Background: Radiomics is a field of research medicine and data science in which quantitative imaging features are extracted from medical images and successively analyzed to develop models for providing diagnostic, prognostic, and predictive information. The purpose of this work was to develop a machine learning model to predict the survival probability of 85 cervical cancer patients using PET and CT radiomic features as predictors. Methods: Initially, the patients were divided into two mutually exclusive sets: a training set containing 80% of the data and a testing set containing the remaining 20%. The entire analysis was separately conducted for CT and PET features. Genetic algorithms and LASSO regression were used to perform feature selection on the initial PET and CT feature sets. Two different survival models were employed: the Cox proportional hazard model and random survival forest. The Cox model was built using the subset of features obtained with the feature selection process, while all the available features were used for the random survival forest model. The models were trained on the training set; cross-validation was used to fine-tune the models and to obtain a preliminary measurement of the performance. The models were then validated on the test set, using the concordance index as the metric. In addition, alternative versions of the models were developed using tumor recurrence as an adjunct feature to evaluate its impact on predictive performance. Finally, the selected CT and PET features were combined to build a further Cox model. Results: The genetic algorithm was superior to the LASSO regression for feature selection. The best performing model was the Cox model, which was built using the selected CT features; it achieved a concordance index score of 0.707. With the addition of tumor recurrence as a predictive feature, the Cox CT model reached a concordance index score of 0.776. PET features, however, proved to be inadequate for survival prediction. The CT model performed better than the model with combined PET and CT features. Conclusions: The results showed that radiomic features can be used to successfully predict survival probability in cervical cancer patients. In particular, CT radiomic features proved to be better predictors than PET radiomic features in this specific case.

Prediction of Overall Survival in Cervical Cancer Patients Using PET/CT Radiomic Features / Carlini, G; Curti, N; Strolin, S; Giampieri, E; Sala, C; Dall'Olio, D; Merlotti, A; Fanti, S; Remondini, D; Nanni, C; Strigari, L; Castellani, G. - In: APPLIED SCIENCES. - ISSN 2076-3417. - ELETTRONICO. - 12:12(2022), pp. 5946.1-5946.10. [10.3390/app12125946]

Prediction of Overall Survival in Cervical Cancer Patients Using PET/CT Radiomic Features

Carlini, G;Curti, N;Giampieri, E;Sala, C;Dall'Olio, D;Merlotti, A;Fanti, S;Remondini, D;Castellani, G
2022

Abstract

Background: Radiomics is a field of research medicine and data science in which quantitative imaging features are extracted from medical images and successively analyzed to develop models for providing diagnostic, prognostic, and predictive information. The purpose of this work was to develop a machine learning model to predict the survival probability of 85 cervical cancer patients using PET and CT radiomic features as predictors. Methods: Initially, the patients were divided into two mutually exclusive sets: a training set containing 80% of the data and a testing set containing the remaining 20%. The entire analysis was separately conducted for CT and PET features. Genetic algorithms and LASSO regression were used to perform feature selection on the initial PET and CT feature sets. Two different survival models were employed: the Cox proportional hazard model and random survival forest. The Cox model was built using the subset of features obtained with the feature selection process, while all the available features were used for the random survival forest model. The models were trained on the training set; cross-validation was used to fine-tune the models and to obtain a preliminary measurement of the performance. The models were then validated on the test set, using the concordance index as the metric. In addition, alternative versions of the models were developed using tumor recurrence as an adjunct feature to evaluate its impact on predictive performance. Finally, the selected CT and PET features were combined to build a further Cox model. Results: The genetic algorithm was superior to the LASSO regression for feature selection. The best performing model was the Cox model, which was built using the selected CT features; it achieved a concordance index score of 0.707. With the addition of tumor recurrence as a predictive feature, the Cox CT model reached a concordance index score of 0.776. PET features, however, proved to be inadequate for survival prediction. The CT model performed better than the model with combined PET and CT features. Conclusions: The results showed that radiomic features can be used to successfully predict survival probability in cervical cancer patients. In particular, CT radiomic features proved to be better predictors than PET radiomic features in this specific case.
2022
Prediction of Overall Survival in Cervical Cancer Patients Using PET/CT Radiomic Features / Carlini, G; Curti, N; Strolin, S; Giampieri, E; Sala, C; Dall'Olio, D; Merlotti, A; Fanti, S; Remondini, D; Nanni, C; Strigari, L; Castellani, G. - In: APPLIED SCIENCES. - ISSN 2076-3417. - ELETTRONICO. - 12:12(2022), pp. 5946.1-5946.10. [10.3390/app12125946]
Carlini, G; Curti, N; Strolin, S; Giampieri, E; Sala, C; Dall'Olio, D; Merlotti, A; Fanti, S; Remondini, D; Nanni, C; Strigari, L; Castellani, G
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/903928
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