Breast Imaging Reporting Research Articles

Abstract PO2-29-01: POSITIVE PREDICTIVE VALUE FOR THE MALIGNANCY OF MAMMOGRAPHIC ABNORMALITIS IN A ONE-STOP CLINIC MODEL FOR BREAST CANCER IN COLOMBI

Abstract Breast cancer (BC) is the most prevalent malignancy among women in Colombia, with the greatest incidence rate among malignancies. According to available data through the year 2020, the Colombian female population has officially documented 52,025 cases of breast cancer. This variety of cancer has emerged as the leading cause of cancer-related mortality among Colombian women. Currently, it is essential to emphasize the significance of the BI-RADS (Breast Imaging Reporting and Data System) classification as a means of categorizing patients’ risk levels based on criteria for subsequent breast pathology evaluation and diagnosis. We aimed to assess the positive predictive value of BIRADS IV and V in predicting the risk of breast cancer in the Colombian population. From September 2018 to December 2022, consecutive patients referred for breast mammography and suspected of having breast cancer underwent triple assessment of breast tumor and were included in this retrospective study. 27.937 patients were included with breast imaging report and BIRADS classification system 4 and 5. Patients with a previously diagnosed case of breast cancer and a BIRADS score of 1,2 or 3 were excluded. The positive predictive value of categories 4 and 5 was determined using histopathologic results of ultrasound- or stereotactically-guided biopsies as the gold standard. 46% of 21,442 patients had BACAF biopsies, 39% TRUCUT, and 15% stereotactic. Category 4A was found in 12,865 patients (60%) with a 7% positivity rate, 4B in 8,362 (25%) with 20%, and 4C and 5 in 3,216 (15%) with 75%. BIRADS 4 and 5 had a 20% positive predictive value, with 4,371 positive participants (31% under 49 and 69% over 50). Categories 4 - 5 were found to be beneficial for predicting the likelihood of malignancy, with categories 4C and 5 yielding the highest rates of positive results. Comparable to previous research, the PPV for malignancy in the present investigation was comparable. Citation Format: Kelly Corredor, Sara Gil, Carlos Agudelo, Clara Piedrahita, Melissa Naranjo. POSITIVE PREDICTIVE VALUE FOR THE MALIGNANCY OF MAMMOGRAPHIC ABNORMALITIS IN A ONE-STOP CLINIC MODEL FOR BREAST CANCER IN COLOMBI [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-29-01.

Prospective study of AI-assisted prediction of breast malignancies in physical health examinations: role of off-the-shelf AI software and comparison to radiologist performance

ObjectiveIn physical health examinations, breast sonography is a commonly used imaging method, but it can lead to repeated exams and unnecessary biopsy due to discrepancies among radiologists and health centers. This study explores the role of off-the-shelf artificial intelligence (AI) software in assisting radiologists to classify incidentally found breast masses in two health centers.MethodsFemale patients undergoing breast ultrasound examinations with incidentally discovered breast masses were categorized according to the 5th edition of the Breast Imaging Reporting and Data System (BI-RADS), with categories 3 to 5 included in this study. The examinations were conducted at two municipal health centers from May 2021 to May 2023.The final pathological results from surgical resection or biopsy served as the gold standard for comparison. Ultrasonographic images were obtained in longitudinal and transverse sections, and two junior radiologists and one senior radiologist independently assessed the images without knowing the pathological findings. The BI-RADS classification was adjusted following AI assistance, and diagnostic performance was compared using receiver operating characteristic curves.ResultsA total of 196 patients with 202 breast masses were included in the study, with pathological results confirming 107 benign and 95 malignant masses. The receiver operating characteristic curve showed that experienced breast radiologists had higher diagnostic performance in BI-RADS classification than junior radiologists, similar to AI classification (AUC = 0.936, 0.806, 0.896, and 0.950, p < 0.05). The AI software improved the accuracy, sensitivity, and negative predictive value of the adjusted BI-RADS classification for the junior radiologists’ group (p< 0.05), while no difference was observed in the senior radiologist group. Furthermore, AI increased the negative predictive value for BI-RADS 4a masses and the positive predictive value for 4b masses among radiologists (p < 0.05). AI enhances the sensitivity of invasive breast cancer detection more effectively than ductal carcinoma in situ and rare subtypes of breast cancer.ConclusionsThe AI software enhances diagnostic efficiency for breast masses, reducing the performance gap between junior and senior radiologists, particularly for BI-RADS 4a and 4b masses. This improvement reduces unnecessary repeat examinations and biopsies, optimizing medical resource utilization and enhancing overall diagnostic effectiveness.

