66: Early Detection of Recurrence in Patients Treated with Lung Stereotactic Ablative Radiotherapy (SABR) Using Radiomics

Kunkyab, Tenzin; Mou, Benjamin; Jirásek, A; Haston, Christina K.; Andrews, Jeffrey G.; Thomas, Steven; Hyde, Derek

doi:10.1016/s0167-8140(20)30958-0

Cited by 1 publication

(1 citation statement)

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“…The open-source Pyradiomics package (https://pyradiomics.readthedocs.io/en/latest/) was used to extract 1488 pathomics features from eight major categories from each of the 10 sub-images of every pathology image for each sample, and the average value was used as the pathomics feature for subsequent data analyses [38] . Z-score standardization of the extracted feature values in the training set was performed using the preprocessing function of the R caret package [39] . Samples were randomly divided into training and validation sets at a 7:3 ratio.…”

Section: Pathomics Image Acquisition Segmentation and Feature Extractionmentioning

confidence: 99%

Machine learning-based pathomics model to predict the infiltration of regulatory T cells and prognosis in isocitrate dehydrogenase-wild- type glioblastoma

Peng,

Wang,

Chen

et al. 2023

Preprint

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Purpose Regulatory T cells (Tregs) have been highlighted as prognostic factors in isocitrate dehydrogenase (IDH)-wild-type (wt) glioblastoma (GBM). However, conventional detection of Tregs with immunohistochemistry is limited for practical application in clinical settings. The aim of this study was to construct a pathomics model to predict Treg infiltration in IDH-wt GBM and explore the related biological processes. Methods Using the Pyradiomics package, pathomics features were extracted from hematoxylin and eosin-stained biopsy images of patients from The Cancer Genome Atlas. The proportion of Tregs was confirmed in orthotopic glioblastoma mouse model via flow cytometry. The pathomics model was constructed using a gradient-boosting machine-learning approach, and the pathomics score (PS) was determined with the minimal redundancy-maximal relevance and relief algorithms. Cox proportional hazard regression analysis was employed to access the association between PS and overall survival (OS). Transcriptomic data were analyzed through GSEA set enrichment, differential gene expression, and correlation analyses. Results PS was positively correlated with high Treg expression. Patients with a high PS had significantly worse overall survival than did those with a low PS. A high PS independently served as a prognostic risk factor for patients with IDH-wt GBM. Gene set enrichment analysis revealed significant associations between PS and the Notch and IL-6/JAK/STAT3 signaling pathways. A high PS was also significantly correlated with elevated RAD50 expression. Conclusion The developed pathomics model based on machine-learning algorithms can offer an alternative non-invasive method to predict Treg infiltration and prognosis in patients with IDH-wt GBM, further suggesting potential targets for immunotherapy.

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Section: Pathomics Image Acquisition Segmentation and Feature Extractionmentioning

confidence: 99%

Machine learning-based pathomics model to predict the infiltration of regulatory T cells and prognosis in isocitrate dehydrogenase-wild- type glioblastoma

Peng,

Wang,

Chen

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

66: Early Detection of Recurrence in Patients Treated with Lung Stereotactic Ablative Radiotherapy (SABR) Using Radiomics

Cited by 1 publication

References 0 publications

Machine learning-based pathomics model to predict the infiltration of regulatory T cells and prognosis in isocitrate dehydrogenase-wild- type glioblastoma

Machine learning-based pathomics model to predict the infiltration of regulatory T cells and prognosis in isocitrate dehydrogenase-wild- type glioblastoma

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