Early death after cancer surgery is higher in low-income and middle-income countries (LMICs) compared with in high-income countries, yet the impact of facility characteristics on early postoperative outcomes is unknown. The aim of this study was to examine the association between hospital infrastructure, resource availability, and processes on early outcomes after cancer surgery worldwide. A multimethods analysis was performed as part of the GlobalSurg 3 study—a multicentre, international, prospective cohort study of patients who had surgery for breast, colorectal, or gastric cancer. The primary outcomes were 30-day mortality and 30-day major complication rates. Potentially beneficial hospital facilities were identified by variable selection to select those associated with 30-day mortality. Adjusted outcomes were determined using generalised estimating equations to account for patient characteristics and country-income group, with population stratification by hospital. Between April 1, 2018, and April 23, 2019, facility-level data were collected for 9685 patients across 238 hospitals in 66 countries (91 hospitals in 20 high-income countries; 57 hospitals in 19 upper-middle-income countries; and 90 hospitals in 27 low-income to lower-middle-income countries). The availability of five hospital facilities was inversely associated with mortality: ultrasound, CT scanner, critical care unit, opioid analgesia, and oncologist. After adjustment for case-mix and country income group, hospitals with three or fewer of these facilities (62 hospitals, 1294 patients) had higher mortality compared with those with four or five (adjusted odds ratio [OR] 3·85 [95% CI 2·58–5·75]; p<0·0001), with excess mortality predominantly explained by a limited capacity to rescue following the development of major complications (63·0% vs 82·7%; OR 0·35 [0·23–0·53]; p<0·0001). Across LMICs, improvements in hospital facilities would prevent one to three deaths for every 100 patients undergoing surgery for cancer. Hospitals with higher levels of infrastructure and resources have better outcomes after cancer surgery, independent of country income. Without urgent strengthening of hospital infrastructure and resources, the reductions in cancer-associated mortality associated with improved access will not be realised. National Institute for Health and Care Research.

Background: This study aims to evaluate the association and dose-response between triglyceride-glucose (TyG) index and breast cancer.Method: This is a multicenter case-control study conducted in six public referral hospitals in Indonesia. Cases are individuals aged 19 years or above who were diagnosed with breast cancer within 1 year of diagnosis, based on histopathology and immunohistochemistry. Controls were recruited from corresponding hospitals. TyG index was determined by the formula: ln (fasting TG [mg/dl] × fasting glucose [mg/dl]).Results: There were 212 participants in the breast cancer group and 212 participants in the control group. TyG index was higher in patients with breast cancer (median 8.65 [7.38, 10.9] vs. 8.30 [7.09, 10.84], p < 0.001). When compared with TyG quartile of Q1, Q4 was associated with an OR of 2.42 (1.77, 3.31), p < 0.001, Q3 was associated with an OR of 1.53 (1.21, 1.93), p < 0.001, Q2 was associated with an OR of 1.39 (1.12, 1.73), p = 0.002 for the risk of breast cancer. The dose-response relationship was nonlinear (p < 0.001). On univariate analysis, smoking (OR 2.15 [1.44, 3.22], p < 0.001), use of contraception (1.73 [1.15, 2.60], p = 0.008), alcohol consumption (OR 2.04 [0.96, 4.35], p = 0.064), and TyG Index >8.87 (OR 3.08 [1.93, 4.93], p < 0.001) were associated with risk of breast cancer. Independently associated with increased risk of breast cancer included smoking (OR 1.93 [1.23, 3.01], p = 0.004), use of contraception (OR 1.59 [1.02, 2.48], p = 0.039), and TyG Index >8.87 (OR 2.93 [1.72, 4.98], p < 0.001)Conclusion: TyG index was associated with breast cancer in a nonlinear dose-response fashion.

Vaccines are an upcoming medical intervention for breast cancer. By targeting the tumor antigen, cancer vaccines can be designed to train the immune system to recognize tumor cells. Therefore, along with technological advances, the vaccine design process is now starting to be carried out with more rational methods such as designing epitope-based peptide vaccines using immunoinformatics methods. Immunoinformatics methods can assist vaccine design in terms of antigenicity and safety. Common protocols used to design epitope-based peptide vaccines include tumor antigen identification, protein structure analysis, T cell epitope prediction, epitope characterization, and evaluation of protein–epitope interactions. Tumor antigen can be divided into two types: tumor associated antigen and tumor specific antigen. We will discuss the identification of tumor antigens using high-throughput technologies. Protein structure analysis comprises the physiochemical, hydrochemical, and antigenicity of the protein. T cell epitope prediction models are widely available with various prediction parameters as well as filtering tools for the prediction results. Epitope characterization such as allergenicity and toxicity can be done in silico as well using allergenicity and toxicity predictors. Evaluation of protein–epitope interactions can also be carried out in silico with molecular simulation. We will also discuss current and future developments of breast cancer vaccines using an immunoinformatics approach. Finally, although prediction models have high accuracy, the opposite can happen after being tested in vitro and in vivo. Therefore, further studies are needed to ensure the effectiveness of the vaccine to be developed. Although epitope-based peptide vaccines have the disadvantage of low immunogenicity, the addition of adjuvants can be a solution.

