Comparison of Core Needle Biopsy and Excision Specimens for the Accurate Evaluation of Breast Cancer Molecular Markers: a Report of 1003 Cases

Multiparametric MRI-based Interpretable Machine Learning Radiomics Model for Distinguishing Between Luminal and Non-luminal Tumors in Breast Cancer: A Multicenter Study

RATIONALE AND OBJECTIVES

To construct and validate an interpretable machine learning (ML) radiomics model derived from multiparametric magnetic resonance imaging (MRI) images to differentiate between luminal and non-luminal breast cancer (BC) subtypes.

METHODS

This study enrolled 1098 BC participants from four medical centers, categorized into a training cohort (n = 580) and validation cohorts 1-3 (n = 252, 89, and 177, respectively). Multiparametric MRI-based radiomics features, including T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), apparent diffusion coefficient (ADC), and dynamic contrast-enhanced (DCE) imaging, were extracted. Five ML algorithms were applied to develop various radiomics models, from which the best performing model was identified. A ML-based combined model including optimal radiomics features and clinical predictors was constructed, with performance assessed through receiver operating characteristic (ROC) analysis. The Shapley additive explanation (SHAP) method was utilized to assess model interpretability.

RESULTS

Tumor size and MR-reported lymph node status were chosen as significant clinical variables. Thirteen radiomics features were identified from multiparametric MRI images. The extreme gradient boosting (XGBoost) radiomics model performed the best, achieving area under the curves (AUCs) of 0.941, 0.903, 0.862, and 0.894 across training and validation cohorts 1-3, respectively. The XGBoost combined model showed favorable discriminative power, with AUCs of 0.956, 0.912, 0.894, and 0.906 in training and validation cohorts 1-3, respectively. The SHAP visualization facilitated global interpretation, identifying "ADC_wavelet-HLH_glszm_ZoneEntropy" and "DCE_wavelet-HLL_gldm_DependenceVariance" as the most significant features for the model's predictions.

CONCLUSION

The XGBoost combined model derived from multiparametric MRI may proficiently differentiate between luminal and non-luminal BC and aid in treatment decision-making.

CRITICAL RELEVANCE STATEMENT

An interpretable machine learning radiomics model can preoperatively predict luminal and non-luminal subtypes in breast cancer, thereby aiding therapeutic decision-making.

Comparing core needle biopsy and surgical excision in breast cancer diagnosis: implications for clinical practice from a retrospective cohort study

BACKGROUND

Preoperative ultrasound-guided core needle biopsy (CNB) is currently the standard procedure for managing breast illnesses. However, the differences in outcomes between CNB and surgical excision (SE) have not been thoroughly assessed. This study aimed to explore the disparities in pathological outcomes between these two procedures, using a large sample dataset.

METHODS

This retrospective study consecutively included patients who underwent CNB and SE at Shenzhen People's Hospital from May 2016 to June 2023. Immunohistochemistry (IHC) was utilized to determine the status of estrogen receptor (ER), progesterone receptor (PgR), human epidermal growth factor receptor-2 (HER2), and Ki-67. Patients presenting with HER2 IHC 2+ underwent additional fluorescence in situ hybridization (FISH) examination. The cutoff value for high Ki-67 expression was established at 14%. Molecular subtypes were classified into four groups (Luminal A, Luminal B, Triple-negative, and HER2-positive) and five groups [Luminal A, Luminal B+ (HER2-positive), Luminal B- (HER2-negative), Triple-negative, and HER2-positive], based on different criteria.

RESULTS

A total of 4,209 patients were included in this study. Post-surgical confirmation revealed 2,410 cases as benign and 1,799 as malignant. Among the malignant cases, 334 were excluded due to either not having undergone direct surgery or having incomplete IHC results. The remaining 1,465 cases underwent IHC testing. CNB demonstrated a 97% concordance rate (CR) in diagnosing benign cases. The CRs for diagnosing invasive breast cancer (IBC) and carcinoma in situ (CIS) were 92% and 54%, respectively. ER, PgR, HER2, and Ki-67 exhibited CRs of 94%, 91%, 98%, and 84%, respectively. In the four-group classification, the overall diagnostic CR was 82%, with CRs for Luminal A, Luminal B, HER2-positive, and triple-negative breast cancer (TNBC) being 84%, 82%, 78%, and 85%, respectively. Under the five-group classification, the overall diagnostic CR was also 82%, with CRs for Luminal A, Luminal B+, Luminal B-, HER2-positive, and TNBC being 86%, 85%, 94%, 88%, and 92%, respectively.

CONCLUSIONS

This study demonstrates that CNB is highly accurate in differentiating benign from malignant breast lesions, particularly showing significant consistency in the diagnosis of molecular subtypes, providing a reliable reference for clinical diagnosis.

