Current Status and Future of BI-RADS in Multimodality Imaging, From the AJR Special Series on Radiology Reporting and Data Systems

Breast lesions on MRI in mass and non-mass enhancement: Kaiser score and modified Kaiser score + for readers of variable experience

OBJECTIVES

To compare the diagnostic performance of three readers using BI-RADS and Kaiser score (KS) based on mass and non-mass enhancement (NME) lesions.

METHODS

A total of 630 lesions, 393 malignant and 237 benign, 458 mass and 172 NME, were analyzed. Three radiologists with 3 years, 6 years, and 13 years of experience made diagnoses. 596 cases had diffusion-weighted imaging, and the apparent diffusion coefficient (ADC) was measured. For lesions with ADC > 1.4 × 10-3 mm2/s, the KS was reduced by 4 as the modified KS +, and the benefit was assessed.

RESULTS

When using BI-RADS, AUC was 0.878, 0.915, and 0.941 for mass, and 0.771, 0.838, 0.902 for NME for Reader-1, 2, and 3, respectively, better for mass than for NME. The diagnostic accuracy of KS was improved compared to BI-RADS for less experienced readers. For Reader-1, AUC was increased from 0.878 to 0.916 for mass (p = 0.005) and from 0.771 to 0.822 for NME (p = 0.124). Based on the cut-off value of BI-RADS ≥ 4B and KS ≥ 5 as malignant, the sensitivity of KS by Readers-1 and -2 was significantly higher for both Mass and NME. When ADC was considered to change to modified KS +, the AUC and the accuracy for all three readers were improved, showing higher specificity with slightly degraded sensitivity.

CONCLUSION

The benefit of KS compared to BI-RADS was most noticeable for the less experienced readers in improving sensitivity. Compared to KS, KS + can improve specificity for all three readers. For NME, the KS and KS + criteria need to be further improved.

CLINICAL RELEVANCE STATEMENT

KS provides an intuitive method for diagnosing lesions on breast MRI. BI-RADS and KS face greater difficulties in evaluating NME compared to mass lesions. Adding ADC to the KS can improve specificity with slightly degraded sensitivity.

KEY POINTS

KS provides an intuitive method for interpreting breast lesions on MRI, most helpful for novice readers. KS, compared to BI-RADS, improved sensitivity in both mass and NME groups for less experienced readers. NME lesions were considered during the development of the KS flowchart, but may need to be better defined.

Intrathoracic Lymph Node Microcalcifications are Associated With a High Prevalence of Malignancy and Anaplastic Lymphoma Kinase Rearrangement: The "Calce" Study

BACKGROUND

Microcalcifications are acknowledged as a malignancy risk factor in multiple cancers. However, the prevalence and association of intrathoracic lymph node (ILN) calcifications with malignancy remain unexplored.

METHODS

In this cross-sectional study, we enrolled patients with known/suspected malignancy and an indication for endosonography for diagnosis or ILN staging. We assessed the prevalence and pattern of calcified ILNs and the prevalence of malignancy in ILNs with and without calcifications. In addition, we evaluated the genomic profile and PD-L1 expression in lung cancer patients, stratifying them based on the presence or absence of ILN calcifications.

RESULTS

A total of 571 ILNs were sampled in 352 patients. Calcifications were detected in 85 (24.1%) patients and in 94 (16.5%) ILNs, with microcalcifications (78/94, 83%) being the predominant type. Compared with ILNs without calcifications (214/477, 44.9%), the prevalence of malignancy was higher in ILNs with microcalcifications (73/78, 93.6%; P<0.0001) but not in those with macrocalcifications (7/16, 43.7%; P=0.93). In patients with lung cancer, the high prevalence of metastatic involvement in ILNs displaying microcalcifications was independent of lymph node size (< or >1 cm) and the clinical stage (advanced disease; cN2/N3 disease; cN0/N1 disease). The anaplastic lymphoma kinase (ALK) rearrangement was significantly more prevalent in patients with than in those without calcified ILNs (17.4% vs. 1.7%, P<0.001), and all of them exhibited microcalcifications.

CONCLUSION

ILN microcalcifications are common in patients undergoing endosonography for suspected malignancy, and they are associated with a high prevalence of metastatic involvement and ALK rearrangement.

