American College Of Radiology Research Articles

Comparison and Utility of Intravenous Iodinated Contrast in Chest, Abdomen, Pelvis Computerized Tomography for Trauma Patients With Blunt Mechanism of Injury Before and After the May 9, 2022 Global Contrast Shortage at a Level II Trauma Center.

Background: Intravenous (IV) contrast improves the sensitivity and specificity of injury detection in computerized tomography (CT). Its use is recommended in the workup of trauma patients by the American College of Surgeons and American College of Radiology. On May 9, 2022, the Food and Drug Administration declared a shortage of iodinated contrast due to the COVID-19 pandemic. Although the shortage has ended, the temporary lack of IV contrast forced physicians to be prudent in ordering CT scans with IV contrast. We sought to determine if there was a change in the percentage of CT contrast studies performed during the contrast shortage and if this change affected patient outcomes.Methods: Retrospective chart review was performed on all adult tier 2 trauma patients at a 619-bed community-based level II trauma center who received CT chest, abdomen, and pelvis imaging as initial workup for blunt trauma from 5/9/2021-6/30/2021 (pre-shortage) and 5/9/2022-6/30/2022 (during shortage).Results: Patients were predominantly male with median age of 31-52 and of White or Hispanic ethnicity. Before the contrast shortage, all 110 trauma patients were scanned with contrast. During the shortage, 29 of 114 patients were scanned with contrast (P < 0.001). Injuries were identified in 59% of patients scanned with contrast (P < 0.001). There were no significant differences in blood transfusion needs, repeat CT, disposition, or mortality when comparing pre-shortage to during shortage or when comparing between non-contrast and contrast studies during the shortage.Discussion: There was a decrease in the percentage of CT contrast studies performed during the shortage. A higher percentage of injuries were identified in the patients scanned with contrast. However, there were no significant differences in patient outcomes. Certain trauma patients may be safely scanned without contrast.

NIFTP-adjusted risk estimation of Bethesda thyroid cytology categories should consider the indication for FNA according to TIRADS.

Non-invasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP) was firstly described in 2016. Since NIFTP is thought a non-malignant tumor, the Bethesda system for thyroid cytology proposes two estimations of risk of malignancy of the diagnostic categories, one considering NIFTP as cancer and another one considering it as a benign neoplasm. The present study aimed to review NIFTPs in a single center, re-assess them across categories of three Thyroid Imaging Reporting and Data Systems (TIRADSs), and define the indication for biopsy according to the category-specific size cut-offs. The study period was from 2017 to 2023. The institutional database was searched for histologically proven NIFTPs with preoperative ultrasound images. NIFTPs were re-assessed according to the American College of Radiology (ACR), European (EU), and Korean (K) TIRADSs. The indication for biopsy was defined according to TIRADS category-specific size threshold. Twenty NIFTPs from 19 patients were included. The median size of the NIFTPs was 23 mm. According to ultrasound, 80-85% of NIFTPs were at low-intermediate risk and 5-15% at high risk without significant difference among the tree TIRADSs (p = 0.91). The indication for FNA, according to three TIRADSs, was found in 52-58% of cases with no significant difference among systems (p = 0.96). NIFTPs have heterogeneous presentation according to TIRADSs with very low indication rate for FNA.

The application value of deep learning-based nomograms in benign–malignant discrimination of TI-RADS category 4 thyroid nodules

Thyroid nodules are a common occurrence, and although most are non-cancerous, some can be malignant. The American College of Radiology has developed the Thyroid Imaging Reporting and Data System (TI-RADS) to standardize the interpretation and reporting of thyroid ultrasound results. Within TI-RADS, a category 4 designation signifies a thyroid nodule with an intermediate level of suspicion for malignancy. Accurate classification of these nodules is crucial for proper management, as it can potentially reduce unnecessary surgeries and improve patient outcomes. This study utilized deep learning techniques to effectively classify TI-RADS category 4 thyroid nodules as either benign or malignant. A total of 500 patients were included in the study and randomly divided into a training group (350 patients) and a test group (150 patients). The YOLOv3 model was constructed and evaluated using various metrics, achieving an 84% accuracy in the classification of TI-RADS category 4 thyroid nodules. Based on the predictions of the model, along with clinical and ultrasound data, a nomogram was developed. The performance of the nomogram was superior in both the training and testing groups. Furthermore, the calibration curve demonstrated good agreement between predicted probabilities and actual outcomes. Decision curve analysis further confirmed that the nomogram provided greater net benefits. Ultimately, the YOLOv3 model and nomogram successfully improved the accuracy of distinguishing between benign and malignant TI-RADS category 4 thyroid nodules, which is crucial for proper management and improved patient outcomes.

