Diagnostic performance of MRI-guided vacuum-assisted breast biopsy (VABB): an essential but still underused technique.

Background: Magnetic resonance imaging (MRI)-guided vacuum-assisted breast biopsy (VABB) is an increasingly requested procedure, but it implies training and experience both in its execution and in determining radiological-pathological concordance and is therefore performed in dedicated breast centers. The purpose of this study is to evaluate the diagnostic performance of MRI-guided vacuum-assisted biopsy and to determine the upgrade rate after surgery or follow-up.

Methods: We retrospectively evaluated all consecutive patients with suspicious MRI findings without corresponding mammographic and ultrasonographic findings who underwent MRI-guided vacuum-assisted breast biopsy (VABB) at our Institution from November 2020 to March 2023.

Decoding Radiomics: A Step-by-Step Guide to Machine Learning Workflow in Hand-Crafted and Deep Learning Radiomics Studies.

Although radiomics research has experienced rapid growth in recent years, with numerous studies dedicated to the automated extraction of diagnostic and prognostic information from various imaging modalities, such as CT, PET, and MRI, only a small fraction of these findings has successfully transitioned into clinical practice. This gap is primarily due to the significant methodological challenges involved in radiomics research, which emphasize the need for a rigorous evaluation of study quality. While many technical aspects may lie outside the expertise of most radiologists, having a foundational knowledge is essential for evaluating the quality of radiomics workflows and contributing, together with data scientists, to the development of models with a real-world clinical impact.

How do large language models answer breast cancer quiz questions? A comparative study of GPT-3.5, GPT-4 and Google Gemini.

Applications of large language models (LLMs) in the healthcare field have shown promising results in processing and summarizing multidisciplinary information. This study evaluated the ability of three publicly available LLMs (GPT-3.5, GPT-4, and Google Gemini-then called Bard) to answer 60 multiple-choice questions (29 sourced from public databases, 31 newly formulated by experienced breast radiologists) about different aspects of breast cancer care: treatment and prognosis, diagnostic and interventional techniques, imaging interpretation, and pathology.

Performance of Contrast-Enhanced Mammography (CEM) for Monitoring Neoadjuvant Chemotherapy Response among Different Breast Cancer Subtypes.

Neoadjuvant chemotherapy (NAT) plays a crucial role in breast cancer (BC) treatment, both in advanced BC and in early-stage BC, with different rates of pathological complete response (pCR) among the different BC molecular subtypes. Imaging monitoring is mandatory to evaluate the NAT efficacy. This study evaluates the diagnostic performance of Contrast-Enhanced Mammography (CEM) in BC patients undergoing NAT.

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).

Radiomics and Artificial Intelligence in Renal Lesion Assessment.

Radiomics, the extraction and analysis of quantitative features from medical images, has emerged as a promising field in radiology with the potential to revolutionize the diagnosis and management of renal lesions. This comprehensive review explores the radiomics workflow, including image acquisition, feature extraction, selection, and classification, and highlights its application in differentiating between benign and malignant renal lesions. The integration of radiomics with artificial intelligence (AI) techniques, such as machine learning and deep learning, can help patients' management and allow the planning of the appropriate treatments.

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.

Efficacy and safety of vacuum-assisted excision (VAE) of fibroadenomas: experience in a tertiary centre.

Purpose: To evaluate the technical success and efficacy rates of US-guided percutaneous vacuum-assisted excision (VAE) of breast fibroadenomas, also assessing procedural complications and long-term patient satisfaction rates.

Materials And Methods: The institutional database of a tertiary breast cancer referral centre was retrospectively reviewed to retrieve all women with fibroadenomas who underwent US-guided VAE between May 2011 and September 2019. We subsequently included in this study all fibroadenomas with a maximum diameter of 3 cm at US and an available histological confirmation obtained by core-needle biopsy before VAE.

Diagnosis and Treatment of Post-Prostatectomy Lymphedema: What's New?

Lymphedema is a chronic progressive disorder that significantly compromises patients' quality of life. In Western countries, it often results from cancer treatment, as in the case of post-radical prostatectomy lymphedema, where it can affect up to 20% of patients, with a significant disease burden. Traditionally, diagnosis, assessment of severity, and management of disease have relied on clinical assessment.

Utility of detection of breast calcifications with integrated real-time radiography system (IRRS) during digital breast tomosynthesis (DBT)-guided vacuum assisted biopsy (VAB): initial single-center experience.

Purpose: To determine whether the presence of calcifications in specimens collected during stereotactic-guided vacuum-assisted breast biopsies (VABB) is sufficient to ascertain their adequacy for final diagnosis at pathology.

Materials And Methods: Digital breast tomosynthesis (DBT)-guided VABBs were performed on 74 patients with calcifications as target. Each biopsy consisted of the collection of 12 samplings with a 9-gauge needle.

Artificial Intelligence in Brain Tumor Imaging: A Step toward Personalized Medicine.

