Dosiomics: An Advanced Radiotherapy Dose Distribution Analysis for Improved Personalized Treatment Planning

Background and Aim Dosiomics, an emerging field in radiotherapy, leverages quantitative measures extracted from dose distributions to facilitate predictive biomarker discovery, thereby advancing personalized radiation oncology. This study conducts a comprehensive review of recent developments in dosiomics research.Methods Our investigation involved a meticulous analysis of literature using keywords such as dosiomics, dosiomic radiotherapy dose textures, spatial dose features, and dose-based radiomics. We sourced data from reputable databases, including PubMed, Web of Science, and Scopus. Dosiomics features were studied in conjunction with clinical data, Dose-Volume Histogram (DVH) analysis, Tumour Control Probability (TCP) modelling, Normal Tissue Complication Probability (NTCP) assessments, radiomics, and dosimetric parameters.Results Our findings are categorized into three main areas: tumour analysis, normal organ analysis, and features reproducibility. Within the realm of dosiomics analysis, we observed substantial variation in parameter selection, combinations, and statistical methodologies. Numerous studies convincingly demonstrated that the incorporation of diverse parameters significantly enhances the accuracy of prediction models, particularly in predicting normal tissue responses. Dosiomic features have proven highly effective in augmenting the Receiver Operating Characteristic (ROC) curve and consistently outperformed conventional dosimetric models. Similarly, in the prediction of local recurrence, dosiomics-based models have consistently exhibited high efficacy and validated their accuracy.Conclusion The limited number of dosiomics studies, frequently constrained by small sample sizes, underscores the need for further exploration, including rigorous assessments of reproducibility and stability. Dosiomics offers a promising avenue for radiation response modelling, unlocking opportunities for therapeutic personalization and ultimately leading to improved treatment outcomes.

Keywords:

Dosiomics , Radiotherapy , Dose distribution texture analysis , Treatment optimization