The association of magnetic resonance imaging features with five molecular subtypes of breast cancer

Objective

To identify the association of magnetic resonance imaging (MRI) features with molecular subtypes of breast cancer (BC).

Materials and methods

A retrospective study was conducted on 112 invasive BC patients with preoperative breast MRI. The confirmed diagnosis and molecular subtypes of BC were based on the postoperative specimens. MRI features were collected by experienced radiologists. The association of MRI features of each subtype was compared to other molecular subtypes in univariate and multivariate logistic regression analyses.

Results

The proportions of luminal A, luminal B HER2-negative, luminal B HER2-positive, HER2-enriched, and triple-negative BC were 14.3 %, 52.7 %, 12.5 %, 10.7 %, and 9.8 %, respectively. Luminal A was associated with hypo-isointensityon T2-weighted images (OR=6.214, 95 % CI: 1.163-33.215) and non-restricted diffusion on DWI-ADC (OR=6.694, 95 % CI: 1.172-38.235). Luminal B HER2-negative was related to the presence of mass (OR=7.245, 95 % CI: 1.760-29.889) and slow/medium initial enhancement pattern (OR=3.654, 95 % CI: 1.588-8.407). There were no associations between MRI features and luminal B HER2-positive. HER2-enriched tended to present as non-mass enhancement lesions (OR=20.498, 95 % CI: 3.145-133.584) with fast uptake in the initial postcontrast phase (OR=9.788, 95 % CI: 1.689-56.740), and distortion (OR=11.471, 95 % CI: 2.250-58.493). Triple-negative were associated with unifocal (OR=7.877, 95 % CI: 1.180-52.589), hyperintensityon T2-weighted images (OR=14.496, 95 % CI: 1.303-161.328), rim-enhanced lesions (OR=18.706, 95 % CI: 1.915-182.764), and surrounding tissue edema (OR=5.768, 95 % CI: 1.040-31.987).

Conclusion

Each molecular subtype of BC has distinct features on breast MRI. These characteristics can serve as an adjunct to immunohistochemistry in diagnosing molecular subtypes, particularly in cases, where traditional methods yield equivocal results.

Predicting hormone receptor status in invasive breast cancer through radiomics analysis of long-axis and short-axis ultrasound planes

The hormone receptor (HR) status plays a significant role in breast cancer, serving as the primary guide for treatment decisions and closely correlating with prognosis. This study aims to investigate the predictive value of radiomics analysis in long-axis and short-axis ultrasound planes for distinguishing between HR-positive and HR-negative breast cancers. A cohort of 505 patients from two hospitals was stratified into discovery (Institute 1, 416 patients) and validation (Institute 2, 89 patients) cohorts. A comprehensive set of 788 ultrasound radiomics features was extracted from both long-axis and short-axis ultrasound planes, respectively. Utilizing least absolute shrinkage and selection operator (LASSO) regression analysis, distinct models were constructed for the long-axis and short-axis data. Subsequently, radiomics scores (Rad-scores) were computed for each patient. Additionally, a combined model was formulated by integrating data from long-axis and short-axis Rad-scores along with clinical factors. The diagnostic efficacy of all models was evaluated by the area under the receiver operating characteristic (ROC) curve (AUC). The long-axis and short-axis models, consisting of 11 features and 15 features, respectively, were established, yielding AUCs of 0.743 and 0.751 in the discovery cohort, and 0.795 and 0.744 in the validation cohort. The calculated long-axis and short-axis Rad-scores exhibited significant differences between HR-positive and HR-negative groups across all cohorts (all p < 0.001). Univariate analysis identified ultrasound-reported tumor size as an independent predictor. The combined model, incorporating long-axis and short-axis Rad-scores along with tumor size, achieved superior AUCs of 0.788 and 0.822 in the discovery and validation cohorts, respectively. The combined model effectively distinguishes between HR-positive and HR-negative breast cancers based on ultrasound radiomics features and tumor size, which may offer a valuable tool to facilitate treatment decision making and prognostic assessment.

An Automated Breast Volume Scanner-Based Intra- and Peritumoral Radiomics Nomogram for the Preoperative Prediction of Expression of Ki-67 in Breast Malignancy

RATIONALE AND OBJECTIVES

This study aimed to create and verify a nomogram for preoperative prediction of Ki-67 expression in breast malignancy to assist in the development of personalized treatment strategies.