The application of artificial intelligence (AI) is accelerating the paradigm shift towards patient-tailored brain tumor management, achieving optimal onco-functional balance for each individual. AI-based models can positively impact different stages of the diagnostic and therapeutic process. Although the histological investigation will remain difficult to replace, in the near future the radiomic approach will allow a complementary, repeatable and non-invasive characterization of the lesion, assisting oncologists and neurosurgeons in selecting the best therapeutic option and the correct molecular target in chemotherapy.

Chest X-ray in Emergency Radiology: What Artificial Intelligence Applications Are Available?

Due to its widespread availability, low cost, feasibility at the patient's bedside and accessibility even in low-resource settings, chest X-ray is one of the most requested examinations in radiology departments. Whilst it provides essential information on thoracic pathology, it can be difficult to interpret and is prone to diagnostic errors, particularly in the emergency setting. The increasing availability of large chest X-ray datasets has allowed the development of reliable Artificial Intelligence (AI) tools to help radiologists in everyday clinical practice.

Artificial Intelligence in Emergency Radiology: Where Are We Going?

Emergency Radiology is a unique branch of imaging, as rapidity in the diagnosis and management of different pathologies is essential to saving patients' lives. Artificial Intelligence (AI) has many potential applications in emergency radiology: firstly, image acquisition can be facilitated by reducing acquisition times through automatic positioning and minimizing artifacts with AI-based reconstruction systems to optimize image quality, even in critical patients; secondly, it enables an efficient workflow (AI algorithms integrated with RIS-PACS workflow), by analyzing the characteristics and images of patients, detecting high-priority examinations and patients with emergent critical findings. Different machine and deep learning algorithms have been trained for the automated detection of different types of emergency disorders (e.

Artificial Intelligence in Lung Cancer Imaging: Unfolding the Future.

Lung cancer is one of the malignancies with higher morbidity and mortality. Imaging plays an essential role in each phase of lung cancer management, from detection to assessment of response to treatment. The development of imaging-based artificial intelligence (AI) models has the potential to play a key role in early detection and customized treatment planning.

Precision Imaging Guidance in the Era of Precision Oncology: An Update of Imaging Tools for Interventional Procedures.

Interventional oncology (IO) procedures have become extremely popular in interventional radiology (IR) and play an essential role in the diagnosis, treatment, and supportive care of oncologic patients through new and safe procedures. IR procedures can be divided into two main groups: vascular and non-vascular. Vascular approaches are mainly based on embolization and concomitant injection of chemotherapeutics directly into the tumor-feeding vessels.

A rare case of unilateral vocal cord paralysis: neurovascular conflict due to an aberrant bronchial artery detected at computed tomography.

We report the case of a 29-year-old patient without medical history presenting with dysphonia associated with left unilateral vocal cord paralysis. The patient underwent a contrast-enhanced computed tomography with an angiographic arterial phase of the head, neck and chest, and the only significant finding was the presence of a large, aberrant right bronchial artery originating directly from the aortic arch, where the recurrent left laryngeal nerve loops. After excluding alternative etiologies, the hypothesis of neurovascular conflict between this vessel and the recurrent left laryngeal nerve was formulated.

Nontraumatic orbital emergencies: a pictorial essay - CT and MRI features for an imaging findings-based approach.

Orbital imaging plays a pivotal role in each hospital with an Ophthalmological Emergency Department. Unenhanced orbital computed tomography (CT) usually represents the first-line tool for the assessment of nontraumatic orbital emergencies, thanks to its quick execution, wide availability, high resolution, and availability of multiplanar reformats/reconstructions. Magnetic resonance imaging (MRI) is an essential tool that allows characterization and a better understanding of the anatomical involvement of different disorders due to its excellent contrast resolution and ability to study the visual pathways, even if, unfortunately, it is not always available in the emergency setting.

Computed tomography in traumatic orbital emergencies: a pictorial essay-imaging findings, tips, and report flowchart.

Computed tomography (CT) is considered the gold standard technique for the assessment of trauma patients with suspected involvement of the eye and orbit. These traumas can result in dramatic consequences to visual function, ocular motility, and aesthetics. CT is a quick and widely available imaging modality, which provides a detailed evaluation of the orbital bony and soft tissue structures, an accurate assessment of the globes, and is used to guide the patients' treatment planning.

Artificial intelligence for the detection of COVID-19 pneumonia on chest CT using multinational datasets.

Chest CT is emerging as a valuable diagnostic tool for clinical management of COVID-19 associated lung disease. Artificial intelligence (AI) has the potential to aid in rapid evaluation of CT scans for differentiation of COVID-19 findings from other clinical entities. Here we show that a series of deep learning algorithms, trained in a diverse multinational cohort of 1280 patients to localize parietal pleura/lung parenchyma followed by classification of COVID-19 pneumonia, can achieve up to 90.