Breast Cancer Status Research Articles

Educational level and characteristics of invasive breast cancer: findings from a French prospective cohort.

Breast cancer (BC) characteristics are known to influence patients survival. Social differences have been reported by previous studies for those characteristics but questions persist because of inconsistent conclusions. We aimed to investigate the impact of education on BC stage, grade, and hormone receptor (HR) status, while adjusting for potential confounders including a broad set of health behaviors, anthropometric measures, and reproductive factors. In the French E3N cohort, 5236 women developed a primary invasive BC for which there was available information on stage, grade, and HR status. No multivariate analyses was performed for BC stage based on the lack of association in bivariate analyses. Odds ratios and confidence intervals were estimated using multinomial logistic regression models for BC grade or binomial logistic regression models for HR status of BC. Women with a lower education were diagnosed with higher grade BC compared to women with a higher education (1.32 [1.12; 1.57]). This association was slightly attenuated after adjustment for covariates independently and more strongly affected in the fully adjusted model (1.20 [0.99; 1.45]). A significant association was observed between lower education and HR- status of BC (1.20 [1.02; 1.42]) attenuated after adjustment for age at first childbirth (1.12 [0.95; 1.33]). In this cohort, education was associated with BC grade and HR status but not stage at diagnosis. The link between education and BC grade was not entirely explained by the different adjustments. A specific mechanism could be at play and deserves further investigations.

Significance of Estrogen/Progesterone Receptor Expression in Metaplastic Breast Carcinoma.

Metaplastic breast carcinoma (MBC) is a rare subgroup of breast neoplasms associated with adverse outcomes because of its aggressive nature. Typically, MBCs show triple-negative hormone receptor (HR) status. Determining the HR status of breast cancer is an integral part because it is an important prognostic factor and helps in the treatment course of the disease. This study aimed to determine the HR status of MBC, its significance, and its association with various clinicopathological parameters. This was a retrospective study conducted at the Department of Histopathology, Liaquat National Hospital. A total of 140 biopsy-proven cases of MBC were enrolled in the study. Clinical and pathological data were retrieved from the institutes' archives. Immunohistochemical studies were conducted to determine the estrogen receptor (ER) and progesterone receptor (PR) status. The mean age of MBC in our population was found to be 52.18 ± 12.19 years. The HR positivity rate in our population was found to be 32.9%. A significant association was found between HR status and tumor laterality, tumor size, tumor grade, tumor stage, and recurrence. ER/PR-negative MBCs were most probably associated with higher grade and higher tumor stage and were larger in size (6.62 ± 3.43 cm) than ER/PR-positive MBCs (4.20 ± 1.88 cm). Moreover, ER/PR-positive MBCs showed a higher recurrence rate than ER/PR-negative MBCs (43.5% vs. 25.5%, respectively). No statistically significant relationship was found between HR status and patient age, histological subtype, or survival rate. MBC is a rare breast neoplasm. MBC was found to be triple negative in most cases, but a significant percentage were HR (ER/PR) positive. Moreover, we found an association between HR status and various clinicopathological features, indicating that HR status is a significant predictor of MBC prognosis.

Deciphering the spatial landscape and plasticity of immunosuppressive fibroblasts in breast cancer

Although heterogeneity of FAP+ Cancer-Associated Fibroblasts (CAF) has been described in breast cancer, their plasticity and spatial distribution remain poorly understood. Here, we analyze trajectory inference, deconvolute spatial transcriptomics at single-cell level and perform functional assays to generate a high-resolution integrated map of breast cancer (BC), with a focus on inflammatory and myofibroblastic (iCAF/myCAF) FAP+ CAF clusters. We identify 10 spatially-organized FAP+ CAF-related cellular niches, called EcoCellTypes, which are differentially localized within tumors. Consistent with their spatial organization, cancer cells drive the transition of detoxification-associated iCAF (Detox-iCAF) towards immunosuppressive extracellular matrix (ECM)-producing myCAF (ECM-myCAF) via a DPP4- and YAP-dependent mechanism. In turn, ECM-myCAF polarize TREM2+ macrophages, regulatory NK and T cells to induce immunosuppressive EcoCellTypes, while Detox-iCAF are associated with FOLR2+ macrophages in an immuno-protective EcoCellType. FAP+ CAF subpopulations accumulate differently according to the invasive BC status and predict invasive recurrence of ductal carcinoma in situ (DCIS), which could help in identifying low-risk DCIS patients eligible for therapeutic de-escalation.

