Digital breast tomosynthesis plus mammography, magnetic resonance imaging plus mammography and mammography alone: A comparison of diagnostic performance in symptomatic women

Dysregulated ceramides metabolism via PTPN11 exposes a metabolic vulnerability to breast cancer metastasis

Breast cancer is a prevalent malignant tumor, posing a significant threat to women's health globally due to its increasing incidence and tendency to affect younger patients. Protein tyrosine phosphatases (PTPs) are a class of enzymes that have emerged as potential targets for various tumors, including breast cancer, because they can modulate oncogenic tyrosine kinases, which are both tumor-suppressive and oncogenic. The regulation of tyrosine phosphorylation levels is crucial for cell proliferation and differentiation. Although the clinical biomarker potential of PTPs is not fully explored, there is evidence to suggest that they may serve as clinical biomarkers and therapeutic targets for breast cancer. We found that increased expression levels of PTPN11 and PTPN3 were associated with a higher risk of death in patients with breast cancer, while PTPN11 and PTPN18 are significantly associated with overall survival in patients with estrogen receptor-positive (ER+) breast cancer. Meanwhile, PTPN11 expression was found to be negatively associated with survival in patients with ER+ breast cancer. Furthermore, PTPN11 exposes a metabolic vulnerability to breast cancer metastasis via dysregulated ceramide metabolism. Therefore, we speculate that PTPN11 has the potential to serve as a therapeutic target for breast cancer by regulating lipid metabolism reprogramming.

Diagnostic value of multiple b-value diffusion-weighted imaging in discriminating the malignant from benign breast lesions

OBJECTIVE

The conventional breast Diffusion-weighted imaging (DWI) was subtly influenced by microcirculation owing to the insufficient selection of the b values. However, the multiparameter derived from multiple b-value exhibits more reliable image quality and maximize the diagnostic accuracy. We aim to evaluate the diagnostic performance of stand-alone parameter or in combination with multiparameter derived from multiple b-value DWI in differentiating malignant from benign breast lesions.

METHODS

A total of forty-one patients diagnosed with benign breast tumor and thirty-eight patients with malignant breast tumor underwent DWI using thirteen b values and other MRI functional sequence at 3.0 T magnetic resonance. Data were accepted mono-exponential, bi-exponential, stretched-exponential, aquaporins (AQP) model analysis. A receiver operating characteristic curve (ROC) was used to evaluate the diagnostic performance of quantitative parameter or multiparametric combination. The Youden index, sensitivity and specificity were used to assess the optimal diagnostic model. T-test, logistic regression analysis, and Z-test were used. P value < 0.05 was considered statistically significant.

RESULT

The ADC_avg, ADC_max, f, and α value of the malignant group were lower than the benign group, while the ADC_fast value was higher instead. The ADC_min, ADC_slow, DDC and ADC_AQP showed no statistical significance. The combination (ADC_avg-ADC_fast) yielded the largest area under curve (AUC = 0.807) with sensitivity (68.42%), specificity (87.8%) and highest Youden index, indicating that multiparametric combination (ADC_avg-ADC_fast) was validated to be a useful model in differentiating the benign from breast malignant lesion.

CONCLUSION

The current study based on the multiple b-value diffusion model demonstrated quantitatively multiparametric combination (ADC_avg-ADC_fast) exhibited the optimal diagnostic efficacy to differentiate malignant from benign breast lesions, suggesting that multiparameter would be a promising non-invasiveness to diagnose breast lesions.

Multi-modality radiomics nomogram based on DCE-MRI and ultrasound images for benign and malignant breast lesion classification

OBJECTIVE

To develop a multi-modality radiomics nomogram based on DCE-MRI, B-mode ultrasound (BMUS) and strain elastography (SE) images for classifying benign and malignant breast lesions.

