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Dynamic contrast-enhanced MRI model selection for predicting tumor aggressiveness in papillary thyroid cancers.

ABSTRACT: The purpose of this study was to identify the optimal tracer kinetic model from T₁ -weighted dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data and evaluate whether parameters estimated from the optimal model predict tumor aggressiveness determined from histopathology in patients with papillary thyroid carcinoma (PTC) prior to surgery. In this prospective study, 18 PTC patients underwent pretreatment DCE-MRI on a 3 T MR scanner prior to thyroidectomy. This study was approved by the institutional review board and informed consent was obtained from all patients. The two-compartment exchange model, compartmental tissue uptake model, extended Tofts model (ETM) and standard Tofts model were compared on a voxel-wise basis to determine the optimal model using the corrected Akaike information criterion (AICc) for PTC. The optimal model is the one with the lowest AICc. Statistical analysis included paired and unpaired t-tests and a one-way analysis of variance. Bonferroni correction was applied for multiple comparisons. Receiver operating characteristic (ROC) curves were generated from the optimal model parameters to differentiate PTC with and without aggressive features, and AUCs were compared. ETM performed best with the lowest AICc and the highest Akaike weight (0.44) among the four models. ETM was preferred in 44% of all 3419 voxels. The ETM estimates of K^trans in PTCs with the aggressive feature extrathyroidal extension (ETE) were significantly higher than those without ETE (0.78 ± 0.29 vs. 0.34 ± 0.18 min^-1 , P = 0.005). From ROC analysis, cut-off values of K^trans , v_e and v_p , which discriminated between PTCs with and without ETE, were determined at 0.45 min^-1 , 0.28 and 0.014 respectively. The sensitivities and specificities were 86 and 82% (K^trans ), 71 and 82% (v_e ), and 86 and 55% (v_p ), respectively. Their respective AUCs were 0.90, 0.71 and 0.71. We conclude that ETM K^trans has shown potential to classify tumors with and without aggressive ETE in patients with PTC.

SUBMITTER: Paudyal R

PROVIDER: S-EPMC7687051 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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Dynamic contrast-enhanced MRI model selection for predicting tumor aggressiveness in papillary thyroid cancers.

Paudyal Ramesh R Lu Yonggang Y Hatzoglou Vaios V Moreira Andre A Stambuk Hilda E HE Oh Jung Hun JH Cunanan Kristen M KM Aramburu Nunez David D Mazaheri Yousef Y Gonen Mithat M Ho Alan A Fagin James A JA Wong Richard J RJ Shaha Ashok A Tuttle R Michael RM Shukla-Dave Amita A

NMR in biomedicine 20191104 1

The purpose of this study was to identify the optimal tracer kinetic model from T<sub>1</sub> -weighted dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data and evaluate whether parameters estimated from the optimal model predict tumor aggressiveness determined from histopathology in patients with papillary thyroid carcinoma (PTC) prior to surgery. In this prospective study, 18 PTC patients underwent pretreatment DCE-MRI on a 3 T MR scanner prior to thyroidectomy. This study was a ...[more]

PMID: 31680360

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Project description:A significant proportion of patients with papillary thyroid cancer (PTC) present with extrathyroidal extension (ETE) and lymph node metastasis (LNM). However, the molecular mechanism of tumor invasiveness in PTC remains to be elucidated. The aim of this study is to understand the role of Hedgehog (Hh) signaling in tumor aggressiveness in patients with PTC. Subjects were patients who underwent thyroidectomy from 2012 to 2013 in a single institution. Frozen or paraffin-embedded tumor tissues with contralateral-matched normal thyroid tissues were collected. Hh signaling activity was analyzed by quantitative RT-PCR (qRT-PCR) and immunohistochemical (IHC) staining. Datasets from Gene Expression Omnibus (GEO) (National Center for Biotechnology Information) were subjected to Gene Set Enrichment Analysis (GSEA). BRAFT1799A and telomerase reverse transcriptase promoter mutation C228T were analyzed by direct sequencing. Among 137 patients with PTC, glioma-associated oncogene homolog 1 (GLI1) group III (patients in whom the ratio of GLI1 messenger ribonucleic acid (mRNA) level in tumor tissue to GLI1 mRNA level in matched normal tissue was in the upper third of the subject population) had elevated risk for ETE (odds ratio [OR] 4.381, 95% confidence interval [CI] 1.414-13.569, P = 0.01) and LNM (OR 5.627, 95% CI 1.674-18.913, P = 0.005). Glioma-associated oncogene homolog 2 (GLI2) group III also had elevated risk for ETE (OR 4.152, 95% CI 1.292-13.342, P = 0.017) and LNM (OR 3.924, 95% CI 1.097-14.042, P = 0.036). GSEA suggested that higher GLI1 expression is associated with expression of the KEGG gene set related to axon guidance (P = 0.031, false discovery rate < 0.05), as verified by qRT-PCR and IHC staining in our subjects.GLI1 and GLI2 expressions were clearly related to aggressive clinicopathological features and aberrant activation of GLI1 involved in the axon guidance pathway. These results may contribute to development of new prognostic markers, as well as novel therapeutic targets.

