Determining optimal clinical target volume margins in high-grade glioma based on microscopic tumor extension and magnetic resonance imaging

Application of preoperative advanced diffusion magnetic resonance imaging in evaluating the postoperative recurrence of lower grade gliomas

BACKGROUND

Recurrence of lower grade glioma (LrGG) appeared to be unavoidable despite considerable research performed in last decades. Thus, we evaluated the postoperative recurrence within two years after the surgery in patients with LrGG by preoperative advanced diffusion magnetic resonance imaging (dMRI).

MATERIALS AND METHODS

48 patients with lower-grade gliomas (23 recurrence, 25 nonrecurrence) were recruited into this study. Different models of dMRI were reconstructed, including apparent fiber density (AFD), white matter tract integrity (WMTI), diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), neurite orientation dispersion and density imaging (NODDI), Bingham NODDI and standard model imaging (SMI). Orthogonal Partial Least Squares-Discriminant Analysis (OPLS-DA) was used to construct a multiparametric prediction model for the diagnosis of postoperative recurrence.

RESULTS

The parameters derived from each dMRI model, including AFD, axon water fraction (AWF), mean diffusivity (MD), mean kurtosis (MK), fractional anisotropy (FA), intracellular volume fraction (ICVF), extra-axonal perpendicular diffusivity (De⊥), extra-axonal parallel diffusivity (De∥) and free water fraction (fw), showed significant differences between nonrecurrence group and recurrence group. The extra-axonal perpendicular diffusivity (De⊥) had the highest area under curve (AUC = 0.885), which was significantly higher than others. The variable importance for the projection (VIP) value of De⊥ was also the highest. The AUC value of the multiparametric prediction model merging AFD, WMTI, DTI, DKI, NODDI, Bingham NODDI and SMI was up to 0.96.

CONCLUSION

Preoperative advanced dMRI showed great efficacy in evaluating postoperative recurrence of LrGG and De⊥ of SMI might be a valuable marker.

[18F]AlF-NOTA-PCP2: a novel PET/CT tracer for enhanced PD-L1 heterogeneity imaging and comparative analysis with [18F]AlF-NOTA-WL12 in glioblastoma xenografts

PURPOSE

The unsatisfactory efficacy of PD-L1 antibodies in glioblastoma (GBM) is largely due to the temporal and spatial heterogeneity of PD-L1 expression. Molecular imaging can enhance understanding of the tumor immune microenvironment and guide immunotherapy. However, highly sensitive imaging agents capable of effectively visualizing PD-L1 heterogeneity are limited. This study introduces a novel PET tracer, offering improved imaging of PD-L1 heterogeneity in GBM xenografts, with a comparative analysis to [18F]AlF-NOTA-WL12.

METHODS

[18F]AlF-NOTA-PCP2 was synthesized with high purity and its affinity for PD-L1 was characterized using surface plasmon resonance (SPR) and cell binding assays. Its specificity for PD-L1 was evaluated both in vitro using various cell lines and in vivo with GBM xenograft models in NOD/SCID mice. PET/CT imaging was conducted to evaluate the tracer's biodistribution, pharmacokinetics, and ability to quantify tumoral spatial heterogeneity of PD-L1 expression. A focused comparative analysis between [18F]AlF-NOTA-PCP2 and [18F]AlF-NOTA-WL12 was conducted, examining binding affinity, biodistribution, pharmacokinetics, and imaging effectiveness in GBM xenografts. Additionally, human radiation dosimetry estimates compared the safety profiles of both tracers.

RESULTS

[18F]AlF-NOTA-PCP2 demonstrated high radiochemical purity (> 95%) and a strong affinity for PD-L1, comparable to [18F]AlF-NOTA-WL12. In vitro and in vivo studies confirmed its specificity for PD-L1, with increased uptake in PD-L1 expressing cells and tumors. Toxicological profiles indicated no significant abnormalities in serum biochemical indicators or major organ tissues. MicroPET/CT imaging showed [18F]AlF-NOTA-PCP2's effectiveness in visualizing PD-L1 expression levels and spatial heterogeneity in GBM xenografts. Comparative studies revealed [18F]AlF-NOTA-PCP2's improved pharmacokinetic properties, including higher tumor-to-blood ratios and lower nonspecific liver uptake, as well as reduced radiation exposure compared to [18F]AlF-NOTA-WL12.

CONCLUSION

[18F]AlF-NOTA-PCP2 distinguishes itself as an exceptionally sensitive PET/CT tracer, adept at non-invasively and accurately quantifying PD-L1 expression and its spatial heterogeneity in tumors, especially in GBM. Its favorable pharmacokinetic properties, safety profile, and high affinity for PD-L1 highlight its potential for enhancing the precision of cancer immunotherapy and guiding individualized treatment strategies. While promising, its clinical translation, especially in brain imaging, necessitates further validation in clinical trials.

