BACKGROUND

The survival advantage of HER2-positive breast cancer from targeted treatment is commonly undermined by catastrophic health expenditure (CHE), particularly in resource-limited areas. Recognizing that financial catastrophe leads to non-adherence to treatment and dissaving practices, we examined the out-of-pocket (OOP) expenses of patients with HER2-positive breast cancer.

The study aimed to estimate the median total per-cycle out-of-pocket expenditure of HER2-positive breast cancer treatment from the patient perspective, in public and private clinics, evaluate the association of catastrophic health expenditure with non-adherence to treatment, and describe dissaving practices.

This was a cross-sectional micro-costing analysis of the treatment of HER2-positive breast cancer from the patient perspective from a tertiary cancer center and select private clinics in the Philippines. Random sampling of patients with HER2-positive breast cancer was done. Using a validated questionnaire, a guided interview was administered. Catastrophic health expenditure was estimated as having OOP of >20% of the household income. OOP costs were assessed retrospectively from the time of confirmed HER2 diagnosis up to the date of survey, while household income referred to the corresponding period. The proportion of patients experiencing catastrophic health expenditure was computed. Fisher's exact was used to assess for any association between CHE and non-adherence to treatment. Descriptive statistics were used to report dissaving practices. All statistical analyses were performed using Stata analytical software version 12.

A total of 101 patients participated in the study. The mean age of participants from the tertiary cancer center and private clinics were 52 and 58 years old respectively. Patients from the private clinics had a median total OOP expenditure of PhP 54,737.06 (IQR = PhP 102,670.00), compared with patients from tertiary cancer center who had a median total OOP expenditure of PhP 13,920.66 (IQR = PhP 20,830.00). The overall prevalence of CHE (90.9%, 95% CI 0.81, 0.95) and nonadherence to treatment with trastuzumab (79%, 95% CI 0.70, 0.87) were high, and similar in both groups. A number of dissaving practices such as resignation from work, borrowing money from friends, selling assets were observed.

The high rate of CHE and treatment delay among patients with HER2-positive breast cancer were not addressed by the existing cancer programs. Most OOP expenditure was for trastuzumab. Current cancer support programs have potential to address the financial impact of their treatment.

Adenoid cystic carcinoma (ACC) is a rare subtype of invasive breast cancer, occurring in <0.1% of all malignant breast tumors. Though majority are triple-negative, ACC of the breast has good prognosis with a low incidence of regional and distant metastases.

A 45-year-old premenopausal female presented with a 5-month history of a gradually enlarging mass on her left breast. After core needle biopsy and subsequent metastatic work-up, she underwent total mastectomy with sentinel lymph node biopsy. Final histopathology showed adenoid cystic carcinoma, 2.1 cm in size and no lymph nodes positive for tumor. She has completed adjuvant radiotherapy of 50 Gy to the chestwall, and is currently well after 6 years of follow-up.

Surgery with either lumpectomy or mastectomy has been established as the mainstay of treatment of adenoid cystic carcinoma of the breast, but the use of adjuvant radiotherapy (RT) and chemotherapy has not been established. While adjuvant RT has been shown to improve cause-specific and overall survival following breast-conserving surgery, its indications after a mastectomy are not as well-defined. The decision to administer adjuvant RT was based on the current evidence indicating the advantages of adjuvant treatment for breast carcinomas, lack of survival difference between invasive ductal carcinomas and adenoid cystic carcinomas, indications for post-mastectomy RT in a retrospective Rare Cancer Network study, and reported incidences of local recurrences following mastectomy alone: 21.4% and 22.2%.

Our patient with adenoid cystic carcinoma of the breast, treated with surgery and adjuvant radiation therapy, showed favorable outcomes after 6 years.

BACKGROUND

Breast cancer remains a leading malignancy among women globally. In addition to established factors like histopathology, hormone receptor status, and lymph node involvement, tools such as immunohistochemistry and molecular tests have been developed to assess tumor behavior and recurrence risk.

This study investigates the concordance between the Ki-67 proliferation index measured by immunohistochemistry and the recurrence risk scores obtained from molecular genomic testing in patients with invasive breast cancer.

This cross-sectional study included patients with invasive breast carcinoma at St. Luke’s Medical Center from 2019 to 2024, who underwent biopsy or mastectomy, with hormone status and Ki-67 index assessed by immunohistochemistry. All patients also had molecular genomic testing using either MammaPrint or OncotypeDX. Concordance between Ki-67 and the genomic recurrence risk score was evaluated using Kappa statistics, and results were further analyzed according to clinical risk and hormone receptor status.

