Machine learning based models of radiotherapy-induced skin induration for breast cancer patients
Cicchetti, A. La Rocca, E. De Santis, M. C. Seibold, P. Azria, D. De Ruysscher, D. Valdagni, R. Dunning, A. M. Elliot, R. Gutierrez-Enriquez, S.
Cicchetti, A.
La Rocca, E.
De Santis, M. C.
Seibold, P.
Azria, D.
De Ruysscher, D.
Valdagni, R.
Dunning, A. M.
Elliot, R.
Gutierrez-Enriquez, S.
Citations
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Abstract
Purpose or Objective
To use data from an international prospective cohort study of breast cancer patients (pts) to predict the risk of skin
induration (SI) after radiotherapy (RT) using a machine learning algorithm that includes dosimetric/clinical/genetic factors.
Materials and Methods
Pts were treated after breast conserving surgery with conventional/moderate or ultra hypo-fractionated RT with or without
a tumour bed boost based on clinical and pathological factors. Pts were enrolled in 7 countries in Europe/US; each centre
followed local clinical practice, but the collection of data and genotyping was standardised and centralised. Our endpoint
was late grade 1+ (G1+) SI 2 years after RT completion. Inclusion criteria were: no SI at baseline and availability of complete
dosimetric and genetic data.
For every pt, skin was defined as a 5-mm inner isotropic expansion from the outer body. To select a relevant portion of the
skin DVH, we extracted the higher dose tail using different volume cutoffs (i.e. 25/50/100/150/200 cc volumes
corresponding to 5x5-20x20cm2 areas). We corrected sub-DHVs for fractionation using two possible a/b values from the
literature (1.8 Gy, Bentzen 1988 & Raza 2012; 3.6 Gy, Jones 2006 & Budach 2015). We calculated Equivalent Uniform Doses
(EUDs) from corrected sub-DVHs, with n spanning from 1 to 0.05. We also considered the minimum dose of the selected
DVH tail as an additional dose parameter (Dmin). Toxicity models were built using feed-forward neural networks (FNNs, 10
neurons and 1 hidden layer) following a wrapper method for feature selection. We used separate datasets for input:
clinical/treatment/genetic features were constant, while the dosimetric factors (EUDs and Dmin) coming from sub-DVHs
varied with volume cutoff and a/b .Results
The 647 pts included in the analysis had a G1+ SI rate at 2 years of 29.4%. 281 variables were considered: 127 published
SNPs (GWAS literature), 40 clinical factors, 93 treatment factors and 21 dosimetric variables (for each volume and a/b).
For volume thresholds <200cc, no dosimetric feature was selected by the wrapper method. Therefore, we derived a
predictive model (16 features, no dosimetric variable) for use before RT planning (Model 1). At sub-DVH_200cc, for
a/b=3.6Gy only Dmin was selected (Model 2) as dosimetric variable, while for a/b=1.8Gy, EUD (n=0.5) and Dmin entered
the FNN (Model 3). Conclusion
A pre-planning SI model was derived that included information on genetics (6 SNPs), treatment (6 RT, 1 oncology) and
clinical factors. Largest volume (200cc) sub-DVH allowed selection of dosimetric features, particularly with a/b=1.8Gy and
EUD with n=0.5. Following validation, the model could be used to personalise use of new RT schedules, such as ultrahigh hypofractionation, to minimise risk of Sl.
Description
Date
2022
Publisher
Collections
Keywords
Type
Meetings and Proceedings
Citation
Cicchetti A, La Rocca E, De Santis MC, Seibold P, Azria D, De Ruysscher D, et al. Machine learning based models of radiotherapy-induced skin induration for breast cancer patients. Radiotherapy and Oncology. 2022 May;170:S720-S1. PubMed PMID: WOS:000806764200348.