MR Imaging Biomarkers for the Prediction of Outcome after Radiofrequency Ablation of Hepatocellular Carcinoma: Qualitative and Quantitative Assessments of the Liver Imaging Reporting and Data System and Radiomic Features

Petukhova-Greenstein, Alexandra; Zeevi, Tal; Yang, Junlin; Chai, Nathan; DiDomenico, Paul; Deng, Yanhong; Ciarleglio, Maria; Haider, Stefan P.; Onyiuke, Ifeyinwa; Malpani, Rohil; Lin, MingDe; Kucukkaya, Ahmet S.; Gottwald, Luzie A.; Gebauer, Bernhard; Revzin, Margarita V.; Onofrey, John A.; Staib, Lawrence H.; Gunabushanam, Gowthaman; Taddei, Tamar H.; Chapiro, Julius

doi:10.1016/j.jvir.2022.04.006

Cited by 10 publications

(9 citation statements)

References 40 publications

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“…Four studies evaluated the use of CT-based radiomics to predict survival after hepatic resection, demonstrating an AUC between 0.71 and 0.81, with two of the four studies performing internal validation[ 39 , 77 - 79 ]. A few other studies evaluated the use of radiomics to predict survival after TACE[ 80 ], TARE[ 81 ], and RFA[ 82 ], all without validation.…”

Section: Applications Of Radiomics In Hccmentioning

confidence: 99%

“…They demonstrated a combined model integrating radiomic features and clinical data (HCC size, Child-Pugh score and AFP) outperformed the clinical sore model or the radiomic score model. Petukhova-Greenstein et al [ 82 ] found that a higher MRI-based radiomic signature based on nodular and perinodular radiomic features predicted poorer survival after RFA. A study evaluated the survival prediction after TARE, using 18-fuoro-deoxyglucose PET-based radiomics[ 81 ].…”

Section: Applications Of Radiomics In Hccmentioning

confidence: 99%

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Current status and future perspectives of radiomics in hepatocellular carcinoma

Miranda¹,

Horvat²,

Fonseca³

et al. 2023

World J Gastroenterol

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Given the frequent co-existence of an aggressive tumor and underlying chronic liver disease, the management of hepatocellular carcinoma (HCC) patients requires experienced multidisciplinary team discussion. Moreover, imaging plays a key role in the diagnosis, staging, restaging, and surveillance of HCC. Currently, imaging assessment of HCC entails the assessment of qualitative characteristics which are prone to inter-reader variability. Radiomics is an emerging field that extracts high-dimensional mineable quantitative features that cannot be assessed visually with the naked eye from medical imaging. The main potential applications of radiomic models in HCC are to predict histology, response to treatment, genetic signature, recurrence, and survival. Despite the encouraging results to date, there are challenges and limitations that need to be overcome before radiomics implementation in clinical practice. The purpose of this article is to review the main concepts and challenges pertaining to radiomics, and to review recent studies and potential applications of radiomics in HCC.

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Section: Applications Of Radiomics In Hccmentioning

confidence: 99%

Section: Applications Of Radiomics In Hccmentioning

confidence: 99%

Current status and future perspectives of radiomics in hepatocellular carcinoma

Miranda¹,

Horvat²,

Fonseca³

et al. 2023

World J Gastroenterol

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“…AI and specifically radiomics, with the extraction of quantitative features from radiological images and the combination of these with clinical and genetic/molecular models, have been improving the prognostic assessment prior to perform both percutaneous and endovascular procedures. 74 – 79 …”

Section: Discussionmentioning

confidence: 99%

“…Many studies show the role of radiomics in terms of prognostic evaluation of recurrence after MWA of liver, lung and kidney diseases. 74 – 79 …”

Section: Artificial Intelligencementioning

confidence: 99%

“…Many studies show the role of radiomics in terms of prognostic evaluation of recurrence after MWA of liver, lung and kidney diseases. [74][75][76][77][78][79] Among the fields of application of AI, augmented reality (AR) or virtual reality (VR) can be supportive tools during the IR procedures. Through advanced 3D rendering and spatial image manipulation, AR and VR allow practitioners to conceptualize difficult anatomy, increase realism in procedural planning (compared to standard 2D images) and improve procedural skills in a previously simulated environment.…”

Section: Artificial Intelligencementioning

confidence: 99%

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Robotics in Interventional Radiology: Review of Current and Future Applications

Lanza

Carriero

Buijs

et al. 2023

Technol Cancer Res Treat

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This review is a brief overview of the current status and the potential role of robotics in interventional radiology. Literature published in the last decades, with an emphasis on the last 5 years, was reviewed and the technical developments in robotics and navigational systems using CT-, MR- and US-image guidance were analyzed. Potential benefits and disadvantages of their current and future use were evaluated. The role of fusion imaging modalities and artificial intelligence was analyzed in both percutaneous and endovascular procedures. A few hundred articles describing results of single or several systems were included in our analysis.

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