radiologists' reading obtained 0.63 sensitivity (95% CI 0.52-0.74) and 0.78 specificity (95% CI 0.61-0.90) in Centre 1 and 0.64 sensitivity (95% CI 0.52-0.74) and 0.86 specificity (95% CI 0.71-0.95) in Centre 2.

CONCLUSIONS

This preliminary experience based on ten CNNs trained on a limited training dataset shows an interesting potential of deep learning for COVID-19 diagnosis. Such tool is in training with new CXRs to further increase its performance.

Machine learning applied on chest x-ray can aid in the diagnosis of COVID-19: a first experience from Lombardy, Italy

BACKGROUND

We aimed to train and test a deep learning classifier to support the diagnosis of coronavirus disease 2019 (COVID-19) using chest x-ray (CXR) on a cohort of subjects from two hospitals in Lombardy, Italy.

METHODS

We used for training and validation an ensemble of ten convolutional neural networks (CNNs) with mainly bedside CXRs of 250 COVID-19 and 250 non-COVID-19 subjects from two hospitals (Centres 1 and 2). We then tested such system on bedside CXRs of an independent group of 110 patients (74 COVID-19, 36 non-COVID-19) from one of the two hospitals. A retrospective reading was performed by two radiologists in the absence of any clinical information, with the aim to differentiate COVID-19 from non-COVID-19 patients. Real-time polymerase chain reaction served as the reference standard.

RESULTS

At 10-fold cross-validation, our deep learning model classified COVID-19 and non-COVID-19 patients with 0.78 sensitivity (95% confidence interval [CI] 0.74-0.81), 0.82 specificity (95% CI 0.78-0.85), and 0.89 area under the curve (AUC) (95% CI 0.86-0.91). For the independent dataset, deep learning showed 0.80 sensitivity (95% CI 0.72-0.86) (59/74), 0.81 specificity (29/36) (95% CI 0.73-0.87), and 0.81 AUC (95% CI 0.73-0.87). Radiologists' reading obtained 0.63 sensitivity (95% CI 0.52-0.74) and 0.78 specificity (95% CI 0.61-0.90) in Centre 1 and 0.64 sensitivity (95% CI 0.52-0.74) and 0.86 specificity (95% CI 0.71-0.95) in Centre 2.

CONCLUSIONS

This preliminary experience based on ten CNNs trained on a limited training dataset shows an interesting potential of deep learning for COVID-19 diagnosis. Such tool is in training with new CXRs to further increase its performance.

Pulmonary thromboembolism in coronavirus disease 2019 patients undergoing thromboprophylaxis

We aimed to investigate the prevalence of pulmonary thromboembolism (PTE) and its association with clinical variables in a cohort of hospitalized coronavirus disease 2019 (COVID-19) patients receiving low-molecular-weight heparin (LMWH) at prophylactic dosage.In this retrospective observational study we included COVID-19 patients receiving prophylactic LMWH from admission but still referred for lower-limbs venous Doppler ultrasound (LL-US) and computed tomography pulmonary angiography (CTPA) for clinical PTE suspicion. A dedicated radiologist reviewed CTPA images to assess PTE presence/extension.From March 1 to April 30, 2020, 45 patients were included (34 men, median age 67 years, interquartile range [IQR] 60-76). Twenty-seven (60%) had PTE signs at CTPA, 17/27 (63%) with bilateral involvement, none with main branch PTE. In 33/45 patients (73%) patients LL-US was performed before CTPA, with 3 patients having superficial vein thrombosis (9%, none with CTPA-confirmed PTE) and 1 patient having deep vein thrombosis (3%, with CTPA-confirmed PTE). Thirty-three patients (73%) had at least one comorbidity, mainly hypertension (23/45, 51%) and cardiovascular disease (15/45, 33%). Before CTPA, 5 patients had high D-dimer (11.21 μg/mL, IQR 9.10-13.02), 19 high fibrinogen (550 mg/dL, IQR 476-590), 26 high interleukin-6 (79 pg/mL, IQR 31-282), and 11 high C-reactive protein (9.60 mg/dL, IQR 6.75-10.65), C-reactive protein being the only laboratory parameter significantly differing between patients with and without PTE (P = .002)High PTE incidence (60%) in COVID-19 hospitalized patients under prophylactic LMWH could substantiate further tailoring of anticoagulation therapy.

