Status Of Breast Cancer Patients Research Articles

Immune marker expression and prognosis of early breast cancer expressing HER3

IntroductionThere is a strong rationale for targeting HER3, as HER3 contributes to tumorigenesis and treatment resistance. However, the prognostic role of HER3 and their association with immunoregulatory protein expression has not been established. MethodsThe main objective of this study was to investigate the prognostic role of HER3 expression and identify immunoregulatory marker expression according to HER3 status. HER3 expression and 10 immunoregulatory protein (PD-1/PD-L1/PD-L2/IDO/TIM-3/OX40/OX40L/B7-H2/B7-H3/B7-H4) expression was identified in 320 stage I-III breast cancer patients who received curative surgery at Seoul National University Hospital in 2008. The median follow-up duration was 88.8 months. Criteria for HER3 IHC was adopted from HER2 IHC score and only those with 3 + was considered positive. ResultsAmong 320 patients, 213 (67.2 %) had luminal A disease, 30 (9.5 %) had luminal B disease, 28 (8.8 %) had HER2-positive disease, and 46 (14.5 %) had triple negative disease. HER3 expression was shown in 153 patients (47.8 %). Tumors with HER3-expression had more immunogenic tumor microenvironment compared to HER3-negative tumor. In addition, patients with HER3 expression had favorable 5-year relapse free survival compared to HER3-negative patients (5-year RFS 92.5 % vs. 85.2 %, p = 0.038). However, in the multivariate analysis, HER3 expression was not a prognostic factor, but expression of immunoregulatory protein was a prognostic factor. ConclusionsThis study identified immunoregulatory protein expression according to HER3 status in breast cancer patients. As tumor with HER3 expression have more immunogenic microenvironment, investigating combination treatment of HER3 targeting agent and immunotherapy in HER3 expressing breast cancer may be promising.

Characterizing Sentinel Lymph Node Status in Breast Cancer Patients Using a Deep-Learning Model Compared With Radiologists' Analysis of Grayscale Ultrasound and Lymphosonography.

The objective of the study was to use a deep learning model to differentiate between benign and malignant sentinel lymph nodes (SLNs) in patients with breast cancer compared to radiologists' assessments.Seventy-nine women with breast cancer were enrolled and underwent lymphosonography and contrast-enhanced ultrasound (CEUS) examination after subcutaneous injection of ultrasound contrast agent around their tumor to identify SLNs. Google AutoML was used to develop image classification model. Grayscale and CEUS images acquired during the ultrasound examination were uploaded with a data distribution of 80% for training/20% for testing. The performance metric used was area under precision/recall curve (AuPRC). In addition, 3 radiologists assessed SLNs as normal or abnormal based on a clinical established classification. Two-hundred seventeen SLNs were divided in 2 for model development; model 1 included all SLNs and model 2 had an equal number of benign and malignant SLNs. Validation results model 1 AuPRC 0.84 (grayscale)/0.91 (CEUS) and model 2 AuPRC 0.91 (grayscale)/0.87 (CEUS). The comparison between artificial intelligence (AI) and readers' showed statistical significant differences between all models and ultrasound modes; model 1 grayscale AI versus readers, P = 0.047, and model 1 CEUS AI versus readers, P < 0.001. Model 2 r grayscale AI versus readers, P = 0.032, and model 2 CEUS AI versus readers, P = 0.041.The interreader agreement overall result showed κ values of 0.20 for grayscale and 0.17 for CEUS.In conclusion, AutoML showed improved diagnostic performance in balance volume datasets. Radiologist performance was not influenced by the dataset's distribution.

Can negative axillary ultrasound reliably predict pathologically negative axillary lymph node status in breast cancer patients with cT ≤3 cm, cN0, and HER2-positive?-a retrospective, single-institution study.