MATERIAL AND METHODS

In this retrospective study, 345 breast lesions from 305 patients who underwent DCE-MRI, BMUS and SE examinations were randomly divided into training (n = 241) and testing (n = 104) datasets. Radiomics features were extracted from manually contoured images. The inter-class correlation coefficient (ICC), Mann-Whitney U test and the least absolute shrinkage and selection operator (LASSO) regression were applied for feature selection and radiomics signature building. Multivariable logistic regression was used to develop a radiomics nomogram incorporating radiomics signature and clinical factors. The performance of the radiomics nomogram was evaluated by its discrimination, calibration, and clinical usefulness and was compared with BI-RADS classification evaluated by a senior breast radiologist.

RESULTS

The All-Combination radiomics signature derived from the combination of DCE-MRI, BMUS and SE images showed better diagnostic performance than signatures derived from single modality alone, with area under the curves (AUCs) of 0.953 and 0.941 in training and testing datasets, respectively. The multi-modality radiomics nomogram incorporating the All-Combination radiomics signature and age showed excellent discrimination with the highest AUCs of 0.964 and 0.951 in two datasets, respectively, which outperformed all single modality radiomics signatures and BI-RADS classification. Furthermore, the specificity of radiomics nomogram was significantly higher than BI-RADS classification (both p < 0.04) with the same sensitivity in both datasets.

CONCLUSION

The proposed multi-modality radiomics nomogram based on DCE-MRI and ultrasound images has the potential to serve as a non-invasive tool for classifying benign and malignant breast lesions and reduce unnecessary biopsy.

Diagnosis of Breast Cancer Using Radiomics Models Built Based on Dynamic Contrast Enhanced MRI Combined With Mammography

Objective

To build radiomics models using features extracted from DCE-MRI and mammography for diagnosis of breast cancer.

Materials and Methods

266 patients receiving MRI and mammography, who had well-enhanced lesions on MRI and histologically confirmed diagnosis were analyzed. Training dataset had 146 malignant and 56 benign, and testing dataset had 48 malignant and 18 benign lesions. Fuzzy-C-means clustering algorithm was used to segment the enhanced lesion on subtraction MRI maps. Two radiologists manually outlined the corresponding lesion on mammography by consensus, with the guidance of MRI maximum intensity projection. Features were extracted using PyRadiomics from three DCE-MRI parametric maps, and from the lesion and a 2-cm bandshell margin on mammography. The support vector machine (SVM) was applied for feature selection and model building, using 5 datasets: DCE-MRI, mammography lesion-ROI, mammography margin-ROI, mammography lesion+margin, and all combined.

Results

In the training dataset evaluated using 10-fold cross-validation, the diagnostic accuracy of the individual model was 83.2% for DCE-MRI, 75.7% for mammography lesion, 64.4% for mammography margin, and 77.2% for lesion+margin. When all features were combined, the accuracy was improved to 89.6%. By adding mammography features to MRI, the specificity was significantly improved from 69.6% (39/56) to 82.1% (46/56), p<0.01. When the developed models were applied to the independent testing dataset, the accuracy was 78.8% for DCE-MRI and 83.3% for combined MRI+Mammography.

Conclusion

The radiomics model built from the combined MRI and mammography has the potential to provide a machine learning-based diagnostic tool and decrease the false positive diagnosis of contrast-enhanced benign lesions on MRI.

Radiomic Evaluations of the Diagnostic Performance of DM, DBT, DCE MRI, DWI, and Their Combination for the Diagnosisof Breast Cancer

Objectives

This study aims to evaluate digital mammography (DM), digital breast tomosynthesis (DBT), dynamic contrast-enhanced (DCE), and diffusion-weighted (DW) MRI, individually and combined, for the values in the diagnosis of breast cancer, and propose a visualized clinical-radiomics nomogram for potential clinical uses.

Methods

A total of 120 patients were enrolled between September 2017 and July 2018, all underwent preoperative DM, DBT, DCE, and DWI scans. Radiomics features were extracted and selected using the least absolute shrinkage and selection operator (LASSO) regression. A radiomics nomogram was constructed integrating the radiomics signature and important clinical predictors, and assessed with the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA).