ACR-ARS Practice Parameter for the Performance of Proton Beam Therapy

Aim/Objectives/BackgroundThis practice parameter for the performance of proton beam radiation therapy was revised collaboratively by the American College of Radiology (ACR) and the American Radium Society (ARS). This practice parameter was developed to serve as a tool in the appropriate application of proton therapy in the care of cancer patients or other patients with conditions in which radiation therapy is indicated. It addresses clinical implementation of proton radiation therapy, including personnel qualifications, quality assurance (QA) standards, indications, and suggested documentation. MethodsThis practice parameter for the performance of proton beam radiation therapy was developed according to the process described under the heading The Process for Developing ACR Practice Parameters and Technical Standards on the ACR website (https://www.acr.org/Clinical-Resources/Practice-Parameters-and-Technical-Standards) by the Committee on Practice Parameters – Radiation Oncology of the ACR Commission on Radiation Oncology in collaboration with the ARS. ResultsThe qualifications and responsibilities of personnel, such as the proton center Chief Medical Officer or Medical Director, Radiation Oncologist, Radiation Physicist, Dosimetrist and Therapist, are outlined, including the necessity for continuing medical education. Proton therapy standard clinical indications and methodologies of treatment management are outlined by disease site and treatment group (e.g. pediatrics) including documentation and the process of proton therapy workflow and equipment specifications. Additionally, this proton therapy practice parameter updates policies and procedures related to a quality assurance and performance improvement program (QAPI), patient education, infection control, and safety. ConclusionsAs proton therapy becomes more accessible to cancer patients, policies and procedures as outlined in this practice parameter will help ensure quality and safety programs are effectively implemented to optimize clinical care.

Performance of GPT-4 on the American College of Radiology In-training Examination: Evaluating Accuracy, Model Drift, and Fine-tuning

Rationale and ObjectivesIn our study we evaluate GPT-4’s performance on the American College of Radiology (ACR) 2022 Diagnostic Radiology In-Training Examination (DXIT). We perform multiple experiments across time points to assess for model drift, as well as after fine-tuning to assess for differences in accuracy. Materials and MethodsQuestions were sequentially input into GPT-4 with a standardized prompt. Each answer was recorded and overall accuracy was calculated, as was logic-adjusted accuracy, and accuracy on image-based questions. This experiment was repeated several months later to assess for model drift, then again after the performance of fine-tuning to assess for changes in GPT’s performance. ResultsGPT-4 achieved 58.5% overall accuracy, lower than the PGY-3 average (61.9%) but higher than the PGY-2 average (52.8%). Adjusted accuracy was 52.8%. GPT-4 showed significantly higher (p = 0.012) confidence for correct answers (87.1%) compared to incorrect (84.0%). Performance on image-based questions was significantly poorer (p < 0.001) at 45.4% compared to text-only questions (80.0%), with adjusted accuracy for image questions of 36.4%. When the questions were repeated, GPT-4 chose a different answer 25.5% of the time and there was no change in accuracy. Fine-tuning did not improve accuracy. ConclusionGPT-4 performed between PGY-2 and PGY-3 levels on the 2022 DXIT, but significantly poorer on image-based questions, and with large variability in answer choices across time points, but without improvement with fine-tuning. This study underscores the potential and risks of using minimally-prompted general AI models in interpreting radiologic images as a diagnostic tool. Implementers of general AI radiology systems should exercise caution given the possibility of spurious yet confident responses.

