Breast lesion size assessment in mastectomy specimens: Correlation of cone-beam breast-CT, digital breast tomosynthesis and full-field digital mammography with histopathology

Performance evaluation of ML models for preoperative prediction of HER2-low BC based on CE-CBBCT radiomic features: A prospective study

To explore the value of machine learning (ML) models based on contrast-enhanced cone-beam breast computed tomography (CE-CBBCT) radiomics features for the preoperative prediction of human epidermal growth factor receptor 2 (HER2)-low expression breast cancer (BC). Fifty-six patients with HER2-negative invasive BC who underwent preoperative CE-CBBCT were prospectively analyzed. Patients were randomly divided into training and validation cohorts at approximately 7:3. A total of 1046 quantitative radiomic features were extracted from CE-CBBCT images and normalized using z-scores. The Pearson correlation coefficient and recursive feature elimination were used to identify the optimal features. Six ML models were constructed based on the selected features: linear discriminant analysis (LDA), random forest (RF), support vector machine (SVM), logistic regression (LR), AdaBoost (AB), and decision tree (DT). To evaluate the performance of these models, receiver operating characteristic curves and area under the curve (AUC) were used. Seven features were selected as the optimal features for constructing the ML models. In the training cohort, the AUC values for SVM, LDA, RF, LR, AB, and DT were 0.984, 0.981, 1.000, 0.970, 1.000, and 1.000, respectively. In the validation cohort, the AUC values for the SVM, LDA, RF, LR, AB, and DT were 0.859, 0.880, 0.781, 0.880, 0.750, and 0.713, respectively. Among all ML models, the LDA and LR models demonstrated the best performance. The DeLong test showed that there were no significant differences among the receiver operating characteristic curves in all ML models in the training cohort (P > .05); however, in the validation cohort, the DeLong test showed that the differences between the AUCs of LDA and RF, AB, and DT were statistically significant (P = .037, .003, .046). The AUCs of LR and RF, AB, and DT were statistically significant (P = .023, .005, .030). Nevertheless, no statistically significant differences were observed when compared to the other ML models. ML models based on CE-CBBCT radiomics features achieved excellent performance in the preoperative prediction of HER2-low BC and could potentially serve as an effective tool to assist in precise and personalized targeted therapy.

Radiomics in cone-beam breast CT for the prediction of axillary lymph node metastasis in breast cancer: a multi-center multi-device study

OBJECTIVES

To develop a radiomics model in contrast-enhanced cone-beam breast CT (CE-CBBCT) for preoperative prediction of axillary lymph node (ALN) status and metastatic burden of breast cancer.

METHODS

Two hundred and seventy-four patients who underwent CE-CBBCT examination with two scanners between 2012 and 2021 from two institutions were enrolled. The primary tumor was annotated in each patient image, from which 1781 radiomics features were extracted with PyRadiomics. After feature selection, support vector machine models were developed to predict ALN status and metastatic burden. To avoid overfitting on a specific patient subset, 100 randomly stratified splits were made to assign the patients to either training/fine-tuning or test set. Area under the receiver operating characteristic curve (AUC) of these radiomics models was compared to those obtained when training the models only with clinical features and combined clinical-radiomics descriptors. Ground truth was established by histopathology.

RESULTS

One hundred and eighteen patients had ALN metastasis (N + (≥ 1)). Of these, 74 had low burden (N + (1~2)) and 44 high burden (N + (≥ 3)). The remaining 156 patients had none (N0). AUC values across the 100 test repeats in predicting ALN status (N0/N + (≥ 1)) were 0.75 ± 0.05 (0.67~0.93, radiomics model), 0.68 ± 0.07 (0.53~0.85, clinical model), and 0.74 ± 0.05 (0.67~0.88, combined model). For metastatic burden prediction (N + (1~2)/N + (≥ 3)), AUC values were 0.65 ± 0.10 (0.50~0.88, radiomics model), 0.55 ± 0.10 (0.40~0.80, clinical model), and 0.64 ± 0.09 (0.50~0.90, combined model), with all the ranges spanning 0.5. In both cases, the radiomics model was significantly better than the clinical model (both p < 0.01) and comparable with the combined model (p = 0.56 and 0.64).

CONCLUSIONS

Radiomics features of primary tumors could have potential in predicting ALN metastasis in CE-CBBCT imaging.

CLINICAL RELEVANCE STATEMENT

The findings support potential clinical use of radiomics for predicting axillary lymph node metastasis in breast cancer patients and addressing the limited axilla coverage of cone-beam breast CT.

KEY POINTS

• Contrast-enhanced cone-beam breast CT-based radiomics could have potential to predict N0 vs. N + (≥ 1) and, to a limited extent, N + (1~2) vs. N + (≥ 3) from primary tumor, and this could help address the limited axilla coverage, pending future verifications on larger cohorts. • The average AUC of radiomics and combined models was significantly higher than that of clinical models but showed no significant difference between themselves. • Radiomics features descriptive of tumor texture were found informative on axillary lymph node status, highlighting a higher heterogeneity for tumor with positive axillary lymph node.

