Exploring diagnostic performance of T2 mapping in diffuse glioma grading

Gu, Weibin; Fang, Shih Chieh; Hou, Xinyi; Ding, Mao; Li, Shaowu

doi:10.21037/qims-20-916

Cited by 15 publications

(9 citation statements)

References 20 publications

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“…Our study demonstrated that quantitative T2 values could differentiate EMC from normal endometrium, which was consistent with tumors in other organs ( 8 , 10 , 16 , 17 ). As for the ADC values, the same applied with the quantitative T2 values, a lower value is suspected to be cancer.…”

Section: Discussionsupporting

confidence: 85%

“…T2 mapping can be used to analyze the changes in the composition of the tissue by measuring the T2 relaxation time, which is related to the water content in the tissue ( 16 ). Previous researchers have reported the value of T2 mapping in the detection and prediction of tumor histopathological features ( 16 , 17 ), but few quantitative studies have focused on T2 mapping in EMC. In this study, T2 mapping can be used to visualize the morphological structure of the myometrium, especially the JZ.…”

Section: Discussionmentioning

confidence: 99%

“…Gu et al. ( 17 ) demonstrated that the T2 values for brain tumors with higher cell density were lower because of reduced extracellular space. Adams et al.…”

Section: Discussionmentioning

confidence: 99%

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Feasibility of accelerated T2 mapping for the preoperative assessment of endometrial carcinoma

Zhang¹,

Jie²,

Wang³

et al. 2023

Front. Oncol.

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ObjectiveThe application value of T2 mapping in evaluating endometrial carcinoma (EMC) features remains unclear. The aim of the study was to determine the quantitative T2 values in EMC using a novel accelerated T2 mapping, and evaluate them for detection, classification,and grading of EMC.Materials and methodsFifty-six patients with pathologically confirmed EMC and 17 healthy volunteers were prospectively enrolled in this study. All participants underwent pelvic magnetic resonance imaging, including DWI and accelerated T2 mapping, before treatment. The T2 and apparent diffusion coefficient (ADC) values of different pathologic EMC features were extracted and compared. Receiver operating characteristic (ROC) curve analysis was performed to analyze the diagnostic efficacy of the T2 and ADC values in distinguishing different pathological features of EMC.ResultsThe T2 values and ADC values were significantly lower in EMC than in normal endometrium (bothl p < 0.05). The T2 and ADC values were significantly different between endometrioid adenocarcinoma (EA) and non-EA (both p < 0.05) and EMC tumor grades (all p < 0.05) but not for EMC clinical types (both p > 0.05) and depth of myometrial invasion (both p > 0.05). The area under the ROC curve (AUC) was higher for T2 values than for ADC values in predicting grade 3 EA (0.939 vs. 0.764, p = 0.048). When combined T2 and ADC values, the AUC for predicting grade 3 EA showed a significant increase to 0.947 (p = 0.03) compared with those of ADC values. The T2 and ADC values were negatively correlated with the tumor grades (r = -0.706 and r = -0.537, respectively).ConclusionQuantitative T2 values demonstrate potential suitability in discriminating between EMC and normal endometrium, EA and non-EA, grade 3 EA and grade 1/2 EA. Combining T2 and ADC values performs better in predicting the histological grades of EA in comparison with ADC values alone.

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Section: Discussionsupporting

confidence: 85%

Section: Discussionmentioning

confidence: 99%

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Feasibility of accelerated T2 mapping for the preoperative assessment of endometrial carcinoma

Zhang¹,

Jie²,

Wang³

et al. 2023

Front. Oncol.

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“…Several previous studies have proposed grading gliomas based on quantitative parameters of magnetic resonance imaging (MRI) techniques, such as magnetic resonance (MR) spectroscopy, perfusion imaging, T2 mapping, and diffusion-weighted imaging (DWI). Of these methods, DWI is the most sensitive and has great potential for grading tasks (4)(5)(6)(7)(8). Many DWI models have been proposed over the past few years.…”

Section: Introductionmentioning

confidence: 99%

Incorporating multiple magnetic resonance diffusion models to differentiate low- and high-grade adult gliomas: a machine learning approach

