Concordance of immunohistochemistry for predictive and prognostic factors in breast cancer between biopsy and surgical excision: a single-centre experience and review of the literature

Photoacoustic-Based Intra- and Peritumoral Radiomics Nomogram for the Preoperative Prediction of Expression of Ki-67 in Breast Malignancy

RATIONALE AND OBJECTIVES

This study investigated the preoperative predictive efficiency of radiomics derived from photoacoustic (PA) imaging, integrated with the clinical features of Ki-67 expression in malignant breast cancer (BC), with a focus on both intratumoral and peritumoral regions.

METHODS

This study involved 359 patients, divided into a training set (n = 251) and a testing set (n = 108). Radiomic features were extracted from intratumoral and peritumoral regions using PA imaging. Multivariate logistic regression was employed to identify significant clinical factors. LASSO regression was used to select the features extracted from the training set. The selected radiomics features were combined with clinical features to develop a radiomics nomogram. The predictive efficiency of the model was assessed using the area under the receiver operating characteristic curve (AUC), and its clinical utility and accuracy were evaluated through decision curve analysis and calibration curves, respectively.

RESULTS

The developed nomogram combined 6 mm peritumoral data with intratumoral and clinical features and showed excellent diagnostic performance, achieving an AUC of 0.899 in the testing set. They both showed good calibrations. The outperformed models based solely on clinical features or other radiomics methods, with the 6 mm surrounding tumor area proving most effective in identifying Ki-67 status in BC patients.

CONCLUSION

Integrating PA radiomics with clinical features offers a robust preoperative tool for predicting Ki-67 status in BC, optimizing the delineation of peritumoral regions for enhanced diagnostic precision. The model's strong performance supports its potential as a non-invasive adjunct to traditional biopsy methods, aiding in the personalized management of BC treatment.

Hormone and HER2-receptor status in breast cancer: determination using sonographically guided core needle biopsy and correlation with excision specimen-a German single institution diagnostic accuracy study

BACKGROUND

Sonographically guided core needle biopsy (CNB) is a well-established tool for diagnosing breast lesions. Preoperative estrogen receptor (ER), progesterone receptor (PR), and HER2-receptor status are essential for a personalized treatment approach.

OBJECTIVES

We evaluated the concordance of the hormone- and HER2-receptor status between the CNB and the surgical specimen to determine the accuracy of the CNB as a diagnostic method.

DESIGN

This is a non-interventional retrospective study analyzing breast cancer patients treated at the breast care center of the University Medical Center Duesseldorf between January 2002 and December 2005.

METHODS

Patients with paired CNB and surgical specimens and a diagnosis of invasive breast cancer were included. ER, PR, and HER2 status were determined by immunohistochemistry (IHC). Patients with IHC 2+ results were further examined by fluorescence in situ hybridization (FISH). Concordance of receptor status was calculated using specificity, sensitivity, and negative and positive predictive values.

RESULTS

We found a very good agreement between CNB and surgical specimens regarding receptor status. A total of 248 patients were analyzed. Concordance rates in cases of primary surgery for ER, PR, and HER2 were 92.9%, 92.9%, and 93%, respectively. In cases of neoadjuvant chemotherapy, the concordance rates for ER, PR, and HER2 were 100%, 87.5%, and 96%, respectively.

CONCLUSION

CNB demonstrated high diagnostic accuracy compared with surgical specimens regarding ER, PR, and HER2-receptor status. Our findings support the recommendation to use sonographically guided CNB as the initial diagnostic method for guiding tailored treatment plans.

Radiomics Analysis of Intratumoral and Various Peritumoral Regions From Automated Breast Volume Scanning for Accurate Ki-67 Prediction in Breast Cancer Using Machine Learning

RATIONALE AND OBJECTIVES

Current radiomics research primarily focuses on intratumoral regions and fixed peritumoral areas, lacking optimization for accurate Ki-67 prediction. This study aimed to develop machine learning (ML) models to analyze radiomic features from Automated Breast Volume Scanning (ABVS) images of different peritumoral region sizes to identify the optimal size for accurate preoperative Ki-67 prediction.

MATERIALS AND METHODS

A total of 668 breast cancer patients were enrolled and divided into training (486) and testing (182) cohorts. In the training cohort, ML models were developed for intratumoral and peritumoral regions (2, 4, 6, 8, and 10 mm). Relevant Ki-67 features for each ROI were identified, and different models were compared to determine the optimal one. These models were validated using a testing cohort to find the most accurate peritumoral region for Ki-67 prediction. SHAP (Shapley Additive Explanations) analysis was performed to identify key radiomic features from the optimal model.

