Longitudinal radiomics algorithm of posttreatment computed tomography images for early detecting recurrence of hepatocellular carcinoma after resection or ablation

Shen, Jingxian; Zhou, Qian; Chen, Zhi-hang; Chen, Qiaofeng; Chen, Shu‐Ling; Feng, Shi‐Ting; Li, Xin; Wu, Tingfan; Peng, Sui

doi:10.1016/j.tranon.2020.100866

Cited by 15 publications

(11 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“… 27 Shen et al reported that a radiomics model based on a post-treatment CT image was built to predict the early recurrence after ablation, and the AUC value was 0.89. 28 As the abovementioned radiomics model takes 200–300 cases, it is not enough to provide big-data information and verification, causing inadequate stability and robustness. In addition, feature extraction is complicated and dependent on the model, which is difficult to apply and lacks image visualization.…”

Section: Discussionmentioning

confidence: 99%

A Machine Learning Model Based on Health Records for Predicting Recurrence After Microwave Ablation of Hepatocellular Carcinoma

An¹,

Yang²,

Yu³

et al. 2022

JHC

View full text Add to dashboard Cite

Background and Aim Early recurrence (ER) presents a challenge for the survival prognosis of patients with hepatocellular carcinoma (HCC). The aim of this study was to investigate machine learning (ML) models using clinical data for predicting ER after microwave ablation (MWA). Methods Between August 2005 and December 2019, 1574 patients with early-stage HCC underwent MWA at four hospitals were reviewed. Then, 36 clinical data points per patient were collected, and the patients were assigned to the training, internal, and external validation set. Apart from traditional logistic regression (LR), three ML models—random forest, support vector machine, and eXtreme Gradient Boosting (XGBoost)—were built and validated for their predictive ability with the area under ROC curve (AUC). Algorithms such as SHapley Additive exPlanations (SHAP) and local interpretable model-agnostic explanations (LIME) were used to realize their interpretability. Results The three ML models all outperformed LR ( P < 0.001 for all) in predictive ability. When nine variables (tumor number, platelet, α-fetoprotein, comorbidity score, white blood cell, cholinesterase, prothrombin time, neutrophils, and etiology) were extracted simultaneously using recursive feature elimination with cross-validation, the XGBoost model achieved the best discrimination among all models, with an AUC value 0.75 (95% CI [confidence interval]: 0.72–0.78) in the training set, 0.74 (95% CI: 0.69–0.80) in the internal validation set, and 0.76 (95% CI: 0.70–0.82) in the external validation set, and it was interpreted depending on the visualization of risk factors by the SHAP and LIME algorithms. The predictive system of post-ablation recurrence risk stratification was provided on online ( http://114.251.235.51:8001/ ) based on XGboost analysis. Conclusion The XGBoost model based on clinical data can effectively predict ER risk after MWA, which can contribute to surveillance, prevention, and treatment strategies for HCC.

show abstract

Section: Discussionmentioning

confidence: 99%

A Machine Learning Model Based on Health Records for Predicting Recurrence After Microwave Ablation of Hepatocellular Carcinoma

An¹,

Yang²,

Yu³

et al. 2022

JHC

View full text Add to dashboard Cite

show abstract

“…Zheng et al tried to predict both HCC recurrence and postoperative survival in patients with solitary HCC, who were selected for LR using a ML-based radiomics model [67] . This model reached a good predictive survival accuracy with a C-index of 0.71.…”

Section: Author Year Country Methods Aim Findingsmentioning

confidence: 99%

“…The two models showed superior prognostic performance, with C-index of 0.733-0.801 compared with models without radiomics Zheng et al [67] 2018 China Radiomics To estimate postoperative recurrence and survival in patients with solitary HCC…”

Section: Author Year Country Methods Aim Findingsmentioning

confidence: 99%

Artificial intelligence and its role in guiding liver-directed therapy for hepatocellular carcinoma: Is it ready for prime time?

Panettieri¹,

Campisi²,

Bianchi³

et al. 2022

Hepatoma Res

View full text Add to dashboard Cite

Artificial intelligence (AI) is an innovative discipline in medicine, impacting both hepatology and hepato-pancreato-biliary surgery, ensuring reliable outcomes because of its repeatable and efficient algorithms. A considerable number of studies about the efficiency of AI in the management of hepatocellular carcinoma (HCC) have been published. While its diagnostic role is well recognized, providing large amounts of quantitative radiological HCC features, its use in HCC treatment is still debated. Innovative use of AI may help to select the best approach for each patient as it is able to predict the outcomes after resection and/or other treatments. In this review, we assess the role of AI in selecting the best therapeutic option and predicting long-term risks after surgical or interventional treatments for HCC patients. Further studies are needed to consolidate AI applications.

show abstract

“…Finally, unlike most models which are based on preoperative imaging, the ML-radiomics one proposed by Shen et al [ 71 ] was trained on post-treatment CECT exams (within one month after resection or ablation), analyzing lesions suspect for HCC recurrence (<2 cm) without classical dynamic features. They aimed to improve early detection of recurrence after therapy to allow more timely treatment.…”

Section: Treatment Response and Prognosis Predictionmentioning

confidence: 99%

State of the Art in Artificial Intelligence and Radiomics in Hepatocellular Carcinoma

et al. 2021

View full text Add to dashboard Cite

The most common liver malignancy is hepatocellular carcinoma (HCC), which is also associated with high mortality. Often HCC develops in a chronic liver disease setting, and early diagnosis as well as accurate screening of high-risk patients is crucial for appropriate and effective management of these patients. While imaging characteristics of HCC are well-defined in the diagnostic phase, challenging cases still occur, and current prognostic and predictive models are limited in their accuracy. Radiomics and machine learning (ML) offer new tools to address these issues and may lead to scientific breakthroughs with the potential to impact clinical practice and improve patient outcomes. In this review, we will present an overview of these technologies in the setting of HCC imaging across different modalities and a range of applications. These include lesion segmentation, diagnosis, prognostic modeling and prediction of treatment response. Finally, limitations preventing clinical application of radiomics and ML at the present time are discussed, together with necessary future developments to bring the field forward and outside of a purely academic endeavor.

show abstract

Longitudinal radiomics algorithm of posttreatment computed tomography images for early detecting recurrence of hepatocellular carcinoma after resection or ablation

Cited by 15 publications

References 33 publications

A Machine Learning Model Based on Health Records for Predicting Recurrence After Microwave Ablation of Hepatocellular Carcinoma

A Machine Learning Model Based on Health Records for Predicting Recurrence After Microwave Ablation of Hepatocellular Carcinoma

Artificial intelligence and its role in guiding liver-directed therapy for hepatocellular carcinoma: Is it ready for prime time?

State of the Art in Artificial Intelligence and Radiomics in Hepatocellular Carcinoma

Contact Info

Product

Resources

About