Transfer Learning-Based Autosegmentation of Primary Tumor Volumes of Glioblastomas Using Preoperative MRI for Radiotherapy Treatment - Tian, Suqing; Wang, Cuiying; Zhang, Ruiping; Dai, Zhuojie; Jia, Lecheng; Zhang, Wei; Wang, Junjie; Liu, Yinglong (Frontiers in Oncology, 2022)
Objectives: Glioblastoma is the most common primary malignant brain tumor in adults and can be cured with radiation therapy. However, tumor target contouring for head radiation therapy is labor intensive and highly dependent on the experience of the radiation oncologist. Recently, autocontouring of the tumor target has been playing an increasingly important role in the development of radiotherapy plans. Therefore, we established a deep learning model and improved its performance in autocontouring the primary gross tumor volume (GTV) of glioblastomas through transfer learning. Methods: The preoperative MRI data of 20 patients with glioblastomas were collected from our department (Suqing Tian) and split into a training set and testing set. We fine-tuned a deep learning model for autosegmentation of the hippocampus on separate MRI scans (Ruiping Zhang) through transfer learning and trained this deep learning model directly using the training set. Finally, we evaluated the performance of both trained models in autosegmenting glioblastomas using the testing set. Results: The fine-tuned model converged within 20 epochs, compared to over 50 epochs for the model trained directly by the same training set, and demonstrated better autosegmentation performance (DSC 0.9404±0.0117, 95HD 1.8107±0.3964, ASD 0.6003±0.1287) than the model trained directly (DSC 0.9158±0.0178, 95HD 2.5761±0.5365, ASD 0.7579±0.1468) with the same test set. The DSC, 95HD and ASD values of the two models were significantly different (P<0.05). Conclusion: A model developed with semisupervised transfer learning and trained on independent data achieved good performance in the autosegmenting glioblastoma. The autocontouring volume of glioblastomas is sufficiently accurate for radiotherapy treatment, which could have a positive impact on tumor control and patient survival.