Accuracy of cone-beam breast computed tomography for assessing breast cancer tumor size-comparison with breast magnetic resonance imaging

Background

Accurate preoperative assessment of tumor size is important in developing a surgical plan for breast cancer. The purpose of this study was to evaluate the accuracy of cone-beam breast computed tomography (CBBCT) and magnetic resonance imaging (MRI) in the assessment of tumor size and to analyze the factors influencing the discordance.

Methods

In this retrospective study, patients with breast cancer who underwent preoperative contrast-enhanced CBBCT (CE-CBBCT) and dynamic contrast-enhanced MRI (DCE-MRI) and received a complete pathologic diagnosis from August 2020 to December 2021 were included, using the pathological result as the gold standard. Two radiologists assessed the CBBCT and MRI features and measured the tumor size with a 2-week washout period. Intraclass correlation coefficient (ICC) and Bland-Altman analyses were used to assess inter-observer reproducibility and agreement based on CBBCT, MRI and pathology. Univariate analyses of differences in clinical, pathological and CBBCT/MRI features between the concordant and discordant groups was performed using the t-test, Mann-Whitney U-test, Chi-squared test and Fisher's exact test. Multivariate analyses were used to identify factors associated with discordance of CBBCT/MRI with pathology.

Results

A total of 115 female breast cancer patients (115 lesions) were included. All patients had a single malignant tumor of the unilateral breast. The reproducibility and the agreement ranged from moderate to excellent (ICC =0.607-0.983). Receiver operating characteristic (ROC) analyses showed that the cut-off values of CBBCT-pathology and MRI-pathology discordance were 2.25 and 2.65 cm, respectively. CBBCT/MRI-pathology concordance was significantly associated with the extent of pathology, lesion type, presence of calcification, human epidermal growth factor receptor 2 (HER2) status and fatty infiltration (P<0.05). In lesions containing calcification, the difference of CBBCT-pathology was significantly smaller than MRI-pathology (P=0.021). Non-mass enhancement (NME) was the main predictor of CBBCT- or MRI-pathology discordance [odds ratio (OR) =3.293-6.469, P<0.05], and HER2 positivity was a predictor of CBBCT-pathology discordance (OR =3.514, P=0.019).

Conclusions

CBBCT and MRI have comparable accuracy in measurement of tumor size, and CBBCT is advantageous in assessing the size of calcified lesions. NME and HER2 positivity are significant predictors of CBBCT-pathology discordance. This suggests that CBBCT might serve as an alternative imaging technique to assess tumor size when patients do not tolerate MRI.

A multi-stage fusion framework to classify breast lesions using deep learning and radiomics features computed from four-view mammograms

BACKGROUND

Developing computer aided diagnosis (CAD) schemes of mammograms to classify between malignant and benign breast lesions has attracted a lot of research attention over the last several decades. However, unlike radiologists who make diagnostic decisions based on the fusion of image features extracted from multi-view mammograms, most CAD schemes are single-view-based schemes, which limit CAD performance and clinical utility.

PURPOSE

This study aims to develop and test a novel CAD framework that optimally fuses information extracted from ipsilateral views of bilateral mammograms using both deep transfer learning (DTL) and radiomics feature extraction methods.

METHODS

An image dataset containing 353 benign and 611 malignant cases is assembled. Each case contains four images: the craniocaudal (CC) and mediolateral oblique (MLO) view of the left and right breast. First, we extract four matching regions of interest (ROIs) from images that surround centers of two suspicious lesion regions seen in CC and MLO views, as well as matching ROIs in the contralateral breasts. Next, the handcrafted radiomics (HCRs) features and VGG16 model-generated automated features are extracted from each ROI resulting in eight feature vectors. Then, after reducing feature dimensionality and quantifying the bilateral and ipsilateral asymmetry of four ROIs to yield four new feature vectors, we test four fusion methods to build three support vector machine (SVM) classifiers by an optimal fusion of asymmetrical image features extracted from four view images.

RESULTS

Using a 10-fold cross-validation method, results show that a SVM classifier trained using an optimal fusion of four view images yields the highest classification performance (AUC = 0.876 ± 0.031), which significantly outperforms SVM classifiers trained using one projection view alone, AUC = 0.817 ± 0.026 and 0.792 ± 0.026 for the CC and MLO view of bilateral mammograms, respectively (p < 0.001).

CONCLUSIONS

The study demonstrates that the shift from single-view CAD to four-view CAD and the inclusion of both DTL and radiomics features significantly increases CAD performance in distinguishing between malignant and benign breast lesions.

