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.

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.

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.

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.

Simultaneous Multislice Readout-Segmented Echo Planar Imaging for Diffusion-Weighted MRI in Patients With Invasive Breast Cancers.

BACKGROUND: In diffusion-weighted imaging (DWI) of breast MRI, simultaneous multislice acceleration techniques can be used for readout-segmented echo planar imaging (rs-EPI) to shorten the scan time. PURPOSE: To compare the image quality, apparent diffusion coefficient (ADC) value, and scan time of rs-EPI and simultaneous multislice rs-EPI (SMS rs-EPI) sequences. STUDY TYPE: Retrospective. SUBJECTS: In all, 134 consecutive women (mean age: 55.3 years) with invasive breast cancer who underwent preoperative MRI. FIELD STRENGTH/ SEQUENCES: 3.0T; rs-EPI sequence, prototypic SMS rs-EPI sequence and dynamic contrast-enhanced MRI (DCE-MRI) sequence ASSESSMENT: For quantitative comparison, two radiologists independently measured the signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), lesion contrast, and apparent diffusion coefficient (ADC). For qualitative comparison, image quality, lesion conspicuity, and reader preference were assessed with a reference of DCE-MRI. STATISTICAL TESTS: Paired t-tests and Mann-Whitney tests were used. RESULTS: For SNR and CNR, there were no differences between the sequences (P = 0.342 and 0.665 for reader 1; P = 0.606 and P = 0.116 for reader 2). Lesion contrast of SMS rs-EPI was higher than that of rs-EPI (P < 0.05 for both reader 1 and reader 2). Mean tumor ADC was similar in rs-EPI and SMS rs-EPI sequences (0.98 ± 0.22 vs. 1.00 ± 0.22; P = 0.291 for reader 1, 0.98 ± 0.21 vs. 1.00 ± 0.22; P = 0.418 for reader 2). Regarding qualitative comparison, image quality and lesion conspicuity were higher in SMS rs-EPI than in rs-EPI (both P < 0.05 for both readers). The two readers regarded SMS rs-EPI as superior or equal to rs-EPI in over 90% of cases. The acquisition time was 4:30 minutes for rs-EPI and 2:31 minutes for SMS rs-EPI. DATA CONCLUSION: The SMS rs-EPI sequence resulted in a similar ADC value and better image quality than the rs-EPI sequence in a 44.1% reduced scan time. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY: 3.

Radiogenomic Analysis of Breast Cancer by Using B-Mode and Vascular US and RNA Sequencing.

Background Radiogenomic investigations for breast cancer provide an understanding of tumor heterogeneity and discover image phenotypes of genetic variation. However, there is little research on the correlations between US features of breast cancer and whole-transcriptome profiling. Purpose To explore US phenotypes reflecting genetic alteration relevant to breast cancer treatment and prognosis by comparing US images of tumor with their RNA sequencing results. Materials and Methods From January to October 2016, B-mode and vascular US images in 31 women (mean age, 49 years ± 9 [standard deviation]) with breast cancer were prospectively analyzed. B-mode features included size, shape, echo pattern, orientation, margin, and calcifications. Vascular features were evaluated by using microvascular US and contrast agent-enhanced US: vascular index, vessel morphologic features, distribution, penetrating vessels, enhancement degree, order, margin, internal homogeneity, and perfusion defect. RNA sequencing was conducted with total RNA obtained from a surgical specimen by using next-generation sequencing. US features were compared with gene expression profiles, and ingenuity pathway analysis was used to analyze gene networks, enriched functions, and canonical pathways associated with breast cancer. The P value for differential expression was extracted by using a parametric F test comparing nested linear models. Results Thirteen US features were associated with various patterns of 340 genes (P < .05). Nonparallel orientation at B-mode US was associated with upregulation of TFF1 (log twofold change [log2FC] = 4.0; P < .001), TFF3 (log2FC = 2.5; P < .001), AREG (log2FC = 2.6; P = .005), and AGR3 (log2FC = 2.6; P = .003). Complex vessel morphologic structure was associated with upregulation of FZD8 (log2FC = 2.0; P = .01) and downregulation of IGF1R (log2FC = -2.0; P = .006) and CRIPAK (log2FC = -2.4; P = .01). The top networks with regard to orientation or vessel morphologic structure were associated with cell cycle, death, and proliferation. Conclusion Compared with RNA sequencing, B-mode and vascular US features reflected genomic alterations associated with hormone receptor status, angiogenesis, or prognosis in breast cancer. © RSNA, 2020 Online supplemental material is available for this article.

