Navigating the landscape of personalized oncology: overcoming challenges and expanding horizons with computational modeling

Objectives We discuss challenges using computational modeling approaches for personalized prediction in clinical practice to predict treatment response for rare diseases treated by novel therapies using clinical oncology as an example context. Several challenges are discussed, including data scarcity, data sparsity, and difficulties in establishing interdisciplinary teams. Machine learning (ML), mechanistic modeling (MM), and hybrid modeling (HM) are discussed in the context of these challenges. Materials and Methods We present an HM approach, combining ML and MM techniques for improved personalized model estimation in the context of chimeric antigen receptor T-cell therapy for aggressive lymphoma. Results The HM approach improved the root mean squared error by 61.27±23.21% compared to using MM alone (MM: 2.36*105∓1.68*105and HM: 9.57*104∓8.37*104, where the units are in cells), computed from 13 patients included in this study. Discussion By exploiting the complementary strengths of ML and MM approaches, the developed HM method addresses common limitations such as data scarcity and sparsity in medical settings, especially common for rare diseases. Conclusion The HM techniques are likely required to overcome data scarcity and sparsity issues in broad medical settings. Developing these techniques requires dedicated interdisciplinary teams.