Spécificités de la prise en charge diagnostique du cancer du sein chez la femme jeune

Breast cancer in women under the age of 40 is a rare diagnosis, but its course is generally less favourable than that of older women. It generally appears as a palpable mass, and its radiological presentation can sometimes be misleading, in the form of a falsely benign mass. The diagnostic work-up for breast cancer in young women should be complete, combining breast ultrasound and mammography, generally supplemented by breast MRI to look for additional lesions. In the specific case of pregnant or breast-feeding women, the Centre de référence des agents tératogènes (Crat), the American College of Radiology (ACR), the European Society of Urogenital Radiology (ESUR), the International Commission on Radiological Protection (ICRP) and the Société française de radiologie (SFR) have issued recommendations to ensure the safety of mother and child during complementary examinations.

ACR benchmark testing of a novel high-speed ring-gantry linac kV-CBCT system.

A new generation cone-beam computed tomography (CBCT) system with new hardware design and advanced image reconstruction algorithms is available for radiation treatment simulation or adaptive radiotherapy (HyperSight CBCT imaging solution, Varian Medical Systems-a Siemens Healthineers company). This study assesses the CBCT image quality metrics using the criteria routinely used for diagnostic CT scanner accreditation as a first step towards the future use of HyperSight CBCT images for treatment planning and target/organ delineations. Image performance was evaluated using American College of Radiology (ACR) Program accreditation phantom tests for diagnostic computed tomography systems (CTs) and compared HyperSight images with a standard treatment planning diagnostic CT scanner (Siemens SOMATOM Edge) and with existing CBCT systems (Varian TrueBeam version 2.7 and Varian Halcyon version 2.0). Image quality performance for all Varian HyperSight CBCT vendor-provided imaging protocols were assessed using ACR head and body ring CT phantoms, then compared to existing imaging modalities. Image quality analysis metrics included contrast-to-noise (CNR), spatial resolution, Hounsfield number (HU) accuracy, image scaling, and uniformity. All image quality assessments were made following the recommendations and passing criteria provided by the ACR. The Varian HyperSight CBCT imaging system demonstrated excellent image quality, with the majority of vendor-provided imaging protocols capable of passing all ACR CT accreditation standards. Nearly all (8/11) vendor-provided protocols passed ACR criteria using the ACR head phantom, with the Abdomen Large, Pelvis Large, and H&N vendor-provided protocols produced HU uniformity values slightly exceeding passing criteria but remained within the allowable minor deviation levels (5-7 HU maximum differences). Compared to other existing CT and CBCT imaging modalities, both HyperSight Head and Pelvis imaging protocols matched the performance of the SOMATOM CT scanner, and both the HyperSight and SOMATOM CT substantially surpassed the performance of the Halcyon 2.0 and TrueBeam version 2.7 systems. Varian HyperSight CBCT imaging system could pass almost all tests for all vendor-provided protocols using ACR accreditation criteria, with image quality similar to those produced by diagnostic CT scanners and significantly better than existing linac-based CBCT imaging systems.

Inappropriate wrist MRI: did guidelines have an impact?

To assess the national prevalence and cost of inappropriate MRI in patients with wrist pain prior to and following American College of Radiology (ACR) guidelinepublication. We used administrative claims from the IBM MarketScan Research Databases to evaluate the appropriateness of wrist MRI in a national cohort of patients with commercial insurance or Medicare Advantage. Adult patients with a diagnosis of wrist pain between 2016 and 2019 were included and followed for 1year. We made assessments of appropriateness based on ACR guidelines for specific wrist pain etiologies. We tabulated the total costs and out-of-pocket expenses associated with inappropriate MRI studies using weighted mean payments for facility and professional fees. We performed segmented logistic regression on interrupted time series data to identify predictors of receiving inappropriate imaging and the impact of guideline publication on MRI use. The study cohort consisted of 867,119 individuals. Of these, 40,164 individuals (4.6%) had MRI, of whom 52.6% received an inappropriate study. Inappropriate studies accounted for $44,493,234 in total payments and $8,307,540 in out-of-pocket expenses. The interrupted time series found an approximately 1% monthly decrease in the odds of receiving an inappropriate study after guidelinedissemination. MRI as a diagnostic tool for wrist pain is often inappropriate and expensive. Our findings support interventions to increase guideline adherence, such as integrated clinical decision support tools.

