Toward predicting the evolution of lung tumors during radiotherapy observed on a longitudinal MR imaging study via a deep learning algorithm Journal Article


Authors: Wang, C.; Rimner, A.; Hu, Y. C.; Tyagi, N.; Jiang, J.; Yorke, E.; Riyahi, S.; Mageras, G.; Deasy, J. O.; Zhang, P.
Article Title: Toward predicting the evolution of lung tumors during radiotherapy observed on a longitudinal MR imaging study via a deep learning algorithm
Abstract: Purpose: To predict the spatial and temporal trajectories of lung tumor during radiotherapy monitored under a longitudinal magnetic resonance imaging (MRI) study via a deep learning algorithm for facilitating adaptive radiotherapy (ART). Methods: We monitored 10 lung cancer patients by acquiring weekly MRI-T2w scans over a course of radiotherapy. Under an ART workflow, we developed a predictive neural network (P-net) to predict the spatial distributions of tumors in the coming weeks utilizing images acquired earlier in the course. The three-step P-net consisted of a convolutional neural network to extract relevant features of the tumor and its environment, followed by a recurrence neural network constructed with gated recurrent units to analyze trajectories of tumor evolution in response to radiotherapy, and finally an attention model to weight the importance of weekly observations and produce the predictions. The performance of P-net was measured with Dice and root mean square surface distance (RMSSD) between the algorithm-predicted and experts-contoured tumors under a leave-one-out scheme. Results: Tumor shrinkage was 60% ± 27% (mean ± standard deviation) by the end of radiotherapy across nine patients. Using images from the first three weeks, P-net predicted tumors on future weeks (4, 5, 6) with a Dice and RMSSD of (0.78 ± 0.22, 0.69 ± 0.24, 0.69 ± 0.26), and (2.1 ± 1.1 mm, 2.3 ± 0.8 mm, 2.6 ± 1.4 mm), respectively. Conclusion: The proposed deep learning algorithm can capture and predict spatial and temporal patterns of tumor regression in a longitudinal imaging study. It closely follows the clinical workflow, and could facilitate the decision-making of ART. A prospective study including more patients is warranted. © 2019 American Association of Physicists in Medicine
Keywords: clinical article; treatment response; cancer radiotherapy; nuclear magnetic resonance imaging; radiotherapy; lung tumor; statistical analysis; algorithm; lung; mri; artificial neural network; longitudinal; human; article; deep learning; convolutional neural network; root mean square surface distance; recurrent neural network
Journal Title: Medical Physics
Volume: 46
Issue: 10
ISSN: 0094-2405
Publisher: American Association of Physicists in Medicine  
Date Published: 2019-10-01
Start Page: 4699
End Page: 4707
Language: English
DOI: 10.1002/mp.13765
PUBMED: 31410855
PROVIDER: scopus
DOI/URL:
Notes: Source: Scopus
Altmetric Score
MSK Authors
  1. Andreas Rimner
    291 Rimner
  2. Pengpeng Zhang
    93 Zhang
  3. Gikas S Mageras
    267 Mageras
  4. Ellen D Yorke
    334 Yorke
  5. Joseph Owen Deasy
    320 Deasy
  6. Yu-Chi Hu
    68 Hu
  7. Neelam Tyagi
    67 Tyagi
  8. Jue Jiang
    16 Jiang
  9. Chuang Wang
    7 Wang