Evaluation of Therapeutic Effects of Computed Tomography Imaging Classification Algorithm-Based Transcatheter Arterial Chemoembolization on Primary Hepatocellular Carcinoma

Li, Qiang; Luo, Guang; Li, Jian

doi:10.1155/2022/5639820

Cited by 3 publications

(1 citation statement)

References 24 publications

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“…The whole-tumor region (i.e., the region of tumor segmentation) was manually outlined using 3D-slicer software (ver. 4.10.2) under the supervision of a hepatobiliary surgeon; more information can be found in previous studies [ 19 , 20 ]. Using Python’s SimpleITK package ( https://simpleitk.org/ ), the whole-tumor and 3 mm peritumoral regions were obtained by taking the whole-tumor region and automatically ballooning it outward; we termed this the whole-peritumor region.…”

Section: Methodsmentioning

confidence: 99%

CT radiomics for noninvasively predicting NQO1 expression levels in hepatocellular carcinoma

He,

Shen,

Wang

et al. 2023

PLoS ONE

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Using noninvasive radiomics to predict pathological biomarkers is an innovative work worthy of exploration. This retrospective cohort study aimed to analyze the correlation between NAD(P)H quinone oxidoreductase 1 (NQO1) expression levels and the prognosis of patients with hepatocellular carcinoma (HCC) and to construct radiomic models to predict the expression levels of NQO1 prior to surgery. Data of patients with HCC from The Cancer Genome Atlas (TCGA) and the corresponding arterial phase-enhanced CT images from The Cancer Imaging Archive were obtained for prognosis analysis, radiomic feature extraction, and model development. In total, 286 patients with HCC from TCGA were included. According to the cut-off value calculated using R, patients were divided into high-expression (n = 143) and low-expression groups (n = 143). Kaplan–Meier survival analysis showed that higher NQO1 expression levels were significantly associated with worse prognosis in patients with HCC (p = 0.017). Further multivariate analysis confirmed that high NQO1 expression was an independent risk factor for poor prognosis (HR = 1.761, 95% CI: 1.136−2.73, p = 0.011). Based on the arterial phase-enhanced CT images, six radiomic features were extracted, and a new bi-regional radiomics model was established, which could noninvasively predict higher NQO1 expression with good performance. The area under the curve (AUC) was 0.9079 (95% CI 0.8127–1.0000). The accuracy, sensitivity, and specificity were 0.86, 0.88, and 0.84, respectively, with a threshold value of 0.404. The data verification of our center showed that this model has good predictive efficiency, with an AUC of 0.8791 (95% CI 0.6979–1.0000). In conclusion, there existed a significant correlation between the CT image features and the expression level of NQO1, which could indirectly reflect the prognosis of patients with HCC. The predictive model based on arterial phase CT imaging features has good stability and diagnostic efficiency and is a potential means of identifying the expression level of NQO1 in HCC tissues before surgery.

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Section: Methodsmentioning

confidence: 99%

CT radiomics for noninvasively predicting NQO1 expression levels in hepatocellular carcinoma

He,

Shen,

Wang

et al. 2023

PLoS ONE

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Radiomics as a tool for prognostic prediction in transarterial chemoembolization for hepatocellular carcinoma: a systematic review and meta-analysis

