Distinct topographic-anatomical patterns in primary and secondary brain tumors and their therapeutic potential

Role of Radiotherapy in PCNSL within the Current Therapeutic Landscape: a Comprehensive Review

OPINION STATEMENT

The therapeutic landscape for primary central nervous system lymphoma (PCNSL) continues to evolve, raising critical questions about the optimal integration of whole-brain radiotherapy (WBRT) to improve patient outcomes. Historically, WBRT has been a cornerstone in PCNSL management, offering effective disease control and relapse prevention. However, the use of high-dose WBRT (HD-WBRT) (≥ 36 Gy), while efficacious, has been associated with significant neurotoxicity, particularly in elderly patients, which has curtailed its long-term applicability. In recent years, high-dose chemotherapy combined with autologous stem cell transplantation (HDT-ASCT) has emerged as a consolidative treatment option, demonstrating efficacy comparable to WBRT, especially in younger patients and those with better performance status, thereby reshaping the therapeutic paradigm. As the therapeutic paradigm shifts, efforts to explore advances in WBRT techniques, such as dose reduction (23.4 Gy) and hyperfractionated protocols, have been aimed at mitigating neurotoxicity while maintaining therapeutic efficacy. These innovations make WBRT a viable option for carefully selected patient populations. Furthermore, this review explores emerging strategies, including localized radiotherapy, novel therapeutic combinations, and individualized treatment paradigms, while identifying key directions for future research to optimize outcomes for PCNSL patients.

Low-grade gliomas do not grow along white matter tracts: evidence from quantitative imaging

Low-grade gliomas are infiltrative brain tumors that can lead to significant neurological deficits due to their invasive nature. The prevailing belief is that low-grade gliomas primarily disseminate along white matter tracts, but quantitative in vivo evidence supporting this concept is lacking. Clarifying their true growth patterns is essential for optimizing therapeutic strategies. We conducted a quantitative analysis of tumor growth patterns in a longitudinal cohort of 43 untreated patients with unigyral World Health Organization grade 2 or 3 gliomas, stratified by their anatomical locations within the neocortex, mesocortex and allocortex. Serial MRI scans were used to generate vector deformation fields, providing detailed three-dimensional representations of tumor evolution over time. These vector deformation fields were compared with diffusion tensor imaging data to assess the alignment of tumor growth with white matter pathways. Quantitative analysis revealed that low-grade gliomas do not predominantly expand along white matter tracts. Instead, they remain confined within specific anatomical boundaries, in respect to their topology of origin. Angular measurements and heat map analysis indicated that tumor growth is directed towards the subventricular zone and may follow their respective radial units. These consistent observations across different anatomical regions challenge the traditional model of glioma progression, suggesting that early-stage glioma expansion is closely governed by ontogenetic factors. In conclusion, this study provides the first quantitative evidence that phenotypical low-grade gliomas do not primarily follow white matter tracts but may instead be influenced by ontogenetic mechanisms. These insights necessitate a re-evaluation of existing models of glioma progression and underscore the importance of incorporating developmental aspects into treatment planning to enhance patient outcomes.

Deep learning-assisted radiomics facilitates multimodal prognostication for personalized treatment strategies in low-grade glioma

Determining the optimal course of treatment for low grade glioma (LGG) patients is challenging and frequently reliant on subjective judgment and limited scientific evidence. Our objective was to develop a comprehensive deep learning assisted radiomics model for assessing not only overall survival in LGG, but also the likelihood of future malignancy and glioma growth velocity. Thus, we retrospectively included 349 LGG patients to develop a prediction model using clinical, anatomical, and preoperative MRI data. Before performing radiomics analysis, a U2-model for glioma segmentation was utilized to prevent bias, yielding a mean whole tumor Dice score of 0.837. Overall survival and time to malignancy were estimated using Cox proportional hazard models. In a postoperative model, we derived a C-index of 0.82 (CI 0.79-0.86) for the training cohort over 10 years and 0.74 (Cl 0.64-0.84) for the test cohort. Preoperative models showed a C-index of 0.77 (Cl 0.73-0.82) for training and 0.67 (Cl 0.57-0.80) test sets. Our findings suggest that we can reliably predict the survival of a heterogeneous population of glioma patients in both preoperative and postoperative scenarios. Further, we demonstrate the utility of radiomics in predicting biological tumor activity, such as the time to malignancy and the LGG growth rate.