O152: Prognostic value of receptor positivity loss following neoadjuvant chemotherapy in breast cancer patients: a systematic review and meta-analysis

Abstract Introduction Receptor status of breast cancer (BC) has been reported to change after neoadjuvant treatment leading to discordance between diagnostic biopsy and surgical specimens. This meta-analysis aims to investigate the prognostic significance of a loss of human epidermal growth factor receptor 2 (HER2) and hormone receptor positivity in residual disease after neoadjuvant therapy. Methods A systematic search of electronic databases was conducted to identify studies that investigated the impact loss of receptor status had on prognosis. Studies reporting disease free survival (DFS) and/or overall survival (OS) hazard ratios (HRs) for loss of HER2 or hormone receptor positivity following neoadjuvant treatment for BC were included. A meta-analysis was undertaken for relevant studies. Pooled hazard ratios were calculated using 95% confidence intervals (CIs). Results Eight studies with a total of 2673 patients were included in the meta-analysis. Patients who lost HER2 positivity had significantly worse OS (pooled HR 1.80, 95% CI 1.01-3.22, p=0.05) and DFS (pooled HR 2.03, 95% CI 1.47-2.82, p<0.001), compared to those who maintained positivity. Similarly, patients with a loss of hormone receptor positivity had significantly worse OS (pooled HR 2.49, 95% CI 1.65-3.77, p<0.001) and DFS (pooled HR 3.71, 95% CI 1.79-7.69, p<0.001) than those who remained hormone receptor positive after neoadjuvant therapy. Conclusion This meta-analysis found that a loss of either HER2 or hormone receptor positivity following neoadjuvant therapy negatively impacted survival outcomes. These findings suggest that retesting of receptor status may offer prognostic information, in addition to potentially altering adjuvant treatment regimens.

False negative breast cancers on imaging and associated risk factors: a single institution six-year analysis.

Mitigating false negative imaging studies remains an important issue given its association with worse morbidity and mortality in patients with breast cancer. We aimed to identify risk factors that predispose to false negative breast imaging exams. In an IRB-approved, HIPAA compliant retrospective study, we identified all patients who were diagnosed with breast cancer within 365days of a negative imaging study assessed as BI-RADS 1-3 between January 1, 2014 and January 31, 2020. A matched cohort based on mammographic breast density was created from randomly selected studies with BI-RADS 4-5 designation that yielded breast cancer at pathology within the same time frame. Patient and cancer characteristics, prior personal history of breast cancer and gene mutation status were collected from patient charts. Pearson chi-squared and Student's t-test on two independent groups with significance at < 0.05 was used for statistical analysis. We identified 155 false negative studies of 129 missed cancers and 128 breast density matched true positive cancers. False negative studies were screening mammograms in 57.42% (89/155), diagnostic mammograms in 29.68% (46/155), ultrasounds in 6.45% (10/155) and MRIs in 6.45% (10/155). Rates of personal (41.09% vs. 18.75%, p < 0.001) and family history of breast cancer (68.22% vs. 49.21%, p = 0.002) were higher in the false negative cohort and remained significant when asymptomatic MRI-detected cancers were removed. Our findings suggest that supplemental screening may be useful in breast cancer survivors.

Prediction of Lymphovascular invasion status in breast cancer based on magnetic resonance imaging radiomics features

ObjectiveThis study intended to investigate the feasibility and effectiveness of using clinical magnetic resonance imaging (MRI) radiomics features to predict lymphovascular invasion (LVI) status in breast cancer (BC) patients. MethodsA total of 182 BC patients were retrospectively collected and randomly divided into a training set (n = 127) and a validation set (n = 55) in a 7:3 ratio. Based on pathological examination results, the training set was further divided into LVI group (n = 60) and non-LVI group (n = 67), and the validation set was divided into LVI group (n = 24) and non-LVI group (n = 31). General data and MRI examination indicators were compared. Multivariate logistic regression was utilized to analyze MRI radiomics features and clinically relevant indicators that were significant in the baseline information of patients in training set, independent risk factors were identified, and a logistic regression model was built. The accuracy of logistic model was validated using ROC curves in training and validation sets. ResultsAge, pathohistological classification, tumor length, tumor width, presence or absence of Magnetic Resonance Spectroscopy (MRS) cho peak, presence or absence of spicule sign, peritumoral enhancement, and peritumoral edema were statistically significant (P < 0.05) between the two groups. Multivariate logistic regression analysis presented that spicule and peritumoral edema were independent risk factors for LVI in BC patients (P < 0.05). The ROC curve illustrated that AUC of the logistic regression model in the training set was 0.807 (95%CI: 0.730–0.885) and that in the validation set was 0.837 (95%CI: 0.731–0.944). ConclusionRadiomics features of spicule sign and peritumoral edema were independent risk factors for LVI in BC patients. A logistic regression model based on these factors, along with age, could accurately predict LVI occurrence in BC patients, providing data support for diagnosis and modeling of LVI in BC patients.