Radiomics nomogram for predicting axillary lymph node metastasis-a potential method to address the limitation of axilla coverage in cone-beam breast CT: a bi-center retrospective study

PURPOSE

Cone-beam breast CT (CBBCT) has an inherent limitation that the axilla cannot be imaged in its entirety. We aimed to develop and validate a nomogram based on clinical factors and contrast-enhanced (CE) CBBCT radiomics features to predict axillary lymph node (ALN) metastasis and complement limited axilla coverage.

MATERIAL AND METHODS

This retrospective study included 312 patients with breast cancer from two hospitals who underwent CE-CBBCT examination in a clinical trial (NCT01792999) during 2012-2020. Patients from TCIH comprised training set (n = 176) and validation set (n = 43), and patients from SYSUCC comprised external test set (n = 93). 3D ROIs were delineated manually and radiomics features were extracted by 3D Slicer software. RadScore was calculated and radiomics model was constructed after feature selection. Clinical model was built on independent predictors. Nomogram was developed with independent clinical predictors and RadScore. Diagnostic performance was compared among three models by ROC curve, and decision curve analysis (DCA) was used to evaluate the clinical utility of nomogram.

RESULTS

A total of 139 patients were ALN positive and 173 patients were negative. Twelve radiomics features remained after feature selection. Location and focality were selected as independent predictors for ALN status. The AUC of nomogram in external test set was higher than that of clinical model (0.80 vs. 0.66, p = 0.012). DCA demonstrated that the nomogram had higher overall net benefit than that of clinical model.

CONCLUSION

The nomogram combined CE-CBBCT-based radiomics features and clinical factors could have potential in distinguishing ALN positive from negative and addressing the limitation of axilla coverage in CBBCT.

New Frontiers in the Role of Locoregional Therapies in Breast Cancer: Proceedings from the Society of Interventional Radiology Foundation Research Consensus Panel

Emerging evidence regarding the effectiveness of locoregional therapies (LRTs) for breast cancer has prompted investigation of the potential role of interventional radiology (IR) in the care continuum of patients with breast cancer. The Society of Interventional Radiology Foundation invited 7 key opinion leaders to develop research priorities to delineate the role of LRTs in both primary and metastatic breast cancer. The objectives of the research consensus panel were to identify knowledge gaps and opportunities pertaining to the treatment of primary and metastatic breast cancer, establish priorities for future breast cancer LRT clinical trials, and highlight lead technologies that will improve breast cancer outcomes either alone or in combination with other therapies. Potential research focus areas were proposed by individual panel members and ranked by all participants according to each focus area's overall impact. The results of this research consensus panel present the current priorities for the IR research community related to the treatment of breast cancer to investigate the clinical impact of minimally invasive therapies in the current breast cancer treatment paradigm.

Accuracy of Preoperative Contrast-enhanced Cone Beam Breast CT in Assessment of Residual Tumor after Neoadjuvant Chemotherapy: A Comparative Study with Breast MRI

RATIONALE AND OBJECTIVES

To compare the accuracy of preoperative contrast-enhanced cone beam breast CT (CE-CBBCT) and MRI in assessment of residual tumor after neoadjuvant chemotherapy (NAC).

MATERIALS AND METHODS

Residual tumor assessments in 91 female patients were performed on preoperative CE-CBBCT and MRI images after NAC. The agreements of tumor size between imaging and pathology were tested by Intraclass Correlation Coefficient (ICC). Subgroup analyses were set according to ductal carcinoma in situ (DCIS), calcifications and molecular subtypes. Correlated-samples Wilcoxon Signed-rank test was used to analyze the difference between imaging and pathology in total and subgroups. AUC, sensitivity, specificity, PPV, and NPV were calculated to compare the performance of CE-CBBCT and MRI in predicting pathological complete response (pCR).

RESULTS

Comparing with pathology, the agreement on CE-CBBCT was good (ICC = 0.64, 95% CI, 0.35-0.78), whereas on MRI was moderate (ICC = 0.59, 95% CI, 0.36-0.77), and overestimation on CE-CBBCT was less than that on MRI (median (interquartile range, IQR): 0.24 [0.00, 1.31] cm vs. 0.67 [0.00, 1.81] cm; p = 0.000). In subgroup analysis, CE-CBBCT showed superior accuracy than MRI when residual DCIS (p = 0.000) and calcifications (p = 0.000) contained, as well as luminal A (p = 0.043) and luminal B (p = 0.009) breast cancer. CE-CBBCT and MRI performed comparable in predicting pCR, AUCs were 0.749 and 0.733 respectively (p > 0.05).

CONCLUSION

CE-CBBCT showed superior accuracy in assessment of residual tumor compared with MRI, especially when residual DCIS or calcifications contained and luminal subtype. The performance of preoperative CE-CBBCT in predicting pCR was comparable to MRI. CE-CBBCT could be an alternative method used for preoperative assessment after NAC.

