Artificial intelligence in assessment of hepatocellular carcinoma treatment response

Spieler, Bradley; Sabottke, Carl; Moawad, Ahmed W; Gabr, Ahmed; Bashir, Mustafa R.; Gian, Richard Kinh; Yaghmai, Vahid; Rozenberg, Radu; Gerena, Marielia; Yacoub, Joseph; Elsayes, Khaled M.

doi:10.1007/s00261-021-03056-1

Cited by 14 publications

(8 citation statements)

References 79 publications

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“…The network uses residual convolution to improve feature utilization, which not only ensures good performance but also increases the network depth, and the gradient disappearance problem in the deep network is alleviated as the network depth increases [ 20 ]. The attention mechanism can be focused on a specific part of the image.…”

Section: Methodsmentioning

confidence: 99%

Preliminary Evaluation of Artificial Intelligence-Based Anti-Hepatocellular Carcinoma Molecular Target Study in Hepatocellular Carcinoma Diagnosis Research

Wang

Wei

Deng³

et al. 2022

BioMed Research International

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In this paper, in-depth research analysis of anti-hepatocellular carcinoma molecular targets for hepatocellular carcinoma diagnosis was conducted using artificial intelligence. Because BRD4 plays an important role in gene transcription for cell cycle regulation and apoptosis, tumor-targeted therapy by inhibiting the expression or function of BRD4 has received increasing attention in the field of antitumor research. Study subjects in small samples were used as the validation set for validating each diagnostic model constructed based on the training set. The diagnostic effect of each model in the validation set is evaluated by calculating the sensitivity, specificity, and compliance rate, and the model with the best and most stable diagnostic value is selected by combining the results of model construction, validation, and evaluation. The total sample was divided into a training set and test set by using a stratified sampling method in the ratio of 7 : 3. Logistic regression, weighted k -nearest neighbor, decision tree, and BP artificial neural network were used in the training set to construct diagnostic models for early-stage liver cancer, respectively, and the optimal parameters of the corresponding models were obtained, and then, the constructed models were validated in the test set. To evaluate the diagnostic efficacy, stability, and generalization ability of the four classification methods more robustly, a 10-fold crossover test was performed for each classification method. BRD4 is an epigenetic regulator that is associated with the upregulation of expression of various oncogenic drivers in tumors. Targeting BRD4 with pharmacological inhibitors has emerged as a novel approach for tumor treatment. However, before we implemented this topic, there were no detailed studies on whether BRD4 could be used for the treatment of HCC, the role of BRD4 in HCC cell proliferation and apoptosis, and the ability of small molecule BRD4 inhibitors to induce apoptosis in hepatocellular carcinoma cells.

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Section: Methodsmentioning

confidence: 99%

Preliminary Evaluation of Artificial Intelligence-Based Anti-Hepatocellular Carcinoma Molecular Target Study in Hepatocellular Carcinoma Diagnosis Research

Wang

Wei

Deng³

et al. 2022

BioMed Research International

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“…The 3 radiologists had read >2,000 liver CT studies per year. When contouring the tumor, the edge of the observed focal lesion within the liver was defined as an imaging appearance that is distinctive from the background according to the Liver Reporting and Data System [20,21]. The image processing and semiautomatic tumor segmentation were performed using In-telliSpace Discovery (Philips, Eindhoven, The Netherlands).…”

Section: Cect Image Segmentation and Radiomic Feature Extractionmentioning

confidence: 99%

Evolutionary Learning-Derived Clinical-Radiomic Models for Predicting Early Recurrence of Hepatocellular Carcinoma after Resection

