American College Of Radiology Research Articles

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.

Evaluating diagnostic accuracy and agreement of TI-RADS scoring in thyroid nodules: A comparative analysis between sonographers and radiologists.

The Thyroid Imaging Reporting and Data System (TI-RADS) is an essential tool for assessing thyroid nodules, primarily used by radiologists. This study aimed to compare the agreement of TI-RADS scores between sonographers and radiologists and to assess the diagnostic performance of these scores against histological findings in suspicious thyroid nodules. In a retrospective analysis, 168 patients with suspicious thyroid nodules classified as TR3 and above by the radiologists were included. Both sonographers and radiologists independently assigned the American College of Radiologists (ACR) TI-RADS scores, which were then compared for inter-reader agreement using Cohen's Kappa statistic. The scores were also evaluated for diagnostic performance against histological results based on the Bethesda system. The study revealed a moderate overall agreement between sonographers and radiologists in TI-RADS scoring (κ = 0.504; 95% CI: 0.409-0.599), with poor agreement noted specifically for nodule margin scores (κ = 0.102; 95% CI: -1.430-0.301). In terms of diagnostic performance against histological outcomes, sonographers' TI-RADS scores showed a sensitivity of 100% and a specificity of 44.6%, while radiologists' scores showed a sensitivity of 100% but a lower specificity of 29.3%. The findings indicate moderate agreement in TI-RADS scoring between sonographers and radiologists, with reproducibility challenges especially in scoring nodule margins. The marginally superior diagnostic performance of sonographers' scores suggests potential efficiency benefits in involving sonographers in preliminary assessments. Future research should aim to encompass a wider range of TI-RADS categories and focus on minimizing scoring variability to enhance the system's clinical utility.

Automated quality control analysis for American College of Radiology (ACR) digital mammography (DM) phantom images.

To develop and validate an automated software analysis method for mammography image quality assessment of the American College of Radiology (ACR) digital mammography (DM) phantom images. Twenty-seven DICOM images were acquired using Fuji mammography systems. All images were evaluated by three expert medical physicists using the Royal Australian and New Zealand College of Radiologists (RANZCR) mammography quality control guideline. To enhance the robustness and sensitivity assessment of our algorithm, an additional set of 12 images from a Hologic mammography system was included to test various phantom positional adjustments. The software automatically chose multiple regions of interest (ROIs) for analysis. A template matching method was primarily used for image analysis, followed by an additional method that locates and scores each target object (speck groups, fibers, and masses). The software performance shows a good to excellent agreement with the average scoring of observers (intraclass correlation coefficient [ICC] of 0.75, 0.79, 0.82 for speck groups, fibers, and masses, respectively). No significant differences were found in the scoring of target objects between human observers and the software. Both methods achieved scores meeting the pass criteria for speck groups and masses. Expert observers allocated lower scores to fiber objects, with diameters less than 0.61mm, when compared to the software. The software was able to accurately score objects when the phantom position changed by up to 25mm laterally, up to 5 degrees rotation, and overhanging the chest wall edge by up to 15mm. Automated software analysis is a feasible method that may help improve the consistency and reproducibility of mammography image quality assessment with reduced reliance on human interaction and processing time.

Complementing Mammography Screening with Automated 3D Ultrasound in Women with High-Density Breasts

Background. In early breast screening structure, an important factor is breast density. Women with types C and D breast density according to American College of Radiology (ACR) classification have 4–6-fold increased risk of cancer compared to women with type A breast density. With this type of density, the effectiveness of diagnostic mammography (MG) is significantly decreased. Today, automated breast 3D ultrasound can be considered as an additional screening method in women with breast structure types C and D according to ACR.Objective: to perform a comparative analysis of the diagnostic efficacy of 2D and 3D ultrasound in women aged 40 years and older with high breast tissue density.Material and methods. Retro-prospective, observational, single-center study was conducted. From February 2019 to May 2023, 1283 patients aged 40 years and older were examined. The patients were divided into two groups. In group A, women underwent 2D ultrasound and MG. In group B, additionally to these methods, 3D ultrasound was performed. In both groups, the results were evaluated according to Breast Imaging-Reporting and Data System (BI-RADS). Based on the obtained data, the following indicators were determined: positive (PPV) and negative (NPV) predictive values, sensitivity, specificity and accuracy of all methods. For 2D and 3D ultrasound, the predictive model areas under curve (AUC) of receiver operating characteristic (ROC) were calculated.Results. MG method showed PPV 0.89, NPV 0.93, sensitivity 0.53, specificity 0.99, and balanced accuracy 0.76. Indicators for 2D ultrasound demonstrated PPV 0.8, NPV 0.98, sensitivity 0.9, specificity 0.97, balanced accuracy 0.93, AUC ROC 0.968. The results for 3D ultrasound were as follows: PPV 0.97, NPV 0.97, sensitivity 0.9, specificity 0.99, balanced accuracy 0.94, AUC ROC 0.98.Conclusion. The diagnostic efficiency of breast automated 3D ultrasound in patients aged 40 years and older is comparable to 2D ultrasound in terms of sensitivity, and it’s better in terms of accuracy, specificity. The prognostic model of 3D ultrasound is also better compared to 2D ultrasound.

