An Unsupervised Autoencoder Developed from Dynamic Contrast-Enhanced (DCE)-MRI Datasets for Classification of Acute Tumor Response in an Animal Model

Purpose/Objective(s): Recent studies have shown that vascular parameters of brain tumors derived from DCE-MRI may act as potential biomarkers for radiation-induced acute effects. However, accurate characterization of the spatial regions affected by radiation therapy (RT) remains challenging. Here, we introduce an unsupervised adaptive model for classification and ranking of the RT-affected regions in an animal model of cerebral U-251n tumors. Materials/Methods: Twenty-three immune-compromised-RNU rats were implanted with human U251n cancer cells to form an orthotopic glioma (IACUC #1509). For each rat, 28 days after implantation, two DCE-MRI studies (Dual Gradient Echo, DGE, FOV: 32 × 32 mm2, TR/(TE1-TE2) = 24 ms/(2 ms-4 ms), flip angle = 18°, 400 acquisitions, 1.55 sec interval with Magnevist contrast agent, CA injection at ∼ 24 sec) were performed 24h apart using a 7T MRI scanner. A single 20 Gy stereotactic radiation exposure was performed before the second MRI, which was acquired 1-6.5 hrs after RT. DCE-MRI analysis was done using a model selection technique to distinguish three different brain regions as follows: Normal vasculature (Model 1: No leakage, only plasma volume, vp, is estimated), leaky tumor tissues with no back-flux to the vasculature (Model 2: vp and forward volumetric transfer constant, Ktrans, are estimated), and leaky tumor tissues with back-flux (Model 3: vp, Ktrans, and interstitial volume fraction, ve, are estimated). Normalized time traces of DCE-MRI information (24 pre, and 24 post-RT for each rat, total of 64108 training datasets) of tumors and their soft surrounding normal tissues were extracted from the 3 different model regions. To eliminate high-dimensional data similarity, an unsupervised autoencoder (AE) was trained to map out the model-derived data into a feature space (latent variables, N=10). For each model, the pre and post RT latent variables were compared (by appropriate tests of significance: ANOVA/Welch, CI=95%) to reveal RT-discriminant features. Pearson correlation coefficients were used to compare the decoded data to rank the effect of RT on different models. Results: The time trace of DCE-MRI information of rat brain in normal (Model 1, non-leaky) and highly permeable (Model 3) regions are less impacted by RT (Higher correlation between pre and post RT: r= 0.8518, p\u3c0.0001 and r= 0.9040, p\u3c0.0001 for Model 1 and Model 3, respectively) compared to the peritumoral regions pertaining to Model 2 (r= 0.8077, p\u3c0.0001). Conclusion: This pilot study suggests that among different brain regions, peritumoral zones (infiltrative tumor borders with enhanced rim) are highly affected by RT. Spatial assessment of RT-affected brain regions can play a key role in optimization of treatment planning in cancer patients, but presents a challenging task in conventional DCE-MRI. This study represents an important step toward classification and ranking the RT-affected brain spatial regions according to their vascular response following hypofractionated RT

Dynamic contrast enhanced (DCE) MRI estimation of vascular parameters using knowledge-based adaptive models

We introduce and validate four adaptive models (AMs) to perform a physiologically based Nested-Model-Selection (NMS) estimation of such microvascular parameters as forward volumetric transfer constant, K(trans), plasma volume fraction, v(p), and extravascular, extracellular space, v(e), directly from Dynamic Contrast-Enhanced (DCE) MRI raw information without the need for an Arterial-Input Function (AIF). In sixty-six immune-compromised-RNU rats implanted with human U-251 cancer cells, DCE-MRI studies estimated pharmacokinetic (PK) parameters using a group-averaged radiological AIF and an extended Patlak-based NMS paradigm. One-hundred-ninety features extracted from raw DCE-MRI information were used to construct and validate (nested-cross-validation, NCV) four AMs for estimation of model-based regions and their three PK parameters. An NMS-based a priori knowledge was used to fine-tune the AMs to improve their performance. Compared to the conventional analysis, AMs produced stable maps of vascular parameters and nested-model regions less impacted by AIF-dispersion. The performance (Correlation coefficient and Adjusted R-squared for NCV test cohorts) of the AMs were: 0.914/0.834, 0.825/0.720, 0.938/0.880, and 0.890/0.792 for predictions of nested model regions, v(p), K(trans), and v(e), respectively. This study demonstrates an application of AMs that quickens and improves DCE-MRI based quantification of microvasculature properties of tumors and normal tissues relative to conventional approaches