Intravoxel incoherent motion diffusion-weighted MRI of invasive breast cancer: Correlation with prognostic factors and kinetic features acquired with computer-aided diagnosis.

BACKGROUND: As both intravoxel incoherent motion (IVIM) modeling and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) provide perfusion parameters, IVIM-derived perfusion parameters might be expected to correlate with the kinetic features from DCE-MRI. PURPOSE: To investigate the association between IVIM parameters and prognostic factors and to evaluate the correlation between IVIM parameters and kinetic features in invasive breast cancer patients using computer-aided diagnosis (CAD). STUDY TYPE: Retrospective. POPULATION: Eighty-five patients (invasive cancers; mean size, 1.8 cm; range, 0.8-4.8 cm) who underwent diffusion-weighted imaging with 12 b-values (0-1000 s/mm2 ). FIELD STRENGTH/SEQUENCE: 3.0T MRI axial, IVIM-DWI epi-sequence, and DCE-MRI. ASSESSMENT: Two radiologists measured the apparent diffusion coefficient (ADC), diffusion coefficient, pseudodiffusion coefficient, and perfusion fraction (f) using IVIM modeling. Kinetic features such as peak enhancement and early and delayed enhancement profiles were acquired using CAD. STATISTICAL TESTS: The correlation between the IVIM parameters and kinetic features and the association between the IVIM parameters and prognostic factors were investigated using Mann-Whitney test and Spearman correlation test. RESULTS: There were no significant associations between IVIM parameters and prognostic factors. When IVIM parameters were correlated with kinetic features by CAD, both the ADC and f values showed correlations with delayed enhancement profiles. The ADC values were lower in tumors with lower persistent components (P = 0.013) and higher washout components (P = 0.045) and showed a positive correlation with persistent proportion (Spearman's rho (r) = 0.222, P = 0.041). The f value was higher in tumors with higher persistent components (P = 0.021) and showed a positive correlation with persistent proportion (r = 0.227, P = 0.029). DATA CONCLUSION: This analysis revealed that IVIM-derived ADC and f values showed correlations with kinetic features at the delayed phase as assessed by CAD. These results indicate the potential of IVIM imaging biomarkers to provide information on the biological and kinetic properties of breast cancers without a contrast agent. LEVEL OF EVIDENCE: 4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:118-130.

Additional value of diffusion-weighted imaging to evaluate multifocal and multicentric breast cancer detected using pre-operative breast MRI.

OBJECTIVES: To investigate whether diffusion-weighted imaging (DWI) aids pre-operative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to evaluate additional lesions in breast cancer patients. METHODS: DCE-MRI and DWI were performed on 131 lesions, with available histopathological results. The apparent diffusion coefficient (ADC) of each lesion was measured, and the cut-off value for differentiation between malignant and benign lesions was calculated. A protocol combining the ADC cut-off value with DCE-MRI was validated in a cohort of 107 lesions in 77 patients. RESULTS: When an ADC cut-off value of 1.11 × 10-3 mm2/s from the development cohort was applied to the additional lesions in the validation cohort, the specificity increased from 18.9% to 67.6% (P < 0.001), and the diagnostic accuracy increased from 61.7% to 82.2% (P = 0.05), without significant loss of sensitivity (98.6% vs. 90.0%, P = 0.07). The negative predictive values of lesions in the same quadrant had decreased, as had those of lesions ≥1 cm in diameter. The ADC cut-off value in the validation cohort was 1.05 × 10-3 mm2/s. CONCLUSIONS: Additional implementation of DWI for breast lesions in pre-operative MRI can help to obviate unnecessary biopsies by increasing specificity. However, to avoid missing cancers, clinicians should closely monitor lesions located in the same quadrant or lesions ≥1 cm. KEY POINTS: • DWI can be used to further differentiate lesions during pre-operative cancer staging. • ADC cut-off values were similar in the development and validation cohorts. • DWI improves both PPV and NPV in cases of multicentric lesions. • DWI improves both PPV and NPV in lesions <1 in diameter. • NPVs are decreased in multifocal lesions and lesions ≥1 cm in diameter.

YKL-40 expression could be a poor prognostic marker in the breast cancer tissue.

