Abstract Background: Estrogen receptor (ER) low-positive (1–10%) breast cancer is a very rare subtype that accounts for only 2–5% of all breast cancer cases and was introduced as a new category in the 2020 American Society of Clinical Oncology/College of American Pathologist Guidelines. There are limited data on the overall benefit of endocrine therapy for patients with ER low-positive cancers. The objective of this study was to analyze the clinicopathological characteristics and treatment patterns in patients with ER low-positive breast cancer and to compare disease-free survival (DFS) between patients who received and did not receive endocrine therapy. Methods: A retrospective search of the prospectively maintained database of our institution identified consecutive women with ER low-positive (1–10%) primary invasive breast cancer who underwent curative surgery at Seoul National University Hospital (Seoul, Korea) between January 2010 and April 2021. Standard immunohistochemical staining techniques were used to assess the level of ER, progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) expression, and semiquantitative scores (0–100%) were reported for ER and PR. DFS was defined as the time from surgery to the development of any recurrence and was compared between women who received and did not receive adjuvant endocrine therapy using univariable and multivariable Cox proportional hazards regression. Results: A total of 721 women (median age, 51 years; range, 24–83 years) with ER low-positive cancer were included in the analysis. Overall, 28% (203/721) had AJCC stage I, 42% (304/721) had stage II, and 30% (214/721) had stage III. PR expression was negative (< 1%) in 79% (571/721), low-positive (1–10%) in 12% (90/721), and high-positive ( >10%) in 8% (60/721). HER2 expression was negative in 54% (390/721) and positive in 46% (331/721). Most women (88% [634/721]) received adjuvant endocrine therapy, including all women with PR expression >10%. Of the 661 women with PR expression ≤10%, 87 (13%) did not receive adjuvant endocrine therapy. HER2-targeted therapy was performed in 81% (268/331) of women with HER2-positive cancer. During a median follow-up of 5.7 years, 98 (14%) had recurrence (25 locoregional/contralateral and 73 distant metastases). AJCC stage III (HR, 3.6; 95% CI: 2.4, 5.4; P < .001) and receiving total mastectomy (HR, 1.8; 95% CI: 1.2, 2.8; P = .004) were associated with worse DFS. Receiving adjuvant endocrine therapy was independently associated with better DFS in the entire cohort (HR, 0.46; 95% CI: 0.28, 0.76; P = .002) and in the HER2-negative subgroup (HR, 0.40; 95% CI: 0.22, 0.75; P = .004). In the HER2-positive subgroup, receiving adjuvant endocrine therapy was not significantly associated with better DFS (HR, 0.55; 95% CI: 0.23, 1.32; P = .18). Conclusion: In this large cohort of women with ER low-positive breast cancers, adjuvant endocrine therapy was administered in the majority of cases, including those with PR >10% expression. Receiving adjuvant endocrine therapy was associated with better DFS, especially when HER2 expression was negative. Citation Format: Su Hyun Lee, Han Suk Ryu, Myoung-jin Jang, Kyung-Hun Lee, Han-Byoel Lee. Survival Benefit of Adjuvant Endocrine Therapy in Patients with Estrogen Receptor Low-Positive Breast Cancer [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 PO1-27-11.

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.

Background Background parenchymal enhancement (BPE) is a known risk factor for breast cancer. However, studies on the association between BPE and second breast cancer risk are still lacking. Purpose To investigate whether BPE at surveillance breast MRI is associated with subsequent second breast cancer risk in women with a personal history of breast cancer. Materials and Methods A retrospective search of the imaging database of an academic medical center identified consecutive surveillance breast MRI examinations performed between January 2008 and December 2017 in women who underwent surgery for primary breast cancer and had no prior diagnosis of second breast cancer. BPE at surveillance breast MRI was qualitatively assessed using a four-category classification of minimal, mild, moderate, or marked. Future second breast cancer was defined as ipsilateral breast tumor recurrence or contralateral breast cancer diagnosed at least 1 year after each surveillance breast MRI examination. Factors associated with future second breast cancer risk were evaluated using the multivariable Fine-Gray subdistribution hazard model. Results Among the 2668 women (mean age at baseline surveillance breast MRI, 49 years ± 8 [SD]), 109 developed a second breast cancer (49 ipsilateral, 58 contralateral, and two ipsilateral and contralateral) at a median follow-up of 5.8 years. Mild, moderate, or marked BPE at surveillance breast MRI (hazard ratio [HR], 2.1 [95% CI: 1.4, 3.1]; P < .001), young age (<45 years) at initial breast cancer diagnosis (HR, 3.4 [95% CI: 1.7, 6.4]; P < .001), positive results from a BRCA1/2 genetic test (HR, 6.5 [95% CI: 3.5, 12.0]; P < .001), and negative hormone receptor expression in the initial breast cancer (HR, 1.6 [95% CI: 1.1, 2.6]; P = .02) were independently associated with an increased risk of future second breast cancer. Conclusion Background parenchymal enhancement at surveillance breast MRI was associated with future second breast cancer risk in women with a personal history of breast cancer. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Niell in this issue.