Abstract PO2-28-06: An on-line deep learning decision support tool, iBRISK, aimed at improving breast cancer risk estimation and reducing unnecessary biopsies for BI-RADS 4 patients

Abstract Probability of malignancy (POM) for Breast Imaging Reporting and Data System (BI-RADS) category 4 designated breast lesions ranges from 2% – 95% and contributes to a high unnecessary biopsy rate. This is as most clinicians often stick to the biopsy option to rule in or out breast cancer early; withholding biopsy could be risky, and biopsies of BI-RADS 4 lesions serve as a quality metric and performance standard. At 21.1%, biopsy-proven positive predictive value (PPV3) rates for BI-RADS 4 have not improved for decades, translating to high false-positive rates of mammography. Unnecessary biopsies are a big issue in the management of BI-RADS 4 lesions with negative implications including increased medical costs, healthcare wastes, unnecessary psychological burdens to the patients, and potential complications and risks. Objectives 1. Optimize the precision breast cancer risk assessment tool, iBRISK, that utilizes artificial intelligence (AI) technologies, including natural language processing (NLP), image processing, and deep learning, with clinical risk factors and imaging features. 2. Develop a user-friendly web interface for iBRISK to facilitate clinicians or insurers in estimating cancer risk and making informed biopsy decisions for BI-RADS 4 lesions. Methods Our intelligent-augmented breast cancer risk calculator (iBRISK) model was trained on multimodal data collected from 10,778 patients, including demographic factors, historical and clinical characteristics, mammographic features, and pathologic signatures. We validated iBRISK using 4,200 patients from multiple leading hospitals, including Houston Methodist Neal Cancer Center, the University of Texas MD Anderson Cancer Center, and the University of Texas Health San Antonio MD Anderson Mays Cancer Center. The iBRISK was connected to a backend server which is linked to a frontend web user interface using technologies like Hypertext Preprocessor (PHP) for the backend application running on an APACHE HTTP server and React-JavaScript for the front-end web application that communicates with the backend using Representational State Transfer Application Programming Interface (RESTful API) and JavaScript Object Notation (JSON) format. The communication was secured using Secure Sockets Layer (SSL) encryption. Results The iBRISK model demonstrated high sensitivity in malignancy prediction and achieved an accuracy of 89.5%, area under the receiver operating characteristic curve of 0.93 (95% CI: 0.92-0.95), sensitivity of 100%, and specificity of 81%. Only 0.16% of lesions determined to have low POM by the model were malignant. Our multi-center study shows that iBRISK achieves at least 50% reduction in unnecessary biopsies of BI-RADS 4 cases. Data elements for the required 20 features are entered into the interface using a variety of imputation methods including direct text, dropdown menus and radio button selections. The user-friendly web interface provides risk scores (0 – 1), risk levels (low, medium, and high), and associated biopsy recommendations. Conclusion The user-friendly iBRISK web interface is proposed as an adjunct to the BI-RADS system, enhancing the precision of BI-RADS 4 lesion cancer risk stratification. It is expected to reduce unnecessary biopsies, lower health costs, and enhance the quality of health care. This approach aims to tackle a critical issue in breast cancer diagnosis by leveraging advanced AI technologies and big data and providing clinicians with a tool to make more informed and precise biopsy decisions for BI-RADS 4 lesions. Citation Format: Chika Ezeana, Xiaohui Yu, Zhihao Wan, Tiancheng He, Tejal Patel, Virginia Kaklamani, Maryam Elmi, Erika Brigmon, Pamela Otto, Kenneth Kist, Lin Wang, Joe Ensor, Heather Speck, Ya-Chen Shih, Bumyang Kim, I-Wen Pan, David Spak, Wei Yang, Jenny Chang, Stephen Wong. An on-line deep learning decision support tool, iBRISK, aimed at improving breast cancer risk estimation and reducing unnecessary biopsies for BI-RADS 4 patients [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-28-06.

Abstract PO2-28-07: Artificial Intelligence-based Computer-aided Diagnosis for Mammography: Predicting Occult Invasive Cancer in Women with Biopsy-proven Ductal Carcinoma in Situ