Factors Associated with False Positive Breast Cancer Results in the Real-Time Sonoelastography Evaluation of Solid Breast Lesions

Background and Objectives: Breast cancer is one of the most widespread cancers among the female population around the world and is curable if diagnosed in an early stage. Consequently, breast cancer screening imaging techniques have greatly evolved and adjusted over the last decades. Alongside mammography, sonoelastography became an important tool for breast cancer detection. However, sonoelastography still has its limitations, namely, there is still a high occurrence of false positive results in the BIRADS 4 category. The aim of our study is to identify potential false positive predictors and to ascertain the factors influencing the quality of strain ultrasound elastography for the evaluation of suspicious solid breast lesions categorized as BIRADS 4B, 4C, and 5. Materials and Methods: We conducted a retrospective study in a single private medical center in Timisoara between January 2017 and January 2022 analyzing 1625 solid breast lesions by the sonoelastography strain using a standardized BIRADS-US lexicon. Results: Our study showed that most sonoelastography factors linked to incorrect and overdiagnosis were due to a nodule dimension (OR = 1.02 per unit increase), posterior acoustic shadowing (OR = 12.26), reactive adenopathy (OR = 6.35), and an increased TES score (TES3 OR = 6.60; TES4 OR = 23.02; TES5 OR = 108.24). Regarding patient characteristics, age (OR = 1.09 per unit increase), BMI, (OR = 1.09 per unit increase), and breastfeeding history (OR = 3.00) were observed to increase the likelihood of false positive results. On the other hand, the nodules less likely to be part of the false positive group exhibited the following characteristics: a regular shape (OR = 0.27), homogenous consistency (OR = 0.42), and avascularity (OR = 0.22). Conclusions: Older age, high BMI, patients with a breastfeeding history, and those who exhibit the following specific nodule characteristics were most often linked to false positive results: large tumors with posterior acoustic shadowing and high elasticity scores, accompanied by reactive adenopathy. On the other hand, homogenous, avascular nodules with regular shapes were less likely to be misdiagnosed.

Nomogram for preoperative differentiation of benign and malignant breast tumors using contrast-enhanced cone-beam breast CT (CE CB-BCT) quantitative imaging and assessment features

PURPOSE

Breast cancer's impact necessitates refined diagnostic approaches. This study develops a nomogram using radiology quantitative features from contrast-enhanced cone-beam breast CT for accurate preoperative classification of benign and malignant breast tumors.

MATERIAL AND METHODS

A retrospective study enrolled 234 females with breast tumors, split into training and test sets. Contrast-enhanced cone-beam breast CT-images were acquired using Koning Breast CT-1000. Quantitative assessment features were extracted via 3D-slicer software, identifying independent predictors. The nomogram was constructed to preoperative differentiation benign and malignant breast tumors. Calibration curve was used to assess whether the model showed favorable correspondence with pathological confirmation. Decision curve analysis confirmed the model's superiority.

RESULTS

The study enrolled 234 female patients with a mean age of 50.2 years (SD ± 9.2). The training set had 164 patients (89 benign, 75 malignant), and the test set had 70 patients (29 benign, 41 malignant). The nomogram achieved excellent predictive performance in distinguishing benign and malignant breast lesions with an AUC of 0.940 (95% CI 0.900-0.940) in the training set and 0.970 (95% CI 0.940-0.970) in the test set.

CONCLUSION

This study illustrates the effectiveness of quantitative radiology features derived from contrast-enhanced cone-beam breast CT in distinguishing between benign and malignant breast tumors. Incorporating these features into a nomogram-based diagnostic model allows for breast tumor diagnoses that are objective and possess good accuracy. The application of these insights could substantially increase reliability and efficacy in the management of breast tumors, offering enhanced diagnostic capability.

