Machine Learning to Improve Prognosis Prediction of Early Hepatocellular Carcinoma After Surgical Resection

Fan, Yonggang; Sun, Dongwei; Wu, Mingyu; Wang, Ke; Li, Xiangcheng; Wang, Xuehao

doi:10.2147/jhc.s320172

Cited by 14 publications

(6 citation statements)

References 27 publications

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“…Hepatocellular carcinoma (HCC) accounts for approximately 85%-90% of primary liver cancer and has been a heavy global health burden in the past few decades. According to a US national survey, HCC is the 5 th and 7 th cancer type of the estimated new cancer deaths for males and females, respectively (1)(2)(3)(4)(5)(6). Due to cytological variants, HCC has several rare pathological subtypes, including fibrolamellar, clear cell, spindle cell, scirrhous and pleomorphic.…”

Section: Introductionmentioning

confidence: 99%

Development and Validation of a Novel Model to Predict Regional Lymph Node Metastasis in Patients With Hepatocellular Carcinoma

Chen

Shi

et al. 2022

Front. Oncol.

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BackgroundThe evaluation of the nodal status of hepatocellular carcinoma (HCC) is a classic but controversial topic. This study aimed to investigate the incidence of lymph node metastasis (LNM), explore the role of lymph node dissection (LND), and develop and validate a novel model to predict LNM in patients with HCC, not other specified (NOS).MethodsThe study cohort was taken from the Surveillance, Epidemiology, and End Results database. The annual percent change (APC) was calculated using the Joinpoint regression. Survival analyses adopted the competing risk model. The nomogram was constructed based on the least absolute shrinkage and selection operator (LASSO) logistic regression algorithm and validated by calibration curves. The area under the receiver operating characteristic curve (AUROC) was obtained to compare prognostic performance. Decision curve and clinical impact curve analyses were introduced to examine the clinical value of the models.ResultsA total of 8,829 patients were finally enrolled in this study, and 1,346 (15.2%) patients received LND. The LND rate showed no noticeable fluctuation in the last decade, with an APC of 0.5% (P=0.593). LNM was identified in 56 (4.2%) patients and confirmed an independent prognostic factor of HCC patients (P=0.005). There were 2,497 lymph nodes retrieved, and 93 (3.7%) of them were positive. After propensity score matching, LND indicated no direct oncologic benefit and did not worsen competing risks. Moreover, an increased number of lymph nodes retrieved could not improve prognoses. 1,346 patients with LND were further randomly divided into the training and validation sets with the ratio of 1:1. Race, tumor size, clinical T stage, extrahepatic bile duct invasion, and tumor grade were independent risk factors for LNM. The constructed model was well calibrated and showed good discrimination power and net benefits in clinical practice.ConclusionLNM is an independent prognostic factor in HCC, but routine LND seems to be unnecessary in HCC patients. The constructed model could predict the presence of LNM in HCC patients with good performance, which is meaningful to patient stratification and individual treatment strategies optimization.

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Section: Introductionmentioning

confidence: 99%

Development and Validation of a Novel Model to Predict Regional Lymph Node Metastasis in Patients With Hepatocellular Carcinoma

Chen

Shi

et al. 2022

Front. Oncol.

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“…Therapies play a decisive role in the prognosis of HCC patients. Several studies have focused on a machine learning approach for predicting the response and prognosis of different treatments ( 21 , 30 , 31 ). Liu et al.…”

Section: Discussionmentioning

confidence: 99%

A Novel Prognostic Score Based on Artificial Intelligence in Hepatocellular Carcinoma: A Long-Term Follow-Up Analysis

Liu

Wang

et al. 2022

Front. Oncol.

