Breast Imaging Research Articles

The Horizon of Thyroid Imaging Reporting and Data System in the Diagnostic Performance of Thyroid Nodules: Clinical Application and Future Perspectives.

The widespread occurrence of thyroid nodules and the typically slow progression of thyroid cancer have led to the development of the thyroid imaging reporting and data system (TI-RADS). The primary objectives behind the development of TI-RADS were to minimize unnecessary biopsies of non-cancerous nodules, enhance the overall precision of diagnosis and establish a uniform risk-stratification framework based on the lexicon to notify healthcare professionals of nodules that require a biopsy. The identification and precise diagnosis of thyroid nodules have led to improved clinical practice examination reports within the general population. TI-RADS is a risk-stratification system related to thyroid lesions and based on ultrasound characteristics and is similar to the structure of the breast imaging reporting and data system. There are various versions of TI-RADS, with some being widely used and adequately validated, while others lacking thorough evaluation. TI-RADS uses a numerical scoring system for characteristics, and its categories are determined by the cumulative score of a thyroid nodule, indicating the likelihood of it being benign or malignant. In this article, the various TI-RADS systems were examined as a successful method for producing precise and comprehensive documentation, with a particular emphasis on their functionality, similarities, distinctions and potential future developments.

Exploring the feasibility of FOCUS DWI with deep learning reconstruction for breast cancer diagnosis: A comparative study with conventional DWI.

This study compared field-of-view (FOV) optimized and constrained undistorted single-shot diffusion-weighted imaging (FOCUS DWI) with deep-learning-based reconstruction (DLR) to conventional DWI for breast imaging. This study prospectively enrolled 49 female patients suspected of breast cancer from July to December 2023. The patients underwent conventional and FOCUS breast DWI and data were reconstructed with and without DLR. Two radiologists independently evaluated three images per patient using a 5-point Likert scale. Objective evaluations, including signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and apparent diffusion coefficient (ADC), were conducted using manual region of interest-based analysis. The subjective and objective evaluations were compared using the Friedman test. The scores for the overall image quality, anatomical details, lesion conspicuity, artifacts, and distortion in FOCUS-DLR DWI were higher than in conventional DWI (all P < 0.001). The SNR of FOCUS-DLR DWI was higher than that of conventional and FOCUS DWI (both P < 0.001), while FOCUS and conventional DWI were similar (P = 0.096). Conventional, FOCUS, and FOCUS-DLR DWI had similar CNR and ADC values. Our findings indicate that images produced by FOCUS-DLR DWI were superior to conventional DWI, supporting the applicability of this technique in clinical practice. DLR provides a new approach to optimize breast DWI.

Performance of breast MRI for high-risk screening during lactation.

To assess the diagnostic performance of breast MRI during lactation in the setting of high-risk breast cancer screening. Screening breast MRIs performed between April 2008 and March 2024 were retrospectively reviewed. Background parenchymal enhancement (BPE) grade was compared between lactating patients and patients who recently stopped lactating using the Mann-Whitney test. Breast Imaging Reporting and Data System (BI-RADS) scores prevalence rates were compared between lactating patients and controls encompassing young non-lactating patients using the Chi-square test. Diagnostic performance was calculated for patients with a biopsy reference or a 1-year radiologic follow-up. One-hundred forty-two screening breast MRIs were performed in lactating patients (n = 104, median age, 36.0 ± 6.0 years). Marked BPE appeared in 82% of cases (116/142), with a higher BPE grade in exams performed during lactation as compared with those performed in patients who had recently ceased lactating (p < 0.001). Screening MRIs performed during lactation had a higher rate of BI-RADS 3 scores (40/142, 28% vs. 683/8922, 7%, p < 0.001) and a lower rate of BI-RADS 1/2 scores (88/142, 62% vs. 7549/8922, 84.6%, p = 0.002) compared with those performed in controls (n = 8922). One pregnancy-associated breast cancer was detected, and one interval-cancer occurred. All MRI-guided biopsies were negative (n = 13). Screening breast MRI during lactation had 50% sensitivity (1/2), 60% specificity (72/120), 2.0% positive predictive value (1/49), and 98.6% negative predictive value (71/82). The efficacy of breast MRI for high-risk screening during lactation is limited by prominent BPE, leading to an increased rate of BI-RADS 3 categorization and diminished overall specificity. Question Studies on breast MRI during lactation were solely focused on studies conducted in patients with known cancer but not in the screening setting. Findings Screening breast MRI during lactation usually results in marked background parenchymal enhancement, negatively impacting its diagnostic performance. Clinical relevance Despite the lower performance, and amidst the significant risk of pregnancy-associated breast cancer, this screening approach remains relevant for lactating patients with high-risk profiles, such as BReast Cancer (BRCA) carriers. Radiologists should be familiar with the normal appearance of breast MRI duringlactation.