Breast Imaging Reporting and Data System (BI-RADS®): a success history and particularities of its use in Brazil

BI-RADS® is a standardization system for breast imaging reports and results created by the American College of Radiology to initially address the lack of uniformity in mammography reporting. The system consists of a lexicon of descriptors, a reporting structure with final categories and recommended management, and a structure for data collection and auditing. It is accepted worldwide by all specialties involved in the care of breast diseases. Its implementation is related to the Mammography Quality Standards Act initiative in the United States (1992) and breast cancer screening. After its initial creation in 1993, four additional editions were published in 1995, 1998, 2003 and 2013. It is adopted in several countries around the world and has been translated into 6 languages. Successful breast cancer screening programs in high-income countries can be attributed in part to the widespread use of BI-RADS®. This success led to the development of similar classification systems for other organs (e.g., lung, liver, thyroid, ovaries, colon). In 1998, the structured report model was adopted in Brazil. This article highlights the pioneering and successful role of BI-RADS®, created by ACR 30 years ago, on the eve of publishing its sixth edition, which has evolved into a comprehensive quality assurance tool for multiple imaging modalities. And, especially, it contextualizes the importance of recognizing how we are using BI-RADS® in Brazil, from its implementation to the present day, with a focus on breast cancer screening.

Multi-MicroRNA Analysis Can Improve the Diagnostic Performance of Mammography in Determining Breast Cancer Risk

The objective of this study was to determine whether multi-microRNA analysis using a combination of four microRNA biomarkers (miR-1246, 202, 21, and 219B) could improve the diagnostic performance of mammography in determining breast cancer risk by age group (under 50 vs. over 50) and distinguish breast cancer from benign breast diseases and other cancers (thyroid, colon, stomach, lung, liver, and cervix cancers). To verify breast cancer classification performance of the four miRNA biomarkers and whether the model providing breast cancer risk score could distinguish between benign breast disease and other cancers, the model was verified using nonlinear support vector machine (SVM) and generalized linear model (GLM) and age and four miRNA qRT-PCR analysis values (dCt) were input to these models. Breast cancer risk scores for each Breast Imaging-Reporting and Data System (BI-RADS) category in multi-microRNA analysis were analyzed to examine the correlation between breast cancer risk scores and mammography categories. We generated two models using two classification algorithms, SVM and GLM, with a combination of four miRNA biomarkers showing high performance and sensitivities of 84.5% and 82.1%, a specificity of 85%, and areas under the curve (AUCs) of 0.967 and 0.965, respectively, which showed consistent performance across all stages of breast cancer and patient ages. The results of this study showed that this multi-microRNA analysis using the four miRNA biomarkers was effective in classifying breast cancer in patients under the age of 50, which is challenging to accurately diagnose. In addition, breast cancer and benign breast diseases can be classified, showing the possibility of helping with diagnosis by mammography. Verification of the performance of the four miRNA biomarkers confirmed that multi-microRNA analysis could be used as a new breast cancer screening aid to improve the accuracy of mammography. However, many factors must be considered for clinical use. Further validation with an appropriate screening population in large clinical trials is required. This trial is registered with (KNUCH 2022-04-036).

Challenges, Barriers, and Successes of Standardized Report Templates: Results of a Society of Interventional Radiology Survey

Registry data are being increasingly used to establish treatment guidelines, set benchmarks, allocate resources, and make payment decisions. Although many registries rely on manual data entry, the Society of Interventional Radiology (SIR) is using automated data extraction for its VIRTEX registry. This process relies on participants using consistent terminology with highly structured data in physician-developed standardized reports (SR). To better understand barriers to adoption, a survey was sent to 3,178 SIR members. Responses were obtained from 451 interventional radiology practitioners (14.2%) from 92 unique academic and 151 unique private practices. Of these, 75% used structured reports and 32% used the SIR SR. The most common barriers to the use of these reports include SR length (35% of respondents), lack of awareness about the SR (31%), and lack of agreement on adoption within practices (27%). The results demonstrated insights regarding barriers in the use and/or adoption of SR and potential solutions.

Are we ready to stratify BI-RADS 4 lesions observed on magnetic resonance imaging? A real-world noninferiority/equivalence analysis

Objective

To demonstrate that positive predictive values (PPVs) for suspicious (category 4) magnetic resonance imaging (MRI) findings that have been stratified are equivalent to those stipulated in the American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) for mammography and ultrasound.

Materials and Methods

This retrospective analysis of electronic medical records generated between January 4, 2016 and December 29, 2021 provided 365 patients in which 419 suspicious (BI-RADS category 4) findings were subcategorized as BI-RADS 4A, 4B or 4C. Malignant and nonmalignant outcomes were determined by pathologic analyses, follow-up, or both. For each subcategory, the level 2 PPV (PPV2) was calculated and tested for equivalence/noninferiority against the established benchmarks.