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ObjectiveT cell immunity plays an important role in anti-tumor effects and immunosuppression often leads to the development and relapse of cancer. This study aimed to investigate the effect of T cell numbers on the long-term prognosis of patients with hepatocellular carcinoma (HCC) and construct an artificial neural network (ANN) model to evaluate its prognostic value.MethodsWe enrolled 3,427 patients with HCC at Beijing Ditan Hospital, Capital Medical University, and randomly divided them into two groups of 1,861 and 809 patients as the training and validation sets, respectively. Cox regression analysis was used to screen for independent risk factors of survival in patients with HCC. These factors were used to build an ANN model using Python. Concordance index, calibration curve, and decision curve analysis were used to evaluate the model performance.ResultsThe 1-year, 3-year, 5-year, and 10-year cumulative overall survival (OS) rates were 66.9%, 45.7%, 34.9%, and 22.6%, respectively. Cox multivariate regression analysis showed that age, white blood cell count, creatinine, total bilirubin, γ-GGT, LDH, tumor size ≥ 5 cm, tumor number ≥ 2, portal vein tumor thrombus, and AFP ≥ 400 ng/ml were independent risk factors for long-term survival in HCC. Antiviral therapy, albumin, T cell, and CD8 T cell counts were independent protective factors. An ANN model was developed for long-term survival. The areas under the receiver operating characteristic (ROC) curve of 1-year, 3-year, and 5-year OS rates by ANNs were 0.838, 0.833, and 0.843, respectively, which were higher than those of the Barcelona Clinic Liver Cancer (BCLC), tumor node metastasis (TNM), Okuda, Chinese University Prognostic Index (CUPI), Cancer of the Liver Italian Program (CLIP), Japan Integrated Staging (JIS), and albumin–bilirubin (ALBI) models (P < 0.0001). According to the ANN model scores, all patients were divided into high-, middle-, and low-risk groups. Compared with low-risk patients, the hazard ratios of 5-year OS of the high-risk group were 8.11 (95% CI: 7.0-9.4) and 6.13 (95% CI: 4.28-8.79) (P<0.0001) in the training and validation sets, respectively.ConclusionHigh levels of circulating T cells and CD8 + T cells in peripheral blood may benefit the long-term survival of patients with HCC. The ANN model has a good individual prediction performance, which can be used to assess the prognosis of HCC and lay the foundation for the implementation of precision treatment in the future.

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“…AI has also been applied to develop prognostic risk scores following hepatectomy [40,41] . Traditionally, prognostic risk stratification has relied on "structured" or pre-specified data including patient characteristics, and tumor size/number.…”

Section: Hepatocellular Carcinomamentioning

confidence: 99%

Application of artificial intelligence to hepatobiliary cancer clinical outcomes research

Endo,

Alaimo,

Catalano

et al. 2024

Art Int Surg

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The rapid evolution of modern technology has made artificial intelligence (AI) an important emerging tool in healthcare. AI, which is a broad field of computer science, can be used to develop systems or machines equipped with the ability to tackle tasks that traditionally necessitate human intelligence. AI can be used to perform multifaceted tasks that involve the synthesis of large amounts of data with the generation of solutions, algorithms, and decision support tools. Various AI approaches, including machine learning (ML) and natural language processing (NLP), are increasingly being used to analyze vast healthcare datasets. In addition, visual AI has the potential to revolutionize surgery and the intraoperative experience for surgeons through augmented reality enhancing surgical navigation in real-time. Specific applications of AI in hepatobiliary tumors such as hepatocellular carcinoma and biliary tract cancer can improve patient diagnosis, prognostic risk stratification, as well as treatment allocation based on ML-based models. The integration of radiomics data and AI models can also improve clinical decision making. We herein review how AI may be of particular interest in the care of patients with complex cancers, such as hepatobiliary tumors, as these patients often require a multimodal treatment approach.

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Machine Learning to Improve Prognosis Prediction of Early Hepatocellular Carcinoma After Surgical Resection

Cited by 14 publications

References 27 publications

Development and Validation of a Novel Model to Predict Regional Lymph Node Metastasis in Patients With Hepatocellular Carcinoma

Development and Validation of a Novel Model to Predict Regional Lymph Node Metastasis in Patients With Hepatocellular Carcinoma

A Novel Prognostic Score Based on Artificial Intelligence in Hepatocellular Carcinoma: A Long-Term Follow-Up Analysis

Application of artificial intelligence to hepatobiliary cancer clinical outcomes research

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