Extraction of influential markers affecting HCC survival chances and its prediction

Singh, Siddhartha; Sinha, Shobhit; Rawat, Sumanu

doi:10.1109/iccci.2019.8821832

Cited by 2 publications

(3 citation statements)

References 4 publications

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“…Regarding the best model, the authors achieved 75.19% classification accuracy and 66.5% F1‐score. Thanks to the public release by Santos et al, 12 their dataset was used by multiple authors 13–30 . All proposed models were predicted in terms of classification accuracy and/or F‐1 score.…”

Section: Introductionmentioning

confidence: 99%

“…The classification performance achieved by the proposed algorithm was accuracy = 78%, F‐score = 80%. While Singh et al 16 created model using boruta and neural network algorithm. This hybrid model gained an accuracy of 84%.…”

Section: Introductionmentioning

confidence: 99%

“…Thanks to the public release by Santos et al, 12 their dataset was used by multiple authors. [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30] All proposed models were predicted in terms of classification accuracy and/or F-1 score. Sawhney et al 13 used a firefly algorithm (BFA) and a random forest (RF) to predict survival of different types of cancer.…”

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confidence: 99%

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NCA‐GA‐SVM: A new two‐level feature selection method based on neighborhood component analysis and genetic algorithm in hepatocellular carcinoma fatality prognosis

Książek

Turza

Pławiak

2022

Numer Methods Biomed Eng

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Hepatocellular carcinoma (HCC) is one of the major challenges facing biomedical research. Despite the high lethality, methods to predict mortality for this type of aggressive malignant tumor are insufficient. Machine learning is recognized by many authors as a valuable, yet poorly studied tool in this field. Undoubtedly, searching for new feature selection methods is significant in building an effective machine-learning model. In this study, we propose the novel hybrid model using neighborhood components analysis, genetic algorithm and support vector machine classifier (NCA-GA-SVM). Because SVM works with default parameters characterized by low classification results, we decided to use GA for the proper optimization and feature selection. As reported in the available literature, NCA and GA obtain high classification results. Here, we decided to combine these approaches, building a two-level algorithm for HCC fatality prognosis. We used a well-known dataset collected from 165 patients at Coimbra's Hospital and University Center, Portugal. Our results revealed 96.36% classification accuracy and 95.52% F1-score. Additionally, we compared all data for these metrics published so far. We demonstrated that our algorithm achieved the highest accuracy and can be successfully applied for the assessment of hepatocellular carcinoma mortality in the future. Our findings bring methodological value for future HCC studies and emphasize the possibility of using machine-learning techniques to improve the quality of medical decisions.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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NCA‐GA‐SVM: A new two‐level feature selection method based on neighborhood component analysis and genetic algorithm in hepatocellular carcinoma fatality prognosis

Książek

Turza

Pławiak

2022

Numer Methods Biomed Eng

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Deep Fusion Models of Multi-Phase CT and Selected Clinical Data for Preoperative Prediction of Early Recurrence in Hepatocellular Carcinoma

et al. 2020

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Post-operative early recurrence (ER) of hepatocellular carcinoma (HCC) is one of the leading causes of death. The prediction of the ER of HCC before treatment contributes to guiding treatment and follow-up protocols. In recent years, CT radiomics signatures have been proven effective in several studies in predicting early recurrence of HCC, there are still two major challenges. First, the radiomics features extracted were low or mid-level features, which may not fully characterize HCC heterogeneity. Second, the fusion approach of clinical textual data and image information is in little consensus. In this paper, we proposed a deep-learning based prediction model to extract high-level features from the triple-phase CT images and compare its performance with traditional radiomics model and clinical model. The accuracy and area under the curve (AUC) of receiver operating characteristics of three models was 69.52%/0.723, 67.04%/0.64, 76.03%/0.75, respectively. In addition, we proposed four fusion models to combine clinical data and highlevel features. Among them, Fusion model D performed best, achieving a higher prediction accuracy of 78.66% and AUC of 0.8248. Moreover, fusion models with a joint loss function can further improve the prediction performance to 80.49% and 0.8331.

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Extraction of influential markers affecting HCC survival chances and its prediction

Cited by 2 publications

References 4 publications

NCA‐GA‐SVM: A new two‐level feature selection method based on neighborhood component analysis and genetic algorithm in hepatocellular carcinoma fatality prognosis

NCA‐GA‐SVM: A new two‐level feature selection method based on neighborhood component analysis and genetic algorithm in hepatocellular carcinoma fatality prognosis

Deep Fusion Models of Multi-Phase CT and Selected Clinical Data for Preoperative Prediction of Early Recurrence in Hepatocellular Carcinoma

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