Journal
CANCERS
Volume 14, Issue 7, Pages -Publisher
MDPI
DOI: 10.3390/cancers14071711
Keywords
diffuse large B-cell lymphoma; lymphoma; predictive modelling; radiomics; machine learning
Categories
Funding
- Innovate UK via the National Consortium of Intelligent Medical Imaging (NCIMI) [104688]
- Royal Academy of Engineering [CiET1819\19]
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This study aimed to predict recurrence in DLBCL patients using radiomics from PET/CT images, and found that a combined radiomic and clinical model outperformed a simple model based on metabolic tumor volume.
Simple Summary Diffuse large B-cell lymphoma (DLBCL) is the most common type of lymphoma. Even with the improvements in the treatment of DLBCL, around a quarter of patients will experience recurrence. The aim of this single centre retrospective study was to predict which patients would have recurrence within 2 years of their treatment using machine learning techniques based on radiomics extracted from the staging PET/CT images. Our study demonstrated that in our dataset of 229 patients (training data = 183, test data = 46) that a combined radiomic and clinical based model performed better than a simple model based on metabolic tumour volume, and that it had a good predictive ability which was maintained when tested on an unseen test set. Background: Approximately 30% of patients with diffuse large B-cell lymphoma (DLBCL) will have recurrence. The aim of this study was to develop a radiomic based model derived from baseline PET/CT to predict 2-year event free survival (2-EFS). Methods: Patients with DLBCL treated with R-CHOP chemotherapy undergoing pre-treatment PET/CT between January 2008 and January 2018 were included. The dataset was split into training and internal unseen test sets (ratio 80:20). A logistic regression model using metabolic tumour volume (MTV) and six different machine learning classifiers created from clinical and radiomic features derived from the baseline PET/CT were trained and tuned using four-fold cross validation. The model with the highest mean validation receiver operator characteristic (ROC) curve area under the curve (AUC) was tested on the unseen test set. Results: 229 DLBCL patients met the inclusion criteria with 62 (27%) having 2-EFS events. The training cohort had 183 patients with 46 patients in the unseen test cohort. The model with the highest mean validation AUC combined clinical and radiomic features in a ridge regression model with a mean validation AUC of 0.75 +/- 0.06 and a test AUC of 0.73. Conclusions: Radiomics based models demonstrate promise in predicting outcomes in DLBCL patients.
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