Abstract Background: Ductal carcinoma in situ (DCIS) is a noninvasive breast cancer confined within the basement membrane. Due to the increase in breast cancer screening, DCIS is increasingly being diagnosed on biopsy. Active surveillance is a novel and evolving treatment strategy for DCIS to reduce overtreatment. Currently ongoing clinical trials of active surveillance include only mammographic findings in their eligibility criteria, however mammography has limited ability to detect occult invasive cancer. The objective of this study was to investigate whether an artificial intelligence-based computer-aided diagnosis (AI-CAD) application for mammography can improve the prediction of occult invasive cancer in women with percutaneous needle biopsy-proven DCIS. Methods: A retrospective search of our database identified consecutive women with percutaneous needle biopsy-proven DCIS who underwent surgery at Seoul National University Hospital (Seoul, Korea) between June 2019 and May 2021. Two board-certified breast radiologists reviewed preoperative mammographic findings of the primary tumor in consensus according to the Breast Imaging Reporting and Data System. Using a commercially available AI-CAD (Lunit INSIGHT for Mammography, Lunit Inc.), quantitative abnormality scores of 0 to 100% of the ipsilateral breast were obtained. Data were collected on age at diagnosis, symptoms, biopsy methods, DCIS grade on biopsy, and presence or absence of invasive cancer and axillary lymph node metastases on surgical pathology. Multivariable logistic regression models were used to evaluate factors associated with prediction of occult invasive cancer. Results: Of 692 biopsy-proven DCIS in 692 women (mean age, 53 years ± 11 [standard deviation]), 298 (43%) presented as calcifications only, 160 (23%) as mass or asymmetry, and 111 (16%) as masses with calcifications. The other 123 (18%) cancers were not demonstrable on mammography. Surgical pathology revealed occult invasive cancer in 278 (40%) women and 18 (3%) had axillary metastasis. Women with occult invasive cancer showed higher abnormality scores on AI-CAD than women with pure DCIS (mean, 70% vs. 51%; P < .001), and women with axillary metastasis had higher abnormality scores on AI-CAD than women without axillary metastasis (mean, 88% vs. 58%; P = .002). High ( >75%) abnormality score on AI-CAD (Odds ratio [OR], 1.8 [95% CI: 1.3, 2.6]; P < .001), age of 55 years or higher (OR, 1.6 [95% CI: 1.2, 2.3]; P =.005), present symptoms in the ipsilateral breast (OR, 2.7 [95% CI: 1.9, 4.0]; P < .001), biopsy performed with a 14-gauge automated gun (OR, 2.5 [95% CI: 1.7, 3.6]; P < .001), and high-grade DCIS at biopsy (OR, 2.5 [95% CI: 1.7, 3.5]; P < .001] were independent predictors of occult invasive cancer with a C-index of 0.75 (95% CI: 0.72, 0.78). Conclusion: The quantitative score of preoperative mammography obtained from a commercially available AI-CAD was able to predict occult invasive cancer in an easy and simple manner. AI-CAD applications for preoperative mammography may be useful in predicting occult invasive cancer in women with biopsy-proven DCIS, especially in those without preoperative breast MRI. Citation Format: Su Hyun Lee, Hajung Kim, Su Min Ha, Soo-Yeon Kim, Jung Min Chang, Nariya Cho, Woo Kyung Moon. Artificial Intelligence-based Computer-aided Diagnosis for Mammography: Predicting Occult Invasive Cancer in Women with Biopsy-proven Ductal Carcinoma in Situ [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-28-07.

Evaluating the Efficacy of 6-Month Follow-Up for BI-RADS 3 Lesions Identified by Screening Ultrasound: A Retrospective Study

Background: The Breast Imaging-Reporting and Data System (BI-RADS), developed to standardize mammographic findings, categorizes 'probably benign' lesions as BI-RADS 3, aiming to reduce unnecessary biopsies from false positives, with a cancer likelihood of less than 2%. In some countries, screening programs have been expanded to include ultrasound alongside mammography, particularly in populations with predominantly dense breast tissue, leading to an increased detection of BI-RADS 3 lesions through this additional modality. This study examines the effectiveness of 6-month follow-ups for these ultrasound-identified BI-RADS 3 lesions. The purpose is to evaluate cancer detection rates and diagnostic performance of ultrasound findings leading to decision for biopsy. Materials and Methods: We retrospectively analyzed 7,577 women who underwent mammography and ultrasound screening from January 2016 to December 2021. Of these, 2,907 were classified as BI-RADS 3 based on ultrasound findings. The study focused on 1,163 patients with normal mammography and assessed BI-RADS 3 from ultrasound findings, who completed a 24-month follow-up. Data analysis included assessing demographic data, breast cancer risk, imaging features, and pathological findings. Results: Among the 1,163 patients, the cancer detection of the entire cohort was 0.7%, with no cancers detected during the first six months. The cancer found at 6 months and 12-24 months were 0 and 0.17%, respectively (p<0.001). The median time to be reassesed from BI-RADS 3 to BI-RADS 4 was 18.4 months. Upon the change of BI-RADS, spiculation and angular margins were the most predictive ultrasound features for malignancy (AUROC=0.75 and 0.69, respectively). Lesion size growth alone was found insufficient as a biopsy criterion. A 28% growth cutoff distinguished between benign and malignant lesions better than 20% cutoff (AUROC=0.68 vs 0.64, p=0.038). Conclusion: A 12-month follow-up interval may be more appropriate than the traditional 6-month interval for average-risk patients with BI-RADS 3 lesions detected by screening ultrasound. Combining suspicious imaging features with size increases enhances diagnostic accuracy, proposing a tailored follow-up approach based on individual risk profiles and imaging characteristics.