Results

The radiomics signature derived from DBT plus DM generated a lower area under the ROC curve (AUC) and sensitivity, but a higher specificity compared with that from DCE plus DWI. The nomogram integrating the combined radiomics signature, age, and menstruation status achieved the best diagnostic performance in the training (AUCs, nomogram vs. combined radiomics signature vs. clinical model, 0.975 vs. 0.964 vs. 0.782) and validation (AUCs, nomogram vs. combined radiomics signature vs. clinical model, 0.983 vs. 0.978 vs. 0.680) cohorts. DCA confirmed the potential clinical usefulness of the nomogram.

Conclusions

The DBT plus DM provided a lower AUC and sensitivity, but a higher specificity than DCE plus DWI for detecting breast cancer. The proposed clinical-radiomics nomogram has diagnostic advantages over each modality, and can be considered as an efficient tool for breast cancer screening.

Digital breast tomosynthesis: sensitivity for cancer in younger symptomatic women

OBJECTIVE

Full-field digital mammography (FFDM) has limited sensitivity for cancer in younger women with denser breasts. Digital breast tomosynthesis (DBT) can reduce the risk of cancer being obscured by overlying tissue. The primary study aim was to compare the sensitivity of FFDM, DBT and FFDM-plus-DBT in women under 60 years old with clinical suspicion of breast cancer.

METHODS

This multicentre study recruited 446 patients from UK breast clinics. Participants underwent both standard FFDM and DBT. A blinded retrospective multireader study involving 12 readers and 300 mammograms (152 malignant and 148 benign cases) was conducted.

RESULTS

Sensitivity for cancer was 86.6% with FFDM [95% CI (85.2-88.0%)], 89.1% with DBT [95% CI (88.2-90%)], and 91.7% with FFDM+DBT [95% CI (90.7-92.6%)]. In the densest breasts, the maximum sensitivity increment with FFDM +DBT over FFDM alone was 10.3%, varying by density measurement method. Overall specificity was 81.4% with FFDM [95% CI (80.5-82.3%)], 84.6% with DBT [95% CI (83.9-85.3%)], and 79.6% with FFDM +DBT [95% CI (79.0-80.2%)]. No differences were detected in accuracy of tumour measurement in unifocal cases.

CONCLUSIONS

Where available, DBT merits first-line use in the under 60 age group in symptomatic breast clinics, particularly in women known to have very dense breasts.

ADVANCES IN KNOWLEDGE

This study is one of very few to address the accuracy of DBT in symptomatic rather than screening patients. It quantifies the diagnostic gains of DBT in direct comparison with standard digital mammography, supporting informed decisions on appropriate use of DBT in this population.

Digital breast tomosynthesis for breast cancer detection: a diagnostic test accuracy systematic review and meta-analysis

OBJECTIVES

No consensus exists on digital breast tomosynthesis (DBT) utilization for breast cancer detection. We performed a diagnostic test accuracy systematic review and meta-analysis comparing DBT, combined DBT and digital mammography (DM), and DM alone for breast cancer detection in average-risk women.

METHODS

MEDLINE and EMBASE were searched until September 2018. Comparative design studies reporting on the diagnostic accuracy of DBT and/or DM for breast cancer detection were included. Demographic, methodologic, and diagnostic accuracy data were extracted. Risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS)-2 tool. Accuracy metrics were pooled using bivariate random-effects meta-analysis. The impact of multiple covariates was assessed using meta-regression.

PROSPERO ID

CRD 42018111287.

RESULTS

Thirty-eight studies reporting on 488,099 patients (13,923 with breast cancer) were included. Eleven studies were at low risk of bias. DBT alone, combined DBT and DM, and DM alone demonstrated sensitivities of 88% (95% confidence interval [CI] 83-92), 88% (CI 83-92), and 79% (CI 75-82), as well as specificities of 84% (CI 76-89), 81% (CI 73-88), and 79% (CI 71-85), respectively. The greater sensitivities of DBT alone and combined DBT and DM compared to DM alone were preserved in the combined meta-regression models accounting for other covariates (p = 0.003-0.006). No significant difference in diagnostic accuracy between DBT alone and combined DBT and DM was identified (p = 0.175-0.581).