Fifty-eight (58) patients met the study criteria. Most had grade 2, hormone receptor-positive, HER2-negative, and node-negative tumors, with high clinical risk based on Adjuvant! Online criteria (adapted from the MINDACT trial). The agreement between categorical Ki-67 and molecular recurrence risk was only fair: 66.7% for MammaPrint (kappa=0.35) and 60% for OncotypeDX (kappa = 0.29) using a 30% Ki-67 cutoff.

There is a fair agreement between Ki-67 and the molecular genomic tests. These findings are consistent with prior studies reporting weak to moderate association. Despite the limited sample size, Ki-67 remains a practical and accessible risk stratification tool, particularly where genomic assays are unavailable. The study supports integrating Ki-67 with clinicopathologic and genomic data to guide therapy, reflecting current best-practice recommendations.

BACKGROUND AND OBJECTIVE

The burden of treatment delay in breast cancer is high, especially among developing countries. Despite adversely affecting morbidity and mortality, treatment delay remains unexplored in the Philippines. This study aimed to determine treatment delays among breast cancer patients in a tertiary hospital during surgery, neoadjuvant chemotherapy, and adjuvant chemotherapy, and to identify predictors of delay.

A cross-sectional study was conducted among breast cancer patients seen between January 1, 2012 to December 31, 2018. The following outcomes were investigated: ≥90 days from initial diagnosis to surgery, ≥8 weeks from diagnosis to initiation of neoadjuvant chemotherapy, and >120 days from diagnosis to initiation of adjuvant chemotherapy. Summary statistics were reported as percent for categorical data and as mean for continuous data. The individual correlations were performed using Chi-square for qualitative data and t-test for quantitative data while predictors were determined through logistic regression.

A total of 324 patients were included in this study. The majority of the patients were less than 65 years old living in urban areas. More than half of the patients were overweight or obese, hypertensive, and diabetic. The following delays were observed: 61.1% (n = 198) with any type of delay, 23.8% (n = 53) with delay in surgery, 53.8% (n = 120) with delay in adjuvant chemotherapy, and 74.3% (n = 75) with delay in neoadjuvant chemotherapy. The patients noted to have any type of delay were more likely to be hypertensive (p = 0.046) and residing in urban areas (p = 0.041). There were no differences in the distribution of age, body mass index, and presence of co-morbid conditions such as hypertension, diabetes mellitus, coronary artery disease, and heart failure among those with any form of delay compared with no delay.

The present study shows the presence of treatment delay among breast cancer patients and may be used to enact policy changes to optimize breast cancer care delivery. Further studies may be done to identify other factors affecting these delays and policy changes are recommended to address these gaps in surgery and chemotherapy administration among breast cancer patients.

Objective To investigate the value of ultrasonographic features combined with immunohistochemistry in predicting axillary lymph node metastasis in middle-aged women with breast cancer.Methods A retrospective analysis was conducted on 827 middle-aged female breast cancer patients who underwent surgical treatment at the Affiliated Hospital of Chengde Medical University from June 2017 to June 2023.Ultrasonographic and immunohistochemical information was collected,and the patients were randomly allocated into a training set(579 patients)and a validation set(248 patients).Univariate and multivariate Logistic regression analyses were performed to identify ultrasonographic and immunohistochemical risk factors associated with axillary lymph node metastasis in these patients,and a nomogram model was developed.Receiver operating characteristic curves and calibration curves were established to evaluate the performance of the nomogram model,and clinical decision curves were built to assess the clinical value of the model.Results The maximum diameter,morphology,boundary,calcification,and expression of human epidermal growth facor receptor 2 and Ki-67 in breast cancer lesions were identified as risk factors for predicting axillary lymph node metastasis in middle-aged women.The areas under the curve of the nomogram model on the training and validation sets were 0.747(0.707-0.787)and 0.714(0.647-0.780),respectively.Calibration curves and clinical decision curves indicated good consistency and performance of the model.Conclusion The nomogram model constructed based on ultrasonographic features and immunohistochemistry of the primary breast cancer lesion demonstrates high value in predicting axillary lymph node metastasis in middle-aged women with breast cancer.
Humans ; Female ; Breast Neoplasms/diagnostic imaging* ; Middle Aged ; Lymphatic Metastasis/diagnostic imaging* ; Axilla ; Retrospective Studies ; Nomograms ; Ultrasonography ; Immunohistochemistry ; Lymph Nodes/diagnostic imaging* ; Risk Factors ; Ki-67 Antigen

