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.

Patients' Perceptions and Attitudes to the Use of Artificial Intelligence in Breast Cancer Diagnosis: A Narrative Review.

Breast cancer remains the most prevalent cancer among women worldwide, necessitating advancements in diagnostic methods. The integration of artificial intelligence (AI) into mammography has shown promise in enhancing diagnostic accuracy. However, understanding patient perspectives, particularly considering the psychological impact of breast cancer diagnoses, is crucial. This narrative review synthesizes literature from 2000 to 2023 to examine breast cancer patients' attitudes towards AI in breast imaging, focusing on trust, acceptance, and demographic influences on these views. Methodologically, we employed a systematic literature search across databases such as PubMed, Embase, Medline, and Scopus, selecting studies that provided insights into patients' perceptions of AI in diagnostics. Our review included a sample of seven key studies after rigorous screening, reflecting varied patient trust and acceptance levels towards AI. Overall, we found a clear preference among patients for AI to augment rather than replace the diagnostic process, emphasizing the necessity of radiologists' expertise in conjunction with AI to enhance decision-making accuracy. This paper highlights the importance of aligning AI implementation in clinical settings with patient needs and expectations, emphasizing the need for human interaction in healthcare. Our findings advocate for a model where AI augments the diagnostic process, underlining the necessity for educational efforts to mitigate concerns and enhance patient trust in AI-enhanced diagnostics.

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.

Bilateral lymphadenopathies on mammograms: a case of mixed connective tissue disease and psoriatic arthropathy.

Axillary lymphadenopathy is defined as abnormality (e.g., increase in size or density) of lymph nodes in the armpit, caused by malignant diseases such as metastases (mainly from primary breast cancer), lymphoma, or leukaemia as well as benign conditions such as infectious or autoimmune systemic diseases. Appropriate imaging and pathological examinations on needle samples, together with accurate clinical correlation are needed for a correct diagnosis and management. Herein, we report a case of a 47-year-old female presented at our department of radiology for her annual mammographic screening. Mammography demonstrated multiple bilateral, enlarged, although benign-appearing axillary lymph nodes. While both breasts showed no sign of malignancy on mammograms, the lymphadenopathies suggested a potential underlying inflammatory process. Previous mammography performed five years before did not present any lymphadenopathy. The patient, recalled for additional breast and axillary ultrasound and for clinical correlation, reported that she had been suffering for at least four years from an autoimmune systemic disease, mixed connective tissue disease, recently overlapping with psoriatic arthropathy, thus explaining the aetiology of reactive lymph nodes enlargement.

The predictive role of right ventricular late gadolinium enhancement in patients with tetralogy of Fallot undergoing pulmonary valve replacement.

BACKGROUND: Our purpose was to evaluate the correlations between right ventricular (RV) late gadolinium enhancement (LGE) at cardiac magnetic resonance (CMR) in patients with tetralogy of Fallot (ToF) scheduled for pulmonary valve replacement (PVR) and post-PVR functional data. METHODS: We retrospectively reviewed ToF patients scheduled for PVR who underwent two CMR examinations at our institution, one before the procedure (CMR-0), including contrast-enhanced sequences, and one after the procedure (CMR-1). Functional left and RV data were obtained by segmenting short-axis stacks on both CMR examinations, and normalised variations were calculated by dividing differences between CMR-1 and CMR-0 by the intercurring time interval, whereas the RV scar burden was assessed on CMR-0 LGE sequences both semiquantitatively and quantitatively. Data were reported as median and interquartile range, differences were appraised with the Mann-Whitney U test, while correlations were assessed with Spearman's ρ. RESULTS: Fifteen patients with a median age of 25 years (16-29), including 9 (60%) males, with a median time interval between CMR-0 and CMR-1 of 17 months (12-23), were retrospectively reviewed. The semiquantitative LGE score at CMR-0 was 7 (6-9), and LGE volume was 4.49 mL (3.70-5.78), covering 5.63% (4.92-7.00) of the RV. RV LGE score showed a moderate positive correlation with the normalised variation of RV stroke volume (ρ = 0.662, p = 0.007) and a borderline moderate positive correlation with the normalised variation of RV end-diastolic indexed volume (ρ = 0.513, p = 0.050). CONCLUSIONS: The assessment of RV LGE before PVR may provide insights on post-PVR functional data, potentially facilitating a patient-tailored treatment pathway.

Klinefelter's syndrome and taurodontism.

OBJECTIVE: Taurodontism is a dental anomaly characterized by an enlarged pulp chamber and apycal displacement of the pulpar floor. The prevalence of taurodontism in normal population is controversial. It has been reported that taurodontism is frequently observed in Klinefelter's patients. The purpose of this study was to assess the prevalence of taurodontism in a group of Italian Klinefelter's patients and in a randomly selected male population of Italy and to compare the results with published data. MATERIALS AND METHODS: Digital panoramic radiographs of 16 Klinefelter's patients and of 100 normal males were retrospectively studied in order to investigate the prevalence of taurodontism in these groups of patients. RESULTS: Taurodont teeth were observed in 2 of the 16 Klinefelter's patients (12.5%) and in 2 of 100 normal males (2.0%). CONCLUSIONS: Our results confirm the higher prevalence of taurodontism in Klinefelter's patients compared to the normal population (12.5% vs. 2.0%). Due to the wide discrepancy of incidence of taurodontism reported in literature (0.04%-48.0% in normal population; 12.5%-88.0% in Klinefelter's patients), we conclude that it is not possible to state which is the prevalence of taurodontism in a normal population nor among Klinefelter's patients.