Prediction of microvascular invasion in solitary hepatocellular carcinoma ≤ 5 cm based on computed tomography radiomics

Peng, Lan; Tan, Xianzhen; Zhang, Ting; Gu, Qianbiao; Mao, Xianhai; Li, Yan-Chun; He, Yaqiong

doi:10.3748/wjg.v27.i17.2015

Cited by 20 publications

(14 citation statements)

References 16 publications

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“…The systematic literature search initially yielded 188 records from the four databases. After removing 82 duplicates, 50 inappropriate types of publications, and 34 ineligible studies, a total of 22 studies were included in this systematic review [ 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 ] ( Figure 2 ).…”

Section: Resultsmentioning

confidence: 99%

Radiomics Models for Predicting Microvascular Invasion in Hepatocellular Carcinoma: A Systematic Review and Radiomics Quality Score Assessment

Wang

Zhang

et al. 2021

Cancers

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Preoperative prediction of microvascular invasion (MVI) is of importance in hepatocellular carcinoma (HCC) patient treatment management. Plenty of radiomics models for MVI prediction have been proposed. This study aimed to elucidate the role of radiomics models in the prediction of MVI and to evaluate their methodological quality. The methodological quality was assessed by the Radiomics Quality Score (RQS), and the risk of bias was evaluated by the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2). Twenty-two studies using CT, MRI, or PET/CT for MVI prediction were included. All were retrospective studies, and only two had an external validation cohort. The AUC values of the prediction models ranged from 0.69 to 0.94 in the test cohort. Substantial methodological heterogeneity existed, and the methodological quality was low, with an average RQS score of 10 (28% of the total). Most studies demonstrated a low or unclear risk of bias in the domains of QUADAS-2. In conclusion, a radiomics model could be an accurate and effective tool for MVI prediction in HCC patients, although the methodological quality has so far been insufficient. Future prospective studies with an external validation cohort in accordance with a standardized radiomics workflow are expected to supply a reliable model that translates into clinical utilization.

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Section: Resultsmentioning

confidence: 99%

Radiomics Models for Predicting Microvascular Invasion in Hepatocellular Carcinoma: A Systematic Review and Radiomics Quality Score Assessment

Wang

Zhang

et al. 2021

Cancers

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“…All patients were diagnosed with HCC based on postoperative pathologic specimens and had available preoperative imaging data including CT, MRI, or US. Fifteen studies were based on a population from China 4 (27)(28)(29)(30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41) and one from the United States (42). Concerning the etiology of HCC, at least 78.46% of patients had HBV or HCV of 4,657 patients across all included studies.…”

Section: Review Of the Included Studiesmentioning

confidence: 99%

“…Concerning the etiology of HCC, at least 78.46% of patients had HBV or HCV of 4,657 patients across all included studies. In patient selection, five articles only included HCC patients with single tumors and excluded multiple tumors (27,34,(39)(40)(41). Based on this diagnostic meta-analysis, 1,946 (40.89%) patients were pathologically diagnosed as MVIpresent and 2,813 patients as MVI-absent after surgical resection or liver transplantation.…”

Section: Review Of the Included Studiesmentioning

confidence: 99%

Diagnostic Accuracy of Artificial Intelligence Based on Imaging Data for Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma: A Systematic Review and Meta-Analysis

Zhang

Huang

et al. 2022

Front. Oncol.

