Objective : To investigate the efficiency of diffusion tensor imaging (DTI) fiber-tracking based neuronavigation and assess its usefulness in the preoperative surgical planning, prognostic prediction, intraoperative course and outcome improvement. Methods : Seventeen patients with cerebral masses adjacent to corticospinal tract (CST) were given standard magnetic resonance imaging and DTI examination. By incorporation of DTI data, the relation between tumor and adjacent white matter tracts was reconstructed and assessed in the neuronavigation system. Distance from tumor border to CST was measured. Results : The sub-portion of CST in closest proximity to tumor was found displaced in all patients. The chief disruptive changes were classified as follows : complete interruption, partial interruption, or simple displacement. Partial interruption was evident in seven patients (41.2%) whose lesions were close to cortex. In the other 10 patients (58.8%), delineated CSTs were intact but distorted. No complete CST interruption was identified. Overall, the mean distance from resection border to CST was 6.12 mm (range, 0-21), as opposed to 8.18 mm (range, 2-21) with simple displacement and 2.33 mm (range, 0-5) with partial interruption. The clinical outcomes were analyzed in groups stratified by intervening distances (close, <5 mm; moderated, 5-10 mm; far, >10 mm). For the primary brain tumor patients, the proportion of completely resected tumors increased progressively from close to far grouping (42.9%, 50%, and 100%, respectively). Five patients out of seven (71.4%) experienced new neurologic deficits postoperatively in the close group. At meantime, motor deterioration was found in six cases in the close group. All patients in the far and moderate groups received excellent (modified Rankin Scale [mRS] score, 0-1) or good (mRS score, 2-3) rankings, but only 57.1% of patients in the close group earned good outcome scores. Conclusion : DTI fiber tracking based neuronavigation has merit in assessing the relation between lesions and adjacent white matter tracts, allowing prediction of patient outcomes based on lesion-CST distance. It has also proven beneficial in formulating surgical strategies.

Purpose To evaluate the efficacy of three‐dimensional (3D) segmentation‐based radiomics analysis of multiparametric MRI combined with proton magnetic resonance spectroscopy (1H‐MRS) and diffusion tensor imaging (DTI) in glioma grading. Method A total of 100 patients with histologically confirmed gliomas (grade II–IV) were examined using conventional MRI, 1H‐MRS, and DTI. Tumor segmentations of T1‐weighted imaging (T1WI), contrast‐enhanced T1WI (T1WI+C), T2‐weighted imaging (T2WI), apparent diffusion coefficient (ADC) mapping, and fractional anisotropy (FA) mapping were performed. In total, 396 radiomics features were extracted and reduced using basic tests and least absolute shrinkage and selection operator (LASSO) regression. The selected features of each sequence were combined, and logistic regression with ten‐fold cross‐validation was applied to develop the grading model. Sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve (AUC) were compared. The model developed from the training set was applied to the test set to measure accuracy. One optimal grading quantitative parameter was selected for each 1H‐MRS and DTI analysis. A radiomics nomogram model including radiomics signature, quantitative parameters, and clinical features was developed. Results T1WI+C exhibited the highest grading efficacy among single sequences (AUC, 0.92; sensitivity, 0.89; specificity, 0.85), but the efficacy of the combined model was higher (AUC, 0.97; sensitivity, 0.94; specificity, 0.91). The AUCs of all models exhibited high accuracy, and no significant differences were observed in AUCs between the training and test sets. The visualized nomogram was developed based on the combined radiomics signature and choline (Cho)/N‐acetyl aspartate (NAA) from 1H‐MRS. Conclusion Multiparametric MRI can be used to predict the pathological grading of high‐grade gliomas (HGGs) and low‐grade gliomas (LGGs) by combining radiomics features with quantitative parameters. The visualized nomogram may provide an intuitive assessment tool in clinical practice. Clinical trial registration This trial was not registered, as it was a retrospective study and was approved by the local institutional review board.