RESULTS

The Extreme Gradient Boosting (XGBoost) model for the intratumoral region combined with the 6 mm peritumoral region achieved the highest predictive accuracy, with an AUC of 0.957 in the training cohort and 0.920 in the testing cohort. Calibration curves confirmed reliability, and decision curve analysis demonstrated the highest net benefit. SHAP indicated that 6 mm peritumoral radiomic features are more significant than intratumoral features.

CONCLUSION

The XGBoost model using ABVS-derived radiomic features from both the intratumoral and 6 mm peritumoral regions provides the most accurate preoperative Ki-67 prediction.

Survival Risk Score for Invasive Nonmetastatic Breast Cancer: A Real-World Analysis

PURPOSE

This study aimed to develop a multivariable, weighted overall survival (OS) risk score (SRS) for nonmetastatic (M0) invasive breast cancer (M0-BC, SRSM0-BC).

MATERIALS AND METHODS

This study included a training (1,890 patients) and a validation cohort (850 patients) from the Reggio Emilia Cancer Registry (RE-CR). Ten traditional prognostic variables were evaluated.

RESULTS

In the training set, all the variables but the human epidermal growth factor receptor were significantly associated with OS at univariable analysis. A multivariable model identified an increased death risk for estrogen receptor (hazard ratio [HR], 2.0 [95% CI, 1.1 to 3.1]; P = .021), tumor stages T2-T3 (HR, 2.4 [95% CI, 1.3 to 4.7]; P = .009) and T4 (HR, 5.1 [95% CI, 2.0 to 13.0]; P < .001), and age >74 years (HR, 5.7 [95% CI, 4.0 to 8.2]; P < .001). By assigning scores according to HRs, four risk categories were generated (P for trend <.001). The HRs of death in the high- (282 patients, 15.6%), intermediate-high (275 patients, 15.2%), and intermediate-risk (349 patients, 19.2%) categories patients were, respectively, 27.3, 12.9, and 3.5 times higher, compared with the low-risk (909 patients, 50%) group. Harrell'C index was 81.1%, and the explained variation in mortality was 66.6. Internal cross-validation performed on the accrual index dates yielded a Harrell'C index ranging from 79.5% to 82.3% and an explained variation in mortality ranging from 60.3% to 69.4%. In the validation set, the same risk categories (P for trend <.001) were devised. The Harrell'C index and the explained variation in mortality were 76.1% and 53.7%, respectively, in the whole cohort, maintaining an elevated percentage according to the two accrual index dates.

CONCLUSION

SRSM0-BC using the real-world RE-CR data set may represent a low-cost, accessible, globally applicable model in daily clinical practice, helping to prognostically stratify patients with invasive M0-BC.

Comparison of Risk Stratification by CanAssist Breast Test Performed on Core Needle Biopsies Versus Surgical Specimens in Hormone Receptor-Positive, Her2-Negative Early Breast Cancer

Introduction Core needle biopsies (CNB) are being increasingly utilized for biomarker, prognostic, and predictive testing in breast cancer (BC). CanAssist Breast (CAB) is a prognostic test performed to assess the 'risk of breast cancer recurrence' in early-stage hormone receptor-positive, Her2-negative BC patients. CAB segregates tumors as 'low risk' or 'high risk' for distant recurrence. Risk assessment done by CAB aids in planning and making adjuvant chemotherapy or hormone therapy decisions. CAB is typically performed on surgical specimens (SS). However, performing it on CNB does offer additional insights into tumor biology leading to different strategies for treatment planning; hence, we aimed to compare the risk stratification performance of CAB using CNB versus SS. Method We analyzed 103 paired formalin-fixed paraffin-embedded CNB and SS samples from hormone receptor-positive, Her2-negative early BC tissue samples submitted for performing CAB at OncoStem Diagnostics between November 2021 and September 2023. Concordance on 'risk categories' of CAB performed on CNB versus SS was reported using overall percentage agreement and Pearson correlation coefficient. Results We found excellent overall concordance of 92.2% for CAB risk stratification between paired CNB and SS tumor samples with a strong Pearson correlation coefficient of r= 0.8351 (p< 0.0001) when either SS or CNB was used as the gold standard. In prognostic testing patients with a 'low risk' of recurrence may avoid chemotherapy and hence it is crucial to assess the accuracy of CAB in the low-risk category. Additionally, in a real-world scenario, it is more likely that CAB will be performed on CNB first. Conclusion CAB when performed on CNB samples showed high concordance with SS thus demonstrating that CNB was a suitable sample for the CanAssist Breast test. The accuracy in the low-risk category is 97.5%, which ensures that physicians can reliably use prognostic information by testing CNB to guide adjuvant therapy decisions.

