TY - GEN
T1 - Deep Learning and Radiomics Based PET/CT Image Feature Extraction from Auto Segmented Tumor Volumes for Recurrence-Free Survival Prediction in Oropharyngeal Cancer Patients
AU - Ma, Baoqiang
AU - Li, Yan
AU - Chu, Hung
AU - Tang, Wei
AU - De la O Arévalo, Luis Ricardo
AU - Guo, Jiapan
AU - van Ooijen, Peter
AU - Both, Stefan
AU - Langendijk, Johannes Albertus
AU - van Dijk, Lisanne V.
AU - Sijtsema, Nanna Maria
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023/3/18
Y1 - 2023/3/18
N2 - Aim: The development and evaluation of deep learning (DL) and radiomics based models for recurrence-free survival (RFS) prediction in oropharyngeal squamous cell carcinoma (OPSCC) patients based on clinical features, positron emission tomography (PET) and computed tomography (CT) scans and GTV (Gross Tumor Volume) contours of primary tumors and pathological lymph nodes. Methods: A DL auto-segmentation algorithm generated the GTV contours (task 1) that were used for imaging biomarkers (IBMs) extraction and as input for the DL model. Multivariable cox regression analysis was used to develop radiomics models based on clinical and IBMs features. Clinical features with a significant correlation with the endpoint in a univariable analysis were selected. The most promising IBMs were selected by forward selection in 1000 times bootstrap resampling in five-fold cross validation. To optimize the DL models, different combinations of clinical features, PET/CT imaging, GTV contours, the selected radiomics features and the radiomics model predictions were used as input. The combination with the best average performance in five-fold cross validation was taken as the final input for the DL model. The final prediction in the test set, was an ensemble average of the predictions from the five models for the different folds. Results: The average C-index in the five-fold cross validation of the radiomics model and the DL model were 0.7069 and 0.7575, respectively. The radiomics and final DL models showed C-indexes of 0.6683 and 0.6455, respectively in the test set. Conclusion: The radiomics model for recurrence free survival prediction based on clinical, GTV and CT image features showed the best predictive performance in the test set with a C-index of 0.6683.
AB - Aim: The development and evaluation of deep learning (DL) and radiomics based models for recurrence-free survival (RFS) prediction in oropharyngeal squamous cell carcinoma (OPSCC) patients based on clinical features, positron emission tomography (PET) and computed tomography (CT) scans and GTV (Gross Tumor Volume) contours of primary tumors and pathological lymph nodes. Methods: A DL auto-segmentation algorithm generated the GTV contours (task 1) that were used for imaging biomarkers (IBMs) extraction and as input for the DL model. Multivariable cox regression analysis was used to develop radiomics models based on clinical and IBMs features. Clinical features with a significant correlation with the endpoint in a univariable analysis were selected. The most promising IBMs were selected by forward selection in 1000 times bootstrap resampling in five-fold cross validation. To optimize the DL models, different combinations of clinical features, PET/CT imaging, GTV contours, the selected radiomics features and the radiomics model predictions were used as input. The combination with the best average performance in five-fold cross validation was taken as the final input for the DL model. The final prediction in the test set, was an ensemble average of the predictions from the five models for the different folds. Results: The average C-index in the five-fold cross validation of the radiomics model and the DL model were 0.7069 and 0.7575, respectively. The radiomics and final DL models showed C-indexes of 0.6683 and 0.6455, respectively in the test set. Conclusion: The radiomics model for recurrence free survival prediction based on clinical, GTV and CT image features showed the best predictive performance in the test set with a C-index of 0.6683.
KW - Deep learning
KW - Head and neck cancer
KW - Oropharyngeal cancer
KW - Outcome prediction
KW - Radiomics
KW - Recurrence-free survival
U2 - 10.1007/978-3-031-27420-6_24
DO - 10.1007/978-3-031-27420-6_24
M3 - Conference contribution
AN - SCOPUS:85151058513
SN - 9783031274190
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 240
EP - 254
BT - Head and Neck Tumor Segmentation and Outcome Prediction - 3rd Challenge, HECKTOR 2022, Held in Conjunction with MICCAI 2022, Proceedings
A2 - Andrearczyk, Vincent
A2 - Oreiller, Valentin
A2 - Depeursinge, Adrien
A2 - Hatt, Mathieu
PB - Springer Science and Business Media Deutschland GmbH
T2 - 3rd 3D Head and Neck Tumor Segmentation in PET/CT Challenge, HECKTOR 2022, held in Conjunction with the 25th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2022
Y2 - 22 September 2022 through 22 September 2022
ER -