| Machine learning models for classifying high-and low-grade gliomas: a systematic review and quality of reporting analysis RC Bahar, S Merkaj, GI Cassinelli Petersen, N Tillmanns, H Subramanian, ... Frontiers in Oncology 12, 856231, 2022 | 7 | 2022 |
| Trends in development of novel machine learning methods for the identification of gliomas in datasets that include non-glioma images: a systematic review H Subramanian, R Dey, WR Brim, N Tillmanns, G Cassinelli Petersen, ... Frontiers in oncology 11, 788819, 2021 | 7 | 2021 |
| Identifying clinically applicable machine learning algorithms for glioma segmentation: recent advances and discoveries N Tillmanns, AE Lum, G Cassinelli, S Merkaj, T Verma, T Zeevi, L Staib, ... Neuro-Oncology Advances 4 (1), vdac093, 2022 | 5 | 2022 |
| NIMG-59. RADIOMIC FEATURE CLUSTER ANALYSIS OF IDH-MUTANT GLIOMA SUBTYPES K Willms, S Chadha, M von Reppert, D Ramakrishnan, J Lost, ... Neuro-Oncology 25 (Supplement_5), v199-v199, 2023 | 1 | 2023 |
| Systematic Literature Review of Machine Learning Algorithms Using Pretherapy Radiologic Imaging for Glioma Molecular Subtype Prediction J Lost, T Verma, L Jekel, M von Reppert, N Tillmanns, S Merkaj, ... American Journal of Neuroradiology 44 (10), 1126-1134, 2023 | 1 | 2023 |
| NIMG-71. Identifying clinically applicable machine learning algorithms for glioma segmentation using a systematic literature review N Tillmanns, A Lum, WR Brim, H Subramanian, M Lin, K Bousabarah, ... Neuro-Oncology 23 (Supplement_6), vi145-vi145, 2021 | 1 | 2021 |
| Comparison of volumetric and 2D-based response methods in the PNOC-001 pediatric low-grade glioma clinical trial M von Reppert, D Ramakrishnan, SC Brüningk, F Memon, S Abi Fadel, ... Neuro-Oncology Advances 6 (1), vdad172, 2024 | | 2024 |
| Application of novel PACS-based informatics platform to identify imaging based predictors of CDKN2A allelic status in glioblastomas N Tillmanns, J Lost, J Tabor, S Vasandani, S Vetsa, N Marianayagam, ... Scientific Reports 13 (1), 22942, 2023 | | 2023 |
| P13. 02. A APPLICATION OF NOVEL PACS-BASED INFORMATICS PLATFORM TO IDENTIFY IMAGING BASED PREDICTORS OF CDKN2A ALLELIC STATUS IN GLIOBLASTOMAS NJ Tillmanns, J Lost, J Tabor, S Vasandani, S Vetsa, N Marianayagam, ... Neuro-Oncology 25 (Supplement_2), ii100-ii101, 2023 | | 2023 |
| Neuro-Oncology Advances M von Reppert, D Ramakrishnan, SC Brüningk, F Memon, S Abi Fadel, ... | | 2023 |
| Bias assessment of Artificial Intelligence papers in Glioma segmentation using TRIPOD (P14-9.005) N Tillmanns, A Lum, W Brim, H Subramanian, S Payabvash, I Ikuta, ... Neurology 98 (18 Supplement), 2022 | | 2022 |
| Machine learning approaches for automated segmentation of gliomas (P3-9.004) N Tillmanns, A Lum, W Brim, H Subramanian, S Payabvash, I Ikuta, ... Neurology 98 (18 Supplement), 2022 | | 2022 |
| Integration of Machine Learning Into Clinical Radiology Practice–Development of a Machine Learning Tool for Preoperative Glioma Grade Prediction (P14-9.002) S Merkaj, T Zeevi, K Bousabarah, E Kazarian, MD Lin, A Pala, ... Neurology 98 (18 Supplement), 2022 | | 2022 |
| Neuro-Oncology Advances N Tillmanns, AE Lum, G Cassinelli, S Merkaj, T Verma, T Zeevi, L Staib, ... | | 2022 |
| NIMG-38. MEASURING ADHERENCE TO TRIPOD OF ARTIFICIAL INTELLIGENCE PAPERS IN THE GLIOMA SEGMENTATION N Tillmanns, A Lum, WR Brim, H Subramanian, M Lin, K Bousabarah, ... Neuro-Oncology 23 (Suppl 6), vi137, 2021 | | 2021 |
| SYSTEMATIC LITERATURE REVIEW OF ARTIFICIAL INTELLIGENCE ALGORITHMS USING PRE-THERAPY MR IMAGING FOR GLIOMA MOLECULAR SUBTYPE CLASSIFICATION J Lost, T Verma, N Tillmanns, WR Brim, H Subramanian, I Ikuta, R Bronen, ... NEURO-ONCOLOGY 23, 139-139, 2021 | | 2021 |
| NIMG-02. PACS-INTEGRATED AUTO-SEGMENTATION WORKFLOW FOR BRAIN METASTASES USING NNU-NET MA RADIOSURGERY Neuro-oncology, 2000 | | 2000 |
| Clinical Implementation of Novel PACS-based Deep Learning Glioma Segmentation Algorithm S Merkaj, K Bousabarah, L MingDe, A Pala, GC Petersen, L Jekel, ... | | |