Exploration of the relationship between tumor-infiltrating lymphocyte score and histological grade in breast cancer

BackgroundThe histological grade is an important factor in the prognosis of invasive breast cancer and is vital to accurately identify the histological grade and reclassify of Grade2 status in breast cancer patients.MethodsIn this study, data were collected from 556 invasive breast cancer patients, and then randomly divided into training cohort (n = 335) and validation cohort (n = 221). All patients were divided into actual low risk group (Grade1) and high risk group (Grade2/3) based on traditional histological grade, and tumor-infiltrating lymphocyte score (TILs-score) obtained from multiphoton images, and the TILs assessment method proposed by International Immuno-Oncology Biomarker Working Group (TILs-WG) were also used to differentiate between high risk group and low risk group of histological grade in patients with invasive breast cancer. Furthermore, TILs-score was used to reclassify Grade2 (G2) into G2 /Low risk and G2/High risk. The coefficients for each TILs in the training cohort were retrieved using ridge regression and TILs-score was created based on the coefficients of the three kinds of TILs.ResultsStatistical analysis shows that TILs-score is significantly correlated with histological grade, and is an independent predictor of histological grade (odds ratio [OR], 2.548; 95%CI, 1.648–3.941; P < 0.0001), but TILs-WG is not an independent predictive factor for grade (P > 0.05 in the univariate analysis). Moreover, the risk of G2/High risk group is higher than that of G2/Low risk group, and the survival rate of patients with G2/Low risk is similar to that of Grade1, while the survival rate of patients with G2/High risk is even worse than that of patients with G3.ConclusionOur results suggest that TILs-score can be used to predict the histological grade of breast cancer and potentially to guide the therapeutic management of breast cancer patients.

ZNF217 Gene Copy Number as a Marker of Response to Standard Therapy Drugs According to ERα Status in Breast Cancer.

The therapeutic decision for the management of breast cancer (BC) patients is based on the evaluation of prognostic factors alongside clinical and pathological parameters. Despite the use of standard biomarkers, response and resistance to therapy represent a challenge for clinicians. Among the new potential biomarkers for BC the ZNF217 gene have gained importance in recent years. However, while associations between ZNF217 gene copy number and clinicopathological characteristics have been established, its correlation with treatment response remains unclear. This study aimed to evaluate the ZNF217 gene copy number and establish its associations with treatment response in estrogen receptor positive (ERα+) and ERα negative (ERα-) BC cell lines. In addition, a validation of the relationship between ZNF217 gene copy number and its prognostic value was performed using datasets of BC patients retrieved from the cBioPortal public database. Our data show that in ERα+ cells, ZNF217 gene copy number increase (amplification), while cell proliferation decreases in response to standard drug treatments. In contrast, both ZNF217 gene copy number (gain) and cell proliferation increases in response to standard drug treatments in ERα- cells. The results obtained align with findings from the cBioPortal database analysis, demonstrating that ERα+/HER2- low proliferation patients, exhibiting ZNF217 gene amplification or gain, have a significantly higher survival probability after treatment, compared to ERα-/HER2- and HER2+ patients. Our results suggest that in ERα+ BC cells, ZNF217 gene amplification could be indicative of a favorable response, while in ERα- BC cells, ZNF217 gene gain could be postulated as a potential predictor of treatment resistance. A broader understanding of the role of ZNF217 gene in treatment response, together with prospective studies in BC patients, could contribute to confirming our data, as well as optimizing existing treatments and exploring novel approaches to improve overall cancer treatment outcomes.

