Neoadjuvant chemotherapy (NACT) is the standard treatment for locally advanced, high-risk breast cancer. Pathological complete response (pCR) improves survival. Peripheral blood-derived indices reflecting systemic inflammation and nutritional status have long been used as predictive and prognostic markers in solid malignancies. This retrospective study investigates whether eight commonly used indices in patients receiving NACT affect pCR and survival. This study includes 624 locally advanced breast cancer patients who received NACT. The biomarker indices were calculated from peripheral blood samples taken two weeks before starting chemotherapy. The indices’ optimal cut-off values were determined using ROC Curve analysis. During a median follow-up period of 42 months, recurrence was detected in 146 patients, and 75 patients died. pCR was observed in 166 patients (26.6%). In univariate analysis, NLR, PLR, SII, PNI, HALP, and HRR were statistically significantly associated (p = 0.00; p = 0.03; p = 0.03; p = 0.02; p = 0.00; p = 0.02 respectively), but in multivariate analysis, only NLR was significantly predictive for pCR(p = 0.04). In multivariate analysis, the HGB/RDW score significantly predicted DFS(p = 0.04). The PNI score was identified as a marker predicting survival for both OS and PFS (p = 0.01, p = 0.01, respectively). In conclusion, peripheral blood-derived indices have prognostic and predictive values on pCR and survival. However, further studies are needed to validate our findings.

Background and Objectives: Lung cancer is the leading cause of cancer-related deaths. Spread through air spaces (STAS) is an adverse prognostic factor that has become increasingly known in recent years. This study aims to investigate the impact of STAS presence on overall survival (OS) and disease-free survival (DFS) in patients with surgically resected stage IA-IIIA lung cancer and to identify clinicopathological features associated with STAS. Materials and Methods: This research involved 311 lung cancer surgery patients. The relationship between the presence of STAS in the patients’ surgical pathology and OS and DFS values was examined. Clinicopathological features associated with the presence of STAS were determined. Results: There were 103 (33%) STAS-positive patients. Adenocarcinoma histological subtype, perineural invasion (PNI), and lymphovascular invasion (LVI) were significantly correlated with being STAS positive. STAS significantly predicted DFS and OS. One-year and five-year DFS rates were significantly lower in the STAS-positive group compared to the STAS-negative group (65% vs. 88%, 29% vs. 62%, respectively, p ≤ 0.001). Similarly, one-year and five-year OS rates were significantly lower in the STAS-positive group compared to the STAS-negative group (92% vs. 94%, 54% vs. 88%, respectively, p ≤ 0.001). In multivariate analysis, STAS was found to be an independent prognostic factor for both DFS and OS (HR: 3.2 (95%CI: 2.1–4.8) and 3.1 (95%CI: 1.7–5.5), p < 0.001 and <0.001, respectively). Conclusions: In our study, STAS was found to be an independent prognostic biomarker in operated stage IA-IIIA lung cancer patients. It may be a beneficial pathological biomarker in predicting the survival of patients and managing their treatments.

Objective: Immunotherapies are commonly employed for the treatment of non-small-cell lung cancer (NSCLC). However, predictive biomarkers still need to be improved to predict responses to these agents. The lymphocyte–albumin (LA) laboratory index has not been evaluated before in this patient group. The aim of this study was to analyze the relation between the LA index and the survival rate of metastatic NSCLC patients who had immunotherapy after at least one round of chemotherapy. Methods: The research included 227 patients diagnosed with metastatic NSCLC, who were administered nivolumab after at least one round of chemotherapy. The LA index was calculated by multiplying lymphocyte count and albumin concentration. The optimal threshold values for the index were established by the examination of the ROC curve for both overall survival (OS) and progression-free survival (PFS). Oncological data were obtained retrospectively from patient files, and survival analyses were performed. Results: The median follow-up was 7.9 months. Progression was observed in 129 (56.9%) patients. A total of 97 (42.7%) patients died during the follow-up. The cutoff values of the LA index to predict OS and PFS were determined as 52.87 and 57.67, respectively. The low-LA group had significantly lowered OS and PFS compared to the high-LA group. LA was found to be an independent prognostic factor for PFS (hazard ratio 4.47; 95% confidence interval, 2.73–7.34; p < 0.001) and OS (hazard ratio 6.24; 95% confidence interval, 3.46–11.25; p < 0.001) in the multivariate regression analysis. Conclusions: In this study, we observed that the LA index independently predicts OS and PFS in immunotherapy-treated metastatic NSCLC patients. Its ease of application, low cost, and noninvasive nature make it a potential guide for clinicians in predicting treatment responses and survival.

