ArticleMedical physics2019
Toward predicting the evolution of lung tumors during radiotherapy observed on a longitudinal MR imaging study via a deep learning algorithm.
Article in Medical physics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
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Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning in MRI-guided radiation therapy: A systematic review.Journal of applied clinical medical physics · 2024Pooled it
- MR-Guided Adaptive Radiotherapy in Lung Cancer: Current Landscape and Future Directions.Cancers · 2026Review
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- Assessing Dosimetric Benefits of Cone Beam Computed Tomography-Guided Online Adaptive Radiation Treatment Frequencies for Lung Cancer.Advances in radiation oncology · 2025Article
- Data Science Opportunities To Improve Radiotherapy Planning and Clinical Decision Making.Seminars in radiation oncology · 2024Review
- Anatomy-specific Progression Classification in Chest Radiographs via Weakly Supervised Learning.Radiology. Artificial intelligence · 2024Article
- Article
- Article
- Seq2Morph: A deep learning deformable image registration algorithm for longitudinal imaging studies and adaptive radiotherapy.Medical physics · 2023Article
- Deep learning of longitudinal mammogram examinations for breast cancer risk prediction.Pattern recognition · 2022Article
- Prediction of Lung Nodule Progression with an Uncertainty-Aware Hierarchical Probabilistic Network.Diagnostics (Basel, Switzerland) · 2022Article
- Patient specific deep learning based segmentation for magnetic resonance guided prostate radiotherapy.Physics and imaging in radiation oncology · 2022Article
- Longitudinal prediction of lung nodule invasiveness by sequential modelling with common clinical computed tomography (CT) measurements: a prediction accuracy study.Translational lung cancer research · 2022Article
- Deep learning driven predictive treatment planning for adaptive radiotherapy of lung cancer.Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology · 2022Article
- Prediction of future imagery of lung nodule as growth modeling with follow-up computed tomography scans using deep learning: a retrospective cohort study.Translational lung cancer research · 2022Article
- Machine-learning-based prediction of the effectiveness of the delivered dose by exhale-gated radiotherapy for locally advanced lung cancer: The additional value of geometric over dosimetric parameters alone.Frontiers in oncology · 2022Article
- Dynamic stochastic deep learning approaches for predicting geometric changes in head and neck cancer.Physics in medicine and biology · 2021Article
- Deformation driven Seq2Seq longitudinal tumor and organs-at-risk prediction for radiotherapy.Medical physics · 2021Article
- Longitudinal Pooling & Consistency Regularization to Model Disease Progression From MRIs.IEEE journal of biomedical and health informatics · 2021Article
- Adaptive Radiation Therapy in the Treatment of Lung Cancer: An Overview of the Current State of the Field.Frontiers in oncology · 2021Review
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Authors and funding
10 authors.
Funding
Abstract
purposeTo 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).
methodsWe 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.
resultsTumor 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.
conclusionThe 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.
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