Ensemble learning based on efficient features combination can predict the outcome of recurrence-free survival in patients with hepatocellular carcinoma within three years after surgery

Wang, Liyang; Wu, Mingmin; Zhu, Chengzhan; Li, Rui; Bao, Shaowen; Yang, Shi‐Zhong; Dong, Jiahong

doi:10.3389/fonc.2022.1019009

Cited by 7 publications

(7 citation statements)

References 42 publications

(47 reference statements)

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“…Recently, a few studies have shown the potential utility of automated segmentation-based CT or MRI radiomics in predicting postsurgical recurrence of HCC [ 28 , 29 ]. For instance, Wang et al employed a DL model to automatically segment tumors on arterial phase images in the external cohort ( n = 31) and reported that an MRI-based radiomic-clinical model achieved good accuracy for predicting postsurgical recurrence [ 29 ].…”

Section: Discussionmentioning

confidence: 99%

MRI radiomics based on deep learning automated segmentation to predict early recurrence of hepatocellular carcinoma

Wei,

Zheng,

Zhang

et al. 2024

Insights Imaging

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Objectives To investigate the utility of deep learning (DL) automated segmentation-based MRI radiomic features and clinical-radiological characteristics in predicting early recurrence after curative resection of single hepatocellular carcinoma (HCC). Methods This single-center, retrospective study included consecutive patients with surgically proven HCC who underwent contrast-enhanced MRI before curative hepatectomy from December 2009 to December 2021. Using 3D U-net-based DL algorithms, automated segmentation of the liver and HCC was performed on six MRI sequences. Radiomic features were extracted from the tumor, tumor border extensions (5 mm, 10 mm, and 20 mm), and the liver. A hybrid model incorporating the optimal radiomic signature and preoperative clinical-radiological characteristics was constructed via Cox regression analyses for early recurrence. Model discrimination was characterized with C-index and time-dependent area under the receiver operating curve (tdAUC) and compared with the widely-adopted BCLC and CNLC staging systems. Results Four hundred and thirty-four patients (median age, 52.0 years; 376 men) were included. Among all radiomic signatures, HCC with5 mmtumorborderextensionandliver showed the optimal predictive performance (training set C-index, 0.696). By incorporating this radiomic signature, rim arterial phase hyperenhancement (APHE), and incomplete tumor “capsule,” a hybrid model demonstrated a validation set C-index of 0.706 and superior 2-year tdAUC (0.743) than both the BCLC (0.550; p < 0.001) and CNLC (0.635; p = 0.032) systems. This model stratified patients into two prognostically distinct risk strata (both datasets p < 0.001). Conclusion A preoperative imaging model incorporating the DL automated segmentation-based radiomic signature with rim APHE and incomplete tumor “capsule” accurately predicted early postsurgical recurrence of a single HCC. Critical relevance statement The DL automated segmentation-based MRI radiomic model with rim APHE and incomplete tumor “capsule” hold the potential to facilitate individualized risk estimation of postsurgical early recurrence in a single HCC. Key Points A hybrid model integrating MRI radiomic signature was constructed for early recurrence prediction of HCC. The hybrid model demonstrated superior 2-year AUC than the BCLC and CNLC systems. The model categorized the low-risk HCC group carried longer RFS. Graphical Abstract

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Section: Discussionmentioning

confidence: 99%

MRI radiomics based on deep learning automated segmentation to predict early recurrence of hepatocellular carcinoma

Wei,

Zheng,

Zhang

et al. 2024

Insights Imaging

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“…Therefore, the previous traditional models may fail to show the goodness of fitting or make accurate predictions. Machine learning could train algorithms to detect and recognize complex patterns and adapt to more complex nonlinear relationships, thus it might be superior than the traditional models in medical research (25). In our study, machine learning algorithms, including normal machine learning, Boosting, Bagging, Stacking, and simple deep learning were used and compared on RFS and OS outcomes of HCC patients received PA-TACE.…”

