2021
DOI: 10.3390/diagnostics11081454
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iPMI: Machine Learning-Aided Identification of Parametrial Invasion in Women with Early-Stage Cervical Cancer

Abstract: Radical hysterectomy is a recommended treatment for early-stage cervical cancer. However, 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-… Show more

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Cited by 8 publications
(4 citation statements)
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“…As the association between PLNM and its predictive variables is commonly non-linear, conventional statistical modeling methods may have significant limitations, and supervised machine-learning models could fit the data better and more suitably represent the association. This has been recently demonstrated in similar settings [ 17 30 31 ]. The predictive performance of the developed models was satisfactory and comparable among the seven algorithms examined in this study.…”
Section: Discussionsupporting
confidence: 61%
“…As the association between PLNM and its predictive variables is commonly non-linear, conventional statistical modeling methods may have significant limitations, and supervised machine-learning models could fit the data better and more suitably represent the association. This has been recently demonstrated in similar settings [ 17 30 31 ]. The predictive performance of the developed models was satisfactory and comparable among the seven algorithms examined in this study.…”
Section: Discussionsupporting
confidence: 61%
“…The accuracy of their method was reported as 81.31%. Naif et al [26] developed ML methods based on image optimization and feature selection using PMS. No specific accuracy results were provided.…”
Section: Discussion Of the Performancementioning
confidence: 99%
“…Naif et al [25] developed several ML methods based on the previous stages: image optimization and selection of vital features for each class by predictive model selection (PMS). Phasit et al [26] developed an RF-based cervical cancer prediction model called iPMI. Features were extracted along with clinical characteristics with characteristics of cervical neighboring regions called iPMI-Econ.…”
Section: Introductionmentioning
confidence: 99%
“…An ML-based feature extraction approach shares massive advantages over all other cancer detection algorithms in obtaining an improved CAD framework. The ML-based technique accomplishes state-of-the-art findings on complicated computer vision applications [ 5 ]. As per existing studies, most cervical precancerous disease classification investigations focus on individual colposcopy visualizations during acetic acid tests, making it challenging to determine cervical cancer.…”
Section: Introductionmentioning
confidence: 99%