Enhancing Ki-67 Prediction in Breast Cancer: Integrating Intratumoral and Peritumoral Radiomics From Automated Breast Ultrasound via Machine Learning

RATIONALE AND OBJECTIVES

Traditional Ki-67 evaluation in breast cancer (BC) via core needle biopsy is limited by repeatability and heterogeneity. The automated breast ultrasound system (ABUS) offers reproducibility but is constrained to morphological and echoic assessments. Radiomics and machine learning (ML) offer solutions, but their integration for improving Ki-67 predictive accuracy in BC remains unexplored. This study aims to enhance ABUS by integrating ML-assisted radiomics for Ki-67 prediction in BC, with a focus on both intratumoral and peritumoral regions.

MATERIALS AND METHODS

A retrospective analysis was conducted on 936 BC patients, split into training (n = 655) and testing (n = 281) cohorts. Radiomics features were extracted from intra- and peritumoral regions via ABUS. Feature selection involved Z-score normalization, intraclass correlation, Wilcoxon rank sum tests, minimum redundancy maximum relevance, and least absolute shrinkage and selection operator logistic regression. ML classifiers were trained and optimized for enhanced predictive accuracy. The interpretability of the optimized model was further augmented by employing Shapley additive explanations (SHAP).

RESULTS

Of the 2632 radiomics features in each patient, 15 were significantly associated with Ki-67 levels. The support vector machine (SVM) was identified as the optimal classifier, with area under the receiver operating characteristic curve values of 0.868 (training) and 0.822 (testing). SHAP analysis indicated that five peritumoral and two intratumoral features, along with age and lymph node status, were key determinants in the predictive model.

CONCLUSION

Integrating ML with ABUS-based radiomics effectively enhances Ki-67 prediction in BC, demonstrating the SVM model's strong performance with both radiomics and clinical factors.

Triple-Negative Breast Cancer Subclassified by Immunohistochemistry: Correlation with Clinical and Pathological Outcomes in Patients Receiving Neoadjuvant Chemotherapy

The intrinsic subtype of triple-negative breast cancer (TNBC) is based on genomic evaluation. In this study, we report the survival and pathological complete response (pCR) rates of TNBC patients subtyped by IHC and treated with neoadjuvant chemotherapy (NACT). A retrospective cohort of 187 TNBC patients who received NACT between 2008 and 2017 was used, and IHC subtyping was performed on biopsy specimens before chemotherapy. The subtyping revealed predominantly basal-like tumors (IHC-BL, 61%), followed by basal-like immune-suppressed tumors (IHC-BLIS, 31%), mesenchymal tumors (12.5%), luminal androgen receptor tumors (IHC-LAR, 12%), and basal-like immune-activated tumors (IHC-BLIA, 10.9%). The pCR rate varied among subtypes, with IHC-BLIA showing the highest (30.0%) and IHC-LAR showing the lowest (4.5%). IHC-BLIS led in recurrence sites. Overall and disease-free survival analyses did not show significant differences among subtypes, although IHC-BLIA demonstrated a trend toward better survival, and IHC-mesenchymal, worse. Patients who achieved pCR exhibited significantly better disease-free survival and overall survival than non-responders. This study underscores the potential of IHC-based subtyping in TNBC management, highlighting distinct response patterns to neoadjuvant chemotherapy and potential implications for treatment strategies. Further research is warranted to validate these findings and explore tailored therapeutic approaches for specific TNBC subtypes.

Development of an interpretable machine learning model for Ki-67 prediction in breast cancer using intratumoral and peritumoral ultrasound radiomics features

Background

Traditional immunohistochemistry assessment of Ki-67 in breast cancer (BC) via core needle biopsy is invasive, inaccurate, and nonrepeatable. While machine learning (ML) provides a promising alternative, its effectiveness depends on extensive data. Although the current mainstream MRI-centered radiomics offers sufficient data, its unsuitability for repeated examinations, along with limited accessibility and an intratumoral focus, constrain the application of predictive models in evaluating Ki-67 levels.

Objective

This study aims to explore ultrasound (US) image-based radiomics, incorporating both intra- and peritumoral features, to develop an interpretable ML model for predicting Ki-67 expression in BC patients.

Methods

A retrospective analysis was conducted on 263 BC patients, divided into training and external validation cohorts. From intratumoral and peritumoral regions of interest (ROIs) in US images, 849 distinctive radiomics features per ROI were derived. These features underwent systematic selection to analyze Ki-67 expression relationships. Four ML models-logistic regression, random forests, support vector machine (SVM), and extreme gradient boosting-were formulated and internally validated to identify the optimal predictive model. External validation was executed to ascertain the robustness of the optimal model, followed by employing Shapley Additive Explanations (SHAP) to reveal the significant features of the model.

