Abstract
Normal tissue complication probability (NTCP) models that were formulated in the Quantitative Analyses of Normal Tissue Effects in the Clinic (QUANTEC) are one of the pillars in support of everyday’s clinical radiation oncology. Because of steady therapeutic refinements and the availability of cutting-edge technical solutions, the ceiling of organs-at-risk-sparing has been reached for photon-based intensity modulated radiotherapy (IMRT). The possibility to capture heterogeneity of patients and tissues in the prediction of toxicity is still an unmet need in modern radiation therapy. Potentially, a major step towards a wider therapeutic index could be obtained from refined assessment of radiation-induced morbidity at an individual level. The rising integration of quantitative imaging and machine learning applications into radiation oncology workflow offers an unprecedented opportunity to further explore the biologic interplay underlying the normal tissue response to radiation. Based on these premises, in this review we focused on the current-state-of-the-art on the use of radiomics for the prediction of toxicity in the field of head and neck, lung, breast and prostate radiotherapy.
Highlights
Frontiers in OncologyBecause of steady therapeutic refinements and the availability of cutting-edge technical solutions, the ceiling of organsat-risk-sparing has been reached for photon-based intensity modulated radiotherapy (IMRT)
The seminal QUANTEC collection [1] provided a comprehensive set of recommendations for the estimation of normal tissue complication probability (NTCP) that were largely based on empirical data, whereas the earlier influential paper by Emami [2] was mainly based on a consensus of experts
This dosiomic approach was replicated through a convolutional deep-neural network analysis [67, 68] in a cohort of 70 non-small cell lung cancer (NSCLC) patients treated with volumetric modulated arc therapy (VMAT), providing a high discriminative power (AUC of 0.84) over standard logistic regression models for the prediction of radiation pneumonitis
Summary
Because of steady therapeutic refinements and the availability of cutting-edge technical solutions, the ceiling of organsat-risk-sparing has been reached for photon-based intensity modulated radiotherapy (IMRT). A major step towards a wider therapeutic index could be obtained from refined assessment of radiation-induced morbidity at an individual level. The rising integration of quantitative imaging and machine learning applications into radiation oncology workflow offers an unprecedented opportunity to further explore the biologic interplay underlying the normal tissue response to radiation. Based on these premises, in this review we focused on the current-state-of-the-art on the use of radiomics for the prediction of toxicity in the field of head and neck, lung, breast and prostate radiotherapy
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