scholarly journals Integrated Machine Learning and Bioinformatic Analyses Constructed a Novel Stemness-Related Classifier to Predict Prognosis and Immunotherapy Responses for Hepatocellular Carcinoma Patients

2022 ◽  
Vol 18 (1) ◽  
pp. 360-373
Author(s):  
Dongjie Chen ◽  
Jixing Liu ◽  
Longjun Zang ◽  
Tijun Xiao ◽  
Xianlin Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hepatocellular Carcinoma

scholarly journals A Novel Epigenetic Machine Learning Model to Define Risk of Progression for Hepatocellular Carcinoma Patients

2021 ◽  
Vol 22 (3) ◽  
pp. 1075
Author(s):  
Luca Bedon ◽  
Michele Dal Bo ◽  
Monica Mossenta ◽  
Davide Busato ◽  
Giuseppe Toffoli ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hepatocellular Carcinoma ◽  
High Risk ◽  
Progression Free Survival ◽  
Low Risk ◽  
Functional Enrichment ◽  
Free Survival ◽  
High Risk Patients ◽  
Risk Of Progression ◽  
Risk Patients

Although extensive advancements have been made in treatment against hepatocellular carcinoma (HCC), the prognosis of HCC patients remains unsatisfied. It is now clearly established that extensive epigenetic changes act as a driver in human tumors. This study exploits HCC epigenetic deregulation to define a novel prognostic model for monitoring the progression of HCC. We analyzed the genome-wide DNA methylation profile of 374 primary tumor specimens using the Illumina 450 K array data from The Cancer Genome Atlas. We initially used a novel combination of Machine Learning algorithms (Recursive Features Selection, Boruta) to capture early tumor progression features. The subsets of probes obtained were used to train and validate Random Forest models to predict a Progression Free Survival greater or less than 6 months. The model based on 34 epigenetic probes showed the best performance, scoring 0.80 accuracy and 0.51 Matthews Correlation Coefficient on testset. Then, we generated and validated a progression signature based on 4 methylation probes capable of stratifying HCC patients at high and low risk of progression. Survival analysis showed that high risk patients are characterized by a poorer progression free survival compared to low risk patients. Moreover, decision curve analysis confirmed the strength of this predictive tool over conventional clinical parameters. Functional enrichment analysis highlighted that high risk patients differentiated themselves by the upregulation of proliferative pathways. Ultimately, we propose the oncogenic MCM2 gene as a methylation-driven gene of which the representative epigenetic markers could serve both as predictive and prognostic markers. Briefly, our work provides several potential HCC progression epigenetic biomarkers as well as a new signature that may enhance patients surveillance and advances in personalized treatment.

A novel machine learning approach for early detection of hepatocellular carcinoma patients

2019 ◽  
Vol 54 ◽  
pp. 116-127 ◽  
Author(s):  
Wojciech Książek ◽  
Moloud Abdar ◽  
U. Rajendra Acharya ◽  
Paweł Pławiak
Keyword(s):  
Machine Learning ◽  
Hepatocellular Carcinoma ◽  
Early Detection ◽  
Learning Approach ◽  
Machine Learning Approach

scholarly journals Development of a deep neural network derived from contours defined by consensus-based guidelines for automatic target segmentation in hepatocellular carcinoma radiotherapy: A study protocol

F1000Research ◽  
2017 ◽  
Vol 6 ◽  
pp. 1929
Author(s):  
Jiandong Zhao ◽  
Jiazhou Wang ◽  
Mingxia Cheng
Keyword(s):  
Machine Learning ◽  
Hepatocellular Carcinoma ◽  
Study Protocol ◽  
Target Identification ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Intraobserver Variability ◽  
Target Delineation ◽  
Artificial Intelligence Technology ◽  
Contouring Guidelines

