Big Data Analytics for Prostate Radiotherapy

Abstract

Radiation therapy is a first-line treatment option for localized prostate cancer and radiation-induced normal tissue damage are often the main limiting factor for modern radiotherapy regimens. Conversely, under-dosing of target volumes in an attempt to spare adjacent healthy tissues limits the likelihood of achieving local, long-term control. Thus, the ability to generate personalized data-driven risk profiles for radiotherapy outcomes would provide valuable prognostic information to help guide both clinicians and patients alike. Big data applied to radiation oncology promises to deliver better understanding of outcomes by harvesting and integrating heterogeneous data types, including patient-specific clinical parameters, treatment-related dose–volume metrics, and biological risk factors. When taken together, such variables make up the basis for a multi-dimensional space (the “RadoncSpace”) in which the presented modeling techniques search in order to identify significant predictors. Herein, we review outcome modeling and big data-mining techniques for both tumor control and radiotherapy-induced normal tissue effects. We apply many of the presented modeling approaches onto a cohort of hypofractionated prostate cancer patients taking into account different data types and a large heterogeneous mix of physical and biological parameters. Cross-validation techniques are also reviewed for the refinement of the proposed framework architecture and checking individual model performance. We conclude by considering advanced modeling techniques that borrow concepts from big data analytics, such as machine learning and artificial intelligence, before discussing the potential future impact of systems radiobiology approaches.

Predictive Solution for Radiation Toxicity Based on Big Data

“…These modeling methods are various, and the neural network and kernel-based methods are widely used. Figure 1 shows that the toxicity prediction results using principle component analysis (PCA) [4]. 48-86%, 35-91%, and 17-79%, respectively, were showed through this study (Figure 2).…”

“…Especially, machine learning is widely used [4][5][6]. Radiotherapy is a treatment method using radiation for cancer treatment based on a patient treatment planning for each radiotherapy machine.…”

“…Coates et al [4] studied the integrated big data research for prostate cancer in radiation oncology. The parameters are dose-volume metrics (EUD), clinical parameter [gastrointestinal (GI) toxicities or rectal bleeding and genitourinary (GU) toxicities or erectile dysfunction (ED)], spatial parameters (zDVH), biological variables (genetic variables), etc., and the risk quantification modeling of TCP and NTCP has performed.…”

A survey on computational intelligence approaches for predictive modeling in prostate cancer

“…Given the heterogeneity of patient populations, it is important 1230 that datasets are large enough to include enough records to reflect the underlying patient population (Coates et al, 2016). Coates et al Coates et al (2016) provide a review of big data-mining techniques that have been applied to radiotherapy outcome modeling. They found that a major limitation in modern outcome modeling is the difficulty in grouping together datasets from multiple 1235 institutions, mainly due to patient privacy and security concerns.…”

Intelligence artificielle appliquée à la radiothérapie

“…Ils ont ainsi intégré les histogrammes dose-volume et ont entraîné trois réseaux neuronaux pour prédire la nocturie, les rectorragies et l'évolution de la concentration sérique d'antigène spécifique de la prostate. La sensibilité et la spécificité restent cependant médiocre (55 %), mais des études plus récentes les ont significativement améliorées (jusque 68 % dans le modèle prédictifs des rectorragies de Tomatis et al) [25][26][27].…”