MATERIALS AND METHODS

This retrospective study received approval from the institutional review board and included a cohort of 197 patients with breast malignancy who were admitted to our hospital. Ki-67 expression was divided into two groups based on a 14% threshold: low and high. A radiomics signature was built utilizing 1702 radiomics features based on an intra- and peritumoral (10 mm) regions of interest. Using multivariate logistic regression, radiomics signature, and ultrasound (US) characteristics, the nomogram was developed. To evaluate the model's calibration, clinical application, and predictive ability, decision curve analysis (DCA), the calibration curve, and the receiver operating characteristic curve were used, respectively.

RESULTS

The final nomogram included three independent predictors: tumor size (P = .037), radiomics signature (P < .001), and US-reported lymph node status (P = .018). The nomogram exhibited satisfactory performance in the training cohort, demonstrating a specificity of 0.944, a sensitivity of 0.745, and an area under the curve (AUC) of 0.905. The validation cohort recorded a specificity of 0.909, a sensitivity of 0.727, and an AUC of 0.882. The DCA showed the nomogram's clinical utility, and the calibration curve revealed a high consistency among the expected and detected values.

CONCLUSION

The nomogram used in this investigation can accurately predict Ki-67 expression in people with malignant breast tumors, helping to develop personalized treatment approaches.

Deep learning radiopathomics based on preoperative US images and biopsy whole slide images can distinguish between luminal and non-luminal tumors in early-stage breast cancers

BACKGROUND

For patients with early-stage breast cancers, neoadjuvant treatment is recommended for non-luminal tumors instead of luminal tumors. Preoperative distinguish between luminal and non-luminal cancers at early stages will facilitate treatment decisions making. However, the molecular immunohistochemical subtypes based on biopsy specimens are not always consistent with final results based on surgical specimens due to the high intra-tumoral heterogeneity. Given that, we aimed to develop and validate a deep learning radiopathomics (DLRP) model to preoperatively distinguish between luminal and non-luminal breast cancers at early stages based on preoperative ultrasound (US) images, and hematoxylin and eosin (H&E)-stained biopsy slides.

METHODS

This multicentre study included three cohorts from a prospective study conducted by our team and registered on the Chinese Clinical Trial Registry (ChiCTR1900027497). Between January 2019 and August 2021, 1809 US images and 603 H&E-stained whole slide images (WSIs) from 603 patients with early-stage breast cancers were obtained. A Resnet18 model pre-trained on ImageNet and a multi-instance learning based attention model were used to extract the features of US and WSIs, respectively. An US-guided Co-Attention module (UCA) was designed for feature fusion of US and WSIs. The DLRP model was constructed based on these three feature sets including deep learning US feature, deep learning WSIs feature and UCA-fused feature from a training cohort (1467 US images and 489 WSIs from 489 patients). The DLRP model's diagnostic performance was validated in an internal validation cohort (342 US images and 114 WSIs from 114 patients) and an external test cohort (270 US images and 90 WSIs from 90 patients). We also compared diagnostic efficacy of the DLRP model with that of deep learning radiomics model and deep learning pathomics model in the external test cohort.

FINDINGS

The DLRP yielded high performance with area under the curve (AUC) values of 0.929 (95% CI 0.865-0.968) in the internal validation cohort, and 0.900 (95% CI 0.819-0.953) in the external test cohort. The DLRP also outperformed deep learning radiomics model based on US images only (AUC 0.815 [0.719-0.889], p = 0.027) and deep learning pathomics model based on WSIs only (AUC 0.802 [0.704-0.878], p = 0.013) in the external test cohort.

INTERPRETATION

The DLRP can effectively distinguish between luminal and non-luminal breast cancers at early stages before surgery based on pretherapeutic US images and biopsy H&E-stained WSIs, providing a tool to facilitate treatment decision making in early-stage breast cancers.

FUNDING

Natural Science Foundation of Guangdong Province (No. 2023A1515011564), and National Natural Science Foundation of China (No. 91959127; No. 81971631).

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

PURPOSE

Accurate evaluation of breast cancer on bioptic samples is of fundamental importance to guide therapeutic decisions, especially in the neoadjuvant or metastatic setting. We aimed to assess concordance for oestrogen receptor (ER), progesterone receptor (PR), c-erbB2/HER2 and Ki-67. We also reviewed the current literature to evaluate our results in the context of the data available at present.

METHODS

We included patients who underwent both biopsy and surgical resection for breast cancer at San Matteo Hospital, Pavia, Italy, between January 2014 and December 2020. ER, PR, c-erbB2, and Ki-67 immunohistochemistry concordance between biopsy and surgical specimen was evaluated. ER was further analysed to include the recently defined ER-low-positive in our analysis.

RESULTS

We evaluated 923 patients. Concordance between biopsy and surgical specimen for ER, ER-low-positive, PR, c-erbB2 and Ki-67 was, respectively, 97.83, 47.8, 94.26, 68 and 86.13%. Cohen's κ for interobserver agreement was very good for ER and good for PR, c-erbB2 and Ki-67. Concordance was especially low (37%) in the c-erbB2 1 + category.