Accuracy of different sonomammographic imaging modalities in assessment of breast tumor size

Background

Accurate breast cancer size is crucial for staging and an important prognostic factor in patient management. Therapeutic decisions heavily depend on tumor size detection by radiological imaging. The purpose of our prospective comparative study is to compare the diagnostic accuracy of different sonomammographic breast imaging modalities, namely DM, DBT, CESM, 2D US and 3D US in the preoperative tumor size measurement.

Results

CESM, 3D US and 2D US achieved moderately strong correlation with the pathological size measurements, while (DM) and (DBT) showed fair correlation with the pathology. CESM showed the highest correlation coefficient (0.789), while (DBT) showed the lowest correlation coefficient (0.411). Regarding the agreement, there was good agreement of the size measured by CESM, 3D US and 2D US with the pathology as the ICC was (0.798), (0.769) and (0.624), respectively. The highest agreement with the pathology was achieved with CESM. The agreement of the size measured by (DM) and (DBT) with the pathology was moderate as the ICC was (0.439) and (0.416), respectively. The lowest agreement was achieved with the size measured by (DBT).

Conclusions

CESM and 3D US are more superior to DM, 2D US and DBT regarding preoperative size measurement. 3D US can be used as preoperative noninvasive technique, especially in patients with impaired renal function who cannot tolerate CESM.

Comparison of background parenchymal enhancement (BPE) on contrast-enhanced cone-beam breast CT (CE-CBBCT) and breast MRI

OBJECTIVES

To compare the background parenchymal enhancement (BPE) levels on contrast-enhanced cone-beam breast CT (CE-CBBCT) and MRI, evaluate inter-reader reliability, and analyze the relationship between clinical factors and BPE level on CE-CBBCT.

METHODS

In this retrospective study, patients who underwent both CE-CBBCT and MRI were analyzed. BPE levels on CE-CBBCT and MRI were assessed by five specialists independently in random fashion, with a wash-out period of 4 weeks. Weighted kappa was used to analyze the agreement between CE-CBBCT and MRI, and intraclass correlation coefficient (ICC) was used to evaluate the inter-reader reliability for each modality. The association between BPE level on CE-CBBCT and clinical factors was evaluated by univariate and multivariate logistic regression.

RESULTS

A total of 221 patients from January 2017 to April 2021 were enrolled. CE-CBBCT showed substantial agreement (weighted kappa = 0.690) with MRI for BPE evaluation, with good degree of inter-reader reliability on both CE-CBBCT (ICC = 0.712) and MRI (ICC = 0.757). Based on majority reports, BPE levels on CE-CBBCT were lower than MRI (p < 0.001). BPE level on CE-CBBCT was significantly associated with menstrual status (odds ratio, OR = 0.125), breast density (OR = 2.308), and previously treated breast cancer (OR = 0.052) (all p < 0.05). BPE level for premenopausal patients was associated with menstrual cycle, with lower BPE level for the 2^nd week of menstrual cycle (OR = 0.246).

CONCLUSIONS

CE-CBBCT showed substantial agreement and comparable inter-reader reliability with MRI for BPE evaluation, indicating that the corresponding BI-RADS lexicons could be used to describe BPE level on CE-CBBCT. The 2^nd week of menstrual cycle timing is suggested as the optimal examination period for CE-CBBCT.

KEY POINTS

• CE-CBBCT showed substantial agreement and comparable inter-reader reliability with MRI for BPE evaluation. • Menstrual status, breast density, and previously treated breast cancer were associated with the BPE level on CE-CBBCT images. • The 2^ndweek of the menstrual cycle is suggested as the optimal examination period for CE-CBBCT.

Dedicated breast CT: state of the art-Part II. Clinical application and future outlook

Dedicated breast CT is being increasingly used for breast imaging. This technique provides images with no compression, removal of tissue overlap, rapid acquisition, and available simultaneous assessment of microcalcifications and contrast enhancement. In this second installment in a 2-part review, the current status of clinical applications and ongoing efforts to develop new imaging systems are discussed, with particular emphasis on how to achieve optimized practice including lesion detection and characterization, response to therapy monitoring, density assessment, intervention, and implant evaluation. The potential for future screening with breast CT is also addressed. KEY POINTS: • Dedicated breast CT is an emerging modality with enormous potential in the future of breast imaging by addressing numerous clinical needs from diagnosis to treatment. • Breast CT shows either noninferiority or superiority with mammography and numerical comparability to MRI after contrast administration in diagnostic statistics, demonstrates excellent performance in lesion characterization, density assessment, and intervention, and exhibits promise in implant evaluation, while potential application to breast cancer screening is still controversial. • New imaging modalities such as phase-contrast breast CT, spectral breast CT, and hybrid imaging are in the progress of R & D.