Sander Roberti

H8.009

Plesmanlaan 121

Amsterdam, The Netherlands

I am currently a PhD candidate at the Netherlands Cancer Institute, advised by Michael Hauptmann. My work focuses on new methods for assessing the risk of developing a second tumor after exposure to therapeutic radiation. These methods, incorporating the radiation dose to every point in the organ at risk of developing such a tumor, are expected to increase the accuracy of risk predictions. Furthermore, they should be able to better discriminate between different radiotherapy treatment plans. Models predicting the absolute risk of breast cancer for female survivors of Hodgkin lymphoma will be be developed using these methods.

Besides my research project, I was a lecturer for the yearly Basic Medical Statistics course, including lectures as well as computer practicals. Finally, I am involved in other research projects as a statistical consultant.

Before joining the Netherlands Cancer Institute, I obtained a MSc in Mathematics with a focus on statistics and probability, with a master’s thesis in biostatistics.

Selected publications

Radiotherapy-Related Dose and Irradiated Volume Effects on Breast Cancer Risk among Hodgkin Lymphoma Survivors

Sander Roberti, Flora E van Leeuwen, Cécile M Ronckers, Inge M Krul, Florent de Vathaire, Cristina Veres, Ibrahima Diallo, Cécile P M Janus, Berthe M P Aleman, Nicola S Russell, and Michael Hauptmann

JNCI: Journal of the National Cancer Institute 2022

Abstract DOI

Background: Breast cancer (BC) risk is increased among Hodgkin lymphoma (HL) survivors treated with chest radiotherapy. Case-control studies showed a linear radiation dose-response relationship for estimated dose to the breast tumor location. However, these relative risks cannot be used for absolute risk prediction of BC anywhere in the breasts. Furthermore, the independent and joint effects of radiation dose and irradiated volumes are unclear. Therefore, we examined the effects of mean breast dose and various dose-volume parameters on BC risk in HL patients. Methods: We conducted a nested casecontrol study of BC among 5-year HL survivors (173 case patients, 464 matched control patients). Dose-volume histograms were obtained from reconstructed voxel-based 3-dimensional dose distributions. Summary parameters of dose-volume histograms were studied next to mean and median breast dose, Gini index, and the new dose metric mean absolute difference of dose, using categorical and linear excess odds ratio (EOR) models. Interactions between dose-volume parameters and mean dose were also examined. Results: Statistically significant linear dose-response relationships were observed for mean breast dose (EOR per Gy \frac14 0.19, 95% confidence interval [CI] \frac14 0.05 to 1.06) and median dose (EOR/Gy \frac14 0.06, 95% CI \frac14 0.02 to 0.19), with no statistically significant curvature. All metrics except Gini and mean absolute difference were positively correlated with each other. These metrics all showed similar patterns of dose-response that were no longer statistically significant when adjusting for mean dose. No statistically significant modification of the effect of mean dose was observed. Conclusion: Mean breast dose predicts subsequent BC risk in long-term HL survivors.
Bias Correction for Estimates from Linear Excess Relative Risk Models in Small Case-Control Studies

Sander Roberti, Flora E. van Leeuwen, Michael Hauptmann, and Ruth M. Pfeiffer

Statistics in Medicine 2021

DOI