CONCLUSIONS

DBT is more sensitive than DM, while the addition of DM to DBT provides no additional diagnostic benefit. Consideration of these findings in breast cancer imaging guidelines is recommended.

KEY POINTS

• Digital breast tomosynthesis with or without additional digital mammography is more sensitive in detecting breast cancer than digital mammography alone in women at average risk for breast cancer. • The addition of digital mammography to digital breast tomosynthesis provides no additional diagnostic benefit in detecting breast cancer compared to digital breast tomosynthesis alone. • The specificity of digital breast tomosynthesis with or without additional digital mammography is no different than digital mammography alone in the detection of breast cancer.

Consensus Meeting of Breast Imaging: BI-RADS® and Beyond

Organizers of medical educational courses are often confronted with questions that are clinically relevant yet trespassing the frontiers of scientifically proven, evidence-based medicine at the point of care. Therefore, since 2007 organizers of breast teaching courses in German language met biannually to find a consensus in clinically relevant questions that have not been definitely answered by science. The questions were prepared during the 3 months before the meeting according to a structured process and finally agreed upon the day before the consensus meeting. At the consensus meeting, the open questions concerning 2D/3D mammography, breast ultrasound, MR mammography, interventions as well as risk-based imaging of the breast were presented first for electronic anonymized voting, and then the results of the audience were separately displayed from the expert votes. Thereafter, an introductory statement of the moderator was followed by pros/cons of two experts, and subsequently the final voting was performed. With ≥75% of votes of the expert panel, an answer qualified as a consensus statement. Seventeen consensus statements were gained, addressing for instance the use of 2D/3D mammography, breast ultrasound in screening, MR mammography in women with intermediate breast cancer risk, markers for localization of pathologic axillary lymph nodes, and standards in risk-based imaging of the breast. After the evaluation, comments from the experts on each field were gathered supplementarily. Methodology, transparency, and soundness of statements achieve a unique yield for all course organizers and provide solid pathways for decision making in breast imaging.

Diagnostic Accuracy of Digital Breast Tomosynthesis in the Evaluation of Palpable Breast Abnormalities

RATIONALE AND OBJECTIVES

The role of digital breast tomosynthesis (DBT) in evaluating palpable abnormalities has not been evaluated and its accuracy compared to 2D mammography is unknown. The purpose of this study was to evaluate combined 2D mammography, DBT, and ultrasound (US) at palpable sites.

MATERIALS AND METHODS

Two breast imagers reviewed blinded consecutive cases with combined 2D mammograms and DBT examinations performed for palpable complaints. By consensus, 2D and DBT findings were recorded and compared to US. Patient characteristics, demographics, subsequent workup, and outcome were recorded.

RESULTS

A total of 229 sites in 188 patients were included, with 50 biopsies performed identifying 18 cancers. All 18 cancers were identified on 2D and US, whereas 17 cancers were identified on DBT. Cancer detection sensitivities for 2D, DBT, and US were 100.0%, 94.4%, and 100.0%. The negative predictive value, when combined with US, was 100% for both. The sensitivity and the specificity for both benign and malignant findings with 2D and DBT were 70.5% versus 75.4% (P = 0.07) and 95.3% versus 99.1% (P = 0.125). Palpable findings not identified by 2D and DBT were smaller than those identified (11.5 ± 8.3 mm vs 23.9 ± 12.8 mm, P < 0.001). Patients with dense breasts were more likely to have mammographically occult findings than patients with nondense breasts (27.4% vs 8.3%).

CONCLUSIONS

DBT did not improve cancer detection over 2D or US. Both mammographic modalities failed to identify sonographically confirmed findings primarily in dense breasts. The diagnostic use of DBT at palpable sites provided limited benefit over combined 2D and US. When utilizing DBT, US should be performed to adequately characterize palpable sites.