Biochemical recurrence prediction after radiotherapy for prostate cancer with T2w magnetic resonance imaging radiomic features

Fernandes, C.D.; Dinh, C.V.; Walraven, I.; Heijmink, S.W.; Smolic, M.; Griethuysen, J.J.M. van; ... ; Heide, U.A. van der (2018)

Article / Letter to editor

Background and purpose: High-risk prostate cancer patients are frequently treated with external-beam radiotherapy (EBRT). Of all patients receiving EBRT, 15–35% will experience biochemical recurrence (BCR) within five years. Magnetic resonance imaging (MRI) is commonly acquired as part of the diagnostic procedure and imaging-derived features have shown promise in tumour characterisation and biochemical recurrence prediction. We investigated the value of imaging features extracted from pre-treatment T2w anatomical MRI to predict five year biochemical recurrence in high-risk patients treated with EBRT. Materials and methods: In a cohort of 120 high-risk patients, imaging features were extracted from the wholeprostate and a margin surrounding it. Intensity, shape and textural features were extracted from the original and filtered T2w-MRI scans. The minimum-redundancy maximum-relevance algorithm was used for feature selection. Random forest and logistic regression classifiers were used in our experiments. The performance of a logistic regression model using the patient’s clinical features was also investigated. To assess the prediction accuracy we used stratified 10-fold cross validation and receiver operating characteristic analysis, quantified by the area under the curve (AUC). Results: A logistic regression model built using whole-prostate imaging features obtained an AUC of 0.63 in the prediction of BCR, outperforming a model solely based on clinical variables (AUC = 0.51). Combining imaging and clinical features did not outperform the accuracy of imaging alone. Conclusions: These results illustrate the potential of imaging features alone to distinguish patients with an increased risk of recurrence, even in a clinically homogeneous cohort.

• Prostate cancer
• T2-weighted MRI
• Radiomics
• External beam radiotherapy

• All authors: Fernandes, C.D.; Dinh, C.V.; Walraven, I.; Heijmink, S.W.; Smolic, M.; Griethuysen, J.J.M. van; Simoes, R.; Losnegard, A.; Poel, H.G. van der; Pos, F.J.; Heide, U.A. van der
• Date: 2018-01-01
• Journal: Physics and Imaging in Radiation Oncology
• Volume: 7
• Pages: 9 - 15
• DOI: doi:10.1016/j.phro.2018.06.005