BACKGROUND: The burden of breast cancer for older adults has been rising with the increasing population aging. This study aims to describe the burden of breast cancer in older adults worldwide, analyze the temporal trends for older breast cancer incidence, and assess the socioeconomic inequalities of breast cancer incidence and mortality with human development index (HDI) levels, which will provide valuable information in preventing and controlling the increasing breast cancer burden in older women. METHODS: The incidence and mortality rates of specific cancer types in older individuals in 2022 were sourced from the Global Cancer Today database. Trends in breast cancer incidence acquired from the Cancer Incidence in Five Continents (CI5) database. HDI and other risk factors were obtained from the United Nations. We used a generalized linear model to estimate the rate ratio and 95% confidence interval (CI) between HDI levels and breast cancer burden in older people. RESULTS: It was estimated approximately 1,058,466 newly diagnosed breast cancer cases and 383,774 breast cancer deaths in women ≥60 years, accounting for 18.9% and 12.7% of global cancer cases and deaths. The age-standardized incidence rate (ASIR) and age-standardized mortality rate (ASMR) were 172.9 and 57.7 per 100,000, ranking first and second among all cancer incidence and mortality in older women. The highest ASIR and ASMR were four-fold higher than the lowest, with ASIR ranging from a peak of 399.1 per 100,000 in Australia-New Zealand to a low of 90.6 per 100,000 in South Central Asia, and ASMR varying from a high of 118.6 per 100,000 in Melanesia to a low of 28.8 per 100,000 in East Asia. The largest increases in ASIR from 1998-2002 to 2013-2017 were observed in South Korea, China, and Estonia. The corresponding estimated 5-year average percentage changes (EAPC) were 6.01%, 2.89%, and 1.93%, respectively. CONCLUSIONS The global burden of breast cancer in older women is increasing fast and varies greatly across countries. Effective prevention strategies are essential to address the increasing breast cancer burden for older women.
Humans ; Female ; Breast Neoplasms/epidemiology* ; Aged ; Incidence ; Middle Aged ; Registries ; Aged, 80 and over ; Risk Factors

Humans ; Class I Phosphatidylinositol 3-Kinases/genetics* ; Breast Neoplasms/diagnosis* ; Mutation ; Female ; China ; DNA Mutational Analysis/methods* ; Genetic Testing/standards*

Study on the mechanism of Fangxia Dihuang Formula(FXDH) in treating breast cancer complicated with depression through the regulation of M1/M2 microglial polarization via the PERK/eIF2α axis. In addition to control group and 4T1 group, a mouse model of breast cancer complicated with depression was established using 4T1 cells combined with corticosterone. The mice were divided into model group, PERK/eIF2α signaling axis agonist(CCT020312, 2 mg·kg~(-1)·d~(-1)) group, CCT020312(2 mg·kg~(-1)·d~(-1)) + FXDH(13.65 g·kg~(-1)·d~(-1)) group, FXDH(13.65 g·kg~(-1)·d~(-1)) group, FXDH(13.65 g·kg~(-1)·d~(-1)) + Capecitabine Tablets(CAP, 390 mg·kg~(-1)·d~(-1)) group, and Fluoxetine Hydrochloride Capsules(FXT, 2.6 mg·kg~(-1)·d~(-1)) + CAP(390 mg·kg~(-1)·d~(-1)) group, with continuous intervention for 21 d. Depression-like behaviors in mice were assessed through sugar preference test and open field test. Hematoxylin-eosin(HE) staining was used to evaluate the morphology of tumor and hippocampal DG region neurons. Nissl staining was employed to detect changes in Nissl bodies in the hippocampal CA3 region. Immunofluorescence was used to observe cluster of differentiation 86(CD86)/ionized calcium-binding adapter molecule 1(Iba-1) and cluster of differentiation 206(CD206)/Iba-1 in hippocampal tissue. Real-time fluorescence quantitative polymerase chain reaction(RT-qPCR) was used to detect the mRNA expression of M1-type microglia [interleukin-6(IL-6), tumor necrosis factor-α(TNF-α)] and M2-type [arginase-1(Arg-1), IL-10] in hippocampal tissue. Western blot was used to detect the protein expression of key factors in the PERK/eIF2α axis, including PERK, eIF2α, activating transcription factor 4(ATF4), and C/EBP homologous protein(CHOP) in hippocampal tissue. The results showed that compared to model group/CCT020312 + FXDH group, FXDH group increased sugar preference index, total movement distance, central zone distance, and central zone entries; reduced tumor mass and volume; tumor cells were sparsely arranged, with a smaller nuclear-to-cytoplasmic ratio and reduced nuclear division figures, increased Nissl body count, and alleviated neuronal nuclear pyknosis; increased CD206-positive M2-type microglia expression, decreased CD86/Iba-1-positive M1-type microglia expression; reduced IL-6 and TNF-α mRNA expression, and increased Arg-1 and IL-10 mRNA expression; downregulated PERK, eIF2α, ATF4, and CHOP protein expression levels. The results indicate that the mechanism of FXDH in treating breast cancer complicated with depression may be related to inhibiting the activity of the PERK/eIF2α axis, reducing the proportion of M1-type microglia, increasing the proportion of M2-type microglia, thereby suppressing neuronal immune inflammation, improving depressive symptoms, and subsequently delaying the progression of breast cancer.
Animals ; Drugs, Chinese Herbal/administration & dosage* ; Female ; Microglia/cytology* ; Mice ; Depression/complications* ; eIF-2 Kinase/genetics* ; Humans ; Breast Neoplasms/psychology* ; Eukaryotic Initiation Factor-2/genetics* ; Mice, Inbred BALB C ; Signal Transduction/drug effects* ; Cell Line, Tumor