Background: Breast cancer treatment-related lymphedema (BCRL) is a chronic progressive morbidity for which a definitive cure has not yet been achieved. Since axillary lymph node dissection (ALND) is the main risk factor, we have studied the pattern of lymphatic drainage and subclinical lymphedema progression with ICG lymphography (ICG-L) after ALND as a part of our strategy to prevent BCRL. Methods: This study was a prospective cohort of breast cancer patients who underwent ALND between October 2022 and August 2024. We prospectively evaluated postoperative lymphatic drainage with ICG-L. Subclinical lymphedema progression to lymphedema was analyzed using the Kaplan–Meier method. Results: Sixty-three patients were analyzed. Five classifications of lymphatic pathways were identified. The most prevalent pattern was arm and chest wall dermal backflow (DB), draining to internal mammary nodes or type III in 54% of cases, followed by arm DB and collateral drainage to clavicular nodes (type II) in 19.1%, and arm DB only (type I) in 14.3% cases. The cumulative incidence of subclinical lymphedema progression to BCRL was 71.7% [95% confidence interval (CI), 47.0–96.4] with a median BCRL incidence of 13.8 months (95% CI, 10.0–17.5). The hazard rate of BCRL reached its peak at 3 and 12 months. Conclusion: The lymphatic drainage pattern of the arm will mainly drain into the internal mammary and clavicular nodes after ALND. A substantial number of subclinical cases progressing to early-stage BCRL can be detected by ICG-L within the first year. This finding could be used to develop strategies for BCRL prevention.

Pancreatic gastrointestinal stromal tumors (PGISTs) are exceptionally rare, accounting for <5% of extra-gastrointestinal stromal tumors (EGISTs) and <1% of malignant pancreatic neoplasms. We present a unique case of concurrent double primary malignancies in a 46-year-old female with a history of recurrent myoepithelial carcinoma of the parotid gland, managed through surgical resection and adjuvant therapy. She presented with an enlarging abdominal mass, initially suggestive of pancreatic metastasis. Immunohistochemical analysis revealed positive staining for smooth-muscle actin (SMA) and CD34 in both parotid and pancreatic tissues. Importantly, CD117 expression was confined to the pancreatic tissue, confirming the diagnosis of PGIST rather than metastasis. Subsequently, a splenic-sparing distal pancreatectomy was performed, followed by immediate imatinib therapy. This case underscores the potential for the coexistence of rare primary malignancies with unique histopathological characteristics and organ involvement. When encountering a newly developed lesion in a distant organ, surgeons must consider the possibility of metastasis to guide therapeutic decision-making. Early diagnosis and appropriate intervention are paramount, particularly in the case of PGIST, given its infrequent presentation and clinical complexities.

The Cancer Genome Atlas (TCGA) has classified papillary thyroid carcinoma (PTC) into indolent RAS-like and aggressive BRAF-like based on its distinct driver gene mutations. This retrospective study aimed to assess clinicopathology and pERK1/2 expression variations between BRAF-like and RAS-like PTCs and establish predictive models for BRAFV600E and RAS-mutated PTCs. A total of 222 PTCs underwent immunohistochemistry staining to assess pERK1/2 expression and Sanger sequencing to analyze the BRAF and RAS genes. Multivariate logistic regression was employed to develop prediction models. Independent predictors of the BRAFV600E mutation include a nuclear score of 3, the absence of capsules, an aggressive histology subtype, and pERK1/2 levels exceeding 10% (X² = 0.128, p > 0.05, AUC = 0.734, p < 0.001). The RAS mutation predictive model includes follicular histology subtype and pERK1/2 expression > 10% (X² = 0.174, p > 0.05, AUC = 0.8, p < 0.001). We propose using the prediction model concurrently with four potential combination group outcomes. PTC cases included in a combination of the low-BRAFV600E-scoring group and high-RAS-scoring group are categorized as RAS-like (adjOR = 4.857, p = 0.01, 95% CI = 1.470–16.049). PTCs included in a combination of the high-BRAFV600E-scoring group and low-RAS-scoring group are categorized as BRAF-like PTCs (adjOR = 3.091, p = 0.001, 95% CI = 1.594–5.995). The different prediction models indicate variations in biological behavior between BRAF-like and RAS-like PTCs.