CONCLUSION

Oestrogen and progesterone receptor status can be safely assessed on preoperative samples. The results of this study advise caution in interpreting biopsy results regarding ER-low-positive, c-erbB2/HER and Ki-67 results due to a still suboptimal concordance. The low concordance for c-erbB2 1 + cases underlines the importance of further training in this area, in the light of the future therapeutic perspectives.

Ki67 increase after core needle biopsy associated with worse disease outcome in HER2-negative breast cancer patients

Ki67 would change after core needle biopsy (CNB) in invasive breast cancer. However, whether Ki67 alteration (ΔKi67) influences disease outcomes remains unclear. Here we aim to evaluate the prognostic value of ΔKi67. Patients with paired CNB and open excision biopsy (OEB) samples between January 2009 and June 2016 were retrospectively analyzed. ΔKi67 was calculated as the absolute difference between Ki67 level in CNB and OEB samples, and the median value of 5% was adopted to category patients into high- and low ΔKi67 groups. Disease-free survival (DFS) and overall survival (OS) were compared between different ΔKi67 groups. Overall, 2173 invasive breast cancer patients were included. Median Ki67 was higher in OEB than CNB samples: 25.00% versus 20.00% (P < 0.001). Axillary nodal status, STI, histological grading, and molecular subtype were independently associated with ΔKi67 (P < 0.05). In the whole population, patients with low ΔKi67 showed superior 5-year DFS (89.6% vs 87.0%, P = 0.026), but similar OS (95.8% vs 94.3%, P = 0.118) compared to those with high ΔKi67. HER2 status at surgery was the only significant factor interacting with ΔKi67 on both DFS (P = 0.026) and OS (P = 0.007). For patients with HER2-negative disease, high ΔKi67 was associated with worse 5-year DFS (87.2% vs 91.2%, P = 0.004) as well as impaired 5-year OS (93.9% vs 96.8%, P = 0.010). ΔKi67 had no significant impact on survival of HER2-positive patients. Ki67 increase after CNB was significantly associated with worse disease outcomes in HER2-negative, but not in HER2-positive patients, which warrants further study.

Diagnostic value of core needle biopsy for determining HER2 status in breast cancer, especially in the HER2-low population

PURPOSE

The status of human epidermal growth factor receptor 2 (HER2) is important for treatment decision-making of breast cancer and was commonly determined by core needle biopsy (CNB). The concordance of CNB with surgical excision biopsy (SEB) has been verified, but remain unclear according to the newly developed classification of HER2 status. Our study aimed to re-evaluate the diagnostic value of CNB for determining HER2 status in breast cancer, especially in the HER2-low population.

METHODS

Eligible breast cancer patients in West China Hospital between January 1, 2007 and December 31, 2021 were enrolled consecutively and data were extracted from the Hospital Information System. The agreement of HER2 status between CNB and SEB was calculated by concordance rate and κ statistics, as well as the sensitivity, specificity, positive, and negative predictive values (PPV & NPV). Logistic models were used to explore potential factors associated with the discordance between both tests.

RESULTS

Of 1829 eligible patients, 1097 (60.0%) and 1358 (74.2%) were consistent between CNB and SEB by pathological and clinical classifications, respectively, with κ value being 0.46 (0.43-0.49) and 0.57 (0.53-0.60). The sensitivity (50.9%-52.7%) and PPV (50.5%-55.2%) of CNB were especially low among IHC 1+ and 2+/ISH - subgroups by pathological classifications; however, it showed the highest sensitivity (77.5%) and the lowest specificity (73.9%) in HER2-low population by clinical classifications. Advanced N stages might be a stable indicator for the discordance between both tests.

CONCLUSION

The diagnostic value of CNB was limited for determining HER2 status in breast cancer, especially in HER2-low population.

Preoperative Computed Tomography Radiomics Analysis for Predicting Receptors Status and Ki-67 Levels in Breast Cancer

BACKGROUND

To assess the prediction performance of preoperative chest computed tomography (CT) based radiomics features for estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor-2 (HER2+), and Ki-67 status of breast cancer.

MATERIALS AND METHODS

This study enrolled 108 breast cancer patients who received preoperative chest CT examinations in our institution from July 2018 to January 2020. Radiomics features were separately extracted from nonenhanced, arterial, and portal-venous phases CT images. The least absolute shrinkage and selection operator logistic regression was used for feature selection. Then the radiomics signatures for each phase and a combined model of 3 phases were built. Finally, the receiver operating characteristic curves and calibration curves were used to confirm the performance of the radiomics signatures and combined model. In addition, the decision curves were performed to estimate the clinical usefulness of the combined model.

