Assessing the malignancy of suspicious breast microcalcifications: the role of contrast enhanced mammography.

PURPOSE: To assess the role of contrast-enhanced mammography (CEM) in predicting the malignancy of breast calcifications. MATERIAL AND METHODS: We retrospectively evaluated patients with suspicious calcifications (BIRADS 4) who underwent CEM and stereotactic vacuum-assisted biopsy (VAB) at our institution. We assessed the sensitivity (SE), specificity (SP), positive predictive value (PPV) and negative predictive value (NPV) of CEM in predicting malignancy of microcalcifications with a 95% confidence interval; we performed an overall analysis and a subgroup analysis stratified into group A-low risk (BIRADS 4a) and group B-medium/high risk (BIRADS 4b-4c). We then evaluated the correlation between enhancement and tumour proliferation index (Ki-67) for all malignant lesions. RESULTS: Data from 182 patients with 184 lesions were collected. Overall the SE of CEM in predicting the malignancy of microcalcifications was 0.70, SP was 0.85, the PPV was 0.82, the NPV was 0.76 and AUC was 0.78. SE in group A was 0.89, SP was 0.89, PPV was 0.57, NPV was 0.98 and AUC was 0.75. SE in group B was 0.68, SP was 0.80, PPV was 0.87, NPV was 0.57 and AUC was 0.75. Among malignant microcalcifications that showed enhancement (N = 52), 61.5% had Ki-67 ≥ 20% and 38.5% had low Ki-67 values. Among the lesions that did not show enhancement (N = 22), 90.9% had Ki-67 < 20% and 9.1% showed high Ki-67 values 20%. CONCLUSIONS: The absence of enhancement can be used as an indicative parameter for the absence of disease in cases of low-suspicious microcalcifications, but not in intermediate-high suspicious ones for which biopsy remains mandatory and can be used to distinguish indolent lesions from more aggressive neoplasms, with consequent reduction of overdiagnosis and overtreatment.

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.

Exploring the Potential of Artificial Intelligence in Breast Ultrasound.

Breast ultrasound has emerged as a valuable imaging modality in the detection and characterization of breast lesions, particularly in women with dense breast tissue or contraindications for mammography. Within this framework, artificial intelligence (AI) has garnered significant attention for its potential to improve diagnostic accuracy in breast ultrasound and revolutionize the workflow. This review article aims to comprehensively explore the current state of research and development in harnessing AI's capabilities for breast ultrasound. We delve into various AI techniques, including machine learning, deep learning, as well as their applications in automating lesion detection, segmentation, and classification tasks. Furthermore, the review addresses the challenges and hurdles faced in implementing AI systems in breast ultrasound diagnostics, such as data privacy, interpretability, and regulatory approval. Ethical considerations pertaining to the integration of AI into clinical practice are also discussed, emphasizing the importance of maintaining a patient-centered approach. The integration of AI into breast ultrasound holds great promise for improving diagnostic accuracy, enhancing efficiency, and ultimately advancing patient's care. By examining the current state of research and identifying future opportunities, this review aims to contribute to the understanding and utilization of AI in breast ultrasound and encourage further interdisciplinary collaboration to maximize its potential in clinical practice.

Magnetic Localization of Breast Lesions: A Large-Scale European Evaluation in a National Cancer Institute.

INTRODUCTION: For decades the standard for preoperative breast lesions' localization has been wire localization. In recent years the options for nonwired localization have significantly expanded and include radioactive seeds, radar reflectors, radiofrequency identification tags and magnetic seeds. The aim of our study is to evaluate on a large scale the performance of preoperative magnetic seed localization of nonpalpable breast lesions. MATERIAL AND METHODS: We prospectively collected data on all patients undergoing image-guided magnetic seed localization from September 2019 to December 2022. We analyzed imaging findings, histological results, and type of surgery. The primary outcome was the successful localization rate. Secondary outcomes were the successful placement rate, the ease of percutaneous positioning, the procedural complications, and the reintervention rate. RESULTS: A total of 1123 magnetic seeds were placed in 1084 patients by 4 radiologists under ultrasound (1053) or stereotactic (70) guidance. All seeds were detectable transcutaneously in all breasts sizes and at all depths by 7 surgeons with a success rate of 100%. A total of 97.5% seeds were correctly placed into the target lesions (only 2.5% were dislocated). All radiologists have shown good compliance during the procedure, and there were no complications or safety issues. The reoperation rate was 5.1%. CONCLUSIONS: Image-guided localization with magnetic seeds is an easy, safe, reliable, and effective method for localizing nonpalpable breast lesions. Both radiologists and surgeons agreed that the technology was intuitive to use and that it can be widely applied in preoperative localization in breast units.

A singular case of primary aorto-duodenal fistula without pre-existing abdominal aortic aneurism: why and when you should suspect it.

Although gastrointestinal hemorrhage from aorto-enteric fistulae (AEF) secondary to previous aortic grafts are well known, a primary aorto-enteric fistula (PAEF) without aortic aneurysm is an extremely rare event resulting in poor prognosis and outcome. PAEF is a rare cause of gastro-intestinal (GI) bleeding that radiologists should consider because often its presence is not easily guessed by clinical features. It is difficult to detect at CT examination therefore PAEF might be not diagnosed until a laparotomy. We report a case of a 74-year-old Italian male who presented to our Emergency Department (ED) with brightly red rectal bleeding that occurred from some hours and a pre-syncopal episode. There was no history of analgesic abuse, peptic ulceration, alcohol excess, and weight loss. Standard resuscitation was commenced with the hope that common sources of bleeding such as peptic ulcers or varices would eventually be discovered by endoscopy and treated definitely. An upper GI endoscopy showed brightly red blood in the stomach and in the first portions of duodenum, but no source of active bleeding was found. Diagnosis of PAEF was made by CT and after confirmed during surgical intervention. Both the duodenum and the aorta were successfully repaired by direct suture and synthetic graft replacement, respectively. Diagnosis of primary aortic duodenal fistula (ADF) has been very difficult in this case especially because our patient did not have abdominal aortic aneurism (AAA) history confirmed by CT examination. Radiologist should remember that upper GI bleeding could however be determined by primary ADF also if atherosclerotic damage is severe as in this case. A technically good and complete exam is mandatory to achieve this rare and complex diagnosis. Particularly, an ultra-tardive acquisition phase (5 min after contrast administration) could be helpful to suspect the presence of PADF: the appearance of contrast into the duodenal lumen is an evocative sign useful to increase clinical and radiological suspicious of ADF. Gl bleeding should be assumed to be caused from a PAEF unless another source can be identified without delay. A timely and accurate diagnosis of primary AEF may be challenging due to insidious episodes of GI bleeding, which are frequently under diagnosed until the occurrence of massive hemorrhage.