AI-Powered Synthesis of Structured Multimodal Breast Ultrasound Reports Integrating Radiologist Annotations and Deep Learning Analysis.

Breast cancer is the most prevalent cancer among women worldwide. B-mode ultrasound (US) is essential for early detection, offering high sensitivity and specificity without radiation exposure. This study introduces a semi-automatic method to streamline breast US report generation, aiming to reduce the burden on radiologists. Our method synthesizes comprehensive breast US reports by combining the extracted information from radiologists' annotations during routine screenings with the analysis results from deep learning algorithms on multimodal US images. Key modules in our method include image classification using visual features (ICVF), type classification via deep learning (TCDL), and automatic report structuring and compilation (ARSC). Experiments showed that the proposed method reduced the average report generation time to 3.8 min compared to manual processes, even when using relatively low-spec hardware. Generated reports perfectly matched ground truth reports for suspicious masses without a single failure on our evaluation datasets. Additionally, the deep-learning-based algorithm, utilizing DenseNet-121 as its core model, achieved an overall accuracy of 0.865, precision of 0.868, recall of 0.847, F1-score of 0.856, and area under the receiver operating characteristics of 0.92 in classifying tissue stiffness in breast US shear-wave elastography (SWE-mode) images. These improvements not only streamline the report generation process but also allow radiologists to dedicate more time and focus on patient care, ultimately enhancing clinical outcomes and patient satisfaction.

Age- and ethnic-driven molecular and clinical disparity of East Asian breast cancers.

BACKGROUND: Breast cancer (BC) is a complex disease with profound genomic aberrations. However, the underlying molecular disparity influenced by age and ethnicity remains elusive. METHODS: In this study, we aimed to investigate the molecular properties of 843 primary and metastatic BC patients enrolled in the K-MASTER program. By categorizing patients into two distinct age subgroups, we explored their unique molecular properties. Additionally, we leveraged large-scale genomic data from the TCGA and MSK-IMPACT studies to examine the ethnic-driven molecular and clinical disparities. RESULTS: We observed a high prevalence of PI3KCA mutations in K-MASTER HER2 + tumors, particularly in older patients. Moreover, we identified increased mutation rates in DNA damage response molecules, including ARID1A, MSH6, and MLH1. The K-MASTER patients were mainly comprised of triple-negative breast cancer (TNBC) and HER2-positive tumors, while the TCGA and MSK-IMPACT cohorts exhibited a predominance of hormone receptor-positive (HR +) subtype tumors. Importantly, GATA3 mutations were less frequently observed in East Asian patients, which correlated with poor clinical outcomes. In addition to characterizing the molecular disparities, we developed a gradient-boosting multivariable model to identify a new molecular signature that could predict the therapeutic response to platinum-based chemotherapy. CONCLUSIONS: Our findings collectively provide unprecedented insights into the significance of age and ethnicity on the molecular and clinical characteristics of BC patients.

Unusual US Findings of Diffuse Large B-Cell Lymphoma of the Breast: A Case Report.

Lymphoma is an uncommon type of breast malignancy, with low prevalence. The ultrasonographic findings of breast lymphoma have been described as nonspecific. Breast lymphoma most commonly appears as a solitary hypoechoic mass on US, and usually shows hypervascularity on color Doppler US. Herein, we report an unusual case of breast lymphoma that presented as multiple bilateral hyperechoic nodules on US.

Tumor Heterogeneity of Breast Cancer Assessed with Computed Tomography Texture Analysis: Association with Disease-Free Survival and Clinicopathological Prognostic Factor.

