Comparing Performances of Predictive Models of Toxicity after Radiotherapy for Breast Cancer Using Different Machine Learning Approaches
Articolo
Data di Pubblicazione:
2024
Citazione:
Comparing Performances of Predictive Models of Toxicity after Radiotherapy for Breast Cancer Using Different Machine Learning Approaches / M. Giulia Ubeira-Gabellini, M. Mori, G. Palazzo, A. Cicchetti, P. Mangili, M. Pavarini, T. Rancati, A. Fodor, A. del Vecchio, N. Gisella Di Muzio, C. Fiorino. - In: CANCERS. - ISSN 2072-6694. - 16:5(2024), pp. 934.1-934.24. [10.3390/cancers16050934]
Abstract:
Purpose. Different ML models were compared to predict toxicity in RT on a large cohort
(n = 1314). Methods. The endpoint was RTOG G2/G3 acute toxicity, resulting in 204/1314 patients
with the event. The dataset, including 25 clinical, anatomical, and dosimetric features, was split
into 984 for training and 330 for internal tests. The dataset was standardized; features with a
high p-value at univariate LR and with Spearman ρ > 0.8 were excluded; synthesized data of the
minority were generated to compensate for class imbalance. Twelve ML methods were considered.
Model optimization and sequential backward selection were run to choose the best models with a
parsimonious feature number. Finally, feature importance was derived for every model. Results.
The model’s performance was compared on a training–test dataset over different metrics: the best
performance model was LightGBM. Logistic regression with three variables (LR3) selected via
bootstrapping showed performances similar to the best-performing models. The AUC of test data is
slightly above 0.65 for the best models (highest value: 0.662 with LightGBM). Conclusions. No model
performed the best for all metrics: more complex ML models had better performances; however,
models with just three features showed performances comparable to the best models using many
(n = 13–19) features.
Tipologia IRIS:
01 - Articolo su periodico
Keywords:
AI models; early-stage breast cancer; modeling; radiotherapy; toxicity
Elenco autori:
M. Giulia Ubeira-Gabellini, M. Mori, G. Palazzo, A. Cicchetti, P. Mangili, M. Pavarini, T. Rancati, A. Fodor, A. del Vecchio, N. Gisella Di Muzio, C. Fiorino
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