YKL-40 is a glycoprotein involved in cellular growth, migration, and the inflammatory process. Elevation in serum levels of YKL-40 has been associated with worse prognosis in various cancers, including breast cancer. Given that the clinical significance of YKL-40 expression in breast cancer tissue is unclear, we aimed to determine the prognostic value of YKL-40 expression in breast cancer tissue using immunohistochemistry. We performed tissue microarray (TMA) analysis of 425 breast cancer tissues collected during operation. Immunohistochemical staining was performed to measure expression of YKL-40 and several breast cancer biomarkers, such as aldehyde dehyadrogenase1, TGF-beta, and Gli-1 as well as hormonal receptor and Her-2/neu status. Statistical analysis of the relationship of YKL-40 expression with clinicopathological characteristics was performed for 390 TMA samples. YKL-40 was expressed to varying degrees in 84.9% of breast cancer tissues. YKL-40 expression was correlated with estrogen receptor and progesterone receptor negativity and was positively correlated with TGF-beta and Gli-1 expression. Strong YKL-40 expression was associated with a larger proportion of Her-2/neu-enriched and basal-like tumors. The results of this study demonstrate that YKL-40 expression in breast cancer tissues is associated with hormone receptor negativity and Her-2/neu-enriched molecular subtypes of breast cancer, and therefore could be considered a poor prognostic predictor.

Nipple Schwannoma: A Case Report and Literature Review on Nipple Mass.

Schwannomas are slow-growing benign tumors originating from the Schwann cells of the peripheral nerve sheaths. Herein, we report the first documented case of a schwannoma presenting as a painful nipple mass in a 32-year-old woman. This mass initially developed six years ago following a period of breastfeeding. Breast magnetic resonance imaging (MRI) scans revealed an iso-intense mass, with an approximate size of 2.2 cm, on a T1-weighted image with internal cystic changes. The mass exhibited heterogeneously delayed enhancement and restricted diffusion. Surgical excision was performed, and the diagnosis of cutaneous plexiform nipple schwannoma was confirmed histopathologically. A literature review revealed that the MRI findings of the nipple mass in our case were consistent with the common features of a schwannoma.

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.

T2 Black Synovitis in Musculoskeletal MRI: Disease Spectrum and Imaging Characteristics of Joint Diseases

The synovium may be affected by a wide spectrum of disorders, including inflammatory, infectious, degenerative, traumatic, hemorrhagic, and tumorous conditions. Magnetic resonance imaging (MRI) is a valuable imaging modality to characterize synovial disorders. Most abnormal lesions appear as areas of nonspecific high signal intensity on T2-weighted images (T2-WI) due to high water content or increased perfusion. However, T2 hypointensity can be attributed to blood components of varying ages, calcification, inorganic crystals, fibrosis, caseous necrosis and/or amyloid deposition. Hypointense lesions on T2-WI are infrequent and additional clinical and imaging characteristics can help to limit the list of differential diagnoses, which may include tenosynovial giant cell tumor, synovial chondromatosis, rheumatoid arthritis, tuberculous arthritis, chronic tophaceous gout, amyloid arthropathy, synovial hemangioma, lipoma arborescens and hemosiderotic synovitis. Recently, susceptibility weighted imaging has been developed and may contribute to more accurate diagnosis for deoxygenated blood and calcium. We review the MRI features of hypointense synovial lesions on MRI and emphasize the characteristic findings that suggest a correct diagnosis.

Evaluation of deep learning-based quantitative computed tomography for opportunistic osteoporosis screening.

To evaluate diagnostic efficacy of deep learning (DL)-based automated bone mineral density (BMD) measurement for opportunistic screening of osteoporosis with routine computed tomography (CT) scans. A DL-based automated quantitative computed tomography (DL-QCT) solution was evaluated with 112 routine clinical CT scans from 84 patients who underwent either chest (N:39), lumbar spine (N:34), or abdominal CT (N:39) scan. The automated BMD measurements (DL-BMD) on L1 and L2 vertebral bodies from DL-QCT were validated with manual BMD (m-BMD) measurement from conventional asynchronous QCT using Pearson's correlation and intraclass correlation. Receiver operating characteristic curve (ROC) analysis identified the diagnostic ability of DL-BMD for low BMD and osteoporosis, determined by dual-energy X-ray absorptiometry (DXA) and m-BMD. Excellent concordance were seen between m-BMD and DL-BMD in total CT scans (r = 0.961/0.979). The ROC-derived AUC of DL-BMD compared to that of central DXA for the low-BMD and osteoporosis patients was 0.847 and 0.770 respectively. The sensitivity, specificity, and accuracy of DL-BMD compared to central DXA for low BMD were 75.0%, 75.0%, and 75.0%, respectively, and those for osteoporosis were 68.0%, 80.5%, and 77.7%. The AUC of DL-BMD compared to the m-BMD for low BMD and osteoporosis diagnosis were 0.990 and 0.943, respectively. The sensitivity, specificity, and accuracy of DL-BMD compared to m-BMD for low BMD were 95.5%, 93.5%, and 94.6%, and those for osteoporosis were 88.2%, 94.5%, and 92.9%, respectively. DL-BMD exhibited excellent agreement with m-BMD on L1 and L2 vertebrae in the various routine clinical CT scans and had comparable diagnostic performance for detecting the low-BMD and osteoporosis on conventional QCT.