Interrater Agreement of BT-RADS for Evaluation of Follow-Up MRI in Treated Primary Brain Tumor Patients.

The Brain Tumor Reporting and Data System (BT-RADS) is a structured radiology reporting algorithm that was introduced to provide uniformity in post-treatment primary brain tumor follow-up and reporting, but its interrater reliability (IRR) assessment has not been widely studied. Our goal is to evaluate the IRR among neuroradiologists and radiology residents in the use of BT-RADS. This retrospective study reviewed 103 consecutive MR studies in 98 adult patients previously diagnosed with and treated for primary brain tumor (January 2019 to February 2019). Six readers with varied experience (4 neuroradiologists and 2 radiology residents) independently evaluated each case and assigned a BT-RADS score. Readers were blinded to the original score reports and the reports from other readers. Cases in which at least one neuroradiologist scored differently were subjected to consensus scoring. After the study, a post-hoc reference score was also assigned by 2 readers using future imaging and clinical information previously unavailable to readers. The interrater reliabilities were assessed using Gwet's AC2 index with ordinal weights and percent agreement. Of the 98 patients evaluated (median age, 53 years; interquartile range, 41-66 years), 53% were males. The most common tumor type was astrocytoma (77%) of which 56% were grade 4 glioblastoma. Gwet's index for interrater reliability among all six readers was 0.83 (95% CI: 0.78, 0.87). The Gwet's index for the neuroradiologists' group (0.84 [95% CI: 0.79, 0.89]) was not statistically different from that for the residents' group (0.79 [95% CI: 0.72, 0.86]) (χ2 = 0.85; p = 0.36). All four neuroradiologists agreed on the same BT-RADS score in 57 of the 103 studies, three neuroradiologists agreed in 21 of the 103 studies, and two neuroradiologists agreed in 21 of the 103 studies. Percent agreement between neuroradiologist blinded scores and post-hoc reference scores ranged from 41%-52%. A very good interrater agreement was found when tumor reports were interpreted by independent blinded readers using BT-RADS criteria. Further study is needed to determine if this high overall agreement can translate into greater consistency in clinical care. BI-RADS = Breast Imaging Reporting and Data System; BT-RADS = Brain Tumor Reporting and Data System; IQR = interquartile range; IRR = interrater reliability; NI-RADS = Neck Imaging Reporting and Data System.

Role of Sonoelastography in Differentiating Benign and Malignant Breast Lesions: A Prospective Study Comparing Elasticity Contrast Index and Tsukuba Score

Introduction: Breast cancer is the most common cancer occurring in women globally which is potentially curable if detected early. Ultrasound elastography is a dynamic technique that estimates tissue stiffness to differentiate between benign and malignant masses. Our study aimed to determine and compare the diagnostic accuracy of B mode Sonography, Elasticity contrast index, and Tsukuba score in differentiating malignant and benign breast masses. Methods: This was a prospective cross-sectional study done including 110 lesions in 102 patients in the age group of 15-73 years. The solid breast lesions seen on sonography were categorized according to the American College of Radiology Breast Imaging Reporting and Data System (ACR BI-RADS) and further evaluated with elastography using both Elasticity Contrast Index (ECI) and Tsukuba score with pathological diagnosis taken as the gold standard. The cut-off value of ECI was obtained. The diagnostic accuracy of B-mode sonography, ECI, and Tsukuba score was compared. Results: We found that B-mode sonography had a sensitivity of 85.7%, specificity of 100%, and accuracy of 96.9 %. The accuracy of Tsukuba scores for differentiating benign and malignant lesions was 81.2%. The cut-off value of ECI obtained was 2.8 and the accuracy was 81.8%. A statistically significant correlation (p < 0.05) existed between sonographic diagnosis, ECI, and Tsukuba score. Conclusions: B mode sonography had the highest diagnostic accuracy while ECI and Tsukuba scores were comparable. ECI can be used for breast masses using a cut-off of 2.8 to differentiate benign from malignant.

Deep-learning model for background parenchymal enhancement classification in contrast-enhanced mammography.