Supplemental breast cancer screening after negative mammography in U.S. women with dense breasts.

The extent and determinants of supplemental screening among women with dense breasts are unclear. We evaluated a retrospective cohort of 498,855 women aged 40-74 years with heterogeneously or extremely dense breasts who obtained 1,176,251 negative screening mammography examinations during 2011-2019 in the United States. Overall, 2.8% and 0.3% of mammograms had supplemental ultrasound or MRI within one year, respectively. Onsite availability was associated with ultrasound (odds ratio [OR]=4.35; 95%CI : 4.21-4.49) but not MRI (OR = 0.94; 95%CI : 0.85-1.04). Facility academic affiliation and for-profit status were inversely associated with supplemental ultrasound (OR = 0.53; 95%CI : 0.49-0.57 and OR = 0.83; 95%CI : 0.81-0.86, respectively) and positively associated with supplemental MRI (OR = 3.04; 95%CI : 2.86-3.46 and OR = 1.88; 95%CI : 1.66-2.12, respectively). Supplemental screening was more likely to occur after passage of state-specific density notification laws than before passage (OR = 3.56; 95%CI 3.30-3.84 and OR = 1.79; 95%CI 1.60-2.00, respectively). These results show that supplemental breast imaging utilization has been uncommon and was related to facility factors and density legislation.

Probing clarity: AI-generated simplified breast imaging reports for enhanced patient comprehension powered by ChatGPT-4o

BackgroundTo assess the reliability and comprehensibility of breast radiology reports simplified by artificial intelligence using the large language model (LLM) ChatGPT-4o.MethodsA radiologist with 20 years’ experience selected 21 anonymized breast radiology reports, 7 mammography, 7 breast ultrasound, and 7 breast magnetic resonance imaging (MRI), categorized according to breast imaging reporting and data system (BI-RADS). These reports underwent simplification by prompting ChatGPT-4o with “Explain this medical report to a patient using simple language”. Five breast radiologists assessed the quality of these simplified reports for factual accuracy, completeness, and potential harm with a 5-point Likert scale from 1 (strongly agree) to 5 (strongly disagree). Another breast radiologist evaluated the text comprehension of five non-healthcare personnel readers using a 5-point Likert scale from 1 (excellent) to 5 (poor). Descriptive statistics, Cronbach’s α, and the Kruskal–Wallis test were used.ResultsMammography, ultrasound, and MRI showed high factual accuracy (median 2) and completeness (median 2) across radiologists, with low potential harm scores (median 5); no significant group differences (p ≥ 0.780), and high internal consistency (α > 0.80) were observed. Non-healthcare readers showed high comprehension (median 2 for mammography and MRI and 1 for ultrasound); no significant group differences across modalities (p = 0.368), and high internal consistency (α > 0.85) were observed. BI-RADS 0, 1, and 2 reports were accurately explained, while BI-RADS 3–6 reports were challenging.ConclusionThe model demonstrated reliability and clarity, offering promise for patients with diverse backgrounds. LLMs like ChatGPT-4o could simplify breast radiology reports, aid in communication, and enhance patient care.Relevance statementSimplified breast radiology reports generated by ChatGPT-4o show potential in enhancing communication with patients, improving comprehension across varying educational backgrounds, and contributing to patient-centered care in radiology practice.Key PointsAI simplifies complex breast imaging reports, enhancing patient understanding.Simplified reports from AI maintain accuracy, improving patient comprehension significantly.Implementing AI reports enhances patient engagement and communication in breast imaging.Graphical

Coverage of mammography imaging in and outside an organized breast cancer screening program - variation by age and sociodemographic groups.