Adaptation of laser interstitial thermal therapy for tumor ablation under MRI monitoring in a rat orthotopic model of glioblastoma

BACKGROUND: Laser interstitial thermal therapy (LITT) under magnetic resonance imaging (MRI) monitoring is being increasingly used in cytoreductive surgery of recurrent brain tumors and tumors located in eloquent brain areas. The objective of this study was to adapt this technique to an animal glioma model. METHODS: A rat model of U251 glioblastoma (GBM) was employed. Tumor location and extent were determined by MRI and dynamic contrast-enhanced (DCE) MRI. A day after assessing tumor appearance, tumors were ablated during diffusion-weighted imaging (DWI)-MRI using a Visualase LITT system (n = 5). Brain images were obtained immediately after ablation and again at 24 h post-ablation to confirm the efficacy of tumor cytoablation. Untreated tumors served as controls (n = 3). Rats were injected with fluorescent isothiocyanate (FITC) dextran and Evans blue that circulated for 10 min after post-LITT MRI. The brains were then removed for fluorescence microscopy and histopathology evaluations using hematoxylin and eosin (H&E) and major histocompatibility complex (MHC) staining. RESULTS: All rats showed a space-occupying tumor with T2 and T1 contrast-enhancement at pre-LITT imaging. The rats that underwent the LITT procedure showed a well-demarcated ablation zone with near-complete ablation of tumor tissue and with peri-ablation contrast enhancement at 24 h post-ablation. Tumor cytoreduction by ablation as seen on MRI was confirmed by H&E and MHC staining. CONCLUSIONS: Data showed that tumor cytoablation using MRI-monitored LITT was possible in preclinical glioma models. Real-time MRI monitoring facilitated visualizing and controlling the area of ablation as it is otherwise performed in clinical applications

Characterization of the Response of 9L and U-251N Orthotopic Brain Tumors to 3D Conformal Radiation Therapy

In a study employing MRI-guided stereotactic radiotherapy (SRS) in two orthotopic rodent brain tumor models, the radiation dose yielding 50% survival (the TCD50) was sought. Syngeneic 9L cells, or human U-251N cells, were implanted stereotactically in 136 Fischer 344 rats or 98 RNU athymic rats, respectively. At approximately 7 days after implantation for 9L, and 18 days for U-251N, rats were imaged with contrast-enhanced MRI (CE-MRI) and then irradiated using a Small Animal Radiation Research Platform (SARRP) operating at 220 kV and 13 mA with an effective energy of ∼70 keV and dose rate of ∼2.5 Gy per min. Radiation doses were delivered as single fractions. Cone-beam CT images were acquired before irradiation, and tumor volumes were defined using co-registered CE-MRI images. Treatment planning using MuriPlan software defined four non-coplanar arcs with an identical isocenter, subsequently accomplished by the SARRP. Thus, the treatment workflow emulated that of current clinical practice. The study endpoint was animal survival to 200 days. The TCD50 inferred from Kaplan-Meier survival estimation was approximately 25 Gy for 9L tumors and below 20 Gy, but within the 95% confidence interval in U-251N tumors. Cox proportional-hazards modeling did not suggest an effect of sex, with the caveat of wide confidence intervals. Having identified the radiation dose at which approximately half of a group of animals was cured, the biological parameters that accompany radiation response can be examined