Breast Background Parenchymal Enhancement (BPE) is correlated with
the risk of breast cancer. BPE level is currently assessed by radiologists in Contrast-Enhanced
Mammography (CEM) using 4 classes: minimal, mild, moderate and marked, as described
in Breast Imaging Reporting and Data System (BI-RADS). However, BPE classification re-
mains subject to intra- and inter-reader variability. Fully automated methods to assess BPE
level have already been developed in breast Contrast-Enhanced MRI (CE-MRI) and have been
shown to provide accurate and repeatable BPE level classification. However, to our knowledge,
no BPE level classification tool is available in the literature for CEM.
Materials & Methods: A BPE level classification tool based on Deep Learning (DL) has
been trained and optimized on 7012 CEM image pairs (low-energy and recombined images)
and evaluated on a dataset of 1013 image pairs. The impact of image resolution, backbone
architecture and loss function were analyzed, as well as the influence of lesion presence and
type on BPE assessment. The evaluation of the model performance was conducted using dif-
ferent metrics including 4-class balanced accuracy and mean absolute error. The results of the
optimized model for a binary classification: minimal/mild versus moderate/marked, were also
investigated.
Results: The optimized model achieved a 4-class balanced accuracy of 71.5% (95% CI:
71.2-71.9) with 98.8% of classification errors between adjacent classes. For binary classifi-
cation, the accuracy reached 93.0%. A slight decrease in model accuracy is observed in the
presence of lesions, but it is not statistically significant, suggesting that our model is robust to
the presence of lesions in the image for a classification task. Visual assessment also confirms
that the model is more affected by non-mass enhancements than by mass-like enhancements.
Conclusion: The proposed BPE classification tool for CEM achieves similar results than
what is published in the literature for CE-MRI.

A prospective cross-sectional observational comparative study: evaluating palpable breast lumps through comparative analysis of sonomammography breast imaging reporting and data system scores and fine needle aspiration cytology

This study protocol outlines a prospective cross-sectional observational investigation aimed at assessing the correlation between Sonomammography Breast Imaging Reporting and Data System (BI-RADS) scores and Fine-Needle Aspiration Cytology (FNAC) findings in diagnosing benign and malignant palpable breast lumps among patients admitted to AVBRH. The primary objective is to understand the diagnostic accuracy of FNAC and Sonomammography BI-RADS scoring in identifying breast lumps’ nature. Four specific objectives guide this study: 1) To determine the diagnostic accuracy of FNAC; 2) To evaluate the diagnostic precision of Sonomammography BI-RADS scores; 3) To compare the diagnostic outcomes of sonomammography and FNAC with HPE, and 4) To examine the age-wise distribution of benign and malignant palpable breast diseases. The study will be conducted at AVBRH, Sawangi (Meghe), Wardha, over three years, commencing in June 2022. The anticipated findings from this study will significantly contribute to refining the diagnostic strategies for palpable breast lumps, enhancing patient care and decision-making.

Screening mammography performance according to breast density: a comparison between radiologists versus standalone intelligence detection

BackgroundArtificial intelligence (AI) algorithms for the independent assessment of screening mammograms have not been well established in a large screening cohort of Asian women. We compared the performance of screening digital mammography considering breast density, between radiologists and AI standalone detection among Korean women.MethodsWe retrospectively included 89,855 Korean women who underwent their initial screening digital mammography from 2009 to 2020. Breast cancer within 12 months of the screening mammography was the reference standard, according to the National Cancer Registry. Lunit software was used to determine the probability of malignancy scores, with a cutoff of 10% for breast cancer detection. The AI’s performance was compared with that of the final Breast Imaging Reporting and Data System category, as recorded by breast radiologists. Breast density was classified into four categories (A–D) based on the radiologist and AI-based assessments. The performance metrics (cancer detection rate [CDR], sensitivity, specificity, positive predictive value [PPV], recall rate, and area under the receiver operating characteristic curve [AUC]) were compared across breast density categories.ResultsMean participant age was 43.5 ± 8.7 years; 143 breast cancer cases were identified within 12 months. The CDRs (1.1/1000 examination) and sensitivity values showed no significant differences between radiologist and AI-based results (69.9% [95% confidence interval [CI], 61.7–77.3] vs. 67.1% [95% CI, 58.8–74.8]). However, the AI algorithm showed better specificity (93.0% [95% CI, 92.9–93.2] vs. 77.6% [95% CI, 61.7–77.9]), PPV (1.5% [95% CI, 1.2–1.9] vs. 0.5% [95% CI, 0.4–0.6]), recall rate (7.1% [95% CI, 6.9–7.2] vs. 22.5% [95% CI, 22.2–22.7]), and AUC values (0.8 [95% CI, 0.76–0.84] vs. 0.74 [95% CI, 0.7–0.78]) (all P < 0.05). Radiologist and AI-based results showed the best performance in the non-dense category; the CDR and sensitivity were higher for radiologists in the heterogeneously dense category (P = 0.059). However, the specificity, PPV, and recall rate consistently favored AI-based results across all categories, including the extremely dense category.ConclusionsAI-based software showed slightly lower sensitivity, although the difference was not statistically significant. However, it outperformed radiologists in recall rate, specificity, PPV, and AUC, with disparities most prominent in extremely dense breast tissue.