Side of contrast injection and breast size correlate with motion artifacts grade and image quality on breast MRI

BACKGROUND

Motion is a relevant cause of artifacts in breast magnetic resonance imaging (MRI), potentially degrading image quality, even with optimized protocols.

PURPOSE

To investigate the causes of motion artifacts (MA) impacting on image quality (IQ) of contrast-enhanced breast MRI.

MATERIAL AND METHODS

Retrospective two-center study on consecutive 1.5-T contrast-enhanced breast MRI, independently reviewed by two radiologists on first subtracted and maximum intensity projection images to define the side most affected by MA. IQ was scored as 1 (optimal), 2 (reduced, but without reduction of diagnostic power), or 3 (reduced, with reduced diagnostic power). Correlations with injection side, breast size (A/B vs. C/D cups), patient age, clinical indication, and MRI scanner/protocol were assessed using χ² and Fisher's exact statistics.

RESULTS

In total, 237 examinations were included, with right injection performed in 124 (52%) and left in 113 (48%). MA were more frequent on the side ipsilateral to the injection (144/237, 61%, 95% confidence interval [CI] 54-67%) than on the contralateral (93/237, 95% CI 33-46%) (P < 0.001); IQ was scored 1 in 154/237 (65%), 2 in 63/237 (27%), and 3 in 20/237 (8%) examinations; patients with A/B cups showed higher IQ score than patients with C/D cups (scores 1, 2, and 3: 54% vs. 70%; 29% vs. 25%; 17% vs. 5%, respectively, P = 0.002). No significant correlations were found for MA (P≥0.106) or IQ (P ≥ 0.318) between ipsi- or contralateral injection and right/left injection, breast size, age, indication, or scanner/protocol.

CONCLUSION

MA were more frequent in breasts ipsilateral to contrast injection and showed a reduced IQ for small breasts.

Chest x-ray severity score in COVID-19 patients on emergency department admission: a two-centre study

BACKGROUND

Integration of imaging and clinical parameters could improve the stratification of COVID-19 patients on emergency department (ED) admission. We aimed to assess the extent of COVID-19 pulmonary abnormalities on chest x-ray (CXR) using a semiquantitative severity score, correlating it with clinical data and testing its interobserver agreement.

METHODS

From February 22 to April 8, 2020, 926 consecutive patients referring to ED of two institutions in Northern Italy for suspected SARS-CoV-2 infection were reviewed. Patients with reverse transcriptase-polymerase chain reaction positive for SARS-CoV-2 and CXR images on ED admission were included (295 patients, median age 69 years, 199 males). Five readers independently and blindly reviewed all CXRs, rating pulmonary parenchymal involvement using a 0-3 semiquantitative score in 1-point increments on 6 lung zones (range 0-18). Interobserver agreement was assessed with weighted Cohen's κ, correlations between median CXR score and clinical data with Spearman's ρ, and the Mann-Whitney U test.

RESULTS

Median score showed negative correlation with SpO₂ (ρ = -0.242, p < 0.001), positive correlation with white cell count (ρ = 0.277, p < 0.001), lactate dehydrogenase (ρ = 0.308, p < 0.001), and C-reactive protein (ρ = 0.367, p < 0.001), being significantly higher in subsequently dead patients (p = 0.003). Considering overall scores, readers' pairings yielded moderate (κ = 0.449, p < 0.001) to almost perfect interobserver agreement (κ = 0.872, p < 0.001), with better interobserver agreement between readers of centre 2 (up to κ = 0.872, p < 0.001) than centre 1 (κ = 0.764, p < 0.001).

CONCLUSIONS

Proposed CXR pulmonary severity score in COVID-19 showed moderate to almost perfect interobserver agreement and significant but weak correlations with clinical parameters, potentially furthering CXR integration in patients' stratification.

Accuracy and inter-reader agreement of breast MRI for cancer staging using 0.08 mmol/kg of gadobutrol

BACKGROUND

Evidence on gadolinium brain accumulation after contrast-enhanced MRI prompted research in dose reduction.

PURPOSE

To estimate accuracy and inter-reader reproducibility of tumor size measurement in breast MRI using 0.08 mmol/kg of gadobutrol.

