期刊
RADIOTHERAPY AND ONCOLOGY
卷 181, 期 -, 页码 -出版社
ELSEVIER IRELAND LTD
DOI: 10.1016/j.radonc.2022.109444
关键词
Cell survival; Cancer stem-like cells; Tumor control probability; Biophysical modeling
By modeling the cells and tumor, this study reveals the close relationship between the efficacy of radiotherapy for non-small cell lung cancer and cancer stem cells. Using the integrated microdosimetric-kinetic model, the survival rate in vitro and clinical outcomes were successfully predicted, providing a precise estimation model of radiotherapy worldwide.
Background: Curative effects of stereotactic body radiotherapy (SBRT) for non-small cell lung cancer (NSCLC) have been evaluated using various biophysical models. Because such model parameters are empirically determined based on clinical experience, there is a large gap between in vitro and clinical studies. In this study, considering the heterogeneous cell population, we performed a translational study to realize the possible linkage based on a modeling approach.Methods: We modeled cell-killing and tumor control probability (TCP) considering two populations: progeny and cancer stem-like cells. The model parameters were determined from in vitro survival data of A549 and EBC-1 cells. Based on the cellular parameters, we predicted TCP and compared it with the corresponding clinical data from 553 patients collected at Hirosaki University Hospital.Results: Using an all-in-one developed model, the so-called integrated microdosimetric-kinetic (IMK) model, we successfully reproduced both in vitro survival after acute irradiation and the 3-year TCP with various fractionation schemes (6-10 Gy per fraction). From the conventional prediction without considering cancer stem cells (CSCs), this study revealed that radioresistant CSCs play a key role in the linkage between in vitro and clinical outcomes. Conclusions: This modeling study provides a possible generalized biophysical model that enables precise estimation of SBRT worldwide.(c) 2022 Elsevier B.V. All rights reserved. Radiotherapy and Oncology 181 (2023) 109444
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