Breast cancer (BC) is the leading cancer in women globally, with human epidermal growth factor receptor 2 (HER2)-positive subtype accounting for 15-20% of cases and exhibiting aggressive behavior. The standard of care for operable BC has evolved to include neoadjuvant systemic therapy, which can guide treatment decisions and improve outcomes, particularly in HER2+ BC. This study aims to investigate whether axillary ultrasound has a good negative predictive value (NPV) for early HER2 BC patients and to identify clinicopathological factors that can impact the axillary lymph node metastasis. This retrospective, single-center study evaluated the medical records of 135 patients with HER2+ BC, cT ≤3 cm, and clinically negative axillary lymph nodes from 2018 to 2020. The study aimed to determine the NPV of axillary ultrasound for pathologically negative axillary lymph node status and to identify factors associated with axillary lymph node metastasis. The NPV of axillary ultrasound was 78.5%, increasing to 89.6% and 93.3% when considering 0-1 and 0-2 metastatic lymph nodes, respectively. Lymphovascular invasion (LVI) was significantly associated with axillary lymph node metastasis, with a 2.2-fold increased risk. Axillary ultrasound shows good predictive value for axillary lymph node negativity in HER2+ BC patients with small tumors. However, the presence of LVI increases the risk of metastasis, suggesting a need for neoadjuvant chemotherapy. These findings contribute to personalized treatment strategies for early HER2+ BC, emphasizing the role of axillary ultrasound in clinical decision-making.

Impact of HER2-targeted PET/CT imaging in patients with breast cancer and therapeutic response monitoring.

Patients with breast cancer exhibit heterogeneity in the expression of the human epithelial growth factor receptor 2 (HER2). Clinically, re-biopsying recurrent or metastatic lesions presents substantial challenges. This study aimed to evaluate the efficacy of HER2-targeted PET/CT imaging in identifying HER2 expression in breast cancer lesions and monitoring therapeutic responses. This exploratory analysis used data from a prospective study that included adult patients with breast cancer who underwent both Al18F-NOTA-HER2-BCH and 18F-FDG PET/CT imaging at Beijing Cancer Hospital between June 2020 and July 2023 (NCT04547309). Fifty-nine participants, with a median age of 55 years, were analyzed. Lesions imaged with HER2-targeted PET/CT before anti-HER2 therapy exhibited higher SUVmax values than after therapy in HER2 immunohistochemistry (IHC) 3 + lesions (19.9, 95% CI: 15.7-25.3 vs 9.8, 95% CI: 5.6-14.7; P = .006). A significant positive correlation was observed between SUVmax on HER2-targeted PET/CT and IHC before therapy (P = .034), with higher SUVmax values noted in lesions with positive HER2 pathology compared to those with negative HER2 status (17.9 ± 13.2 vs 1.1 ± 0.3; P = .007). HER2 expression heterogeneity was confirmed both between primary and metastatic lesions (22.9%) and among different metastatic sites (26.7%) as assessed by HER2-targeted PET/CT. A superior therapeutic response correlated with higher pretreatment SUVmax values. The HER2-targeted PET/CT procedure was well-tolerated by all patients. HER2-targeted PET/CT imaging offers a practical, non-invasive, and quantitative approach for assessing HER2 status in breast cancer patients, facilitating the optimization and personalization of therapeutic strategies by oncologists.

Artificial intelligence assisted ultrasound for the non-invasive prediction of axillary lymph node metastasis in breast cancer

PurposeA practical noninvasive method is needed to identify lymph node (LN) status in breast cancer patients diagnosed with a suspicious axillary lymph node (ALN) at ultrasound but a negative clinical physical examination. To predict ALN metastasis effectively and noninvasively, we developed an artificial intelligence-assisted ultrasound system and validated it in a retrospective study.MethodsA total of 266 patients treated with sentinel LN biopsy and ALN dissection at Peking Union Medical College & Hospital(PUMCH) between the year 2017 and 2019 were assigned to training, validation and test sets (8:1:1). A deep learning model architecture named DeepLabV3 + was used together with ResNet-101 as the backbone network to create an ultrasound image segmentation diagnosis model. Subsequently, the segmented images are classified by a Convolutional Neural Network to predict ALN metastasis.ResultsThe area under the receiver operating characteristic curve of the model for identifying metastasis was 0.799 (95% CI: 0.514–1.000), with good end-to-end classification accuracy of 0.889 (95% CI: 0.741–1.000). Moreover, the specificity and positive predictive value of this model was 100%, providing high accuracy for clinical diagnosis.ConclusionThis model can be a direct and reliable tool for the evaluation of individual LN status. Our study focuses on predicting ALN metastasis by radiomic analysis, which can be used to guide further treatment planning in breast cancer.

