GlioPredictor: a deep learning model for identification of high-risk adult IDH-mutant glioma towards adjuvant treatment planning.

Identification of isocitrate dehydrogenase (IDH)-mutant glioma patients at high risk of early progression is critical for radiotherapy treatment planning. Currently tools to stratify risk of early progression are lacking. We sought to identify a combination of molecular markers that could be used to identify patients who may have a greater need for adjuvant radiation therapy machine learning technology. 507 WHO Grade 2 and 3 glioma cases from The Cancer Genome Atlas, and 1309 cases from AACR GENIE v13.0 datasets were studied for genetic disparities between IDH1-wildtype and IDH1-mutant cohorts, and between different age groups. Genetic features such as mutations and copy number variations (CNVs) correlated with IDH1 mutation status were selected as potential inputs to train artificial neural networks (ANNs) to predict IDH1 mutation status. Grade 2 and 3 glioma cases from the Memorial Sloan Kettering dataset (n = 404) and Grade 3 glioma cases with subtotal resection (STR) from Northwestern University (NU) (n = 21) were used to further evaluate the best performing ANN model as independent datasets. IDH1 mutation is associated with decreased CNVs of EGFR (21% vs. 3%), CDKN2A (20% vs. 6%), PTEN (14% vs. 1.7%), and increased percentage of mutations for TP53 (15% vs. 63%), and ATRX (10% vs. 54%), which were all statistically significant (p < 0.001). Age > 40 was unable to identify high-risk IDH1-mutant with early progression. A glioma early progression risk prediction (GlioPredictor) score generated from the best performing ANN model (6/6/6/6/2/1) with 6 inputs, including CNVs of EGFR, PTEN and CDKN2A, mutation status of TP53 and ATRX, patient's age can predict IDH1 mutation status with over 90% accuracy. The GlioPredictor score identified a subgroup of high-risk IDH1-mutant in TCGA and NU datasets with early disease progression (p = 0.0019, 0.0238, respectively). The GlioPredictor that integrates age at diagnosis, CNVs of EGFR, CDKN2A, PTEN and mutation status of TP53, and ATRX can identify a small cohort of IDH-mutant with high risk of early progression. The current version of GlioPredictor mainly incorporated clinically often tested genetic biomarkers. Considering complexity of clinical and genetic features that correlate with glioma progression, future derivatives of GlioPredictor incorporating more inputs can be a potential supplement for adjuvant radiotherapy patient selection of IDH-mutant glioma patients.

Whole-brain radiotherapy associated with structural changes resembling aging as determined by anatomic surface-based deep learning.

BACKGROUND: Brain metastases are the most common intracranial tumors in adults and are associated with significant morbidity and mortality. Whole-brain radiotherapy (WBRT) is used frequently in patients for palliation, but can result in neurocognitive deficits. While dose-dependent injury to individual areas such as the hippocampus has been demonstrated, global structural shape changes after WBRT remain to be studied. METHODS: We studied healthy controls and patients with brain metastases and examined MRI brain anatomic surface data before and after WBRT. We implemented a validated graph convolutional neural network model to estimate patient's "brain age". We further developed a mixed-effects linear model to compare the estimated age of the whole brain and substructures before and after WBRT. RESULTS: 4220 subjects were analyzed (4148 healthy controls and 72 patients). The median radiation dose was 30 Gy (range 25-37.5 Gy). The whole brain and substructures underwent structural change resembling rapid aging in radiated patients compared to healthy controls; the whole brain "aged" 9.32 times faster, the cortex 8.05 times faster, the subcortical structures 12.57 times faster, and the hippocampus 10.14 times faster. In a subset analysis, the hippocampus "aged" 8.88 times faster in patients after conventional WBRT versus after hippocampal avoidance (HA)-WBRT. CONCLUSIONS: Our findings suggest that WBRT causes the brain and its substructures to undergo structural changes at a pace up to 13x of the normal aging pace, where hippocampal avoidance offers focal structural protection. Correlating these structural imaging changes with neurocognitive outcomes following WBRT or HA-WBRT would benefit from future analysis.

History of Technological Advancements towards MR-Linac: The Future of Image-Guided Radiotherapy.

