Pediatric brain tumors are the leading cause of cancer-related death in children. Current curative approaches in management rely, in most cases, on complete surgical resection, followed by irradiation and chemotherapy [4, 25]. Histologic assessment of tumor cellularity, infiltration and necrosis is critical in the diagnosis and grading, as well as subsequent clinical decision-making for patient management and follow-up . The current clinical gold standard, i.e. histologic examination, requires stereotactic biopsy or surgical resection , which carries potential risks including infections, seizures, stroke, coma, as well as brain swelling or bleeding . Sometimes inconclusive pathological findings result from inadequate sampling, necessitating repeat biopsy, with all its attendant risks . A noninvasive neuroimaging approach to facilitate diagnosis or guide surgical planning will ensure better treatment response assessment, ultimately improving patient care .
While MRI remains the most common clinical imaging technique for evaluating CNS tumors , conventional MRI sequences such as T1WI and T2WI correlated poorly with pathologies of high-grade brain tumors. For example, hyperintense regions in T2W and FLAIR images surrounding the Gadolinium (Gd)-enhancing lesion cannot distinguish between infiltrative tumor, vasogenic edema, or immune cell . Gd-enhancement in T1WI also could occur due to either tumor progression or radiation necrosis . Furthermore, conventional T1W and T2W image contrasts vary from scan to scan and are not quantitative, as they depend not only on the MR characteristics of brain tissue, but also on the scanner models, magnet strength, and pulse sequences.
To address the limitations of conventional MRI bridging the gap between histology and MRI for pediatric brain tumor diagnoses, we developed a novel image processing technique, i.e., DHI, taking advantage of previously developed DBSI and DNN algorithm. DBSI provides a simple tensor expression to visualize morphological features resulting from both tumor and non-tumor elements of the brain that are indistinguishable by conventional MRI. In our previous studies, we demonstrated how DBSI-derived restricted fraction positively correlated with adult GBM tumor cellularity identified by H&E staining . In this study, we demonstrated that the hyperintense restricted fraction regions also accurately identify densely cellular tumor areas (Fig. 2). Group analysis across multiple samples with various tumor types also indicated densely cellular tumor had higher restricted fraction values than either normal WM, less densely cellular tumor, infiltrative edges or necrosis. From the areas of necrosis, infiltrating edge, less densely cellular tumor, and densely cellular tumor, we observed a trend towards a gradual increase in restricted fraction values across these four types of histological areas. Thus, the restricted fraction could serve as an appropriate biomarker to assess tumor cellularity in high-grade pediatric brain tumors.
In addition, necrosis exhibited higher values in hindered diffusion fraction and free diffusion fraction than all other histologic components, indicating that these two diffusion metrics are strongly associated with tumor necrosis. Furthermore, our results showed comparable ADC, FA, restricted fraction, and fiber fraction values between the infiltrative edge and normal WM, suggesting that these diffusion metrics lack adequate specificity to distinguish between infiltrating tumor cellularity and white matter. Note that infiltrative edges showed higher isotropic ADC and hindered fraction values than do normal WM, potentially pointing to how tumor infiltration displaces normal parenchyma , destructs of white matter tracts , and/or forming vasogenic edema to disrupt blood brain barrier .
In this study, we demonstrated that DHI, i.e., DBSI-derived metrics as the input classifiers for DNN, differentiates 6 major types of tumor pathologies with an overall accuracy of 83.3%. In detecting and distinguishing individual tumor histology, ROC analysis of DHI models calculated the AUC, sensitivity, and specificity values of all selected tumor pathologies to be higher than 0.950, 89.4% and 87.2%, respectively. In the precision-recall analysis, the prediction of infiltrative edge was relatively low for AUC (0.796) and in F1-score (0.703), likely due to the highly variable degrees of infiltration or inherent cellularity differences among brain tumors and infiltrated brain regions. For example, infiltrative edges with mild to intermediate tumor cellularity could be falsely predicted to be normal WM. Similar phenomenon was observed from the results of confusion matrix (Fig. 4B).
In contrast to previous studies, we adopted a voxel-wise analysis through precise co-registration between histology and MR images, to bridge MRI and histology. Application of this approach accurately detected distinct regions within pediatric brain tumors that were histologically heterogeneous [36, 37]. Image voxels within a region of interest from a specimen could vary differently from each other, reflecting histological heterogeneity. Since DBSI models diffusion-weighted MRI signals independent of neighboring image voxels , each image voxel has its own DBSI metric profiles. Thus, DBSI provides a unique opportunity to assess the heterogeneous tumor pathology-associated structural changes within individual image voxels. DBSI-derived structural metrics are thus ideal to serve as the unique input classifiers for the DNN algorithm. Patient-wise analysis has been typically studied by correlating image metrics with clinical scores or survival rates. There have been attempts to correlate MRI signal with tumor histopathology using stereotactic biopsy [38, 39]. However, the analyses have been hindered by spatial misalignment between MRI-defined lesions and biopsy location, in addition to the high histological heterogeneity of high-grade pediatric brain tumors.
Although the relatively small number of subjects (n = 9) limited the broad applicability of the results, we performed voxel-wise analyses (matching the DBSI voxel-based modeling characteristics) on a total of 94,453 image voxels from 45 brain specimens covering different areas of the brain alleviating the limitation on sample size while providing a proof-of-concept demonstration of DHI. The unbalanced data distribution amongst different tumor histologic components imposed another limit since the imbalance could compromise the performance of a DNN model. We have addressed the concern by employing an oversampling approach to balance the training data and adopted precision-recall analyses to provide complement ROC analyses.
In conclusion, we have demonstrated that DHI can accurately characterize and classify multiple histologies in fixed postmortem specimens of pediatric high-grade brain tumors. While precise prediction of infiltrative edges was suboptimal, the collective findings are encouraging. The efficacy of DHI classification of pediatric brain tumor pathologies still requires in vivo application with image-guided stereotactic biopsy validation.