Comparing shear wave elastography of breast tumors and axillary nodes in the axillary assessment after neoadjuvant chemotherapy in patients with node-positive breast cancer

BackgroundAccurately identifying patients with axillary pathologic complete response (pCR) after neoadjuvant chemotherapy (NAC) in breast cancer patients remains challenging.PurposeTo compare the feasibility of shear wave elastography (SWE) performed on breast tumors and axillary lymph nodes (LNs) in predicting the axillary status after NAC.Materials and MethodsThis prospective study included a total of 319 breast cancer patients with biopsy-proven positive node who received NAC followed by axillary lymph node dissection from 2019 to 2022. The correlations between shear wave velocity (SWV) and pathologic characteristics were analyzed separately for both breast tumors and LNs after NAC. We compared the performance of SWV between breast tumors and LNs in predicting the axillary status after NAC. Additionally, we evaluated the performance of the most significantly correlated pathologic characteristic in breast tumors and LNs to investigate the pathologic evidence supporting the use of breast or axilla SWE.ResultsAxillary pCR was achieved in 51.41% of patients with node-positive breast cancer. In breast tumors, there is a stronger correlation between SWV and collagen volume fraction (CVF) (r = 0.52, p < 0.001) compared to tumor cell density (TCD) (r = 0.37, p < 0.001). In axillary LNs, SWV was weakly correlated with CVF (r = 0.31, p = 0.177) and TCD (r = 0.29, p = 0.213). No significant correlation was found between SWV and necrosis proportion in breast tumors or axillary LNs. The predictive performances of both SWV and CVF for axillary pCR were found to be superior in breast tumors (AUC = 0.87 and 0.85, respectively) compared to axillary LNs (AUC = 0.70 and 0.74, respectively).ConclusionSWE has the ability to characterize the extracellular matrix, and serves as a promising modality for evaluating axillary LNs after NAC. Notably, breast SWE outperform axilla SWE in determining the axillary status in breast cancer patients after NAC.

Effects of different exercise types and cycles on pain and quality of life in breast cancer patients: A systematic review and network meta-analysis.

To determine the effect of different combinations of different exercise modalities with different training cycles on the improvement of quality of life and pain symptoms in breast cancer patients. The databases PubMed, Web of Science, Embase, and Scopus were searched through a computer network with a search deadline of 23 August 2023. Two researchers independently screened the literature, extracted data and performed methodological quality assessment of the included literature, and then performed the corresponding statistical analyses and graphing using stata17.0. Thirty-six randomized control trial (RCT) studies involving 3003 participants and seven exercise modalities were included. Most of the exercise modalities improved patients' quality of life compared to usual care, with long-term aerobic combined with resistance exercise [SMD = 0.83,95% CI = 0.34,1.33,p = 0.001] and YOGA [SMD = 0.61,95% CI = 0.06,1.16,p = 0.029] treatments having a significant effect. For pain and fatigue-related outcome indicators, the treatment effect was not significant for all exercise modalities included in the analysis compared to the control group, but tended to be beneficial for patients. Long-term aerobic combined with resistance exercise was the most effective in improving quality of life and fatigue status in breast cancer patients, and aerobic exercise was more effective in improving pain symptoms in breast cancer patients.

Revolutionizing breast cancer Ki-67 diagnosis: ultrasound radiomics and fully connected neural networks (FCNN) combination method.