Results

Of the 419 findings evaluated, 168 (40.1%) were categorized as malignant and 251 (59.9%) were categorized as nonmalignant. The PPV2 for subcategory 4A was 14.2% (95% CI: 9.3-20.4%), whereas it was 41.2% (95% CI: 32.8-49.9%) for subcategory 4B and 77.2% (95% CI: 68.4-84.5%) for subcategory 4C. Multivariate analysis showed a significantly different cancer yield for each subcategory (p < 0.001).

Conclusion

We found that stratification of suspicious findings by MRI criteria is feasible, and malignancy probabilities for sub-categories 4B and 4C are equivalent to the values established for the other imaging methods in the BI-RADS. Nevertheless, low suspicion (4A) findings might show slightly higher malignancy rates.

Impact of Imaging Biomarkers and AI on Breast Cancer Management: A Brief Review

Breast cancer stands out as the most frequently identified malignancy, ranking as the fifth leading cause of global cancer-related deaths. The American College of Radiology (ACR) introduced the Breast Imaging Reporting and Data System (BI-RADS) as a standard terminology facilitating communication between radiologists and clinicians; however, an update is now imperative to encompass the latest imaging modalities developed subsequent to the 5th edition of BI-RADS. Within this review article, we provide a concise history of BI-RADS, delve into advanced mammography techniques, ultrasonography (US), magnetic resonance imaging (MRI), PET/CT images, and microwave breast imaging, and subsequently furnish comprehensive, updated insights into Molecular Breast Imaging (MBI), diagnostic imaging biomarkers, and the assessment of treatment responses. This endeavor aims to enhance radiologists' proficiency in catering to the personalized needs of breast cancer patients. Lastly, we explore the augmented benefits of artificial intelligence (AI), machine learning (ML), and deep learning (DL) applications in segmenting, detecting, and diagnosing breast cancer, as well as the early prediction of the response of tumors to neoadjuvant chemotherapy (NAC). By assimilating state-of-the-art computer algorithms capable of deciphering intricate imaging data and aiding radiologists in rendering precise and effective diagnoses, AI has profoundly revolutionized the landscape of breast cancer radiology. Its vast potential holds the promise of bolstering radiologists' capabilities and ameliorating patient outcomes in the realm of breast cancer management.

A Nomogram for Enhancing the Diagnostic Effectiveness of Solid Breast BI-RADS 3-5 Masses to Determine Malignancy Based on Imaging Aspects of Conventional Ultrasonography and Contrast-Enhanced Ultrasound

BACKGROUND

To establish and validate a nomogram model, which can incorporate clinical data, and imaging features of ultrasound (US) and contrast-enhanced ultrasound (CEUS), for improving the diagnostic efficiency of solid breast lesions.

PATIENTS AND METHODS

A total of 493 patients with solid breast lesions were randomly divided into training (n = 345) and validation (n = 148) cohorts with a ratio of 7:3 and, clinical data and image features of US and CEUS were reviewed and retrospectively analyzed. The breast lesions in both the training and validation cohorts were analyzed using the BI-RADS and nomogram models.

RESULTS

Five variables, including the shape and calcification features of conventional US, enhancement type and size after enhancement features of CEUS, and BI-RADS, were selected to construct the nomogram model. As compared to the BI-RADS model, the nomogram model demonstrated satisfactory discriminative function (area under the receiver operating characteristic [ROC] curves [AUC], 0.940; 95% confidence interval [CI], 0.909 to 0.971; sensitivity, 0.905; and specificity, 0.902 in the training cohort and AUC, 0.968; 95% CI, 0.941 to 0.995; sensitivity, 0.971; and specificity, 0.867 in the validation cohort). In addition, the nomogram model showed good consistency and clinical potential according to the calibration curve and DCA.

CONCLUSION

The nomogram model could identify benign from malignant breast lesions with good performance.

