Improving the Reliability of Pharmacokinetic Parameters at Dynamic                    Contrast-enhanced MRI in Astrocytomas: A Deep Learning Approach

Choi, Kyu Sung; You, Sung-Hye; Han, Yoseob; Ye, Jong Chul; Jeong, Bumseok; Choi, Seung Hong

doi:10.1148/radiol.2020192763

Cited by 18 publications

(15 citation statements)

References 20 publications

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“…K trans derived from DCE‐MR imaging provides information on vascular permeability in vivo. [ 37 ] The K trans values of the lesion were higher in the TBI, AME, and AMEC groups, compared with the sham group at 1 day post‐treatment. Compared with the TBI group, the K trans values of the lesion were remarkably reduced in both AME and AMEC groups at 1 day post‐treatment ( p < 0.01).…”

Section: Resultsmentioning

confidence: 99%

Intrinsically Bioactive Manganese–Eumelanin Nanocomposites Mediated Antioxidation and Anti‐Neuroinflammation for Targeted Theranostics of Traumatic Brain Injury

Sun

Liu

et al. 2022

Adv Healthcare Materials

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Overproduced reactive oxygen species and the induced oxidative stress and neuroinflammation often result in secondary injury, which is associated with unfavorable prognosis in traumatic brain injury (TBI). Unfortunately, current medications cannot effectively ameliorate the secondary injury at traumatic sites. Here, it is reported that intrinsically bioactive multifunctional nanocomposites (ANG‐MnEMNPs‐Cur, AMEC) mediate antioxidation and anti‐neuroinflammation for targeted TBI theranostics, which are engineered by loading the neuroprotective agent curcumin on angiopep‐2 functionalized and manganese doped eumelanin‐like nanoparticles. After intravenous delivery, efficient AMEC accumulation is observed in lesions of TBI mice models established by controlled cortical impact method, evidenced by T1–T2 magnetic resonance and photoacoustic dual‐modal imaging. Therapeutically, AMEC effectively alleviates neuroinflammation, protects blood–brain barrier integrity, relieves brain edema, reduces brain tissue loss, and improves the cognition of TBI mice. Mechanistically, following the penetration into the traumatic tissues via angiopep‐2 mediated targeting effect, the efficacy of AMEC is synergistically improved by combined functional moieties of curcumin and eumelanin. This is achieved by the alleviation of oxidative stress, inhibition of neuroinflammation via M1‐to‐M2 macrophage reprogramming, and promotion of neuronal regeneration. The as‐developed AMEC with well‐defined mechanisms of action may represent a promising targeted theranostics strategy for TBI and other neuroinflammation‐associated intracranial diseases.

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Section: Resultsmentioning

confidence: 99%

Intrinsically Bioactive Manganese–Eumelanin Nanocomposites Mediated Antioxidation and Anti‐Neuroinflammation for Targeted Theranostics of Traumatic Brain Injury

Sun

Liu

et al. 2022

Adv Healthcare Materials

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“…Networks (CNNs) to generate more accurate and stable estimates of PK vascular parameters by extracting timedependent features from DCE-MRI [24][25][26][27][28][29][30] 62 ), or that R1 is proportional. As to the first assumption, in vasculature that leaks, it is clear from a vast literature that competing R1 and R2* effects strongly affect the relationship between [CA] and the R2* MR contrast.…”

Section: In Recent Years Studies Have Investigated the Development Of...mentioning

confidence: 99%

“…Unlike other similar works [24][25][26][27][28][29][30] that have used deep learning and CNNs for adaptive modeling that require a large number of samples for their hyper-parameter tuning, training, and validation, the proposed work uses shallow…”

Section: In Recent Years Studies Have Investigated the Development Of...mentioning

confidence: 99%

Dynamic Contrast Enhanced (DCE) MRI Estimation of Vascular Parameters using Knowledge-Based Adaptive Models

Bagher‐Ebadian

Brown

Ghassemi

et al. 2023

Preprint

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Purpose 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, Ktrans, plasma volume fraction, vp, and extravascular, extracellular space, ve, directly from Dynamic Contrast-Enhanced (DCE) MRI raw information without the need for an Arterial-Input Function (AIF). Methods 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. Results 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, vp, Ktrans, and ve, respectively. Conclusions 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.

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“…Recent studies have sought implementation of deep learning to facilitate clinical PK parameter generation [ 25 – 27 ]. Nalepa et al .…”

Section: Introductionmentioning

confidence: 99%

“…Choi el al . [ 25 ] implemented deep learning to improve the reliability of the AIF generation in astrocytomas to improve PK parameter accuracy; however, similar to Nalepa et al . [ 26 ], this approach still requires conventional PK modeling.…”

Section: Introductionmentioning

confidence: 99%

Deep learning quantification of vascular pharmacokinetic parameters in mouse brain tumor models

Arledge¹,

Sankepalle²,

Crowe³

et al. 2022

Front. Biosci. (Landmark Ed)

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Background: Dynamic contrast-enhanced (DCE) MRI is widely used to assess vascular perfusion and permeability in cancer. In small animal applications, conventional modeling of pharmacokinetic (PK) parameters from DCE MRI images is complex and time consuming. This study is aimed at developing a deep learning approach to fully automate the generation of kinetic parameter maps, Ktrans (volume transfer coefficient) and Vp (blood plasma volume ratio), as a potential surrogate to conventional PK modeling in mouse brain tumor models based on DCE MRI. Methods: Using a 7T MRI, DCE MRI was conducted in U87 glioma xenografts growing orthotopically in nude mice. Vascular permeability Ktrans and Vp maps were generated using the classical Tofts model as well as the extended-Tofts model. These vascular permeability maps were then processed as target images to a twenty-four layer convolutional neural network (CNN). The CNN was trained on T 1 -weighted DCE images as source images and designed with parallel dual pathways to capture multiscale features. Furthermore, we performed a transfer study of this glioma trained CNN on a breast cancer brain metastasis (BCBM) mouse model to assess the potential of the network for alternative brain tumors. Results: Our data showed a good match for both Ktrans and Vp maps generated between the target PK parameter maps and the respective CNN maps for gliomas. Pixel-by-pixel analysis revealed intratumoral heterogeneous permeability, which was consistent between the CNN and PK models. The utility of the deep learning approach was further demonstrated in the transfer study of BCBM. Conclusions: Because of its rapid and accurate estimation of vascular PK parameters directly from the DCE dynamic images without complex mathematical modeling, the deep learning approach can serve as an efficient tool to assess tumor vascular permeability to facilitate small animal brain tumor research.

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Improving the Reliability of Pharmacokinetic Parameters at Dynamic Contrast-enhanced MRI in Astrocytomas: A Deep Learning Approach

Cited by 18 publications

References 20 publications

Intrinsically Bioactive Manganese–Eumelanin Nanocomposites Mediated Antioxidation and Anti‐Neuroinflammation for Targeted Theranostics of Traumatic Brain Injury

Intrinsically Bioactive Manganese–Eumelanin Nanocomposites Mediated Antioxidation and Anti‐Neuroinflammation for Targeted Theranostics of Traumatic Brain Injury

Dynamic Contrast Enhanced (DCE) MRI Estimation of Vascular Parameters using Knowledge-Based Adaptive Models

Deep learning quantification of vascular pharmacokinetic parameters in mouse brain tumor models

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