This study aims to assess the diagnostic value of ultrasound habitat sub-region radiomics feature parameters using a fully connected neural networks (FCNN) combination method L2,1-norm in relation to breast cancer Ki-67 status. Ultrasound images from 528 cases of female breast cancer at the Affiliated Hospital of Xiangnan University and 232 cases of female breast cancer at the Affiliated Rehabilitation Hospital of Xiangnan University were selected for this study. We utilized deep learning methods to automatically outline the gross tumor volume and perform habitat clustering. Subsequently, habitat sub-regions were extracted to identify radiomics features and underwent feature engineering using the L1,2-norm. A prediction model for the Ki-67 status of breast cancer patients was then developed using a FCNN. The model's performance was evaluated using accuracy, area under the curve (AUC), specificity (Spe), positive predictive value (PPV), negative predictive value (NPV), Recall, and F1. In addition, calibration curves and clinical decision curves were plotted for the test set to visually assess the predictive accuracy and clinical benefit of the models. Based on the feature engineering using the L1,2-norm, a total of 9 core features were identified. The predictive model, constructed by the FCNN model based on these 9 features, achieved the following scores: ACC 0.856, AUC 0.915, Spe 0.843, PPV 0.920, NPV 0.747, Recall 0.974, and F1 0.890. Furthermore, calibration curves and clinical decision curves of the validation set demonstrated a high level of confidence in the model's performance and its clinical benefit. Habitat clustering of ultrasound images of breast cancer is effectively supported by the combined implementation of the L1,2-norm and FCNN algorithms, allowing for the accurate classification of the Ki-67 status in breast cancer patients.

Association Between Vitamin D Deficiency and Tumor Characteristics in Breast Cancer Patients.

Introduction Breast cancer is the most frequent cancer among women worldwide, but there is little literature regarding the effects of vitamin Donbreast cancer patients in the Indian population. Hence, this study was planned to determine the correlation between vitamin D deficiency and tumor characteristics in breast cancer patients. Methods This was a cross-sectional study conducted in a tertiary healthcare facilityin central India among allnewly diagnosed patients with breast carcinoma who had received primary surgery and pathological confirmation. We performed universal sampling and included 50 patients in the study. We excluded patients with insufficient histopathological reports, those unfit for surgery, and those with hepatic or renal failure, metabolic bone disease, malabsorption, orrecent consumption of vitamin D (patients who had received oral vitamin D in the preceding two weeks, or vitamin D injection in the preceding six months). Results Among the 50 patients, 86% were vitamin D deficient, with a mean deficiency of 23.54. Vitamin D deficiency is most common in the age groups 41-50 years and >60 years, with the mean age group of 51.49 years. The left side is more involved than the right in vitamin D-deficient patients. Most patients were moderately and poorly differentiated, suggesting a significant association between vitamin D deficiency and tumor differentiation. Almost half the patients were estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (Her-2/neu) status negative with vitamin D deficiency. Vitamin D deficiency was highest in Her-2/neu amplified, luminal A, and B patients. The mean lymph node-positive participants was 4.04, and the mean number of lymph nodes extracted was 15.58 in vitamin D-deficient breast cancer patients. Conclusion The prevalence of low vitamin D status was high among breast cancer patients. There is an association between vitamin D deficiency and tumors with poor prognostic features. Low vitamin D levels were considered a risk factor for ER, PR, and Her-2/neu-negative tumors along with positive lymph node status in breast cancer patients. Vitamin D status is a modifiable risk factor for breast cancer.Thus, it is concluded from this study that vitamin D has a potential role in the prevention of breast cancer, it may reduce its aggressiveness, and its deficiency is associated with an increased risk of breast cancer.

