Non‐Gaussian diffusion imaging with a fractional order calculus model to predict response of gastrointestinal stromal tumor to second‐line sunitinib therapy

Tang, Lei; Sui, Yi; Zhong, Zheng; Damen, Frederick W.; Li, Jian; Shen, Lin; Sun, Ying‐Shi; Zhou, Xiaohong Joe

doi:10.1002/mrm.26798

Cited by 33 publications

(32 citation statements)

References 37 publications

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“…Using an FROC model with a combination of the pretreatment β value and posttreatment change of ADC at 2 weeks, Tang et al demonstrated that GIST response to sunitinib second-line targeted therapy can be assessed at early as 2 weeks after the initiation of therapy. 14 Figure 9 shows an example illustrating the sensitivity of β from the FROC model to sunitinib targeted therapy. Parameters from Gaussian and non-Gaussian diffusion models can also be combined across a broad b-value range to improve the performance of response to chemotherapy, as recently demonstrated by Zhu et al, for locally advanced rectal cancer.…”

Section: Dwi For Assessment Of Early Responsementioning

confidence: 99%

“…5 Although the majority of tissue microstructural studies are performed on the brain, applications to the non-central nervous system (CNS) have emerged, including but not limited to prostate cancer, 12 breast cancer, 13 and gastrointestinal stromal tumor (GIST). 14 In this review we focus on the three aforementioned aspects-cellularity, vascularity, and microstructuresthat diffusion MRI can offer for cancer detection, characterization, and therapy evaluation. A special emphasis will be placed on microstructural characterization at high b-values, given its novelty and lack of systematic review in this rapidly growing area with immense potential.…”

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confidence: 99%

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Diffusion MRI of cancer: From low to high b‐values

Tang

Zhou

2018

Magnetic Resonance Imaging

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Following its success in early detection of cerebral ischemia, diffusion-weighted imaging (DWI) has been increasingly used in cancer diagnosis and treatment evaluation. These applications are propelled by the rapid development of novel diffusion models to extract biologically valuable information from diffusion-weighted MR signals, and significant advance in MR hardware that has enabled image acquisition with high b-values. This article reviews recent technical developments and clinical applications in cancer imaging using DWI, with a special emphasis on high b-value diffusion models. The article is organized in four sections. First, we provide an overview of diffusion models that are relevant to cancer imaging. The model parameters are discussed in relation to three tissue properties – cellularly, vascularity, and microstructures. An emphasis is placed on characterization of microstructural heterogeneity, given its novelty and close relevance to cancer. Second, we illustrate diffusion MR clinical applications in each of the following three categories: (a) cancer detection and diagnosis; (b) cancer grading, staging, and classification; and (c) cancer treatment response prediction and evaluation. Third, we discuss several practical issues, including selection of image acquisition parameters, reproducibility and reliability, motion management, image distortion, etc., that are commonly encountered when applying DWI to cancer in clinical settings. Lastly, we highlight a few ongoing challenges and provide some possible future directions, particularly in the area of establishing standards via well-organized multi-center clinical trials to accelerate clinical translation of advanced DWI techniques to improving cancer care on a large scale.

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Section: Dwi For Assessment Of Early Responsementioning

confidence: 99%

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confidence: 99%

Diffusion MRI of cancer: From low to high b‐values

Tang

Zhou

2018

Magnetic Resonance Imaging

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154

112

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“…First, the lowest b value possible is only 0 s/mm 2 on our machine, and the ADC value may be influenced by microscopic perfusion. The use of low b values of 200–400 s/mm 2 with the IVIM model and FROC model [ 16 – 18 ] would be more sensitive to diffusion. Second, we could not use the contrast-enhanced sequence repeatedly over a short time period because of ethical reasons, which may have introduced bias in the judgement of cystic or myxoid degeneration only by T1WI and T2WI.…”

Section: Discussionmentioning

confidence: 99%

MRI in predicting the response of gastrointestinal stromal tumor to targeted therapy: a patient-based multi-parameter study

