Determining the Optimal Cut-Off Values of Serum E2 and FSH for Evaluating the Menopausal Status of Breast Cancer Patients in a Southern Chinese Population

Yang, Lu; Li, Rucai; Yu, Mingcan; Huang, Fen; Zeng, Jiangzheng; LU, Yanda; Yang, Changcheng

doi:10.1155/2022/8716160

Cited by 2 publications

(1 citation statement)

References 24 publications

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“…Thus, machine learning can effectively reduce the cost of drug research and development and improve the stability and accuracy of prediction models ( Wang et al, 2006 ). Scholars have determined that the optimal cut-off values for E2 and FSH in serum can assess CIA in breast cancer populations in southern China ( Yang et al, 2022 ). Estrogen receptors alpha (ERα) in estrogen has been found to be an important target for the treatment of breast cancer ( Chang et al, 2013 ).…”

Section: Discussionmentioning

confidence: 99%

A machine learning-based approach to ERα bioactivity and drug ADMET prediction

Chen

et al. 2023

Front. Genet.

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By predicting ERα bioactivity and mining the potential relationship between Absorption, Distribution, Metabolism, Excretion, Toxicity (ADMET) attributes in drug research and development, the development efficiency of specific drugs for breast cancer will be effectively improved and the misjudgment rate of R&D personnel will be reduced. The quantitative prediction model of ERα bioactivity and classification prediction model of Absorption, Distribution, Metabolism, Excretion, Toxicity properties were constructed. The prediction results of ERα bioactivity were compared by XGBoot, Light GBM, Random Forest and MLP neural network. Two models with high prediction accuracy were selected and fused to obtain ERα bioactivity prediction model from Mean absolute error (MAE), mean squared error (MSE) and R2. The data were further subjected to model-based feature selection and FDR/FPR-based feature selection, respectively, and the results were placed in a voting machine to obtain Absorption, Distribution, Metabolism, Excretion, Toxicity classification prediction model. In this study, 430 molecular descriptors were removed, and finally 20 molecular descriptors with the most significant effect on biological activity obtained by the dual feature screening combined optimization method were used to establish a compound molecular descriptor prediction model for ERα biological activity, and further classification and prediction of the Absorption, Distribution, Metabolism, Excretion, Toxicity properties of the drugs were made. Eighty variables were selected by the model ExtraTreesClassifier Classifie, and 40 variables were selected by the model GradientBoostingClassifier to complete the model-based feature selection. At the same time, the feature selection method based on FDR/FPR is also selected, and the three classification models obtained by the two methods are placed into the voting machine to obtain the final model. The experimental results showed that the model‘s evaluation indexes and roc diagram were excellent and could accurately predict ERα bioactivity and Absorption, Distribution, Metabolism, Excretion, Toxicity properties. The model constructed in this study has high accuracy, fast convergence and robustness, has a very high accuracy for Absorption, Distribution, Metabolism, Excretion, Toxicity and ERα classification prediction, has bright prospects in the biopharmaceutical field, and is an important method for energy conservation and yield increase in the future.

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Section: Discussionmentioning

confidence: 99%

A machine learning-based approach to ERα bioactivity and drug ADMET prediction

Chen

et al. 2023

Front. Genet.

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Radiation-induced Ovarian Ablation and Survival in Breast Cancer: A Single-Institutional Evaluation

Wani,

Khan,

Wani

et al. 2024

Journal of Radiation and Cancer Research

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Background: Radiation-induced ovarian ablation (ROA) was one of the main modalities in the early years in inducing suppression of hormone levels of estradiol (E2) and follicle-stimulating hormones (FSHs) in estrogen receptor/progesterone receptor-positive breast cancer patients. However, as newer techniques such as medical ovarian suppression (MOS) and laparoscopic oophorectomies (LOs) emerged, ROA began to take a back seat. Nonetheless, ROA remains a valuable option for certain patients with specific indications. It is worth noting that ROA is still as effective as MOS and LO in achieving desired hormonal suppression levels. Materials and Methods: After receiving approval from the institutional ethics committee, we enrolled 41 female breast cancer patients who were scheduled for ROA at our institute between 2016 and 2018. Two patients declined to participate and were, therefore, excluded from the final analysis. Patients were evaluated for pre- and postradiation hormone levels, specifically E2 and FSH, cessation of menstrual periods, radiation-induced toxicity, disease-free survival (DFS), overall survival (OS), and the clinical profile as well. Results: Among 39 patients, 89.74% (n = 35) achieved postmenopausal hormone levels after ROA. Only 17.94% (n = 7) patients were menstruating before radiation exposure to the pelvis (postchemotherapy) and among these, 3 patients (42.85%) attained amenorrhea after ROA. The mean age of patients was 42 years, and the most common stage at presentation was Stage III (n = 15) 38.5%. Her2 positivity was seen in (n = 11) 28.2%. Breast conservation surgery was done in 64.1% (n = 25) and mastectomy was done in 23.1% (n = 9). About 71.7% (n = 28) have received tamoxifen, 43.5% (n = 17) were on letrozole, and 17.9% (n = 7) have received trastuzumab. None had shown any Grade 3 or 4 acute skin toxicity during treatment and late radiation-induced complications during their follow-up. The mean 5-year DFS was 62.2% and the mean 5-year OS was 59%. Conclusion: ROA is not widely used anymore due to its irreversible nature, and there are now reversible and noninvasive methods for ovarian suppression. Observing the effectiveness of ROA, it may still be an option for a selective group of breast cancer patients especially those who have contraindications for MOS or are willing for a short course of treatment.

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Determining the Optimal Cut-Off Values of Serum E2 and FSH for Evaluating the Menopausal Status of Breast Cancer Patients in a Southern Chinese Population

Cited by 2 publications

References 24 publications

A machine learning-based approach to ERα bioactivity and drug ADMET prediction

A machine learning-based approach to ERα bioactivity and drug ADMET prediction

Radiation-induced Ovarian Ablation and Survival in Breast Cancer: A Single-Institutional Evaluation

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