The relationship between preeclampsia, pregnancy-induced hypertension and maternal risk of breast cancer: a meta-analysis.

BACKGROUND: It has long been recognized that some human breast cancers are hormone dependent. Preeclampsia is a syndrome of pregnancy defined by the onset of hypertension and proteinuria and characterized by dysfunction of the maternal endothelium. Many hormonal changes occur with preeclampsia, and we hypothesize that these changes may influence the risk of maternal breast cancer. We also analyzed the relation between pregnancy-induced hypertension (PIH) and maternal risk of breast cancer. METHODS: Among 13 relevant publications about preeclampsia and six relevant publications about PIH, some studies find preeclampsia associated with a lower risk of breast cancer, but others did not. Therefore, these results are inconclusive. We conducted meta-analysis to evaluate more precisely the relationship between preeclampsia, PIH and maternal risk of breast cancer. RESULTS: The pooled estimate of the hazard ratio (HR) associated with preeclampsia was 0.86 (95% CI 0.73-1.01), and that associated with PIH was 0.83 (0.66-1.06), both based on the random effects model. CONCLUSION: Some suggestive but not entirely consistent nor conclusive evidence was found on the association between the history of preeclampsia or PIH with the subsequent risk of breast cancer.

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.

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.

Analysis of Prognoses according to Breast MRI Results in Patients with Axillary Lymph Node Metastases from an Unknown Primary Origin.

PURPOSE: To compare the prognosis of patients with axillary adenocarcinoma from an unknown primary (ACUPax) origin with negative MRI results and those with MRI-detected primary breast cancers. MATERIALS AND METHODS: The breast MRI images of 32 patients with ACUPax without signs of primary breast cancer on mammography and ultrasound (US) were analyzed. Spot compression-magnification mammography and second-look US were performed for the area of MRI abnormality in patients with positive results; any positive findings corresponding to the MRI abnormality were confirmed by biopsy. If suspicious MRI lesions could not be localized on mammography or US, MR-guided biopsy or excision biopsy after MR-guided localization was performed. We compared the prognosis of patients with negative breast MRI with that for patients with MRI-detected primary breast cancers. RESULTS: Primary breast cancers were confirmed in 8 (25%) patients after breast MRI. Primary breast cancers were not detected on MRI in 24 (75%) patients, including five cases of false-positive MRI results. Twenty-three patients underwent axillary lymph node dissection (ALND) followed by whole breast radiation therapy (WBRT) and chemotherapy (n=17) or subsequent chemotherapy only (n=2). Recurrence or distant metastasis did not occur during follow up in 7/8 patients with MRI-detected primary breast cancers and 22/24 patients with negative MRI results. Regional recurrence or distant metastasis did not occur in any MR-negative patient who received adjuvant chemotherapy after ALND and WBRT. CONCLUSION: The prognoses of MR-negative patients with ACUPax who received ALND and WBRT followed by chemotherapy were as good as those of patients with MRI-detected primary breast cancers.

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.

The relationship between twin births and maternal risk of breast cancer: a meta-analysis.

Women who undergo a greater number of menstrual cycles may be at increased risk of breast cancer, possibly due to cumulative exposure to ovarian hormones. Pregnancy reduces the lifetime number of menstrual cycles and also influences the levels of ovarian hormones. Twin pregnancies differ from singleton pregnancies in both hormone levels and perinatal changes. To date, a meta-analysis on the effects of twin birth on the risk of maternal breast cancer has not been conducted. Among 17 relevant publications identified in a systematic search, some suggest that twin births may be associated with lower breast cancer risk but others do not; therefore, the results are inconclusive. Although our pooled results of all 17 published studies did not show a reduced maternal risk of breast cancer for twin births (HR 0.94; 95% CI = 0.87-1.02; P = 0.127), a trend toward reduced maternal risk of breast cancer was identified in a subgroup analysis of cohort studies (HR 0.91; 95% CI = 0.83-1.01; P = 0.068). The results of this meta-analysis suggest that twin pregnancy does not significantly decrease the maternal risk of breast cancer.