METHODS

We retrospectively analyzed all women who underwent 1.5-T breast MRI for cancer staging at our department with 0.08 mmol/kg of gadobutrol. Two readers (R1 and R2, 12 and 3 years-experience) measured the largest lesion diameter. Accuracy was estimated both as correlation with pathology and rate of absolute (>5 mm) overestimation and underestimation, inter-reader reproducibility using the Bland-Altman method. Data are given as median and interquartile range.

RESULTS

Thirty-six patients were analyzed (median age 56 years, 49-66) for a total of 38 lesions, 24 (63%) mass enhancement, 14 (37%) non-mass enhancement. Histopathological median size (mm) of all lesions was 15 (9-25): 13 (9-19) for mass lesions, 19 (11-39) for non-mass lesions. On MRI, R1 measured (mm) 14 (10-22) for all lesions, 13 (10-19) for mass lesions, 19 (11-49) for non-mass lesions. MRI-pathology correlation was very high for all lesion categories (ρ ≥ 0.766). On MRI, R1 overestimated lesion size in 6 cases (16%), and underestimated in 3 (8%); R2, overestimated 7 cases (18%) and underestimated 3 cases (8%). At inter-reader reproducibility analysis (mm): bias 0.9, coefficient of reproducibility 13 for all lesions; -0.1 and 6 for mass lesions; 2.5 and 20 for non-mass lesions.

CONCLUSIONS

Breast MRI may be performed using 0.08 mmol/kg of gadobutrol with high accuracy and acceptable inter-reader agreement.

Breast imaging and cancer diagnosis during the COVID-19 pandemic: recommendations from the Italian College of Breast Radiologists by SIRM

The Italian College of Breast Radiologists by the Italian Society of Medical Radiology (SIRM) provides recommendations for breast care provision and procedural prioritization during COVID-19 pandemic, being aware that medical decisions must be currently taken balancing patient's individual and community safety: (1) patients having a scheduled or to-be-scheduled appointment for in-depth diagnostic breast imaging or needle biopsy should confirm the appointment or obtain a new one; (2) patients who have suspicious symptoms of breast cancer (in particular: new onset palpable nodule; skin or nipple retraction; orange peel skin; unilateral secretion from the nipple) should request non-deferrable tests at radiology services; (3) asymptomatic women performing annual mammographic follow-up after breast cancer treatment should preferably schedule the appointment within 1 year and 3 months from the previous check, compatibly with the local organizational conditions; (4) asymptomatic women who have not responded to the invitation for screening mammography after the onset of the pandemic or have been informed of the suspension of the screening activity should schedule the check preferably within 3 months from the date of the not performed check, compatibly with local organizational conditions. The Italian College of Breast Radiologists by SIRM recommends precautions to protect both patients and healthcare workers (radiologists, radiographers, nurses, and reception staff) from infection or disease spread on the occasion of breast imaging procedures, particularly mammography, breast ultrasound, breast magnetic resonance imaging, and breast intervention procedures.

Pulmonary thromboembolism in hospitalised COVID-19 patients at moderate to high risk by Wells score: a report from Lombardy, Italy

Objectives:

To present a single-centre experience on CT pulmonary angiography (CTPA) for the assessment of hospitalised COVID-19 patients with moderate-to-high risk of pulmonary thromboembolism (PTE).

Methods:

We analysed consecutive COVID-19 patients (RT-PCR confirmed) undergoing CTPA in March 2020 for PTE clinical suspicion. Clinical data were retrieved. Two experienced radiologists reviewed CTPAs to assess pulmonary parenchyma and vascular findings.

Results:

Among 34 patients who underwent CTPA, 26 had PTE (76%, 20 males, median age 61 years, interquartile range 54–70), 20/26 (77%) with comorbidities (mainly hypertension, 44%), and 8 (31%) subsequently dying. Eight PTE patients were under thromboprophylaxis with low-molecular-weight heparin, four PTE patients had lower-limbs deep vein thrombosis at ultrasound examination (performed in 33/34 patients). Bilateral PTE characterised 19/26 cases, with main branches involved in 10/26 cases. Twelve patients had a parenchymal involvement >75%, the predominant pneumonia pattern being consolidation in 10/26 patients, ground glass opacities in 9/26, crazy paving in 5/26, and both ground glass opacities and consolidation in 2/26.

Conclusion:

COVID-19 patients are prone to PTE.

Advances in knowledge:

PTE, potentially attributable to an underlying thrombophilic status, may be more frequent than expected in COVID-19 patients. Extension of prophylaxis and adaptation of diagnostic criteria should be considered.