OBJECTIVE The primary objective of this anatomical study was to apply innovative imaging techniques to increase understanding of the microanatomical structures of the brainstem related to safe entry zones. The authors hypothesized that such a high-detail overview would enhance neurosurgeons’ abilities to approach and define anatomical safe entry zones for use with microsurgical resection techniques for intrinsic brainstem lesions. METHODS The brainstems of 13 cadavers were studied with polarized light imaging (PLI) and 11.7-T MRI. The brainstem was divided into 3 compartments—mesencephalon, pons, and medulla—for evaluation with MRI. Tissue was further sectioned to 100 μm with a microtome. MATLAB was used for further data processing. Segmentation of the internal structures of the brainstem was performed with the BigBrain database. RESULTS Thirteen entry zones were reported and assessed for their safety, including the anterior mesencephalic zone, lateral mesencephalic sulcus, interpeduncular zone, intercollicular region, supratrigeminal zone, peritrigeminal zone, lateral pontine zone, median sulcus, infracollicular zone, supracollicular zone, olivary zone, lateral medullary zone, and anterolateral sulcus. The microanatomy, safety, and approaches are discussed. CONCLUSIONS PLI and 11.7-T MRI data show that a neurosurgeon possibly does not need to consider the microanatomical structures that would not be visible on conventional MRI and tractography when entering the mentioned safe entry zones. However, the detailed anatomical images may help neurosurgeons increase their understanding of the internal architecture of the human brainstem, which in turn could lead to safer neurosurgical intervention.

Background In a considerable subgroup of glioma patients treated with (chemo) radiation new lesions develop either representing tumor progression (TP) or treatment-related abnormalities (TRA). Quantitative diffusion imaging metrics such as the Apparent Diffusion Coefficient (ADC) and Fractional Anisotropy (FA) have been reported as potential metrics to noninvasively differentiate between these two phenomena. Variability in performance scores of these metrics and absence of a critical overview of the literature contribute to the lack of clinical implementation. This meta-analysis therefore critically reviewed the literature and meta-analyzed the performance scores. Methods Systematic searching was carried out in PubMed, EMBASE and The Cochrane Library. Using predefined criteria, papers were reviewed. Diagnostic accuracy values of suitable papers were meta-analyzed quantitatively. Results Of 1252 identified papers, 10 ADC papers, totaling 414 patients, and 4 FA papers, with 154 patients were eligible for meta-analysis. Mean ADC values of the patients in the TP/TRA groups were 1.13 × 10−3mm2/s (95% CI 0.912 × 10–3–1.32 × 10−3mm2/s) and 1.38 × 10−3mm2/s (95% CI 1.33 × 10–3–1.45 × 10−3mm2/s, respectively. Mean FA values of TP/TRA was 0.19 (95% CI 0.189–0.194) and 0.14 (95% CI 0.137–0.143) respectively. A significant mean difference between ADC and FA values in TP versus TRA was observed (p = 0.005). Conclusions Quantitative ADC and FA values could be useful for distinguishing TP from TRA on a meta-level. Further studies using serial imaging of individual patients are warranted to determine the role of diffusion imaging in glioma patients.