ArticleScientific reports2020
Robust deep learning classification of adamantinomatous craniopharyngioma from limited preoperative radiographic images.
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 23 papers, 2 of them syntheses that pooled it.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
23 citing papers in PubMed, 2 syntheses or guidelines pooled it, 33 citations in OpenAlex.
- The spectrum of radiation therapy options for craniopharyngioma: a systematic review.Journal of neuro-oncology · 2025Pooled it
- Application of artificial intelligence and radiomics in pituitary neuroendocrine and sellar tumors: a quantitative and qualitative synthesis.Neuroradiology · 2022Pooled it
- Radiomic Applications in Skull Base Pathology: A Systematic Review of Potential Clinical Uses.Journal of neurological surgery. Part B, Skull base · 2025Article
- Review
- Review
- Context aware machine learning techniques for brain tumor classification and detection - A review.Heliyon · 2025Review
- A Visual Analytics Framework for Assessing Interactive AI for Clinical Decision Support.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2025Article
- Review
- Applications and Integration of Radiomics for Skull Base Oncology.Advances in experimental medicine and biology · 2024Review
- Current state and promise of user-centered design to harness explainable AI in clinical decision-support systems for patients with CNS tumors.Frontiers in radiology · 2024Review
- Impact of AI Decision Support on Clinical Experts' Radiographic Interpretation of Adamantinomatous Craniopharyngioma.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- The Iterative Design Process of an Explainable AI Application for Non-Invasive Diagnosis of CNS Tumors: A User-Centered Approach.... IEEE Workshop on Visual Analytics in Healthcare. IEEE Workshop on Visual Analytics in Healthcare · 2023Article
- Generative Adversarial Network (Generative Artificial Intelligence) in Pediatric Radiology: A Systematic Review.Children (Basel, Switzerland) · 2023Review
- EASL: A Framework for Designing, Implementing, and Evaluating ML Solutions in Clinical Healthcare Settings.Proceedings of machine learning research · 2023Article
- Automated segmentation of craniopharyngioma on MR images using U-Net-based deep convolutional neural network.European radiology · 2023Article
- Uncertainty-Aware Deep Learning Classification of Adamantinomatous Craniopharyngioma from Preoperative MRI.Diagnostics (Basel, Switzerland) · 2023Article
- Diagnostic Performance of Artificial Intelligence-Based Computer-Aided Detection and Diagnosis in Pediatric Radiology: A Systematic Review.Children (Basel, Switzerland) · 2023Review
- Artificial intelligence applications in pediatric oncology diagnosis.Exploration of targeted anti-tumor therapy · 2023Review
- Machine Learning for the Detection and Segmentation of Benign Tumors of the Central Nervous System: A Systematic Review.Cancers · 2022Review
- Molecular Biology in Treatment Decision Processes-Neuro-Oncology Edition.International journal of molecular sciences · 2021Review
Corrections and comments
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Authors and funding
27 authors at 20 institutions in 2 countries.
Funding
Abstract
Deep learning (DL) is a widely applied mathematical modeling technique. Classically, DL models utilize large volumes of training data, which are not available in many healthcare contexts. For patients with brain tumors, non-invasive diagnosis would represent a substantial clinical advance, potentially sparing patients from the risks associated with surgical intervention on the brain. Such an approach will depend upon highly accurate models built using the limited datasets that are available. Herein, we present a novel genetic algorithm (GA) that identifies optimal architecture parameters using feature embeddings from state-of-the-art image classification networks to identify the pediatric brain tumor, adamantinomatous craniopharyngioma (ACP). We optimized classification models for preoperative Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and combined CT and MRI datasets with demonstrated test accuracies of 85.3%, 83.3%, and 87.8%, respectively. Notably, our GA improved baseline model performance by up to 38%. This work advances DL and its applications within healthcare by identifying optimized networks in small-scale data contexts. The proposed system is easily implementable and scalable for non-invasive computer-aided diagnosis, even for uncommon diseases.
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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.