Cited By
View all- Lazaros KAdam SKrokidis MExarchos TVlamos PVrahatis A(2025)Non-Invasive Biomarkers in the Era of Big Data and Machine LearningSensors10.3390/s2505139625:5(1396)Online publication date: 25-Feb-2025
Towards non-invasive biomarkers in colorectal cancer management: A study on integrating radiomics and deep learning-based image processing for tumor-stroma interaction
Pages 58 - 64
Abstract
Colorectal cancer (CRC) management, particularly for invasive histopathology analysis, has benefited from the prognostic insights the tumor-stroma ratio (TSR) provides. Despite advances in colonography, histopathology, and radiomics, the relationship between TSR and radiomic features still needs to be studied, given the potential non-invasive nature of radiomics biomarkers. This study explores the relationship between TSR and radiomics that can reduce the reliance on invasive biopsies in CRC management by identifying noninvasive radiomic-based markers. A retrospective analysis was conducted on radiology slides and corresponding histopathology slides from the colorectal cancer patients (n = 15) sourced from The Cancer Genomic Atlas (TCGA). A specialist radiologist manually segmented the tumors, and radiomic feature extraction was performed on radiology slides. A pre-trained vision transformer model characterized pathology slides. Using a tile-based approach, each tile was classified into one of nine distinct classes, resulting in a complete characterization of the entire slide. TSR was estimated as the stroma ratio to tumor area on pathology slides. Statistical correlation analysis and machine learning algorithms were employed to find associations between TSR and radiomic features, including feature importance assessment. Our findings indicate a significant correlation between specific gray-level measurement features and TSR, suggesting the potential of these radiomic features as noninvasive prognostic indicators. In summary, the interaction between radiomics and tumor-stroma ratio (TSR) presents potential opportunities for improving colorectal cancer (CRC) prognosis, treatment decision-making, and patient stratification.
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Index Terms
- Towards non-invasive biomarkers in colorectal cancer management: A study on integrating radiomics and deep learning-based image processing for tumor-stroma interaction
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May 2024
349 pages
ISBN:9798400716874
DOI:10.1145/3673971
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Published: 09 September 2024
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- Sichuan Natural Science Foundation
- National Natural Science Foundation
- University of Electronic Science and Technology of China
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ICMHI 2024
ICMHI 2024: 2024 8th International Conference on Medical and Health Informatics
May 17 - 19, 2024
Yokohama, Japan
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View all- Lazaros KAdam SKrokidis MExarchos TVlamos PVrahatis A(2025)Non-Invasive Biomarkers in the Era of Big Data and Machine LearningSensors10.3390/s2505139625:5(1396)Online publication date: 25-Feb-2025
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