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.

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.

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. AI models have shown remarkable accuracy in predicting tumor aggressiveness, treatment response, and patient outcomes. This review provides insights into the current state of radiomics and AI in renal lesion assessment and outlines future directions for research in this rapidly evolving field.

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. Immediately after VAE, technical success (defined as the correct VAE execution) and the occurrence of procedural complications were evaluated. Imaging follow-up (US ± mammography) after 6, 12, 24 and 36 months was performed to evaluate technical efficacy (defined as the absence of fibroadenoma recurrence at 6-month follow-up). Long-term patient satisfaction was evaluated with telephonic interviews in October 2022. RESULTS: We retrospectively included 108 women (median age 46 years) with 110 fibroadenomas diagnosed at core-needle biopsy with a median lesion size at US of 12 mm. Technical success was obtained in 110/110 VAEs (100%). Minor procedural complications (haematomas) occurred in 7/110 VAEs (6%), whereas 8/110 patients had a fibroadenoma recurrence at 6-month follow-up, resulting in a 93% technical efficacy (102/110 VAEs). All patients available for telephonic follow-up (104/104, 100%) reported high satisfaction with VAE results. CONCLUSION: US-guided VAE is a safe and effective procedure for the excision of fibroadenomas, representing a viable alternative to surgery, with a low complication rate and high patient satisfaction.

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. In this landscape, physical and conservative treatments, including bandages and lymphatic drainage have shown limited results. Recent advances in imaging technology are revolutionizing the approach to this disorder: magnetic resonance imaging has shown satisfactory results in differential diagnosis, quantitative classification of severity, and most appropriate treatment planning. Further innovations in microsurgical techniques, based on the use of indocyanine green to map lymphatic vessels during surgery, have improved the efficacy of secondary LE treatment and led to the development of new surgical approaches. Physiologic surgical interventions, including lymphovenous anastomosis (LVA) and vascularized lymph node transplant (VLNT), are going to face widespread diffusion. A combined approach to microsurgical treatment provides the best results: LVA is effective in promoting lymphatic drainage, bridging VLNT delayed lymphangiogenic and immunological effects in the lymphatic impairment site. Simultaneous VLNT and LVA are safe and effective for patients with both early and advanced stages of post-prostatectomy LE. A new perspective is now represented by the combination of microsurgical treatments with the positioning of nano fibrillar collagen scaffolds (BioBridgeTM) to favor restoring the lymphatic function, allowing for improved and sustained volume reduction. In this narrative review, we proposed an overview of new strategies for diagnosing and treating post-prostatectomy lymphedema to get the most appropriate and successful patient treatment with an overview of the main artificial intelligence applications in the prevention, diagnosis, and management of lymphedema.

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. This technique was integrated with a real-time radiography system (IRRS) which allowed the operator to determine whether calcifications were included in the specimens at the end of each of the 12 tissue collections through the acquisition of a radiograph of every sampling. Calcified and non-calcified specimens were separately sent to pathology and evaluated. RESULTS: A total of 888 specimens were retrieved, 471 containing calcifications and 417 without. In 105 (22.2%) samples out of 471 with calcifications cancer was detected, while the remaining 366 (77.7%) were non-cancerous. Out of 417 specimens without calcifications 56 (13.4%) were cancerous, whereas 361 (86.5%) were non-cancerous. Seven hundred and twenty-seven specimens out of all 888 were cancer-free (81.8%, 95%CI 79-84%). CONCLUSION: Although there is a statistical significative difference between calcified and non-calcified samples and the detection of cancer (p < 0.001), our study shows that the sole presence of calcifications in the specimens is not sufficient to determine their adequacy for final diagnosis at pathology because non-calcified samples can be cancerous and vice-versa. Ending biopsies when calcifications are first detected through IRRS could lead to false negative results.

