The implications of machine learning in predicting glioblastoma recurrence: a correspondence
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s10143-024-02403-2.pdf
Reference6 articles.
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3. Shafizadeh M, Farzaneh F, Kankam SB, Jangholi E, Shafizadeh Y, Khoshnevisan A (2023) Effects of postoperative intravenous cyclosporine treatment on the survival and functional performance status of patients with glioblastoma: a randomized, triple-blinded, placebo-controlled clinical trial. World Neurosurg 176:e548–e556
4. Yoon J, Baek N, Yoo RE, Choi SH, Kim TM, Park CK, Park SH, Won JK, Lee JH, Lee ST, Choi KS (2024) Added value of dynamic contrast-enhanced MR imaging in deep learning-based prediction of local recurrence in grade 4 adult-type diffuse gliomas patients. Sci Rep 14(1):2171
5. Shahzadi I, Seidlitz A, Beuthien-Baumann B, Zwanenburg A, Platzek I, Kotzerke J, Baumann M, Krause M, Troost EG, Löck S (2024) Radiomics for residual tumour detection and prognosis in newly diagnosed glioblastoma based on postoperative [11 C] methionine PET and T1c-w MRI. Sci Rep 14(1):4576
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