RESULTS

The 20 most predictive features were finally selected to build radiomics signatures for each phase. The combined model achieved the overall best performance than using either of the nonenhanced, arterial and portal-venous phases alone, achieving an area under the receiver operating characteristic curve of 0.870 for ER+ versus ER-, 0.797 for PR+ versus PR-, 0.881 for HER2+ versus HER2-, and 0.726 for Ki-67. The decision curve demonstrated that the CT-based radiomics features were clinically useful.

CONCLUSION

This study indicated preopreative chest CT radiomics analysis might be able to assess ER, PR, HER2+, and Ki-67 status of breast cancer. The findings need further to be verified in future larger studies.

Systematically higher Ki67 scores on core biopsy samples compared to corresponding resection specimen in breast cancer: a multi-operator and multi-institutional study

Ki67 has potential clinical importance in breast cancer but has yet to see broad acceptance due to inter-laboratory variability. Here we tested an open source and calibrated automated digital image analysis (DIA) platform to: (i) investigate the comparability of Ki67 measurement across corresponding core biopsy and resection specimen cases, and (ii) assess section to section differences in Ki67 scoring. Two sets of 60 previously stained slides containing 30 core-cut biopsy and 30 corresponding resection specimens from 30 estrogen receptor-positive breast cancer patients were sent to 17 participating labs for automated assessment of average Ki67 expression. The blocks were centrally cut and immunohistochemically (IHC) stained for Ki67 (MIB-1 antibody). The QuPath platform was used to evaluate tumoral Ki67 expression. Calibration of the DIA method was performed as in published studies. A guideline for building an automated Ki67 scoring algorithm was sent to participating labs. Very high correlation and no systematic error (p = 0.08) was found between consecutive Ki67 IHC sections. Ki67 scores were higher for core biopsy slides compared to paired whole sections from resections (p ≤ 0.001; median difference: 5.31%). The systematic discrepancy between core biopsy and corresponding whole sections was likely due to pre-analytical factors (tissue handling, fixation). Therefore, Ki67 IHC should be tested on core biopsy samples to best reflect the biological status of the tumor.

Association of Ki-67 Change Pattern After Core Needle Biopsy and Prognosis in HR+/HER2− Early Breast Cancer Patients

Background

To investigate the association of Ki-67 change pattern after core needle biopsy (CNB) and prognosis in HR+/HER2− early breast cancer patients.

Method

Eligible patients were categorized into three groups: Low group, Elevation group, and High group. Chi-square test and logistic regression analysis were used to compare the clinic-pathological characteristics. Kaplan–Meier method was used to estimate the rates of recurrence-free interval (RFI) and breast cancer-specific survival (BCSS), which were compared via the Log-rank test. Cox proportional hazard analysis was performed to investigate independent prognostic factors.

Results

A total of 2,858 patients were included: 1,179 (41.3%), 482 (16.9%), and 1,197 (41.8%) patients were classified into the low, elevation, and high groups, respectively. Age, tumor size, histological grade, lymph-vascular invasion (LVI), and ER level status were associated with Ki-67 change pattern after CNB. With a median follow-up of 53.6 months, the estimated 5-year RFI rates for the low group, elevation, and high groups were 96.4%, 95.3% and 90.9%, respectively (P < 0.001). And 5-year BCSS rates were 99.3%, 98.3% and 96.8%, respectively (P = 0.001). Compared with patients in the low group, patients in the high group had significantly worse RFI (hazard ratio [HR] 1.71, 95% confidence interval [CI] 1.16–2.54) in multivariate analysis.

Conclusions

Ki-67 change after CNB was associated with prognosis in HR+/HER2− early breast cancer. Patients with Ki-67 high or elevation after CNB had an inferior disease outcome, indicating the necessity of re-evaluating Ki-67 on surgical specimens after CNB.

Concordance of breast cancer biomarker testing in core needle biopsy and surgical specimens: A single institution experience

BACKGROUND

Accurate diagnostic biomarker testing is crucial to treatment decisions in breast cancer. Biomarker testing is performed on core needle biopsies (CNB) and is often repeated in the surgical specimen (SS) after resection. As differences between CNB and SS testing may alter treatment decisions, we evaluated concordance between CNB and SS as well as associated changes in treatment and clinical outcomes.

METHODS

We performed a retrospective analysis of breast cancer patients at our institution between January 2010 and May 2020. Concordance between CNB and SS was assessed for estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) by immunohistochemistry (IHC) and fluorescence in situ hybridization (FISH). Survival in patients, including recurrence, metastatic recurrence, and death, were assessed using chi-squared likelihood ratio.