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. AI-driven tools are already playing an important role in surgical planning, delimiting the extent of the lesion (segmentation) and its relationships with the brain structures, thus allowing precision brain surgery as radical as reasonably acceptable to preserve the quality of life. Finally, AI-assisted models allow the prediction of complications, recurrences and therapeutic response, suggesting the most appropriate follow-up. Looking to the future, AI-powered models promise to integrate biochemical and clinical data to stratify risk and direct patients to personalized screening protocols.

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. AI integration into the diagnostic workflow would benefit patients, radiologists, and healthcare systems in terms of improved and standardized reporting accuracy, quicker diagnosis, more efficient management, and appropriateness of the therapy. This review article aims to provide an overview of the applications of AI for chest X-rays in the emergency setting, emphasizing the detection and evaluation of pneumothorax, pneumonia, heart failure, and pleural effusion.

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.g., intracranial hemorrhage, bone fractures, pneumonia), to help radiologists to detect relevant findings. AI-based smart reporting, summarizing patients' clinical data, and analyzing the grading of the imaging abnormalities, can provide an objective indicator of the disease's severity, resulting in quick and optimized treatment planning. In this review, we provide an overview of the different AI tools available in emergency radiology, to keep radiologists up to date on the current technological evolution in this field.

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. Computer-aided detection of lung nodules in screening programs has revolutionized the early detection of the disease. Moreover, the possibility to use AI approaches to identify patients at risk of developing lung cancer during their life can help a more targeted screening program. The combination of imaging features and clinical and laboratory data through AI models is giving promising results in the prediction of patients' outcomes, response to specific therapies, and risk for toxic reaction development. In this review, we provide an overview of the main imaging AI-based tools in lung cancer imaging, including automated lesion detection, characterization, segmentation, prediction of outcome, and treatment response to provide radiologists and clinicians with the foundation for these applications in a clinical scenario.

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. Percutaneous approaches are a type of non-vascular procedures and include percutaneous image-guided biopsies and different ablation techniques with radiofrequency, microwaves, cryoablation, and focused ultrasound. The use of these techniques requires precise imaging pretreatment planning and guidance that can be provided through different imaging techniques: ultrasound, computed tomography, cone-beam computed tomography, and magnetic resonance. These imaging modalities can be used alone or in combination, thanks to fusion imaging, to further improve the confidence of the operators and the efficacy and safety of the procedures. This article aims is to provide an overview of the available IO procedures based on clinical imaging guidance to develop a targeted and optimal approach to cancer patients.

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. To the best of our knowledge, this is the first case reported in the literature. Thanks to its high spatial resolution, contrast-enhanced computed tomography is the examination of choice for the study of anatomical variants and should be included in the routine work-up of patients presenting with unilateral vocal cord paralysis.

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. It represents the first imaging choice in pediatric patients, due to the absence of ionizing radiation. When available, CT and MRI are often used together to diagnose, assess the extent, and provide treatment plans for various orbital nontraumatic emergencies, including infective, inflammatory, vascular, and neoplastic diseases. Familiarity with the imaging appearances of these disorders helps the radiologists to establish the correct diagnosis in the emergency setting, which contributes to timely clinical management. This pictorial essay provides a description of the clinical presentation and imaging findings of nontraumatic orbital emergencies.

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. For a timely and accurate diagnosis, radiologists should be aware of fracture patterns and possible associated complications, ocular detachments and hemorrhages, and different appearances of intraorbital foreign bodies. This educational review aims to describe all post-traumatic orbital abnormalities that can be identified on CT, providing a list of tips and a diagnostic flowchart to help radiologists deal with this complex condition.

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 90.8% accuracy, with 84% sensitivity and 93% specificity, as evaluated in an independent test set (not included in training and validation) of 1337 patients. Normal controls included chest CTs from oncology, emergency, and pneumonia-related indications. The false positive rate in 140 patients with laboratory confirmed other (non COVID-19) pneumonias was 10%. AI-based algorithms can readily identify CT scans with COVID-19 associated pneumonia, as well as distinguish non-COVID related pneumonias with high specificity in diverse patient populations.