Fully automated diagnosis of thyroid nodule ultrasound using brain-inspired inference

The interpretability of artificial intelligence (AI) based medical diagnostic systems is crucial to make the diagnosis adequately convincible. Deep learning has been extensively investigated and utilized in the area of medical assistance diagnosis in recent decades due to its outstanding performance and objective prediction. However, a huge semantic chasm dividing clinicians and unexplainable deep models emerges. Here we design a brain-inspired inference framework from medical images to explainable features, then to the final diagnostic conclusions. The fast thinking module is responsible for recognizing medical features in ultrasound (US) images, and the slow-thinking module builds a model for inferring from medical features to diagnostic results by constructing a knowledge graph of medical features with tensor decomposition. The whole model infers through intuition and thinking like a human being, and gives the recognized medical image features while inferring the diagnosis, which greatly improves the interpretability of the model. We conducted studies on thyroid cancer diagnoses using US images. The American College of Radiology (ACR) Thyroid Imaging Reporting and Data System (TI-RADS) characteristics are employed as medical features describing thyroid nodules. Our brain-inspired medical inference framework outperforms commonly used deep learning algorithms, with an AUC score of 0.963 (95% confidential interval (CI)=0.923–1.000) for thyroid US image diagnosis. Results indicate that our framework improves diagnostic objectivity and interpretability while providing performance that is better than deep models. Our proposed brain-inspired medical inference framework could improve the efficiency of diagnosis and our technique is performant, objective and interpretable.

Ultrasound assessment of ovarian lesions: O-RADS approach

Lesions of ovarian or adnexal origin are common in all age groups, but there is significant variability in the interpretation of the words, definitions, and morphologic descriptions by the radiologists and the clinicians. There has also been a paradigm shift in the diagnosis and management of ovarian lesions during the last 20 years. Ovarian-Adnexal Reporting and Data System (O-RADS) by the American College of Radiology (ACR) was introduced in 2018. ORADS US (ultrasonography) serves as a tool to simplify the characterization of adnexal lesions, minimize the use of misleading terminology, and aid in the management of such lesions. It maintains the six risk assessment categories (O-RADS US 0–5) with an increasing predicted risk of malignancy from O-RADS US 1 to 5. In this pictorial essay, we briefly summarize the O-RADS and its descriptors, followed by representative ultrasound images to help clinicians understand what the O-RADS descriptors in the ultrasound report would mean.

Comparative analysis of the diagnostic efficiency of the four thyroid imaging reporting and data systems

The purpose of this study was to comparative analyze the diagnostic efficiency of Thyroid Imaging Reporting and Data System proposed by Kwak, American College of Radiology, the European Thyroid Association and the Chinese Medical Association, respectively (Kwak TI-RADS, ACR TI-RADS, EU-TIRADS and C-TIRADS, respectively) for malignant risk stratification of thyroid nodules. A total of 1142 thyroid nodules confirmed by cytological or surgical pathologies were retrospectively classified according to Kwak TI-RADS, ACR TI-RADS, EU-TIRADS and C-TIRADS. The area under the receiver operating characteristic curve (AUC), the diagnostic sensitivity, specificity, Youden index, and accuracy of each TI-RADS for nodules was calculated and compared. The diagnostic sensitivity of ACR TI-RADS was 85.5%, which was lowest (all P < 0.05). The diagnostic specificity of ACR TI-RADS was higher than that of Kwak TI-RADS and EU-TIRADS (all P < 0.05), but there was no significant difference compared with that of C-TIRADS (x2 = 2.335, P = 0.126). The diagnostic accuracies of the four methods were more than 85%, and there were no significant differences (all P > 0.05). The AUC of C-TIRADS was highest among the four methods (all P < 0.05). The four methods have high diagnostic efficiency in malignant risk stratification of thyroid nodules, and the AUC of C-TIRADS was higher than that of the other three methods.