Xu¹,

Ren²,

Zhao³

et al. 2022

Quant Imaging Med Surg

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Background: Accurate grading of gliomas is a challenge in imaging diagnosis. This study aimed to evaluate the performance of a machine learning (ML) approach based on multiparametric diffusion-weighted imaging (DWI) in differentiating low-and high-grade adult gliomas. Methods: A model was developed from an initial cohort containing 74 patients with pathology-confirmed gliomas, who underwent 3 tesla (3T) diffusion magnetic resonance imaging (MRI) with 21 b values. In all, 112 histogram features were extracted from 16 parameters derived from seven diffusion models [monoexponential, intravoxel incoherent motion (IVIM), diffusion kurtosis imaging (DKI), fractional order calculus (FROC), continuous-time random walk (CTRW), stretched-exponential, and statistical]. Feature selection and model training were performed using five randomly permuted five-fold cross-validations. An internal test set (15 cases of the primary dataset) and an external cohort (n=55) imaged on a different scanner were used to validate the model. The diagnostic performance of the model was compared with that of a single DWI model and DWI radiomics using accuracy, sensitivity, specificity, and the area under the curve (AUC). Results: Seven significant multiparametric DWI features (two from the stretched-exponential and FROC models, and three from the CTRW model) were selected to construct the model. The multiparametric DWI model achieved the highest AUC (0.84, versus 0.71 for the single DWI model, P<0.05), an accuracy of 0.80 in the internal test, and both AUC and accuracy of 0.76 in the external test. Conclusions: Our multiparametric DWI model differentiated low-(LGG) from high-grade glioma (HGG) with better generalization performance than the established single DWI model. This result suggests that the ^ ORCID: Junqi Xu,

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“…In recent years, quantitative MRI techniques have been used for evaluating the changes of the microenvironment in fibrotic endometrium 16,17 . Quantitative T2 relaxation time (spin–spin relaxation time), the time constant representing the decay of transverse magnetization, is obtained by a T2 mapping MRI sequence, and can reflect water accumulation and blood volume within tissues 18,19 . T2 mapping has been successfully applied to evaluate myocardial fibrosis and liver fibrosis 20,21 .…”

mentioning

confidence: 99%

Quantification of Endometrial Fibrosis Using Noninvasive MRI T2 Mapping: Initial Findings

Zhou

Zhu

Jiang

et al. 2023

Magnetic Resonance Imaging

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BackgroundEndometrial fibrosis may cause infertility. Accurate evaluation of endometrial fibrosis helps clinicians to schedule timely therapy.PurposeTo explore T2 mapping for assessing endometrial fibrosis.Study TypeProspective.PopulationNinety‐seven women with severe endometrial fibrosis (SEF) and 21 patients with mild to moderate endometrial fibrosis (MMEF), diagnosed by hysteroscopy, and 37 healthy women.Field Strength/Sequence3T, T2‐weighted turbo spin echo (T2‐weighted imaging) and multi‐echo turbo spin echo (T2 mapping) sequences.AssessmentEndometrial MRI parameters (T2, thickness [ET], area [EA], and volume [EV]) were measured by N.Z. and Q.H. (9‐ and 4‐years' experience in pelvic MRI) and compared between the three subgroups. A multivariable model including MRI parameters and clinical variables (including age and body mass index [BMI]) was developed to predict endometrial fibrosis assessed by hysteroscopy.Statistical TestsKruskal–Wallis; ANOVA; Spearman's correlation coefficient (rho); area under the receiver operating characteristic curve (AUC); binary logistic regression; intraclass correlation coefficient (ICC). P value <0.05 for statistical significance.ResultsEndometrial T2, ET, EA, and EV of MMEF patients (185 msec, 8.2 mm, 168 mm2, and 2181 mm3) and SEF patients (164 msec, 6.7 mm, 120 mm2, and 1762 mm3) were significantly lower than those of healthy women (222 msec, 11.7 mm, 316 mm2, and 3960 mm3). Endometrial T2 and ET of SEF patients were significantly lower than those of MMEF patients. Endometrial T2, ET, EA, and EV were significantly correlated to the degree of endometrial fibrosis (rho = −0.623, −0.695, −0.694, −0.595). There were significant strong correlations between ET, EA, and EV in healthy women and MMEF patients (rho = 0.850–0.908). Endometrial MRI parameters and the multivariable model accurately distinguished MMEF or SEF from normal endometrium (AUCs >0.800). Age, BMI, and MRI parameters in univariable analysis and age and T2 in multivariable analysis significantly predicted endometrial fibrosis. The reproducibility of MRI parameters was excellent (ICC, 0.859–0.980).Data ConclusionT2 mapping has potential to noninvasively and quantitatively evaluate the degree of endometrial fibrosis.Evidence Level: 2Technical Efficacy: Stage 2

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Exploring diagnostic performance of T2 mapping in diffuse glioma grading

Cited by 15 publications

References 20 publications

Feasibility of accelerated T2 mapping for the preoperative assessment of endometrial carcinoma

Feasibility of accelerated T2 mapping for the preoperative assessment of endometrial carcinoma

Incorporating multiple magnetic resonance diffusion models to differentiate low- and high-grade adult gliomas: a machine learning approach

Quantification of Endometrial Fibrosis Using Noninvasive MRI T2 Mapping: Initial Findings

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