Deng,

Chen,

Leng

et al. 2024

Radiol med

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Introduction Transarterial chemoembolization (TACE) is one of the predominant locoregional therapeutic modalities for addressing hepatocellular carcinoma (HCC). However, achieving precise prognostic predictions and effective patient selection remains a challenging pursuit. The primary objective of this systematic review and meta-analysis is to evaluate the efficacy of radiomics in forecasting the prognosis associated with TACE treatment. Methods A comprehensive exploration of pertinent original studies was undertaken, encompassing databases of PubMed, Web of Science and Embase. The studies' quality was meticulously evaluated employing the quality assessment of diagnostic accuracy studies 2 (QUADAS-2), the radiomics quality score (RQS) and the METhodological RadiomICs Score (METRICS). Pooled statistics, along with 95% confidence intervals (95% CI), were computed for sensitivity, specificity, positive likelihood ratio (PLR), and negative likelihood ratio (NLR). Additionally, a summary receiver operating characteristic curve (sROC) was generated. To discern potential sources of heterogeneity, meta-regression and subgroup analyses were performed. Results The systematic review incorporated 29 studies, comprising a total of 5483 patients, with 14 studies involving 2691 patients qualifying for inclusion in the meta-analysis. The assessed studies exhibited commendable quality with regard to bias risk, with mean RQS of 12.90 ± 5.13 (35.82% ± 14.25%) and mean METRICS of 62.98% ± 14.58%. The pooled sensitivity was 0.83 (95% CI: 0.78–0.87), specificity was 0.86 (95% CI: 0.79–0.92), PLR was 6.13 (95% CI: 3.79–9.90), and NLR was 0.20 (95% CI: 0.15–0.27). The area under the sROC was 0.90 (95% CI: 0.87–0.93). Significant heterogeneity within all the included studies was observed, while meta-regression and subgroup analyses revealed homogeneous and promising findings in subgroups where principal methodological variables such as modeling algorithms, imaging modalities, and imaging phases were specified. Conclusion Radiomics models have exhibited robust predictive capabilities concerning prognosis subsequent to TACE, thereby presenting promising prospects for clinical translation.

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Quantitative analysis of artificial intelligence on liver cancer: A bibliometric analysis

Xiong

Xu²,

Zhao³

et al. 2023

Front. Oncol.

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ObjectiveTo provide the current research progress, hotspots, and emerging trends for AI in liver cancer, we have compiled a relative comprehensive and quantitative report on the research of liver disease using artificial intelligence by employing bibliometrics in this study.MethodsIn this study, the Web of Science Core Collection (WoSCC) database was used to perform systematic searches using keywords and a manual screening strategy, VOSviewer was used to analyze the degree of cooperation between countries/regions and institutions, as well as the co-occurrence of cooperation between authors and cited authors. Citespace was applied to generate a dual map to analyze the relationship of citing journals and citied journals and conduct a strong citation bursts ranking analysis of references. Online SRplot was used for in-depth keyword analysis and Microsoft Excel 2019 was used to collect the targeted variables from retrieved articles.Results1724 papers were collected in this study, including 1547 original articles and 177 reviews. The study of AI in liver cancer mostly began from 2003 and has developed rapidly from 2017. China has the largest number of publications, and the United States has the highest H-index and total citation counts. The top three most productive institutions are the League of European Research Universities, Sun Yat Sen University, and Zhejiang University. Jasjit S. Suri and Frontiers in Oncology are the most published author and journal, respectively. Keyword analysis showed that in addition to the research on liver cancer, research on liver cirrhosis, fatty liver disease, and liver fibrosis were also common. Computed tomography was the most used diagnostic tool, followed by ultrasound and magnetic resonance imaging. The diagnosis and differential diagnosis of liver cancer are currently the most widely adopted research goals, and comprehensive analyses of multi-type data and postoperative analysis of patients with advanced liver cancer are rare. The use of convolutional neural networks is the main technical method used in studies of AI on liver cancer.ConclusionAI has undergone rapid development and has a wide application in the diagnosis and treatment of liver diseases, especially in China. Imaging is an indispensable tool in this filed. Mmulti-type data fusion analysis and development of multimodal treatment plans for liver cancer could become the major trend of future research in AI in liver cancer.

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Evaluation of Therapeutic Effects of Computed Tomography Imaging Classification Algorithm-Based Transcatheter Arterial Chemoembolization on Primary Hepatocellular Carcinoma

Cited by 3 publications

References 24 publications

CT radiomics for noninvasively predicting NQO1 expression levels in hepatocellular carcinoma

CT radiomics for noninvasively predicting NQO1 expression levels in hepatocellular carcinoma

Radiomics as a tool for prognostic prediction in transarterial chemoembolization for hepatocellular carcinoma: a systematic review and meta-analysis

Quantitative analysis of artificial intelligence on liver cancer: A bibliometric analysis

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