Correlation between quantitative perfusion histogram parameters of DCE-MRI and PTEN, P-Akt and m-TOR in different pathological types of lung cancer

Qian, Zhendong; Zhao, Zhenhua; Huang, Yanan; Mao, Haijia; Zou, Mingyue; Wang, Cheng; Yu, Guanzhen; Zhang, Minming

doi:10.1186/s12880-021-00604-5

Cited by 5 publications

(5 citation statements)

References 25 publications

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“…The heterogeneity of the tumor is highlighted by GLDM, which is based on voxel values assessing the differences between neighboring voxels and describes the correlation of grayscale in pictures with considerable dependency. According to changes in growth factor activity, angiogenesis, and the tumor microenvironment, local tumor cell proliferation or death, variations in metabolic activity, and enhanced or reduced angiogenesis may result from tumor heterogeneity ( 19 ). According to research, there is a significant association between GLDM and CD8, which is similar with the findings of the current study ( 20 ).…”

Section: Discussionmentioning

confidence: 99%

“…The effectiveness and generalizability of imaging histology will thus be enhanced in future investigations by increasing the sample size to include more lung cancer patients with additional pathological kinds and adding more clinical data. Powerful modeling tools are now available to mine the vast quantity of image data that is currently accessible and show the underlying intricate biological pathways thanks to the advent of quantitative imaging techniques and machine learning ( 19 ). To create models with the best prediction performance, more sophisticated radiomics techniques like machine learning and deep learning should be built.…”

Section: Limitations Of Our Studymentioning

confidence: 99%

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Prediction of CD3 T cells and CD8 T cells expression levels in non-small cell lung cancer based on radiomic features of CT images

Chen

Chen²,

Ni³

et al. 2023

Front. Oncol.

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BackgroundIn this work, radiomics characteristics based on CT scans were used to build a model for preoperative evaluation of CD3 and CD8 T cells expression levels in patients with non-small cell lung cancer (NSCLC).MethodsTwo radiomics models for evaluating tumor-infiltrating CD3 and CD8 T cells were created and validated using computed tomography (CT) images and pathology information from NSCLC patients. From January 2020 to December 2021, 105 NSCLC patients with surgical and histological confirmation underwent this retrospective analysis. Immunohistochemistry (IHC) was used to determine CD3 and CD8 T cells expression, and all patients were classified into groups with high and low CD3 T cells expression and high and low CD8 T cells expression. The CT area of interest had 1316 radiomic characteristics that were retrieved. The minimal absolute shrinkage and selection operator (Lasso) technique was used to choose components from the IHC data, and two radiomics models based on CD3 and CD8 T cells abundance were created. Receiver operating characteristic (ROC), calibration curve, and decision curve analyses were used to examine the models’ ability to discriminate and their clinical relevance (DCA).ResultsA CD3 T cells radiomics model with 10 radiological characteristics and a CD8 T cells radiomics model with 6 radiological features that we created both demonstrated strong discrimination in the training and validation cohorts. The CD3 radiomics model has an area under the curve (AUC) of 0.943 (95% CI 0.886-1), sensitivities, specificities, and accuracy of 96%, 89%, and 93%, respectively, in the validation cohort. The AUC of the CD8 radiomics model was 0.837 (95% CI 0.745-0.930) in the validation cohort, with sensitivity, specificity, and accuracy values of 70%, 93%, and 80%, respectively. Patients with high levels of CD3 and CD8 expression had better radiographic results than patients with low levels of expression in both cohorts (p<0.05). Both radiomic models were therapeutically useful, as demonstrated by DCA.ConclusionsWhen making judgments on therapeutic immunotherapy, CT-based radiomic models can be utilized as a non-invasive way to evaluate the expression of tumor-infiltrating CD3 and CD8 T cells in NSCLC patients.

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Section: Discussionmentioning

confidence: 99%

Section: Limitations Of Our Studymentioning

confidence: 99%

Prediction of CD3 T cells and CD8 T cells expression levels in non-small cell lung cancer based on radiomic features of CT images

Chen

Chen²,

Ni³

et al. 2023

Front. Oncol.

