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DC Field | Value | Language |
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dc.contributor.author | Phasit Charoenkwan | en_US |
dc.contributor.author | Watshara Shoombuatong | en_US |
dc.contributor.author | Chalaithorn Nantasupha | en_US |
dc.contributor.author | Tanarat Muangmool | en_US |
dc.contributor.author | Prapaporn Suprasert | en_US |
dc.contributor.author | Kittipat Charoenkwan | en_US |
dc.date.accessioned | 2022-10-16T07:01:04Z | - |
dc.date.available | 2022-10-16T07:01:04Z | - |
dc.date.issued | 2021-08-01 | en_US |
dc.identifier.issn | 20754418 | en_US |
dc.identifier.other | 2-s2.0-85112751764 | en_US |
dc.identifier.other | 10.3390/diagnostics11081454 | en_US |
dc.identifier.uri | https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85112751764&origin=inward | en_US |
dc.identifier.uri | http://cmuir.cmu.ac.th/jspui/handle/6653943832/75595 | - |
dc.description.abstract | Radical hysterectomy is a recommended treatment for early-stage cervical cancer. How-ever, the procedure is associated with significant morbidities resulting from the removal of the parametrium. Parametrial cancer invasion (PMI) is found in a minority of patients but the efficient system used to predict it is lacking. In this study, we develop a novel machine learning (ML)-based predictive model based on a random forest model (called iPMI) for the practical identification of PMI in women. Data of 1112 stage IA-IIA cervical cancer patients who underwent primary surgery were collected and considered as the training dataset, while data from an independent cohort of 116 consec-utive patients were used as the independent test dataset. Based on these datasets, iPMI-Econ was then developed by using basic clinicopathological data available prior to surgery, while iPMI-Power was also introduced by adding pelvic node metastasis and uterine corpus invasion to the iPMI-Econ. Both 10-fold cross-validations and independent test results showed that iPMI-Power outperformed other well-known ML classifiers (e.g., logistic regression, decision tree, k-nearest neighbor, multi-layer perceptron, naive Bayes, support vector machine, and extreme gradient boosting). Upon comparison, it was found that iPMI-Power was effective and had a superior performance to other well-known ML classifiers in predicting PMI. It is anticipated that the proposed iPMI may serve as a cost-effective and rapid approach to guide important clinical decision-making. | en_US |
dc.subject | Biochemistry, Genetics and Molecular Biology | en_US |
dc.title | Article ipmi: Machine learning-aided identification of parametrial invasion in women with early-stage cervical cancer | en_US |
dc.type | Journal | en_US |
article.title.sourcetitle | Diagnostics | en_US |
article.volume | 11 | en_US |
article.stream.affiliations | Mahidol University | en_US |
article.stream.affiliations | Chiang Mai University | en_US |
Appears in Collections: | CMUL: Journal Articles |
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