A nomogram based on clinicopathological and ultrasound characteristics to predict central neck lymph node metastases in papillary thyroid cancer.

Papillary thyroid cancer (PTC) has grown rapidly in prevalence over the past few decades, and central neck lymph node metastasis (CNLNM) is associated with poor prognoses. However, whether to carry out preventive central neck lymph node dissection (CNLND) is still controversial. We aimed to construct a prediction model of CNLNM to facilitate making clinical surgical regimens. A total of 691 patients with PTC between November 2018 and December 2021 were included in our study. Univariate and multivariate analyses were performed on basic information and clinicopathological characteristics, as well as ultrasound characteristics (American College of Radiology (ACR) scores). The prediction model was constructed and performed using a nomogram, and then discriminability, calibrations, and clinical applicability were evaluated. Five variables, namely, male, age >55 years, clinical lymph node positivity, tumor size ≥1 cm, and ACR scores ≥6, were independent predictors of CNLNM in the multivariate analysis, which were eventually included to construct a nomogram model. The area under the curve (AUC) of the model was 0.717, demonstrating great discriminability. A calibration curve was developed to validate the calibration of the present model by bootstrap resampling, which indicated that the predicted and actual values were in good agreement and had no differentiation from the ideal model. The decision curve analysis (DCA) indicated that the prediction model has good clinical applicability. Our non-invasive prediction model combines ACR scores with clinicopathological features presented through nomogram and has shown good performance and application prospects for the prediction of CNLNM in PTCs.

PhiΦBreast New Diagnostic Techniques for Breast Cancer Detection

Background: Today, breast cancer is one of the most aggressive cancers in women and new cases continue to increase worldwide. In order to reduce mortality, we need to detect this life-threatening disease at an earlier stage. For two years, we have conducted a study for the identification and characterization of suspicious breast lesions using a new diagnostic technique applied to ultrasonography and mammography called “PhiΦBreast.” Methods: We have conducted a study for the identification and characterization of suspicious breast lesions using a new diagnostic technique applied to mammography and ultrasonography. Ultrasound systems with high frequency probes was used, 10-15 MHz, and Digital Breast Tomosynthesis (DBT). Identification and characterization of category C4-C5 lesions of the breast with high Predictive Positive PPV value, with a new innovative method called “PhiΦBreast” using the Golden Ratio (Phi, or Φ 1.618...) Fibonacci sequence and a Predictive Algorithm, applied to the ultrasonography and mammography with subsequent deepening with cytological examination using Fine Needle Aspiration Cytology (FNAC), according to evaluation criteria of the Breast Imaging Report Data System (BI-RADS) and the American College of Radiology (ACR). Usefulness of this research and the use of this new diagnostic tecnique is to detect the breast cancer in early stage. In addition to develop a classification model of the histological type identified in the section areas and the percentage of probability in relation between the Golden Ratio (Φ) and Fibonacci sequence. Results: With the use of Golden Ratio and Fibonacci sequence, applied to ultrasonography and mammography, we have experimented and developed a diagnostic map with characteristics of high probability of identifying suspicious lesions at an early stage. We examined 987 women, 55 lesions detected with PhiΦBreast pattern were classified according to BI-RADS descriptors for US-imaging, including morphologic features that had a high predictive value for the malignancy (p &lt;0.001) This innovative diagnostic technique has shown a sensitivity of 95%, a specificity of 97%, a positive predictive value (PPV) of 97%, and negative predictive value (NPV) of 96%. Furthermore, with a Predictive Algorithm associated with malignant cytology after FNAC, we have classified different types of potentially life-threatening cancers for patients. Conclusion: PhiΦBreast could be an important new model diagnostic technique to be applied mammography and ultrasound for detection of malignant lesions of category C4-C5. In diagnostic imaging is fundamental to recognize predictively the characteristics of a potentially aggressive tumor. This new model could represent the cornerstone as an important contribution for early diagnosis of breast cancer.

Channelized hotelling observer-based low-contrast detectability on the ACR CT accreditation phantom: Part II. Repeatability study.

