arXiv subjects

Antonio Gómez-Caamaño

Publications and source records attributed to Antonio Gómez-Caamaño.

3 recordsLinked to original sources

arXiv2023

Variation of the relative biological effectiveness with fractionation in proton therapy: analysis of prostate cancer response

Purpose: To present a methodology to analyze the variation of RBE with fractionation from clinical data of tumor control probability (TCP) and to apply it to study the response of prostate cancer to proton therapy. M&M: We analyzed the dependence of the RBE on the dose per fraction by using the LQ model and the Poisson TCP formalism. Clinical TCPs for prostate cancer treated with photon and proton therapy for conventional fractionation (2 Gy(RBE)x37 fractions), moderate hypofractionation (3 Gy(RBE)x20 fractions) and hypofractionation (7.25 Gy(RBE)x5 fractions) were obtained from the literature and analyzed. Results: The theoretical analysis showed three distinct regions with RBE monotonically decreasing, increasing or staying constant with the dose per fraction, depending on the change of (α, \{beta}) values between photon and proton irradiation (the equilibrium point being at(α_p/\{beta}_p)=(α_X/\{beta}_X)(α_X/α_p)). An analysis of the clinical data showed RBE values that decline with increasing dose per fraction: for low risk RBE=1.124, 1.119, and 1.102 for 1.82 Gy, 2.73 Gy and 6.59 Gy per fraction (physical proton doses), respectively; for intermediate risk RBE=1.119, and 1.102 for 1.82 Gy, and 6.59 Gy per fraction (physical proton doses), respectively. These values are nonetheless very close to the nominal 1.1 value. Conclusions: We presented a methodology to analyze the RBE for different fractionations, and we used it to study clinical data for prostate cancer. The analysis shows a monotonically decreasing RBE with increasing dose per fraction, which is expected from the LQ formalism and the changes in (α, \{beta}) between photon and proton irradiation. However, the calculations in this study have to be considered with care as they may be biased by limitations in the modeling and/or by the clinical data set used for the analysis.

physics.med-ph↗

arXiv2022

A biomathematical model of tumor response to radioimmunotherapy with $α$PDL1 and $α$CTLA4

There is evidence of synergy between radiotherapy and immunotherapy. Radiotherapy can increase liberation of tumor antigens, causing activation of antitumor T-cells. This effect can be boosted with immunotherapy. Radioimmunotherapy has potential to increase tumor control rates. Biomathematical models of response to radioimmunotherapy may help on understanding of the mechanisms affecting response, and assist clinicians on the design of optimal treatment strategies. In this work we present a biomathematical model of tumor response to radioimmunotherapy. The model uses the linear-quadratic response of tumor cells to radiation (or variation of it), and builds on previous developments to include the radiation-induced immune effect. We have focused this study on the combined effect of radiotherapy and $α$PDL1/$α$CTLA4 therapies. The model can fit preclinical data of volume dynamics and control obtained with different dose fractionations and $α$PDL1/$α$CTLA4. A biomathematical study of optimal combination strategies suggests that a good understanding of the involved biological delays, the biokinetics of the immunotherapy drug, and the interplay between them, may be of paramount importance to design optimal radioimmunotherapy schedules. Biomathematical models like the one we present can help to interpret experimental data on the synergy between radiotherapy and immunotherapy, and to assist in the design of more effective treatments.

physics.med-ph↗

arXiv2022

Evaluation of indirect damage and damage saturation effects in dose-response curves of hypofractionated radiotherapy of early-stage NSCLC and brain metastases

Background and purpose: To investigate the possible contribution of indirect damage and damage saturation to tumour control obtained with SBRT/SRS treatments for early-stage NSCLC and brain metastases. Methods and Materials: We have constructed a dataset of early-stage NSCLC and brain metastases dose-response. These data were fitted to models based on the linear-quadratic (LQ), the linear-quadratic-linear (LQL), and phenomenological modifications of the LQ-model to account for indirect cell damage. We use the Akaike-Information-Criterion formalism to compare performance, and studied the stability of the results with changes in fitting parameters and perturbations on dose/TCP values. Results: In NSCLC, a modified LQ-model with a beta-term increasing with dose yields the best-fits for $α$/$β$=10 Gy. Only the inclusion of very fast accelerated proliferation or low $α$/$β$ values can eliminate such superiority. In brain, the LQL model yields the best-fits, and the ranking is not affected by variations of fitting parameters or dose/TCP perturbations. Conclusions: For $α$/$β$=10 Gy, a modified LQ-model with a beta-term increasing with dose provides better fits to NSCLC dose-response curves. For brain metastases, the LQL provides the best fit. This might be interpreted as a hint of indirect damage in NSCLC, and damage saturation in brain metastases. The results for NSCLC are strongly dependent on the value of $α$/$β$ and may require further investigation, while those for brain seem to be clearly significant. Our results can assist in the design of improved radiotherapy for NSCLC and brain metastases, aiming at avoiding over/under-treatment.

physics.med-ph↗