Statistical Meta-Analysis of Risk Factors for Endometrial Cancer and Development of a Risk Prediction Model Using an Artificial Neural Network Algorithm

Hutt, Suzanna; Mihaies, Denis; Karteris, Emmanouíl; Michael, Agnieszka; Payne, Annette; Chatterjee, Jayanta

doi:10.3390/cancers13153689

Cited by 9 publications

(8 citation statements)

References 82 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A systematic review suggested an increased risk of endometrial hyperplasia with MP containing estrogen plus progestogen therapy (EPT) 4 . A meta-analysis suggested increased risk of endometrial cancer (relative risk [RR], 1.2) with noncontinuous combined EPT (type of progestogen not specified) but not with continuous EPT 10 . Oral MP should be adequately dosed for prevention of endometrial hyperplasia (eg, 200 mg/d for 12-14 d/mo) 11,12 .…”

Section: Formulation Dosing Routes Of Administration and Safetymentioning

confidence: 99%

“…4 A meta-analysis suggested increased risk of endometrial cancer (relative risk [RR], 1.2) with noncontinuous combined EPT (type of progestogen not specified) but not with continuous EPT. 10 Oral MP should be adequately dosed for prevention of endometrial hyperplasia (eg, 200 mg/d for 12-14 d/mo). 11,12 Off-label use of a levonorgestrelcontaining intrauterine device to prevent endometrial hyperplasia may avoid adverse systemic effects of progestogens and can protect against unwanted pregnancy in women initiating hormone therapy for symptom management before their final menstrual period.…”

Section: Table 1 Frequency Of Adverse Drug Reactionsmentioning

confidence: 99%

See 1 more Smart Citation

The 2022 hormone therapy position statement of The North American Menopause Society

2022

Menopause

283

219

View full text Add to dashboard Cite

and identifies future research needs. An Advisory Panel of clinicians and researchers expert in the field of women's health and menopause was recruited by NAMS to review the 2017 Position Statement, evaluate new literature, assess the evidence, and reach consensus on recommendations, using the level of evidence to identify the strength of recommendations and the quality of the evidence. The Advisory Panel's recommendations were reviewed and approved by the NAMS Board of Trustees.Hormone therapy remains the most effective treatment for vasomotor symptoms (VMS) and the genitourinary syndrome of menopause and has been shown to prevent bone loss and fracture. The risks of hormone therapy differ depending on type, dose, duration of use, route of administration, timing of initiation, and whether a progestogen is used. Treatment should be individualized using the best available evidence to maximize benefits and minimize risks, with periodic reevaluation of the benefits and risks of continuing therapy.For women aged younger than 60 years or who are within 10 years of menopause onset and have no contraindications, the benefit-risk ratio is favorable for treatment of bothersome VMS and prevention of bone loss. For women who initiate hormone therapy more than 10 years from menopause onset or who are aged older than 60 years, the benefit-risk ratio appears less favorable because of the greater absolute risks of coronary heart disease, stroke, venous thromboembolism, and dementia. Longer durations of therapy should be for documented indications such as persistent VMS, with shared decision-making and periodic reevaluation. For bothersome genitourinary syndrome of menopause symptoms not relieved with over-the-counter therapies in women without indications for use of systemic hormone therapy, low-dose vaginal estrogen therapy or other therapies (eg, vaginal dehydroepiandrosterone or oral ospemifene) are recommended.

show abstract

Section: Formulation Dosing Routes Of Administration and Safetymentioning

confidence: 99%

Section: Table 1 Frequency Of Adverse Drug Reactionsmentioning

confidence: 99%

The 2022 hormone therapy position statement of The North American Menopause Society

2022

Menopause

283

219

View full text Add to dashboard Cite

show abstract

“…The findings of the study effectively reduce the amount of unnecessary invasive testing of EC patients. This might be a valuable tool for physicians to utilize in concert with additional indicators to determine whether individuals require enhanced preventative measures before the potential development of EC ( 126 ).…”