In recent decades, attendance to organized breast cancer screening has been decreasing in European countries. This could be partly due to an increase in the use of opportunistic screening. The aim of this study was to assess the coverage of imaging in and outside the screening program in Finland during the period of 1999-2018. We also compared the usage of imaging services across sociodemographic groups in the more recent years (2017-2018). Our initial data consisted of 1,159,000 screening-target-aged women (50-69 years) in 1999-2018 and 1,849,000 women aged 30-89 years in 2017-2018. Data on organized breast cancer screening program was drawn from the Finnish Cancer Registry and supplemented with comprehensive individual data on mammograms and ultrasounds performed outside the program. Among the screening-aged women (50-69), a clear decline in the overall imaging coverage was observed during the study period (from 89 to 85%). The use of outside imaging increased slightly but not enough to compensate for the overall decrease. There were large differences in coverages between sociodemographic groups. Compared to manual workers and the unemployed, upper-level employees were around two times more active in using outside imaging (8.2% vs. 3.6% and 4.3%, respectively). Overall breast imaging coverage has slowly decreased during the 2000s, while outside imaging has increased slightly. The coverage of imaging in and outside the program largely followed the same trends, with the highest usage concentrating on higher socioeconomical groups, native speakers and highly educated women.

Added Value of a Previous Exam Comparison in the Final Breast Imaging and Reporting Data System (BI-RADS®) Assessment in Screening Mammography

Added Value of a Previous Exam Comparison in the Final Breast Imaging and Reporting Data System (BI-RADS®) Assessment in Screening Mammography

A Multi-label Artificial Intelligence Approach for Improving Breast Cancer Detection With Mammographic Image Analysis.

Breast cancer remains a major global health concern. This study aimed to develop a deep-learning-based artificial intelligence (AI) model that predicts the malignancy of mammographic lesions and reduces unnecessary biopsies in patients with breast cancer. In this retrospective study, we used deep-learning-based AI to predict whether lesions in mammographic images are malignant. The AI model learned the malignancy as well as margins and shapes of mass lesions through multi-label training, similar to the diagnostic process of a radiologist. We used the Curated Breast Imaging Subset of Digital Database for Screening Mammography. This dataset includes annotations for mass lesions, and we developed an algorithm to determine the exact location of the lesions for accurate classification. A multi-label classification approach enabled the model to recognize malignancy and lesion attributes. Our multi-label classification model, trained on both lesion shape and margin, demonstrated superior performance compared with models trained solely on malignancy. Gradient-weighted class activation mapping analysis revealed that by considering the margin and shape, the model assigned higher importance to border areas and analyzed pixels more uniformly when classifying malignant lesions. This approach improved diagnostic accuracy, particularly in challenging cases, such as American College of Radiology Breast Imaging-Reporting and Data System categories 3 and 4, where the breast density exceeded 50%. This study highlights the potential of AI in improving the diagnosis of breast cancer. By integrating advanced techniques and modern neural network designs, we developed an AI model with enhanced accuracy for mammographic image analysis.

Artificial Intelligence-based Software for Breast Arterial Calcification Detection on Mammograms.

The performance of a commercially available artificial intelligence (AI)-based software that detects breast arterial calcifications (BACs) on mammograms is presented. This retrospective study was exempt from IRB approval and adhered to the HIPAA regulations. Breast arterial calcification detection using AI was assessed in 253 patients who underwent 314 digital mammography (DM) examinations and 143 patients who underwent 277 digital breast tomosynthesis (DBT) examinations between October 2004 and September 2022. Artificial intelligence performance for binary BAC detection was compared with ground truth (GT) determined by the majority consensus of breast imaging radiologists. Area under the receiver operating curve (AUC), sensitivity, specificity, positive predictive value and negative predictive value (NPV), accuracy, and BAC prevalence rates of the AI algorithm were compared. The case-level AUCs of AI were 0.96 (0.93-0.98) for DM and 0.95 (0.92-0.98) for DBT. Sensitivity, specificity, and accuracy were 87% (79%-93%), 92% (88%-96%), and 91% (87%-94%) for DM and 88% (80%-94%), 90% (84%-94%), and 89% (85%-92%) for DBT. Positive predictive value and NPV were 82% (72%-89%) and 95% (92%-97%) for DM and 84% (76%-90%) and 92% (88%-96%) for DBT, respectively. Results are 95% confidence intervals. Breast arterial calcification prevalence was similar for both AI and GT assessments. Breast AI software for detection of BAC presence on mammograms showed promising performance for both DM and DBT examinations. Artificial intelligence has potential to aid radiologists in detection and reporting of BAC on mammograms, which is a known cardiovascular risk marker specific to women.