Breast cancer is a heterogeneous disease, and computed tomography texture analysis (CTTA), which reflects the tumor heterogeneity, may predict the prognosis. We investigated the usefulness of CTTA for the prediction of disease-free survival (DFS) and prognostic factors in patients with invasive breast cancer. A total of 256 consecutive women who underwent preoperative chest CT and surgery in our institution were included. The Cox proportional hazards model was used to determine the relationship between textural features and DFS. Logistic regression analysis was used to reveal the relationship between textural features and prognostic factors. Of 256 patients, 21 (8.2%) had disease recurrence over a median follow-up of 60 months. For the prediction of shorter DFS, higher histological grade (hazard ratio [HR], 6.12; p < 0.001) and lymphovascular invasion (HR, 2.93; p = 0.029) showed significance, as well as textural features such as lower mean attenuation (HR, 4.71; p = 0.003) and higher entropy (HR, 2.77; p = 0.036). Lower mean attenuation showed a correlation with higher tumor size, and higher entropy showed correlations with higher tumor size and Ki-67. In conclusion, CTTA-derived textural features can be used as a noninvasive imaging biomarker to predict shorter DFS and prognostic factors in patients with invasive breast cancer.

Value of Breast MRI and Nomogram After Negative Axillary Ultrasound for Predicting Axillary Lymph Node Metastasis in Patients With Clinically T1-2 N0 Breast Cancer.

BACKGROUND: There are increasing concerns about that sentinel lymph node biopsy (SLNB) could be omitted in patients with clinically T1-2 N0 breast cancers who has negative axillary ultrasound (AUS). This study aims to assess the false negative result (FNR) of AUS, the rate of high nodal burden (HNB) in clinically T1-2 N0 breast cancer patients, and the diagnostic performance of breast magnetic resonance imaging (MRI) and nomogram. METHODS: We identified 948 consecutive patients with clinically T1-2 N0 cancers who had negative AUS, subsequent MRI, and breast conserving therapy between 2013 and 2020 from two tertiary medical centers. Patients from two centers were assigned to development and validation sets, respectively. Among 948 patients, 402 (mean age ± standard deviation, 57.61 ± 11.58) were within development cohort and 546 (54.43 ± 10.02) within validation cohort. Using logistic regression analyses, clinical-imaging factors associated with lymph node (LN) metastasis were analyzed in the development set from which nomogram was created. The performance of MRI and nomogram was assessed. HNB was defined as ≥ 3 positive LNs. RESULTS: The FNR of AUS was 20.1% (81 of 402) and 19.2% (105 of 546) and the rates of HNB were 1.2% (5/402) and 2.2% (12/546), respectively. Clinical and imaging features associated with LN metastasis were progesterone receptor positivity, outer tumor location on mammography, breast imaging reporting and data system category 5 assessment of cancer on ultrasound, and positive axilla on MRI. In validation cohorts, the positive predictive value (PPV) and negative predictive value (NPV) of MRI and clinical-imaging nomogram was 58.5% and 86.5%, and 56.0% and 82.0%, respectively. CONCLUSION: The FNR of AUS was approximately 20% but the rate of HNB was low. The diagnostic performance of MRI was not satisfactory with low PPV but MRI had merit in reaffirming negative AUS with high NPV. Patients who had low probability scores from our clinical-imaging nomogram might be possible candidates for the omission of SLNB.

Therapy-related myeloid neoplasm in early breast cancer patients treated with adjuvant chemotherapy.