Objective and quantitative evaluation for low-contrast detectability that correlates with human observer performance is lacking for routine CT quality control testing. Channelized Hotelling observer (CHO) is considered a strong candidate to fill the need but has long been deemed impractical to implement due to its requirement of a large number of repeated scans in order to provide accurate and precise estimates of index of detectability (d'). In our previous work, we optimized a CHO model observer on the American College of Radiology (ACR) CT accreditation phantom and achieved accurate measurement of d' with only 1-3 repeat scans. In this work, we aim to validate the repeatability of the proposed CHO-based low-contrast evaluation on four scanner models using the ACR CT accreditation phantom. The repeatability test was performed on four different scanners from two major CT manufacturers: Siemens Force and Alpha; Canon Prism and Prime SP. An ACR CT phantom was scanned 10 times, each time after repositioning of the phantom. For each repositioning, 3 repeated scans were acquired at 24, 12, and 6 mGy on all four scanner models. CHO was applied at the measured dose levels for different low-contrast object sizes (4-6mm). The CHO was also applied to images created using deep learning-based reconstructions on Canon Prism and to four different scan/reconstruction modes on the Siemens Alpha, a photon-counting-detector (PCD)-CT. The repeatability was evaluated by the probability that a measurement would fall within the±15% tolerance (P<15% ). With the CHO setting optimized for the ACR phantom and the use of 3 repeated scans and 9 non-overlapping slices per scan, the CHO measurement could provide high repeatability with P<15% of 98.8%-99.9% at 12 mGy with IR reconstruction on all four scanners. On scanner A, P<15% were 91.5%-99.9% at the three dose levels and for all three object sizes while the numbers were 93.6%-99.998% on scanner B. P<15% were 96.5%-97.2% for the two deep learning reconstructions and 97.0%-99.97% for the four scan/reconstruction modes on the PCD-CT. The CHO provided highly repeatable measurements with over 95% probability that a CHO measurement would lie within the±15% tolerance for most of the dose levels and object sizes on the ACR phantom. The repeatability was maintained when the CHO was applied to images created with a commercial deep learning-based reconstruction and various scan/reconstruction modes on a PCD-CT. This study demonstrates that practical implementation of CHO for routine quality control and performance evaluation is feasible.

Abstract B085: Increasing utilization of breast cancer risk assessment tools: Identifying and adapting implementation strategies using multiple stakeholder groups

Abstract Background: Each year ~12,000 female breast cancers are diagnosed in US women ages 18-45 with overrepresentation of aggressive cancers, advanced stage disease at diagnosis, and inferior outcomes. The American College of Radiology and American Society of Breast Surgeons recommend that breast cancer risk assessment (RA) start no later than age 30 to identify high-risk individuals in whom to initiate screening earlier. However, RA tools are currently utilized in as few as 3-26% of eligible women. Methods: Our study aims are to assess feasibility, reach, acceptability, and appropriateness of customized strategies to increase uptake of breast cancer RA. We identified barriers to increasing utilization of RA tools through discussions with long-term community partners in the Tennessee Breast and Cervical Cancer Screening Program (TBCSP), a three-phase adapted Feasibility of Intervention Measure (FIM) in five health clinics, and one-on-one interviews guided by the Consolidated Framework for Implementation Research with 24 providers in public health settings and 37 interviews guided by the Health Belief Model with Black women age 30-50 years with no personal history of breast cancer. Interview data contributed to the development of a conceptual framework to depict influences of provider behavior in the utilization of breast cancer risk assessment. Using the CFIR-ERIC Matching Tool and working with the study’s Community Advisory Board (CAB), the research team identified three potential strategies to increase the use of breast cancer RA in public health settings. Findings: Selected strategies included having a site champion for each clinic, offering a short series of education modules, and distributing print educational materials including a provider education sheet and patient-facing information cards. These strategies were further revised in collaboration with the CAB and patients and providers in Community Engagement Studios. Implications for D&amp;I Research: This project demonstrates the contributions of a long-standing relationship with a state-wide public health agency, implementation science tools, and collaboration with community advisors for identifying and addressing barriers to breast cancer RA. The study has generated replicable scripts for educating providers and community-informed reproducible patient education materials to increase breast cancer RA. Evaluation of the impact of these strategies on provider behavior in six clinical settings is currently underway. Citation Format: Rebecca Selove, Lucy Spalluto, Maureen Sanderson, Laura Ernst, Carolyn Audet. Increasing utilization of breast cancer risk assessment tools: Identifying and adapting implementation strategies using multiple stakeholder groups [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Breast Cancer Research; 2023 Oct 19-22; San Diego, California. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_1):Abstract nr B085.

