Ji Hyun Youk

To evaluate the association between automatically measured mammographic breast density (MBD) and osteoporosis in postmenopausal women and determine whether

Background/Objectives: This study aimed to investigate the preoperative clinicopathologic and imaging features associated with subsequent breast cancer events detected on postoperative abbreviated MRI in early-stage breast cancer patients following breast and axillary surgery. Methods: A retrospective analysis was conducted on 1171 patients (median age, 53 years; range, 24–90 years) diagnosed with clinical stage I or II breast cancer between January 2013 and December 2017. Logistic regression analysis was used to evaluate preoperative imaging features—including breast density assessed on mammography and MRI descriptors—along with clinicopathologic characteristics, to identify factors independently associated with subsequent breast cancer events during abbreviated MRI screening. Results: Among the patients, 57 (4.9%) experienced subsequent breast cancer events at a median follow-up of 74 months. In the multivariable analysis, high nuclear grade (odds ratio [OR] = 2.821; 95% confidence interval [CI], 1.427–5.577; p = 0.003), dense breast tissue on mammography (OR = 4.680; 95% CI, 1.113–19.684; p = 0.035), and absence of heterogeneous internal enhancement on preoperative MRI (OR = 0.429; 95% CI, 0.206–0.891; p = 0.023) were independently associated with subsequent breast cancer events detected using an abbreviated breast MRI protocol. Age ≥ 40 years (OR = 0.448; 95% CI, 0.193–1.039; p = 0.061) and clinical T2 stage (OR = 1.744; 95% CI, 0.969–3.139; p = 0.064) showed borderline significance. Conclusions: High nuclear grade, dense breast tissue on mammography, and absence of heterogeneous internal enhancement on preoperative MRI were associated with an increased risk of subsequent breast cancer events in patients undergoing abbreviated MRI surveillance following surgery for early-stage breast cancer.

To investigate automated breast ultrasound (ABUS) features affecting the use of a multiview convolutional neural network (CNN) for breast lesions according to the level of experience of radiologists.A total of 656 breast lesions (152 malignant and 504 benign lesions) were included and reviewed by 6 radiologists for background echotexture, glandular tissue component (GTC), and lesion type and size without as well as with a multiview CNN. The sensitivity, specificity, and the area under the receiver operating curve (AUC) for ABUS features were compared between 2 sessions according to the level of the radiologists’ experience.Radiology residents showed significant AUC improvement with the multiview CNN for mass (0.81–0.91, P=0.003) and non-mass lesions (0.56–0.90, P=0.007), all background echotextures (homogeneous-fat: 0.84–0.94, P=0.04; homogeneous-fibroglandular: 0.85–0.93, P=0.01; heterogeneous: 0.68–0.88, P=0.002), all GTC levels (minimal: 0.86–0.93, P=0.001; mild: 0.82–0.94, P=0.003; moderate: 0.75–0.88, P=0.01; marked: 0.68–0.89, P<0.001), and lesions ≤10mm (≤5mm: 0.69–0.86, P<0.001; 6–10mm: 0.83–0.92, P<0.001). Breast specialists showed significant AUC improvement with the multiview CNN in heterogeneous echotexture (0.90–0.95, P=0.03), marked GTC (0.88–0.95, P<0.001), and lesions ≤10mm (≤5mm: 0.89–0.93, P=0.02; 6–10mm: 0.95–0.98, P=0.01).With the multiview CNN, ABUS performance among radiology residents was improved regardless of lesion type, background echotexture, or GTC. For breast lesions smaller than 10mm, both radiology residents and breast specialists achieved better ABUS performance.