Role of Image-Guided Biopsy in Nonpalpable Breast Lesions: A Study in the Sub-Himalayan Region of North India

Background Nonpalpable breast lesions pose a challenge in their early diagnosis. Image-guided biopsy is preferred in these cases so that a pathological diagnosis of breast carcinoma is reached timely for a better prognosis as the disease has an increased chance of successful outcome with early identification and treatment. Objective The study aims at evaluating the role of stereotactic core needle biopsy (CNB) and percutaneous ultrasound-guided core needle biopsy (US-CNB) in diagnosing suspicious nonpalpable breast lesions. Methods Our study included 35 patients with nonpalpable breast lesions and having a Breast Imaging Reporting and Data System (BI-RADS) risk assessment category IV or V on mammography or sonography. These 35 lesions were subjected to percutaneous image-guided (stereotactic or US) biopsy for histopathological analysis. Results Out of a total of 35 cases, 17 were pathologically malignant (48.6%), with the most common subtype being invasive ductal carcinoma (82.3%). Twenty-nine cases underwent US-CNB, 16 (55.1%) of which were malignant and 13 (44.8%) were benign on histopathological evaluation (HPE). The remaining six cases, which on mammography showed no mass but suspicious malignant calcification only, were subjected to stereotactic CNB, out of which one (16.6%) was malignant and five (83.3%) were benign on HPE. Hence, the lesions visible on sonography were more likely to be malignant. Conclusion Sonography and mammography play a complimentary role in detecting breast carcinoma. Percutaneous biopsy under image guidance can be used as an accurate diagnostic alternative to open surgical excisional biopsy to avoid diagnostic delay.

Breast cancer imaging-clinical experience with two-dimensional-shear wave elastography: A retrospective study.

Breast cancer morbidity has been increasing worldwide, but treatments are improving. The therapeutic response depends on the stage at which the disease is diagnosed. Therefore, early diagnosis has never been more essential for successful treatment and a reduction in mortality rates. Radiology plays a pivotal role in cancer detection, and advances in ultrasound (US) palpation have shown promising results for breast cancer imaging. The addition of two-dimensional-shear wave elastography (2D-SWE) US in the routine breast imaging exam can increase early cancer detection and promote better surveillance. To evaluate the clinical applications of 2D-SWE US in breast cancer detection and its combination with other imaging modalities. The 200 consecutive female patients aged 50-80 were examined to evaluate palpable breast lesions. All patients underwent mammography, bright mode (B-mode) US, and 2D-SWE followed by US-guided biopsy in two consecutive sessions. Combining B-mode and shear wave US imaging with X-ray mammography revealed 100% of the suspicious lesions, resulting in greater sensitivity, specificity, and negative predictive value. The result improves compared to either B-mode or 2D-SWE alone (P = 0.02). Combining 2D-SWE with conventional US and X-ray techniques improves the chance of early cancer detection. Including 2D-SWE in regular breast imaging routines can reduce the need for biopsies and improve the chance of early cancer detection and survivability with the proper line of therapy.

Automated breast density assessment for full-field digital mammography and digital breast tomosynthesis.

Mammographic density is a strong risk factor for breast cancer (BC) and is reported clinically as part of Breast Imaging Reporting and Data System (BI-RADS) results issued by radiologists. Automated assessment of density is needed that can be used for both full-field digital mammography (FFDM) and digital breast tomosynthesis (DBT) as both types of exams are acquired in standard clinical practice. We trained a deep learning model to automate the estimation of BI-RADS density from a prospective Washington University (WashU) clinic-based cohort of 9,714 women, entering into the cohort in 2013 with follow-up through, October 31, 2020. The cohort included 27% non-Hispanic Black women. The trained algorithm was assessed in an external validation cohort that included 18,360 women screened at Emory from January 1, 2013 and followed through December 31, 2020 that included 42% non-Hispanic Black women. Our model-estimated BI-RADS density demonstrated substantial agreement with the density as assessed by radiologist. In the external validation, the agreement with radiologists for category B 81% and C 77% for FFDM and B 83% and C 74% for DBT show important distinction for separation of women with dense breast. We obtained a Cohen's κ of 0.72 (95% CI, 0.71, 0.73) in FFDM and 0.71 (95% CI 0.69, 0.73) in DBT. We provided a consistent and fully automated BI-RADS estimation for both FFDM and DBT using a deep learning model. The software can be easily implemented anywhere for clinical use and risk prediction.