Self Cite

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“…Deep learning technology can fully utilize the spatial structure information of 3D laser scanning technology [15]. e combination of 3D laser scanning technology and deep learning technology can effectively extract useful features of images [16].…”

Section: E Application Of DLmentioning

confidence: 99%

Application of Deep Learning Intelligent Laser Scanning Technology in Mural Digitization

Dong

2022

Mathematical Problems in Engineering

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Ancient Chinese murals have a long history and a large number of types. They are witnesses to the development of ancient Chinese civilization. They still have important historical, artistic, and scientific values, as well as other values such as cultural relics, economy, and missionary education. Yet, the clear and smooth images of earlier have been damaged and covered by fog after the ancient murals were destroyed by human being and the nature. Therefore, the ancient murals that exist till now have been bothered by different damages and sickness. Protection and prevention are needed the most. In recent years, the application of scientific and technological means has played a role in the protection of ancient murals, making the work methods of cultural relics protection more scientific and diverse. In the context of the increasingly rich digital protection of cultural relics, the protection of murals requires more innovative work. However, at present, the resolution of ancient murals is low and the texture details are ambiguous, which leads to the problems of insufficient viewing and low research value. This paper focuses on ancient Chinese murals and conducts exploration on the phenomenon of mural damage and blurred colors. The deep learning intelligent laser scanning technology is used to extract the damaged ancient mural images. In this thesis, the images of murals have been restored using the new-super resolution technology to achieve the optimal mural images so that the mural images are more beautiful and artistic, which provides new ideas for the protection of murals.

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“…It can also provide more quantitative information, such as standard deviation, percentile, energy, entropy, skewness, and kurtosis values ( 10 ), which can be used as a potential noninvasive method for tumor diagnosis, pathological classification, grading, staging, and evaluation of efficacy and prognosis ( 11 ). Moreover, it does not require additional hardware or sequences and has high repeatability and consistency ( 12 ). DKI was first proposed by Jensen et al.…”

Section: Introductionmentioning

confidence: 99%

Differential value of diffusion kurtosis imaging and intravoxel incoherent motion in benign and malignant solitary pulmonary lesions

Liu

Yang

et al. 2023

Front. Oncol.

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ObjectiveTo investigate the diagnostic value of diffusion kurtosis imaging (DKI) and intravoxel incoherent motion (IVIM) whole-lesion histogram parameters in differentiating benign and malignant solitary pulmonary lesions (SPLs).Materials and MethodsPatients with SPLs detected by chest CT examination and with further routine MRI, DKI and IVIM-DWI functional sequence scanning data were recruited. According to the pathological results, SPLs were divided into a benign group and a malignant group. Independent samples t tests (normal distribution) or Mann‒Whitney U tests (nonnormal distribution) were used to compare the differences in DKI (Dk, K), IVIM (D, D*, f) and ADC whole-lesion histogram parameters between the benign and malignant SPL groups. The receiver operating characteristic (ROC) curve was used to evaluate the diagnostic efficiency of the histogram parameters and determine the optimal threshold. The area under the curve (AUC) of each histogram parameter was compared by the DeLong method. Spearman rank correlation was used to analyze the correlation between histogram parameters and malignant SPLs.ResultsMost of the histogram parameters for diffusion-related values (Dk, D, ADC) of malignant SPLs were significantly lower than those of benign SPLs, while most of the histogram parameters for the K value of malignant SPLs were significantly higher than those of benign SPLs. DKI (Dk, K), IVIM (D) and ADC were effective in differentiating benign and malignant SPLs and combined with multiple parameters of the whole-lesion histogram for the D value, had the highest diagnostic efficiency, with an AUC of 0.967, a sensitivity of 90.00% and a specificity of 94.03%. Most of the histogram parameters for the Dk, D and ADC values were negatively correlated with malignant SPLs, while most of the histogram parameters for the K value were positively correlated with malignant SPLs.ConclusionsDKI (Dk, K) and IVIM (D) whole-lesion histogram parameters can noninvasively distinguish benign and malignant SPLs, and the diagnostic performance is better than that of DWI. Moreover, they can provide additional information on SPL microstructure, which has important significance for guiding clinical individualized precision diagnosis and treatment and has potential clinical application value.

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Correlation between quantitative perfusion histogram parameters of DCE-MRI and PTEN, P-Akt and m-TOR in different pathological types of lung cancer

Cited by 5 publications

References 25 publications

Prediction of CD3 T cells and CD8 T cells expression levels in non-small cell lung cancer based on radiomic features of CT images

Prediction of CD3 T cells and CD8 T cells expression levels in non-small cell lung cancer based on radiomic features of CT images

Application of Deep Learning Intelligent Laser Scanning Technology in Mural Digitization

Differential value of diffusion kurtosis imaging and intravoxel incoherent motion in benign and malignant solitary pulmonary lesions

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