The accurate segmentation of breast ultrasound images is an important precondition for the lesion determination. The existing segmentation approaches embrace massive parameters, sluggish inference speed, and huge memory consumption. To tackle this problem, we propose T ²KD Attention U-Net (dual-Teacher Knowledge Distillation Attention U-Net), a lightweight semantic segmentation method combined double-path joint distillation in breast ultrasound images. Primarily, we designed two teacher models to learn the fine-grained features from each class of images according to different feature representation and semantic information of benign and malignant breast lesions. Then we leveraged the joint distillation to train a lightweight student model. Finally, we constructed a novel weight balance loss to focus on the semantic feature of small objection, solving the unbalance problem of tumor and background. Specifically, the extensive experiments conducted on Dataset BUSI and Dataset B demonstrated that the T ²KD Attention U-Net outperformed various knowledge distillation counterparts. Concretely, the accuracy, recall, precision, Dice, and mIoU of proposed method were 95.26%, 86.23%, 85.09%, 83.59%and 77.78% on Dataset BUSI, respectively. And these performance indexes were 97.95%, 92.80%, 88.33%, 88.40% and 82.42% on Dataset B, respectively. Compared with other models, the performance of this model was significantly improved. Meanwhile, compared with the teacher model, the number, size, and complexity of student model were significantly reduced (2.2×10 ⁶ vs. 106.1×10 ⁶, 8.4 MB vs. 414 MB, 16.59 GFLOPs vs. 205.98 GFLOPs, respectively). Indeedy, the proposed model guarantees the performances while greatly decreasing the amount of computation, which provides a new method for the deployment of clinical medical scenarios.
Humans ; Breast Neoplasms/diagnostic imaging* ; Female ; Ultrasonography, Mammary/methods* ; Image Processing, Computer-Assisted/methods* ; Algorithms ; Neural Networks, Computer ; Breast/diagnostic imaging*

With the rising incidence of breast cancer among women, neoadjuvant chemotherapy (NAC) is becoming increasingly crucial as a preoperative treatment modality, enabling tumor downstaging and volume reduction. However, its efficacy varies significantly among patients, underscoring the importance of predicting pathological complete response (pCR) following NAC. Early research relied on statistical methods to integrate clinical data for predicting treatment outcomes. With the advent of artificial intelligence (AI), traditional machine learning approaches were subsequently employed for efficacy prediction. Deep learning emerged to dominate this field, and demonstrated the capability to automatically extract imaging features and integrate multimodal data for pCR prediction. This review comprehensively examined the applications and limitations of these three methodologies in predicting breast cancer pCR. Future efforts must prioritize the development of superior predictive models to achieve precise predictions, integrate them into clinical workflows, enhance patient care, and ultimately improve therapeutic outcomes and quality of life.
Humans ; Breast Neoplasms/pathology* ; Neoadjuvant Therapy ; Artificial Intelligence ; Female ; Machine Learning ; Deep Learning ; Chemotherapy, Adjuvant ; Treatment Outcome