TECRR: a benchmark dataset of radiological reports for BI-RADS classification with machine learning, deep learning, and large language model baselines

BackgroundRecently, machine learning (ML), deep learning (DL), and natural language processing (NLP) have provided promising results in the free-form radiological reports’ classification in the respective medical domain. In order to classify radiological reports properly, a high-quality annotated and curated dataset is required. Currently, no publicly available breast imaging-based radiological dataset exists for the classification of Breast Imaging Reporting and Data System (BI-RADS) categories and breast density scores, as characterized by the American College of Radiology (ACR). To tackle this problem, we construct and annotate a breast imaging-based radiological reports dataset and its benchmark results. The dataset was originally in Spanish. Board-certified radiologists collected and annotated it according to the BI-RADS lexicon and categories at the Breast Radiology department, TecSalud Hospitals Monterrey, Mexico. Initially, it was translated into English language using Google Translate. Afterwards, it was preprocessed by removing duplicates and missing values. After preprocessing, the final dataset consists of 5046 unique reports from 5046 patients with an average age of 53 years and 100% women. Furthermore, we used word-level NLP-based embedding techniques, term frequency-inverse document frequency (TF-IDF) and word2vec to extract semantic and syntactic information. We also compared the performance of ML, DL and large language models (LLMs) classifiers for BI-RADS category classification.ResultsThe final breast imaging-based radiological reports dataset contains 5046 unique reports. We compared K-Nearest Neighbour (KNN), Support Vector Machine (SVM), Naive Bayes (NB), Random Forest (RF), Adaptive Boosting (AdaBoost), Gradient-Boosting (GB), Extreme Gradient Boosting (XGB), Long Short-Term Memory (LSTM), Bidirectional Encoder Representations from Transformers (BERT) and Biomedical Generative Pre-trained Transformer (BioGPT) classifiers. It is observed that the BioGPT classifier with preprocessed data performed 6% better with a mean sensitivity of 0.60 (95% confidence interval (CI), 0.391-0.812) compared to the second best performing classifier BERT, which achieved mean sensitivity of 0.54 (95% CI, 0.477-0.607).ConclusionIn this work, we propose a curated and annotated benchmark dataset that can be used for BI-RADS and breast density category classification. We also provide baseline results of most ML, DL and LLMs models for BI-RADS classification that can be used as a starting point for future investigation. The main objective of this investigation is to provide a repository for the investigators who wish to enter the field to push the boundaries further.

The Utility of Second-Look US to Evaluate Abnormal Molecular Breast Imaging Findings: A Retrospective Study.

The purpose of this study was to evaluate the utility of US for identifying and characterizing lesions detected on molecular breast imaging (MBI). A retrospective single-institution review was performed of patients with MBI studies with subsequent US for abnormal MBI findings between January 1, 2015, and September 30, 2021. Medical records, imaging, and histopathology were reviewed. The reference standard was histopathology and/or imaging follow-up. Associations among MBI findings, the presence of an US correlate, and histopathology were evaluated by Fisher exact tests. The 32 lesions detected on MBI in 25 patients were evaluated by US, and 19 lesions had an US correlate (19/32, 59%). Mass uptake was more likely to have an US correlate (11/13, 85%; P = .02) than nonmass uptake (7/19, 37%), and mass uptake was more likely to be malignant (5/13, 38%; P = .01). Of the 13 lesions without an US correlate, 5 were evaluated and subsequently biopsied by MRI (2 high-risk lesions and 3 benign lesions). Follow-up MBIs demonstrated stability/resolution for 5 lesions in 4 patients at 6 months or longer. Three patients had no further imaging. Mass lesions identified on MBI were more likely to have an US correlate and were more likely to be malignant than nonmass lesions.