Results

Among 231 selected BC patients, 67.5% exhibited high Ki-67 expression, with consistency observed across both training and validation cohorts as well as other clinical characteristics. Of the 1698 radiomics features identified, 15 were significantly correlated with Ki-67 expression. The SVM model, utilizing combined ROI, demonstrated the highest accuracy [area under the receiver operating characteristic curve (AUROC): 0.88], making it the most suitable for predicting Ki-67 expression. External validation sustained an AUROC of 0.82, affirming the model's robustness above a 40% threshold. SHAP analysis identified five influential features from intra- and peritumoral ROIs, offering insight into individual prediction.

Conclusion

This study emphasized the potential of SVM model using radiomics features from both intra- and peritumoral US images, for predicting elevated Ki-67 levels in BC patients. The model exhibited strong performance in validations, indicating its promise as a noninvasive tool to enable personalized decision-making in BC care.

The Clinical and Biological Significance of Estrogen Receptor-Low Positive Breast Cancer

Estrogen receptor (ER) status in breast cancer (BC) is determined using immunohistochemistry (IHC) with nuclear expression in ≥1% of cells defined as ER-positive. BC with 1%-9% expression (ER-low-positive), is a clinically and biologically unique subgroup. In this study, we hypothesized that ER-low-positive BC represents a heterogeneous group with a mixture of ER-positive and ER-negative tumor, which may explain their divergent clinical behavior. A large BC cohort (n = 8171) was investigated and categorized into 3 groups: ER-low-positive (1%-9%), ER-positive (≥10%), and ER-negative (<1%) where clinicopathological and outcome characteristics were compared. A subset of ER-low-positive cases was further evaluated using IHC, RNAscope, and RT-qPCR. PAM50 subtyping and ESR1 mRNA expression levels were assessed in ER-low-positive cases within The Cancer Genome Atlas data set. The reliability of image analysis software in assessment of ER expression in the ER-low-positive category was also assessed. ER-low-positive tumors constituted <2% of BC cases examined and showed significant clinicopathological similarity to ER-negative tumors. Most of these tumors were nonluminal types showing low ESR1 mRNA expression. Further validation of ER status revealed that 45% of these tumors were ER-negative with repeated IHC staining and confirmed by RNAscope and RT-qPCR. ER-low-positive tumors diagnosed on needle core biopsy were enriched with false-positive ER staining. BCs with 10% ER behaved similar to ER-positive, rather than ER-negative or low-positive BCs. Moderate concordance was found in assessment of ER-low-positive tumors, and this was not improved by image analysis. Routinely diagnosed ER-low-positive BC includes a proportion of ER-negative cases. We recommend repeat testing of BC showing 1%-9% ER expression and using a cutoff ≥10% expression to define ER positivity to help better inform treatment decisions.

Intra-patient and inter-metastasis heterogeneity of HER2-low status in metastatic breast cancer

INTRODUCTION

Anti-HER2 antibody-drug conjugates (ADCs) have shown important efficacy in HER2-low metastatic breast cancer (mBC). Criteria for receiving ADCs are based on a single assay on the primary tumour or a small metastatic biopsy. We assessed the intra-patient inter-metastasis heterogeneity of HER2-low status in HER2-negative mBC.

PATIENTS AND METHODS

We included samples of 10 patients (7 ER-positive and 3 ER-negative) donated in the context of our post-mortem tissue donation program UPTIDER. Excisional post-mortem biopsies of 257 metastases and 8 breast tumours underwent central HER2 immunohistochemistry (IHC), alongside 41 pre-mortem primary or metastatic samples. They were classified as HER2-zero, HER2-low (HER2-1+ or HER2-2+, in situ hybridisation [ISH] negative) or HER2-positive (HER2-3+ or HER2-2+, ISH-positive) following ASCO/CAP guidelines 2018. HER2-zero was further subdivided into HER2-undetected (no staining) and HER2-ultralow (faint staining in ≤10% of tumour cells).

RESULTS

Median post-mortem interval was 2.5 h. In 8/10 patients, HER2-low and HER2-zero metastases co-existed, with the proportion of HER2-low lesions ranging from 5% to 89%. A total of 32% of metastases currently classified as HER2-zero were HER2-ultralow. Intra-organ inter-metastasis heterogeneity of HER2-scores was observed in the liver in 3/6 patients. Patients with primary ER-positive disease had a higher proportion of HER2-low metastases as compared to ER-negative disease (46% versus 8%, respectively). At the metastasis level, higher percentages of ER-expressing cells were observed in HER2-low or -ultralow as compared to HER2-undetected metastases.

CONCLUSIONS

Important intra-patient inter-metastasis heterogeneity of HER2-low status exists. This questions the validity of HER2-low in its current form as a theranostic marker.