정희진, et al. J Korean Soc Radiol. 2025 Jan;86(1):114-126. https://doi.org/10.3348/jksr.2024.0016

Purpose: To investigate whether preoperative ultrasonographic (US) features of the index cancer and metastatic lymph nodes (LNs) are associated with level II LN metastasis in N1b papillary rmfthyroid carcinoma (PTC) patients. Materials and methods: We enrolled 517 patients (mean age, 42 [range, 6–80] years) who underwent total thyroidectomy and lateral compartment LN dissection between January 2009 and December 2015. We reviewed the clinicopathologic and US features of the index cancer and metastatic LNs in the lateral neck. Logistic regression analysis was performed to analyze features associated with level II LN metastasis. Results: Among the patients, 196 (37.9%) had level II metastasis on final pathology. In the preoperative model, larger tumor size (odds ratios [ORs], 1.031; 95% confidence interval [CI]: 1.011–1.051, p = 0.002), nonparallel tumor shape (OR, 1.963; 95% CI: 1.322–2.915, p = 0.001), multilevel LN involvement (OR, 1.906; 95% CI: 1.242–2.925, p = 0.003), and level III involvement (OR, 1.867; 95% CI: 1.223–2.850, p = 0.004), were independently associated with level II LN metastasis. In the postoperative model, non-conventional pathology remained a significant predictor for level II LN metastasis (OR, 1.951; 95% CI: 1.121–3.396; p = 0.018), alongside the presence of extrathyroidal extension (OR, 1.867; 95% CI: 1.060–3.331; p = 0.031), and higher LN ratio (OR, 1.057; 95% CI: 1.039–1.076; p < 0.001). Conclusions: Preoperative US features of the index tumor and LN may be helpful in guiding surgery in N1b PTC. These findings could enhance preoperative planning and decision-making, potentially reducing surgical morbidities by identifying those at higher risk of level II LN metastasis and tailoring surgical approaches accordingly.

Purpose To develop and evaluate artificial intelligence (AI) algorithms for ultrasound (US) microflow imaging (MFI) in breast cancer diagnosis. Materials and Methods We retrospectively collected a dataset consisting of 516 breast lesions (364 benign and 152 malignant) in 471 women who underwent B-mode US and MFI. The internal dataset was split into training (n = 410) and test datasets (n = 106) for developing AI algorithms from deep convolutional neural networks from MFI. AI algorithms were trained to provide malignancy risk (0–100%). The developed AI algorithms were further validated with an independent external dataset of 264 lesions (229 benign and 35 malignant). The diagnostic performance of B-mode US, AI algorithms, or their combinations was evaluated by calculating the area under the receiver operating characteristic curve (AUROC). Results The AUROC of the developed three AI algorithms (0.955–0.966) was higher than that of B-mode US (0.842, P < 0.0001). The AUROC of the AI algorithms on the external validation dataset (0.892–0.920) was similar to that of the test dataset. Among the AI algorithms, no significant difference was found in all performance metrics combined with or without B-mode US. Combined B-mode US and AI algorithms had a higher AUROC (0.963–0.972) than that of B-mode US (P < 0.0001). Combining B-mode US and AI algorithms significantly decreased the false-positive rate of BI-RADS category 4A lesions from 87% to 13% (P < 0.0001). Conclusion AI-based MFI diagnosed breast cancers with better performance than B-mode US, eliminating 74% of false-positive diagnoses in BI-RADS category 4A lesions.

Background: We aimed to elucidate the clinical significance of tumor stiffness across breast cancer subtypes and establish its correlation with the tumor-infiltrating lymphocyte (TIL) levels using shear-wave elastography (SWE). Methods: SWE was used to measure tumor stiffness in breast cancer patients from January 2016 to August 2020. The association of tumor stiffness and clinicopathologic parameters, including the TIL levels, was analyzed in three breast cancer subtypes. Results: A total of 803 patients were evaluated. Maximal elasticity (E_max) showed a consistent positive association with an invasive size and the pT stage in all cases, while it negatively correlated with the TIL level. A subgroup-specific analysis revealed that the already known parameters for high stiffness (lymphovascular invasion, lymph node metastasis, Ki67 levels) were significant only in hormone receptor-positive and HER2-negative breast cancer (HR + HER2-BC). In the multivariate logistic regression, an invasive size and low TIL levels were significantly associated with E_max in HR + HER2-BC and HER2 + BC. In triple-negative breast cancer, only TIL levels were significantly associated with low E_max. Linear regression confirmed a consistent negative correlation between TIL and E_max in all subtypes. Conclusions: Breast cancer stiffness presents varying clinical implications dependent on the tumor subtype. Elevated stiffness indicates a more aggressive tumor biology in HR + HER2-BC, but is less significant in other subtypes. High TIL levels consistently correlate with lower tumor stiffness across all subtypes.