Background Accurate preoperative prediction of upstaging in women with biopsy-proven ductal carcinoma in situ (DCIS) is important for surgical planning, but published models using predictive MRI features remain lacking. Purpose To develop and validate a predictive model based on preoperative breast MRI to predict upstaging in women with biopsy-proven DCIS and to select high-risk women who may benefit from sentinel lymph node biopsy at initial surgery. Materials and methods Consecutive women with biopsy-proven DCIS who underwent preoperative 3.0-T breast MRI including dynamic contrast-enhanced (DCE) MRI and diffusion-weighted imaging (DWI) and who underwent surgery between June 2019 and March 2020 were retrospectively identified (development set) from an academic medical center. The apparent diffusion coefficients of lesions from DWI, lesion size and morphologic features on DCE MRI scans, mammographic findings, age, symptoms, biopsy method, and DCIS grade at biopsy were collected. The presence of invasive cancer and axillary metastases was determined with surgical pathology. A predictive model for upstaging was developed by using multivariable logistic regression and validated in a subsequent prospective internal validation set recruited between July 2020 and April 2021. Results Fifty-seven (41%) of 140 women (mean age, 53 years ± 11 [SD]) in the development set and 43 (41%) of 105 women (mean age, 53 years ± 10) in the validation set were upstaged after surgery. The predictive model combining DWI and clinical-pathologic factors showed the areas under the receiver operating characteristic curve at 0.87 (95% CI: 0.80, 0.92) in the development set and 0.76 (95% CI: 0.67, 0.84) in the validation set. The predicted probability of invasive cancer showed good interobserver agreement (intraclass correlation coefficient, 0.79); the positive predictive value was 85% (28 of 33), and the negative predictive value was 92% (22 of 24). Conclusion A predictive model based on diffusion-weighted breast MRI identified women at high risk of upstaging. © RSNA, 2022 Online supplemental material is available for this article See also the editorial by Baltzer in this issue. An earlier incorrect version appeared online. This article was corrected on July 7, 2022.

Abstract Background: Estrogen receptor (ER) low-positive (1–10%) breast cancer is a very rare subtype that accounts for only 2–5% of all breast cancer cases and was introduced as a new category in the 2020 American Society of Clinical Oncology/College of American Pathologist Guidelines. There are limited data on the overall benefit of endocrine therapy for patients with ER low-positive cancers. The objective of this study was to analyze the clinicopathological characteristics and treatment patterns in patients with ER low-positive breast cancer and to compare disease-free survival (DFS) between patients who received and did not receive endocrine therapy. Methods: A retrospective search of the prospectively maintained database of our institution identified consecutive women with ER low-positive (1–10%) primary invasive breast cancer who underwent curative surgery at Seoul National University Hospital (Seoul, Korea) between January 2010 and April 2021. Standard immunohistochemical staining techniques were used to assess the level of ER, progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) expression, and semiquantitative scores (0–100%) were reported for ER and PR. DFS was defined as the time from surgery to the development of any recurrence and was compared between women who received and did not receive adjuvant endocrine therapy using univariable and multivariable Cox proportional hazards regression. Results: A total of 721 women (median age, 51 years; range, 24–83 years) with ER low-positive cancer were included in the analysis. Overall, 28% (203/721) had AJCC stage I, 42% (304/721) had stage II, and 30% (214/721) had stage III. PR expression was negative (< 1%) in 79% (571/721), low-positive (1–10%) in 12% (90/721), and high-positive ( >10%) in 8% (60/721). HER2 expression was negative in 54% (390/721) and positive in 46% (331/721). Most women (88% [634/721]) received adjuvant endocrine therapy, including all women with PR expression >10%. Of the 661 women with PR expression ≤10%, 87 (13%) did not receive adjuvant endocrine therapy. HER2-targeted therapy was performed in 81% (268/331) of women with HER2-positive cancer. During a median follow-up of 5.7 years, 98 (14%) had recurrence (25 locoregional/contralateral and 73 distant metastases). AJCC stage III (HR, 3.6; 95% CI: 2.4, 5.4; P < .001) and receiving total mastectomy (HR, 1.8; 95% CI: 1.2, 2.8; P = .004) were associated with worse DFS. Receiving adjuvant endocrine therapy was independently associated with better DFS in the entire cohort (HR, 0.46; 95% CI: 0.28, 0.76; P = .002) and in the HER2-negative subgroup (HR, 0.40; 95% CI: 0.22, 0.75; P = .004). In the HER2-positive subgroup, receiving adjuvant endocrine therapy was not significantly associated with better DFS (HR, 0.55; 95% CI: 0.23, 1.32; P = .18). Conclusion: In this large cohort of women with ER low-positive breast cancers, adjuvant endocrine therapy was administered in the majority of cases, including those with PR >10% expression. Receiving adjuvant endocrine therapy was associated with better DFS, especially when HER2 expression was negative. Citation Format: Su Hyun Lee, Han Suk Ryu, Myoung-jin Jang, Kyung-Hun Lee, Han-Byoel Lee. Survival Benefit of Adjuvant Endocrine Therapy in Patients with Estrogen Receptor Low-Positive Breast Cancer [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 PO1-27-11.