Tang

et al. 2018

BMC Cancer

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BackgroundTo investigate the performance of quantitative indicators of MRI in early prediction of the response of gastrointestinal stromal tumor (GIST) to targeted therapy in a patient-based study.MethodsMRI examinations were performed on 62 patients with GIST using 1.5 T scanners before and at two and 12 weeks after treatment with targeted agents. The longest diameter (LD) and contrast-to-noise ratio (CNR) of the tumors were measured by T2-weighted imaging (T2WI), and the apparent diffusion coefficient (ADC) was determined using diffusion-weighted imaging (DWI). The pre-therapy and early percentage changes (%Δ) of the three parameters were compared with regard to their abilities to differentiate responder and non- responder patients, using ROC curves.ResultsThere were 42 patients in responder and 20 in non-responder group. After two weeks of therapy, the percentage changes in the ADC and LD were significantly different between the two groups (ADC: responder 30% vs. non- responder 1%, Z = − 4.819, P < 0.001; LD: responder − 7% vs. non- responder − 2%, Z = − 3.238, P = 0.001), but not in T2WI-CNR (responder − 3% vs. non-responder 9%, Z = − 0.663, P = 0.508). The AUCs on ROC for %ΔLD, %ΔT2WI-CNR and %ΔADC after two weeks of therapy were 0.756, 0.552 and 0.881, respectively, for response differentiation. When %ΔADC ≥15% was used to predict responder, the PPV was 93.3%.ConclusionsThe percentage change of the ADC after two weeks of therapy outperformed T2WI-CNR and longest diameter in predicting the early response of GIST to targeted therapy.

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“…Among numerous anomalous diffusion models, the continuous‐time random‐walk (CTRW) model and the fractional motion (FM) model are actively pursued by the biophysics community and regarded as the major “archrivals” . The CTRW model, which recognizes intra‐voxel diffusion heterogeneity in both time and space, has already been introduced to the diffusion MRI community and successfully demonstrated in a number of clinical applications . The FM model, on the other hand, describes the complex diffusion process based on the intricate statistical properties, such as variances and correlations .…”

Section: Introductionmentioning

confidence: 99%

“…17 The CTRW model, which recognizes intra-voxel diffusion heterogeneity in both time and space, has already been introduced to the diffusion MRI community and successfully demonstrated in a number of clinical applications. [3][4][5][6][7][8][18][19][20] The FM model, on the other hand, describes the complex diffusion process based on the intricate statistical properties, such as variances and correlations. 15,17 Based on the Langevin equation, 21 one of the fundamental differential equations to describe stochastic dynamics of motion in a randomly fluctuating environment, 22,23 the FM model provides an alternative avenue to probing the complexity and the stochastic characteristics of diffusion in biological tissues.…”

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confidence: 99%

A fractional motion diffusion model for a twice‐refocused spin‐echo pulse sequence

Karaman

Zhou

2018

NMR in Biomedicine

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The purpose of this study was to develop an analytical expression for a fractional motion (FM) diffusion model to characterize diffusion-induced signal attenuation in a twice-refocused spin-echo (TRSE) sequence that is resilient to eddy currents, and to demonstrate its applicability to human brain imaging in vivo. Based on the FM theory, which provides a unified statistical description for Langevin motions, the diffusion-weighted (DW) MR signal was measured with a TRSE sequence that balances the concomitant gradients. The analytical expression was fitted to a set of DW images acquired with 14 b-values (0-4000 s/mm ) from a total of 10 healthy human subjects at 3 T, yielding three FM parameter maps based on anomalous diffusion coefficient D , diffusion increment variance φ, and diffusion correlation ψ, respectively. These parameters were used to characterize different brain regions in gray matter (GM), white matter (WM), and cerebrospinal fluid. The analytical expression for the TRSE-based FM model accurately described diffusion signal attenuation in healthy brain tissues at high b-values. TRSE's robustness against eddy currents was illustrated by comparing results from an expression for a conventional Stejskal-Tanner sequence. The TRSE-based FM model also produced consistent GM-WM contrast (p < 0.01) across all brain regions studied, whereas the consistency was not observed with the Stejskal-Tanner-based FM model. This new analytical expression is expected to enable further investigations to probe tissue structures by exploiting anomalous diffusion properties without being hindered by eddy-current perturbations at high b-values.

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Non‐Gaussian diffusion imaging with a fractional order calculus model to predict response of gastrointestinal stromal tumor to second‐line sunitinib therapy

Cited by 33 publications

References 37 publications

Diffusion MRI of cancer: From low to high b‐values

Diffusion MRI of cancer: From low to high b‐values

MRI in predicting the response of gastrointestinal stromal tumor to targeted therapy: a patient-based multi-parameter study

A fractional motion diffusion model for a twice‐refocused spin‐echo pulse sequence

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