Breast Tomographic Ultrasound: The Spectrum from Current Dense Breast Cancer Screenings to Future Theranostic Treatments.

This review provides unique insights to the scientific scope and clinical visions of the inventors and pioneers of the SoftVue breast tomographic ultrasound (BTUS). Their >20-year collaboration produced extensive basic research and technology developments, culminating in SoftVue, which recently received the Food and Drug Administration's approval as an adjunct to breast cancer screening in women with dense breasts. SoftVue's multi-center trial confirmed the diagnostic goals of the tissue characterization and localization of quantitative acoustic tissue differences in 2D and 3D coronal image sequences. SoftVue mass characterizations are also reviewed within the standard cancer risk categories of the Breast Imaging Reporting and Data System. As a quantitative diagnostic modality, SoftVue can also function as a cost-effective platform for artificial intelligence-assisted breast cancer identification. Finally, SoftVue's quantitative acoustic maps facilitate noninvasive temperature monitoring and a unique form of time-reversed, focused US in a single theranostic device that actually focuses acoustic energy better within the highly scattering breast tissues, allowing for localized hyperthermia, drug delivery, and/or ablation. Women also prefer the comfort of SoftVue over mammograms and will continue to seek out less-invasive breast care, from diagnosis to treatment.

A Case Report of Breast Myofibroblastoma: A Bad Looking Benign Tumor

This study aims to analyze the relevance of the correct evaluation in the breast pathology, using a standardized method for reporting breast imaging studies, the Breast Imaging Report And Data System (BI-RADS). Myofibroblastoma (MFB) of the breast is a rare, benign, mesenchymal tumor of breast, can be a diagnostic challenge for the non-experienced general radiologist or radiology resident, due to its clinical, mammographic and ultrasonographic characteristics. In this report case we present a 57-year-old women with a breast lump and non-specific imaging findings, through her mammographic and ultrasonographic evaluation, requiring histopathological correlation, making the diagnoses of MFB, leading to lumpectomy as treatment.

Use of an AI Score Combining Cancer Signs, Masking, and Risk to Select Patients for Supplemental Breast Cancer Screening.

Background Mammographic density measurements are used to identify patients who should undergo supplemental imaging for breast cancer detection, but artificial intelligence (AI) image analysis may be more effective. Purpose To assess whether AISmartDensity-an AI-based score integrating cancer signs, masking, and risk-surpasses measurements of mammographic density in identifying patients for supplemental breast imaging after a negative screening mammogram. Materials and Methods This retrospective study included randomly selected individuals who underwent screening mammography at Karolinska University Hospital between January 2008 and December 2015. The models in AISmartDensity were trained and validated using nonoverlapping data. The ability of AISmartDensity to identify future cancer in patients with a negative screening mammogram was evaluated and compared with that of mammographic density models. Sensitivity and positive predictive value (PPV) were calculated for the top 8% of scores, mimicking the proportion of patients in the Breast Imaging Reporting and Data System "extremely dense" category. Model performance was evaluated using area under the receiver operating characteristic curve (AUC) and was compared using the DeLong test. Results The study population included 65 325 examinations (median patient age, 53 years [IQR, 47-62 years])-64 870 examinations in healthy patients and 455 examinations in patients with breast cancer diagnosed within 3 years of a negative screening mammogram. The AUC for detecting subsequent cancers was 0.72 and 0.61 (P < .001) for AISmartDensity and the best-performing density model (age-adjusted dense area), respectively. For examinations with scores in the top 8%, AISmartDensity identified 152 of 455 (33%) future cancers with a PPV of 2.91%, whereas the best-performing density model (age-adjusted dense area) identified 57 of 455 (13%) future cancers with a PPV of 1.09% (P < .001). AISmartDensity identified 32% (41 of 130) and 34% (111 of 325) of interval and next-round screen-detected cancers, whereas the best-performing density model (dense area) identified 16% (21 of 130) and 9% (30 of 325), respectively. Conclusion AISmartDensity, integrating cancer signs, masking, and risk, outperformed traditional density models in identifying patients for supplemental imaging after a negative screening mammogram. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Kim and Chang in this issue.

BI-RADS Category Assignments by GPT-3.5, GPT-4, and Google Bard: A Multilanguage Study.