Leveraging artificial intelligence for timely detection and referral of incidental pulmonary nodules at a safety-net hospital.

422 Background: Incidental pulmonary nodules (IPNs) are commonly reported through a multitude of radiologic exams. A small number of these IPNs can harbor lung cancer (LC). To aide LC detection rates, hospitals must identify and validate the completion of appropriate follow-up regarding IPNs in a timely manner. The problem with timely referral and completion of work up is found to be amplified in safety-net settings with limited resources. John Peter Smith (JPS) hospital, a safety-net hospital, has developed a method to identify, track, and refer patients presenting with IPNs. Methods: JPS has created an algorithm to classify all IPNs by means of Thynk Health, an Artificial Intelligence (AI) platform that uses Natural Language Processing (NLP) to identify and track incidental findings and lung cancer screening patients (LCS). The algorithm created a scoring system by combining Fleischner Society (2017) and the American College of Radiology (ACR) LCS (Lung-RADS) guideline recommendations. The scoring system was designed to mimic Lung-RADS established by the ACR (1, 2, 3, 4A, and 4B). The assigned score creates a risk stratification to assist providers in the patient’s care plan. Using NLP, the project queried radiologic exams performed at JPS searching for key terminology relating to IPNs. Findings were then reviewed by radiology staff for validity and assigned a score based on the algorithm. Program exclusions, when identified in the radiology report, included: patient < 35 years, patient has known cancer or under surveillance of oncology team, abnormalities favoring infectious process, LCS patients and when nodule size is not reported in the radiologist’s dictation. Results: A current state analysis was completed from 3/25/24 to 4/25/24. The AI platform analyzed 5,276 CT exams (2049 abdomen, 1347 head/neck, and 1880 chest) involving partial or complete imaging of the lungs. A total of 727 (13.8%) IPNs were flagged for review. After excluding exams falling outside of the set program parameters, a total of 178 (3.4%) IPNs were identified. 21 (11.8%) of the IPNs identified received a score of 4A or 4B. Patients found to have a score of 4A (11, 6.2%) and 4B (10, 5.6%) underwent additional review. Conclusions: Our team has created a unique system that can be used as a model for other large health systems. By leveraging technology, we are able to successfully identify key factors associated with IPNs to help ensure patients receive the recommended care in a timely manner. Results of JPS incidental pulmonary nodule program: first monthly analysis. JPS Pulmonary Nodule Score 4A 4B Number of Patients (% of IPNs) 11 (6.2%) 10 (5.6%) Median Age (years) 70 68.5 JPS Assigned PCP 1 7 (63.6%) 7 (70%) Median Smoking History (PY) 2 6.4 24.5 Average Nodule Size (mm) 11.5 34.3 Pulmonary Referral 3/11 (27.3%) 7/10 (70%) Positive for Cancer 0 3/4 (75%) 1 Primary care provider. 2 Pack years.