BACKGROUND: Long-term complications are becoming more important as the survival rate of breast cancer improves. Treatment-related myeloid neoplasm is an important long-term complication in breast cancer survivors as it has a poor prognosis. OBJECTIVE: We evaluated the incidence and risk factors for the development of treatment-related acute myeloid leukaemia (AML)/myelodysplastic syndrome (MDS) in patients treated with early breast cancer. METHODS: We accessed the national Korean database to identify 153,565 patients diagnosed with breast cancer between January 2007 and October 2016 who underwent surgery for breast cancer. We estimated the cumulative incidence of AML/MDS and analysed the risk factors for developing AML/MDS. RESULTS: Of 153,575 patients, 79,321 received anthracycline-based adjuvant therapy, 14,317 received adjuvant therapy without anthracyclines and 46,657 did not receive adjuvant chemotherapy. Overall, 120 developed AML (105 in the anthracycline group, 9 in the non-anthracycline group and 6 in the control group), and 128 developed MDS (96, 9 and 23 in each group). The 10-year cumulative incidence of AML/MDS was the highest in the anthracycline group (0.221% and 0.199%), followed by the non-anthracycline group (0.122% and 0.163%) and the control group (0.024% and 0.089%). The risk of developing AML/MDS was significantly higher in patients treated with anthracyclines (hazard ratio [HR] 9.531; p < 0.0001 for AML and HR 2.559; p < 0.0001 for MDS) compared to patients in the control group. CONCLUSION: This study found that anthracycline-based adjuvant therapy significantly increased the risk of AML/MDS in Korean breast cancer patients, with the risk persisting for at least 10 years. While the cumulative incidence was low, the long-term risks of AML/MDS should be taken into account considering the poor outcomes associated with these neoplasms.

MRI-based breast cancer radiogenomics using RNA profiling: association with subtypes in a single-center prospective study.

BACKGROUND: There are few prospective studies on the correlations between MRI features and whole RNA-sequencing data in breast cancer according to molecular subtypes. The purpose of our study was to explore the association between genetic profiles and MRI phenotypes of breast cancer and to identify imaging markers that influences the prognosis and treatment according to subtypes. METHODS: From June 2017 to August 2018, MRIs of 95 women with invasive breast cancer were prospectively analyzed, using the breast imaging-reporting and data system and texture analysis. Whole RNA obtained from surgical specimens was analyzed using next-generation sequencing. The association between MRI features and gene expression profiles was analyzed in the entire tumor and subtypes. Gene networks, enriched functions, and canonical pathways were analyzed using Ingenuity Pathway Analysis. The P value for differential expression was obtained using a parametric F test comparing nested linear models and adjusted for multiple testing by reporting Q value. RESULTS: In 95 participants (mean age, 53 years ± 11 [standard deviation]), mass lesion type was associated with upregulation of CCL3L1 (sevenfold) and irregular mass shape was associated with downregulation of MIR421 (sixfold). In estrogen receptor-positive cancer with mass lesion type, CCL3L1 (21-fold), SNHG12 (11-fold), and MIR206 (sevenfold) were upregulated, and MIR597 (265-fold), MIR126 (12-fold), and SOX17 (fivefold) were downregulated. In triple-negative breast cancer with increased standard deviation of texture analysis on precontrast T1-weighted imaging, CLEC3A (23-fold), SRGN (13-fold), HSPG2 (sevenfold), KMT2D (fivefold), and VMP1 (fivefold) were upregulated, and IGLC2 (73-fold) and PRDX4 (sevenfold) were downregulated (all, P < 0.05 and Q < 0.1). Gene network and functional analysis showed that mass type estrogen receptor-positive cancers were associated with cell growth, anti-estrogen resistance, and poor survival. CONCLUSION: MRI characteristics are associated with the different expressions of genes related to metastasis, anti-drug resistance, and prognosis, depending on the molecular subtypes of breast cancer.

Prediction of Axillary Lymph Node Metastasis in Early-stage Triple-Negative Breast Cancer Using Multiparametric and Radiomic Features of Breast MRI.