Image-guided radiotherapy (IGRT) enables optimal tumor targeting and sparing of organs-at-risk, which ultimately results in improved outcomes for patients. Magnetic resonance imaging (MRI) revolutionized diagnostic imaging with its superior soft tissue contrast, high spatiotemporal resolution, and freedom from ionizing radiation exposure. Over the past few years there has been burgeoning interest in MR-guided radiotherapy (MRgRT) to overcome current challenges in X-ray-based IGRT, including but not limited to, suboptimal soft tissue contrast, lack of efficient daily adaptation, and incremental exposure to ionizing radiation. In this review, we present an overview of the technologic advancements in IGRT that led to MRI-linear accelerator (MRL) integration. Our report is organized in three parts: (1) a historical timeline tracing the origins of radiotherapy and evolution of IGRT, (2) currently available MRL technology, and (3) future directions and aspirations for MRL applications.

Towards Accurate and Precise Image-Guided Radiotherapy: Clinical Applications of the MR-Linac.

Advances in image-guided radiotherapy have brought about improved oncologic outcomes and reduced toxicity. The next generation of image guidance in the form of magnetic resonance imaging (MRI) will improve visualization of tumors and make radiation treatment adaptation possible. In this review, we discuss the role that MRI plays in radiotherapy, with a focus on the integration of MRI with the linear accelerator. The MR linear accelerator (MR-Linac) will provide real-time imaging, help assess motion management, and provide online adaptive therapy. Potential advantages and the current state of these MR-Linacs are highlighted, with a discussion of six different clinical scenarios, leading into a discussion on the future role of these machines in clinical workflows.

Tumor-associated alterations in white matter connectivity have prognostic significance in MGMT-unmethylated glioblastoma.

PURPOSE: We investigated the prognostic significance of tumor-associated white matter (TA-WM) tracts in glioblastoma (GBM) using magnetic resonance-diffusion tensor imaging (MR-DTI). We hypothesized that (1) TA-WM tracts harbor microscopic disease not targeted through surgery or radiotherapy (RT), and (2) the greater the extent of TA-WM involvement, the worse the survival outcomes. METHODS: We studied a retrospective cohort of 76 GBM patients. TA-WM tracts were identified by MR-DTI fractional anisotropy (FA) maps. For each patient, 22 TA-WM tracts were analyzed and each tract was graded 1-3 based on FA. A TA-WM score (TA-WMS) was computed based on number of involved tracts and corresponding FA grade of involvement. Kaplan-Meier statistics were utilized to determine survival outcomes, log-rank test was used to compare survival between groups, and Cox regression was utilized to determine prognostic variables. RESULTS: For the MGMT-unmethylated cohort, there was a decrease in OS for increasing TA-WMS (median OS 16.5 months for TA-WMS 0-4; 13.6 months for TA-WMS 5-8; 7.3 months for TA-WMS > 9; p = 0.0002). This trend was not observed in the MGMT-methylated cohort. For MGMT-unmethylated patients with TA-WMS > 6 and involvement of tracts passing through brainstem or contralateral hemisphere, median OS was 8.3 months versus median OS 14.1 months with TA-WMS > 6 but not involving aforementioned critical tracts (p = 0.003 log-rank test). For MGMT-unmethylated patients, TA-WMS was predictive of overall survival in multivariate analysis (HR = 1.14, 95% CI 1.03-1.27, p = 0.012) while age, gender, and largest tumor dimension were non-significant. CONCLUSION: Increased TA-WMS and involvement of critical tracts are associated with decreased overall survival in MGMT-unmethylated GBM.

Water-Soluble Nanoconjugate for Enhanced Cellular Delivery of Receptor-Targeted Magnetic Resonance Contrast Agents.

ProGlo is an efficient steroid receptor-targeted magnetic resonance (MR) imaging contrast agent (CA). It has been shown to bind to the progesterone receptor (PR) and produce enhanced image contrast in PR-positive cells and tissues in vitro and in vivo. However, the hydrophobicity of the steroid targeting domain of ProGlo (logP = 1.4) limits its formulation and delivery at clinically relevant doses. In this work, a hydrophobic moiety was utilized to drive efficient adsorption onto nanodiamond (ND) clusters to form a water-soluble nanoconstruct (logP = -2.4) with 80% release in 8 h under biological conditions. In cell culture, the ND-ProGlo construct delivered increased concentrations of ProGlo to target cells compared to ProGlo alone. Importantly, these results were accomplished without the use of solvents such as DMSO, providing a significant advance toward formulating ProGlo for translational applications. Biodistribution studies confirm the delivery of ProGlo to PR(+) tissues with enhanced efficacy over untargeted controls. These results demonstrate the potential for a noncovalent ND-CA construct as a general strategy for solubilizing and delivering hydrophobic targeted MR CAs.