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.

Background Background parenchymal enhancement (BPE) is a known risk factor for breast cancer. However, studies on the association between BPE and second breast cancer risk are still lacking. Purpose To investigate whether BPE at surveillance breast MRI is associated with subsequent second breast cancer risk in women with a personal history of breast cancer. Materials and Methods A retrospective search of the imaging database of an academic medical center identified consecutive surveillance breast MRI examinations performed between January 2008 and December 2017 in women who underwent surgery for primary breast cancer and had no prior diagnosis of second breast cancer. BPE at surveillance breast MRI was qualitatively assessed using a four-category classification of minimal, mild, moderate, or marked. Future second breast cancer was defined as ipsilateral breast tumor recurrence or contralateral breast cancer diagnosed at least 1 year after each surveillance breast MRI examination. Factors associated with future second breast cancer risk were evaluated using the multivariable Fine-Gray subdistribution hazard model. Results Among the 2668 women (mean age at baseline surveillance breast MRI, 49 years ± 8 [SD]), 109 developed a second breast cancer (49 ipsilateral, 58 contralateral, and two ipsilateral and contralateral) at a median follow-up of 5.8 years. Mild, moderate, or marked BPE at surveillance breast MRI (hazard ratio [HR], 2.1 [95% CI: 1.4, 3.1]; P < .001), young age (<45 years) at initial breast cancer diagnosis (HR, 3.4 [95% CI: 1.7, 6.4]; P < .001), positive results from a BRCA1/2 genetic test (HR, 6.5 [95% CI: 3.5, 12.0]; P < .001), and negative hormone receptor expression in the initial breast cancer (HR, 1.6 [95% CI: 1.1, 2.6]; P = .02) were independently associated with an increased risk of future second breast cancer. Conclusion Background parenchymal enhancement at surveillance breast MRI was associated with future second breast cancer risk in women with a personal history of breast cancer. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Niell in this issue.

Background Accurate preoperative prediction of upstaging in women with biopsy-proven ductal carcinoma in situ (DCIS) is important for surgical planning, but published models using predictive MRI features remain lacking. Purpose To develop and validate a predictive model based on preoperative breast MRI to predict upstaging in women with biopsy-proven DCIS and to select high-risk women who may benefit from sentinel lymph node biopsy at initial surgery. Materials and methods Consecutive women with biopsy-proven DCIS who underwent preoperative 3.0-T breast MRI including dynamic contrast-enhanced (DCE) MRI and diffusion-weighted imaging (DWI) and who underwent surgery between June 2019 and March 2020 were retrospectively identified (development set) from an academic medical center. The apparent diffusion coefficients of lesions from DWI, lesion size and morphologic features on DCE MRI scans, mammographic findings, age, symptoms, biopsy method, and DCIS grade at biopsy were collected. The presence of invasive cancer and axillary metastases was determined with surgical pathology. A predictive model for upstaging was developed by using multivariable logistic regression and validated in a subsequent prospective internal validation set recruited between July 2020 and April 2021. Results Fifty-seven (41%) of 140 women (mean age, 53 years ± 11 [SD]) in the development set and 43 (41%) of 105 women (mean age, 53 years ± 10) in the validation set were upstaged after surgery. The predictive model combining DWI and clinical-pathologic factors showed the areas under the receiver operating characteristic curve at 0.87 (95% CI: 0.80, 0.92) in the development set and 0.76 (95% CI: 0.67, 0.84) in the validation set. The predicted probability of invasive cancer showed good interobserver agreement (intraclass correlation coefficient, 0.79); the positive predictive value was 85% (28 of 33), and the negative predictive value was 92% (22 of 24). Conclusion A predictive model based on diffusion-weighted breast MRI identified women at high risk of upstaging. © RSNA, 2022 Online supplemental material is available for this article See also the editorial by Baltzer in this issue. An earlier incorrect version appeared online. This article was corrected on July 7, 2022.

