A predictive model for personalization of nanotechnology-based phototherapy in cancer treatment

Eli Varon; Gaddi Blumrosen; Orit Shefi

doi:10.3389/fonc.2022.1037419

A predictive model for personalization of nanotechnology-based phototherapy in cancer treatment

Front Oncol. 2023 Jan 4:12:1037419. doi: 10.3389/fonc.2022.1037419. eCollection 2022.

Authors

Eli Varon^{1

2}, Gaddi Blumrosen^{3

4}, Orit Shefi^{1

2

5}

Affiliations

¹ Faculty of Engineering, Bar-Ilan University, Ramat Gan, Israel.
² Bar-Ilan Institute of Nanotechnology and Advanced Materials, Bar-Ilan University, Ramat Gan, Israel.
³ Department of Digital Medical Technologies, Holon Institute of Technology, Holon, Israel.
⁴ Department of Computer Science, Holon Institute of Technology, Holon, Israel.
⁵ Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat Gan, Israel.

Abstract

A major challenge in radiation oncology is the prediction and optimization of clinical responses in a personalized manner. Recently, nanotechnology-based cancer treatments are being combined with photodynamic therapy (PDT) and photothermal therapy (PTT). Predictive models based on machine learning techniques can be used to optimize the clinical setup configuration, including such parameters as laser radiation intensity, treatment duration, and nanoparticle features. In this article we demonstrate a methodology that can be used to identify the optimal treatment parameters for PDT and PTT by collecting data from in vitro cytotoxicity assay of PDT/PTT-induced cell death using a single nanocomplex. We construct three machine learning prediction models, employing regression, interpolation, and low- degree analytical function fitting, to predict the laser radiation intensity and duration settings that maximize the treatment efficiency. To examine the accuracy of these prediction models, we construct a dedicated dataset for PDT, PTT, and a combined treatment; this dataset is based on cell death measurements after light radiation treatment and is divided into training and test sets. The preliminary results show that the performance of all three models is sufficient, with death rate errors of 0.09, 0.15, and 0.12 for the regression, interpolation, and analytical function fitting approaches, respectively. Nevertheless, due to its simple form, the analytical function method has an advantage in clinical application and can be used for further analysis of the sensitivity of performance to the treatment parameters. Overall, the results of this study form a baseline for a future personalized prediction model based on machine learning in the domain of combined nanotechnology- and phototherapy-based cancer treatment.

Keywords: PDT (photodynamic therapy); PTT (photothermal therapy); biomedical model development; cancer; personalized medicine; prediction medicine; radiation.