Current status and future perspectives of radiomics in hepatocellular carcinoma

Miranda, Joao; Horvat, Natally; Fonseca, Gilton Marques; Araújo-Filho, José de Arimateia Batista; Fernandes, Maria Clara; Charbel, Charlotte; Chakraborty, Jayasree; Coelho, Fabrício Ferreira; Nomura, César Higa; Herman, Paulo

doi:10.3748/wjg.v29.i1.43

Cited by 14 publications

(7 citation statements)

References 106 publications

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“…Another important ndings emerge from the XAI analysis. Overall, imaging features extracted from VOIs of the largest liver tumors have consistently proven to be the most relevant variables in our models, in line with previous radiomics studies 5,8,[27][28][29] . However, the most intriguing aspect of our study is the relatively high importance of features derived from non-tumoral VOIs.…”

Section: Discussionsupporting

confidence: 84%

A Novel Radiomics Approach for Predicting TACE Outcomes in Hepatocellular Carcinoma Patients Using Deep Learning for Multi-organ Segmentation

Bartnik,

Krzyziński,

Bartczak

et al. 2023

Preprint

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Transarterial chemoembolization (TACE) represent the standard of therapy for non-operative hepatocellular carcinoma (HCC), while prediction of long term treatment outcomes is a complex and multifactorial task. In this study, we present a novel machine learning approach utilizing radiomics features from multiple organ volumes of interest (VOIs) to predict TACE outcomes for 252 HCC patients. Unlike conventional radiomics models requiring laborious manual segmentation limited to tumoral regions, our approach captures information comprehensively across various VOIs using a fully automated, pretrained deep learning model applied to pre-TACE CT images. Evaluation of radiomics random survival forest models against clinical ones using Cox proportional hazard demonstrated comparable performance in predicting overall survival. However, radiomics outperformed clinical models in predicting progression-free survival. Explainable analysis highlighted the significance of non-tumoral VOI features, with their cumulative importance superior to features from the largest liver tumor. The proposed approach overcomes the limitations of manual VOI segmentation, requires no radiologist input and highlight the clinical relevance of features beyond tumor regions. Our findings suggest the potential of this radiomics models in predicting TACE outcomes, with possible implications for other clinical scenarios.

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Section: Discussionsupporting

confidence: 84%

A Novel Radiomics Approach for Predicting TACE Outcomes in Hepatocellular Carcinoma Patients Using Deep Learning for Multi-organ Segmentation

Bartnik,

Krzyziński,

Bartczak

et al. 2023

Preprint

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“…WJG v29i1: I found the paper “Current status and future perspectives of radiomics in hepatocellular carcinoma” to be very well-conceived and executed[ 1 ].…”

Section: Hot Articles and Insufficient Articlesmentioning

confidence: 99%

Editor-in-Chief articles of choice and comments at the year-end of 2023

Tarnawski

2024

World J Gastroenterol

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As the Editor-in-Chief, every week prior to a new issue’s online publication, I perform a careful review of all encompassed articles, including the title, clinical and/or research importance, originality, novelty, and ratings by the peer reviewers. Based on this review, I select the papers of choice and suggest pertinent changes (e.g. , in the title) to the Company Editors responsible for publication. This process, while time-consuming, is very important for assuring the quality of publications and highlighting important articles that Readers may revisit.

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“…Thus, a non-invasive additional support might improve the discrimination between benign and malignant LNs. In this contest, radiomics could be a useful imaging tool, there being the expectancy that it will influence treatment decision making and predict patient prognosis based on nodal microarchitecture, microenvironment, and heterogeneity by extracting quantitative features from volumetric LN segmentation [11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Is CT Radiomics Superior to Morphological Evaluation for pN0 Characterization? A Pilot Study in Colon Cancer

Zerunian,

Nacci,

Caruso

et al. 2024

Cancers

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The aim of this study was to compare CT radiomics and morphological features when assessing benign lymph nodes (LNs) in colon cancer (CC). This retrospective study included 100 CC patients (test cohort) who underwent a preoperative CT examination and were diagnosed as pN0 after surgery. Regional LNs were scored with a morphological Likert scale (NODE-SCORE) and divided into two groups: low likelihood (LLM: 0–2 points) and high likelihood (HLM: 3–7 points) of malignancy. The T-test and the Mann–Whitney test were used to compare 107 radiomic features extracted from the two groups. Radiomic features were also extracted from primary lesions (PLs), and the receiver operating characteristic (ROC) was used to test a LN/PL ratio when assessing the LN’s status identified with radiomics and with the NODE-SCORE. An amount of 337 LNs were divided into 167 with LLM and 170 with HLM. Radiomics showed 15/107 features, with a significant difference (p < 0.02) between the two groups. The comparison of selected features between 81 PLs and the corresponding LNs showed all significant differences (p < 0.0001). According to the LN/PL ratio, the selected features recognized a higher number of LNs than the NODE-SCORE (p < 0.001). On validation of the cohort of 20 patients (10 pN0, 10 pN2), significant ROC curves were obtained for LN/PL busyness (AUC = 0.91; 0.69–0.99; 95% C.I.; and p < 0.001) and for LN/PL dependence entropy (AUC = 0.76; 0.52–0.92; 95% C.I.; and p = 0.03). The radiomics ratio between CC and LNs is more accurate for noninvasively discriminating benign LNs compared to CT morphological features.

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Current status and future perspectives of radiomics in hepatocellular carcinoma

Cited by 14 publications

References 106 publications

A Novel Radiomics Approach for Predicting TACE Outcomes in Hepatocellular Carcinoma Patients Using Deep Learning for Multi-organ Segmentation

A Novel Radiomics Approach for Predicting TACE Outcomes in Hepatocellular Carcinoma Patients Using Deep Learning for Multi-organ Segmentation

Editor-in-Chief articles of choice and comments at the year-end of 2023

Is CT Radiomics Superior to Morphological Evaluation for pN0 Characterization? A Pilot Study in Colon Cancer

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