Gail model utilization in predicting breast cancer risk in Egyptian women: a cross-sectional study

The effect of the use of the Gail model on breast cancer diagnosis in BIRADs 4a cases

Objective: The BI-RADS classification system and the Gail Model are the scoring systems that contribute to the diagnosis of breast cancer. The aim of the study was to determine the contribution of Gail Model to the diagnosis of breast lesions that were radiologically categorized as BI-RADS 4A.

Material and Methods: We retrospectively examined the medical records of 320 patients between January 2011 and December 2020 whose lesions had been categorized as BI-RADS 4A. Radiological parameters of breast lesions and clinical parameters according to the Gail Model were collected. The relationship between malignant BI-RADS 4A lesions and radiological and clinical parameters was evaluated. In addition, the effect of the Gail Model on diagnosis in malignant BI-RADS 4A lesions was evaluated.

Results: Among radiological features, there were significant differences between lesion size, contour, microcalcification content, echogenicity, and presence of ectasia with respect to the pathological diagnosis (p< 0.05). No significant difference was found between the lesions’ pathological diagnosis and the patients’ Gail score (p> 0.05). An analysis of the features of the Gail model revealed that there was no significant difference between the age of menarche, age at first live birth, presence of a first-degree relative with breast cancer, and a history of breast biopsy and the pathological diagnosis (p> 0.05).

Conclusion: As a conclusion Gail Model does not contribute to the diagnosis of BC, especially in patients with BI-RADS 4A lesions.

Involvement of INF-γ functional single nucleotide polymorphism +874 T/A (rs2430561) in breast cancer risk

According Global Cancer Statistics 2020 GLOBOCAN estimates female breast cancer was found as the most commonly diagnosed cancer, with an estimated 2.3 million new cases (11.7%), and the fourth leading cause (6.9%) of cancer death among women worldwide. Identification of new diagnostic marker sharply characterize the tumor feature is intensive need. The present work was performed to investigate the involvement of the INF-γ + 874 T/A gene polymorphism in different breast cancer prognostic factors. Polymorphism detection analysis was performed on 163 subjects from breast cancer patients, 79 with inflamed cells of breast patients and 144 controls. The gene polymorphism was detected using the amplification refractory mutation system- polymerase chain reaction method (ARMS-PCR). The distribution of INF-γ T + 874A gene polymorphism shows strong significant association between INF-γ + 874 T/A genotypes TT in BC patients (ORTT: 6.41 [95% CI = 2.72-15.1] P < 0.0001) as well as strong significant association regarding T allele (ORT: 1.99 [95% CI = 1.43-2.76] P < 0.0001) when compared to the healthy control. In ICB group the strong association was noted with INF-γ + 874 T/A genotypes AT genotype (ORAT: 2.28 [95% CI = 1.22-4.29] P = 0.007). From the different histological BC hormonal markers the human epidermal growth factor receptor 2 (HER2) was showing significant association in INF-γ + 874 T/A genotypes TT (P = 0.03) and recessive model (TT versus AA + AT P = 0.03). Concerning different BC prognostic models, the poor prognostic one of luminal B, (ER+ve PR+ve Her2+ve) show significant association in the host INF-γ + 874 T/A genotype (TT, P = 0.03) and recessive model (TT versus AA + AT P = 0.02) when compared to the good prognostic hormonal status luminal A model, (ER+ve PR+ve Her2-ve). It seems that this is the first study that interested in correlate the INF-γ + 874 T/A gene polymorphisms in Egyptian BC patients. T allele, TT genotype and recessive model of the INF-γ + 874 T/A gene variants were documented as risk factors for BC pathogenesis. It may be used as practical biomarker to guide the BC carcinogenesis and risk process.

Nomograms predict survival of patients with lymph node-positive, luminal a breast cancer

Background

To develop nomograms for the prediction of the 1-, 3-, and 5-year overall survival (OS) and breast cancer-specific survival (BCSS) for patients with lymph node positive, luminal A breast cancer.

Methods

Thirty-nine thousand fifty-one patients from The Surveillance, Epidemiology, and End Results (SEER) database were included in our study and were set into a training group (n = 19,526) and a validation group (n = 19,525). Univariate analysis and Cox proportional hazards analysis were used to select variables and set up nomogram models on the basis of the training group. Kaplan-Meier curves and the log-rank test were adopted in the survival analysis and curves plotting. C-index, calibration plots and ROC curves were used to performed internal and external validation on the training group and validation group.

Results

Following independent factors were included in our nomograms: Age, marital status, grade, ethnic group, T stage, positive lymph nodes numbers, Metastasis, surgery, radiotherapy, chemotherapy. In both the training group and testing group, the calibration plots show that the actual and nomogram-predicted survival probabilities are consistent greatly. The C-index values of the nomograms in the training and validation cohorts were 0.782 and 0.806 for OS and 0.783 and 0.804 for BCSS, respectively. The ROC curves show that our nomograms have good discrimination.

Conclusions

The nomograms may assist clinicians predict the 1-, 3-, and 5-year OS and BCSS of patients with lymph node positive, luminal A breast cancer.