International Expert Consensus on US Lexicon for Thyroid Nodules

Multiple US-based systems for risk stratification of thyroid nodules are in use worldwide. Unfortunately, the malignancy probability assigned to a nodule varies, and terms and definitions are not consistent, leading to confusion and making it challenging to compare study results and craft revisions. Consistent application of these systems is further hampered by interobserver variability in identifying the sonographic features on which they are founded. In 2018, an international multidisciplinary group of 19 physicians with expertise in thyroid sonography (termed the International Thyroid Nodule Ultrasound Working Group) was convened with the goal of developing an international system, tentatively called the International Thyroid Imaging Reporting and Data System, or I-TIRADS, in two phases: (phase I) creation of a lexicon and atlas of US descriptors of thyroid nodules and (phase II) development of a system that estimates the malignancy risk of a thyroid nodule. This article presents the methods and results of phase I. The purpose herein is to show what has been accomplished thus far, as well as generate interest in and support for this effort in the global thyroid community.

Implementation strategies to support ultrasound thyroid nodule risk stratification: A systematic review

BACKGROUND

Ultrasound risk stratification can improve the care of patients with thyroid nodules by providing a structured and systematic approach for the evaluation of thyroid nodule features and thyroid cancer risk. The optimal strategies to support implementation of high quality thyroid nodule risk stratification are unknown. This study seeks to summarise strategies used to support implementation of thyroid nodule ultrasound risk stratification in practice and their effects on implementation and service outcomes.

METHODS

This is a systematic review of studies evaluating implementation strategies published between January 2000 and June 2022 that were identified on Ovid MEDLINE, Ovid EMBASE, Ovid Cochrane, Scopus, or Web of Science. Screening of eligible studies, data collection and assessment for risk of bias was completed independently and in duplicate. Implementation strategies and their effects on implementation and service outcomes were evaluated and summarised.

RESULTS

We identified 2666 potentially eligible studies of which 8 were included. Most implementation strategies were directed towards radiologists. Common strategies to support the implementation of thyroid nodule risk stratification included: tools to standardise thyroid ultrasound reports, education on thyroid nodule risk stratification and use of templates/forms for reporting, and reminders at the point of care. System based strategies, local consensus or audit were less commonly described. Overall, the use of these strategies supported the implementation process of thyroid nodule risk stratification with variable effects on service outcomes.

CONCLUSIONS

Implementation of thyroid nodule risk stratification can be supported by development of standardised reporting templates, education of users on risk stratification and reminders of use at the point of care. Additional studies evaluating the value of implementation strategies in different contexts are urgently needed.

Challenges in Interpretation of US Breast Findings in the Emergency Setting

Emergencies in breast imaging are infrequent but not rare. Although infectious conditions such as mastitis and breast abscess are the most common breast diseases encountered in acute care settings, other entities that may require additional imaging or different treatment approaches are also seen and include traumatic injury and breast cancer. While mammography is widely available for breast evaluation in outpatient facilities, most emergency departments do not have mammography units. This makes evaluation of patients with breast disease incomplete in the acute care setting and emphasizes the role of appropriate US techniques for interpretation. It also highlights the importance of effective sonographer-to-radiologist communication to ensure patient safety and diagnostic accuracy, especially in an era of increasing adoption of teleradiology. The authors discuss the challenges in image acquisition and remote interpretation that are commonly faced by radiologists when they assess breast anomalies in the emergency setting. They present strategies to overcome these challenges by describing techniques for proper US evaluation, highlighting the importance of sonographer-radiologist communication, defining the goals of the evaluation, reviewing common differential diagnoses, and providing appropriate follow-up recommendations. ©RSNA, 2023 Quiz questions for this article are available in the supplemental material.

[Breast Imaging Reporting and Data System (BI-RADS): Advantages and Limitations]

Breast Imaging Reporting and Data System (BI-RADS) is a communication and data tracking system that standardizes and controls the quality of reporting by presenting lexicon descriptors, assessment categories, and recommendations for managing breast lesions. Using standardized terminology recommended by BI-RADS, radiologists can concisely and reproducibly communicate breast imaging results to clinicians. They can also provide the estimated malignant probability of the lesions found and guide management for them by determining the final assessment category. The limitations of BI-RADS 5th edition currently in use are that there are some areas for which standardized terminologies still need to be established, and that the diagnostic criteria of MRI assessment categories 3 and 4 are ambiguous compared to those for mammography or ultrasound. The next revision of BI-RADS is expected to include solutions for overcoming current limitations.