Abstract PO5-06-05: DISCORDANCE BETWEEN HORMONE RECEPTORS AND HER2 STATUS IN BREAST CANCER PATIENTS RELAPSE

Abstract Introduction: Breast cancer (BC) is a heterogeneous disease, and during its progression, the tumor phenotype can undergo changes that are often associated with a poor prognosis. In this study, we aim to compare the discordance in estrogen receptor (ER), progesterone receptor (PgR), human epidermal growth factor receptor 2 (HER-2), and Ki-67 expression between primary and recurrent/metastatic lesions. Methods: This study is a retrospective analysis of 132 non-advanced breast cancer patients who experienced confirmed recurrence/metastasis at the Instituto Nacional de Enfermedades Neoplasicas between 2011 and 2022. Descriptive analysis was conducted to assess the expressions of ER, PgR, HER-2, and Ki-67 in primary and metastatic breast cancer, as well as clinical variables. Normality tests, including Kolmogorov-Smirnov and Shapiro-Wilk, were performed. The paired samples Wilcoxon test was used to determine significant differences between primary and recurrent/metastatic tumors. Additionally, Spearman's rank correlation coefficient was employed to evaluate the relationships of expression discordance. Results: Fifty-nine percent of patients presented locoregional recurrence, while 41% presented visceral metastasis. In the primary tumor, ER and PR expression were found in 63.6% and 55.3% of cases, respectively. HER2 status was determined as 0 (36.3%), +1 (22%), +2 (14.4%), +3 (26.5%), and not determined (0.8%) by IHC. For the recurrent/metastatic tumor, ER and PR expression were found in 59% and 34% of cases, respectively. HER2 status was determined as 0 (27.3%), +1 (19.7%), +2 (25%), +3 (25%), and not determined (3%). The median Ki-67 levels were 37% in the primary tumor and 40% in the recurrent/metastatic tumor. Significant differences were found for ER (p=0.014), PR (p&amp;lt; 0.001), and HER2 (p=0.019), but not for Ki-67 percentage (p=0.139). The discordance rates of ER, PR, HER2, and Ki-67 expression were 27.9%, 47.7%, and 26.3%, respectively. Conclussions: Discordance in hormone receptor expression between primary and recurrent/metastatic tumors is a common phenomenon, with ER showing a discordance rate of 27.9% and PR showing a higher rate of 47.7%. HER2 status also displayed significant discordance, with a 26.3% rate of discordant expression between primary and recurrent/metastatic tumors. The findings from this study emphasize the dynamic nature of breast cancer and the importance of comprehensive evaluation of biomarker expression in both primary and recurrent/metastatic tumors, to choose the best treatment options. Citation Format: Katia Roque, Iris Otoya, Natalia Valdivieso, Zaida Morante, Hugo Fuentes, Silvia Neciosup, Henry Gómez, Jorge Cotito, Gonzalo Ziegler, Estefania Peña, Tatiana Vidaurre, Ramon Andrade de Mello, Carlos Castañeda. DISCORDANCE BETWEEN HORMONE RECEPTORS AND HER2 STATUS IN BREAST CANCER PATIENTS RELAPSE [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-06-05.

TP53-specific mutations serve as a potential biomarker for homologous recombination deficiency in breast cancer: a clinical next-generation sequencing study.

TP53 mutations and homologous recombination deficiency (HRD) occur frequently in breast cancer. However, the characteristics of TP53 pathogenic mutations in breast cancer patients with/without HRD are not clear. Clinical next-generation sequencing (NGS) of both tumor and paired blood DNA from 119 breast cancer patients (BRCA-119 cohort) was performed with a 520-gene panel. Mutations, tumor mutation burden (TMB), and genomic HRD scores were assessed from NGS data. NGS data from 47 breast cancer patients in the HRD test cohort were analyzed for further verification. All TP53 pathogenic mutations in patients had somatic origin, which was associated with the protein expression of estrogen receptor and progestogen receptor. Compared to patients without TP53 pathologic mutations, patients with TP53 pathologic mutations had higher levels of HRD scores and different genomic alterations. The frequency of TP53 pathologic mutation was higher in the HRD-high group (HRD score≥42) relative to that in the HRD-low group (HRD score<42). TP53 has different mutational characteristics between the HRD-low and HRD-high groups. TP53-specific mutation subgroups had diverse genomic features and TMB. Notably, TP53 pathogenic mutations predicted the HRD status of breast cancer patients with an area under the curve (AUC) of 0.61. TP53-specific mutations, namely HRD-low mutation, HRD-high mutation, and HRD common mutation, predicted the HRD status of breast cancer patients with AUC values of 0.32, 0.72, and 0.58, respectively. Interestingly, TP53 HRD-high mutation and HRD common mutation combinations showed the highest AUC values (0.80) in predicting HRD status. TP53-specific mutation combinations predict the HRD status of patients, indicating that TP53 pathogenic mutations could serve as a potential biomarker for poly-ADP-ribose polymerase (PARP) inhibitors in breast cancer patients .