272 (PB-088) Poster - Association between prognosis, carboplatin treatment response and homologous recombination deficiency status in early triple-negative breast cancer

272 (PB-088) Poster - Association between prognosis, carboplatin treatment response and homologous recombination deficiency status in early triple-negative breast cancer

Intra- and peritumoral radiomics features based on multicenter automatic breast volume scanner for noninvasive and preoperative prediction of HER2 status in breast cancer: a model ensemble research

The aim to investigate the predictive efficacy of automatic breast volume scanner (ABVS), clinical and serological features alone or in combination at model level for predicting HER2 status. The model weighted combination method was developed to identify HER2 status compared with single data source model method and feature combination method. 271 patients with invasive breast cancer were included in the retrospective study, of which 174 patients in our center were randomized into the training and validation sets, and 97 patients in the external center were as the test set. Radiomics features extracted from the ABVS-based tumor, peritumoral 3 mm region, and peritumoral 5 mm region and clinical features were used to construct the four types of the optimal single data source models, Tumor, R3mm, R5mm, and Clinical model, respectively. Then, the model weighted combination and feature combination methods were performed to optimize the combination models. The proposed weighted combination models in predicting HER2 status achieved better performance both in validation set and test set. For the validation set, the single data source model, the feature combination model, and the weighted combination model achieved the highest area under the curve (AUC) of 0.803 (95% confidence interval [CI] 0.660–947), 0.739 (CI 0.556,0.921), and 0.826 (95% CI 0.689,0.962), respectively; with the sensitivity and specificity were 100%, 62.5%; 81.8%, 66.7%; 90.9%,75.0%; respectively. For the test set, the single data source model, the feature combination model, and the weighted combination model attained the best AUC of 0.695 (95% CI 0.583, 0.807), 0.668 (95% CI 0.555,0.782), and 0.700 (95% CI 0.590,0.811), respectively; with the sensitivity and specificity were 86.1%, 41.9%; 61.1%, 71.0%; 86.1%, 41.9%; respectively. The model weighted combination was a better method to construct a combination model. The optimized weighted combination models composed of ABVS-based intratumoral and peritumoral radiomics features and clinical features may be potential biomarkers for the noninvasive and preoperative prediction of HER2 status in breast cancer.

Joint global and local interpretation method for CIN status classification in breast cancer

Breast cancer is among the cancer types with the highest numbers of new cases. The study of this disease from a microscopic perspective has been a prominent research topic. Previous studies have shown that microRNAs (miRNAs) are closely linked to chromosomal instability (CIN). Correctly predicting CIN status from miRNAs can help to improve the survival of breast cancer patients. In this study, a joint global and local interpretation method called GL_XGBoost is proposed for predicting CIN status in breast cancer. GL_XGBoost integrates the eXtreme Gradient Boosting (XGBoost) and SHapley Additive exPlanation (SHAP) methods. XGBoost is used to predict CIN status from miRNA data, whereas SHAP is used to select miRNA features that have strong relationships with CIN. Furthermore, SHAP's rich visualization strategies enhance the interpretability of the entire model at the global and local levels. The performance of GL_XGBoost is validated on the TCGA-BRCA dataset, and it is shown to have an accuracy of 78.57% and an area under the curve value of 0.87. Rich visual analysis is used to explain the relationships between miRNAs and CIN status from different perspectives. Our study demonstrates an intuitive way of exploring the relationship between CIN and cancer from a microscopic perspective.

Discrimination between HER2-overexpressing, -low-expressing, and -zero-expressing statuses in breast cancer using multiparametric MRI-based radiomics.

