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Cancer classification with radiomics in controlled preclinical models
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  • Published: 29 January 2026

Cancer classification with radiomics in controlled preclinical models

  • Kyle Drover1,
  • David A. Simon Davis1,
  • Katharine Gosling1,
  • Jason Price2,
  • Naomi Otoo3,
  • Ines Atmosukarto2,
  • Kylie Jung1,5,
  • Hany Elsaleh1,4,
  • Farhan M. Syed1,5 &
  • …
  • Benjamin J. C. Quah1,5 

Scientific Reports , Article number:  (2026) Cite this article

We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Biomarkers
  • Cancer
  • Image processing
  • Preclinical research

Abstract

The premise of radiomics involves extracting high-dimensional quantitative features from medical images to aid clinical decision-making. While radiomics has shown promise in predicting disease characteristics, concerns regarding confounders, reproducibility, and interpretability limit its clinical adoption. In this study, we assessed the ability of radiomic features extracted from contoured CT images to classify two distinct tumour models, CT26 colorectal cancer (CRC) and 4T1 breast cancer (BC), in a highly controlled murine setting. We aimed to provide compelling data for the role of radiomics as a reliable cancer biomarker. We benchmarked radiomics-based classification against previously established blood-based biomarkers, including leukocyte populations and plasma proteins. Feature filtering reduced the original 1409 radiomic features to 18 non-redundant, high-importance predictors, primarily texture-based transformations. Unsupervised clustering via UMAP revealed that radiomics-based features did not segregate tumour types as effectively as blood biomarkers, suggesting potential confounding factors. Supervised machine learning using Random Forest showed that radiomic features achieved a classification accuracy of 0.87, lower than the 0.96 and 0.99 accuracies obtained with cell and plasma biomarkers, respectively. Furthermore, integrating radiomics with blood biomarkers did not enhance classification performance, and feature importance analysis using SHAP identified blood-based markers as the dominant predictors. These findings suggest that while radiomics retains some predictive value, it is less effective than blood biomarkers in this classification task and does not significantly contribute to multimodal tumour classification models. Our study underscores the need for further standardization and validation of radiomics before its clinical implementation.

Data availability

The initial (before filtering) radiomics features are available in Supplementary File (2) The raw sample data for the blood biomarkers are available in the original paper[19] and Supplementary File (1) The R code for the analysis is in Supplementary File (3) The tumour conformal contours and underlying DICOMS images are available in the Australian National University (ANU) DATA COMMONS repository at DOI: https://doi.org/10.25911/zkcm-ab43. Sphere contours and underlying DICOMS images are available in the Zenodo repository at DOI: https://doi.org/10.5281/zenodo.15070060.

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Funding

This work was supported by the Radiation Oncology Private Practice Trust Fund, Canberra Health Services and with assistance from the ACT Government’s Research and Innovation Fund.

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Authors and Affiliations

  1. Irradiation Immunity Interaction Lab, Division of Genome Sciences and Cancer, John Curtin School of Medical Research, Australian National University, Canberra, Australia

    Kyle Drover, David A. Simon Davis, Katharine Gosling, Kylie Jung, Hany Elsaleh, Farhan M. Syed & Benjamin J. C. Quah

  2. Division of Immunology and Infectious Disease, John Curtin School of Medical Research, Australian National University, Canberra, Australia

    Jason Price & Ines Atmosukarto

  3. Division of Genome Sciences and Cancer, John Curtin School of Medical Research, Australian National University, Canberra, Australia

    Naomi Otoo

  4. Radiation Oncology Department, Alfred Health, Melbourne, Australia

    Hany Elsaleh

  5. Radiation Oncology Department, Canberra Hospital, Canberra Health Services, Canberra, Australia

    Kylie Jung, Farhan M. Syed & Benjamin J. C. Quah

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Contributions

BJCQ, FMS, IA and HE conceived and designed the work; BJCQ, KD, DASD, JP, KG, NO, and KJ acquired the data; BJCQ, and KD analysed and interpreted the data; BJCQ drafted the manuscript; and KD, KG, KJ, and FMS revised the manuscript.

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Correspondence to Benjamin J. C. Quah.

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Drover, K., Davis, D.A.S., Gosling, K. et al. Cancer classification with radiomics in controlled preclinical models. Sci Rep (2026). https://doi.org/10.1038/s41598-026-37757-8

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  • Received: 09 April 2025

  • Accepted: 24 January 2026

  • Published: 29 January 2026

  • DOI: https://doi.org/10.1038/s41598-026-37757-8

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Keywords

  • Radiomics
  • Cancer
  • Biomarkers
  • Medical imaging
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