Systematic review: radiomics for the diagnosis and prognosis of hepatocellular carcinoma

Harding-Theobald, Emily; Louissaint, Jeremy; Maraj, Bharat; Cuaresma, Edward; Townsend, Whitney; Mendiratta‐Lala, Mishal; Singal, Amit G.; Su, Grace L.; Lok, Anna S.; Parikh, Neehar D.

doi:10.1111/apt.16563

Cited by 80 publications

(40 citation statements)

References 101 publications

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“…There have been many liver cancer studies based on radiomics. However, these studies focused on the distinguishment of microvasculature and prediction of prognosis while may not meet the requirements for clinical application [ 29 ]. In this study, we established the model by analyzing the image features of patients and selected the features related to the pathological types of patients through logistic regression analysis.…”

Section: Discussionmentioning

confidence: 99%

Classification of Hepatocellular Carcinoma and Intrahepatic Cholangiocarcinoma Based on Radiomic Analysis

Mao

Tang

et al. 2022

Computational and Mathematical Methods in Medicine

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Introduction. Considering the narrow window of surgery, early diagnosis of liver cancer is still a fundamental issue to explore. Hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICCA) are considered as two different types of liver cancer because of their distinct pathogenesis, pathological features, prognosis, and responses to adjuvant therapies. Qualitative analysis of image is not enough to make a discrimination of liver cancer, especially early-stage HCC or ICCA. Methods. This retrospective study developed a radiomic-based model in a training cohort of 122 patients. Radiomic features were extracted from computed tomography (CT) scans. Feature selection was operated with the least absolute shrinkage and operator (LASSO) logistic method. The support vector machine (SVM) was selected to build a model. An internal validation was conducted in 89 patients. Results. In the training set, the AUC of the evaluation of the radiomics was 0.855 higher than for radiologists at 0.689. In the valuation cohorts, the AUC of the evaluation was 0.847 and the validation was 0.659, which indicated that the established model has a significantly better performance in distinguishing the HCC from ICCA. Conclusion. We developed a radiomic diagnosis model based on CT image that can quickly distinguish HCC from ICCA, which may facilitate the differential diagnosis of HCC and ICCA in the future.

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Section: Discussionmentioning

confidence: 99%

Classification of Hepatocellular Carcinoma and Intrahepatic Cholangiocarcinoma Based on Radiomic Analysis

Mao

Tang

et al. 2022

Computational and Mathematical Methods in Medicine

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“…Second, in terms of scanning devices and parameters, both ours and Niu et al's study involved only 1 device (64-row CT) and the same tube voltage of 120Kv, whereas the other two studies involved 2 and more scanning devices and the tube voltage was not constant. Nonetheless, a recent systematic review 26 noted that no two studies can be directly compared with each other because proprietary feature extraction tools generate thousands of quantitative variables that have no meaning outside the context of individual studies, and suggested that standardization of protocols and outcome measures, sharing of algorithms and analysis methods, and external validation are necessary to avoid model heterogeneity.…”

Section: Discussionmentioning

confidence: 99%

Noninvasive Imaging Evaluation Based on Computed Tomography of the Efficacy of Initial Transarterial Chemoembolization to Predict Outcome in Patients with Hepatocellular Carcinoma

Dai¹,

Jiang²,

Feng³

et al. 2022

JHC

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Purpose This study aims to develop a new model to more comprehensively and accurately predict the survival of patients with HCC after initial TACE. Patients and Methods The whole cohort (n = 102) was randomly divided into a training cohort and a validation cohort in the ratio of 8:2. The optimal radiomics signatures were screened using the least absolute shrinkage and selection operator algorithm (LASSO) regression for constructing the radscore to predict overall survival (OS). The C-index (95% confidence interval, CI), calibration curve, and decision curve analysis (DCA) were used to evaluate the performance of the models. The independent risk factors (hazard ratio, HR) for predicting OS were stratified by Kaplan–Meier (K-M) analysis and the Log rank test. Results The median OS was 439 days (95% CI: 215.795–662.205) in whole cohort, and in the training cohort and validation cohort, the median OS was 552 days (95% CI: 171.172–932.828), 395 days (95% CI: 309.415–480.585), respectively ( P = 0.889). After multivariate cox regression, the combined radscore-clinical model was consisted of radscore (HR: 2.065, 95% CI: 1.285–3.316; P = 0.0029) and post-response (HR: 1.880, 95% CI: 1.310–2.697; P = 0.0007), both of which were independent risk factors for the OS. In the validation cohort, the efficacy of both the radscore (C-index: 0.769, 95% CI: 0.496–1.000) and combined model (C-index: 0.770, 95% CI: 0.581–0.806) were higher than that of the clinical model (C-index: 0.655, 95% CI: 0.508–0.802). The calibration curve of the combined model for predicting OS presented good consistency between observations and predictions in both the training cohort and validation cohort. Conclusion Noninvasive imaging has a good prediction performance of survival after initial TACE in patients with HCC. The combined model consisting of post-response and radscore may be able to better predict outcome.