RATIONALE AND OBJECTIVES: To investigate whether machine learning (ML) approaches using breast magnetic resonance imaging (MRI)-derived multiparametric and radiomic features could predict axillary lymph node metastasis (ALNM) in stage I-II triple-negative breast cancer (TNBC). MATERIALS AND METHODS: Between 2013 and 2019, 86 consecutive patients with TNBC who underwent preoperative MRI and surgery were enrolled and divided into ALNM (N = 27) and non-ALNM (n = 59) groups according to histopathologic results. For multiparametric features, kinetic features using computer-aided diagnosis (CAD), morphologic features, and apparent diffusion coefficient (ADC) values at diffusion-weighted images were evaluated. For extracting radiomic features, three-dimensional segmentation of tumors using T2-weighted images (T2WI) and T1-weighted subtraction images were respectively performed by two radiologists. Each predictive model using three ML algorithms was built using multiparametric features or radiomic features, or both. The diagnostic performances of models were compared using the DeLong method. RESULTS: Among multiparametric features, non-circumscribed margin, peritumoral edema, larger tumor size, and larger angio-volume at CAD were associated with ALNM in univariate analysis. In multivariate analysis, larger angio-volume was the sole statistically significant predictor for ALNM (odds ratio = 1.33, P = 0.008). Regarding ADC values, there were no significant differences according to ALNM status. The area under the receiver operating characteristic curve for predicting ALNM was 0.74 using multiparametric features, 0.77 using radiomic features from T1-weighted subtraction images, 0.80 using radiomic features from T2WI, and 0.82 using all features. CONCLUSION: A predictive model incorporating breast MRI-derived multiparametric and radiomic features may be valuable in predicting ALNM preoperatively in patients with TNBC.

Comparison of CT- and MRI-Based Quantification of Tumor Heterogeneity and Vascularity for Correlations with Prognostic Biomarkers and Survival Outcomes: A Single-Center Prospective Cohort Study.

BACKGROUND: Tumor heterogeneity and vascularity can be noninvasively quantified using histogram and perfusion analyses on computed tomography (CT) and magnetic resonance imaging (MRI). We compared the association of histogram and perfusion features with histological prognostic factors and progression-free survival (PFS) in breast cancer patients on low-dose CT and MRI. METHODS: This prospective study enrolled 147 women diagnosed with invasive breast cancer who simultaneously underwent contrast-enhanced MRI and CT before treatment. We extracted histogram and perfusion parameters from each tumor on MRI and CT, assessed associations between imaging features and histological biomarkers, and estimated PFS using the Kaplan-Meier analysis. RESULTS: Out of 54 histogram and perfusion parameters, entropy on T2- and postcontrast T1-weighted MRI and postcontrast CT, and perfusion (blood flow) on CT were significantly associated with the status of subtypes, hormone receptors, and human epidermal growth factor receptor 2 (p < 0.05). Patients with high entropy on postcontrast CT showed worse PFS than patients with low entropy (p = 0.053) and high entropy on postcontrast CT negatively affected PFS in the Ki67-positive group (p = 0.046). CONCLUSIONS: Low-dose CT histogram and perfusion analysis were comparable to MRI, and the entropy of postcontrast CT could be a feasible parameter to predict PFS in breast cancer patients.

Association between vascular ultrasound features and DNA sequencing in breast cancer: a preliminary study.

There are few radiogenomic studies to correlate ultrasound features of breast cancer with genomic changes. We investigated whether vascular ultrasound phenotypes are associated with breast cancer gene profiles for predicting angiogenesis and prognosis. We prospectively correlated quantitative and qualitative features of microvascular ultrasound (vascular index, vessel morphology, distribution, and penetrating vessel) and contrast-enhanced ultrasound (time-intensity curve parameters and enhancement pattern) with genomic characteristics in 31 breast cancers. DNA obtained from breast tumors and normal tissues were analyzed using targeted next-generation sequencing of 105 genes. The single-variant association test was used to identify correlations between vascular ultrasound features and genomic profiles. Chi-square analysis was used to detect single nucleotide polymorphisms (SNPs) associated with ultrasound features by estimating p values and odds ratios (ORs). Eight ultrasound features were significantly associated with 9 SNPs (p < 0.05). Among them, four ultrasound features were positively associated with 5 SNPs: high vascular index with rs1136201 in ERBB2 (p = 0.04, OR = 7.75); large area under the curve on contrast-enhanced ultrasound with rs35597368 in PDGFRA (p = 0.04, OR = 4.07); high peak intensity with rs35597368 in PDGFRA (p = 0.049, OR = 4.05) and rs2305948 in KDR (p = 0.04, OR = 5.10); and long mean transit time with rs2275237 in ARNT (p = 0.02, OR = 10.25) and rs755793 in FGFR2 (p = 0.02, OR = 10.25). We identified 198 non-silent SNPs in 71 various cancer-related genes. Vascular ultrasound features can reflect genomic changes associated with angiogenesis and prognosis in breast cancer.

