Evaluation of Apparent Diffusion Coefficient Thresholds for Diagnosis of Medulloblastoma Using Diffusion-Weighted Imaging

Abstract

We assess a diffusion-weighted imaging (DWI) analysis technique as a potential basis for computer-aided diagnosis (CAD) of pediatric posterior fossa tumors. A retrospective medical record search identified 103 children (mean age: 87 months) with posterior fossa tumors having a total of 126 preoperative MR scans with DWI. The minimum ADC (ADC_min) and normalized ADC (nADC) values [ratio of ADC_min values in tumor compared to normal tissue] were measured by a single observer blinded to diagnosis. Receiver operating characteristic (ROC) curves were generated to determine the optimal threshold for which the nADC and ADC_min values would predict tumor histology. Inter-rater reliability for predicting tumor type was evaluated using values measured by two additional observers. At histology, ten tumor types were identified, with astrocytoma (n=50), medulloblastoma (n=33), and ependymoma (n=9) accounting for 89%. Mean ADC_min (0.54 times 10⁻³ mm²/s) and nADC (0.70) were lowest for medulloblastoma. Mean ADC-_min (1.28 times 10⁻³ mm²/s) and nADC (1.64) were highest for astrocytoma. For the ROC analysis, the area under the curve when discriminating medulloblastoma from other tumors using nADC was 0.939 and 0.965 when using ADC_min. The optimal ADC_min threshold was 0.66 times 10⁻³ mm²/s, which yielded an 86% positive predictive value, 97% negative predictive value, and 93% accuracy. Interobserver variability was very low, with near perfect agreement among all observers in predicting medulloblastoma. Our data indicate that both ADC_min and nADC could serve as the basis for a CAD program to distinguish medulloblastoma from other posterior fossa tumors with a high degree of accuracy.