MRI radiomics features predict immuno-oncological characteristics of hepatocellular carcinoma

Hectors, Stefanie; Lewis, Sara; Besa, Cecilia; King, Michael; Said, Daniela; Putra, Juan; Ward, Stephen; Higashi, Taishi; Thung, Swan N.; Shen, Yao; Laface, Ilaria; Schwartz, Myron; Gnjatic, Sacha; Mérad, Miriam; Hoshida, Yujin; Taouli, Bachir

doi:10.1007/s00330-020-06675-2

Cited by 117 publications

(73 citation statements)

References 39 publications

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“…Data used in radiomics studies can be single‐centre or multi‐centre, and retrospective or prospective. Here, we searched PubMed (8 October 2019) for radiomics studies on liver diseases using terms (liver diseases AND radiomics), and found 36 clinical target‐oriented published work 13‐48 . Most (33 out of 36) studies were performed on single‐centre with retrospective cohort, while only two studies were performed on multi‐centre and prospective cohort (Table 1).…”

Section: Methodology Of Radiomics In Liver Diseasesmentioning

confidence: 99%

“…In addition to imaging data, clinical factors were also involved in radiomics analysis, including patient age, gender, Child‐Pugh stage, histologic grading, BCLC stage, cirrhosis and its cause, etc 13‐48 . Laboratory examination indexes comprise serum α‐fetoprotein (AFP) level, prothrombin induced by vitamin K absence‐II (PIVKA‐II) level, carbohydrate antigen 19‐9 (CA 19‐9) level, hepatitis B virus surface antigen (HBsAg), serum alanine aminotransferase (ALT), aspartate aminotransferase (AST), serum total bilirubin (TB), conjugated bilirubin (CB), serum albumin (ALB), prothrombin time (PT), platelet count (PLT), etc 13‐48 …”

Section: Methodology Of Radiomics In Liver Diseasesmentioning

confidence: 99%

“…This groundbreaking result laid a foundation and greatly encouraged researchers to explore the potential of quantitative imaging tool in preoperative genetic/pathological outcome prediction. Hence, a great deal of radiomics studies have been conducted using multi‐parametric and multi‐modality imaging in terms of liver disease diagnosis and treatment decision making 13‐48 . In certain scenarios, this artificial intelligence‐based technique could even compete pathological gold standard, providing new ways for unsolved clinical problems in the paradigm of liver disease management 16 .…”

Section: Introductionmentioning

confidence: 99%

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Radiomics in liver diseases: Current progress and future opportunities

Wei

Jiang

et al. 2020

Liver International

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Liver diseases, a wide spectrum of pathologies from inflammation to neoplasm, have become an increasingly significant health problem worldwide. Noninvasive imaging plays a critical role in the clinical workflow of liver diseases, but conventional imaging assessment may provide limited information. Accurate detection, characterization and monitoring remain challenging. With progress in quantitative imaging analysis techniques, radiomics emerged as an efficient tool that shows promise to aid in personalized diagnosis and treatment decision‐making. Radiomics could reflect the heterogeneity of liver lesions via extracting high‐throughput and high‐dimensional features from multi‐modality imaging. Machine learning algorithms are then used to construct clinical target‐oriented imaging biomarkers to assist disease management. Here, we review the methodological process in liver disease radiomics studies in a stepwise fashion from data acquisition and curation, region of interest segmentation, liver‐specific feature extraction, to task‐oriented modelling. Furthermore, the applications of radiomics in liver diseases are outlined in aspects of diagnosis and staging, evaluation of liver tumour biological behaviours, and prognosis according to different disease type. Finally, we discuss the current limitations of radiomics in liver disease studies and explore its future opportunities.

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Section: Methodology Of Radiomics In Liver Diseasesmentioning

confidence: 99%

Section: Methodology Of Radiomics In Liver Diseasesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Radiomics in liver diseases: Current progress and future opportunities

Wei

Jiang

et al. 2020

Liver International

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“…Radiomics, the automated high-throughput extraction and analysis of quantitative and phenotypic features from radiographic images [107] , has emerged as a non-invasive tool for diagnosis and prognostication in several cancers, including HCC [108] . Qualitative and quantitative radiomics features may predict HCC recurrence and treatment response [109] , and are promising as novel biomarkers that may be complementary to existing serum biomarkers for HCC surveillance and treatment response assessment. However, lack of reproducibility is a major challenge and further validation studies are needed prior to the adoption of radiomics in routine clinical practice.…”

Section: Future Directions For Imaging In Hccmentioning

confidence: 99%

Role of imaging in management of hepatocellular carcinoma: surveillance, diagnosis, and treatment response

Osho¹,

Rich²,

Singal³

2020

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Imaging plays a notable role in hepatocellular carcinoma (HCC) surveillance, diagnosis, and treatment response assessment. Whereas HCC surveillance among at-risk patients, including those with cirrhosis, has traditionally been ultrasound-based, there are increasing data showing that this strategy is operator-dependent and has insufficient sensitivity when used alone. Several novel blood-based and imaging modalities are currently being evaluated to increase sensitivity for early HCC detection. Multi-phase computed tomography (CT) or contrastenhanced magnetic resonance imaging (MRI) should be performed in patients with positive surveillance tests to confirm a diagnosis of HCC and perform cancer staging, as needed. HCC is a unique cancer in that most cases can be diagnosed radiographically without histological confirmation when demonstrating characteristic features such as arterial phase hyperenhancement and delayed phase washout. The Liver Imaging Reporting and Data System offers a standardized nomenclature for reporting CT or MRI liver findings among at-risk patients. Finally, crosssectional imaging plays a critical role for assessing response to any HCC therapy as well as monitoring for HCC recurrence in those who achieve complete response.

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“…A recent radiogenomic study in patients with HCC demonstrated an association of radiomic features with immune infilatration. 18 Qualitative and quantitative textural analyses based on MRI images correlated with infiltrating immune cells, as well as mRNA expression of PD-L1 and CTLA4. This work suggested that it may be feasible to identify molecular radiomic signatures to serve as important relevant actionable targets.…”

mentioning

confidence: 96%