PIK3CA Mutation is Associated with Poor Response to HER2-Targeted Therapy in Breast Cancer Patients.

PURPOSE: Mutations in the PIK3CA gene occur frequently in breast cancer patients. Activating PIK3CA mutations confer resistance to human epidermal growth factor receptor 2 (HER2)-targeted treatments. In this study, we investigated whether PIK3CA mutations were correlated with treatment response or duration in patients with HER2-positive (HER2+) breast cancer. Materials and Methods: We retrospectively reviewed the clinical information of patients with HER2+ breast cancer who received HER2-targeted therapy for early-stage or metastatic cancers. The pathologic complete response (pCR), progression-free survival (PFS), and overall survival were compared between patients with wild-type PIK3CA (PIK3CAw) and those with mutated PIK3CA (PIK3CAm). Next-generation sequencing was combined with examination of PFS associated with anti-HER2 monoclonal antibody (mAb) treatment. RESULTS: Data from 90 patients with HER2+ breast cancer were analyzed. Overall, 34 (37.8%) patients had pathogenic PIK3CA mutations. The pCR rate of the PIK3CAm group was lower than that of the PIK3CAw group among patients who received neoadjuvant chemotherapy for early-stage cancer. In the metastatic setting, the PIK3CAm group showed a significantly shorter mean PFS (mPFS) with first-line anti-HER2 mAb. The mPFS of second-line T-DM1 was lower in the PIK3CAm group than that in the PIK3CAw group. Sequencing revealed differences in the mutational landscape between PIK3CAm and PIK3CAw tumors. CONCLUSION: Patients with HER2+ breast cancer with activating PIK3CA mutations had lower pCR rates and shorter PFS with palliative HER2-targeted therapy than those with wild-type PIK3CA. Precise targeted-therapy is needed to improve survival of patients with HER2+/PIK3CAm breast cancer.

Granular Cell Tumor Originating from the Pectoral Muscle: A Rare Extramammary Finding on Mammography.

A granular cell tumor (GCT) is a rare soft tissue tumor that usually arises from the striated muscle of the tongue. Few literatures have reported pectoral muscle involvement of the GCT. Herein, we report a rare case of a GCT originating from the pectoral muscle below the breast with multimodal imaging appearance.

Diagnostic Performance of Digital Breast Tomosynthesis with the Two-Dimensional Synthesized Mammogram for Suspicious Breast Microcalcifications Compared to Full-Field Digital Mammography in Stereotactic Breast Biopsy.

Purpose: To evaluate the diagnostic performance of digital breast tomosynthesis (DBT) with the two-dimensional synthesized mammogram (2DSM), compared to full-field digital mammography (FFDM), for suspicious microcalcifications in the breast ahead of stereotactic biopsy and to assess the diagnostic image visibility of the images. Materials and Methods: This retrospective study involved 189 patients with microcalcifications, which were histopathologically verified by stereotactic breast biopsy, who underwent DBT with 2DSM and FFDM between January 8, 2015, and January 20, 2020. Two radiologists assessed all cases of microcalcifications based on Breast Imaging Reporting and Data System (BI-RADS) independently. They were blinded to the histopathologic outcome and additionally evaluated lesion visibility using a five-point scoring scale. Results: Overall, the inter-observer agreement was excellent (0.9559). Under the setting of category 4A as negative due to the low possibility of malignancy and to avoid the dilution of malignancy criteria in our study, McNemar tests confirmed no significant difference between the performances of the two modalities in detecting microcalcifications with a high potential for malignancy (4B, 4C, or 5; p = 0.1573); however, the tests showed a significant difference between their performances in detecting microcalcifications with a high potential for benignancy (4A; p = 0.0009). DBT with 2DSM demonstrated superior visibility and diagnostic performance than FFDM in dense breasts. Conclusion: DBT with 2DSM is superior to FFDM in terms of total diagnostic accuracy and lesion visibility for benign microcalcifications in dense breasts. This study suggests a promising role for DBT with 2DSM as an accommodating tool for stereotactic biopsy in female with dense breasts and suspicious breast microcalcifications.