To explore the performance of multiparametric MRI-based radiomics in discriminating different human epidermal growth factor receptor 2 (HER2) expressing statuses (i.e., HER2-overexpressing, HER2-low-expressing, and HER2-zero-expressing) in breast cancer. A total of 771 breast cancer patients from two institutions were retrospectively studied. Five-hundred-eighty-one patients from Institution I were divided into a training dataset (n1 = 407) and an independent validation dataset (n1 = 174); 190 patients from Institution II formed the external validation dataset. All patients were categorized into HER2-overexpressing, HER2-low-expressing, and HER2-zero-expressing groups based on pathologic examination. Multiparametric (including T2-weighted imaging with fat suppression [T2WI-FS], diffusion-weighted imaging [DWI], apparent diffusion coefficient [ADC], and dynamic contrast-enhanced [DCE]) MRI-based radiomics features were extracted and then selected from the training dataset using the least absolute shrinkage and selection operator (LASSO) regression. Three predictive models to discriminate HER2-overexpressing vs. others, HER2-low expressing vs. others, and HER2-zero-expressing vs. others were developed based on the selected features. The model performance was evaluated using the area under the receiver operating characteristic curve (AUC). Eleven radiomics features from DWI, ADC, and DCE; one radiomics feature from DWI; and 17 radiomics features from DWI, ADC, and DCE were selected to build three predictive models, respectively. In training, independent validation, and external validation datasets, radiomics models achieved AUCs of 0.809, 0.737, and 0.725 in differentiating HER2-overexpressing from others; 0.779, 0.778, and 0.782 in differentiating HER2-low-expressing from others; and 0.889, 0.867, and 0.813 in differentiating HER2-zero-expressing from others, respectively. Multiparametric MRI-based radiomics model may preoperatively predict HER2 statuses in breast cancer patients. The MRI-based radiomics models could be used to noninvasively identify the new three-classification of HER2 expressing status in breast cancer, which is helpful to the decision-making for HER2-target therapies. • Detecting HER2-overexpressing, HER2-low-expressing, and HER2-zero-expressing status in breast cancer patients is crucial for determining candidates for anti-HER2 therapy. • Radiomics features from multiparametric MRI significantly differed among HER2-overexpressing, HER2-low expressing, and HER2-zero-expressing breast cancers. • Multiparametric MRI-based radiomics could preoperatively evaluate three different HER2-expressing statuses and help to determine potential candidates for anti-HER2 therapy in breast cancer patients.

Association between Homologous Recombination Repair Defect Status and Long-Term Prognosis of Early HER2-Low Breast Cancer: A Retrospective Cohort Study.

As a newly identified subtype of HER2-negative tumors associated with a less favorable prognosis, it remains crucial to evaluate potential prognostic and predictive factors, particularly non-invasive biomarkers, for individuals with human epidermal growth factor 2 (HER2) low early-stage breast cancer (EBC). Multiple investigations have highlighted that HER2-negative patients with EBC exhibiting high homologous recombination deficiency (HRD) scores display lower rates of pathological complete response (PCR) to neoadjuvant chemotherapy (NAC). Nevertheless, no study to date has explored the correlation between HRD and the long-term prognosis in HER2-low patients with EBC. This retrospective observational study focuses on primary EBC sourced from The Cancer Genome Atlas dataset (TCGA). It reveals the gene mutation landscape in EBC with low HER2 expression and elucidates the tumor immune landscape across different HRD states. Utilizing bioinformatics analysis and Cox proportional models, along with the Kaplan-Meier method, the study assesses the correlation between HRD status and disease-specific survival (DSS), disease-free interval (DFI), and progression-free interval (PFI). Subgroup analyses were conducted to identify potential variations in the association between HRD and prognosis. In the patients with HER2-low breast cancer, patients with homologous recombination related genes (HRRGs) defects had an HRD score about twice that of those without related genes mutations, and were at higher risk of acquiring ARID1A, ATM, and BRCA2 mutations. We also found that most immune cell abundances were significantly higher in EBC tumors with high HRD than in EBC tumors with low HRD or HRD-medium, particularly plasma B-cell abundance, CD8 T-cell abundance, and M1 macrophages. In addition, these tumors with HRD-high also appear to have significantly higher tumor immune scores and lower interstitial scores. Then, we analyzed the relationship between different HRD status and prognosis. There was statistical significance (P = .036 and P = .046, respectively) in DSS and PFI between the HRD-low and HRD-high groups, and patients with HRD-high EBC showed relatively poor survival outcomes. A medium HRD score (hazard ratio, HR = 2.15, 95% CI: 1.04-4.41, P = .038) was a significant risk factor for PFI. Hormone receptor positivity is an important factor in obtaining medium-high HRD score and poor prognosis. Higher HRD scores were associated with poorer PFI outcomes, particularly in people with HR+/HER2-low. Varied HRD states exhibited distinctions in HRRGs and the tumor immune landscape. These insights have the potential to assist clinicians in promptly identifying high-risk groups and tailoring personalized treatments for patients with HER2-low EBC, aiming to enhance long-term outcomes.

