Breaking new ground: can artificial intelligence and machine learning transform papillary glioneuronal tumor diagnosis?
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s10143-024-02504-y.pdf
Reference5 articles.
1. Rosenblum MK (2007) The 2007 WHO classification of nervous system tumors: newly recognized members of the mixed glioneuronal group. Brain Pathol 17:308–313. https://doi.org/10.1111/j.1750-3639.2007.00079.x
2. Ahmed A-K, Dawood HY, Gerard J, Smith TR (2017) Surgical Resection and Cellular Proliferation Index Predict Prognosis for patients with Papillary Glioneuronal Tumor: systematic review and pooled analysis. World Neurosurg 107:534–541. https://doi.org/10.1016/j.wneu.2017.08.041
3. Du X, He Y, Li F, Wang X, Kong X, Ye M, Chen X (2024) Imaging manifestations of papillary glioneuronal tumors. Neurosurg Rev 47:179. https://doi.org/10.1007/s10143-024-02393-1
4. Saeedi S, Rezayi S, Keshavarz H, Niakan Kalhori R S (2023) MRI-based brain tumor detection using convolutional deep learning methods and chosen machine learning techniques. BMC Med Inf Decis Mak 23:16. https://doi.org/10.1186/s12911-023-02114-6
5. Hollon TC, Pandian B, Adapa AR, Urias E, Save AV, Khalsa SSS, Eichberg DG, D’Amico RS, Farooq ZU, Lewis S, Petridis PD, Marie T, Shah AH, Garton HJL, Maher CO, Heth JA, McKean EL, Sullivan SE, Hervey-Jumper SL, Patil PG, Thompson BG, Sagher O, McKhann GM, Komotar RJ, Ivan ME, Snuderl M, Otten ML, Johnson TD, Sisti MB, Bruce JN, Muraszko KM, Trautman J, Freudiger CW, Canoll P, Lee H, Camelo-Piragua S, Orringer DA (2020) Near real-time intraoperative brain tumor diagnosis using stimulated Raman histology and deep neural networks. Nat Med 26:52–58. https://doi.org/10.1038/s41591-019-0715-9
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