RESULTS

In total, 961 patients met eligibility criteria. Concordance, minor discordance, total concordance (concordance plus minor discordance), and major discordance between CNB and SS were reported for ER (87.7%, 9.2%, 90.8%, and 2.9%), PR (58.1%, 29.1%, 87.2%, and 12.8%), and HER2 IHC (52.5%, 20.9%, 73.4%, 26.6%), respectively. HER2 FISH concordance and major discordance were 58.5% and 1.2%, respectively. Of major discordance, ER (48.2%, p < 0.001) and HER2 FISH (50.0%) led to more management changes than HER2 IHC (2.4%, p = 0.04) and PR (1.6%, p = 0.10). Patients with ER major discordance had increased risk of death (6.7% concordance vs. 22.2% major discordance, p = 0.004).

CONCLUSION

Overall, retesting ER and HER2 was more clinically beneficial than retesting PR. To aid decision-making and minimize healthcare costs, we propose patient-centered guidelines on retesting biomarker profiles.

Decoding the molecular subtypes of breast cancer seen on multimodal ultrasound images using an assembled convolutional neural network model: A prospective and multicentre study

BACKGROUND

Preoperative determination of breast cancer molecular subtypes facilitates individualized treatment plan-making and improves patient prognosis. We aimed to develop an assembled convolutional neural network (ACNN) model for the preoperative prediction of molecular subtypes using multimodal ultrasound (US) images.

METHODS

This multicentre study prospectively evaluated a dataset of greyscale US, colour Doppler flow imaging (CDFI), and shear-wave elastography (SWE) images in 807 patients with 818 breast cancers from November 2016 to February 2021. The St. Gallen molecular subtypes of breast cancer were confirmed by postoperative immunohistochemical examination. The monomodal ACNN model based on greyscale US images, the dual-modal ACNN model based on greyscale US and CDFI images, and the multimodal ACNN model based on greyscale US and CDFI as well as SWE images were constructed in the training cohort. The performances of three ACNN models in predicting four- and five-classification molecular subtypes and identifying triple negative from non-triple negative subtypes were assessed and compared. The performance of the multimodal ACNN was also compared with preoperative core needle biopsy (CNB).

FINDING

The performance of the multimodal ACNN model (macroaverage area under the curve [AUC]: 0.89-0.96) was superior to that of the dual-modal ACNN model (macroaverage AUC: 0.81-0.84) and the monomodal ACNN model (macroaverage AUC: 0.73-0.75) in predicting four-classification breast cancer molecular subtypes, which was also better than that of preoperative CNB (AUC: 0.89-0.99 vs. 0.67-0.82, p < 0.05). In addition, the multimodal ACNN model outperformed the other two ACNN models in predicting five-classification molecular subtypes (AUC: 0.87-0.94 vs. 0.78-0.81 vs. 0.71-0.78) and identifying triple negative from non-triple negative breast cancers (AUC: 0.934-0.970 vs. 0.688-0.830 vs. 0.536-0.650, p < 0.05). Moreover, the multimodal ACNN model obtained satisfactory prediction performance for both T1 and non-T1 lesions (AUC: 0.957-0.958 and 0.932-0.985).

INTERPRETATION

The multimodal US-based ACNN model is a potential noninvasive decision-making method for the management of patients with breast cancer in clinical practice.

FUNDING

This work was supported in part by the National Natural Science Foundation of China (Grants 81725008 and 81927801), Shanghai Municipal Health Commission (Grants 2019LJ21 and SHSLCZDZK03502), and the Science and Technology Commission of Shanghai Municipality (Grants 19441903200, 19DZ2251100, and 21Y11910800).

Concordance between core needle biopsy and surgical excision specimens for Ki-67 in breast cancer - a systematic review of the literature

AIMS

The biomarkers oestrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor 2 (HER2) are routinely measured in patients with breast cancer with international consensus on how they should be interpreted. There is evidence to support use of other biomarkers to give more detailed predictive and prognostic information. Ki-67 is one example, and measures the proliferative activity of cancer cells. It is important that this can be performed at diagnosis of breast cancer for patients who do not have initial surgical treatment (mainly older women) and those receiving neoadjuvant therapies.

METHODS AND RESULTS

A systematic review was performed to assess concordance of measurement of Ki-67 between core needle biopsy (CNB) samples and surgical excision (SE) samples in patients with invasive breast cancer. MEDLINE and Embase databases were searched. Studies were eligible if performed within the last 10 years; included quantitative measurement of Ki-67 in both CNB and SE samples with no prior breast cancer treatment; measured concordance between two samples; and had full text available. A total of 22 studies, including 5982 paired CNB and SE samples on which Ki-67 was measured, were appraised. Overall, there appeared to be concordance; however, reliability was unclear. Where given, the Cohen's kappa coefficient (κ) of correlation between samples ranged from 0.261 to 0.712. The concordance rate between CNB and SE where measured as a percentage had a range from 70.3 to 92.7% CONCLUSIONS: Assessment of level of concordance of Ki-67 between CNB and SE samples is hampered by different methodologies. International consensus on Ki-67 measurement is urgently needed.

