TY - JOUR

T1 - Neural Networks for Deep Radiotherapy Dose Analysis and Prediction of Liver SBRT Outcomes

AU - Ibragimov, Bulat

AU - Toesca, Diego A.S.

AU - Yuan, Yixuan

AU - Koong, Albert C.

AU - Chang, Daniel T.

AU - Xing, Lei

PY - 2019

Y1 - 2019

N2 - Stereotactic body radiation therapy (SBRT) is a relatively novel treatment modality, with little post-treatment prognostic information reported. This study proposes a novel neural network based paradigm for accurate prediction of liver SBRT outcomes. We assembled a database of patients treated with liver SBRT at our institution. Together with a three-dimensional (3-D) dose delivery plans for each SBRT treatment, other variables such as patients' demographics, quantified abdominal anatomy, history of liver comorbidities, other liver-directed therapies, and liver function tests were collected. We developed a multi-path neural network with the convolutional path for 3-D dose plan analysis and fully connected path for other variables analysis, where the network was trained to predict post-SBRT survival and local cancer progression. To enhance the network robustness, it was initially pre-trained on a large database of computed tomography images. Following n-fold cross-validation, the network automatically identified patients that are likely to have longer survival or late cancer recurrence, i.e., patients with the positive predicted outcome (PPO) of SBRT, and vice versa, i.e., negative predicted outcome (NPO). The predicted results agreed with actual SBRT outcomes with 56% of PPO patients and 0% NPO patients with primary liver cancer survived more than two years after SBRT. Similarly, 82% of PPO patients and 0% of NPO patients with metastatic liver cancer survived two-year threshold. The obtained results were superior to the performance of support vector machine and random forest classifiers. Furthermore, the network was able to identify the critical-to-spare liver regions, and the critical clinical features associated with the highest risks of negative SBRT outcomes.

AB - Stereotactic body radiation therapy (SBRT) is a relatively novel treatment modality, with little post-treatment prognostic information reported. This study proposes a novel neural network based paradigm for accurate prediction of liver SBRT outcomes. We assembled a database of patients treated with liver SBRT at our institution. Together with a three-dimensional (3-D) dose delivery plans for each SBRT treatment, other variables such as patients' demographics, quantified abdominal anatomy, history of liver comorbidities, other liver-directed therapies, and liver function tests were collected. We developed a multi-path neural network with the convolutional path for 3-D dose plan analysis and fully connected path for other variables analysis, where the network was trained to predict post-SBRT survival and local cancer progression. To enhance the network robustness, it was initially pre-trained on a large database of computed tomography images. Following n-fold cross-validation, the network automatically identified patients that are likely to have longer survival or late cancer recurrence, i.e., patients with the positive predicted outcome (PPO) of SBRT, and vice versa, i.e., negative predicted outcome (NPO). The predicted results agreed with actual SBRT outcomes with 56% of PPO patients and 0% NPO patients with primary liver cancer survived more than two years after SBRT. Similarly, 82% of PPO patients and 0% of NPO patients with metastatic liver cancer survived two-year threshold. The obtained results were superior to the performance of support vector machine and random forest classifiers. Furthermore, the network was able to identify the critical-to-spare liver regions, and the critical clinical features associated with the highest risks of negative SBRT outcomes.

KW - convolutional neural networks

KW - deep learning

KW - Liver cancer

KW - local progression prediction

KW - SBRT

KW - survival prediction

UR - http://www.scopus.com/inward/record.url?scp=85071896965&partnerID=8YFLogxK

U2 - 10.1109/JBHI.2019.2904078

DO - 10.1109/JBHI.2019.2904078

M3 - Journal article

C2 - 30869633

AN - SCOPUS:85071896965

VL - 23

SP - 1821

EP - 1833

JO - IEEE Journal of Biomedical and Health Informatics

JF - IEEE Journal of Biomedical and Health Informatics

SN - 2168-2194

IS - 5

M1 - 8664101

ER -