ArticleScientific reports2020
Discriminating pseudoprogression and true progression in diffuse infiltrating glioma using multi-parametric MRI data through deep learning.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 3 of them syntheses that pooled it.
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Who cites it
38 citing papers in PubMed, 3 syntheses or guidelines pooled it, 64 citations in OpenAlex.
- Radiomics for differentiating radiation-induced brain injury from recurrence in gliomas: systematic review, meta-analysis, and methodological quality evaluation using METRICS and RQS.European radiology · 2025Pooled it
- Machine learning imaging applications in the differentiation of true tumour progression from treatment-related effects in brain tumours: A systematic review and meta-analysis.Journal of medical imaging and radiation oncology · 2022Pooled it
- Imaging Biomarkers of Glioblastoma Treatment Response: A Systematic Review and Meta-Analysis of Recent Machine Learning Studies.Frontiers in oncology · 2022Pooled it
- Integrating ConvNeXt tiny and radiomics in a nomogram to differentiate true progression from pseudoprogression in glioma.Neuroradiology · 2026Article
- Spatially identifying regions of tumor recurrence in patients with suspected recurrent glioma using physiologic MRI and machine learning.NPJ digital medicine · 2026Article
- Deep learning in fetal, infant, and toddler neuroimaging research.Developmental cognitive neuroscience · 2026Review
- AI-driven radiomics and radiogenomics: supporting the assessment and differentiation of pseudoprogression in cellular immunotherapy for glioblastoma.Frontiers in immunology · 2026Review
- Comprehensive comparative analysis of explainable deep learning model for differentiation of brucellar spondylitis and tuberculous spondylitis through MRI sequences.European journal of medical research · 2025Article
- Timepoint-Specific Benchmarking of Deep Learning Models for Glioblastoma Follow-Up MRI.Cancers · 2025Article
- Predicting Remaining Survival of Glioblastoma Patients with Radiomics Analysis Based onCancers · 2025Article
- AI-Driven Innovations in Neuroradiology and Neurosurgery: Scoping Review of Current Evidence and Future Directions.Cancers · 2025Review
- A self-supervised multimodal deep learning approach to differentiate post-radiotherapy progression from pseudoprogression in glioblastoma.Scientific reports · 2025Article
- Multiparametric MRI-based radiomics combined with 3D deep transfer learning to predict cervical stromal invasion in patients with endometrial carcinoma.Abdominal radiology (New York) · 2025Article
- A review of deep learning for brain tumor analysis in MRI.NPJ precision oncology · 2025Review
- Predicting Glioma Recurrence Using 18F-FDG PET/CT, MRI, and Tumor Markers: A Combined Approach.International journal of general medicine · 2025Article
- Biologically informed deep neural networks provide quantitative assessment of intratumoral heterogeneity in post treatment glioblastoma.NPJ digital medicine · 2024Article
- Enhanced tumor targeting and penetration of fluorophores via iRGD peptide conjugation: a strategy for the precision targeting of lung cancer.Translational lung cancer research · 2024Article
- Post-ictal changes presenting as late pseudoprogression on MRI and PET in a patient with diffuse glioma: Case report and brief literature review.The neuroradiology journal · 2024Review
- Incidence and outcome of pseudoprogression after radiation therapy in glioblastoma patients: A cohort study.Neuro-oncology practice · 2024Article
- Pseudoprogression versus true progression in glioblastoma: what neurosurgeons need to know.Journal of neurosurgery · 2023Review
Corrections and comments
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Authors and funding
13 authors at 1 institution in 1 country.
Funding
Abstract
Differentiating pseudoprogression from true tumor progression has become a significant challenge in follow-up of diffuse infiltrating gliomas, particularly high grade, which leads to a potential treatment delay for patients with early glioma recurrence. In this study, we proposed to use a multiparametric MRI data as a sequence input for the convolutional neural network with the recurrent neural network based deep learning structure to discriminate between pseudoprogression and true tumor progression. In this study, 43 biopsy-proven patient data identified as diffuse infiltrating glioma patients whose disease progressed/recurred were used. The dataset consists of five original MRI sequences; pre-contrast T1-weighted, post-contrast T1-weighted, T2-weighted, FLAIR, and ADC images as well as two engineered sequences; T1post-T1pre and T2-FLAIR. Next, we used three CNN-LSTM models with a different set of sequences as input sequences to pass through CNN-LSTM layers. We performed threefold cross-validation in the training dataset and generated the boxplot, accuracy, and ROC curve, AUC from each trained model with the test dataset to evaluate models. The mean accuracy for VGG16 models ranged from 0.44 to 0.60 and the mean AUC ranged from 0.47 to 0.59. For CNN-LSTM model, the mean accuracy ranged from 0.62 to 0.75 and the mean AUC ranged from 0.64 to 0.81. The performance of the proposed CNN-LSTM with multiparametric sequence data was found to outperform the popular convolutional CNN with a single MRI sequence. In conclusion, incorporating all available MRI sequences into a sequence input for a CNN-LSTM model improved diagnostic performance for discriminating between pseudoprogression and true tumor progression.
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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.