Aim: To investigate how COVID-19 fear and anxiety (COV-FA) affects chemotherapy adherence in patients with cancer. Materials & methods: The records of 3661 patients with chemotherapy (CT) appointme...

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...

Introduction: Due to variety of treatment alternatives for testicular tumours, parameters other than existing staging criteria are also needed. Most studies have revealed the correlation between cancer and inflammation. In this study, we aimed to investigate the value of preoperative inflammatory markers between early-stage testicular tumours and patients with advanced-stage, their relationship with tumour pathology and their importance in predicting stage. To calculate the differences between inflammatory markers, stage 1 tumours localized to the testis and advanced-stage tumours spread beyond the testis were classified into 2 groups according to tumour pathology. Materials and Methods: The data of 112 patients undergoing inguinal orchiectomy in between 2008 and 2018 were recorded retrospectively. Neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR) and systemic immune-inflammation index (SII) were calculated by using the numbers of blood cell counts based systemic markers of inflammation. The differences between markers of inflammation were calculated by dividing tumours into 2 groups including early-stage and advanced- stage testicle tumours. Results: According to the results of preoperative inflammatory markers in predicting the stage; in the seminoma group, the difference between the median NLR (2.37 vs. 4.39, p = 0.012), LMR (3.80 vs. 2.40, p = 0.018) and SII (612 vs. 1,127, p = 0.009) of stage 1 and advanced stage were statistically significant, while in the non-seminoma group, only the difference between median PLR (99 vs. 154, p = 0.002) of stage 1 and advanced stage was statistically significant. Sensitivity and specificity of predicting advanced stage according to cut-off values of markers were 69 and 75% in NLR (3.21), 83 and 75% in LMR, and 59 and 75% in SII in the seminoma group; on the other hand, in the non-seminoma group, the sensitivity, and specificity of predicting the advanced stage of PLR cut-off (104) were 71 and 88% respectively. Conclusions: The clinical use of inflammatory biomarkers in testicular tumours may represent an important step in understanding germ cell tumours biology and in supporting staging criteria and prognostic criteria.

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.

Background: There is no standard treatment recommended at category 1 level in international guidelines for subsequent therapy after cyclin-dependent kinase 4/6 inhibitor (CDK4/6) based therapy. We aimed to evaluate which subsequent treatment oncologists prefer in patients with disease progression under CDKi. In addition, we aimed to show the effectiveness of systemic treatments after CDKi and whether there is a survival difference between hormonal treatments (monotherapy vs. mTOR-based). Methods: A total of 609 patients from 53 centers were included in the study. Progression-free-survivals (PFS) of subsequent treatments (chemotherapy (CT, n:434) or endocrine therapy (ET, n:175)) after CDKi were calculated. Patients were evaluated in three groups as those who received CDKi in first-line (group A, n:202), second-line (group B, n: 153) and ≥ 3rd-line (group C, n: 254). PFS was compared according to the use of ET and CT. In addition, ET was compared as monotherapy versus everolimus-based combination therapy. Results: The median duration of CDKi in the ET arms of Group A, B, and C was 17.0, 11.0, and 8.5 months in respectively; it was 9.0, 7.0, and 5.0 months in the CT arm. Median PFS after CDKi was 9.5 (5.0–14.0) months in the ET arm of group A, and 5.3 (3.9–6.8) months in the CT arm (p = 0.073). It was 6.7 (5.8–7.7) months in the ET arm of group B, and 5.7 (4.6–6.7) months in the CT arm (p = 0.311). It was 5.3 (2.5–8.0) months in the ET arm of group C and 4.0 (3.5–4.6) months in the CT arm (p = 0.434). Patients who received ET after CDKi were compared as those who received everolimus-based combination therapy versus those who received monotherapy ET: the median PFS in group A, B, and C was 11.0 vs. 5.9 (p = 0.047), 6.7 vs. 5.0 (p = 0.164), 6.7 vs. 3.9 (p = 0.763) months. Conclusion: Physicians preferred CT rather than ET in patients with early progression under CDKi. It has been shown that subsequent ET after CDKi can be as effective as CT. It was also observed that better PFS could be achieved with the subsequent everolimus-based treatments after first-line CDKi compared to monotherapy ET.