Shear-wave elastography (SWE) is an effective tool in discriminating malignant lesions of breast and axillary lymph node metastasis in patients with breast cancer. However, the association between the baseline elasticity value of breast cancer and the treatment response of neoadjuvant chemotherapy is yet to be elucidated. Baseline SWE measured mean stiffness (E-mean) and maximum stiffness (E-max) in 830 patients who underwent neoadjuvant chemotherapy and surgery from January 2012 to December 2022. Association of elasticity values with breast pCR (defined as ypTis/T0), pCR (defined as ypTis/T0, N0), and tumor-infiltrating lymphocytes (TILs) was analyzed. Of 830 patients, 356 (42.9%) achieved breast pCR, and 324 (39.0%) achieved pCR. The patients with low elasticity values had higher breast pCR and pCR rates than those with high elasticity values. A low E-mean (adjusted odds ratio (OR): 0.620; 95% confidence interval (CI): 0.437 to 0.878; p = 0.007) and low E-max (adjusted OR: 0.701; 95% CI: 0.494 to 0.996; p = 0.047) were independent predictive factors for breast pCR. Low elasticity values were significantly correlated with high TILs. Pretreatment elasticity values measured using SWE were significantly associated with treatment response and inversely correlated with TILs, particularly in HR+HER2- breast cancer and TNBC.

To evaluate the consistency in the performance of Artificial Intelligence (AI)-based diagnostic support software in short-term digital mammography reimaging after core needle biopsy. Of 276 women who underwent short-term (P<0.0001). In comparison of serial exams, no significant difference was found in AI-based score. AI-based score difference between serial exams was significantly different according to biopsy performed or not (-0.25 vs. 0.07, P = 0.035). In linear regression analysis, there was no significant interaction effect of all clinical and mammographic characteristics with mammographic examinations performed after biopsy or not. The results from AI-based diagnostic support software for digital mammography was relatively consistent in short-term reimaging even after core needle biopsy.

Purpose: To analyze BE on ABUS using BI‐RADS and a modified classification in association with mammographic density and clinical features.Methods: Menopausal status, parity, and family history of breast cancer were collected for 496 women who underwent ABUS and mammography. Three radiologists independently reviewed all ABUS BE and mammographic density. Statistical analyses including kappa statistics (κ) for interobserver agreement, Fisher's exact test, and univariate and multivariate multinomial logistic regression were performed.Results: BE distribution between the two classifications and between each classification and mammographic density were associated (P < 0.001). BI‐RADS homogeneous‐fibroglandular (76.8%) and modified heterogeneous BE (71.3%, 75.7%, and 87.5% of mild, moderate, and marked heterogeneous background echotexture, respectively) tended to be dense. BE was correlated between BI‐RADS homogeneous‐fat and modified homogeneous background (95.1%) and between BI‐RADS homogeneous‐fibroglandular or heterogeneous (90.6%) and modified heterogeneous (86.9%) (P < 0.001). In multinomial logistic regression, age < 50 years was independently associated with heterogeneous BE (OR, 8.89, P = 0.003, in BI‐RADS; OR, 3.74; P = 0.020 in modified classification).Conclusion: BI‐RADS homogeneous‐fat and modified homogeneous BE on ABUS was likely to be mammographically fatty. However, BI‐RADS homogeneous‐fibroglandular or heterogeneous BE might be classified as any modified BE. Younger age was independently associated with heterogeneous BE.