Is the 21-Gene Recurrence Score on Core Needle Biopsy Equivalent to Surgical Specimen in Early-Stage Breast Cancer? A Comparison of Gene Expression Between Paired Core Needle Biopsy and Surgical Specimens

BACKGROUND

Molecular testing on surgical specimens predicts disease recurrence and benefit of adjuvant chemotherapy in hormone receptor-positive (HR+), human epidermal growth factor receptor 2-negative (HER2-) early-stage breast cancer (EBC). Testing on core biopsies has become common practice despite limited evidence of concordance between core/surgical samples. In this study, we compared the gene expression of the 21 genes and the recurrence score (RS) between paired core/surgical specimens.

METHODS

Eighty patients with HR+/HER2- EBC were evaluated from two publicly available gene expression datasets (GSE73235, GSE76728) with paired core/surgical specimens without neoadjuvant systemic therapy. The expression of the 21 genes was compared in paired samples. A microarray-based RS was calculated and a value ≥ 26 was defined as high-RS. The concordance rate and kappa statistic were used to evaluate the agreement between the RS of paired samples.

RESULTS

Overall, there was no significant difference and a high correlation in the gene expression levels of the 21 genes between paired samples. However, CD68 and RPLP0 in GSE73235, AURKA, BAG1, and TFRC in GSE76728, and MYLBL2 and ACTB in both datasets exhibited weak to moderate correlation (r < 0.5). There was a high correlation of the microarray-based RS between paired samples in GSE76728 (r = 0.91, 95% confidence interval [CI] 0.81-0.96) and GSE73235 (r = 0.82, 95% CI 0.71-0.89). There were no changes in RS category in GSE76728, whereas 82% of patients remained in the same RS category in GSE73235 (κ = 0.64).

CONCLUSIONS

Gene expression levels of the 21-gene RS showed a high correlation between paired specimens. Potential sampling and biological variability on a set of genes need to be considered to better estimate the RS from core needle biopsy.

Concordance of the molecular subtype classification between core needle biopsy and surgical specimen in primary breast cancer

BACKGROUND

Molecular profiling of breast cancer (BC) classifies several intrinsic subtypes based on different patterns of gene expression. Multigene assays estimate the risk of recurrence and help to select high-risk patients for adjuvant chemotherapy. However, these tests are associated with significant costs. Immunohistochemistry (IHC) offers a surrogate classification for molecular subtypes by determining estrogen (ER) and progesterone receptors (PR), human epidermal growth factor (Her2neu), as well as the proliferation marker Ki67. Core needle biopsy (CNB) is well established in BC diagnosis and allows a pre-operative assessment of biomarkers. The aim of this study was to analyze the concordance of these markers between CNB and surgical specimens to assess whether re-testing of the surgical specimen is mandatory.

MATERIALS AND METHODS

Within a 3-year period, patients with primary BC and paired samples of CNB and surgical specimens were analyzed retrospectively. Concordance rates of ER, PR, Her2neu, Ki67, and the surrogate classification for molecular subtypes were calculated using the Landis and Koch agreement grades.

RESULTS

Out of 2254 patients with primary breast cancer, 1307 paired specimens without pre-operative treatment were available for analysis Concordance rates for ER, PR, Her2neu, and Ki67 status showed substantial-to-almost perfect agreement grades (κ = 0.91, 0.75, 0.89, and 0.61, respectively). Though substantial concordance was also found for the subtype classification (κ = 0.70), the molecular subtype changed in 18.5% of patients based on the testing of the surgical specimen, mainly from luminal A-like to luminal B-like.

CONCLUSIONS

Though the concordance rates for single markers were convincing, a significant proportion of the molecular subtypes differed between CNB and the surgical specimen. Re-testing of PR and Ki67 is mandatory to ensure optimal treatment decisions. Further research is necessary to define safe, efficient, and cost-effective predictive models in adjuvant breast cancer therapy.