Hepatocellular carcinoma (HCC) is a leading cause of cancer death in China and around the world. Tumoricidal doses of modern radiation therapy (RT) can now be safely delivered with excellent local control and minimal toxicity. Delivering adequate doses of radiation to the primary tumor, while preserving adjacent healthy organs, depends on accurate target identification. In recent years, different novel machine learning techniques, including artificial intelligence technology, have been exploited in RT with impressive results in automatic image segmentation. If the machine learning algorithms are trained on delineated contours, according to consensus contouring guidelines, it promises greatly reduced interobserver and intraobserver variability in target delineation, thus substantially improving the quality and efficiency of HCC radiotherapy. This study protocol proposes to develop a fully-automated target structure contouring system, which is based on deep neural networks trained on contours delineated according to consensus contouring guidelines in HCC radiotherapy. In addition, the study will evaluate the contouring system’s feasibility and performance during application in normal clinical operations. The study is ongoing (data analysis).

One- year mortality in patients with advanced hepatocellular carcinoma on immunotherapy: Prediction using machine learning models (Preprint)

2021 ◽  
Author(s):  
Thomas Ka-Luen Lui ◽  
Ka Shing, Michael Cheung ◽  
Wai Keung Leung
Keyword(s):  
Machine Learning ◽  
Hepatocellular Carcinoma ◽  
Characteristic Curve ◽  
False Negative ◽  
False Negative Rate ◽  
Absolute Error ◽  
Advanced Hepatocellular Carcinoma ◽  
Data Set ◽  
One Year ◽  
Related Mortality

BACKGROUND Immunotherapy is a new promising treatment for patients with advanced hepatocellular carcinoma (HCC), but is costly and potentially associated with considerable side effects. OBJECTIVE This study aimed to evaluate the role of machine learning (ML) models in predicting the one-year cancer-related mortality in advanced HCC patients treated with immunotherapy METHODS 395 HCC patients who had received immunotherapy (including nivolumab, pembrolizumab or ipilimumab) in 2014 - 2019 in Hong Kong were included. The whole data set were randomly divided into training (n=316) and validation (n=79) set. The data set, including 45 clinical variables, was used to construct six different ML models in predicting the risk of one-year mortality. The performances of ML models were measured by the area under receiver operating characteristic curve (AUC) and the mean absolute error (MAE) using calibration analysis. RESULTS The overall one-year cancer-related mortality was 51.1%. Of the six ML models, the random forest (RF) has the highest AUC of 0.93 (95%CI: 0.86-0.98), which was better than logistic regression (0.82, p=0.01) and XGBoost (0.86, p=0.04). RF also had the lowest false positive (6.7%) and false negative rate (2.8%). High baseline AFP, bilirubin and alkaline phosphatase were three common risk factors identified by all ML models. CONCLUSIONS ML models could predict one-year cancer-related mortality of HCC patients treated with immunotherapy, which may help to select patients who would most benefit from this new treatment option.

scholarly journals Predicting overall survival of patients with hepatocellular carcinoma using a three‐category method based on DNA methylation and machine learning

2019 ◽  
Vol 23 (5) ◽  
pp. 3369-3374 ◽  
Author(s):  
Rui‐Zhao Dong ◽  
Xuan Yang ◽  
Xin‐Yu Zhang ◽  
Ping‐Ting Gao ◽  
Ai‐Wu Ke ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hepatocellular Carcinoma ◽  
Dna Methylation ◽  
Overall Survival

scholarly journals Phospholipids are A Potentially Important Source of Tissue Biomarkers for Hepatocellular Carcinoma: Results of a Pilot Study Involving Targeted Metabolomics

Diagnostics ◽  
2019 ◽  
Vol 9 (4) ◽  
pp. 167
Author(s):  
Erin B. Evangelista ◽  
Sandi A. Kwee ◽  
Miles M. Sato ◽  
Lu Wang ◽  
Christoph Rettenmeier ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hepatocellular Carcinoma ◽  
Fatty Acids ◽  
Free Fatty Acids ◽  
Small Molecules ◽  
Bile Acids ◽  
Random Forests ◽  
Liver Tissue ◽  
Classification Performance ◽  
Tissue Biomarkers

