NIMG-16. COMPARISON OF A STIR- AND T1-WEIGHTED-BASED RADIOMICS MODEL TO DIFFERENTIATE BETWEEN PLEXIFORM NEUROFIBROMAS AND MALIGNANT PERIPHERAL NERVE SHEATH TUMORS IN NEUROFIBROMATOSIS TYPE 1 (NF1). (14th November 2022)

• Record Type: Journal Article
• Authors: Ly, Ina; Liu, Tianyu; Cai, Wenli; Michaels, Olivia; Kwon, Daniel; Bredella, Miriam; Jordan, Justin; Borcherding, Dana; Boswell, Demetrius; Burgess, Crystal; Chi, Ping; de Blank, Peter; Dombi, Eva; Hirbe, Angela; Korf, Bruce; Lee, Shernine; Mautner, Victor; Melecio-Vázquez, Mairim; Mulder, Zachary; Pollard, Kai; Pratilas, Christine; Salamon, Johannes; Srihari, Divya; Steensma, Matthew; Widemann, Brigitte; Blakeley, Jaishri; Plotkin, Scott
• Is Part Of: Neuro-oncology. Volume 24(2022)Supplement 7
• Page Start: vii164
• Page End: vii165
• Publication Date: 2022-11-14
• Subjects: Brain Neoplasms -- Periodicals; Brain -- Tumors -- Periodicals; Brain -- Cancer -- Periodicals; Nervous system -- Cancer -- Periodicals; 616.99481
• DOI: 10.1093/neuonc/noac209.634
• Languages: English
• ISSNs: 1522-8517

Abstract

Abstract: BACKGROUND: Plexiform neurofibromas (PNF) and malignant peripheral nerve sheath tumors (MPNST) are best visualized on short TI inversion recovery (STIR) sequences on MRI. However, STIR sequences are not routinely acquired in the clinical setting. T1-weighted pre-contrast (T1W) sequences are more standardly obtained but provide insufficient contrast for tumor identification. We developed a radiomics model based on STIR and T1W sequences to differentiate between NF1-associated PNF and MPNST. METHODS: Using a 3D quantitative imaging analysis software (3DQI), 68 MPNST and 79 PNF from 134 participants at nine centers were segmented on STIR sequences (if available) or T2 fat-saturated or T1-weighted fat-saturated post-contrast sequences. Tumor regions of interest were co-registered to T1W sequences. Standard pre-processing included N4 bias field correction, intensity normalization (mean 120 SI, SD 80 SI), and resampling (1 mm 3 voxel resolution). 107 radiomic features were extracted using PyRadiomics. To classify tumors as PNF or MPNST, we applied the Boruta algorithm and correlation removal for selection of important features. A Random Forest model was built using the top five selected features. The data were divided into a training/validation and test set (7:3 ratio). Five-fold cross-validation was performed and repeated 100 times. Model performance was evaluated using AUC, sensitivity, specificity, accuracy, and 95% CI. RESULTS: For the STIR-based model, AUC was 0.856 (95% CI 0.727-0.984), sensitivity 0.6, specificity 0.833, and accuracy 0.727 in the test set. For the T1W-based model, AUC was 0.867 (95% CI 0.743-0.990), sensitivity 0.8, specificity 0.79, and accuracy 0.794 in the test set. CONCLUSIONS: Our radiomics models demonstrate high and comparable performance to distinguish between PNF and MPNST on STIR and T1W sequences. Our inclusion of multicenter MRIs enhances model generalizability. These models can potentially be integrated into the radiologic workflow to help clinicians in the early identification of MPNST or pre-malignant atypical neurofibromas on clinical MRIs.