PPV of the Molecular Breast Imaging Lexicon

BACKGROUND. Molecular breast imaging (MBI) is used for various breast imaging indications. An MBI lexicon has been developed, although the likelihood of malignancy of the lexicon descriptors has not been assessed to our knowledge. OBJECTIVE. The purpose of this article was to evaluate the PPV for malignancy of the MBI lexicon imaging descriptors. METHODS. This retrospective study included MBI examinations performed from August 1, 2005, through August 31, 2017, that were positive (BI-RADS analogous categories 0, 3, 4, 5, or 6) according to the clinical report and had an available reference standard. Examinations were performed using dual-detector cadmium zinc telluride MBI systems after injection of ^99mTc sestamibi. Category 3 lesions had pathologic correlation, at least 2 years of imaging follow-up, or final resolution on follow-up imaging as category 1 or 2; category 4 and 5 lesions had pathologic correlation. MBI examinations were reviewed by one of two radiologists to assess lesions on the basis of the published MBI lexicon for type (mass vs nonmass uptake), distribution (if nonmass uptake), uptake intensity, and number of MBI views on which the lesion was seen. PPV for malignancy was summarized. RESULTS. The analysis included 643 lesions (479 benign, 164 malignant; 83 mass, 560 nonmass uptake) in 509 patients (median age, 56 years). PPV was 73.5% (61/83) for masses and 18.4% (103/560) for nonmass uptake. Among the nonmass uptake lesions, PPV was 36.2% (17/47) for segmental, 20.1% (77/384) for focal, 30.8% (4/13) for diffuse, and 4.3% (5/116) for regional or multiple regional distribution. PPV was 5.3% (5/94) for one view, 15.2% (32/210) for two views, 14.6% (13/89) for three views, and 45.4% (113/249) for four views showing the lesion. PPV was 14.0% (43/307) for mild, 22.4% (51/228) for moderate, and 64.8% (70/108) for marked uptake intensity. CONCLUSION. The MBI lexicon lesion descriptors are associated with likelihood of malignancy. PPV was higher for masses, lesions seen on multiple MBI views, and lesions with marked uptake intensity. Among nonmass uptake lesions, PPV was highest for those with segmental distribution. CLINICAL IMPACT. Insight into the likelihood of malignancy associated with the MBI lexicon descriptors can inform radiologists' interpretations and guide potential future incorporation of the MBI lexicon into the ACR BI-RADS Atlas.

Assessment of breast lesions by the Kaiser score for differential diagnosis on MRI: the added value of ADC and machine learning modeling

OBJECTIVES

To evaluate the diagnostic performance of Kaiser score (KS) adjusted with the apparent diffusion coefficient (ADC) (KS+) and machine learning (ML) modeling.

METHODS

A dataset of 402 malignant and 257 benign lesions was identified. Two radiologists assigned the KS. If a lesion with KS > 4 had ADC > 1.4 × 10^-3 mm²/s, the KS was reduced by 4 to become KS+. In order to consider the full spectrum of ADC as a continuous variable, the KS and ADC values were used to train diagnostic models using 5 ML algorithms. The performance was evaluated using the ROC analysis, compared by the DeLong test. The sensitivity, specificity, and accuracy achieved using the threshold of KS > 4, KS+ > 4, and ADC ≤ 1.4 × 10^-3 mm²/s were obtained and compared by the McNemar test.

RESULTS

The ROC curves of KS, KS+, and all ML models had comparable AUC in the range of 0.883-0.921, significantly higher than that of ADC (0.837, p < 0.0001). The KS had sensitivity = 97.3% and specificity = 59.1%; and the KS+ had sensitivity = 95.5% with significantly improved specificity to 68.5% (p < 0.0001). However, when setting at the same sensitivity of 97.3%, KS+ could not improve specificity. In ML analysis, the logistic regression model had the best performance. At sensitivity = 97.3% and specificity = 65.3%, i.e., compared to KS, 16 false-positives may be avoided without affecting true cancer diagnosis (p = 0.0015).

CONCLUSION

Using dichotomized ADC to modify KS to KS+ can improve specificity, but at the price of lowered sensitivity. Machine learning algorithms may be applied to consider the ADC as a continuous variable to build more accurate diagnostic models.