Ranking the Relative Importance of Image Quality Features in CT by Consensus Survey

ObjectiveThis study sought to determine consensus opinions from subspecialty radiologists and imaging physicists on the relative importance of image quality features in CT. MethodsA prospective survey of subspecialty radiologists and medical physicists was conducted to collect consensus opinions on the relative importance of ten image quality features: axial sharpness, blooming, contrast, longitudinal sharpness, low contrast axial sharpness, metal artifact, motion, noise magnitude, noise texture, and streaking. The survey was first sent to subspecialty radiologists in volunteer leadership roles in the American College of Radiology and Radiological Society of North America, thereafter relying on snowball sampling. Surveyed subspecialties were abdominal, cardiac, emergency, musculoskeletal, neuro, pediatric, and thoracic radiology, and medical physics. Individual respondents’ ratings were normalized for calculation of mean normalized ratings and priority rankings for each feature within subspecialties. Also calculated were intraclass correlation coefficients across image quality features within subspecialties, and analysis of variance across subspecialties within each feature. ResultsMost subspecialties had moderate to excellent intraclass agreement. For every radiology subspecialty except musculoskeletal, motion was the most important image quality feature. There was agreement across subspecialties that axial sharpness and contrast are only moderately important. There was disagreement across subspecialties on the relative importance of noise magnitude. Blooming was highly important to cardiac radiologists, and noise texture was highly important to musculoskeletal radiologists. ConclusionImage quality preferences differ based on clinical tasks and challenges in each anatomical radiology subspecialty. CT image analysis and development of quantitative measures of quality and protocol optimization—and related policy initiatives—should be specific to radiology subspecialty.

Enhancing chatbot performance for imaging recommendations: Leveraging GPT-4 and context-awareness for trustworthy clinical guidance

PurposeTo investigate if GPT-4 improves the accuracy, consistency, and trustworthiness of a context-aware chatbot to provide personalized imaging recommendations from American College of Radiology (ACR) appropriateness criteria documents using semantic similarity processing: In addition, we sought to enable auditability of the output by revealing the information source the decision relies on. Material and MethodsWe refined an existing chatbot that incorporated specialized knowledge of the ACR guidelines by upgrading GPT-3.5-Turbo to its successor GPT-4 by OpenAI, using the latest version of LlamaIndex, and improving the prompting strategy. This chatbot was compared to the previous version, generic GPT-3.5-Turbo and GPT-4, and general radiologists regarding the performance in applying the ACR appropriateness guidelines. ResultsThe refined context-aware chatbot performed superior to the previous version using GPT-3.5-Turbo, generic chatbots GPT-3.5-Turbo and GPT-4, and general radiologists in providing “usually or may be appropriate” recommendations according to the ACR guidelines (all p < 0.001). It also outperformed GPT-3.5-Turbo and general radiologists in respect to “usually appropriate” recommendations (both p < 0.001). Moreover, the consistency in correct answers was higher with 78 % consistent correct “usually appropriate” answers and 94 % for “usually or may be appropriate” recommendations. In all cases, the same source documents were chosen, ensuring transparency. ConclusionOur study demonstrates the significance of context awareness in ensuring the use of appropriate knowledge and proposes a strategy to enhance trust in chatbot-based outputs to provide transparency. The improvements in accuracy, consistency, and source transparency address trust issues and enhance the clinical decision support process.Abbreviations: ACR, American College of Radiology; accGPT, appropriateness criteria context aware GPT; accGPT-4, appropriateness criteria context aware GPT using GPT-4; GPT, generative pre-trained transformer; LLM, Large Language Model.

2023 American Academy of Orthopaedic Surgeons Management of Osteoarthritis of the Hip Evidence-Based Clinical Practice Guideline: Case Studies.

The American Academy of Orthopaedic Surgeons, together with The Hip Society, American Association of Hip and Knee Surgeons, American College of Radiology, and American Physical Therapy Association, recently released the updated 2023 Management of Osteoarthritis of the Hip Evidence-Based Clinical Practice Guidelines. 8 recommendations and 9 options comprise these guidelines intended to optimize treatment outcomes in a patient population that is growing exponentially over time. In this article, we present case studies that illustrate utilization of the clinical practice guideline workgroup's evidence-based recommendations in clinical practice.

ASNR Statement on Gadolinium-Based Contrast Agent Use in Patients with Chronic Kidney Disease.