et al. 2021

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Background and Aims: Current prediction models for early recurrence of hepatocellular carcinoma (HCC) after surgical resection remain unsatisfactory. The aim of this study was to develop evolutionary learning-derived prediction models with interpretability using both clinical and radiomic features to predict early recurrence of HCC after surgical resection. Methods: Consecutive 517 HCC patients receiving surgical resection with available contrast-enhanced computed tomography (CECT) images before resection were retrospectively enrolled. Patients were randomly assigned to a training set (n = 362) and a test set (n = 155) in a ratio of 7:3. Tumor segmentation of all CECT images including noncontrast phase, arterial phase, and portal venous phase was manually performed for radiomic feature extraction. A novel evolutionary learning-derived method called genetic algorithm for predicting recurrence after surgery of liver cancer (GARSL) was proposed to design prediction models for early recurrence of HCC within 2 years after surgery. Results: A total of 143 features, including 26 preoperative clinical features, 5 postoperative pathological features, and 112 radiomic features were used to develop GARSL preoperative and postoperative models. The area under the receiver operating characteristic curves (AUCs) for early recurrence of HCC within 2 years were 0.781 and 0.767, respectively, in the training set, and 0.739 and 0.741, respectively, in the test set. The accuracy of GARSL models derived from the evolutionary learning method was significantly better than models derived from other well-known machine learning methods or the early recurrence after surgery for liver tumor (ERASL) preoperative (AUC = 0.687, p < 0.001 vs. GARSL preoperative) and ERASL postoperative (AUC = 0.688, p < 0.001 vs. GARSL postoperative) models using clinical features only. Conclusion: The GARSL models using both clinical and radiomic features significantly improved the accuracy to predict early recurrence of HCC after surgical resection, which was significantly better than other well-known machine learning-derived models and currently available clinical models.

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“… 105 In hepatocellular carcinoma, AI can provide great benefits in patients’ management by predicting the response to a variety of treatments, including transarterial chemoembolization. 106 , 107 …”

Section: Predicting Prognosis and Treatment Responsementioning

confidence: 99%

“…105 In hepatocellular carcinoma, AI can provide great benefits in patients' management by predicting the response to a variety of treatments, including transarterial chemoembolization. 106,107 Immunotherapy is one of the most promising tools in oncological treatment. However, despite its remarkable success rate, immunotherapy is still curbed by high costs and toxicities, while its clinical benefit is limited to a specific subset of patients.…”

Section: Predicting Prognosis and Treatment Responsementioning

confidence: 99%

Artificial Intelligence in the Era of Precision Oncological Imaging

Cellina

Cè

Khenkina

et al. 2022

Technol Cancer Res Treat

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Rapid-paced development and adaptability of artificial intelligence algorithms have secured their almost ubiquitous presence in the field of oncological imaging. Artificial intelligence models have been created for a variety of tasks, including risk stratification, automated detection, and segmentation of lesions, characterization, grading and staging, prediction of prognosis, and treatment response. Soon, artificial intelligence could become an essential part of every step of oncological workup and patient management. Integration of neural networks and deep learning into radiological artificial intelligence algorithms allow for extrapolating imaging features otherwise inaccessible to human operators and pave the way to truly personalized management of oncological patients. Although a significant proportion of currently available artificial intelligence solutions belong to basic and translational cancer imaging research, their progressive transfer to clinical routine is imminent, contributing to the development of a personalized approach in oncology. We thereby review the main applications of artificial intelligence in oncological imaging, describe the example of their successful integration into research and clinical practice, and highlight the challenges and future perspectives that will shape the field of oncological radiology.

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Artificial intelligence in assessment of hepatocellular carcinoma treatment response

Cited by 14 publications

References 79 publications

Preliminary Evaluation of Artificial Intelligence-Based Anti-Hepatocellular Carcinoma Molecular Target Study in Hepatocellular Carcinoma Diagnosis Research

Preliminary Evaluation of Artificial Intelligence-Based Anti-Hepatocellular Carcinoma Molecular Target Study in Hepatocellular Carcinoma Diagnosis Research

Evolutionary Learning-Derived Clinical-Radiomic Models for Predicting Early Recurrence of Hepatocellular Carcinoma after Resection

Artificial Intelligence in the Era of Precision Oncological Imaging

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