Background and Objectives: Venous thromboembolism (VTE) is a serious complication frequently encountered in cancer patients and is associated with high morbidity. In patients undergoing cancer treatment—particularly those receiving chemotherapy—VTE increases treatment-related complications and has a direct impact on mortality. The development of VTE in oncology patients varies depending on cancer type, treatment protocols, and individual patient characteristics. The Khorana Risk Score (KRS) is a validated risk assessment tool used to estimate the risk of VTE development in patients receiving chemotherapy. KRS provides risk estimations based on the patient’s clinical features, cancer type, and treatment process. This study aims to investigate the prognostic value of the Khorana Risk Score in predicting VTE development and overall survival in patients with metastatic gastric cancer. Materials and Methods: This retrospective study used data from 337 metastatic gastric cancer patients who presented to Kartal Dr. Lütfi Kırdar City Hospital between January 2012 and June 2024. Patients were categorized into intermediate- and high-risk groups according to the Khorana Risk Score. The study’s primary endpoints were the development of VTE and overall survival. Results: There was no statistically significant difference in VTE incidence (p = 0.27) or overall survival (11.9 months vs. 11.5 months, p = 0.23) between patients in the intermediate- and high-risk groups. Conclusions: These results indicate that the Khorana Risk Score is insufficient in predicting VTE development in patients with metastatic gastric cancer and has a weak association with overall survival outcomes. In conclusion, this study demonstrates the KRS’s inadequacy in predicting VTE and survival outcomes in patients with metastatic gastric cancer, highlighting the need for more tailored approaches.

Background/Objectives: Triple-negative breast cancer (TNBC) is a heterogeneous entity lacking ER, PR, and HER2, with aggressive biology and high recurrence risk. Neoadjuvant chemotherapy (NACT) is the standard of care, and a pathological complete response (pCR) is a surrogate marker for survival. Within TNBC, apocrine differentiation (TNAC) is a distinct subtype, often androgen receptor (AR)-positive, with lower chemosensitivity but a favorable prognosis. Comparative studies of TNAC versus classical TNBC remain limited. This study aimed to define clinical and biological differences between TNAC and non-apocrine TNBC (NA-TNBC), representing the largest TNAC cohort to date. Methods: This retrospective study included 129 non-metastatic TNBC patients treated with NACT and surgery (2010–2020). Patients were classified as TNAC or NA-TNBC. Demographic, clinicopathological, and immunohistochemical data (including Ki-67 and AR) were collected. Tumor-infiltrating lymphocytes (TILs), delta Ki-67, pathological complete response (pCR), and survival outcomes were evaluated. Results: Of 129 TNBC patients, 45 (34.9%) were TNAC. AR positivity occurred in 64.4% of TNACs. TNAC patients were predominantly postmenopausal. pCR rates were significantly lower in TNAC (6.6% vs. 30.9%, p = 0.002). TNACs exhibited lower baseline Ki-67, delta Ki-67, and TIL positivity (13.3% vs. 30%). Despite this, 5-year overall survival was higher in TNAC (86% vs. 78%). Delta Ki-67 > 20% strongly predicted pCR across the cohort (p < 0.001). Carboplatin was rarely used in TNAC (8.3%), but was associated with a higher pCR rate (50% vs. 2.4%, p = 0.018). Conclusions: TNAC represents a biologically distinct TNBC subtype, characterized by low pCR but favorable survival. Recognition of its unique features may guide treatment de-escalation and exploration of AR-targeted therapies. Prospective studies focusing on TNAC are warranted.

Background: Systemic inflammation is an essential factor in the formation of the tumor microenvironment and has an impact on patient response to immune checkpoint inhibitors. Although there is a growing interest in biomarkers of inflammation, there is a gap in understanding their predictive value for response to nivolumab in clinical practice. The objective of this research was to design and assess a multi-algorithmic machine learning (ML) model based on regular systemic inflammation measurements to forecast the response of treatment to nivolumab. Methods: An analysis of a retrospective real-world cohort of 177 nivolumab-treated patients was performed. Baseline inflammatory biomarkers, such as neutrophils, lymphocytes, platelets, CRP, LDH, albumin, and derived indices (NLR, PLR, SII), were derived. After preprocessing, 5 ML models (Logistic Regression, Random Forest, Gradient Boosting, Support Vector Machine, and Neural Network) were trained and tested on a 70/30 stratified split. Accuracy, AUC, precision, recall, F1-score, and Brier score were used to evaluate predictive performance. The interpretability of the model was analyzed based on feature-importance ranking and SHAP. Results: Gradient Boosting performed best in terms of discriminative (AUC = 0.816), whereas Support Vector Machine performed best on overall predictive profile (accuracy = 0.833; F1 = 0.909; recall = 1.00; and Brier Score = 0.134) performance. CRP and LDH became the most common predictors of all models, and then neutrophils and platelets. SHAP analysis has verified that high CRP and LDH were strong predictors that forced the prediction to non-response, whereas higher lymphocyte levels were weak predictors that increased the response probability prediction. Conclusions: Machine learning models based on common inflammatory systemic markers give useful predictive information about nivolumab response. Their discriminative ability is moderate, but the high performance of SVM and Gradient Boosting pays attention to the opportunities of inflammation-based ML tools in making personalized decisions regarding immunotherapy. A combination of clinical, radiomic, and molecular biomarkers in the future can increase predictive capabilities and clinical use.