Correlation between rCBV Delineation Similarity and Overall Survival in a Prospective Cohort of High-Grade Gliomas Patients: The Hidden Value of Multimodal MRI?

PURPOSE

The accuracy of target delineation in radiation treatment planning of high-grade gliomas (HGGs) is crucial to achieve high tumor control, while minimizing treatment-related toxicity. Magnetic resonance imaging (MRI) represents the standard imaging modality for delineation of gliomas with inherent limitations in accurately determining the microscopic extent of tumors. The purpose of this study was to assess the survival impact of multi-observer delineation variability of multiparametric MRI (mpMRI) and [18F]-FET PET/CT.

MATERIALS AND METHODS

Thirty prospectively included patients with histologically confirmed HGGs underwent a PET/CT and mpMRI including diffusion-weighted imaging (DWI: b0, b1000, ADC), contrast-enhanced T1-weighted imaging (T1-Gado), T2-weighted fluid-attenuated inversion recovery (T2Flair), and perfusion-weighted imaging with computation of relative cerebral blood volume (rCBV) and K2 maps. Nine radiation oncologists delineated the PET/CT and MRI sequences. Spatial similarity (Dice similarity coefficient: DSC) was calculated between the readers for each sequence. Impact of the DSC on progression-free survival (PFS) and overall survival (OS) was assessed using Kaplan-Meier curves and the log-rank test.

RESULTS

The highest DSC mean values were reached for morphological sequences, ranging from 0.71 +/- 0.18 to 0.84 +/- 0.09 for T2Flair and T1Gado, respectively, while metabolic volumes defined by PET/CT achieved a mean DSC of 0.75 +/- 0.11. rCBV variability (mean DSC0.32 +/- 0.20) significantly impacted PFS (p = 0.02) and OS (p = 0.002).

CONCLUSIONS

Our data suggest that the T1-Gado and T2Flair sequences were the most reproducible sequences, followed by PET/CT. Reproducibility for functional sequences was low, but rCBV inter-reader similarity significantly impacted PFS and OS.

Development and External Validation of an MRI-based Radiomics Nomogram to Distinguish Circumscribed Astrocytic Gliomas and Diffuse Gliomas: A Multicenter Study

RATIONALE AND OBJECTIVES

The 5th edition of the World Health Organization classification of tumors of the Central Nervous System (WHO CNS) has introduced the term "diffuse" and its counterpart "circumscribed" to the category of gliomas. This study aimed to develop and validate models for distinguishing circumscribed astrocytic gliomas (CAGs) from diffuse gliomas (DGs).

MATERIALS AND METHODS

We retrospectively analyzed magnetic resonance imaging (MRI) data from patients with CAGs and DGs across three institutions. After tumor segmentation, three volume of interest (VOI) types were obtained: VOItumor and peritumor, VOIwhole, and VOIinterface. Clinical and combined models (incorporating radiomics and clinical features) were also established. To address imbalances in training dataset, Synthetic Minority Oversampling Technique was employed.

RESULTS

A total of 475 patients (DGs: n = 338, CAGs: n = 137) were analyzed. The VOIinterface model demonstrated the best performance for differentiating CAGs from DGs, achieving an area under the curve (AUC) of 0.806 and area under the precision-recall curve (PRAUC)of 0.894 in the cross-validation set. Using analysis of variance (ANOVA) feature selector and Support Vector Machine (SVM) classifier, seven features were selected. The model achieved an AUC and AUPRC of 0.912 and 0.972 in the internal validation dataset, and 0.897 and 0.930 in the external validation dataset. The combined model, incorporating interface radiomics and clinical features, showed improved performance in the external validation set, with an AUC of 0.94 and PRAUC of 0.959.

CONCLUSION

Radiomics models incorporating the peritumoral area demonstrate greater potential for distinguishing CAGs from DGs compared to intratumoral models. These findings may hold promise for evaluating tumor nature before surgery and improving clinical management of glioma patients.

Recurrence analysis of glioblastoma cases based on distance and dose information

BACKGROUND AND PURPOSE

Radiation therapy for glioblastoma (GBM) typically involves large target volumes. The aim of this study was to examine the recurrence pattern of GBM following modern radiochemotherapy according to EORTC guidelines and provide dose and distance information for the choice of optimal target volume margins.

MATERIALS AND METHODS

In this study, the recurrences of 97 GBM patients, treated with radiochemotherapy from 2013 to 2017 at the Medical Center- University of Freiburg, Germany were analysed. Dose and distance based metrices were used to derive recurrence patterns.

RESULTS

The majority of recurrences (75%) occurred locally within the primary tumor area. Smaller GTVs had a higher rate of distant recurrences. Larger treated volumes did not show a clinical benefit regarding progression free and overall survival.

