Predicting postoperative liver cancer death outcomes with machine learning

Wang, Yong; Ji, Chaopeng; Wang, Ying; Ji, Mu‐Huo; Yang, Jianjun; Zhou, Chengmao

doi:10.1080/03007995.2021.1885361

Cited by 13 publications

(8 citation statements)

References 23 publications

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“…Patients with advanced HCC can also demonstrate a normal level of AFP. 13 The increased CT value (ΔA) and increased ratio of CT value to arterial phase (ΔA%) showed no significant difference between TR and TN groups (P > 0.05). The possible reason was that the heterogeneous parenchyma and enhanced patterns within one tumor lesion and the background of hepatic fibrosis or cirrhosis may have a negative effect on the CT findings.…”

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confidence: 86%

“…12 Wang et al explored factors influencing the postoperative outcomes of patients with HCC through a machine-learning approach with different algorithms, and found that the RF model had the best efficiency in predicting postoperative mortality, with an AUC of 0.803. 13 To our knowledge, there are few studies focusing on model construction to predict TACE treatment responses using different machine learning algorithms based on clinical and radiological characteristics of patients with unresectable HCCs. Therefore, this study aimed to explore and verify the feasibility of machine learning models based on clinical and contrast-enhanced computed tomography (CT) image features to predict response after initial TACE, and compare the prediction efficiency of various machine learning models to determine a model with the best prediction performance.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of Early Treatment Response to Initial Conventional Transarterial Chemoembolization Therapy for Hepatocellular Carcinoma by Machine-Learning Model Based on Computed Tomography

Dong¹,

Lin²,

Lin³

et al. 2021

JHC

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The treatment response to initial conventional transarterial chemoembolization (cTACE) is essential for the prognosis of patients with hepatocellular carcinoma (HCC). This study explored and verified the feasibility of machine-learning models based on clinical data and contrast-enhanced computed tomography (CT) image findings to predict early responses of HCC patients after initial cTACE treatment. Patients and Methods: Overall, 110 consecutive unresectable HCC patients who were treated with cTACE for the first time were retrospectively enrolled. Clinical data and imaging features based on contrast-enhanced CT were collected for the selection of characteristics. Treatment responses were evaluated based on the modified Response Evaluation Criteria in Solid Tumors (mRECIST) by postoperative CT examination within 2 months after the procedure. Python (version 3.70) was used to develop machine learning models. Least absolute shrinkage and selection operator (LASSO) algorithm was applied to select features with the impact on predicting treatment response after the first TACE procedure. Six machine learning algorithms were used to build predictive models, including XGBoost, decision tree, support vector machine, random forest, k-nearest neighbor, and fully convolutional networks, and their performances were compared using receiver operator characteristic (ROC) curves to determine the best performing model. Results: Following TACE, 31 patients (28.2%) were described as responsive to TACE, while 72 patients (71.8%) were nonresponsive to TACE. Portal vein tumor thrombosis type, albumin level, and distribution of tumors within the liver were selected for predictive model building. Among the models, the RF model showed the best performance, with area under the curve (AUC), accuracy, sensitivity, and specificity of 0.802, 0.784, 0.904, and 0.480, respectively. Conclusion: Machine learning models can provide an accurate prediction of the early response of initial TACE treatment for HCC, which can help in individualizing clinical decision-making and modification of further treatment strategies for patients with unresectable HCC.

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confidence: 86%

Section: Introductionmentioning

confidence: 99%

Prediction of Early Treatment Response to Initial Conventional Transarterial Chemoembolization Therapy for Hepatocellular Carcinoma by Machine-Learning Model Based on Computed Tomography

Dong¹,

Lin²,

Lin³

et al. 2021

JHC

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“…Aside from CNN, the reference literature lists SVM, RF, and DT as some of the most common algorithms utilized widely in MLBDD. Furthermore, several researchers are emphasizing ensemble techniques in MLBDD [ 127 , 130 ]. Nonetheless, when compared to other ML algorithms, CNN is the most dominating.…”

Section: Discussionmentioning

confidence: 99%

Machine-Learning-Based Disease Diagnosis: A Comprehensive Review

2022

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Globally, there is a substantial unmet need to diagnose various diseases effectively. The complexity of the different disease mechanisms and underlying symptoms of the patient population presents massive challenges in developing the early diagnosis tool and effective treatment. Machine learning (ML), an area of artificial intelligence (AI), enables researchers, physicians, and patients to solve some of these issues. Based on relevant research, this review explains how machine learning (ML) is being used to help in the early identification of numerous diseases. Initially, a bibliometric analysis of the publication is carried out using data from the Scopus and Web of Science (WOS) databases. The bibliometric study of 1216 publications was undertaken to determine the most prolific authors, nations, organizations, and most cited articles. The review then summarizes the most recent trends and approaches in machine-learning-based disease diagnosis (MLBDD), considering the following factors: algorithm, disease types, data type, application, and evaluation metrics. Finally, in this paper, we highlight key results and provides insight into future trends and opportunities in the MLBDD area.

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“…The ML protocol is shown in Figure 1. ML has been widely used for medical research and has shown excellent performance in multiple aspects, such as diagnosis, efficacy evaluation, and prognosis prediction (7)(8)(9)(10).…”

Section: Challenges In Diagnosis and Prognosis Prediction For Tsccmentioning

confidence: 99%

A narrative review on machine learning in diagnosis and prognosis prediction for tongue squamous cell carcinoma

Lin¹,

Tang²,

Liu³

et al. 2022

Transl Cancer Res

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Background: Tongue squamous cell carcinoma (TSCC) is the most common subtype of oral cavity squamous cell carcinoma (OCSCC), and it also has the worst prognosis. It is crucial to find an effective way to solve the challenges in diagnosis and prognosis prediction for TSCC. Machine learning (ML) has been widely used in medical research and has shown good performance. It can be used for feature extraction, feature selection, model construction, etc. Radiomics and deep learning (DL), the new components of ML, have also been utilized to explore the relationship between image features and diseases. The current study aimed to highlight the importance of ML as a potential method for addressing the challenges in diagnosis and prognosis prediction of TSCC by reviewing studies on ML in TSCC. Methods: The studies on ML in TSCC in PubMed, Scopus, Web of Science, and China National Knowledge Infrastructure published between the dates of inception of these databases and April 30, 2022, were reviewed. Key Content and Findings: ML (including radiomics and DL) which was used in diagnosis and prognosis prediction for TSCC, has shown promising performance.Conclusions: Despite its limitations, ML is still a potential approach that can help to deal with the challenges in diagnosis and prognosis prediction for TSCC. Nevertheless, more efforts are needed to enhance the usefulness of ML in this field.

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Predicting postoperative liver cancer death outcomes with machine learning

Cited by 13 publications

References 23 publications

Prediction of Early Treatment Response to Initial Conventional Transarterial Chemoembolization Therapy for Hepatocellular Carcinoma by Machine-Learning Model Based on Computed Tomography

Prediction of Early Treatment Response to Initial Conventional Transarterial Chemoembolization Therapy for Hepatocellular Carcinoma by Machine-Learning Model Based on Computed Tomography

Machine-Learning-Based Disease Diagnosis: A Comprehensive Review

A narrative review on machine learning in diagnosis and prognosis prediction for tongue squamous cell carcinoma

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