Hypervascular hepatic focal lesions on dynamic contrast-enhanced CT: preliminary data from arterial phase scans texture analysis for classification

Song, Shuangshuang; Li, Z.; Niu, Lei; Zhou, Xiaoming; Wang, G.; Gao, Yong; Wang, J.; Liu, F.; Qinglan, Sui; Jiao, Linlin; Liu, Jie

doi:10.1016/j.crad.2019.05.010

Cited by 17 publications

(14 citation statements)

References 23 publications

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“…Of the 54 studies met eligibility criteria, nine evaluated aspects of HCC diagnosis (Table 1). Four studies primarily focussed on distinguishing hepatic haemangiomas from HCC 32‐35 . Mokrane et al evaluated 178 patients with indeterminate nodules and sought to categorise the nodules as high‐ or low‐risk for HCC.…”

Section: Resultsmentioning

confidence: 99%

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

Harding-Theobald

Louissaint

Maraj

et al. 2021

Aliment Pharmacol Ther

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Summary Background Advances in imaging technology have the potential to transform the early diagnosis and treatment of hepatocellular carcinoma (HCC) through quantitative image analysis. Computational “radiomic” techniques extract biomarker information from images which can be used to improve diagnosis and predict tumour biology. Aims To perform a systematic review on radiomic features in HCC diagnosis and prognosis, with a focus on reporting metrics and methodologic standardisation. Methods We performed a systematic review of all full‐text articles published from inception through December 1, 2019. Standardised data extraction and quality assessment metrics were applied to all studies. Results A total of 54 studies were included for analysis. Radiomic features demonstrated good discriminatory performance to differentiate HCC from other solid lesions (c‐statistics 0.66‐0.95), and to predict microvascular invasion (c‐statistic 0.76‐0.92), early recurrence after hepatectomy (c‐statistics 0.71‐0.86), and prognosis after locoregional or systemic therapies (c‐statistics 0.74‐0.81). Common stratifying features for diagnostic and prognostic radiomic tools included analyses of imaging skewness, analysis of the peritumoural region, and feature extraction from the arterial imaging phase. The overall quality of the included studies was low, with common deficiencies in both internal and external validation, standardised imaging segmentation, and lack of comparison to a gold standard. Conclusions Quantitative image analysis demonstrates promise as a non‐invasive biomarker to improve HCC diagnosis and management. However, standardisation of protocols and outcome measurement, sharing of algorithms and analytic methods, and external validation are necessary prior to widespread application of radiomics to HCC diagnosis and prognosis in clinical practice.

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Section: Resultsmentioning

confidence: 99%

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

Harding-Theobald

Louissaint

Maraj

et al. 2021

Aliment Pharmacol Ther

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“…Most papers ( n = 28) described retrospective analyses, while four reported planned secondary analyses of prospectively acquired data [ 32 , 33 , 34 , 35 ]. Nineteen authors analyzed computed tomography (CT) [ 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 ], eight magnetic resonance imaging (MRI) [ 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 ], three positron-emission tomography (PET)/CT [ 59 , 60 , 61 ], and two multiple imaging modalities (CT and MRI; PET and MRI, respectively) [ 62 , 63 ]. Various software applications were used for texture analysis, with these being custom-made in a large proportion of cases ( n = 10).…”

Section: Resultsmentioning

confidence: 99%

“…Differential diagnosis of the two malignant entities (LM and HCC) required a more complex model, with a higher number of features, than differential diagnosis between benign and malignant lesions (LM vs. hemangiomas or HCC vs. hemangiomas). Finally, a study by Song et al, identified kurtosis, variance, and inverse difference moment as distinguishing criteria between benign and malignant hypervascular lesions [ 48 ]. Only the latter study used pathology data as the reference standard for all the analyzed patients.…”

Section: Resultsmentioning

confidence: 99%

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Radiomics of Liver Metastases: A Systematic Review

Fiz

Viganò

Gennaro

et al. 2020

Cancers

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Multidisciplinary management of patients with liver metastases (LM) requires a precision medicine approach, based on adequate profiling of tumor biology and robust biomarkers. Radiomics, defined as the high-throughput identification, analysis, and translational applications of radiological textural features, could fulfill this need. The present review aims to elucidate the contribution of radiomic analyses to the management of patients with LM. We performed a systematic review of the literature through the most relevant databases and web sources. English language original articles published before June 2020 and concerning radiomics of LM extracted from CT, MRI, or PET-CT were considered. Thirty-two papers were identified. Baseline higher entropy and lower homogeneity of LM were associated with better survival and higher chemotherapy response rates. A decrease in entropy and an increase in homogeneity after chemotherapy correlated with radiological tumor response. Entropy and homogeneity were also highly predictive of tumor regression grade. In comparison with RECIST criteria, radiomic features provided an earlier prediction of response to chemotherapy. Lastly, texture analyses could differentiate LM from other liver tumors. The commonest limitations of studies were small sample size, retrospective design, lack of validation datasets, and unavailability of univocal cut-off values of radiomic features. In conclusion, radiomics can potentially contribute to the precision medicine approach to patients with LM, but interdisciplinarity, standardization, and adequate software tools are needed to translate the anticipated potentialities into clinical practice.

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“…In human medicine the computed tomographic (CT) features of different FLLs in the arterial, portal and delayed phase are welldescribed, and, therefore, it is possible to infer the histopathologic subtype of a FLL from its CT features (5)(6)(7). For example, the presence of a hypervascular pattern in a heterogeneous enhancing hepatic lesion during the arterial phase, is a feature often associated with HCCs (6).…”

Section: Introductionmentioning

confidence: 99%

Diagnostic Accuracy of Delayed Phase Post Contrast Computed Tomographic Images in the Diagnosis of Focal Liver Lesions in Dogs: 69 Cases

et al. 2021

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To describe the computed tomographic (CT) features of focal liver lesions (FLLs) in dogs, that could enable predicting lesion histotype. Dogs diagnosed with FLLs through both CT and cytopathology and/or histopathology were retrospectively collected. Ten qualitative and 6 quantitative CT features have been described for each case. Lastly, a machine learning-based decision tree was developed to predict the lesion histotype. Four categories of FLLs - hepatocellular carcinoma (HCC, n = 13), nodular hyperplasia (NH, n = 19), other benign lesions (OBL, n = 18), and other malignant lesions (OML, n = 19) - were evaluated in 69 dogs. Five of the observed qualitative CT features resulted to be statistically significant in the distinction between the 4 categories: surface, appearance, lymph-node appearance, capsule formation, and homogeneity of contrast medium distribution. Three of the observed quantitative CT features were significantly different between the 4 categories: the Hounsfield Units (HU) of the radiologically normal liver parenchyma during the pre-contrast scan, the maximum dimension, and the ellipsoid volume of the lesion. Using the machine learning-based decision tree, it was possible to correctly classify NHs, OBLs, HCCs, and OMLs with an accuracy of 0.74, 0.88, 0.87, and 0.75, respectively. The developed decision tree could be an easy-to-use tool to predict the histotype of different FLLs in dogs. Cytology and histology are necessary to obtain the final diagnosis of the lesions.

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Hypervascular hepatic focal lesions on dynamic contrast-enhanced CT: preliminary data from arterial phase scans texture analysis for classification

Cited by 17 publications

References 23 publications

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

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

Radiomics of Liver Metastases: A Systematic Review

Diagnostic Accuracy of Delayed Phase Post Contrast Computed Tomographic Images in the Diagnosis of Focal Liver Lesions in Dogs: 69 Cases

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