Radiomic machine learning for predicting prognostic biomarkers and molecular subtypes of breast cancer using tumor heterogeneity and angiogenesis properties on MRI.

OBJECTIVES: To investigate machine learning approaches for radiomics-based prediction of prognostic biomarkers and molecular subtypes of breast cancer using quantification of tumor heterogeneity and angiogenesis properties on magnetic resonance imaging (MRI). METHODS: This prospective study examined 291 invasive cancers in 288 patients who underwent breast MRI at 3 T before treatment between May 2017 and July 2019. Texture and perfusion analyses were performed and a total of 160 parameters for each cancer were extracted. Relationships between MRI parameters and prognostic biomarkers were analyzed using five machine learning algorithms. Each model was built using only texture features, only perfusion features, or both. Model performance was compared using the area under the receiver-operating characteristic curve (AUC) and the DeLong method, and the importance of MRI parameters in prediction was derived. RESULTS: Texture parameters were associated with the status of hormone receptors, human epidermal growth factor receptor 2, and Ki67, tumor size, grade, and molecular subtypes (p < 0.002). Perfusion parameters were associated with the status of hormone receptors and Ki67, grade, and molecular subtypes (p < 0.003). The random forest model integrating texture and perfusion parameters showed the highest performance (AUC = 0.75). The performance of the random forest model was the best with a special scale filter of 0 (AUC = 0.80). The important parameters for prediction were texture irregularity (entropy) and relative extracellular extravascular space (Ve). CONCLUSIONS: Radiomic machine learning that integrates tumor heterogeneity and angiogenesis properties on MRI has the potential to noninvasively predict prognostic factors of breast cancer. KEY POINTS: • Machine learning, integrating tumor heterogeneity and angiogenesis properties on MRI, can be applied to predict prognostic biomarkers and molecular subtypes in breast cancer. • The random forest model showed the best predictive performance among the five machine learning models (logistic regression, decision tree, naïve Bayes, random forest, and artificial neural network). • The most important MRI parameters for predicting prognostic factors in breast cancer were texture irregularity (entropy) among texture parameters and relative extracellular extravascular space (Ve) among perfusion parameters.

Machine learning with multiparametric breast MRI for prediction of Ki-67 and histologic grade in early-stage luminal breast cancer.

OBJECTIVES: To investigate whether machine learning-based prediction models using 3-T multiparametric MRI (mpMRI) can predict Ki-67 and histologic grade in stage I-II luminal cancer. METHODS: Between 2013 and 2019, consecutive women with luminal cancers who underwent preoperative MRI with diffusion-weighted imaging (DWI) and surgery were included. For prediction models, morphology, kinetic features using computer-aided diagnosis (CAD), and apparent diffusion coefficient (ADC) at DWI were evaluated by two radiologists. Logistic regression analysis was used to identify mpMRI features for predicting Ki-67 and grade. Diagnostic performance was assessed using eight machine learning algorithms incorporating mpMRI features and compared using the DeLong method. RESULTS: Of 300 women, 203 (67.7%) had low Ki-67 and 97 (32.3%) had high Ki-67; 242 (80.7%) had low grade and 58 (19.3%) had high grade. In multivariate analysis, independent predictors for higher Ki-67 were washout component > 13.5% (odds ratio [OR] = 4.16; p < 0.001) and intratumoral high SI on T2-weighted image (OR = 1.89; p = 0.022). Those for higher grade were washout component > 15.5% (OR = 7.22; p < 0.001), rim enhancement (OR = 2.59; p = 0.022), and ADC value < 0.945 × 10-3 mm2/s (OR = 2.47; p = 0.015). Among eight models using these predictors, six models showed the equivalent performance for Ki-67 (area under the receiver operating characteristic curve [AUC]: 0.70) and Naive Bayes classifier showed the highest performance for grade (AUC: 0.79). CONCLUSIONS: A prediction model incorporating mpMRI features shows good diagnostic performance for predicting Ki-67 and histologic grade in patients with luminal breast cancers. KEY POINTS: • Among multiparametric MRI features, kinetic feature of washout component >13.5% and intratumoral high signal intensity on T2-weighted image were associated with higher Ki-67. • Washout component >15.5%, rim enhancement, and mean apparent diffusion coefficient value < 0.945 × 10-3 mm2/s were associated with higher histologic grade. • Machine learning-based prediction models incorporating multiparametric MRI features showed good diagnostic performance for Ki-67 and histologic grade in luminal breast cancers.