Beginning in 2006, neuroradiologists became increasingly aware of the risk of nephrogenic system fibrosis (NSF) when patients suffering from chronic kidney disease (CKD) received gadolinium-based contrast agents (GBCAs) in conjunction with MRI scans. Radiology practices began withholding GBCAs from MRI patients with substantial CKD and instated a variety of safety measures to ensure that these individuals did not inadvertently receive GBCAs. As a result, the worldwide incidence of NSF was dramatically reduced. Since that time, a wealth of research on NSF and its etiology has found few unconfounded cases associated with those GBCAs categorized as Group II agents by the American College of Radiology. In 2023 and 2024, members of the American Society of Neuroradiology (ASNR) Standards and Guidelines Committee reviewed new research evidence on GBCA safety and its relevance to current MRI contrast administration guidelines for patients with CKD. This focused on systematic reviews and meta-analyses conducted during the past five years. Upon consideration of this literature, recommendations for administration of GBCAs to patients with CKD were formulated. For neuroimaging applications, the ASNR recommends that Group II GBCAs no longer be withheld in patients with CKD when these agents are medically indicated for diagnosis. Moreover, if Group II GBCAs are exclusively used in an MRI practice, other safety measures such as checking renal function or querying patients about chronic kidney disease can be discontinued. ACR = American College of Radiology; ASNR = American Society of Neuroradiology; CKD = chronic kidney disease; GBCA = gadolinium-based contrast agent; NSF = nephrogenic systemic fibrosis.

Quantifying the reproducibility and longitudinal repeatability of radiomics features in magnetic resonance Image-Guide accelerator Imaging: A phantom study

ObjectiveThis study aimed to quantitatively evaluate the inter-platform reproducibility and longitudinal acquisition repeatability of MRI radiomics features in Fluid-Attenuated Inversion Recovery (FLAIR), T2-weighted (T2W), and T1-weighted (T1W) sequences on MR-Linac systems using an American College of Radiology (ACR) phantom. Materials and MethodsThis study used two MR-Linac systems (A and B) in different cancer centers. The ACR phantom was scanned on system A daily for 30 consecutive days to evaluate longitudinal repeatability. Additionally, retest data were collected after repositioning the phantom. Inter-platform reproducibility was assessed by conducting scans under identical conditions using system B. Regions of interest were delineated on the T1W sequence from system A and mapped to other sequences via rigid registration. Intra-observer and inter-observer comparisons were conducted. Repeatability and reproducibility were assessed using the intraclass correlation coefficient (ICC) and coefficient of variation (CV). Robust radiomics features were identified based on ICC>0.9 and CV<10 %. ResultsAnalysis showed that a higher proportion of radiomics features derived from longitudinal FLAIR sequence (51.65 %) met robustness criteria compared to T2W (48.35 %) and T1W (43.96 %). Additionally, more inter-platform features from the FLAIR sequence (62.64 %) were robust compared to T2W (42.86 %) and T1W (39.56 %). Test-retest and intra-observer repeatability were excellent across all sequences, with a median ICC of 0.99 and CV<5%. However, inter-observer reproducibility was inferior, especially for the T1W sequence. ConclusionsDifferent sequences show variations in repeatability and reproducibility. The FLAIR sequence demonstrated advantages in both longitudinal repeatability and inter-platform reproducibility. Caution is warranted when interpreting data, particularly in longitudinal or multiplatform radiomics studies.

Preliminary experiments on interpretable ChatGPT-assisted diagnosis for breast ultrasound radiologists.

Ultrasound is essential for detecting breast lesions. The American College of Radiology's Breast Imaging Reporting and Data System (BI-RADS) classification system is widely used, but its subjectivity can lead to inconsistency in diagnostic outcomes. Artificial intelligence (AI) models, such as ChatGPT-3.5, may potentially enhance diagnostic accuracy and efficiency in medical settings. This study aimed to assess the utility of the ChatGPT-3.5 model in generating BI-RADS classifications for breast ultrasound reports and its ability to replicate the "chain of thought" (CoT) in clinical decision-making to improve model interpretability. Breast ultrasound reports were collected, and ChatGPT-3.5 was used to generate diagnoses and treatment plans. We evaluated GPT-4's performance by comparing its generated reports to those from doctors with various levels of experience. We also conducted a Turing test and a consistency analysis. To enhance the interpretability of the model, we applied the CoT method to deconstruct the decision-making chain of the GPT model. A total of 131 patients were evaluated, with 57 doctors participating in the experiment. ChatGPT-3.5 showed promising performance in structure and organization (S&O), professional terminology and expression (PTE), treatment recommendations (TR), and clarity and comprehensibility (C&C). However, improvements are needed in BI-RADS classification, malignancy diagnosis (MD), likelihood of being written by a physician (LWBP), and ultrasound doctor artificial intelligence acceptance (UDAIA). Turing test results indicated that AI-generated reports convincingly resembled human-authored reports. Reproducibility experiments displayed consistent performance. Erroneous report analysis revealed issues related to incorrect diagnosis, inconsistencies, and overdiagnosis. The CoT investigation supports the potential of ChatGPT to replicate the clinical decision-making process and offers insights into AI interpretability. The ChatGPT-3.5 model holds potential as a valuable tool for assisting in the efficient determination of BI-RADS classifications and enhancing diagnostic performance.