Gd(III)-Gold Nanoconjugates Provide Remarkable Cell Labeling for High Field Magnetic Resonance Imaging.

In vivo cell tracking is vital for understanding migrating cell populations, particularly cancer and immune cells. Magnetic resonance (MR) imaging for long-term tracking of transplanted cells in live organisms requires cells to effectively internalize Gd(III) contrast agents (CAs). Clinical Gd(III)-based CAs require high dosing concentrations and extended incubation times for cellular internalization. To combat this, we have devised a series of Gd(III)-gold nanoconjugates (Gd@AuNPs) with varied chelate structure and nanoparticle-chelate linker length, with the goal of labeling and imaging breast cancer cells. These new Gd@AuNPs demonstrate significantly enhanced labeling compared to previous Gd(III)-gold-DNA nanoconstructs. Variations in Gd(III) loading, surface packing, and cell uptake were observed among four different Gd@AuNP formulations suggesting that linker length and surface charge play an important role in cell labeling. The best performing Gd@AuNPs afforded 23.6 ± 3.6 fmol of Gd(III) per cell at an incubation concentration of 27.5 µM-this efficiency of Gd(III) payload delivery (Gd(III)/cell normalized to dose) exceeds that of previous Gd(III)-Au conjugates and most other Gd(III)-nanoparticle formulations. Further, Gd@AuNPs were well-tolerated in vivo in terms of biodistribution and clearance, and supports future cell tracking applications in whole-animal models.

Nanodiamond-Gadolinium(III) Aggregates for Tracking Cancer Growth In Vivo at High Field.

The ability to track labeled cancer cells in vivo would allow researchers to study their distribution, growth, and metastatic potential within the intact organism. Magnetic resonance (MR) imaging is invaluable for tracking cancer cells in vivo as it benefits from high spatial resolution and the absence of ionizing radiation. However, many MR contrast agents (CAs) required to label cells either do not significantly accumulate in cells or are not biologically compatible for translational studies. We have developed carbon-based nanodiamond-gadolinium(III) aggregates (NDG) for MR imaging that demonstrated remarkable properties for cell tracking in vivo. First, NDG had high relaxivity independent of field strength, a finding unprecedented for gadolinium(III) [Gd(III)]-nanoparticle conjugates. Second, NDG demonstrated a 300-fold increase in the cellular delivery of Gd(III) compared to that of clinical Gd(III) chelates without sacrificing biocompatibility. Further, we were able to monitor the tumor growth of NDG-labeled flank tumors by T1- and T2-weighted MR imaging for 26 days in vivo, longer than was reported for other MR CAs or nuclear agents. Finally, by utilizing quantitative maps of relaxation times, we were able to describe tumor morphology and heterogeneity (corroborated by histological analysis), which would not be possible with competing molecular imaging modalities.

Gd(III)-Dithiolane Gold Nanoparticles for T1-Weighted Magnetic Resonance Imaging of the Pancreas.

Pancreatic adenocarcinoma has a 5 year survival of approximately 3% and median survival of 6 months and is among the most dismal of prognoses in all of medicine. This poor prognosis is largely due to delayed diagnosis where patients remain asymptomatic until advanced disease is present. Therefore, techniques to allow early detection of pancreatic adenocarcinoma are desperately needed. Imaging of pancreatic tissue is notoriously difficult, and the development of new imaging techniques would impact our understanding of organ physiology and pathology with applications in disease diagnosis, staging, and longitudinal response to therapy in vivo. Magnetic resonance imaging (MRI) provides numerous advantages for these types of investigations; however, it is unable to delineate the pancreas due to low inherent contrast within this tissue type. To overcome this limitation, we have prepared a new Gd(III) contrast agent that accumulates in the pancreas and provides significant contrast enhancement by MR imaging. We describe the synthesis and characterization of a new dithiolane-Gd(III) complex and a straightforward and scalable approach for conjugation to a gold nanoparticle. We present data that show the nanoconjugates exhibit very high per particle values of r1 relaxivity at both low and high magnetic field strengths due to the high Gd(III) payload. We provide evidence of pancreatic tissue labeling that includes MR images, post-mortem biodistribution analysis, and pancreatic tissue evaluation of particle localization. Significant contrast enhancement was observed allowing clear identification of the pancreas with contrast-to-noise ratios exceeding 35:1.