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“…In studies on CT radiomics, automatic acquisition protocol and test-retest analysis have proven to be useful in overcoming the bias of acquisition protocols (Caruso et al, 2021). In light of the above limitations, radiomic models based on MRI are more difficult to reproduce across institutions than those based on CT images (Harding-Theobald et al, 2021). CT is recommended prior to TURBT according to the NCCN guidelines, and is still the most commonly used imaging method worldwide in diagnosing and staging BCa, mainly because CT is fast and inexpensive (Babjuk et al, 2019;Flaig et al, 2020).…”

Section: Discussionmentioning

confidence: 99%

Radiomics of Contrast-Enhanced Computed Tomography: A Potential Biomarker for Pretreatment Prediction of the Response to Bacillus Calmette-Guerin Immunotherapy in Non-Muscle-Invasive Bladder Cancer

Chen

et al. 2022

Front. Cell Dev. Biol.

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Background:Bacillus Calmette-Guerin (BCG) instillation is recommended postoperatively after transurethral resection of bladder cancer (TURBT) in patients with high-risk non-muscle-invasive bladder cancer (NMIBC). An accurate prediction model for the BCG response can help identify patients with NMIBC who may benefit from alternative therapy.Objective: To investigate the value of computed tomography (CT) radiomics features in predicting the response to BCG instillation among patients with primary high-risk NMIBC.Methods: Patients with pathologically confirmed high-risk NMIBC were retrospectively reviewed. Patients who underwent contrast-enhanced CT examination within one to 2 weeks before TURBT and received ≥5 BCG instillation treatments in two independent hospitals were enrolled. Patients with a routine follow-up of at least 1 year at the outpatient department were included in the final cohort. Radiomics features based on CT images were extracted from the tumor and its periphery in the training cohort, and a radiomics signature was built with recursive feature elimination. Selected features further underwent an unsupervised radiomics analysis using the newly introduced method, non-negative matrix factorization (NMF), to compute factor factorization decompositions of the radiomics matrix. Finally, a robust component, which was most associated with BCG failure in 1 year, was selected. The performance of the selected component was assessed and tested in an external validation cohort.Results: Overall, 128 patients (training cohort, n = 104; external validation cohort, n = 24) were included, including 12 BCG failures in the training cohort and 11 failures in the validation cohort each. NMF revealed five components, of which component 3 was selected for the best discrimination of BCG failure; it had an area under the curve (AUC) of .79, sensitivity of .79, and specificity of .65 in the training set. In the external validation cohort, it achieved an AUC of .68, sensitivity of .73, and specificity of .69. Survival analysis showed that patients with higher component scores had poor recurrence-free survival (RFS) in both cohorts (C-index: training cohort, .69; validation cohort, .68).Conclusion: The study suggested that radiomics components based on NMF might be a potential biomarker to predict BCG response and RFS after BCG treatment in patients with high-risk NMIBC.

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Systematic review: radiomics for the diagnosis and prognosis of hepatocellular carcinoma

Cited by 80 publications

References 101 publications

Classification of Hepatocellular Carcinoma and Intrahepatic Cholangiocarcinoma Based on Radiomic Analysis

Classification of Hepatocellular Carcinoma and Intrahepatic Cholangiocarcinoma Based on Radiomic Analysis

Noninvasive Imaging Evaluation Based on Computed Tomography of the Efficacy of Initial Transarterial Chemoembolization to Predict Outcome in Patients with Hepatocellular Carcinoma

Radiomics of Contrast-Enhanced Computed Tomography: A Potential Biomarker for Pretreatment Prediction of the Response to Bacillus Calmette-Guerin Immunotherapy in Non-Muscle-Invasive Bladder Cancer

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