Association of Nutritional Status with Quality of Life in Breast Cancer Patients on Chemotherapy

Background: Cancer patients often experience a decline in their nutritional status due to chemotherapy, which can result in malnutrition and negatively impact their quality of life. However, there has been limited research on the prevalence of these conditions in breast cancer patients undergoing chemotherapy in Indonesia. Purposes: This research aims to explore the connection between the quality of life and the nutritional status of breast cancer patients receiving chemotherapy treatment. Methods: A cross-sectional study was conducted in a private hospital in Pekanbaru using non-probability sampling. Demographic profile, nutritional status assessment by PG-SGA, and quality of life assessment by the EORTC QLQ C-30 module were obtained. Mann-Whitney and Spearman's tests analyzed the association between nutritional status and QoL. Results: The average age of breast cancer patients was 45.46±14.375. The prevalence of overweight and obesity was 64.1%. According to the PG-SGA score, the vast majority of patients, approximately 91%, were discovered to be well-nourished. The nutritional status was significantly associated with most functional and symptom scales (p&lt;0.05). Improved nutritional status is associated with increased functionality and a reduction in symptoms, while malnutrition has been linked to decreased quality of life across multiple dimensions. Conclusion: The nutritional status of breast cancer patients affects some dimensions of quality of life. Well-nourished patients had better function and fewer symptoms.

Abstract 3758: Evaluation of breast cancer-derived extracellular vesicles as diagnostic indicator for HER2 status

Abstract HER2-low is a new distinct type of breast cancer that is characterized by low levels of the HER2 protein. As treatment paradigms for HER2-low breast cancer are rapidly evolving with the approval of antibody-drug conjugates, identification of HER2-low breast cancer with more sensitive and reproducible HER2 assays is needed. We aimed to investigate the utility of liquid biopsy in isolating tumor-derived extracellular vesicles (TDEs) for diagnosis of HER2 status. We adopted an affinity-based approach to isolate TDEs and performed CNV analysis of the ERBB2/neu genes using the tumor DNA present in TDEs (etDNA). HER2 status of etDNA had been determined by droplet digital PCR using HER2/EIF2C ratio, a highly accurate method to quantify DNA copy number. In our study, we utilized a variety of breast cancer cell lines categorized as HER2-low (MDA-MB-231, MCF7, ZR-75-1) and HER2-high (MDA-MB-453, BT-474, SK-BR-3) to confirm HER2 expression in extracellular vesicles (EVs) by flow cytometry. This analysis successfully demonstrated that EVs effectively reflect the HER2 status of the tumor cells, evidenced by a strong Pearson correlation coefficient (r=0.987, p&amp;lt;0.001). Further clinical validation was conducted with plasma samples from 22 breast cancer patients. In the clinical study, we observed a significant correlation between the CNV of tumor DNA present in TDEs and the HER2 status of primary tumors (p&amp;lt;0.01). Additionally, the Receiver Operating Characteristic (ROC) analysis yielded an impressive area under the curve (AUC) value of 0.838 (p&amp;lt;0.01), demonstrating a high level of accuracy in differentiating between high and low HER2 groups, with a sensitivity of 55.6% and a specificity of 100%. In summary, we show potential diagnostic tools for real-time, less invasive determination of HER2 status in primary tumors, providing continuous monitoring methods for HER2 status. Although further validation in larger cohorts and prospective studies is needed, our study indicates that TDE analysis could contribute to the assays for identification of the HER2 status in breast cancer patients. Citation Format: Sol Moon, Min Woo Kim, Young Kim, Suji Lee, Hyojung Lee, Joon Ye Kim, Seung Il Kim, Jee Ye Kim. Evaluation of breast cancer-derived extracellular vesicles as diagnostic indicator for HER2 status [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3758.