Classification of BI-RADS using convolutional neural network and effecientNet-B7

From its development, image processing technology has become increasingly complex, such as detecting cancer in an image. In this context, the American College of Radiology (ACR) has developed a standard format and terminology called the breast imaging reporting and data system (BI-RADS). The aim of this research is to determine whether CNN can classify BI-RADS types (C1, C2, C3, C4), the best model, and the classification results of BI-RADS types on mammograms using CNN, and the best values for accuracy, sensitivity, specificity, and precision. Google Colaboratory and machine learning libraries were used in this research to create machine learning for BI-RADS classification. Then, a dataset from Kaggle titled "Mini-DDSM: Mammography-based Automatic Age Estimation" by Lekamlage et al., from the Association for Computing Machinery (ACM), Kyoto, Japan, was utilized. Based on the research, an accuracy of 85.9% was achieved for Category 1 classification. Category 2 classification attained an accuracy of 77.9%. The accuracy for Category 3 classification was 81.4%, and for Category 4, it reached 90.8%.

Biopsy or no biopsy? Risk of Malignancy in Thyroid Nodules Based on TI-RADS Criteria

Purpose: To assess the risk of malignancy in thyroid nodules recommended for biopsy using the American College of Radiology (ACR) Thyroid Imaging Reporting &amp; Data System (TIRADS).&#x0D; Methods: We conducted a retrospective review of ultrasound (US) guided thyroid biopsies performed from 2018- 2023 at IU Health hospitals for nodules which were recommended for biopsy based on ACR TI-RADS criteria and compared with histopathology results. The research was conducted at University and Methodist Hospital radiology reading rooms.&#x0D; Results: Of the total 210 thyroid nodules recommended for biopsy, only 8.57% of nodules biopsied were malignant. Positive malignancy rate for TR-3 was 10.2%, TR-4 was 7.1%, and TR-5 was 12.5%. TR-3 nodules less than 2.9 cm were all benign on histopathology. TR-4 nodules less than 1.5 cm were all benign on histopathology. TR-5 nodules were all benign on histopathology. The most common malignancy was papillary thyroid carcinoma.&#x0D; Conclusion: ACR TI-RADS is currently the standard of care for guidelines and lexicon for radiologists for characterizing thyroid nodules. Our results demonstrate that all TR-3 nodules less than 2.9 cm were all benign and most of the TR-4 and TR-5 nodules biopsied were benign on histopathology. The lower biopsy rate for positive malignancy even for TR-4 and TR-5 nodules respectively raises the need for revision for existing TI-RADS criteria, as more nodules could be followed up with ultrasound rather than percutaneous biopsy, which will alleviate patient anxiety as well as decrease overall healthcare costs.

An economically efficient strategy for diagnosing atypia of undetermined significance or follicular lesion of undetermined significance thyroid nodules with ultrasound-based risk stratification systems and BRAFV600E testing.