Background The performance of publicly available large language models (LLMs) remains unclear for complex clinical tasks. Purpose To evaluate the agreement between human readers and LLMs for Breast Imaging Reporting and Data System (BI-RADS) categories assigned based on breast imaging reports written in three languages and to assess the impact of discordant category assignments on clinical management. Materials and Methods This retrospective study included reports for women who underwent MRI, mammography, and/or US for breast cancer screening or diagnostic purposes at three referral centers. Reports with findings categorized as BI-RADS 1-5 and written in Italian, English, or Dutch were collected between January 2000 and October 2023. Board-certified breast radiologists and the LLMs GPT-3.5 and GPT-4 (OpenAI) and Bard, now called Gemini (Google), assigned BI-RADS categories using only the findings described by the original radiologists. Agreement between human readers and LLMs for BI-RADS categories was assessed using the Gwet agreement coefficient (AC1 value). Frequencies were calculated for changes in BI-RADS category assignments that would affect clinical management (ie, BI-RADS 0 vs BI-RADS 1 or 2 vs BI-RADS 3 vs BI-RADS 4 or 5) and compared using the McNemar test. Results Across 2400 reports, agreement between the original and reviewing radiologists was almost perfect (AC1 = 0.91), while agreement between the original radiologists and GPT-4, GPT-3.5, and Bard was moderate (AC1 = 0.52, 0.48, and 0.42, respectively). Across human readers and LLMs, differences were observed in the frequency of BI-RADS category upgrades or downgrades that would result in changed clinical management (118 of 2400 [4.9%] for human readers, 611 of 2400 [25.5%] for Bard, 573 of 2400 [23.9%] for GPT-3.5, and 435 of 2400 [18.1%] for GPT-4; P < .001) and that would negatively impact clinical management (37 of 2400 [1.5%] for human readers, 435 of 2400 [18.1%] for Bard, 344 of 2400 [14.3%] for GPT-3.5, and 255 of 2400 [10.6%] for GPT-4; P < .001). Conclusion LLMs achieved moderate agreement with human reader-assigned BI-RADS categories across reports written in three languages but also yielded a high percentage of discordant BI-RADS categories that would negatively impact clinical management. © RSNA, 2024 Supplemental material is available for this article.

The application of computer-aided diagnosis in Breast Imaging Reporting and Data System ultrasound training for residents—a randomized controlled study

The application of computer-aided diagnosis in Breast Imaging Reporting and Data System ultrasound training for residents—a randomized controlled study

Mammographic density changes after neoadjuvant chemotherapy in triple-negative breast cancer: Association with treatment and survival outcome

PurposeTo investigate the association of mammographic breast density with treatment and survival outcomes in patients with triple-negative breast cancer (TNBC) undergoing neoadjuvant chemotherapy (NAC). MethodsThis retrospective study evaluated 306 women with TNBC who underwent NAC followed by surgery between 2010 and 2019. The baseline density and the density changes after NAC were evaluated. Qualitative breast density (a-d) was evaluated using the Breast Imaging Reporting and Data System. Quantitative breast density (%) was evaluated using fully automated software (the Laboratory for Individualized Breast Radiodensity Assessment) in the contralateral breast. Multivariable logistic regression analysis was used to evaluate the association between breast density and pathologic complete response (pCR), stratified by menopausal status. Cox proportional hazard regression analysis was used to evaluate the association among breast density, the development of contralateral breast cancer, and the development of locoregional recurrence and/or distant metastasis. ResultsContralateral density reduction ≥10 % was independently associated with pCR in premenopausal women (odds ratio [OR], 2.5; p = 0.022) but not in postmenopausal women (OR, 0.9; p = 0.823). During a mean follow-up of 65 months, 10 (3 %) women developed contralateral breast cancer, and 68 (22 %) women developed locoregional recurrences and/or distant metastases. Contralateral density reduction ≥10 % showed no association with the occurrence of contralateral breast cancer (hazard ratio [HR], 3.1; p = 0.308) or with locoregional recurrence and/or distant metastasis (HR, 1.1; p = 0.794). ConclusionIn premenopausal women, a contralateral breast density reduction of ≥10 % after NAC was independently associated with pCR, although it did not translate into improved outcomes.

Investigation of imaging features in contrast-enhanced magnetic resonance imaging of benign and malignant breast lesions.