Background: Hepatocellular carcinoma (HCC) pathogenesis involves the alteration of multiple liver-specific metabolic pathways. We systematically profiled cancer- and liver-related classes of metabolites in HCC and adjacent liver tissues and applied supervised machine learning to compare their potential yield for HCC biomarkers. Methods: Tumor and corresponding liver tissue samples were profiled as follows: Bile acids by ultra-performance liquid chromatography (LC) coupled to tandem mass spectrometry (MS), phospholipids by LC-MS/MS, and other small molecules including free fatty acids by gas chromatography—time of flight MS. The overall classification performance of metabolomic signatures derived by support vector machine (SVM) and random forests machine learning algorithms was then compared across classes of metabolite. Results: For each metabolite class, there was a plateau in classification performance with signatures of 10 metabolites. Phospholipid signatures consistently showed the highest discrimination for HCC followed by signatures derived from small molecules, free fatty acids, and bile acids with area under the receiver operating characteristic curve (AUC) values of 0.963, 0.934, 0.895, 0.695, respectively, for SVM-generated signatures comprised of 10 metabolites. Similar classification performance patterns were observed with signatures derived by random forests. Conclusion: Membrane phospholipids are a promising source of tissue biomarkers for discriminating between HCC tumor and liver tissue.

Prediction of hepatocellular carcinoma patient survival using machine learning classification rules.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e15649-e15649
Author(s):  
Wei Zhou ◽  
Huan Chen ◽  
Wenbo Han ◽  
Ji He ◽  
Henghui Zhang
Keyword(s):  
Machine Learning ◽  
Hepatocellular Carcinoma ◽  
Gradient Boosting ◽  
Great Promise ◽  
Survival Prediction ◽  
Surgical Removal ◽  
Features Selection ◽  
Machine Learning Classification ◽  
Learning Techniques ◽  
Pathologic Features

e15649 Background: The outcome prediction of hepatocellular carcinoma (HCC) is conventionally determined by evaluating tissue samples obtained during surgical removal of the primary tumor focusing on their clinical and pathologic features. Recently, accumulating evidence suggests that cancer development is comprehensively modulated by the host’s immune system underlying the importance of immunological biomarkers for the prediction of HCC prognosis. However, an integrated predictive algorism incorporating clinical characteristic and immune features still remain to be established. Methods: We obtained respectable stage II HCC specimens, along with adjacent para-tumor tissues from 221 patients who underwent surgical resection at Eastern Hepatobiliary Surgery Hospital, (Shanghai, China) from 2015 through April 2018. Characteristics such as CD8+, CD163+, tumor-infiltrating lymphocytes (TILs) were obtained for further model construction used to predict the status of 3 survival indexes: Overall Survival (OS ,≤ 24 or > 24 month), Progression Free Survival (PFS, ≤ 6 or > 6 month), and Recurrence/Death (RD). Mutual information and coefficient between each feature and the survival indexes were tested to remove low scoring features after data cleaning and standardization. Furthermore, recursive features selection was preformed to obtain the optimal features combination. Finally, supervised learning techniques include either boosting or bagging strategy were used to fit and predict model with a grid-search method optimizing the parameters. Meanwhile, a cross validation procedure with 0.2 proportion of test cohort was randomly carried out for 10 times to evaluate the model. Results: We finally confirmed 15 biomarkers from the 46 candidates as features for the survival status prediction by using a 221 patients cohort. Among them, the top 10 most important biomarkers, included both clinical and immune attributes. The AUC of our model for survival indexes (OS, PFS, RD) was ranged from 0.76 (RD) to 0.8 (PFS), and the accuracy was above 0.85. Conclusions: We describe the integrative analysis of the clinical and immune features which collectively contribute to the survival index of HCC. Machine learning techniques, such as Gradient Boosting and random forest classifier , have a great promise for using in HCC cancer survival prediction.

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