The application of machine learning for predicting recurrence in patients with early-stage endometrial cancer: a pilot study

Akazawa, Munetoshi; Hashimoto, Kazunori; Noda, Kazuhiro; Yoshida, Kenichi

doi:10.5468/ogs.20248

Cited by 18 publications

(8 citation statements)

References 7 publications

(20 reference statements)

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“…Hence, they reported LR as the best predictive model for the study ( 108 ). They demonstrated the feasibility of AI prediction in patients with EC through the current investigation and concluded that, in the early stage of EC, the application of an ML algorithm made it possible to predict recurrence ( 111 , 112 ). This finding can help to improve the efficiency and accuracy to predict recurrence and treatment response.…”

Section: Algorithms In Ec Prognosismentioning

confidence: 77%

Machine Learning for Endometrial Cancer Prediction and Prognostication

et al. 2022

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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.

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Section: Algorithms In Ec Prognosismentioning

confidence: 77%

Machine Learning for Endometrial Cancer Prediction and Prognostication

et al. 2022

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“…As in other medical or academic fields, the application of artificial intelligence technologies in maternal-fetal medicine has seen an exponential increase in the number recently and it is getting increasingly utilized in diagnostic and therapeutic medical decisions. Although classical statistical techniques so far have made significant progress in disease causes, pathophysiology, diagnosis, treatment, and prognosis prediction, artificial intelligence technologies appear to be the basis and the next standard methodology for the huge wave of data-based Fourth Industrial Revolution [ 75 ]. Since maternal-fetal medicine deals with two lives (mother and fetus), stricter ethical standards will be needed for the application of artificial intelligence technologies; hence, more attention should be paid to the amount, quality, and accuracy of data and research inputs in this direction.…”

Section: Discussionmentioning

confidence: 99%

Artificial intelligence in obstetrics

Ahn

Lee

2022

Obstet Gynecol Sci

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This study reviews recent advances on the application of artificial intelligence for the early diagnosis of various maternal-fetal conditions such as preterm birth and abnormal fetal growth. It is found in this study that various machine learning methods have been successfully employed for different kinds of data capture with regard to early diagnosis of maternal-fetal conditions. With the more popular use of artificial intelligence, ethical issues should also be considered accordingly.

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“…[31][32][33][34][35][36] Machine learning tools have also been deployed to predict the presence or absence of sperm in testicular biopsy in patients with nonobstructive azoospermia, pregnancy loss after vitro fertilization-embryo transfer, the response of patients with overactive bladder syndrome to anticholinergic medications, and disease recurrence in women with early-stage endometrial or cervical cancer. [37][38][39][40][41] Though promising, these models have not yet been deployed clinically.…”

Section: Clinical Applications Of Machine Learning In Obstetrics and ...mentioning

confidence: 99%