CONCLUSION

The identified recurrence pattern suggests that adjustments or reductions in target volume margins are feasible and could result in similar survival rates, potentially combined with a lower risk of side effects.

Rapid detection of isocitrate dehydrogenase 1 mutation status in glioma based on Crispr-Cas12a

The aim is to use Crispr-Cas12a for the rapid detection of the single nucleotide polymorphism (SNP) of isocitrate dehydrogenase 1 (IDH1)-R132H locus and explore the effectiveness and consistency of this method with direct sequencing method for detecting IDH1-R132H of glioma tissue samples. 58 previous frozen tissue and 46 recent fresh tissue samples of adult diffuse glioma were selected to detect IDH1-R132H using Crispr-Cas12a. The results of immunohistochemistry (IHC) and direct sequencing methods were analyzed. We calculated the efficiency index of Crispr-Cas12a and IHC, and analyzed the consistency among Crispr-Cas12a, IHC and direct sequencing method using paired Chi-sequare test and Kappa identity test. We accomplished the rapid detection of IDH1-R132H in 60 min using Crispr-Cas12a. Regarding direct sequencing method as the gold standard, the sensitivity, specificity and consistency rate of Crispr-Cas12a was 91.4%, 95.7% and 93.1% in the frozen sample group, while 96.1%, 89.7% and 92.0% in the fresh sample group, respectively. Kappa test showed good consistency between the two methods (k = 0.858). Crispr-Cas12a can quickly and accurately detect IDH1-R132H and has good stability. It is a promising method to detect IDH1 mutation status intraoperatively.

Data-driven spatio-temporal modelling of glioblastoma

Mathematical oncology provides unique and invaluable insights into tumour growth on both the microscopic and macroscopic levels. This review presents state-of-the-art modelling techniques and focuses on their role in understanding glioblastoma, a malignant form of brain cancer. For each approach, we summarize the scope, drawbacks and assets. We highlight the potential clinical applications of each modelling technique and discuss the connections between the mathematical models and the molecular and imaging data used to inform them. By doing so, we aim to prime cancer researchers with current and emerging computational tools for understanding tumour progression. By providing an in-depth picture of the different modelling techniques, we also aim to assist researchers who seek to build and develop their own models and the associated inference frameworks. Our article thus strikes a unique balance. On the one hand, we provide a comprehensive overview of the available modelling techniques and their applications, including key mathematical expressions. On the other hand, the content is accessible to mathematicians and biomedical scientists alike to accommodate the interdisciplinary nature of cancer research.

Clinicopathologic analysis of microscopic tumor extension in glioma for external beam radiotherapy planning

BACKGROUND

There is no consensus regarding the clinical target volume (CTV) margins in radiotherapy for glioma. In this study, we aimed to perform a complete macropathologic analysis examining microscopic tumor extension (ME) to more accurately define the CTV in glioma.

METHODS

Thirty-eight supra-total resection specimens of glioma patients were examined on histologic sections. The ME distance, defined as the maximum linear distance from the tumor border to the invasive tumor cells, was measured at each section. We defined the CTV based on the relationships between ME distance and clinicopathologic features.

RESULTS

Between February 2016 and July 2020, a total of 814 slides were examined, corresponding to 162 slides for low-grade glioma (LGG) and 652 slides for high-grade glioma (HGG). The ME value was 0.69 ± 0.43 cm for LGG and 1.29 ± 0.54 cm for HGG (P < 0.001). After multivariate analysis, tumor grade, O⁶-methylguanine-DNA-methyltransferase promoter methylated status (MGMT_m), isocitrate dehydrogenase wild-type status (IDH_wt), and 1p/19q non-co-deleted status (non-codel) were positively correlated with ME distance (all P < 0.05). We defined the CTV of glioma based on tumor grade. To take into account approximately 95% of the ME, a margin of 1.00 cm, 1.50 cm, and 2.00 cm were chosen for grade II, grade III, and grade IV glioma, respectively. Paired analysis of molecularly defined patients confirmed that tumors that had all three molecular alterations (i.e., MGMT_m/IDH_wt/non-codel) were the most aggressive subgroups (all P < 0.05). For these patients, the margin could be up to 1.50 cm, 2.00 cm, and 2.50 cm for grade II, grade III, and grade IV glioma, respectively, to cover the subclinical lesions in 95% of cases.

CONCLUSIONS

The ME was different between the grades of gliomas. It may be reasonable to recommend 1.00 cm, 1.50 cm, and 2.00 cm CTV margins for grade II, grade III, and grade IV glioma, respectively. Considering the highly aggressive nature of MGMT_m/IDH_wt/non-codel tumors, for these patients, the margin could be further expanded by 0.5 cm. These recommendations would encompass microscopic disease extension in 95% of cases.

TRIAL REGISTRATION

The trial was registered with Chinese Clinical Trial Registry ( ChiCTR2100049376 ).