The anatomy of neuroepithelial tumours

Many neurological conditions conceal specific anatomical patterns. Their study contributes to the understanding of disease biology and to tailored diagnostics and therapy. Neuroepithelial tumours exhibit distinct anatomical phenotypes and spatiotemporal dynamics that differ from those of other brain tumours. Brain metastases display a preference for the cortico-subcortical boundaries of watershed areas and have a predominantly spherical growth. Primary CNS lymphomas localize to the white matter and generally invade along fibre tracts. In neuroepithelial tumours, topographic probability mapping and unsupervised topological clustering have identified an inherent radial anatomy and adherence to ventriculopial configurations of specific hierarchical orders. Spatiotemporal probability and multivariate survival analyses have identified a temporal and prognostic sequence underlying the anatomical phenotypes of neuroepithelial tumours. Gradual neuroepithelial de-differentiation and declining prognosis follow (i) an expansion into higher order radial units; (ii) a subventricular spread; and (iii) the presence of mesenchymal patterns (expansion along white matter tracts, leptomeningeal or perivascular invasion, CSF spread). While different pathophysiological hypotheses have been proposed, the cellular and molecular mechanisms dictating this anatomical behaviour remain largely unknown. Here we adopt an ontogenetic approach towards the understanding of neuroepithelial tumour anatomy. Contemporary perception of histo- and morphogenetic processes during neurodevelopment permit us to conceptualize the architecture of the brain into hierarchically organized radial units. The anatomical phenotypes in neuroepithelial tumours and their temporal and prognostic sequences share remarkable similarities with the ontogenetic organization of the brain and the anatomical specifications that occur during neurodevelopment. This macroscopic coherence is reinforced by cellular and molecular observations that the initiation of various neuroepithelial tumours, their intratumoural hierarchy and tumour progression are associated with the aberrant reactivation of surprisingly normal ontogenetic programs. Generalizable topological phenotypes could provide the basis for an anatomical refinement of the current classification of neuroepithelial tumours. In addition, we have proposed a staging system for adult-type diffuse gliomas that is based on the prognostically critical steps along the sequence of anatomical tumour progression. Considering the parallels in anatomical behaviour between different neuroepithelial tumours, analogous staging systems may be implemented for other neuroepithelial tumour types and subtypes. Both the anatomical stage of a neuroepithelial tumour and the spatial configuration of its hosting radial unit harbour the potential to stratify treatment decisions at diagnosis and during follow-up. More data on specific neuroepithelial tumour types and subtypes are needed to increase the anatomical granularity in their classification and to determine the clinical impact of stage-adapted and anatomically tailored therapy and surveillance.

Meta-topologies define distinct anatomical classes of brain tumours linked to histology and survival

The current World Health Organization classification integrates histological and molecular features of brain tumours. The aim of this study was to identify generalizable topological patterns with the potential to add an anatomical dimension to the classification of brain tumours. We applied non-negative matrix factorization as an unsupervised pattern discovery strategy to the fine-grained topographic tumour profiles of 936 patients with neuroepithelial tumours and brain metastases. From the anatomical features alone, this machine learning algorithm enabled the extraction of latent topological tumour patterns, termed meta-topologies. The optimal part-based representation was automatically determined in 10 000 split-half iterations. We further characterized each meta-topology's unique histopathologic profile and survival probability, thus linking important biological and clinical information to the underlying anatomical patterns. In neuroepithelial tumours, six meta-topologies were extracted, each detailing a transpallial pattern with distinct parenchymal and ventricular compositions. We identified one infratentorial, one allopallial, three neopallial (parieto-occipital, frontal, temporal) and one unisegmental meta-topology. Each meta-topology mapped to distinct histopathologic and molecular profiles. The unisegmental meta-topology showed the strongest anatomical-clinical link demonstrating a survival advantage in histologically identical tumours. Brain metastases separated to an infra- and supratentorial meta-topology with anatomical patterns highlighting their affinity to the cortico-subcortical boundary of arterial watershed areas.Using a novel data-driven approach, we identified generalizable topological patterns in both neuroepithelial tumours and brain metastases. Differences in the histopathologic profiles and prognosis of these anatomical tumour classes provide insights into the heterogeneity of tumour biology and might add to personalized clinical decision-making.

