TY  - JOUR
AU  - Farias, Matheus dos Santos
AU  - Batista, Juciene dos Santos
AU  - Ferreira, Joiciane Sousa de Carvalho
PY  - 2026
DA  - 2026/09/27
TI  - A Taylor-Series-Based Computational Framework for Simulating Lung Tumor Regression During Radiotherapy: Toward Model-Informed Decision Support in Precision Oncology
JO  - Biomedical Informatics and Smart Healthcare
T2  - Biomedical Informatics and Smart Healthcare
JF  - Biomedical Informatics and Smart Healthcare
VL  - 2
IS  - 3
SP  - 152
EP  - 165
DO  - 10.62762/BISH.2026.811248
UR  - https://www.icck.org/article/abs/BISH.2026.811248
KW  - computational oncology
KW  - Taylor series
KW  - forward Euler method
KW  - tumor regression
KW  - radiotherapy
KW  - non-small cell lung cancer
KW  - Geant4
KW  - clinical decision support
AB  - Mechanistic tumor-response models that are transparent and updatable with patient data are important for model-informed precision radiotherapy. This study presents a reproducible computational framework integrating an explicit mathematical derivation, numerical solver, clinical protocol-derived parameters, and three-dimensional visualization to simulate simplified lung tumor regression during radiotherapy for non-small cell lung cancer (NSCLC). A first-order Taylor approximation, equivalent to the forward Euler method, is derived from Taylor's formula with integral remainder, with local truncation error of order $h^2$. The resulting update rule is applied to a first-order regression model with a constant coefficient $k=0.0317$ per fraction. Treatment parameters were extracted from the standard-dose arm of the phase III RTOG 0617 trial (60 Gy in 30 fractions of 2 Gy with concurrent weekly paclitaxel and carboplatin), enabling fraction-by-fraction simulation of the documented treatment schedule. The simulated trajectory drives a three-dimensional tumor representation rendered using Geant4. The relative difference between numerical and exact exponential solutions remained below 1% through fraction 19 and reached 1.53% at fraction 30. These results establish mathematical and computational consistency, rather than clinical validation of response magnitude. The explicit and auditable framework provides a transparent baseline for future patient-specific calibration using longitudinal imaging, adaptive radiotherapy, and digital-twin applications in smart healthcare.
SN  - 3068-5524
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  - 

@article{Farias2026A,
  author = {Matheus dos Santos Farias and Juciene dos Santos Batista and Joiciane Sousa de Carvalho Ferreira},
  title = {A Taylor-Series-Based Computational Framework for Simulating Lung Tumor Regression During Radiotherapy: Toward Model-Informed Decision Support in Precision Oncology},
  journal = {Biomedical Informatics and Smart Healthcare},
  year = {2026},
  volume = {2},
  number = {3},
  pages = {152-165},
  doi = {10.62762/BISH.2026.811248},
  url = {https://www.icck.org/article/abs/BISH.2026.811248},
  abstract = {Mechanistic tumor-response models that are transparent and updatable with patient data are important for model-informed precision radiotherapy. This study presents a reproducible computational framework integrating an explicit mathematical derivation, numerical solver, clinical protocol-derived parameters, and three-dimensional visualization to simulate simplified lung tumor regression during radiotherapy for non-small cell lung cancer (NSCLC). A first-order Taylor approximation, equivalent to the forward Euler method, is derived from Taylor's formula with integral remainder, with local truncation error of order \$h^2\$. The resulting update rule is applied to a first-order regression model with a constant coefficient \$k=0.0317\$ per fraction. Treatment parameters were extracted from the standard-dose arm of the phase III RTOG 0617 trial (60 Gy in 30 fractions of 2 Gy with concurrent weekly paclitaxel and carboplatin), enabling fraction-by-fraction simulation of the documented treatment schedule. The simulated trajectory drives a three-dimensional tumor representation rendered using Geant4. The relative difference between numerical and exact exponential solutions remained below 1\% through fraction 19 and reached 1.53\% at fraction 30. These results establish mathematical and computational consistency, rather than clinical validation of response magnitude. The explicit and auditable framework provides a transparent baseline for future patient-specific calibration using longitudinal imaging, adaptive radiotherapy, and digital-twin applications in smart healthcare.},
  keywords = {computational oncology, Taylor series, forward Euler method, tumor regression, radiotherapy, non-small cell lung cancer, Geant4, clinical decision support},
  issn = {3068-5524},
  publisher = {Institute of Central Computation and Knowledge}
}