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<p>Quantitative imaging is an emerging field that may allow prediction of oncological outcomes. We investigate whether radiomics and deep learning can predict outcomes in metastatic non-small cell lung cancer utilizing randomized trials of PD-1 inhibitors + /− stereotactic ablative body radiotherapy: PEMBRO-RT(NCT02492568), NIVORAD(ACTRN12616000352404) and MDACC(NCT02444741). A random forest model developed on PEMBRO-RT using radiomics features had an AUC of 0.57 for prediction of per-lesion progressive disease on immunotherapy compared to an AUC of 0.92 for a deep learning model. A random forest survival model using radiomics features for overall survival (progression free survival) had a concordance index of 0.63(0.59) and improved to 0.67(0.65) by adding clinical features, including PD-L1 and treatment arm. Validation on NIVORAD and MDACC revealed reduced AUCs. Overall, a deep learning compared to a radiomics model demonstrated excellent predictive value for per-lesion progressive disease for patients on immunotherapy. Models had reduced performance on external validation. Research improving generalizability is required for clinical translation.</p>

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Deep learning and radiomics models in patients with advanced non-small cell lung cancer treated with immunotherapy combined with stereotactic radiotherapy

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月28日

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  • G. Kothari,
  • N. Hardcastle,
  • R. Perera,
  • P. Jackson,
  • S. Lee,
  • J. D. Silver,
  • M. Gaudreault,
  • J. Li,
  • C. Brown,
  • P. L. R. Mitchell,
  • T. Kron,
  • J. W. Welsh,
  • W. S. M. E. Theelen,
  • S. Siva

摘要

Quantitative imaging is an emerging field that may allow prediction of oncological outcomes. We investigate whether radiomics and deep learning can predict outcomes in metastatic non-small cell lung cancer utilizing randomized trials of PD-1 inhibitors + /− stereotactic ablative body radiotherapy: PEMBRO-RT(NCT02492568), NIVORAD(ACTRN12616000352404) and MDACC(NCT02444741). A random forest model developed on PEMBRO-RT using radiomics features had an AUC of 0.57 for prediction of per-lesion progressive disease on immunotherapy compared to an AUC of 0.92 for a deep learning model. A random forest survival model using radiomics features for overall survival (progression free survival) had a concordance index of 0.63(0.59) and improved to 0.67(0.65) by adding clinical features, including PD-L1 and treatment arm. Validation on NIVORAD and MDACC revealed reduced AUCs. Overall, a deep learning compared to a radiomics model demonstrated excellent predictive value for per-lesion progressive disease for patients on immunotherapy. Models had reduced performance on external validation. Research improving generalizability is required for clinical translation.