Epidemiology of pediatric central nervous system tumors in Uyghur: experience from a single center

PURPOSE

Retrospective analysis of clinical and epidemiological characteristics of central nervous system (CNS)tumors in Uyghur children from a single center in Xinjiang.

METHODS

Between January 2013 and December 2021, 243 children (0-17 years old) with a clear pathological type of CNS tumor are collected and analyzed for tumor size, grade, and category, as well as their relationship with the child's gender, age, and region of origin according to the 2021 edition of the new WHO CNS tumor classification.

OUTCOME

The 243 cases of CNS tumors in Uyghur children are predominantly from rural areas, with 144 cases (59.26%) of supratentorial tumors and 129 cases (53.09%) of low-grade tumors. With an overall male-to-female ratio of 1.43:1, a peak age of incidence of 6 to 8 years.

CONCLUDING

The present study is based on a 9-year analysis of pediatric CNS data from a single center, and the center is the largest tertiary hospital in Xinjiang with large numbers of admitted patients, which may reflect some extent the clinical characteristics and epidemiological features characteristics of pediatric CNS tumors in Uyghur in Xinjiang.

Anatomical phenotyping and staging of brain tumours

Unlike other tumors, the anatomical extent of brain tumors is not objectified and quantified through staging. Staging systems are based on understanding the anatomical sequence of tumor progression and its relationship to histopathological dedifferentiation and survival. The aim of this study was to describe the spatiotemporal phenotype of the most frequent brain tumor entities, to assess the association of anatomical tumor features with survival probability and to develop a staging system for WHO grade 2 and 3 gliomas and glioblastoma. Anatomical phenotyping was performed on a consecutive cohort of 1000 patients with first diagnosis of a primary or secondary brain tumor. Tumor probability in different topographic, phylogenetic and ontogenetic parcellation units was assessed on preoperative MRI through normalization of the relative tumor prevalence to the relative volume of the respective structure. We analyzed the spatiotemporal tumor dynamics by cross-referencing preoperative against preceding and subsequent MRIs of the respective patient. The association between anatomical phenotype and outcome defined prognostically critical anatomical tumor features at diagnosis. Based on a hypothesized sequence of anatomical tumor progression, we developed a three-level staging system for WHO grade 2 and 3 gliomas and glioblastoma. This staging system was validated internally in the original cohort and externally in an independent cohort of 300 consecutive patients. While primary central nervous system lymphoma showed highest probability along white matter tracts, metastases enriched along terminal arterial flow areas. Neuroepithelial tumors mapped along all sectors of the ventriculocortical axis, while adjacent units were spared, consistent with a transpallial behavior within phylo-ontogenetic radial units. Their topographic pattern correlated with morphogenetic processes of convergence and divergence of radial units during phylo- and ontogenesis. While a ventriculofugal growth dominated in neuroepithelial tumors, a gradual deviation from this neuroepithelial spatiotemporal behavior was found with progressive histopathological dedifferentiation. The proposed three-level staging system for WHO grade 2 and 3 gliomas and glioblastoma correlated with the degree of histological dedifferentiation and proved accurate in terms of survival upon both internal and external validation. In conclusion, this study identified specific spatiotemporal phenotypes in brain tumors through topographic probability and growth pattern assessment. The association of anatomical tumor features with survival defined critical steps in the anatomical sequence of neuroepithelial tumor progression, based on which a staging system for WHO grade 2 and 3 gliomas and glioblastoma was developed and validated.