Circulating Leukocyte Subsets Before and After a Breast Cancer Diagnosis and Therapy

Changes in leukocyte composition often precede chronic disease onset. Patients with a history of breast cancer (hereinafter referred to as breast cancer survivors) are at increased risk for subsequent chronic diseases, but the long-term changes in peripheral leukocyte composition following a breast cancer diagnosis and treatment remain unknown. To examine longitudinal changes in peripheral leukocyte composition in women who did and did not develop breast cancer and identify whether differences in breast cancer survivors were associated with specific treatments. In this prospective cohort study, paired blood samples were collected from 2315 women enrolled in The Sister Study, a US-nationwide prospective cohort study of 50 884 women, at baseline (July 2003 to March 2009) and follow-up (October 2013 to March 2015) home visits, with a mean (SD) follow-up interval of 7.6 (1.4) years. By design, approximately half of the included women had been diagnosed and treated for breast cancer after enrollment and before the second blood draw. A total of 410 women were included in the present study, including 185 breast cancer survivors and 225 who remained free of breast cancer over a comparable follow-up period. Data were analyzed from April 21 to September 9, 2022. Breast cancer status and, among breast cancer survivors, cancer treatment type (chemotherapy, radiotherapy, endocrine therapy, or surgery). Blood DNA methylation data were generated in 2019 using a genome-wide methylation screening tool and deconvolved to estimate percentages of 12 circulating leukocyte subsets. Of the 410 women included in the analysis, the mean (SD) age at enrollment was 56 (9) years. Compared with breast cancer-free women, breast cancer survivors had decreased percentages of circulating eosinophils (-0.45% [95% CI, -0.87% to -0.03%]; P = .03), total CD4+ helper T cells (-1.50% [95% CI, -2.56% to -0.44%]; P = .01), and memory B cells (-0.22% [95% CI, -0.34% to -0.09%]; P = .001) and increased percentages of circulating naive B cells (0.46% [95% CI, 0.17%-0.75%]; P = .002). In breast cancer survivor-only analyses, radiotherapy was associated with decreases in total CD4+ T cell levels, whereas chemotherapy was associated with increases in naive B cell levels. Surgery and endocrine therapy were not meaningfully associated with leukocyte changes. In this cohort study of 410 women, breast cancer survivors experienced lasting changes in peripheral leukocyte composition compared with women who remained free of breast cancer. These changes may be related to treatment with chemotherapy or radiotherapy and could influence future chronic disease risk.

Investigating the role of tumour-to-skin proximity in predicting nodal metastasis in breast cancer.

Understanding the factors influencing nodal status in breast cancer is vital for axillary staging, therapy, and patient survival. The nodal stage remains a crucial factor in prognostication indices. This study investigates the relationship between tumour-to-skin distance (in T1-T3 tumours where the skin is not clinically involved) and the risk of nodal metastasis. We retrospectively reviewed data from 100 patients who underwent neoadjuvant chemotherapy (NACT). Besides patient demographics and tumour variables, a radiologist retrospectively reviewed pre-operative MRI to measure tumour-to-skin distance. R core packages were used for univariate (χ2 and T-Wilcoxon tests) and bivariate logistic regression statistical analysis. Of 95 analysable datasets, patients' median age was 51years (IQR: 42-61), 97% were symptomatic (rest screen detected), and the median tumour size was 43mm (IQR, 26-52). On multivariate analysis, increasing invasive tumour size (p = 0.02), ER positivity (p = 0.007)and shorter tumour-to-skin distance (p = 0.05) correlated with nodal metastasis.HER2was not included in multivariate analysis as there wasno association with nodal status on univariate analysis. In node-positive tumours, as tumour size increased, the tumour-to-skin distance decreased (r = -0.34, p = 0.026). In node-negative tumours, there was no correlation (r = + 0.18, p = 0.23). This study shows that non-locally advanced cancers closer to the skin (and consequent proximity to subdermal lymphatics) are associated with a greater risk of nodal metastasis. Pre-operative identification of those more likely to be node positive may suggest the need for a second-look USS since a higher nodal stage may lead to a change in therapeutic strategies, such as upfront systemic therapy, node marking, and axillary clearance without the need to return to theatre following sentinel node biopsy.

Identification of ipsilateral supraclavicular lymph node metastasis in breast cancer based on LASSO regression with a high penalty factor.

