Radiomics of cholangiocarcinoma on pretreatment CT can identify patients who would best respond to radioembolisation

Mosconi, Cristina; Cucchetti, Alessandro; Bruno, Antonio; Cappelli, Alberta; Bargellini, Irene; Benedittis, Caterina De; Lorenzoni, Giulia; Gramenzi, Annagiulia; Tarantino, Francesco; Parini, Lorenza; Pettinato, Cinzia; Modestino, Francesco; Peta, Giuliano; Cioni, Roberto; Golfieri, Rita

doi:10.1007/s00330-020-06795-9

Cited by 38 publications

(28 citation statements)

References 29 publications

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“…Morphologic variables and RF were derived from venous phase in our retrospective database, whereas previous studies selected arterial phase. Mosconi et al reported that textural RF are best extracted from arterial phase, in a population selected for transarterial radioembolization [24]. In that study, venous phase remained significant for first-order features, including "Mean", partly consistent with our observation.…”

Section: Variability and Selection Of Rfsupporting

confidence: 91%

“…Accurate non-invasive prognostic descriptors of IMCC are deemed of major clinical support for personalized therapeutic approach because IMCC is one form of intrahepatic cholangiocarcinoma for surgical option, and therefore radiomics might represent a relevant complement in pre-surgical clinical management. Most of the published research focused on population selected by clinical treatment, namely surgery or embolization [23,24]. However, the application of radiomics on broader population is still lacking, especially for management throughout the full process of treatment decision.…”

Section: Introductionmentioning

confidence: 99%

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Integrated prognostication of intrahepatic cholangiocarcinoma by contrast-enhanced computed tomography: the adjunct yield of radiomics

et al. 2021

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Purpose To test radiomics for prognostication of intrahepatic mass-forming cholangiocarcinoma (IMCC) and to develop a comprehensive risk model. Methods Histologically proven IMCC (representing the full range of stages) were retrospectively analyzed by volume segmentation on baseline hepatic venous phase computed tomography (CT), by two readers with different experience (R1 and R2). Morphological CT features included: tumor size, hepatic satellite lesions, lymph node and distant metastases. Radiomic features (RF) were compared across CT protocols and readers. Univariate analysis against overall survival (OS) warranted ranking and selection of RF into radiomic signature (RSign), which was dichotomized into high and low-risk strata (RSign*). Models without and with RSign* (Model 1 and 2, respectively) were compared. Results Among 78 patients (median follow-up 262 days, IQR 73–957), 62/78 (79%) died during the study period, 46/78 (59%) died within 1 year. Up to 10% RF showed variability across CT protocols; 37/108 (34%) RF showed variability due to manual segmentation. RSign stratified OS (univariate: HR 1.37 for R1, HR 1.28 for R2), RSign* was different between readers (R1 0.39; R2 0.57). Model 1 showed AUC 0.71, which increased in Model 2: AUC 0.81 (p < 0.001) and AIC 89 for R1, AUC 0.81 (p = 0.001) and AIC 90.2 for R2. Conclusion The use of RF into a unified RSign score stratified OS in patients with IMCC. Dichotomized RSign* classified survival strata, its inclusion in risk models showed adjunct yield. The cut-off value of RSign* was different between readers, suggesting that the use of reference values is hampered by interobserver variability.

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Section: Variability and Selection Of Rfsupporting

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Integrated prognostication of intrahepatic cholangiocarcinoma by contrast-enhanced computed tomography: the adjunct yield of radiomics

et al. 2021

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“…To predict treatment response, Mosconi et al . used radiomic analysis to quantify vascularization and homogeneity of ICC in patients receiving transarterial radioembolization 47 . The results showed that patients with a favorable radiomic signature had a significantly longer median progression‐free survival (12.1 months) than that of patients with an unfavorable signature (5.1 months).…”

Section: Biliary Diseasesmentioning

confidence: 99%

Applications of artificial intelligence in pancreatic and biliary diseases

Chen

Chang

et al. 2021

J of Gastro and Hepatol

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The application of artificial intelligence (AI) in medicine has increased rapidly with respect to tasks including disease detection/diagnosis, risk stratification, and prognosis prediction. With recent advances in computing power and algorithms, AI has shown promise in taking advantage of vast electronic health data and imaging studies to supplement clinicians. Machine learning and deep learning are the most widely used AI methodologies for medical research and have been applied in pancreatobiliary diseases for which diagnosis and treatment selection are often complicated and require joint consideration of data from multiple sources. The aim of this review is to provide a concise introduction of the major AI methodologies and the current landscape of AI research in pancreatobiliary diseases.

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“…The usefulness of CTTA to identify ICCs, which best respond to trans-arterial radioembolization (TARE) was also explored in a recent publication. Mosconi et al [29] performed a retrospective texture analysis on pretreatment CT scans of 55 ICC patients. Tumors showing objective response after TARE had higher iodine uptake implicated by the higher mean histogram intensity, and more homogeneous distribution of iodine implicated by the lower kurtosis, GLCM contrast, and GLCM dissimilarity, as well as the higher GLCM correlation and GLCM homogeneity at the baseline CT images.…”

Section: Radiomics Analysis Of Primary Liver Tumorsmentioning

confidence: 99%

CT texture analysis of abdominal lesions – Part I.: Liver lesions

Budai

Frank

Shariati

et al. 2021

Imaging

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Artificial Intelligence and the use of radiomics analysis have been of great interest in the last decade in the field of imaging. CT texture analysis (CTTA) is a new and emerging field in radiomics, which seems promising in the assessment and diagnosis of both focal and diffuse liver lesions. The utilization of CTTA has only been receiving great attention recently, especially for response evaluation and prognostication of different oncological diagnoses. Radiomics, combined with machine learning techniques, offers a promising opportunity to accurately detect or differentiate between focal liver lesions based on their unique texture parameters. In this review article, we discuss the unique ability of radiomics in the diagnostics and prognostication of both focal and diffuse liver lesions. We also provide a brief review of radiogenomics and summarize its potential role of in the non-invasive diagnosis of malignant liver tumors.

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Radiomics of cholangiocarcinoma on pretreatment CT can identify patients who would best respond to radioembolisation

Cited by 38 publications

References 29 publications

Integrated prognostication of intrahepatic cholangiocarcinoma by contrast-enhanced computed tomography: the adjunct yield of radiomics

Integrated prognostication of intrahepatic cholangiocarcinoma by contrast-enhanced computed tomography: the adjunct yield of radiomics

Applications of artificial intelligence in pancreatic and biliary diseases

CT texture analysis of abdominal lesions – Part I.: Liver lesions

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