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Introduction: Breast cancer is a prevalent global health concern characterized by uncontrolled cell growth in breast tissue. In 2020, approximately 2.3 million cases were reported worldwide, with 162,468 new cases and 87,090 fatalities documented in India in 2018. Early diagnosis is crucial for reducing mortality. Our study focused on the use of markers such as the triglyceride-glycemic index and hematological markers to distinguish between benign and malignant breast masses. Methods: A prospective cross-sectional study included female patients with breast mass complaints. The target sample size was 200. Data collection included medical history, clinical breast examination, mammography, cytological assessment via fine-needle aspiration cytology (FNAC), and blood sample collection. The analyzed parameters included neutrophil-to-lymphocyte Ratio (NLR), platelet-to-lymphocyte Ratio (PLR), and triglyceride-glycemic index (TyG). Histopathological examination confirmed the FNAC results. Statistical analysis including propensity score matching, Kolmogorov-Smirnov tests, Mann-Whitney U tests, receiver's operator curve (ROC) analysis, and logistic regression models was conducted using SPSS and R Software. Additional validation was performed on 25 participants. Results: This study included 200 participants. 109 had benign tumors and 91 had malignant tumors. Propensity score matching balanced covariates. NLR did not significantly differ between the groups, while PLR and TyG index differed significantly. NLR correlated strongly with the breast cancer stage, but not with the BI-RADS score. PLR and TyG index showed moderate positive correlations with the BI-RADS score. ROC analysis was used to determine the optimal cutoff values for PLR and TyG index. Logistic regression models combining PLR and TyG index significantly improved malignancy prediction. Conclusions: TyG index and PLR show potential as adjunctive markers for distinguishing breast masses. NLR correlated with cancer stage but not lesion type. Combining TyG and PLR improves prediction, aiding clinical decisions, but large-scale multicenter trials and long-term validation are required for clinical implementation.
RESUMO
BACKGROUND: Oral cancer poses a significant global health burden, with India having the highest prevalence. Effective detection is crucial in effective prevention. This study aimed to evaluate nuclear morphometric parameters (NMPs) in buccal mucosa cells of smokers, correlate NMPs with dysplasia, establish cut off values for grading dysplasia, and investigate the relationship between NMPs and smoking. METHODS: After obtaining ethical approval and informed consent, patients were recruited from the outpatient department of our institution. A target sample size of 250 was calculated. The data included smoking exposure quantified in pack-years, nuclear morphometric analysis (NMA) of buccal mucosa cells obtained through oral cytology using Image J, and the severity of dysplasia of the slides assessed by pathologists. Statistical analysis assessed the impact of dysplasia and the association between nuclear characteristics and smoking exposure. Receiver operating characteristic (ROC) plots determined the potential of these parameters to distinguish dysplasia levels. RESULTS: Significant differences in NMPs were observed among different smoking groups. Dysplasia severity had a significant correlation with NMPs, and strong correlations were found between NMPs and lifetime smoking exposure. ROC analysis established cut off values for NMPs with good sensitivity and specificity for classifying dysplasia severity. CONCLUSIONS: This study highlights the potential of NMA as a tool for oral cancer screening. NMPs can distinguish dysplasia severity and correlate with tobacco (smoking). The efficiency of NMA in a non-invasive oral cytology offers promise for patient-centered screening Additionally, the findings suggest future applications in telepathology and the potential for AI integration in automated screening after conducting multicentric large-scale studies.