Aiming at the problems of small sample size and large feature dimension in the identification of ipsilateral supraclavicular lymph node metastasis status in breast cancer using ultrasound radiomics, an optimized feature combination search algorithm is proposed to construct linear classification models with high interpretability. The genetic algorithm (GA) is used to search for feature combinations within the feature subspace using least absolute shrinkage and selection operator (LASSO) regression. The search is optimized by applying a high penalty to the L1 norm of LASSO to retain excellent features in the crossover operation of the GA. The experimental results show that the linear model constructed using this method outperforms those using the conventional LASSO regression and standard GA. Therefore, this method can be used to build linear models with higher classification performance and more robustness.

Prediction of human epidermal growth factor receptor 2 (HER2) status in breast cancer by mammographic radiomics features and clinical characteristics: a multicenter study.

To preoperatively evaluate the human epidermal growth factor 2 (HER2) status in breast cancer using mammographic radiomics features and clinical characteristics on a multi-vendor and multi-center basis. This multi-center study included a cohort of 1512 Chinese female with invasive ductal carcinoma of no special type (IDC-NST) from two different hospitals and five devices (1332 from Institution A, used for training and testing the models, and 180 women from Institution B, as the external validation cohort). The Gradient Boosting Machine (GBM) was employed to establish radiomics and multiomics models. Model efficacy was evaluated by the area under the curve (AUC). The number of HER2-positive patients in the training, testing, and external validation cohort were 245(26.3%), 105 (26.3.8%), and 51(28.3%), respectively, with no statistical differences among the three cohorts (p = 0.842, chi-square test). The radiomics model, based solely on the radiomics features, achieved an AUC of 0.814 (95% CI, 0.784-0.844) in the training cohort, 0.776 (95% CI, 0.727-0.825) in the testing cohort, and 0.702 (95% CI, 0.614-0.790) in the external validation cohort. The multiomics model, incorporated radiomics features with clinical characteristics, consistently outperformed the radiomics model with AUC values of 0.838 (95% CI, 0.810-0.866) in the training cohort, 0.788 (95% CI, 0.741-0.835) in the testing cohort, and 0.722 (95% CI, 0.637-0.811) in the external validation cohort. Our study demonstrates that a model based on radiomics features and clinical characteristics has the potential to accurately predict HER2 status of breast cancer patients across multiple devices and centers. By predicting the HER2 status of breast cancer reliably, the presented model built upon radiomics features and clinical characteristics on a multi-vendor and multi-center basis can help in bolstering the model's applicability and generalizability in real-world clinical scenarios. • The mammographic presentation of breast cancer is closely associated with the status of human epidermal growth factor receptor 2 (HER2). • The radiomics model, based solely on radiomics features, exhibits sub-optimal performance in the external validation cohort. • By combining radiomics features and clinical characteristics, the multiomics model can improve the prediction ability in external data.

Whole-tumor histogram models based on quantitative maps from synthetic MRI for predicting axillary lymph node status in invasive ductal breast cancer

PurposeTo investigate the potential of using histogram analysis of synthetic MRI (SyMRI) images before and after contrast enhancement to predict axillary lymph node (ALN) status in patients with invasive ductal carcinoma (IDC). MethodsFrom January 2022 to October 2022, a total of 212 patients with IDC underwent breast MRI examination including SyMRI. Standard T2 weight images, DCE-MRI and quantitative maps of SyMRI were obtained. 13 features of the entire tumor were extracted from these quantitative maps, standard T2 weight images and DCE-MRI. Statistical analyses, including Student’s t-test, Mann-Whiney U test, logistic regression, and receiver operating characteristic (ROC) curves, were used to evaluate the data. The mean values of SyMRI quantitative parameters derived from the conventional 2D region of interest (ROI) were also evaluated. ResultsThe combined model based on T1-Gd quantitative map (energy, minimum, and variance) and clinical features (age and multifocality) achieved the best diagnostic performance in the prediction of ALN between N0 (with non-metastatic ALN) and N+ group (metastatic ALN ≥ 1) with the AUC of 0.879. Among individual quantitative maps and standard sequence-derived models, the synthetic T1-Gd model showed the best performance for the prediction of ALN between N0 and N+ groups (AUC = 0.823). Synthetic T2_entropy and PD-Gd_energy were useful for distinguishing N1 group (metastatic ALN ≥ 1 and ≤ 3) from the N2-3 group (metastatic ALN > 3) with an AUC of 0.722. ConclusionsWhole-tumor histogram features derived from quantitative parameters of SyMRI can serve as a complementary noninvasive method for preoperatively predicting ALN metastases.

