Establishment of Two Data Mining Models of Lung Cancer Screening Based on Three Gene Promoter Methylations Combined with Telomere Damage

Wang, Wei; Feng, Xiaolei; Duan, Xiaoran; Tan, Shengkui; Wang, Sihua; Wang, Tuanwei; Feng, Feifei; Yi-ming, WU; Wu, Yongjun

doi:10.5301/jbm.5000232

Cited by 10 publications

(12 citation statements)

References 23 publications

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“…DTs are tree-structured schemes where the nodes represent the input variables, and the leaves correspond to decision outcomes [26]. They are widely used for classification purposes and can be intuitive [3]. ANNs are developed on the basis of biological neurons of the human brain and trained to generate an output outcome as a weighted combination of AGING the input variables [29,30].…”

Section: Discussionmentioning

confidence: 99%

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Development of a machine learning-based multimode diagnosis system for lung cancer

Duan

Cao

Hong

et al. 2020

Aging

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As an emerging technology, artificial intelligence has been applied to identify various physical disorders. Here, we developed a three-layer diagnosis system for lung cancer, in which three machine learning approaches including decision tree C5.0, artificial neural network (ANN) and support vector machine (SVM) were involved. The area under the curve (AUC) was employed to evaluate their decision powers. In the first layer, the AUCs of C5.0, ANN and SVM were 0.676, 0.736 and 0.640, ANN was better than C5.0 and SVM. In the second layer, ANN was similar with SVM but superior to C5.0 supported by the AUCs of 0.804, 0.889 and 0.825. Much higher AUCs of 0.908, 0.910 and 0.849 were identified in the third layer, where the highest sensitivity of 94.12% was found in C5.0. These data proposed a three-layer diagnosis system for lung cancer: ANN was used as a broadspectrum screening subsystem basing on 14 epidemiological data and clinical symptoms, which was firstly adopted to screen high-risk groups; then, combining with additional 5 tumor biomarkers, ANN was used as an auxiliary diagnosis subsystem to determine the suspected lung cancer patients; C5.0 was finally employed to confirm lung cancer patients basing on 22 CT nodule-based radiomic features.

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Section: Discussionmentioning

confidence: 99%

“…Nowadays, chemical diagnosis, imaging diagnosis, cell and histocytological diagnosis are the primary diagnostic methods of lung cancer [3]. Among them, computed tomography (CT)-based imaging diagnosis is the AGING primary tool to detect lung cancer at early stages [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Development of a machine learning-based multimode diagnosis system for lung cancer

Duan

Cao

Hong

et al. 2020

Aging

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“…The accuracy for three model validation sets was 84.15%, 75.61%, and 80.49%, respectively. The combined Fisher model showed good ability to detect lung cancer, which is superior to the lung cancer diagnosis Fisher model (0.670, 95%CI 0.569-0.761) established with FHIT, RASSF1A, p16 promoter methylation, and relative telomere length in our prophase research [20] . This may be due to the miRNAs biomarkers has better specificity compared with gene or other biomarkers.…”

Section: Discussionmentioning

confidence: 64%

“…SVM model were established for lung cancer diagnostic in our study, which combined 10 miRNAs and 6 symptoms, had a higher accuracy. The combined SVM model with miRNAs was superior in lung cancer diagnosis in this study compared to models with methylation and telomere biomarkers in our prophase research [20] . The accuracy and AUC of combined SVM model in our study were also better than the results of other studies on gene and other biomarkers using ANN or SVM and so on.…”

Section: Discussionmentioning

confidence: 71%

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Diagnostic Value of Plasma MicroRNAs for Lung Cancer Using Support Vector Machine Model