KEY POINTS

• When using ADC to modify the Kaiser score to KS+, the diagnostic specificity according to the results of two independent readers was improved by 9.4-9.7%, at the price of slightly degraded sensitivity by 1.5-1.8%, and overall had improved accuracy by 2.6-2.9%. • When the KS and the continuous ADC values were combined to train models by machine learning algorithms, the diagnostic specificity achieved by the logistic regression model could be significantly improved from 59.1 to 65.3% (p = 0.0015), while maintaining at the high sensitivity of KS = 97.3%, and thus, the results demonstrated the potential of ML modeling to further evaluate the contribution of ADC. • When setting the sensitivity at the same levels, the modified KS+ and the original KS have comparable specificity; therefore, KS+ with consideration of ADC may not offer much practical help, and the original KS without ADC remains as an excellent robust diagnostic method.

Palpable breast lumps: An age-based approach to evaluation and diagnosis

A palpable breast lump is a common presentation of breast disease to a general practitioner. Fortunately, investigation of most of these lumps will lead to a benign diagnosis. It is essential to have a clear and systematic approach when investigating a palpable breast lump to avoid over investigation with the resultant increase in healthcare cost and anxiety. This article will discuss an approach to evaluating and diagnosing a palpable breast lump in the primary care setting.

Structured Reporting in Radiological Settings: Pitfalls and Perspectives

Objective: The aim of this manuscript is to give an overview of structured reporting in radiological settings. Materials and Method: This article is a narrative review on structured reporting in radiological settings. Particularly, limitations and future perspectives are analyzed. RESULTS: The radiological report is a communication tool for the referring physician and the patients. It was conceived as a free text report (FTR) to allow radiologists to have their own individuality in the description of the radiological findings. However, this form could suffer from content, style, and presentation discrepancies, with a probability of transferring incorrect radiological data. Quality, datafication/quantification, and accessibility represent the three main goals in moving from FTRs to structured reports (SRs). In fact, the quality is related to standardization, which aims to improve communication and clarification. Moreover, a “structured” checklist, which allows all the fundamental items for a particular radiological study to be reported and permits the connection of the radiological data with clinical features, allowing a personalized medicine. With regard to accessibility, since radiological reports can be considered a source of research data, SR allows data mining to obtain new biomarkers and to help the development of new application domains, especially in the field of radiomics. Conclusions: Structured reporting could eliminate radiologist individuality, allowing a standardized approach.

Breast Cancer—How Can Imaging Help?

Breast cancer is the most common malignant disease among women, causing death and suffering worldwide. It is known that, for the improvement of the survival rate and the psychological impact it has on patients, early detection is crucial. For this to happen, the imaging techniques should be used at their full potential. We selected and examined 44 articles that had as subject the use of a specific imaging method in breast cancer management (mammography, ultrasound, MRI, ultrasound-guided biopsy, PET-CT). After analyzing their data, we summarized and concluded which are the best ways to use each one of the mentioned techniques for a good outcome. We created a simplified algorithm with easy steps that can be followed by radiologists when facing this type of neoplasia.

Lung Opacity and Coronary Artery Calcium Score: A Combined Tool for Risk Stratification and Outcome Prediction in COVID-19 Patients

PURPOSE

To assess and correlate pulmonary involvement and outcome of SARS-CoV-2 pneumonia with the degree of coronary plaque burden based on the CAC-DRS classification (Coronary Artery Calcium Data and Reporting System).

METHODS

This retrospective study included 142 patients with confirmed SARS-CoV-2 pneumonia (58 ± 16 years; 57 women) who underwent non-contrast CT between January 2020 and August 2021 and were followed up for 129 ± 72 days. One experienced blinded radiologist analyzed CT series for the presence and extent of calcified plaque burden according to the visual and quantitative HU-based CAC-DRS Score. Pulmonary involvement was automatically evaluated with a dedicated software prototype by another two experienced radiologists and expressed as Opacity Score.