Breast US is an essential breast imaging tool that complements mammography and MRI. US is also often the primary imaging modality used to evaluate palpable breast masses and axillary lymph nodes and to guide percutaneous biopsy of breast masses and lymph nodes. Screening whole-breast US, with either handheld or automated technique, serves as a supplementary modality to screening mammography, particularly in women with dense breasts. Artificial intelligence (AI) has been adopted in US examinations to improve diagnostic accuracy and workflow. Analysis and quantification of background echotexture are emerging as a novel biomarker for breast cancer risk assessment. As US technology evolves and the scope of breast US widens, radiologists must understand the current and emerging US technology. They must also apply meticulous US scanning techniques to optimize image quality and ensure accurate diagnosis. This review provides a state-of-the-art summary of US technology and clinical applications as an adjuvant technique to mammography, MRI, and the clinical breast examination. The utility of breast US for screening, preoperative staging, and neoadjuvant treatment monitoring for breast cancer, breast intervention, and new techniques including AI, US tomography, optoacoustic imaging, and contrast-enhanced US will also be presented.

10564 Background: Supplemental MRI screening for women at increased breast cancer risk can detect additional cancers that are occult on mammography. However, its adoption is limited by restricted availability and high false-positive rates. Improved risk stratification is therefore needed to optimize the use of supplemental MRI screening. This study aimed to evaluate the feasibility of a mammography-based artificial intelligence (AI) system for stratifying women at intermediate to high risk for supplemental MRI screening. Methods: This retrospective study included consecutive women who underwent abbreviated breast MRI following negative mammography between January 2020 and December 2024. The study cohort comprised women at intermediate to high breast cancer risk, including those with known genetic predisposition, personal history of breast cancer, prior benign or high-risk breast biopsy, or family history of breast cancer. A commercially available AI system was applied to all mammograms, generating both exam-level and breast-level scores representing the likelihood of a malignancy on a scale of 0 to 100. MRI outcomes, false-positive examinations, cancer detection, and positive predictive value (PPV) were evaluated. Univariate analyses were performed to explore associations between breast density and AI-based selection results. Results: In 1,588 women (median age, 51 years; range, 24-87), 1,804 screening mammograms and MRI examinations were performed. Of these examinations, 220 resulted in recall, yielding 24 mammographically occult breast cancers (12 ductal carcinoma in situ and 12 invasive cancers) diagnosed within one year and 166 false-positive examinations. The AI system achieved an area under the curve (AUC) of 0.694 (95% CI, 0.499-0.888) in women without a personal history of breast cancer and 0.611 (95% CI, 0.430-0.792) in women with a personal history of breast cancer. Using AI-based selection, 239 of 1,588 women (14.9%) were selected for supplemental MRI, resulting in an 81% reduction in false-positive examinations (31 vs 166) and an improvement in PPV to 20.5% (8 of 39). 6 of 8 detected cancers (75%) were invasive. Breast density was not associated with the AI-based selection strategy. Conclusions: Mammography-based AI substantially reduced false-positive supplemental MRI examinations while preferentially selecting women at higher risk for invasive breast cancers, without compromising cancer detection. These findings suggest that AI can refine risk stratification and optimize selection of women to improve the efficiency of supplemental MRI screening.

Objective: To investigate whether reader training improves the performance and agreement of radiologists in interpreting unenhanced breast magnetic resonance imaging (MRI) scans using diffusion-weighted imaging (DWI). Materials and Methods:A study of 96 breasts (35 cancers, 24 benign, and 37 negative) in 48 asymptomatic women was performed between June 2019 and October 2020.High-resolution DWI with b-values of 0, 800, and 1200 sec/mm 2 was performed using a 3.0-T system.Sixteen breast radiologists independently reviewed the DWI, apparent diffusion coefficient maps, and T1-weighted MRI scans and recorded the Breast Imaging Reporting and Data System (BI-RADS) category for each breast.After a 2-h training session and a 5-month washout period, they re-evaluated the BI-RADS categories.A BI-RADS category of 4 (lesions with at least two suspicious criteria) or 5 (more than two suspicious criteria) was considered positive.The per-breast diagnostic performance of each reader was compared between the first and second reviews.Inter-reader agreement was evaluated using a multi-rater κ analysis and intraclass correlation coefficient (ICC).Results: Before training, the mean sensitivity, specificity, and accuracy of the 16 readers were 70.7% (95% confidence interval [CI]: 59.4-79.9),90.8% (95% CI: 85.6-94.2),and 83.5% (95% CI: 78.6-87.4),respectively.After training, significant improvements in specificity (95.2%; 95% CI: 90.8-97.5;P = 0.001) and accuracy (85.9%; 95% CI: 80.9-89.8;P = 0.01) were observed, but no difference in sensitivity (69.8%; 95% CI: 58.1-79.4;P = 0.58) was observed.Regarding inter-

Supplemental screening US demonstrated additional node-negative early breast cancers that were overlooked by mammography and artificial intelligence in women with dense breasts, while increasing false-positive findings.