The management of thyroid nodules classified as atypia of undetermined significance or follicular lesion of undetermined significance (AUS/FLUS) has been a subject of ongoing debate. Therefore, the aim of this study was to investigate a cost-effective approach for managing these nodules by combining BRAFV600E mutation analysis with the guidelines provided by the American Thyroid Association (ATA) or the American College of Radiology (ACR) Thyroid Imaging Reporting and Data System (TIRADS). This study included 762 AUS/FLUS nodules in 551 patients with a postoperative pathology. A preoperative BRAFV600E gene test and an evaluation using the ATA guidelines and ACR-TIRADS were performed. Two combined diagnostic approaches were employed: In method 1, all nodules underwent BRAFV600E gene testing, and nodules testing positive for BRAFV600E or for risk stratification systems (RSSs) were diagnosed as malignant, while those with negative results in both tests were considered benign. In method 2 (modified combination method), nodules were reclassified into low-risk (category 2 and 3 in the ATA guidelines and ACR-TIRADS), medium-risk (category 4), and high-risk (category 5) groups based on the malignancy rate of the RSSs. BRAFV600E gene testing was applied only with the medium-risk group. Nodules with positive BRAFV600E mutation were upgraded to the high-risk group, while negative cases remained in the medium-risk group. Both malignancy rates and positive BRAFV600E mutation rates increased with the increase in RSS category (P<0.001). The combination of ACR with BRAFV600E gene testing significantly improved the area under the curve (AUC) compared to the use of ACR or BRAFV600E alone (the AUCs for ACR combined with BRAFV600E, modified ACR combined with BRAFV600E, ACR alone, and BRAFV600E alone were 0.875, 0.878, 0.832, and 0.839, respectively; P<0.05 for both combinations vs. ACR or BRAFV600E alone). Similarly, ATA combined with BRAFV600E showed significant improvements in AUC compared to ATA alone (the AUCs for ATA combined with BRAFV600E, modified ATA combined with BRAFV600E, and ATA alone were 0.851, 0.846, 0.809, respectively; P<0.001 for both combination methods vs. ATA alone), but there was no significant difference observed compared to using BRAFV600E alone (P=0.450 and P=0.680 for both combination methods vs. BRAFV600E). Notably, the AUC of ACR combined with BRAFV600E was greater than that of ATA combined with BRAFV600E (P=0.047 and P=0.007 for both combination methods, respectively). There were no significant differences in diagnostic performance between the two combination approaches (P=0.428 for ACR combined with BRAFV600E and P=0.314 for ATA combined with BRAFV600E). Performing BRAFV600E gene testing only on the medium-risk groups (modified combination method) significantly reduced the rate of BRAFV600E gene testing (P<0.001) without increasing the false-negative rate (P=0.818 and P=0.394 for ACR and ATA, respectively). Incorporating the BRAFV600E gene test exclusively for nodules in the medium-risk group significantly improved diagnostic efficacy, reduced the utilization of gene tests, and maintained a consistent false-negative rate.

The Saudi Central Board for Accreditation of Healthcare Institutions (CBAHI) compared with the Joint Commission International (JCI) and the American College of Radiology (ACR)

In this paper we compare the Saudi Central Board for Accreditation of Healthcare Institutions (CBAHI) with the Joint Commission International (JCI) and the American College of Radiology (ACR) regarding accreditation of hospitals or radiology departments. CBAHI was established in 2005 by the Saudi Arabian Ministry of Health to improve the health care service to the public and to localize the protocols and the policies. CBAHI sets standards for healthcare facilities like hospitals, clinics, and laboratories according to CBAHI standards. The applying of these standards vary on the degree of enforcement of these standards in private or governmental sectors.

DICOM: A Revolution in Facet of Maxillofacial Imaging

Digital Imaging and Communications in Medicine (DICOM) is a paradigm that enables interoperability between health-related digital applications, imaging devices, and Picture Archiving and Communications Systems (PACS). DICOM has its foundation in the USA, established by the American College of Radiology (ACR) and the National Electrical Manufacturers Association (NEMA) in 1983 with the objective of establishing a “gold standard” for monitor and print-copy devices. In 1996, the American Dental Association (ADA) became a member of DICOM, followed by the American Academy of Oral and Maxillofacial Radiology and the American Association of Orthodontics. A working committee specifically for dentistry (WG-22) was initiated in 2003, and in 2005, it was finally adopted as an international standard. Currently, it is internationally recognised as the standard configuration for compatibility between dental radiological images and various scanners and digital X-ray devices. DICOM synchronises network communication and facilitates the exchange of patient data through a transmission protocol. The purpose of the present paper is to emphasise the reliance of digital dentistry on DICOM and its relationship with Three-Dimensional (3D) printing.