Project description:In the context of neurologic disorders, dynamic susceptibility contrast (DSC) and dynamic contrast enhanced (DCE) MRI provide valuable insights into cerebral vascular function, integrity, and architecture. Even after two decades of use, these modalities continue to evolve as their biophysical and kinetic basis is better understood, with improvements in pulse sequences and accelerated imaging techniques and through application of more robust and automated data analysis strategies. Here, we systematically review each of these elements, with a focus on how their integration improves kinetic parameter accuracy and the development of new hemodynamic biomarkers that provide sub-voxel sensitivity (e.g., capillary transit time and flow heterogeneity). Regarding contrast mechanisms, we discuss the dipole-dipole interactions and susceptibility effects that give rise to simultaneous T1, T2 and T2∗ relaxation effects, including their quantification, influence on pulse sequence parameter optimization, and use in methods such as vessel size and vessel architectural imaging. The application of technologic advancements, such as parallel imaging, simultaneous multi-slice, undersampled k-space acquisitions, and sliding window strategies, enables improved spatial and/or temporal resolution of DSC and DCE acquisitions. Such acceleration techniques have also enabled the implementation of, clinically feasible, simultaneous multi-echo spin- and gradient echo acquisitions, providing more comprehensive and quantitative interrogation of T1, T2 and T2∗ changes. Characterizing these relaxation rate changes through different post-processing options allows for the quantification of hemodynamics and vascular permeability. The application of different biophysical models provides insight into traditional hemodynamic parameters (e.g., cerebral blood volume) and more advanced parameters (e.g., capillary transit time heterogeneity). We provide insight into the appropriate selection of biophysical models and the necessary post-processing steps to ensure reliable measurements while minimizing potential sources of error. We show representative examples of advanced DSC- and DCE-MRI methods applied to pathologic conditions affecting the cerebral microcirculation, including brain tumors, stroke, aging, and multiple sclerosis. The maturation and standardization of conventional DSC- and DCE-MRI techniques has enabled their increased integration into clinical practice and use in clinical trials, which has, in turn, spurred renewed interest in their technological and biophysical development, paving the way towards a more comprehensive assessment of cerebral hemodynamics.

Project description:ObjectiveFew attempts have been made to investigate the prognostic value of dynamic contrast-enhanced (DCE) MRI or dynamic susceptibility contrast (DSC) MRI of non-enhancing, T2-high-signal-intensity (T2-HSI) lesions of glioblastoma multiforme (GBM) in newly diagnosed patients. This study aimed to investigate the prognostic values of DCE MRI and DSC MRI parameters from non-enhancing, T2-HSI lesions of GBM.Materials and methodsA total of 76 patients with GBM who underwent preoperative DCE MRI and DSC MRI and standard treatment were retrospectively included. Six months after surgery, the patients were categorized into early progression (n = 15) and non-early progression (n = 61) groups. We extracted and analyzed the permeability and perfusion parameters of both modalities for the non-enhancing, T2-HSI lesions of the tumors. The optimal percentiles of the respective parameters obtained from cumulative histograms were determined using receiver operating characteristic (ROC) curve and univariable Cox regression analyses. The results were compared using multivariable Cox proportional hazards regression analysis of progression-free survival.ResultsThe 95th percentile value (PV) of Ktrans, mean Ktrans, and median Ve were significant predictors of early progression as identified by the ROC curve analysis (area under the ROC curve [AUC] = 0.704, p = 0.005; AUC = 0.684, p = 0.021; and AUC = 0.670, p = 0.0325, respectively). Univariable Cox regression analysis of the above three parametric values showed that the 95th PV of Ktrans and the mean Ktrans were significant predictors of early progression (hazard ratio [HR] = 1.06, p = 0.009; HR = 1.25, p = 0.017, respectively). Multivariable Cox regression analysis, which also incorporated clinical parameters, revealed that the 95th PV of Ktrans was the sole significant independent predictor of early progression (HR = 1.062, p < 0.009).ConclusionThe 95th PV of Ktrans from the non-enhancing, T2-HSI lesions of GBM is a potential prognostic marker for disease progression.

Dataset Information

Dynamic contrast-enhanced MRI model selection for predicting tumor aggressiveness in papillary thyroid cancers.

Publications

Dynamic contrast-enhanced MRI model selection for predicting tumor aggressiveness in papillary thyroid cancers.

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