Machine Learning Models That Integrate Tumor Texture and Perfusion Characteristics Using Low-Dose Breast Computed Tomography Are Promising for Predicting Histological Biomarkers and Treatment Failure in Breast Cancer Patients.

This prospective study enrolled 147 women with invasive breast cancer who underwent low-dose breast CT (80 kVp, 25 mAs, 1.01-1.38 mSv) before treatment. From each tumor, we extracted eight perfusion parameters using the maximum slope algorithm and 36 texture parameters using the filtered histogram technique. Relationships between CT parameters and histological factors were analyzed using five machine learning algorithms. Performance was compared using the area under the receiver-operating characteristic curve (AUC) with the DeLong test. The AUCs of the machine learning models increased when using both features instead of the perfusion or texture features alone. The random forest model that integrated texture and perfusion features was the best model for prediction (AUC = 0.76). In the integrated random forest model, the AUCs for predicting human epidermal growth factor receptor 2 positivity, estrogen receptor positivity, progesterone receptor positivity, ki67 positivity, high tumor grade, and molecular subtype were 0.86, 0.76, 0.69, 0.65, 0.75, and 0.79, respectively. Entropy of pre- and postcontrast images and perfusion, time to peak, and peak enhancement intensity of hot spots are the five most important CT parameters for prediction. In conclusion, machine learning using texture and perfusion characteristics of breast cancer with low-dose CT has potential value for predicting prognostic factors and risk stratification in breast cancer patients.

Prediction of Inflammatory Breast Cancer Survival Outcomes Using Computed Tomography-Based Texture Analysis.

Background: Although inflammatory breast cancer (IBC) has poor overall survival (OS), there is little information about using imaging features for predicting the prognosis. Computed tomography (CT)-based texture analysis, a non-invasive technique to quantify tumor heterogeneity, could be a potentially useful imaging biomarker. The aim of the article was to investigate the usefulness of chest CT-based texture analysis to predict OS in IBC patients. Methods: Of the 3,130 patients with primary breast cancers between 2006 and 2016, 104 patients (3.3%) with IBC were identified. Among them, 98 patients who underwent pre-treatment contrast-enhanced chest CT scans, got treatment in our institution, and had a follow-up period of more than 2 years were finally included for CT-based texture analysis. Texture analysis was performed on CT images of 98 patients, using commercially available software by two breast radiologists. Histogram-based textural features, such as quantification of variation in CT attenuation (mean, standard deviation, mean of positive pixels [MPP], entropy, skewness, and kurtosis), were recorded. To dichotomize textural features for survival analysis, receiver operating characteristic curve analysis was used to determine cutoff points. Clinicopathologic variables, such as age, node stage, metastasis stage at the time of diagnosis, hormonal receptor positivity, human epidermal growth factor receptor 2 positivity, and molecular subtype, were assessed. A Cox proportional hazards model was used to determine the association of textural features and clinicopathologic variables with OS. Results: During a mean follow-up period of 47.9 months, 41 of 98 patients (41.8%) died, with a median OS of 20.0 months. The textural features of lower mean attenuation, standard deviation, MPP, and entropy on CT images were significantly associated with worse OS, as was the M1 stage among clinicopathologic variables (all P-values < 0.05). In multivariate analysis, lower mean attenuation (hazard ratio [HR], 3.26; P = 0.003), lower MPP (HR, 3.03; P = 0.002), and lower entropy (HR, 2.70; P = 0.009) on chest CT images were significant factors independent from the M1 stage for predicting worse OS. Conclusions: Lower mean attenuation, MPP, and entropy on chest CT images predicted worse OS in patients with IBC, suggesting that CT-based texture analysis provides additional predictors for OS.

