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Background: Artificial intelligence-based computer-assisted diagnosis (AI-CAD) is increasingly used for mammographic exams, and its role in mammographic density assessment should be evaluated. Purpose: To assess the inter-modality agreement between radiologists, automated volumetric density measurement program (Volpara), and AI-CAD system in breast density categorization using the Breast Imaging-Reporting and Data System (BI-RADS) density categories. Material and Methods: A retrospective review was conducted on 1015 screening digital mammograms that were performed in Asian female patients (mean age = 56 years ± 10 years) in our health examination center between December 2022 and January 2023. Four radiologists with two different levels of experience (expert and general radiologists) performed density assessments. Agreement between the radiologists, Volpara, and AI-CAD (Lunit INSIGHT MMG) was evaluated using weighted kappa statistics and matched rates. Results: Inter-reader agreement between expert and general radiologists was substantial (k = 0.65) with a matched rate of 72.8%. The agreement was substantial between expert or general radiologists and Volpara (k = 0.64–0.67) with a matched rate of 72.0% but moderate between expert or general radiologists and AI-CAD (k = 0.45–0.58) with matched rates of 56.7%–67.0%. The agreement between Volpara and AI-CAD was moderate (k = 0.53) with a matched rate of 60.8%. Conclusion: The agreement in breast density categorization between radiologists and automated volumetric density measurement program (Volpara) was higher than the agreement between radiologists and AI-CAD (Lunit INSIGHT MMG). [ABSTRACT FROM AUTHOR]

Background: MammaPrint is a 70-gene signature microarray assay that predicts the likelihood of recurrence of breast cancer and chemotherapeutic benefits. Purpose: To investigate the association between mammography and ultrasound (US) features and MammaPrint results in patients with estrogen receptor (ER)-positive, HER2-negative, node-positive invasive breast cancer, and to identify the predictive factors for high risk of recurrence. Material and Methods: This retrospective study included 251 patients with ER-positive, HER2-negative, 1–3 node-positive invasive breast cancer. Mammography and US findings were reviewed according to the BI-RADS criteria. The association between MammaPrint results and the clinicopathological and imaging features was evaluated. Logistic regression analysis was performed to identify independent predictors for high risk of recurrence. Results: Of the patients, 143 (57.0%) and 108 (43.0%) had low and high risks for recurrence on MammaPrint, respectively. Young age (odds ratio [OR] 1.08; 95% confidence interval (CI) 1.04–1.12; P <0.001), posterior enhancement on US (OR 2.45; 95% CI 1.16–5.20; P = 0.019), absence of posterior shadowing on US (OR 3.19; 95% CI 1.17–8.62; P = 0.023), high histologic grade (OR 113.36; 95% CI 6.79–1893.53; P = 0.001), and high Ki-67 level (OR 4.90; 95% CI 2.62–9.17; P <0.001) were independently associated with high risk of recurrence on multivariate logistic regression analysis. Conclusion: Posterior features in US may predict a high risk of recurrence in patients with ER-positive, HER2-negative, node-positive invasive breast cancer, which may be useful in enhancing the diagnostic value of MammaPrint and aid in the decision-making process regarding treatment. [ABSTRACT FROM AUTHOR]

Background: High levels of tumor-infiltrating lymphocytes (TILs) are associated with improved prognosis and response to therapy in patients with human epidermal growth factor receptor 2 (HER2)-positive breast cancer. Purpose: This study investigated the associations between TIL levels and magnetic resonance imaging (MRI) findings in patients with estrogen receptor (ER)-negative HER-2 positive breast cancer. Material and Methods: This study included 110 consecutive patients with surgically confirmed ER-negative HER2-positive breast cancers who underwent preoperative MRI from January to December 2015. Images of all lesions were reviewed in accordance with the BI-RADS lexicon by radiologists blinded to clinicopathologic findings. Tumor kinetic features were acquired by computer-aided diagnosis (CAD). Patients were divided into three TIL groups: low (<10%); intermediate (10–50%); and high (>50%). Associations between TIL levels and clinicopathologic and imaging features were evaluated; independent predictors of high and low TIL were identified by multiple logistic regression analysis. Results: The 110 patients included 29 (26.4%) with low, 45 (40.9%) with intermediate, and 36 (32.7%) with high TIL levels. Multiple logistic regression analysis showed that older age (odds ratio [OR] = 1.08; P = 0.017), high peak enhancement (OR = 1.01; P = 0.019), positive CK5/6 (OR = 4.36; P = 0.024), and low Ki-67 (OR = 14.29; P = 0.037) were significantly associated with low TILs; low peak enhancement (OR = 1.01; P = 0.020) was significantly associated with high TILs. Conclusion: MRI features may predict TIL levels in patients with ER-negative HER-2 positive breast cancer, enhancing the ability to diagnose and treat these patients. [ABSTRACT FROM AUTHOR]

Background: Invasive micropapillary carcinoma (IMPC) is a rare subtype of breast cancer and is presumed to have a poorer survival outcome than invasive ductal carcinoma (IDC). However, studies for clinical outcomes including imaging features are still scarce. Purpose: To investigate differences in clinical outcomes between IMPC and IDC and to determine prognostic factors indicating survival, including imaging features. Material and Methods: This retrospective study was approved by the institutional review board. Between 2007 and 2012, 308 women diagnosed with IMPC were matched with patients from a group of 6816 women diagnosed with IDC. Patients were matched with 17 clinicopathologic covariates using propensity score matching. Recurrence-free survival (RFS) and overall survival (OS) were compared between the IMPC and IDC groups using Kaplan–Meier estimates. Log-rank tests were performed to compare the survival curves. A Cox proportional hazard model was used to analyze the association of imaging features with survival. Results: In total, 308 matched patient pairs were available for survival analysis. The IMPC group showed worse total RFS (hazard ratio [HR] = 1.63, P = 0.016), local RFS (HR = 2.86, P = 0.042), and distant RFS (HR = 1.85 P = 0.018), but there was no significant difference in OS (HR = 1.30, P = 0.335). A mass with calcification on mammography was an independent factor for worse RFS in the IMPC group and combined IMPC and IDC groups. IMPC subtype was a significant independent factor for worse RFS in the combined groups. Conclusion: The IMPC group showed poorer recurrence-free survival outcomes than the IDC group. A mass with calcification on mammography were associated with poor RFS. [ABSTRACT FROM AUTHOR]

Background Only few studies have assessed variability in the results obtained by the readers with different experience levels in comparison with automated volumetric breast density measurements. Purpose To examine the variations in breast density assessment according to BI-RADS categories among readers with different experience levels and to compare it with the results of automated quantitative measurements. Material and Methods Density assignment was done for 1000 screening mammograms by six readers with three different experience levels (breast-imaging experts, general radiologists, and students). Agreement level between the results obtained by the readers and the Volpara automated volumetric breast density measurements was assessed. The agreement analysis using two categories-non-dense and dense breast tissue-was also performed. Results Intra-reader agreement for experts, general radiologists, and students were almost perfect or substantial (k = 0.74-0.95). The agreement between visual assessments of the breast-imaging experts and volumetric assessments by Volpara was substantial (k = 0.77). The agreement was moderate between the experts and general radiologists (k = 0.67) and slight between the students and Volpara (k = 0.01). The agreement for the two category groups (nondense and dense) was almost perfect between the experts and Volpara (k = 0.83). The agreement was substantial between the experts and general radiologists (k = 0.78). Conclusion We observed similar high agreement levels between visual assessments of breast density performed by radiologists and the volumetric assessments. However, agreement levels were substantially lower for the untrained readers. [ABSTRACT FROM AUTHOR]