Section: Algorithms In Ec Prognosismentioning

confidence: 99%

Machine Learning for Endometrial Cancer Prediction and Prognostication

et al. 2022

View full text Add to dashboard Cite

Endometrial cancer (EC) is a prevalent uterine cancer that remains a major contributor to cancer-associated morbidity and mortality. EC diagnosed at advanced stages shows a poor therapeutic response. The clinically utilized EC diagnostic approaches are costly, time-consuming, and are not readily available to all patients. The rapid growth in computational biology has enticed substantial research attention from both data scientists and oncologists, leading to the development of rapid and cost-effective computer-aided cancer surveillance systems. Machine learning (ML), a subcategory of artificial intelligence, provides opportunities for drug discovery, early cancer diagnosis, effective treatment, and choice of treatment modalities. The application of ML approaches in EC diagnosis, therapies, and prognosis may be particularly relevant. Considering the significance of customized treatment and the growing trend of using ML approaches in cancer prediction and monitoring, a critical survey of ML utility in EC may provide impetus research in EC and assist oncologists, molecular biologists, biomedical engineers, and bioinformaticians to further collaborative research in EC. In this review, an overview of EC along with risk factors and diagnostic methods is discussed, followed by a comprehensive analysis of the potential ML modalities for prevention, screening, detection, and prognosis of EC patients.

show abstract

“…This capability of machine learning algorithms offers a possibility for the investigation and development of risk prediction and diagnostic prediction models in cancer research [36]. It is evident that the use of machine learning methods can improve our understanding of cancer occurrence and progression [35,37]. Thus, developing machine learning-based breast cancer risk prediction models with improved discriminatory power can stratify women into different risk groups, which are useful for guiding the choice for personalized breast cancer screening in order to achieve a good balance in the risk benefit and cost benefit for breast cancer screening.…”

Section: Principal Findingsmentioning

confidence: 99%

An Assessment of the Predictive Performance of Current Machine Learning–Based Breast Cancer Risk Prediction Models: Systematic Review

Gao¹,

Li²,

Jin³

et al. 2022

JMIR Public Health Surveill

View full text Add to dashboard Cite

Background Several studies have explored the predictive performance of machine learning–based breast cancer risk prediction models and have shown controversial conclusions. Thus, the performance of the current machine learning–based breast cancer risk prediction models and their benefits and weakness need to be evaluated for the future development of feasible and efficient risk prediction models. Objective The aim of this review was to assess the performance and the clinical feasibility of the currently available machine learning–based breast cancer risk prediction models. Methods We searched for papers published until June 9, 2021, on machine learning–based breast cancer risk prediction models in PubMed, Embase, and Web of Science. Studies describing the development or validation models for predicting future breast cancer risk were included. The Prediction Model Risk of Bias Assessment Tool (PROBAST) was used to assess the risk of bias and the clinical applicability of the included studies. The pooled area under the curve (AUC) was calculated using the DerSimonian and Laird random-effects model. Results A total of 8 studies with 10 data sets were included. Neural network was the most common machine learning method for the development of breast cancer risk prediction models. The pooled AUC of the machine learning–based optimal risk prediction model reported in each study was 0.73 (95% CI 0.66-0.80; approximate 95% prediction interval 0.56-0.96), with a high level of heterogeneity between studies (Q=576.07, I2=98.44%; P<.001). The results of head-to-head comparison of the performance difference between the 2 types of models trained by the same data set showed that machine learning models had a slightly higher advantage than traditional risk factor–based models in predicting future breast cancer risk. The pooled AUC of the neural network–based risk prediction model was higher than that of the nonneural network–based optimal risk prediction model (0.71 vs 0.68, respectively). Subgroup analysis showed that the incorporation of imaging features in risk models resulted in a higher pooled AUC than the nonincorporation of imaging features in risk models (0.73 vs 0.61; Pheterogeneity=.001, respectively). The PROBAST analysis indicated that many machine learning models had high risk of bias and poorly reported calibration analysis. Conclusions Our review shows that the current machine learning–based breast cancer risk prediction models have some technical pitfalls and that their clinical feasibility and reliability are unsatisfactory.

show abstract

Statistical Meta-Analysis of Risk Factors for Endometrial Cancer and Development of a Risk Prediction Model Using an Artificial Neural Network Algorithm

Cited by 9 publications

References 82 publications

The 2022 hormone therapy position statement of The North American Menopause Society

The 2022 hormone therapy position statement of The North American Menopause Society

Machine Learning for Endometrial Cancer Prediction and Prognostication

An Assessment of the Predictive Performance of Current Machine Learning–Based Breast Cancer Risk Prediction Models: Systematic Review

Contact Info

Product

Resources

About