Wang¹,

Ding²,

Duan³

et al. 2019

J. Cancer

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Aim: Small single-stranded non-coding RNAs (miRNAs) play an important role in carcinogenesis through degrading target mRNAs. However, the diagnostic value of miRNAs was not explored in lung cancers. In this study, a support-vector-machine (SVM) model for diagnosis of lung cancer was established based on plasma miRNAs biomarkers, clinical symptoms and epidemiology material. Methods: The expressions of plasma miRNA were examined with SYBR Green-based quantitative real-time PCR. Results: We identified that the expressions of 10 plasma miRNAs (miR-21, miR-20a, miR-210, miR-145, miR-126, miR-223, miR-197, miR-30a, miR-30d, miR-25), smoking status, fever, cough, chest pain or tightness, bloody phlegm, haemoptysis, were significantly different between lung cancer and control groups (P<0.05). The accuracies of the combined SVM, miRNAs SVM, symptom SVM, combined Fisher, miRNAs Fisher and symptom Fisher were 96.34%, 80.49%, 84.15%, 84.15%, 75.61%, and 80.49%, respectively; AUC of these six model were 0.976, 0.841, 0.838, 0.865, 0.750, and 0.801, respectively. The accuracy and AUC of combined SVM were higher than the other 5 models (P<0.05). Conclusions: Our findings indicate that SVM model based on plasma miRNAs biomarkers may serve as a novel, accurate, noninvasive method for auxiliary diagnosis of lung cancer.

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Combination of deep learning and ensemble machine learning using intraoperative video images strongly predicts recovery of urinary continence after robot‐assisted radical prostatectomy

et al. 2023

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BackgroundWe recently reported the importance of deep learning (DL) of pelvic magnetic resonance imaging in predicting the degree of urinary incontinence (UI) following robot‐assisted radical prostatectomy (RARP). However, our results were limited because the prediction accuracy was approximately 70%.AimTo develop a more precise prediction model that can inform patients about UI recovery post‐RARP surgery using a DL model based on intraoperative video images.Methods and ResultsThe study cohort comprised of 101 patients with localized prostate cancer undergoing RARP. Three snapshots from intraoperative video recordings showing the pelvic cavity (prior to bladder neck incision, immediately following prostate removal, and after vesicourethral anastomosis) were evaluated, including pre‐ and intraoperative parameters. We evaluated the DL model plus simple or ensemble machine learning (ML), and the area under the receiver operating characteristic curve (AUC) was analyzed through sensitivity and specificity. Of 101, 64 and 37 patients demonstrated “early continence (using 0 or 1 safety pad at 3 months post‐RARP)” and “late continence (others),” respectively, at 3 months postoperatively. The combination of DL and simple ML using intraoperative video snapshots with clinicopathological parameters had a notably high performance (AUC, 0.683–0.749) to predict early recovery from UI after surgery. Furthermore, combining DL with ensemble artificial neural network using intraoperative video snapshots had the highest performance (AUC, 0.882; sensitivity, 92.2%; specificity, 78.4%; overall accuracy, 85.3%) to predict early recovery from post‐RARP incontinence, with similar results by internal validation. The addition of clinicopathological parameters showed no additive effects for each analysis using DL, EL and simple ML.ConclusionOur findings suggest that the DL algorithm with intraoperative video imaging is a reliable method for informing patients about the severity of their recovery from UI after RARP, although it is not clear if our methods are reproducible for predicting long‐term UI and pad‐free continence.

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Establishment of Two Data Mining Models of Lung Cancer Screening Based on Three Gene Promoter Methylations Combined with Telomere Damage

Abstract: The SVM and DT models for diagnosing lung cancer were successfully developed through the combined detection of p16, RASSF1A and FHIT promoter methylation and RTL, which provided useful tools for screening lung cancer.

Cited by 10 publications

References 23 publications

Development of a machine learning-based multimode diagnosis system for lung cancer

Development of a machine learning-based multimode diagnosis system for lung cancer

Diagnostic Value of Plasma MicroRNAs for Lung Cancer Using Support Vector Machine Model

Combination of deep learning and ensemble machine learning using intraoperative video images strongly predicts recovery of urinary continence after robot‐assisted radical prostatectomy

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