AAPM Task Group Report 325: MRI static magnetic field homogeneity measurement and evaluation procedures - Guidance and resources.

Measurement of static magnetic field (B0) homogeneity is an essential component of routine MRI system evaluation. This report summarizes the work of AAPM Task Group (TG) 325 on vendor-specific methods of B0 homogeneity measurement and evaluation. TG 325 was charged with producing a set of detailed, step-by-step instructions to implement B0 homogeneity measurement methods discussed in the American College of Radiology (ACR) MRI Quality Control Manual using specific makes and models of MRI scanners. The TG produced such instructions for as many approaches as was relevant and practical on six currently available vendor platforms including details of software/tools, settings, phantoms, and other experimental details needed for a reproducible protocol. Because edits to these instructions may need to be made as vendors enter and exit the market and change available tools, interfaces, and access levels over time, the step-by-step instructions are published as a living document on the AAPM website. This summary document provides an introduction to B0 homogeneity testing in MRI and several of the common methods for its measurement and evaluation. A living document on the AAPM website provides vendor-specific step-by-step instructions for performing these tests to facilitate accurate and reproducible B0 homogeneity evaluation on a routine basis.

Comparison of ultrasound findings of papillary thyroid carcinoma subtypes based on the 2022 WHO classification of thyroid neoplasms.

The present study aimed to analyze and compare sonographic features of papillary thyroid carcinoma (PTC) subtypes to determine whether ultrasound (US) may aid in differentiating particular subtypes. This retrospective study enrolled 133 patients diagnosed with 142 histopathologically proven PTCs as per the fifth edition of the World Health Organization classification of thyroid neoplasms between January 2013 and May 2023. US features based on the American College of Radiology and European Thyroid Imaging and Reporting Data Systems (TIRADS), and histopathological characteristics of nodules were assessed and compared. Histopathological analysis yielded 55 (38.7%) classic PTC, 32 (22.5%) invasive encapsulated follicular variant (IEFV) PTC, 20 (14.1%) oncocytic subtype, 14 (9.9%) non-invasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP), 11 (7.8%) infiltrative follicular subtype, 7 (4.9%) tall cell subtype, 2 (1.4%) solid subtype, and 1 (0.7%) diffuse sclerosing subtype. The US findings indicating malignancy, such as taller-than-wide shape, irregular margins, echogenic foci, and higher TIRADS categories, were more frequently demonstrated in nodules with classic PTC and the tall cell subtype, in line with their histopathological features. Conversely, IEFV-PTC and NIFTP rarely exhibited these high-risk sonographic features. US appearance of the oncocytic subtype more frequently overlapped with IEFV-PTC, yet hypo/very hypoechoic nodules with larger nodular diameters and higher TIRADS scores may favor the diagnosis of this subtype. US features of certain subtypes may guide the differential diagnosis regarding shape, margin, echogenic foci, and TIRADS category of nodules; however, definitive subtyping is not yet possible using US images alone.

Current State of Evidence-Based Long-Term Monitoring Protocols for Breast Plastic Surgery Patients

BackgroundRecommendations for breast surveillance following breast plastic surgery are frequently changing. Establishing guidelines for long-term monitoring protocols may help identify treatable conditions and prevent untoward sequelae. We sought to evaluate the current state of evidence-based long-term monitoring protocols for patients following breast augmentation, reduction, and breast reconstruction.MethodsOfficial guidelines from various American societies and international societies were analyzed for alignment in evidence-based recommendations regarding breast surveillance.ResultsThe most recent US FDA update recommends magnetic resonance imaging or ultrasound starting 5–6 years after surgery and every 2–3 years thereafter. Discrepancies exist among professional societies: the American Society of Plastic Surgeons (ASPS) aligns with the FDA, while the American Society of Breast Surgeons and American College of Radiology (ACR) find no role for imaging for asymptomatic cases. Ultrasound is first-line for any implant concerns, with MRI if necessary. European societies oppose routine breast implant imaging. Breast reduction patients lack unique screening protocols; monitoring aligns with age and cancer risk factors. Following mastectomy and breast reconstruction, most organizations advocate for annual clinical examinations, with more frequent examinations initially. Evidence suggests that physical examination is sufficient to detect local cancer recurrence, with imaging only indicated if there is concern for recurrence. No surveillance imaging is recommended by the American Society of Clinical Oncology, National Comprehensive Cancer Network, or ASPS; however, ACR recommends mammography for autologous reconstruction only.ConclusionMultispecialty and regulatory body alignment may promote provider and patient adherence. Ongoing studies of long-term outcomes are needed to strengthen the level of evidence for monitoring guidelines.