Comparative Analysis of Large Language Models' Performance in Breast Imaging

Aim: To evaluate the performance of the flagship models, OpenAI's GPT-4o and Anthropic's Claude 3.5 Sonnet, in breast imaging cases. Materials and Methods: The dataset consisted of cases from the publicly available Case of the Month archive by the Society of Breast Imaging. Questions were classified as text-based or containing images from mammography, ultrasound, magnetic resonance imaging, or hybrid imaging. The accuracy rates of GPT-4o and Claude 3.5 Sonnet were compared using the Mann-Whitney U test. Results: Of the total 94 questions, 61.7% were image-based. The overall accuracy rate of GPT-4o was higher than that of Claude 3.5 Sonnet (75.4% vs. 67.7%, p=0.432). GPT-4o achieved higher scores on questions based on ultrasound and hybrid imaging, while Claude 3.5 Sonnet performed better on mammography-based questions. In tumor group cases, both models reached higher accuracy rates compared to the non-tumor group (both, p&gt;0.05). The models' performance in breast imaging cases overall exceeded 75%, ranging between 64-83% for questions involving different imaging modalities. Conclusion: In breast imaging cases, although GPT-4o generally achieved higher accuracy rates than Claude 3.5 Sonnet in image-based and other types of questions, their performances were comparable.

Predictors of breast cancer HER2-receptor positivity by MRI intuitive imaging features

BackgroundToday, breast cancer is the most diagnosed cancer worldwide. There are many different clinical presentations, radiological characteristics, and histological types of breast cancer. HER2 is overexpressed in a significant number of breast cancer cases reaching 20% of all breast cancers, and its overexpression is seen directly proportionate with a poor outcome and prognosis.MethodsWe started this cross-sectional research from January 2022–December 2023 on 202 breast cancer patients who had 220 lesions. The molecular subtypes of the different lesions were determined in all the included cases. Magnetic resonance imaging (MRI) studies were conducted in all included cases. The MRI parameters included conventional MRI, diffusion-weighted analysis, and dynamic post-contrast T1-weighted imaging.ResultsThe prevalence of irregular margins (P < 0.001), linear and segmental distribution (P = 0.044), heterogeneous pattern (P < 0.001), and type 2 curve was statistically significantly higher in the HER2-positive lesions. Nipple infiltration incidence showed statistically significant elevation in the HER2-positive lesions (P = 0.017). The lesions’ ADC and perilesional ADC in the HER2-positive lesions were also statistically significantly elevated. The best cutoff point of ADC to detect lesions with positive HER2 expression was > 0.885 × 10–3 mm2/s, with 65.7% sensitivity and 60% specificity, with a statistically significant value (p = 0.005).ConclusionsMagnetic resonance imaging of breast imaging is a promising noninvasive method for identifying breast tumors with the HER2 molecular subtype. Combining various radiological features by MRI may provide a conclusion for recognizing positive HER2 lesions.

ALK-Positive Histiocytosis and its Distinctive Manifestation in the Breast

Abstract Introduction/Objective ALK-positive histiocytosis (APH) is a rare histiocytic proliferation disease that harbors ALK rearrangements, often involving fusion with KIF5B. Primary cases in the unilateral breast are exceptionally uncommon, with only a few reported cases globally, characterizing it as a unique histiocytic proliferation disorder. Methods/Case Report In our study, we report a case of unilateral multifocal breast involvement of ALK-positive histiocytosis following bilateral breast core biopsies in a 56-year-old Hispanic woman who presented with bilateral breast masses. Right breast imaging demonstrated multiple nodular densities, further characterized on ultrasound as a hypoechoic region of 8 cm with increased vascularity. On right breast biopsy, prominent proliferation of histiocytes (lacking high-grade cytologic atypia) with chronic inflammation, few giant cells, and focal cholesterol clefts were identified. On immunohistochemical studies, the lesional cells were variably positive for histiocytic markers CD68, CD163, and PU.1 and were negative for S100 and pancytokeratin. ALK1 showed multifocal staining in the lesional cells as confirmed on repeat study; however, ALK (D5F3), GMS and acid fast studies were negative. The presence of focal cholesterol clefts, scattered giant cells, and chronic inflammation lead us to consider aberrant ALK expression in a reactive process. However, APH was definitively diagnosed through confirmation of clonal histiocyte proliferation, observed findings on morphology, immunophenotyping, and the presence of KIF5B-ALK gene fusion as detected by comprehensive solid tumor fusion panel testing. Results (if a Case Study enter NA) NA Conclusion It is noteworthy that ALK-positive histiocytosis can occasionally present alongside features of a reactive process in the breast. Therefore, considering any history of prior surgery or trauma at the lesion site is crucial. However, we recommend a comprehensive approach in most cases, which includes morphological, immunohistochemical, and molecular analysis on all suspected histiocytosis cases. This approach aims to increase awareness and detection of this rarely described entity and to precisely distinguish it from other diseases for prognostic and therapeutic purposes.

