A radiomics nomogram for predicting transcatheter arterial chemoembolization refractoriness of hepatocellular carcinoma without extrahepatic metastasis or macrovascular invasion

Sheen, Heesoon; Kim, Jin Sil; Lee, Jeong Kyong; Choi, Sun Young; Baek, Seung Yon; Kim, Jung Young

doi:10.1007/s00261-020-02884-x

Cited by 16 publications

(14 citation statements)

References 28 publications

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“…The results showed significantly higher pyradiomics scores in the response group. The response group also showed smaller tumor size, which was consistent with previous reports ( 32 , 33 ). There was a significantly negative correlation between the pyradiomics score and tumor size.…”

Section: Discussionsupporting

confidence: 92%

Development and validation of a pyradiomics signature to predict initial treatment response and prognosis during transarterial chemoembolization in hepatocellular carcinoma

Peng

Lu²,

Huang³

et al. 2022

Front. Oncol.

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We aimed to develop and validate a pyradiomics model for preoperative prediction of initial treatment response to transarterial chemoembolization (TACE) in patients with hepatocellular carcinoma (HCC). To this end, computed tomography (CT) images were acquired from multi-centers. Numerous pyradiomics features were extracted and machine learning approach was used to build a model for predicting initial response of TACE treatment. The predictive accuracy, overall survival (OS), and progression-free survival (PFS) were analyzed. Gene Set Enrichment Analysis (GSEA) was further used to explore signaling pathways in The Cancer Genome Atlas (TCGA)-HCC cohort. Overall, 24 of the 1,209 pyradiomic features were selected using the least absolute shrinkage and selection operator (LASSO) algorithm. The pyradiomics signature showed high predictive accuracy across the discovery set (AUC: 0.917, 95% confidence interval [CI]: 86.93-96.39), validation set 1 (AUC: 0.902, 95% CI: 84.81-95.59), and validation set 2 (AUC: 0.911; 95% CI: 83.26-98.98). Based on the classification of pyradiomics model, we found that a group with high values base on pyramidomics score showed good PFS and OS (both P<0.001) and was negatively correlated with glycolysis pathway. The proposed pyradiomics signature could accurately predict initial treatment response and prognosis, which may be helpful for clinicians to better screen patients who are likely to benefit from TACE.

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Section: Discussionsupporting

confidence: 92%

Development and validation of a pyradiomics signature to predict initial treatment response and prognosis during transarterial chemoembolization in hepatocellular carcinoma

Peng

Lu²,

Huang³

et al. 2022

Front. Oncol.

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“…A number of authors previously assessed radiomics features correlated with the biological behavior and recurrence of HCC after surgical resection or in response to treatment [ 26 , 27 , 28 , 29 , 30 , 31 ]. Wu et al investigated the clinical significance of MRI-based radiomics signatures for the preoperative prediction of HCC grade and reported an AUC of 0.80 for the model using radiomics combined with clinical factors [ 28 ].…”

Section: Discussionmentioning

confidence: 99%

Machine Learning-Based Radiomic Features on Pre-Ablation MRI as Predictors of Pathologic Response in Patients with Hepatocellular Carcinoma Who Underwent Hepatic Transplant

Tabari

D'Amore

Cox

et al. 2023

Cancers

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Background: The aim was to investigate the role of pre-ablation tumor radiomics in predicting pathologic treatment response in patients with early-stage hepatocellular carcinoma (HCC) who underwent liver transplant. Methods: Using data collected from 2005–2015, we included adult patients who (1) had a contrast-enhanced MRI within 3 months prior to ablation therapy and (2) underwent liver transplantation. Demographics were obtained for each patient. The treated hepatic tumor volume was manually segmented on the arterial phase T1 MRI images. A vector with 112 radiomic features (shape, first-order, and texture) was extracted from each tumor. Feature selection was employed through minimum redundancy and maximum relevance using a training set. A random forest model was developed based on top radiomic and demographic features. Model performance was evaluated by ROC analysis. SHAP plots were constructed in order to visualize feature importance in model predictions. Results: Ninety-seven patients (117 tumors, 31 (32%) microwave ablation, 66 (68%) radiofrequency ablation) were included. The mean model for end-stage liver disease (MELD) score was 10.5 ± 3. The mean follow-up time was 336.2 ± 179 days. Complete response on pathology review was achieved in 62% of patients at the time of transplant. Incomplete pathologic response was associated with four features: two first-order and two GLRM features using univariate logistic regression analysis (p < 0.05). The random forest model included two radiomic features (diagnostics maximum and first-order maximum) and four clinical features (pre-procedure creatinine, pre-procedure albumin, age, and gender) achieving an AUC of 0.83, a sensitivity of 82%, a specificity of 67%, a PPV of 69%, and an NPV of 80%. Conclusions: Pre-ablation MRI radiomics could act as a valuable imaging biomarker for the prediction of tumor pathologic response in patients with HCC.

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“…By combining these two radiomics features (forming the Rad-score) with the T stage of the TNM classification, the predictive model had an AUC of 0.95, a sensitivity of 91%, and a specificity of 75%. The two radiomics features also showed good predictive performance when combined with the bilobar or nonbilobar character of the nodules (AUC 0.91) or with logAFP (Alpha-fetoprotein) (AUC 0.91) [ 30 ].…”

Section: Radiomics In Practice: Example Of Hepatocellular Carcinomamentioning

confidence: 99%

Radiomics, a Promising New Discipline: Example of Hepatocellular Carcinoma

Lévi-Strauss

Tortorici²,

Lopez³

et al. 2023

Diagnostics

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Radiomics is a discipline that involves studying medical images through their digital data. Using “artificial intelligence” algorithms, radiomics utilizes quantitative and high-throughput analysis of an image’s textural richness to obtain relevant information for clinicians, from diagnosis assistance to therapeutic guidance. Exploitation of these data could allow for a more detailed characterization of each phenotype, for each patient, making radiomics a new biomarker of interest, highly promising in the era of precision medicine. Moreover, radiomics is non-invasive, cost-effective, and easily reproducible in time. In the field of oncology, it performs an analysis of the entire tumor, which is impossible with a single biopsy but is essential for understanding the tumor’s heterogeneity and is known to be closely related to prognosis. However, current results are sometimes less accurate than expected and often require the addition of non-radiomics data to create a performing model. To highlight the strengths and weaknesses of this new technology, we take the example of hepatocellular carcinoma and show how radiomics could facilitate its diagnosis in difficult cases, predict certain histological features, and estimate treatment response, whether medical or surgical.

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A radiomics nomogram for predicting transcatheter arterial chemoembolization refractoriness of hepatocellular carcinoma without extrahepatic metastasis or macrovascular invasion

Cited by 16 publications

References 28 publications

Development and validation of a pyradiomics signature to predict initial treatment response and prognosis during transarterial chemoembolization in hepatocellular carcinoma

Development and validation of a pyradiomics signature to predict initial treatment response and prognosis during transarterial chemoembolization in hepatocellular carcinoma

Machine Learning-Based Radiomic Features on Pre-Ablation MRI as Predictors of Pathologic Response in Patients with Hepatocellular Carcinoma Who Underwent Hepatic Transplant

Radiomics, a Promising New Discipline: Example of Hepatocellular Carcinoma

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