Assessment of Additional MRI-Detected Breast Lesions Using the Quantitative Analysis of Contrast-Enhanced Ultrasound Scans and Its Comparability with Dynamic Contrast-Enhanced MRI Findings of the Breast.

Purpose: To assess the diagnostic performance of contrast-enhanced ultrasound (CEUS) for additional MR-detected enhancing lesions and to determine whether or not kinetic pattern results comparable to dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of the breast can be obtained using the quantitative analysis of CEUS. Materials and Methods: In this single-center prospective study, a total of 71 additional MR-detected breast lesions were included. CEUS examination was performed, and lesions were categorized according to the Breast Imaging-Reporting and Data System (BI-RADS). The sensitivity, specificity, and diagnostic accuracy of CEUS were calculated by comparing the BI-RADS category to the final pathology results. The degree of agreement between CEUS and DCE-MRI kinetic patterns was evaluated using weighted kappa. Results: On CEUS, 46 lesions were assigned as BI-RADS category 4B, 4C, or 5, while 25 lesions category 3 or 4A. The diagnostic performance of CEUS for enhancing lesions on DCE-MRI was excellent, with 84.9% sensitivity, 94.4% specificity, and 97.8% positive predictive value. A total of 57/71 (80%) lesions had correlating kinetic patterns and showed good agreement (weighted kappa = 0.66) between CEUS and DCE-MRI. Benign lesions showed excellent agreement (weighted kappa = 0.84), and invasive ductal carcinoma (IDC) showed good agreement (weighted kappa = 0.69). Conclusion: The diagnostic performance of CEUS for additional MR-detected breast lesions was excellent. Accurate kinetic pattern assessment, fairly comparable to DCE-MRI, can be obtained for benign and IDC lesions using CEUS.

Progressive Transformation of Germinal Centers in Axillary Lymph Nodes Mimicking Metastatic Lymphadenopathy after Breast Cancer Surgery: A Case Report.

Progressive transformation of germinal centers (PTGC) is a rarely diagnosed, benign disease of the lymph nodes that commonly manifests as chronic lymphadenopathy. PTGC may be characterized by single or multiple non-tender lymph nodes, and it commonly involves the cervical, axillary, and inguinal areas. Although PTGC is identified with concurrent lymphoma in some patients, it is not considered as a premalignant entity. Histopathologic diagnosis of PTGC is rarely made, and imaging findings have been reported in very few studies. We present a case of PTGC that occurred at the contralateral axillary lymph nodes and mimicked metastatic lymphadenopathy after breast cancer surgery. We also discuss its imaging findings.

Mammographic, Sonographic, and MRI Features of Primary Neuroendocrine Carcinoma of the Breast: A Case Report.

Primary neuroendocrine carcinomas of the breast are a rare, distinct category of breast carcinomas that require immunohistochemical staining for diagnosis. Currently, there is not enough evidence on the clinical pattern, prognosis, and proper management of the disease. Only few case series have described the imaging findings of neuroendocrine carcinomas of the breast. We herein present a case of a primary neuroendocrine carcinoma of the breast (small cell) presenting as a locally aggressive tumor with metastatic disease, and describe the radiologic findings.