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BackgroundThe presence of microvascular invasion (MVI) is considered an independent prognostic factor associated with early recurrence and poor survival in hepatocellular carcinoma (HCC) patients after resection. Artificial intelligence (AI), mainly consisting of non-deep learning algorithms (NDLAs) and deep learning algorithms (DLAs), has been widely used for MVI prediction in medical imaging.AimTo assess the diagnostic accuracy of AI algorithms for non-invasive, preoperative prediction of MVI based on imaging data.MethodsOriginal studies reporting AI algorithms for non-invasive, preoperative prediction of MVI based on quantitative imaging data were identified in the databases PubMed, Embase, and Web of Science. The quality of the included studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) scale. The pooled sensitivity, specificity, positive likelihood ratio (PLR), and negative likelihood ratio (NLR) were calculated using a random-effects model with 95% CIs. A summary receiver operating characteristic curve and the area under the curve (AUC) were generated to assess the diagnostic accuracy of the deep learning and non-deep learning models. In the non-deep learning group, we further performed meta-regression and subgroup analyses to identify the source of heterogeneity.ResultsData from 16 included studies with 4,759 cases were available for meta-analysis. Four studies on deep learning models, 12 studies on non-deep learning models, and two studies compared the efficiency of the two types. For predictive performance of deep learning models, the pooled sensitivity, specificity, PLR, NLR, and AUC values were 0.84 [0.75–0.90], 0.84 [0.77–0.89], 5.14 [3.53–7.48], 0.2 [0.12–0.31], and 0.90 [0.87–0.93]; and for non-deep learning models, they were 0.77 [0.71–0.82], 0.77 [0.73–0.80], 3.30 [2.83–3.84], 0.30 [0.24–0.38], and 0.82 [0.79–0.85], respectively. Subgroup analyses showed a significant difference between the single tumor subgroup and the multiple tumor subgroup in the pooled sensitivity, NLR, and AUC.ConclusionThis meta-analysis demonstrates the high diagnostic accuracy of non-deep learning and deep learning methods for MVI status prediction and their promising potential for clinical decision-making. Deep learning models perform better than non-deep learning models in terms of the accuracy of MVI prediction, methodology, and cost-effectiveness.Systematic Review Registrationhttps://www.crd.york.ac.uk/PROSPERO/display_record.php? RecordID=260891, ID:CRD42021260891.

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“…Thus, new biomarkers for the early detection of microvascular invasion are needed urgently. Liu P and collaborators showed the role of radiogenomic analysis in determining microvascular invasion in preoperative patients [ 36 ]. Moreover, another study corroborated the prognostic value of these radiogenomic biomarkers of microvascular invasion in hepatocellular carcinoma to predict patients’ recurrence and survival [ 37 ].…”

Section: Liver Premetastatic Niche Formation In Crc Patientsmentioning

confidence: 99%

“…Reasonably, the different phases of metastatic niche formation (each one represented by a puzzle piece in the figure) could be detected by radiogenomics approaches in the near future. The references included in the figure show related manuscript with information about tumor cell extravasation [ 34 , 35 ], neoangiogenesis [ 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 ], immunosurveillance evasion [ 44 , 47 , 48 , 49 , 50 , 51 , 52 , 53 ] and tumor growth [ 41 ] that could be theoretically translated to metastatic niche detection. Created with BioRender.com.…”

Section: Figurementioning

confidence: 99%

Radiogenomics: Hunting Down Liver Metastasis in Colorectal Cancer Patients

Pinta

Castillo

Collado

et al. 2021

Cancers

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Radiomics is a developing new discipline that analyzes conventional medical images to extract quantifiable data that can be mined for new biomarkers that show the biology of pathological processes at microscopic levels. These data can be converted into image-based signatures to improve diagnostic, prognostic and predictive accuracy in cancer patients. The combination of radiomics and molecular data, called radiogenomics, has clear implications for cancer patients’ management. Though some studies have focused on radiogenomics signatures in hepatocellular carcinoma patients, only a few have examined colorectal cancer metastatic lesions in the liver. Moreover, the need to differentiate between liver lesions is fundamental for accurate diagnosis and treatment. In this review, we summarize the knowledge gained from radiomics and radiogenomics studies in hepatic metastatic colorectal cancer patients and their use in early diagnosis, response assessment and treatment decisions. We also investigate their value as possible prognostic biomarkers. In addition, the great potential of image mining to provide a comprehensive view of liver niche formation is examined thoroughly. Finally, new challenges and current limitations for the early detection of the liver premetastatic niche, based on radiomics and radiogenomics, are also discussed.

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Prediction of microvascular invasion in solitary hepatocellular carcinoma ≤ 5 cm based on computed tomography radiomics

Cited by 20 publications

References 16 publications

Radiomics Models for Predicting Microvascular Invasion in Hepatocellular Carcinoma: A Systematic Review and Radiomics Quality Score Assessment

Radiomics Models for Predicting Microvascular Invasion in Hepatocellular Carcinoma: A Systematic Review and Radiomics Quality Score Assessment

Diagnostic Accuracy of Artificial Intelligence Based on Imaging Data for Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma: A Systematic Review and Meta-Analysis

Radiogenomics: Hunting Down Liver Metastasis in Colorectal Cancer Patients

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