Abstract
Intensity-modulated radiation therapy (IMRT) has been used for high-accurate physical dose distribution sculpture and employed to modulate different dose levels into Gross Tumor Volume (GTV), Clinical Target Volume (CTV) and Planning Target Volume (PTV). GTV, CTV and PTV can be prescribed at different dose levels, however, there is an emphasis that their dose distributions need to be uniform, despite the fact that most types of tumour are heterogeneous. With traditional radiomics and artificial intelligence (AI) techniques, we can identify biological target volume from functional images against conventional GTV derived from anatomical imaging. Functional imaging, such as multi parameter MRI and PET can be used to implement dose painting, which allows us to achieve dose escalation by increasing doses in certain areas that are therapy-resistant in the GTV and reducing doses in less aggressive areas. In this review, we firstly discuss several quantitative functional imaging techniques including PET-CT and multi-parameter MRI. Furthermore, theoretical and experimental comparisons for dose painting by contours (DPBC) and dose painting by numbers (DPBN), along with outcome analysis after dose painting are provided. The state-of-the-art AI-based biomarker diagnosis techniques is reviewed. Finally, we conclude major challenges and future directions in AI-based biomarkers to improve cancer diagnosis and radiotherapy treatment.
Highlights
For treatment planning and outcome management, medical imaging plays a significant role to guarantee satisfying treatment of radiation therapy (RT) [1]
dose painting by numbers (DPBN) assumes that the recurrence risk of a certain pixel in the tumor area is positively correlated with the parameter intensity of its specific function image pixel, and the radiation dose of a certain pixel is directly related to its corresponding functional image pixel information
We reviewed the state-of-the-art functional imaging techniques which facilitates the development of dose painting
Summary
For treatment planning and outcome management, medical imaging plays a significant role to guarantee satisfying treatment of radiation therapy (RT) [1]. Conventional anatomical imaging techniques provide limited insight into tumour macro- and micro-environments, especially regarding biological function, such as metabolic activity, cell proliferation, perfusion, hypoxia etc This information can facilitate evaluating the severity of disease, improving tumour staging and the subsequent patient stratification and treatment [1]. In the 2000s, Ling et al employed biological imaging to achieve “biological conformality”, where higher doses are applied to some areas with higher clonogenic cell density and radiation resistance in a tumour, while lower doses to less aggressive areas [3] In this way, tumor cells can be eliminated, and healthy tissues can recover faster [4, 5]. On this basis, a biological target volume can be defined by identifying biomarkers from functional images.
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