ArticleIEEE transactions on medical imaging2018
Convolutional Invasion and Expansion Networks for Tumor Growth Prediction.
Article in IEEE transactions on medical imaging, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
12 citing papers in PubMed, 70 citations in OpenAlex.
- A narrative review on CT-based evaluation and prediction of lung nodule growth: current status and future directions.Translational lung cancer research · 2026Review
- Predicting Tumor Dynamics Post-Staged GKRS: Machine Learning Models in Brain Metastases Prognosis.Diagnostics (Basel, Switzerland) · 2024Article
- Prediction of Lung Nodule Progression with an Uncertainty-Aware Hierarchical Probabilistic Network.Diagnostics (Basel, Switzerland) · 2022Article
- Molecular Pathology of Lung Cancer.Cold Spring Harbor perspectives in medicine · 2022Review
- Chemotherapy response prediction with diffuser elapser network.Scientific reports · 2022Article
- Deformation driven Seq2Seq longitudinal tumor and organs-at-risk prediction for radiotherapy.Medical physics · 2021Article
- Predicting spatial esophageal changes in a multimodal longitudinal imaging study via a convolutional recurrent neural network.Physics in medicine and biology · 2020Article
- Spatio-Temporal Convolutional LSTMs for Tumor Growth Prediction by Learning 4D Longitudinal Patient Data.IEEE transactions on medical imaging · 2020Article
- Toward predicting the evolution of lung tumors during radiotherapy observed on a longitudinal MR imaging study via a deep learning algorithm.Medical physics · 2019Article
- Deep learning in medical imaging and radiation therapy.Medical physics · 2019Review
- An Interplay Between Reaction-Diffusion and Cell-Matrix Adhesion Regulates Multiscale Invasion in Early Breast Carcinomatosis.Frontiers in physiology · 2019Article
- DeepLesion: automated mining of large-scale lesion annotations and universal lesion detection with deep learning.Journal of medical imaging (Bellingham, Wash.) · 2018Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
Tumor growth is associated with cell invasion and mass-effect, which are traditionally formulated by mathematical models, namely reaction-diffusion equations and biomechanics. Such models can be personalized based on clinical measurements to build the predictive models for tumor growth. In this paper, we investigate the possibility of using deep convolutional neural networks to directly represent and learn the cell invasion and mass-effect, and to predict the subsequent involvement regions of a tumor. The invasion network learns the cell invasion from information related to metabolic rate, cell density, and tumor boundary derived from multimodal imaging data. The expansion network models the mass-effect from the growing motion of tumor mass. We also study different architectures that fuse the invasion and expansion networks, in order to exploit the inherent correlations among them. Our network can easily be trained on population data and personalized to a target patient, unlike most previous mathematical modeling methods that fail to incorporate population data. Quantitative experiments on a pancreatic tumor data set show that the proposed method substantially outperforms a state-of-the-art mathematical model-based approach in both accuracy and efficiency, and that the information captured by each of the two subnetworks is complementary.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.