Differential Access to Breast Magnetic Resonance Imaging Compared with Mammography and Ultrasound

For high-risk women, breast magnetic resonance (MR) is the preferred supplemental imaging option, but spatial access differences may exacerbate disparities in breast care. This was a cross-sectional study examining distance between ZIP codes and the nearest breast imaging facility (MR, mammography, ultrasound) using 2023 data from the Food and Drug Administration and the American College of Radiology. Linear regression was used to assess distance differences controlling for Area Deprivation Index (ADI), urbanicity, and population size. Analyses were conducted in 2024. Among the 29,629 ZIP codes with an ADI and known urbanicity, unadjusted mean distance to breast MR was 23.2±25.1 miles (SD) compared with 8.2±8.3 for mammography and 22.2±25.0 for ultrasound. Hence, the average distance to breast MR facilities was 2.8 times further than to mammography facilities. ADI and urbanicity were associated with increased distance to the nearest breast imaging facility. The additional miles associated with the least advantaged areas compared with most advantaged areas was 12.2 (95%CI: 11.3, 13.2) for MR, 11.5 miles (95%CI: 10.6, 12.3) for ultrasound, and 2.4 (95%CI: 2.1, 2.7) for mammography. Compared with metropolitan areas, the additional miles to breast MR facilities was 23.2 (95%CI: 22.5, 24.0) for small/rural areas. Spatial access is substantially better for mammography sites compared with breast MR or ultrasound sites. Given these findings, consideration of options to mitigate the impact of differential access should be considered. For example, mammography sites could offer contrast-enhanced mammography. Future research should examine the feasibility and effectiveness of this and other options.

Capturing anatomy in computed tomography scans for genital pathology.

In this cross-sectional study,we aimed to characterize how frequently the anatomy of interest (AOI) was excluded when evaluating genital pathology using the current CT pelvis protocol recommended by the American College of Radiology and evaluate how AOI exclusion affects patient management. We retrospectively reviewed medical records, using diagnosis and CPT codes, of patients admitted with genital pathology who obtained a CT scan at our institution from July 1, 2020-April 30, 2023. Baseline patient demographics were included. Data about each index CT scan (scan obtained at our institution) were recorded and assessed for exclusion of the AOI. Statistical analysis was performed to determine the rate of AOI exclusion and to compare patient management between patients with AOI excluded versus those without AOI exclusion. 113 presentations for genital pathology included an index CT scan and were included for analysis. Patients were primarily men (98%) with a mean age of 53.1 years (SD 13.9). The most common diagnoses were Fournier's gangrene (35%), scrotal abscess (22%) and unspecified infection (19%). 26/113 scans (23%) did not capture the entire AOI. When the AOI was missed during the index scan, there was a higher rate of obtaining additional scans (38% vs. 21%), but a similar rate of intervention (77% vs. 63%) when compared to index scans that captured the entire AOI.35 scans (31%) had protocol-extending instructions;index scans that captured the entire AOI were more likely to have specific protocol-extending instructions(38% vs. 8% p < 0.01). Creating a specific CT protocol for genital pathology could decrease the amount of inappropriate irradiation and improve AOI capture rates without relying on specific request for protocol deviation.

Trends in Radiation at a Level I Trauma Center.

