The immune system plays a fundamental role in the response to neoadjuvant chemotherapy (NAC) of locally advanced breast cancer (LABC) patients. Patients with pathological complete response (pCR) after NAC have a higher survival rate. Neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and lymphocyte-to-monocyte ratio (LMR) are peripheral blood indicators of inflammatory response. This investigates the correlation between NLR, PLR, LMR, and other clinicopathological features of breast cancer patients before receiving NAC and pCR. Data of LABC patients who underwent NAC between 2009 and 2018 were retrospectively reviewed. Each patient's peripheral complete blood count was recorded before starting NAC. The cut-off values for neutrophils, lymphocytes, monocytes, and platelets in the peripheral blood and NLR, PLR, and LMR were determined by receiver operating characteristic curve analyses. The records of 131 patients were analyzed and divided into two groups, pCR (+ve) and pCR (−ve), and their clinicopathological features and laboratory findings were compared. pCR was achieved in 23.6% of patients. The cut-off values of neutrophils, lymphocytes, monocytes, and platelets at the time of diagnosis and NLR, PLR, and LMR were, respectively, 4150 μL, 2000 μL, 635 μL, 271 × 10³ μL, 1.95, 119, and 3.35. The pCR rate was higher in patients with low neutrophil count, low NLR, and high lymphocyte count (P = .002, <.001, and .040, respectively). As per the findings of multivariate logistic regression analysis, the independent predictive factors of pCR were clinical tumor size T1 and T2, grade 3, ER negativity, and low NLR (P = .015, .001, .020, .022, and .001, respectively). While NLR was found to be an independent predictive factor of pCR in LABC patients receiving NAC, a similar result was not observed for PLR and LMR. NLR can be a useful biomarker for predicting the response of patients receiving NAC.

Aim: To evaluate the immunogenicity and safety of the CoronaVac vaccine in patients with cancer receiving active systemic therapy. Methods: This multicenter, prospective, observational study was co... Tweetable abstract Immunogenicity developed with CoronaVac in 25 (59.5%) of 42 patients who received at least one cytotoxic drug and in all patients (n = 5) who received monoclonal antibody or immu...

Background/Objectives: Artificial intelligence (AI) has emerged as a promising field in the era of personalized oncology due to its potential to save time and workforce while serving as a supportive tool in patient management decisions. Although several studies in the literature have explored the integration of AI into oncology practice across different tumor types, available data remain limited. In our study, we aimed to evaluate the role of AI in the management of complex cancer cases by comparing the decisions of an in-house tumor board and ChatGPT-4o for patients with various tumor types. Methods: A total of 102 patients with diverse cancer types were included. Treatment and follow-up decisions proposed by both the tumor board and ChatGPT-4o were independently evaluated by two medical oncologists using a 5-point Likert scale. Results: Analysis of agreement levels showed high inter-rater reliability (κ = 0.722, p < 0.001 for tumor board decisions; κ = 0.794, p < 0.001 for ChatGPT decisions). However, concordance between the tumor board and ChatGPT was low, as reflected in the assessments of both raters (Rater 1: κ = 0.211, p = 0.003; Rater 2: κ = 0.376, p < 0.001). Both raters more frequently agreed with the tumor board decisions, and a statistically significant difference between tumor board and AI decisions was observed for both (Rater 1: Z = +4.548, p < 0.001; Rater 2: Z = +3.990, p < 0.001). Conclusions: These findings suggest that AI, in its current form, is not yet capable of functioning as a standalone decision-maker in the management of challenging oncology cases. Clinical experience and expert judgment remain the most critical factors in guiding patient care.

Colorectal cancer (CRC) is the third most common cancer in the world and is the second leading cause of cancer-related deaths. Several mutations are involved in the development of CRC. The prognostic significance of the KRAS mutation has been discussed in many studies. We aimed to investigate the prognostic significance of the number of KRAS mutations in metastatic CRC (mCRC). Patients with mutations in the KRAS gene were included in the study. They were divided into 2 groups as single mutation and multiple mutations in the KRAS gene. For the study, 425 CRC patients were screened. KRAS mutation was positive in 191 patients (45%). One hundred ninety-one patients were included in the study, 171 patients (90%) had single mutations and 20 patients (10%) had multiple mutations. Median progression-free survival was 12.8 months in patients with multiple mutations, while it was 8.8 months in patients with single mutations (P: .05). The median overall survival of patients with multiple mutations was 40.7 months, while it was 22.7 months for patients with single mutations (P = .01) We found that the presence of multiple mutations in KRAS mutant patients was associated with better overall survival and progression-free survival than a single mutation.