Breast cancer tumor heterogeneity has only little impact on the estimation of the Oncotype DX® recurrence score using Magee Equations and Magee Decision Algorithm™

Oncotype DX® assay is used to guide therapeutic decisions in early-stage invasive breast carcinoma but remains expensive. Magee Equations (MEs) and Magee Decision Algorithm (MDA) predict the Oncotype DX® recurrence score (RS) on the basis of histopathological parameters. The influence of intratumor heterogeneity on MEs and MDA remains uncertain. We compared Ki-67, estrogen and progesterone receptors, and human erb-b2 receptor tyrosine kinase 2 (HER2) status on tissue microarray cores with the corresponding findings on the whole slides to calculate MEs scores and to decide if Oncotype DX® testing was required as per MDA in two sets of 175 and 59 tumors, without and with Oncotype DX® results, respectively. Agreements in the interpretation of Ki-67, estrogen and progesterone receptors, and HER2 status were very good between limited areas and whole-slide analyses. This resulted also in very good agreements about the results of MEs and MDA. For 7 of 175 (4%) and 3 of 59 (5.1%) cases, MEs and MDA results in different tumor areas would have changed the indication to perform or not perform Oncotype DX® assays. Oncotype DX® RSs were significantly correlated with MEs and MDA results, but among cases initially predicted to have an RS ≤25 using MDA, 3 of 34 cases (8.8%) had in fact an RS >25. Tumor heterogeneity appears to have little impact on the estimation of the Oncotype DX® RS using MEs and MDA and would have permitted to avoid half of Oncotype DX® assays in our series. Caution is nevertheless required in discarding Oncotype DX® assay in cases with ME scores >18 associated with low mitotic activity.

Estrogen Receptor, Progesterone Receptor, and Human Epidermal Growth Factor Receptor-2 Testing in Breast Cancer: Assessing the Value of Repeated Centralized Testing in Excision Specimens

At some tertiary breast care centers, where many patients are referred from other institutions, it is routine to repeat testing for estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor-2 (HER2/neu) in excision specimens if these tests were performed on the preceding biopsy at the referring facility. The goal of this study is to assess the value of this practice. We documented results from ER, PR, and HER2 testing in 541 consecutive invasive breast cancers excised over a 2.5-year period and analyzed the subset (n=153) for which testing was performed on the excision specimen solely due to the fact that testing on the preceding biopsy was performed at an outside institution. The rates and directions of biopsy-to-excision change were as follows: ER [1.3% (2/153), 100% from (+) to (-)]; PR [4% (6/153), 83% from (+) to (-)]; HER2/neu assessed by immunohistochemistry [21% (29/137)]; HER2/neu assessed by fluorescence in situ hybridization [3.3% (2/61); 50% from amplified to nonamplified and 50% vice versa]. There were no ER(-) and PR(-) biopsy cases that became ER and/or PR(+) in the excision. By coordinate analysis for the hormone receptors [ie, ER and/or PR(+) being indicative of "hormone receptor" (HR) positivity], there were no cases that changed from HR(+) in the biopsy to HR(-) in the excision (or vice versa), which suggests that repeat testing for ER and PR in this setting is of limited value. In an analysis that incorporated both immunohistochemistry and in situ fluorescence hybridization results, there were 2 cases with a clinically significant biopsy-to-excision change in HER2/neu status in which that change was detected primarily because the excision was retested. These findings provide baseline data for formulating policies on whether repeat testing should routinely be performed in the described scenario.

Analysis of the molecular subtypes of preoperative core needle biopsy and surgical specimens in invasive breast cancer

Background

Accurate molecular classification of breast core needle biopsy (CNB) tissue is important for determining neoadjuvant systemic therapies for invasive breast cancer. The researchers aimed to evaluate the concordance rate (CR) of molecular subtypes between CNBs and surgical specimens.

Methods

This study was conducted with invasive breast cancer patients who underwent surgery after CNB at Seoul St. Mary’s Hospital between December 2014 and December 2017. Estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), and Ki67 were analyzed using immunohistochemistry. ER and PR were evaluated by Allred score (0–8). HER2 was graded from 0 to +3, and all 2+ cases were reflex tested with silver in situ hybridization. The labeling index of Ki67 was counted by either manual scoring or digital image analysis. Molecular subtypes were classified using the above surrogate markers.

Results

In total, 629 patients were evaluated. The CRs of ER, PR, HER2, and Ki67 were 96.5% (kappa, 0.883; p<.001), 93.0% (kappa, 0.824; p<.001), 99.7% (kappa, 0.988; p<.001), and 78.7% (kappa, 0.577; p<.001), respectively. Digital image analysis of Ki67 in CNB showed better concordance with Ki67 in surgical specimens (CR, 82.3%; kappa, 0.639 for digital image analysis vs. CR, 76.2%; kappa, 0.534 for manual counting). The CRs of luminal A, luminal B, HER2, and triple negative types were 89.0%, 70.0%, 82.9%, and 77.2%, respectively.

Conclusions

CNB was reasonably accurate for determining ER, PR, HER2, Ki67, and molecular subtypes. Using digital image analysis for Ki67 in CNB produced more accurate molecular classifications.