Introduction Following traumatic injury, patients often require imaging modalities that subject them to large amounts of radiation. The current protocol for imaging workup in trauma patients includes plain radiographs and computed tomography (CT). Although these imaging modalities have improved quality and efficiency in trauma interventions, it is essential to understand their potential limitations and adverse effects.The large amounts of radiation from these imaging tests have raised concern for particularly vulnerable populations, including children and adolescents. Objective To evaluate the trends in radiation exposure in trauma assessment and imaging seen at a Level I trauma center. Methods This retrospective analysis used a de-identified dataset from the trauma registry at Desert Regional Medical Center Level I Trauma Center in Palm Springs, CA. Total radiation amounts were calculated based on the type of diagnostic modality. An effective radiation dose was assigned to each examination (radiograph and CT scan) based on the values provided by the American College of Radiology. Results There was a statistically significant positive correlation between injury severity scoreand effective radiation dose from imaging workup. From 2016 to 2021, radiation amount and year were found to be negatively correlated. There was a larger, statistically significant amount of radiation dosage among adult trauma patients (16.32 mSv) when compared to pediatric (6.32 mSv) and geriatric (12.67 mSv) groups. Conclusion Our Level I trauma center has observed a decline in radiation exposure with our current trauma protocols in place. On average, adult patients received the highest effective radiation dose to pediatric and geriatric patients. CT scans and radiography are essential tools in initial trauma workup and should be used only in appropriate patients.

Male breast cancer: Case report and review of literature

Introduction: Male breast cancer (MBC) is an uncommon disease that makes up less than 1% of all breast cancer diagnoses worldwide. While breast carcinomas have some similarities in both sexes, there are significant differences. Case Report: We present a case of breast cancer in a 57-year-old man who had a left breast mass of 3.5 cm classified as breast imaging reporting and data system (BIRADS 4) by the American College of Radiology (ACR). The presence of invasive breast carcinoma of grade II Scarff–Bloom–Richardson (SBR) with a negative molecular phenotype B HER2 was confirmed by a tru-cut biopsy. The axillary cytopuncture did not show any abnormalities. The patient was later treated for a left mastectomy with homolateral axillary dissection. Conclusion: Male breast cancer represents a therapeutic dilemma. A multidisciplinary approach is mandatory.

Incidental Findings in Lung Cancer Screening.

While low-dose computed tomography (LDCT) for lung cancer screening (LCS) has been recognized for its effectiveness in reducing lung cancer mortality, it often simultaneously leads to the detection of incidental findings (IFs) unrelated to the primary screening indication. These IFs present diagnostic and management challenges, potentially causing unnecessary anxiety and further invasive diagnostic procedures for patients. This review article provides an overview of IFs encountered in LDCT, emphasizing their clinical significance and recommended management strategies. We categorize IFs based on their anatomical locations (intrathoracic-intrapulmonary, intrathoracic-extrapulmonary, and extrathoracic) and discuss the most common findings. We highlight the importance of utilizing guidelines and standardized reporting systems by the American College of Radiology (ACR) to guide appropriate follow-ups. For each category, we present specific IF examples, their radiologic features, and the suggested management approach. This review aims to provide radiologists and clinicians with a comprehensive understanding of IFs in LCS for accurate assessment and management, ultimately enhancing patient care. Finally, we outline a few key aspects for future research and development in managing IFs.

Comparative evaluation of image-based vs. text-based vs. multimodal AI approaches for automatic breast density assessment in mammograms

Background and objectivesIn the last decade, there has been a growing interest in applying artificial intelligence (AI) systems to breast cancer assessment, including breast density evaluation. However, few models have been developed to integrate textual mammographic reports and mammographic images. Our aims are (1) to generate a natural language processing (NLP)-based AI system, (2) to evaluate an external image-based software, and (3) to develop a multimodal system, using the late fusion approach, by integrating image and text inferences for the automatic classification of breast density according to the American College of Radiology (ACR) guidelines in mammograms and radiological reports. MethodsWe first compared different NLP models, three based on n-gram term frequency – inverse document frequency and two transformer-based architectures, using 1533 unstructured mammogram reports as a training set and 303 reports as a test set. Subsequently, we evaluated an external image-based software using 303 mammogram images. Finally, we assessed our multimodal system taking into account both text and mammogram images. ResultsOur best NLP model achieved 88 % accuracy, while the external software and the multimodal system achieved 75 % and 80 % accuracy, respectively, in classifying ACR breast densities. ConclusionAlthough our multimodal system outperforms the image-based tool, it currently does not improve the results offered by the NLP model for ACR breast density classification. Nevertheless, the promising results observed here open the possibility to more comprehensive studies regarding the utilization of multimodal tools in the assessment of breast density.