Section: Discussionmentioning

confidence: 99%

“…The rise of artificial intelligence (AI) technology has also brought many new machine learning strategies to predict patient prognosis. In recent researches, several ensemble learning strategies, including Boosting and Bagging algorithm have been developed for HCC and achieved encouraging results (25)(26)(27). Different from other machine learning methods that typically apply one model or one algorithm to a specific task, ensemble learning performs greater flexibility in model selection.…”

Section: Introductionmentioning

confidence: 99%

Development of ensemble learning models for prognosis of hepatocellular carcinoma patients underwent postoperative adjuvant transarterial chemoembolization

et al. 2023

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BackgroundPostoperative adjuvant transarterial chemoembolization (PA-TACE) has been increasing widely used to improve the prognosis of hepatocellular carcinoma (HCC) patients. However, clinical outcomes vary from patient to patient, which calls for individualized prognostic prediction and early management.MethodsA total of 274 HCC patients who underwent PA-TACE were enrolled in this study. The prediction performance of five machine learning models was compared and the prognostic variables of postoperative outcomes were identified.ResultsCompared with other machine learning models, the risk prediction model based on ensemble learning strategies, including Boosting, Bagging, and Stacking algorithms, presented better prediction performance for overall mortality and HCC recurrence. Moreover, the results showed that the Stacking algorithm had relatively low time consumption, good discriminative ability, and the best prediction performance. In addition, according to time-dependent ROC analysis, the ensemble learning strategies were found to perform well in predicting both OS and RFS for the patients. Our study also found that BCLC Stage, hsCRP/ALB and frequency of PA-TACE were relatively important variables in both overall mortality and recurrence, while MVI contributed more to the recurrence of the patients.ConclusionAmong the five machine learning models, the ensemble learning strategies, especially the Stacking algorithm, could better predict the prognosis of HCC patients following PA-TACE. Machine learning models could also help clinicians identify the important prognostic factors that are clinically useful in individualized patient monitoring and management.

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“…By integrating clinical features into the predictive model, superior prognostic performance was realized with a C-index of 0.733-0.801. Several similar studies employed various machine learning modeling methods, including random forest and SVM [20,25,27,50], to establish predictive models and achieved remarkable results in predicting HCC recurrence, with area under the receiver operator characteristic curves (AUCs) ranging from 0.834 to 0.948. These advancements facilitate a more precise evaluation of recurrence in HCC patients following surgical resection.…”

Section: Surgical Therapy For Malignant Liver Tumorsmentioning

confidence: 99%

Machine Learning Combined with Radiomics Facilitating the Personal Treatment of Malignant Liver Tumors

Sheng,

Yang,

Chen

et al. 2023

Biomedicines

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In the realm of managing malignant liver tumors, the convergence of radiomics and machine learning has redefined the landscape of medical practice. The field of radiomics employs advanced algorithms to extract thousands of quantitative features (including intensity, texture, and structure) from medical images. Machine learning, including its subset deep learning, aids in the comprehensive analysis and integration of these features from diverse image sources. This potent synergy enables the prediction of responses of malignant liver tumors to various treatments and outcomes. In this comprehensive review, we examine the evolution of the field of radiomics and its procedural framework. Furthermore, the applications of radiomics combined with machine learning in the context of personalized treatment for malignant liver tumors are outlined in aspects of surgical therapy and non-surgical treatments such as ablation, transarterial chemoembolization, radiotherapy, and systemic therapies. Finally, we discuss the current challenges in the amalgamation of radiomics and machine learning in the study of malignant liver tumors and explore future opportunities.

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Ensemble learning based on efficient features combination can predict the outcome of recurrence-free survival in patients with hepatocellular carcinoma within three years after surgery

Cited by 7 publications

References 42 publications

MRI radiomics based on deep learning automated segmentation to predict early recurrence of hepatocellular carcinoma

MRI radiomics based on deep learning automated segmentation to predict early recurrence of hepatocellular carcinoma

Development of ensemble learning models for prognosis of hepatocellular carcinoma patients underwent postoperative adjuvant transarterial chemoembolization

Machine Learning Combined with Radiomics Facilitating the Personal Treatment of Malignant Liver Tumors

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