MRI-Based Kinetic Heterogeneity Evaluation in the Accurate Access of Axillary Lymph Node Status in Breast Cancer Using a Hybrid CNN-RNN Model.

Accurate evaluation of the axillary lymph node (ALN) status is needed for determining the treatment protocol for breast cancer (BC). The value of magnetic resonance imaging (MRI)-based tumor heterogeneity in assessing ALN metastasis in BC is unclear. To assess the value of deep learning (DL)-derived kinetic heterogeneity parameters based on BC dynamic contrast-enhanced (DCE)-MRI to infer the ALN status. Retrospective. 1256/539/153/115 patients in the training cohort, internal validation cohort, and external validation cohorts I and II, respectively. 1.5 T/3.0 T, non-contrast T1-weighted spin-echo sequence imaging (T1WI), DCE-T1WI, and diffusion-weighted imaging. Clinical pathological and MRI semantic features were obtained by reviewing histopathology and MRI reports. The segmentation of the tumor lesion on the first phase of T1WI DCE-MRI images was applied to other phases after registration. A DL architecture termed convolutional recurrent neural network (ConvRNN) was developed to generate the KHimage (kinetic heterogeneity of DCE-MRI image) score that indicated the ALN status in patients with BC. The model was trained and optimized on training and internal validation cohorts, tested on two external validation cohorts. We compared ConvRNN model with other 10 models and the subgroup analyses of tumor size, magnetic field strength, and molecular subtype were also evaluated. Chi-squared, Fisher's exact, Student's t, Mann-Whitney U tests, and receiver operating characteristics (ROC) analysis were performed. P < 0.05 was considered significant. The ConvRNN model achieved area under the curve (AUC) of 0.802 in the internal validation cohort and 0.785-0.806 in the external validation cohorts. The ConvRNN model could well evaluate the ALN status of the four molecular subtypes (AUC = 0.685-0.868). The patients with larger tumor sizes (>5 cm) were more susceptible to ALN metastasis with KHimage scores of 0.527-0.827. A ConvRNN model outperformed traditional models for determining the ALN status in patients with BC. 3 TECHNICAL EFFICACY: Stage 2.

Radiomics signatures for predicting the Ki-67 level and HER-2 status based on bone metastasis from primary breast cancer.

This study explores the potential of radiomics to predict the proliferation marker protein Ki-67 levels and human epidermal growth factor receptor 2 (HER-2) status based on MRI images of patients with spinal metastasis from primary breast cancer. A total of 110 patients with pathologically confirmed spinal metastases from primary breast cancer were enrolled between Dec. 2017 and Dec. 2021. All patients underwent T1-weighted contrast-enhanced MRI scans. The PyRadiomics package was used to extract features from the MRI images based on the intraclass correlation coefficient and least absolute shrinkage and selection operator. The most predictive features were used to develop the radiomics signature. The Chi-Square test, Fisher's exact test, Student's t-test, and Mann-Whitney U test were used to evaluate the clinical and pathological characteristics between the high- and low-level Ki-67 groups and the HER-2 positive/negative groups. The radiomics models were compared using receiver operating characteristic curve analysis. The area under the receiver operating characteristic curve (AUC), sensitivity (SEN), and specificity (SPE) were generated as comparison metrics. From the spinal MRI scans, five and two features were identified as the most predictive for the Ki-67 level and HER-2 status, respectively. The developed radiomics signatures generated good prediction performance for the Ki-67 level in the training (AUC = 0.812, 95% CI: 0.710-0.914, SEN = 0.667, SPE = 0.846) and validation (AUC = 0.799, 95% CI: 0.652-0.947, SEN = 0.722, SPE = 0.833) cohorts. Good prediction performance for the HER-2 status was also achieved in the training (AUC = 0.796, 95% CI: 0.686-0.906, SEN = 0.720, SPE = 0.776) and validation (AUC = 0.705, 95% CI: 0.506-0.904, SEN = 0.733, SPE = 0.762) cohorts. The results of this study provide a better understanding of the potential clinical implications of spinal MRI-based radiomics on the prediction of Ki-67 levels and HER-2 status in breast cancer.