Assessing the Factors Leading to Missed Breast Cancer Diagnoses in Mammography Among Pakistani Women: A Prospective Cross-Sectional Study.

Objective To determine the frequency of false-negative mammograms, and identify the factors contributing to missed breast cancer diagnoses in Pakistani women. Materials and methods This descriptive, prospective cross-sectional study was conducted at a tertiary care hospital from December 15, 2020, to December 10, 2023, including 150 women aged 30 to 60 who underwent bilateral mammography and concurrent breast ultrasound. The study analyzed the frequency and causes of false negatives, categorizing them into patient-related, tumor-related, technical-related, and provider-related factors. Stratification was performed based on age groups and Breast Imaging Reporting and Data System (BI-RADS) scores, and statistical significance was assessed using Chi-square tests. Results The study found a 5.1% frequency of false-negative mammograms. Lesion-related factors were seen in 59 (39.7%) patients; patient-related factors were seen in 40 (26.7%) patients; provider-related factors were seen in 29 (19.3%) patients; and technical-related factors were seen in 22 (26.7%) patients. Conclusion Dense breast tissue significantly contributes to missed breast cancer diagnoses in Pakistani women. While lesion-related, provider-related, and technical-related factors uniformly affect mammography outcomes, addressing patient-specific challenges - particularly in younger women with dense breasts - is crucial. The study suggests incorporating supplementary imaging modalities, like ultrasound, in routine screening for better detection, potentially informing national breast cancer screening guidelines in Pakistan.

Understanding the risk of ionizing radiation in breast imaging: Concepts and quantities, clinical importance, and future directions

BackgroundConventional mammography remains the primary imaging modality for state-of-the-art breast imaging practice and its benefit (both on diagnostic and screening) was largely reported. In mammography, the typical Mean Glandular Dose (MGD) from X-ray radiation to the breast spans, on average, from 1 to 10 mGy, depending on breast thicknesses, percentage of fibroglandular tissue, and on the examination purpose. MethodsThe aim of this narrative review is to describe the extent of radiation risk in X-ray breast imaging and discuss the main steps and parameters (e.g. MGD, screening frequency and number of examination views) that may have an influence on the radiation risk assessment. ResultsEven though the radiation doses used with these examinations are very low, as compared to other medical or natural radiation exposures, there is a non-negligible associated risk of radiation-induced cancer. Accurate radiation risk assessment permits to better balance the overall estimation of the benefit-to-risk ratio in X-ray breast imaging. ConclusionsIt is expected that a better knowledge about radiation-induced cancer risk among population could improve the communications skills between patients and clinicians and could help to increase the awareness in women about radiation risk perception for a transparent and proper informed choice of imaging exam.

Resistant inflammatory breast lesions: can AI exclude malignancy?

BackgroundNumerous underlying causes can lead to inflammatory breast disorders. A wide range of non-specific symptoms may be presenting symptoms, which could cause a delay in diagnosis and thus improper therapy. Studies on artificial intelligence (AI) are rapidly developing and offer a wide range of possible uses in breast imaging. Artificial intelligence-based computer-assisted diagnosis (AI-CAD) holds promise in the field of mammography. It demonstrated diagnostic performances that are equivalent to or even better than those achieved by stand-alone methods. The current work aimed to identify whether AI can improve the performance of mammography in diagnosing inflammatory breast diseases and excluding the underlying malignancy in cases resistant to treatment that may reduce the need for interventional procedures such as biopsy. MethodsOur study was a retrograde one done on 34 patients with pathologically proven inflammatory breast lesions.ResultsSuppurative breast lesions gave high false positive results. This was also the case with granulomatous mastitis; while simple inflammatory lesions gave true negative results on AI interrogation.ConclusionsArtificial intelligence can be of great value in diagnosing simple inflammatory breast lesions thus following up on such lesions can usually be sufficient without asking for unneeded biopsies. On the other hand, our study showed that AI had high false positive results in suppurative lesions and granulomatous mastitis. Consequently, ultrasonography can be more reliable in their diagnosis.