The effect of diaphragmatic breathing on nausea, vomiting and functional status among breast cancer patients undergoing chemotherapy

PurposeTo examine the effect of diaphragmatic breathing (DB) on nausea, vomiting, and functional status among breast cancer patients undergoing chemotherapy in Indonesia. MethodsA quasi experimental study with non-equivalent pretest and posttest control group was conducted. A total of forty-eight breast cancer patients (24 DB and 24 control participants) undergoing chemotherapy participated in this study selected conveniently. DB intervention was performed to the intervention group after chemotherapy cycle twice a day for six days, meanwhile the control group received usual care. A set of questionnaires was used to collect data consisting of Patient Information Form, Rhodes Index Nausea, Vomiting and Retching (RINVR) and The Functional Living Index-Cancer (FLI-C). Data were analyzed and interpreted using Generalized Linear Model, Wilcoxon Test, Paired T Test and Mann-Whitney U test. ResultsThere were significant changes in RINVR mean scores for the intervention group that started on the third day after chemotherapy (p = 0.000); meanwhile, the significant changes in RINVR mean scores for the control group began on the fifth day (p = 0.000). The total score of FLI-C was significantly different between the intervention and control groups (p = 0.000). ConclusionDB could decrease nausea and vomiting, and increase functional status of breast cancer patients undergoing chemotherapy. It can be promoted as a useful low-cost self-management approach and an additional and complementary therapy to manage chemotherapy-related nausea and vomiting.

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.

Assessing the Impact of Organ Failure and Metastases on Quality of Life in Breast Cancer Patients: A Prospective Study Based on Utilizing EORTC QLQ-C30 and EORTC QLQ-BR45 Questionnaires in Romania.

Breast cancer (BC) significantly impacts the quality of life (QoL) of affected individuals. This study, conducted at Colțea Clinical Hospital, Bucharest, aimed to assess the impact of organ failures and metastases on QoL in breast cancer patients using EORTC QLQ-C30 and EORTC QLQ-BR45 questionnaires and the survival rate to understand the clinical journey and the quality of life status in breast cancer patients. From January 2019 to October 2022, a prospective, observational study surveyed 874 patients, revealing 201 fatalities, 66 refusals, and 607 eligible participants. Results indicated statistically significant differences in various QoL aspects for patients experiencing heart failure, including physical functioning, pain, insomnia, global health status, and overall summary score. Kidney failure exhibited significance in physical functioning for QLQ-C30 and body image, sexual functioning, and endocrine sexual symptoms for QLQ-BR45. Respiratory failure demonstrated significant differences across multiple QoL domains. Patients with bone metastases reported lower physical functioning (p = 0.006) and increased pain (p = 0.002). This study has revealed an overall 5-year life expectancy of 68.8%, with survival rates of 93.8% for Stage I, 86.3% for Stage II, and 77.2% for Stage III breast cancer. Metastatic cancer patients have shown a 35.6% survival rate over 45 months, with a median survival duration of 36 months. A significant limitation of our study was the administration of the questionnaire only once, preventing us from quantifying the impact of specific treatment types on quality of life. This study emphasizes the necessity of using standardized QoL assessments in clinical practice from the initial presentation to ongoing follow-up.

Understanding changes in pulmonary function and functional status in breast cancer patients after systemic chemotherapy and radiotherapy: a prospective study

BackgroundRespiratory complications in breast cancer (BC) patients after chemotherapy (CT) and radiotherapy (RT) have been well acquainted and these complications should be investigated to prevent secondary problems and/or improve BC patients’ clinical outcomes. Therefore, this study aimed to assess the potential acute effect of systemic chemotherapy and radiotherapy on respiratory function and functional status of patients with breast cancer.MethodsA total of 25 BC patients who were candidates for systemic chemotherapy and radiotherapy were recruited after oncological examination and included in this study. Respiratory function and functional status were assessed with the Pulmonary Function Test (PFT) and the Six-Minute Walk Test (6MWT), respectively. Patients were assessed before CT (c0), after CT (c1), and after RT (r1).Results25 BC patients were assessed in c0 and c1 while only 15 out of 25 patients (60%) were assessed in r1. The actual values of Forced vital capacity (FVC) (t = 2.338, p =.028), Forced expiratory volume in 1s (FEV1 (t = 2.708, p =.012), and the forced expiratory flow of between 25% and 75% of vital capacity (FEF25-75%) (t = 2.200, p =.038) were found significantly different after systemic CT. Inspiratory (MIP) and expiratory (MEP) muscle strength also did not show a significant change from c0 to c1. A significant effect of the type of surgery was found (Wilks’ lambda, F [1, 19] = 6.561, p =.019, ηp2 = 0.25) between c0 and c1 in actual FVC value. The main effect of time was found significant in FVC (F [2, 28] = 4.840, p =.016, ηp2 = 0.25) from c0 to r1. Pairwise comparisons with Bonferroni correction showed that there was a significant difference between c0 and r1 (p =.037).DiscussionThe present study showed decreased FVC and FEV1 actual values and percent predicted rates from baseline to the completion of treatment. Since the interactional effect of the type of surgery was significant, we suggest that clinical and demographic factors such as age should be considered when interpreting the early changes in PFT. In addition, the significant linear trend of decreasing in some specific outcomes in respiratory function also highlighted the need for continuous monitoring of potential respiratory problems in patients with BC from baseline to the completion of chemotherapy and radiotherapy.

