Recurrence predictive models for patients with hepatocellular carcinoma after radiofrequency ablation using support vector machines with feature selection methods

Liang, Ja-Der; Tseng, Yi-Ju; Huang, Guan-Tarn; Lai, Feipei; Yang, Pei–Ming

doi:10.1016/j.cmpb.2014.09.001

Cited by 40 publications

(33 citation statements)

References 54 publications

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“…It is difficult to ensure that there is no difference in the positive rate of VI at each random cross‐validation. Some predictive models for early recurrence of HCC based on clinicopathological characteristics have been proposed . Several recent studies screening biomarkers of VI in HCC were from the perspective of genes or microRNA; these studies tried to predict recurrence risk by predicting VI.…”

Section: Discussionmentioning

confidence: 99%

Long non‐coding RNA expression profile can predict early recurrence in hepatocellular carcinoma after curative resection

Wei

Huang

et al. 2018

Hepatology Research

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Section: Discussionmentioning

confidence: 99%

Long non‐coding RNA expression profile can predict early recurrence in hepatocellular carcinoma after curative resection

Wei

Huang

et al. 2018

Hepatology Research

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“…7 ANN and logistic regression uses data mining technique for web-based liver cancer prediction within 6 years of diagnosis with type II diabetes. 10 In traditional SVM the extracted features are limited and the setting of the characteristics is often based on subjective human experience. In 2017, Al-antari et al 9 classified a medical image for breast cancer via DBN.…”

Section: Introductionmentioning

confidence: 99%

“…8 RF is an eminent classifier, but it contains errors, noise and image artifacts. 10 In traditional SVM the extracted features are limited and the setting of the characteristics is often based on subjective human experience. 11 Various algorithms like genetic algorithm (GA), 12 particle swarm optimization (PSO), ant colony optimization (ACO), harmony search, artificial bee colony (ABC), and firefly algorithm (FA) 13 are used for DBN parameters tuning.…”

Section: Introductionmentioning

confidence: 99%

Optimizing deep belief network parameters using grasshopper algorithm for liver disease classification

Renukadevi¹,

Karunakaran²

2019

Int J Imaging Syst Tech

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Image processing plays a vital role in many areas such as healthcare, military, scientific and business due to its wide variety of advantages and applications. Detection of computed tomography (CT) liver disease is one of the difficult tasks in the medical field. Hand crafted features and classifications are the two types of methods used in the previous approaches, to classify liver disease. But these classification results are not optimal. In this article, we propose a novel method utilizing deep belief network (DBN) with grasshopper optimization algorithm (GOA) for liver disease classification. Initially, the image quality is enhanced by preprocessing techniques and then features like texture, color and shape are extracted. The extracted features are reduced by utilizing the dimensionality reduction method like principal component analysis (PCA). Here, the DBN parameters are optimized using GOA for recognizing liver disease. The experiments are performed on the real time and open source CT image datasets which embraces normal, cyst, hepatoma, and cavernous hemangiomas, fatty liver, metastasis, cirrhosis, and tumor samples. The proposed method yields 98% accuracy, 95.82% sensitivity, 97.52% specificity, 98.53% precision, and 96.8% F-1 score in simulation process when compared with other existing techniques. K E Y W O R D S deep belief network (DBN), grasshopper optimization algorithm (GOA), liver disease classification, principal component analysis (PCA)

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“…Radical resection or liver transplantation is the best treatment for primary HCC, but not suitable for patients with poor liver function and metastases occurring. The 5‐year survival rate postoperation is only 30–40%, as a result of high frequency of relapse . Therefore, it is urgent to explore more accurate biomarkers for identifying HCC and predicting clinical outcome.…”

Section: Introductionmentioning

confidence: 99%

“…The 5-year survival rate postoperation is only 30-40%, as a result of high frequency of relapse. 4 Therefore, it is urgent to explore more accurate biomarkers for identifying HCC and predicting clinical outcome. Recently, miRNA have been reported to be valuable for diagnosing and predicting HCC.…”

Section: Introductionmentioning

confidence: 99%

miRNA for diagnosis and clinical implications of human hepatocellular carcinoma

Wang

Tian

2015

Hepatology Research

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Hepatocellular carcinoma (HCC) is one of the most prevalent malignancies, as a result of being asymptomatic at early stage, subsequent late clinical confirmation and poor prognosis. It is urgent to search more accurate biomarkers for diagnosing early HCC and predicting prognosis. Many factors participate in liver carcinogenesis, including dysregulation of miRNA. miRNA were endogenously expressed non-coding single-stranded small RNA with 19-25 nucleotides. Accumulating evidences have showed that miRNA from circulation and solitary tumors may be useful to classify the differentiation degree and stages of HCC, detect the hepatitis B/C virus-related HCC, and predict the survival rate after surgical resection or orthotopic liver transplantation. In this review, we summarize dysregulated miRNA, their roles in diagnosis and clinical implications of HCC.

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Recurrence predictive models for patients with hepatocellular carcinoma after radiofrequency ablation using support vector machines with feature selection methods

Cited by 40 publications

References 54 publications

Long non‐coding RNA expression profile can predict early recurrence in hepatocellular carcinoma after curative resection

Long non‐coding RNA expression profile can predict early recurrence in hepatocellular carcinoma after curative resection

Optimizing deep belief network parameters using grasshopper algorithm for liver disease classification

miRNA for diagnosis and clinical implications of human hepatocellular carcinoma

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