RESULTS

CAC-DRS Scores derived from visual and quantitative image evaluation correlated well with the Opacity Score (r=0.81, 95% CI 0.76-0.86, and r=0.83, 95% CI 0.77-0.89, respectively; p<0.0001) with higher correlation in severe than in mild stage SARS-CoV-2 pneumonia (p<0.0001). Combined, CAC-DRS and Opacity Scores revealed great potential to discriminate fatal outcomes from a mild course of disease (AUC 0.938, 95% CI 0.89-0.97), and the need for intensive care treatment (AUC 0.801, 95% CI 0.77-0.83). Visual and quantitative CAC-DRS Scores provided independent prognostic information on all-cause mortality (p=0.0016 and p<0.0001, respectively), both in univariate and multivariate analysis.

CONCLUSIONS

Coronary plaque burden is strongly correlated to pulmonary involvement, adverse outcome, and death due to respiratory failure in patients with SARS-CoV-2 pneumonia, offering great potential to identify individuals at high risk.

Structured reporting of x-ray mammography in the first diagnosis of breast cancer: a Delphi consensus proposal

Background

Radiology is an essential tool in the management of a patient. The aim of this manuscript was to build structured report (SR) Mammography based in Breast Cancer.

Methods

A working team of 16 experts (group A) was composed to create a SR for Mammography Breast Cancer. A further working group of 4 experts (group B), blinded to the activities of the group A, was composed to assess the quality and clinical usefulness of the SR final draft. Modified Delphi process was used to assess level of agreement for all report sections. Cronbach’s alpha (Cα) correlation coefficient was used to assess internal consistency and to measure quality analysis according to the average inter-item correlation.

Results

The final SR version was built by including n = 2 items in Personal Data, n = 4 items in Setting, n = 2 items in Comparison with previous breast examination, n = 19 items in Anamnesis and clinical context; n = 10 items in Technique; n = 1 item in Radiation dose; n = 5 items Parenchymal pattern; n = 28 items in Description of the finding; n = 12 items in Diagnostic categories and Report and n = 1 item in Conclusions. The overall mean score of the experts and the sum of score for structured report were 4.9 and 807 in the second round. The Cronbach’s alpha (Cα) correlation coefficient was 0.82 in the second round. About the quality evaluation, the overall mean score of the experts was 3.3. The Cronbach’s alpha (Cα) correlation coefficient was 0.90.

Conclusions

Structured reporting improves the quality, clarity and reproducibility of reports across departments, cities, countries and internationally and will assist patient management and improve breast health care and facilitate research.

A National Chinese Survey on Ultrasound Feature Interpretation and Risk Assessment of Breast Masses Under ACR BI-RADS

Purpose

Through this nationwide survey on ACR BI-RADS including ultrasound images of 10 selected breast lesions, we aimed to learn about consistency in feature interpretation and assessment categories and to identify factors that might contribute to inconsistencies, thereby promoting the application of BI-RADS in China.

Materials and Methods

The survey was delivered through a self-developed website about blinded image interpretation and was released to the public through online platforms and social media. A total of 10 representative lesions were selected by an experienced radiologist to gather information about the general practice of BI-RADS lexicons and categories. The Kappa statistic, the chi-squared test, and descriptive statistics were used for data analysis.

Results

Nine hundred ultrasound workers completed the questionnaire, coming from all provinces and major cities in China. They had different positions, grades of work organization, and seniority. The interrater agreement of BI-RADS features was fair to substantial (kappa value: 0.37–0.66). For BI-RADS categories, the highest agreement was observed in the typical benign group (average constituent rate = 74.78%), and generally lower agreement was observed in the typical malignant (average constituent rate = 36.03%) and suspicious groups (average constituent rate = 39.02%).

Conclusion

We found inconsistencies in BI-RADS applications, providing direction for image feature research using big data. Therefore, we call for more efforts to improve the consistency of BI-RADS application and provide an evidence-based basis for identifying benign and malignant lesions by sonographic features.

Artificial Intelligence for Quality Improvement in Radiology

Artificial intelligence (AI) and informatics promise to improve the quality and efficiency of diagnostic radiology but will require substantially more standardization and operational coordination to realize and measure those improvements. As radiology steps into the AI-driven future we should work hard to identify the needs and desires of our customers and develop process controls to ensure we are meeting them. Rather than focusing on easy-to-measure turnaround times as surrogates for quality, AI and informatics can support more comprehensive quality metrics, such as ensuring that reports are accurate, readable, and useful to patients and health care providers.