This study aimed to enhance the diagnostic accuracy of contrast-enhanced breast magnetic resonance imaging (MRI) using gadobutrol for differentiating benign breast lesions from malignant ones. Moreover, this study sought to address the limitations of current imaging techniques and criteria based on the Breast Imaging Reporting and Data System (BI-RADS). In a multicenter retrospective study conducted in Japan, 200 women were included, comprising 100 with benign lesions and 100 with malignant lesions, all classified under BI-RADS categories 3 and 4. The MRI protocol included 3D fast gradient echo T1- weighted images with fat suppression, with gadobutrol as the contrast agent. The analysis involved evaluating patient and lesion characteristics, including age, size, location, fibroglandular tissue, background parenchymal enhancement (BPE), signal intensity, and the findings of mass and non-mass enhancement. In this study, univariate and multivariate logistic regression analyses were performed, along with decision tree analysis, to identify significant predictors for the classification of lesions. Differences in lesion characteristics were identified, which may influence malignancy risk. The multivariate logistic regression model revealed age, lesion location, shape, and signal intensity as significant predictors of malignancy. Decision tree analysis identified additional diagnostic factors, including lesion margin and BPE level. The decision tree models demonstrated high diagnostic accuracy, with the logistic regression model showing an area under the curve of 0.925 for masses and 0.829 for non-mass enhancements. This study underscores the importance of integrating patient age, lesion location, and BPE level into the BI-RADS criteria to improve the differentiation between benign and malignant breast lesions. This approach could minimize unnecessary biopsies and enhance clinical decision-making in breast cancer diagnostics, highlighting the effectiveness of gadobutrol in breast MRI evaluations.

Breast Imaging Reporting and Data System evaluation of breast lesions improved with virtual touch tissue imaging average grayscale values.

Early breast cancer diagnosis is of great clinical importance for selecting treatment options, improving prognosis, and enhancing the quality of patients' survival. We investigated the value of virtual touch tissue imaging average grayscale values (VAGV) helper Breast Imaging Reporting and Data System (BI-RADS) in diagnosing breast malignancy. We retrospectively analyzed 141 breast tumors in 134 patients. All breast lesions were diagnosed pathologically by biopsy or surgical excision. All patients first underwent conventional ultrasound (US) followed by virtual touch tissue imaging (VTI). The measurement of the VAGV of the lesion was performed by Image J software. BI-RADS classification was performed for each lesion according to the US. We performed a two-by-two comparison of the diagnostic values of VAGV, BI-RADS, and BI-RADS+VAGV. VAGV was lower in malignant tumors than in benign ones (35.82 ± 13.39 versus 73.58 ± 42.69, P< 0.001). The area under the receiver operating characteristic curve (AUC) value, sensitivity, and specificity of VAGV was 0.834, 84.09%, and 69.07%, respectively. Among BI-RADS, VAGV, and BI-RADS+VAGV, BI-RADS+VAGV had the highest AUC (0.926 versus 0.882, P= 0.0066; 0.926 versus 0.834, P= 0.0012). There was perfect agreement between the two radiologists using VAGV (ICC= 0.9796) and substantial agreement using BI-RADS (Kappa= 0.725). Our study shows that VAGV can accurately diagnose breast cancer. VAGV effectively improves the diagnostic performance of BI-RADS.

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

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

Associations between breast cancer risk factors and mammographic breast density in a large cross-section of Korean women.

We investigated the association between established risk factors for breast cancer and mammographic breast density in Korean women. This large cross-sectional study included 8 460 928 women aged >40 years, who were screened for breast cancer between 2009 and 2018. Breast density was assessed using the Breast Imaging Reporting and Data System. This study used multiple logistic regression analyses of age, BMI, age at menarche, menopausal status, menopausal age, parity, breastfeeding status, oral contraceptive use, family history of breast cancer, physical activity, smoking, drinking and hormone replacement therapy use to investigate their associations with mammographic breast density. Analyses were performed using SAS software. Of 8 460 928 women, 4 139 869 (48.9%) had nondense breasts and 4 321 059 (51.1%) had dense breasts. Factors associated with dense breasts were: earlier age at menarche [<15 vs. ≥15; adjusted odds ratio (aOR), 1.18; 95% confidence interval (CI), 1.17-1.18], premenopausal status (aOR, 2.01; 95% CI, 2.00-2.02), later age at menopause (≥52 vs. <52; aOR, 1.23; 95% CI, 1.22-1.23), nulliparity (aOR, 1.64; 95% CI, 1.63-1.65), never breastfed (aOR, 1.23; 95% CI, 1.23-1.24) and use of hormone replacement therapy (aOR, 1.29; 95% CI, 1.28-1.29). Women with a higher BMI and the use of oral contraceptives were more likely to have nondense breasts. Lower BMI, reproductive health and behavioral factors were associated with dense breasts in Korean women. Additional research should investigate the relationship between mammographic breast density, breast cancer risk factors and breast cancer risk.