Topographic volume-standardization atlas of the human brain

Specific anatomical patterns are seen in various diseases affecting the brain. Clinical studies on the topography of pathologies are often limited by the absence of a normalization of the prevalence of pathologies to the relative volume of the affected anatomical structures. A comprehensive reference on the relative volumes of clinically relevant anatomical structures serving for such a normalization, is currently lacking. The analyses are based on anatomical high-resolution three-dimensional T1-weighted magnetic resonance imaging data of 30 healthy Caucasian volunteers, including 14 females (mean age 37.79 years, SD 13.04) and 16 males (mean age 38.31 years, SD 16.91). Semi-automated anatomical segmentation was used, guided by a neuroanatomical parcellation algorithm differentiating 96 structures. Relative volumes were derived by normalizing parenchymal structures to the total individual encephalic volume and ventricular segments to the total individual ventricular volume. The present investigation provides the absolute and relative volumes of 96 anatomical parcellation units of the human encephalon. A larger absolute volume in males than in females is found for almost all parcellation units. While parenchymal structures display a trend towards decreasing volumes with increasing age, a significant inverse effect is seen with the ventricular system. The variances in volumes as well as the effects of gender and age are given for each structure before and after normalization. The provided atlas constitutes an anatomically detailed and comprehensive analysis of the absolute and relative volumes of the human encephalic structures using a clinically oriented parcellation algorithm. It is intended to serve as a reference for volume-standardization in clinical studies on the topographic prevalence of pathologies.

The Infratentorial Localization of Brain Metastases May Correlate with Specific Clinical Characteristics and Portend Worse Outcomes Based on Voxel-Wise Mapping

The infratentorial regions are vulnerable to develop brain metastases (BMs). However, the associations between the infratentorial localization of BMs and clinical characteristics remained unclear. We retrospectively studied 1102 patients with 4365 BM lesions. Voxel-wise mapping of MRI was applied to construct the tumor frequency heatmaps after normalization and segmentation. The analysis of differential involvement (ADIFFI) was further used to obtain statistically significant clusters. Kaplan-Meier method and Cox regression were used to analyze the prognosis. The parietal, insular and left occipital lobes, and cerebellum were vulnerable to BMs with high relative metastatic risks. Infratentorial areas were site-specifically affected by the lung, breast, and colorectal cancer BMs, but inversely avoided by melanoma BMs. Significant infratentorial clusters were associated with young age, male sex, lung neuroendocrine and squamous cell carcinomas, high expression of Ki-67 of primaries and BMs, and patients with poorer prognosis. Inferior OS was observed in patients with ≥3 BMs and those who received whole-brain radiotherapy alone. Infratentorial involvement of BMs was an independent risk factor of poor prognosis for patients who received surgery (p = 0.023, hazard ratio = 1.473, 95% confidence interval = 1.055-2.058). The current study may add valuable clinical recognition of BMs and provide references for BMs diagnosis, treatment evaluation, and prognostic prediction.

Topographic brain tumor anatomy drives seizure risk and enables machine learning based prediction

OBJECTIVE

The aim of this study was to identify relevant risk factors for epileptic seizures upon initial diagnosis of a brain tumor and to develop and validate a machine learning based prediction to allow for a tailored risk-based antiepileptic therapy.

METHODS

Clinical, electrophysiological and high-resolution imaging data was obtained from a consecutive cohort of 1051 patients with newly diagnosed brain tumors. Factor-associated seizure risk difference allowed to determine the relevance of specific topographic, demographic and histopathologic variables available at the time of diagnosis for seizure risk. The data was divided in a 70/30 ratio into a training and test set. Different machine learning based predictive models were evaluated before a generalized additive model (GAM) was selected considering its traceability while maintaining high performance. Based on a clinical stratification of the risk factors, three different GAM were trained and internally validated.

RESULTS

A total of 923 patients had full data and were included. Specific topographic anatomical patterns that drive seizure risk could be identified. The involvement of allopallial, mesopallial or primary motor/somatosensory neopallial structures by brain tumors results in a significant and clinically relevant increase in seizure risk. While topographic input was most relevant for the GAM, the best prediction was achieved by a combination of topographic, demographic and histopathologic information (Validation: AUC: 0.79, Accuracy: 0.72, Sensitivity: 0.81, Specificity: 0.66).

CONCLUSIONS

This study identifies specific phylogenetic anatomical patterns as epileptic drivers. A GAM allowed the prediction of seizure risk using topographic, demographic and histopathologic data achieving fair performance while maintaining transparency.