Strategies to Promote Empowerment Status of Breast Cancer Women.

As the second leading cause of death in women in the world, breast cancer has several physical and psychological effects. Nowadays, nonclinical approaches such as patient empowerment have been considered by physicians along with clinical care. Given the increasing number of breast cancer women worldwide, promoting the empowerment of these patients is one of the key factors affecting their survival and quality of life. Therefore, because of no comprehensive research on the empowerment needs and related improvement strategies, this study is aimed at determining the empowerment status of breast cancer patients referred to the Shahid Motahari Breast Cancer Clinic in Iran, Shiraz, and at providing strategies to improve their empowerment in 2021. This applied study was conducted in two phases. In the quantitative phase, 310 Cancer-Related Patient Empowerment Scale questionnaires (Persian format) were distributed among the studied patients selected through the random sampling method in the clinic, and the items with "unacceptable status" became the basis for determining the empowerment strategies through the scoping review and semistructured interviews with 22 medical staff and patients through the thematic analysis. The collected data were analyzed using the SPSS 20.0 and MAXQDA10 software. The mean score of the participants' empowerment strategies was 3.58. The results showed that trust in the physician, family support, and spiritual beliefs could affect the empowerment of the studied patients. Moreover, the participants needed empowerment strategies in 11 scale items with unacceptable status, for which 46 strategies were determined in the scoping review and interview phase. The results of this study provided useful strategies for empowering breast cancer patients, the most important of which were classified into five categories of financial support, informational support, interaction with the physician, occupational support, and complementary therapies, the use of which by the stakeholders could help to improve the patients' quality of life while improving their empowerment.

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.

Machine learning-based model constructed from ultrasound radiomics and clinical features for predicting HER2 status in breast cancer patients with indeterminate (2+) immunohistochemical results.

We aimed to predict human epidermal growth factor receptor 2 (HER2) 2+ status in patients with breast cancer by constructing and validating machine learning models utilizing ultrasound (US) radiomics and clinical features. We analyzed 203 breast cancer cases immunohistochemically determined as HER2 2+ and used fluorescence insitu hybridization (FISH) as the confirmation method. From each case, the study analyzed 840 extracted radiomics features and 11 clinicopathologic features. Cases were randomly split into training (n = 141) and validation sets (n = 62) at a 7:3 ratio. Univariate logistic regression analysis was first performed on the 11 clinicopathologic characteristics. The least absolute shrinkage and selection operator (LASSO) and decision tree (DT) techniques were employed for post-feature selection. Finally, 19 radiomics features were utilized in logistic regression (LR) and Naive Bayesian (NB) classifiers. Model performance was gauged using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. Our models exhibited notable diagnostic efficacy in differentiating HER2-positive from negative breast cancer cases. In the validation sets, the LR model outperformed the NB model with an AUC of 0.860 and accuracy of 83.8% compared to NB's AUC of 0.684 and accuracy of 79.0%. The LR model demonstrated higher sensitivity (92.3% vs. 46.2%) while the NB model had a better specificity (91.8% vs. 63.3%) in the validation set. Machine learning models grounded on radiomics efficiently predicted IHC HER2 2+ status in breast cancer patients, suggesting potential enhancements in clinical decision-making for treatment and management.