Radiomics analysis of [18F] FDG PET/CT for microvascular invasion and prognosis prediction in very-early and early-stage hepatocellular carcinoma

Li, Wenfei; Shakir, Tahir Mehmood; Zhao, Yuemei; Wang, Zhanqiu

doi:10.1007/s00259-021-05479-w

Cited by 3 publications

(2 citation statements)

References 4 publications

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“…Radiomics is an emerging method to acquire a large amount of high-dimensional data through the analysis of entire disease lesions and can quantify the non-uniformity between adjacent voxels. Measuring the textural indices of PET/CT images to quantify intratumor heterogeneity has been proposed as an adjunct to predict outcomes (17,18,20). Moreover, due to the differences in radiomic features and numbers extracted, it is difficult to make direct comparisons between studies.…”

Section: Discussionmentioning

confidence: 99%

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Prognostic Value of Radiomic Features of 18F-FDG PET/CT in Patients With B-Cell Lymphoma Treated With CD19/CD22 Dual-Targeted Chimeric Antigen Receptor T Cells

Zhou

Zhang

et al. 2022

Front. Oncol.

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ObjectiveIn the present study, we aimed to evaluate the prognostic value of PET/CT-derived radiomic features for patients with B-cell lymphoma (BCL), who were treated with CD19/CD22 dual-targeted chimeric antigen receptor (CAR) T cells. Moreover, we explored the relationship between baseline radiomic features and the occurrence probability of cytokine release syndrome (CRS).MethodsA total of 24 BCL patients who received 18F-FDG PET/CT before CAR T-cell infusion were enrolled in the present study. Radiomic features from PET and CT images were extracted using LIFEx software, and the least absolute shrinkage and selection operator (LASSO) regression was used to select the most useful predictive features of progression-free survival (PFS) and overall survival (OS). Receiver operating characteristic curves, Cox proportional hazards model, and Kaplan-Meier curves were conducted to assess the potential prognostic value.ResultsContrast extracted from neighbourhood grey-level different matrix (NGLDM) was an independent predictor of PFS (HR = 15.16, p = 0.023). MYC and BCL2 double-expressor (DE) was of prognostic significance for PFS (HR = 7.02, p = 0.047) and OS (HR = 10.37, p = 0.041). The combination of NGLDM_ContrastPET and DE yielded three risk groups with zero (n = 7), one (n = 11), or two (n = 6) factors (p < 0.0001 and p = 0.0004, for PFS and OS), respectively. The PFS was 85.7%, 63.6%, and 0%, respectively, and the OS was 100%, 90.9%, and 16.7%, respectively. Moreover, there was no significant association between PET/CT variables and CRS.ConclusionsIn conclusion, radiomic features extracted from baseline 18F-FDG PET/CT images in combination with genomic factors could predict the survival outcomes of BCL patients receiving CAR T-cell therapy.

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Section: Discussionmentioning

confidence: 99%

“…Radiomics is the high-throughput extraction of a large number of quantitative image features from medical images and can capture information on the intensity, texture, and shape of lesions. Recently, although radiomics has made significant progress in various malignancies (20,21), radiomic information for R/R B-NHL is still limited.…”

Section: Introductionmentioning

confidence: 99%

Prognostic Value of Radiomic Features of 18F-FDG PET/CT in Patients With B-Cell Lymphoma Treated With CD19/CD22 Dual-Targeted Chimeric Antigen Receptor T Cells

Zhou

Zhang

et al. 2022

Front. Oncol.

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Deep learning-aided respiratory motion compensation in PET/CT: addressing motion induced resolution loss, attenuation correction artifacts and PET-CT misalignment

Lu,

Kang,

Zhang

et al. 2024

Eur J Nucl Med Mol Imaging

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Purpose Respiratory motion (RM) significantly impacts image quality in thoracoabdominal PET/CT imaging. This study introduces a unified data-driven respiratory motion correction (uRMC) method, utilizing deep learning neural networks, to solve all the major issues caused by RM, i.e., PET resolution loss, attenuation correction artifacts, and PET-CT misalignment. Methods In a retrospective study, 737 patients underwent [18F]FDG PET/CT scans using the uMI Panorama PET/CT scanner. Ninety-nine patients, who also had respiration monitoring device (VSM), formed the validation set. The remaining data of the 638 patients were used to train neural networks used in the uRMC. The uRMC primarily consists of three key components: (1) data-driven respiratory signal extraction, (2) attenuation map generation, and (3) PET-CT alignment. SUV metrics were calculated within 906 lesions for three approaches, i.e., data-driven uRMC (proposed), VSM-based uRMC, and OSEM without motion correction (NMC). RM magnitude of major organs were estimated. Results uRMC enhanced diagnostic capabilities by revealing previously undetected lesions, sharpening lesion contours, increasing SUV values, and improving PET-CT alignment. Compared to NMC, uRMC showed increases of 10% and 17% in SUVmax and SUVmean across 906 lesions. Sub-group analysis showed significant SUV increases in small and medium-sized lesions with uRMC. Minor differences were found between VSM-based and data-driven uRMC methods, with the SUVmax was found statistically marginal significant or insignificant between the two methods. The study observed varied motion amplitudes in major organs, typically ranging from 10 to 20 mm. Conclusion A data-driven solution for respiratory motion in PET/CT has been developed, validated and evaluated. To the best of our knowledge, this is the first unified solution that compensates for the motion blur within PET, the attenuation mismatch artifacts caused by PET-CT misalignment, and the misalignment between PET and CT.

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Anatomic versus non-anatomic resection of hepatocellular carcinoma with microvascular invasion: A systematic review and meta-analysis

Sun

Shi

et al. 2021

Asian Journal of Surgery

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Radiomics analysis of [18F] FDG PET/CT for microvascular invasion and prognosis prediction in very-early and early-stage hepatocellular carcinoma

Cited by 3 publications

References 4 publications

Prognostic Value of Radiomic Features of 18F-FDG PET/CT in Patients With B-Cell Lymphoma Treated With CD19/CD22 Dual-Targeted Chimeric Antigen Receptor T Cells

Prognostic Value of Radiomic Features of 18F-FDG PET/CT in Patients With B-Cell Lymphoma Treated With CD19/CD22 Dual-Targeted Chimeric Antigen Receptor T Cells

Deep learning-aided respiratory motion compensation in PET/CT: addressing motion induced resolution loss, attenuation correction artifacts and PET-CT misalignment

Anatomic versus non-anatomic resection of hepatocellular carcinoma with microvascular invasion: A systematic review and meta-analysis

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