ArticleFrontiers in oncology2020
Non-Invasive Radiomics Approach Predict Invasiveness of Adamantinomatous Craniopharyngioma Before Surgery.
Article in Frontiers in oncology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Application of artificial intelligence and radiomics in pituitary neuroendocrine and sellar tumors: a quantitative and qualitative synthesis.Neuroradiology · 2022Pooled it
- FAP as a prognostic biomarker and radiomics-based predictor of angiogenesis-associated recurrence in Adamantinomatous craniopharyngioma.Pituitary · 2025Article
- Molecular subtypes of adamantinomatous craniopharyngiomas.Neuro-oncology · 2025Review
- Radiomic study of common sellar region lesions differentiation in magnetic resonance imaging based on multi-classification machine learning model.BMC medical imaging · 2025Article
- Finger-like protrusions in adamantinomatous craniopharyngiomas: implications for recurrence and prognosis following complete tumor resection and predictive factors.Neurosurgical review · 2025Article
- Review
- Development and validation of a radiomics nomogram for preoperative prediction of BRAFNeurosurgical review · 2024Article
- Machine learning models from computed tomography to diagnose thymic epithelial tumors requiring combined resection.Journal of thoracic disease · 2024Article
- Review
- Applications and Integration of Radiomics for Skull Base Oncology.Advances in experimental medicine and biology · 2024Review
- Automated segmentation of craniopharyngioma on MR images using U-Net-based deep convolutional neural network.European radiology · 2023Article
- Advances in the treatment of Adamantinomatous craniopharyngioma: How to balance tumor control and quality of life in the current environment: a narrative review.Frontiers in oncology · 2023Review
- Pathological Diagnosis of Adult Craniopharyngioma on MR Images: An Automated End-to-End Approach Based on Deep Neural Networks Requiring No Manual Segmentation.Journal of clinical medicine · 2022Article
- Machine Learning for the Detection and Segmentation of Benign Tumors of the Central Nervous System: A Systematic Review.Cancers · 2022Review
- Current Advances in the Management of Adult Craniopharyngiomas.Current oncology (Toronto, Ont.) · 2022Review
- Clinical Outcomes of Transcranial and Endoscopic Endonasal Surgery for Craniopharyngiomas: A Single-Institution Experience.Frontiers in oncology · 2022Article
- Nomograms to Predict Endocrinological Deficiency in Patients With Surgically Treated Craniopharyngioma.Frontiers in oncology · 2022Article
- Application of Artificial Intelligence in Diagnosis of Craniopharyngioma.Frontiers in neurology · 2021Review
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8 authors.
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No grant is acknowledged in the PubMed record.
Abstract
purposeCraniopharyngiomas (CPs) are benign tumors, complete tumor resection is considered to be the optimal treatment. However, although histologically benign, the local invasiveness of CPs commonly contributes to incomplete resection and a poor prognosis. At present, some advocate less aggressive surgery combined with radiotherapy as a more reasonable and effective means of protecting hypothalamus function and preventing recurrence in patients with tight tumor adhesion to the hypothalamus. Hence, if a method can be developed to predict the invasiveness of CP preoperatively, it will help in the development of a more personalized surgical strategy. The aim of the study was to report a radiomics-clinical nomogram for the individualized preoperative prediction of the invasiveness of adamantinomatous CP (ACPs) before surgery.
methodsIn total, 1,874 radiomics features were extracted from whole tumors on contrast-enhanced T1-weighted images. A support vector machine trained a predictive model that was validated using receiver operating characteristic (ROC) analysis on an independent test set. Moreover, a nomogram was constructed incorporating clinical characteristics and the radiomics signature for individual prediction.
resultsEleven features associated with the invasiveness of ACPs were selected by using the least absolute shrinkage and selection operator (LASSO) method. These features yielded area under the curve (AUC) values of 79.09 and 73.5% for the training and test sets, respectively. The nomogram incorporating peritumoral edema and the radiomics signature yielded good calibration in the training and test sets with the AUCs of 84.79 and 76.48%, respectively.
conclusionThe developed model yields good performance, indicating that the invasiveness of APCs can be predicted using noninvasive radiological data. This reliable, noninvasive tool can help clinical decision making and improve patient prognosis.
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