Convolutional neural network and transfer learning for dose volume histogram prediction for prostate cancer radiotherapy
Por:
Ambroa, EM, Perez-Alija, J, Gallego, P
Publicada:
1 ene 2021
Ahead of Print:
1 oct 2021
Resumen:
To adopt a transfer learning approach and establish a convolutional neural network (CNN) model for the prediction of rectum and bladder dose-volume histograms (DVH) in prostate patients treated with a VMAT technique. One hundred forty-four VMAT patients with intermediate or high-risk prostate cancer were included in this study. Data were split into two sets: 120 and 24 patients, respectively. The second set was used for final validation. To ensure the accuracy of the training data, we developed a ground-truth analysis for detecting and correcting for all potential outliers. We used transfer learning in combination with a pre-trained VGG-16 network. We dropped the fully connected layers from the VGG-16 and added a new fully connected neural network. The inputs for the CNN were a 2D image of the volumes contoured in the CT, but we only retained the geometrical infor-mation of every CT-slice. The outputs were the corresponding rectum and bladder DVH for every slice. We used a confusion matrix to analyze the performance of our model. Our model achieved 100% and 81% of true positive and true negative predictions, respectively. We have an overall accuracy of 87.5%, a misclassification rate of 12.5%, and a precision of 100%. We have successfully developed a model for reliable prediction of rectum and bladder DVH in prostate patients by applying a previously pre-trained CNN. To our knowledge, this is the first attempt to apply transfer learning to the prediction of DVHs that accounts for the ground truth problem.
Filiaciones:
Ambroa, EM:
Consorci Sanitari Terrassa, Dept Radiat Oncol, Med Phys Unit, Terrassa, Spain
Perez-Alija, J:
Hosp Santa Creu & Sant Pau, Dept Med Phys, Carrer St Quinti 89, Barcelona 08041, Spain
Gallego, P:
Hosp Santa Creu & Sant Pau, Dept Med Phys, Carrer St Quinti 89, Barcelona 08041, Spain
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