Machine Learning Enabled Prediction of Biologically Relevant Gene Expression Using CT‐Based Radiomic Features in Non‐Small Cell Lung Cancer
ABSTRACT Background Non‐small‐cell lung cancer (NSCLC) remains a global health challenge, driving morbidity and mortality. The emerging field of radiogenomics utilizes statistical methods to correlate radiographic tumor features with genomic characteristics from biopsy samples. Radiomic techniques a...
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| Language: | English |
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Wiley
2024-12-01
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| Series: | Cancer Medicine |
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| Online Access: | https://doi.org/10.1002/cam4.70509 |
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| author | Shrey S. Sukhadia Christoph Sadée Olivier Gevaert Shivashankar H. Nagaraj |
| author_facet | Shrey S. Sukhadia Christoph Sadée Olivier Gevaert Shivashankar H. Nagaraj |
| author_sort | Shrey S. Sukhadia |
| collection | DOAJ |
| description | ABSTRACT Background Non‐small‐cell lung cancer (NSCLC) remains a global health challenge, driving morbidity and mortality. The emerging field of radiogenomics utilizes statistical methods to correlate radiographic tumor features with genomic characteristics from biopsy samples. Radiomic techniques automate the precise extraction of imaging features from tumor regions in radiographic scans, which are subjected to machine learning (ML) to predict genomic attributes. Methods In a retrospective study of two NSCLC patient cohorts separated by 5 years, we performed a radiogenomic analysis of previously disseminated data from 2018 (n = 116) and newly acquired data from 2023 (n = 44) using RNA sequencing and lung CT images. Combining the data from two cohorts post binarization (of gene expression) or batch normalization (of radiomic features) in each cohort proved to be a better approach as compared to training the model on one cohort and validating on the other. Results Our ML‐based radiogenomic modeling identified specific imaging features—wavelet, three‐dimensional local binary patterns, and logarithmic sigma of gray‐level variance—as predictive indicators for high (1) vs. low (0) gene expression of pivotal NSCLC‐related genes: SLC35C1, BCL2L1, and MAPK1. These genes have recognized implications in a variety of biological pathways and mechanisms of drug resistance pertinent to NSCLC. Conclusion The successful integration of heterogeneous radiogenomic datasets underscores the potential of imaging biomarkers in uncovering NSCLC biological processes through gene expression profiles. |
| format | Article |
| id | doaj-art-9fe2dafd0b9046c5849de9cd186e4f53 |
| institution | Kabale University |
| issn | 2045-7634 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | Cancer Medicine |
| spelling | doaj-art-9fe2dafd0b9046c5849de9cd186e4f532025-01-20T10:51:32ZengWileyCancer Medicine2045-76342024-12-011324n/an/a10.1002/cam4.70509Machine Learning Enabled Prediction of Biologically Relevant Gene Expression Using CT‐Based Radiomic Features in Non‐Small Cell Lung CancerShrey S. Sukhadia0Christoph Sadée1Olivier Gevaert2Shivashankar H. Nagaraj3Centre for Genomics and Personalized Health and School of Biomedical Sciences Queensland University of Technology Brisbane Queensland AustraliaStanford Center for Biomedical Informatics Research, Department of Medicine and Biomedical Data Science Stanford University California USAStanford Center for Biomedical Informatics Research, Department of Medicine and Biomedical Data Science Stanford University California USACentre for Genomics and Personalized Health and School of Biomedical Sciences Queensland University of Technology Brisbane Queensland AustraliaABSTRACT Background Non‐small‐cell lung cancer (NSCLC) remains a global health challenge, driving morbidity and mortality. The emerging field of radiogenomics utilizes statistical methods to correlate radiographic tumor features with genomic characteristics from biopsy samples. Radiomic techniques automate the precise extraction of imaging features from tumor regions in radiographic scans, which are subjected to machine learning (ML) to predict genomic attributes. Methods In a retrospective study of two NSCLC patient cohorts separated by 5 years, we performed a radiogenomic analysis of previously disseminated data from 2018 (n = 116) and newly acquired data from 2023 (n = 44) using RNA sequencing and lung CT images. Combining the data from two cohorts post binarization (of gene expression) or batch normalization (of radiomic features) in each cohort proved to be a better approach as compared to training the model on one cohort and validating on the other. Results Our ML‐based radiogenomic modeling identified specific imaging features—wavelet, three‐dimensional local binary patterns, and logarithmic sigma of gray‐level variance—as predictive indicators for high (1) vs. low (0) gene expression of pivotal NSCLC‐related genes: SLC35C1, BCL2L1, and MAPK1. These genes have recognized implications in a variety of biological pathways and mechanisms of drug resistance pertinent to NSCLC. Conclusion The successful integration of heterogeneous radiogenomic datasets underscores the potential of imaging biomarkers in uncovering NSCLC biological processes through gene expression profiles.https://doi.org/10.1002/cam4.70509gene expression and non‐small cell lung cancermachine learningradiogenomicsradiomics |
| spellingShingle | Shrey S. Sukhadia Christoph Sadée Olivier Gevaert Shivashankar H. Nagaraj Machine Learning Enabled Prediction of Biologically Relevant Gene Expression Using CT‐Based Radiomic Features in Non‐Small Cell Lung Cancer Cancer Medicine gene expression and non‐small cell lung cancer machine learning radiogenomics radiomics |
| title | Machine Learning Enabled Prediction of Biologically Relevant Gene Expression Using CT‐Based Radiomic Features in Non‐Small Cell Lung Cancer |
| title_full | Machine Learning Enabled Prediction of Biologically Relevant Gene Expression Using CT‐Based Radiomic Features in Non‐Small Cell Lung Cancer |
| title_fullStr | Machine Learning Enabled Prediction of Biologically Relevant Gene Expression Using CT‐Based Radiomic Features in Non‐Small Cell Lung Cancer |
| title_full_unstemmed | Machine Learning Enabled Prediction of Biologically Relevant Gene Expression Using CT‐Based Radiomic Features in Non‐Small Cell Lung Cancer |
| title_short | Machine Learning Enabled Prediction of Biologically Relevant Gene Expression Using CT‐Based Radiomic Features in Non‐Small Cell Lung Cancer |
| title_sort | machine learning enabled prediction of biologically relevant gene expression using ct based radiomic features in non small cell lung cancer |
| topic | gene expression and non‐small cell lung cancer machine learning radiogenomics radiomics |
| url | https://doi.org/10.1002/cam4.70509 |
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