Diffusion-weighted MRI for predicting treatment response in patients with nasopharyngeal carcinoma: a systematic review and meta-analysis

Lee, Min Kyoung; Choi, Yangsean; Jung, Seungwon

doi:10.1038/s41598-021-98508-5

Cited by 14 publications

(9 citation statements)

References 47 publications

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“…ADC has been proven to have good diagnostic performance in the identification of benign and malignant lesions such as the brain, thyroid, pancreas, and uterus (38)(39)(40)(41)(42). In addition, ADC has also been confirmed in other studies to better predict the response evaluation of liver cancer, nasopharyngeal cancer, colon cancer with liver metastasis after treatment (43)(44)(45). Whether the ADC value can better distinguish benign and malignant ovarian or appendages is different in some previous studies (46,47).…”

Section: Discussionmentioning

confidence: 99%

The Diagnostic Performance of Maximum Uptake Value and Apparent Diffusion Coefficient in Differentiating Benign and Malignant Ovarian or Adnexal Masses: A Meta-Analysis

Liang

Zhang

et al. 2022

Front. Oncol.

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ObjectiveThe purpose of this meta-analysis was to provide evidence for using maximum uptake value (SUVmax) and apparent diffusion coefficient (ADC) to quantitatively differentiate benign and malignant ovarian or adnexal masses, and to indirectly compare their diagnostic performance.Material and MethodsThe association between SUVmax, ADC and ovarian or adnexal benign and malignant masses was searched in PubMed, Cochrane Library, and Embase databases until October 1, 2021. Two authors independently extracted the data. Studies included in the analysis were required to provide data for the construction of a 2 × 2 contingency table to evaluate the diagnostic performance of SUVmax or ADC in differentiating benign and malignant ovarian or adnexal masses. The quality of the enrolled studies was evaluated by Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) instrument, and the meta-analysis was conducted using Stata software version 14.0. Forest plots were generated according to the sensitivity and specificity of SUVmax and ADC, and meta-regression analysis was further used to assess heterogeneity between studies.ResultsA total of 14 studies were finally included in this meta-analysis by gradually excluding duplicate literatures, conference abstracts, guidelines, reviews, case reports, animal studies and so on. The pooled sensitivity and specificity of SUVmax for quantitative differentiation of benign and malignant ovarian or adnexal masses were 0.88 and 0.89, respectively, and the pooled sensitivity and specificity for ADC were 0.87 and 0.80, respectively.ConclusionQuantitative SUVmax and ADC values have good diagnostic performance in differentiating benign and malignant ovarian or adnexal masses, and SUVmax has higher accuracy than ADC. Future prospective studies with large sample sizes are needed for the analysis of the role of SUVmax and ADC in the differentiation of benign and malignant ovarian or adnexal masses.

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Section: Discussionmentioning

confidence: 99%

The Diagnostic Performance of Maximum Uptake Value and Apparent Diffusion Coefficient in Differentiating Benign and Malignant Ovarian or Adnexal Masses: A Meta-Analysis

Liang

Zhang

et al. 2022

Front. Oncol.

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“…However, HNC is typically heterogeneous, and this heterogeneity may be due The histogram analysis of ADC values within the primary tumor has been frequently used to adaptively evaluate the heterogeneity of the primary tumor relative to the complex tumor microenvironment (11). Such analysis has proven helpful in predicting the histologic grade (23), HPV status (24), and treatment response (25,26) in patients with HNC. In the present study, although primary tumor ∆ADC mean and ∆ADC median values were higher in responders than in non-responders, which is compatible with the results of previous studies (18,20), this finding did not reach statistical significance.…”

Section: Discussionmentioning

confidence: 99%

Interval changes of histogram-derived diffusion indices predict treatment response to induction chemotherapy in head and neck cancer: a feasibility study

Cheng¹,

Lu²,

Lin³

et al. 2022

Quant Imaging Med Surg

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Background: This retrospective study investigated whether the interval change of apparent diffusion coefficient (∆ADC) [baseline and after the first cycle of induction chemotherapy (ICT)] can be used as a valid predictive imaging biomarker of the treatment response to ICT in head and neck cancer (HNC). Methods: A total of 19 consecutive patients with HNC who underwent diffusion-weighted magnetic resonance imaging (DWI) at baseline and after the first cycle of ICT were included. Whole-tumor ADC histogram parameters (mean, median, kurtosis, skewness, entropy, minimal, maximum, 25th percentile, and 75th percentile) were obtained. The correlations of ∆ADC histogram parameters, volume, T-stage, N-stage, and age with the treatment response were examined using the Mann-Whitney U test. The predictive value of histogram parameters was examined using receiver operating characteristic (ROC) curves.Results: Responders showed significantly higher values of ∆ADC 25 (0.19±0.23) and ∆ADC min (1.78±2.98) than non-responders (−0.09±0.15 and −0.73±0.36; P=0.035 and 0.009, respectively). When ∆ADC 25 and ∆ADC min were used for predicting the treatment response, the area under the ROC curve was 0.850/0.933 with a sensitivity of 73.3%/80.0% and specificity of 100%/100% (P=0.036 and 0.009, respectively).Conclusions: ∆ADC 25 and ∆ADC min derived from whole-tumor histogram analysis are valuable imaging biomarkers for the early prediction of the ICT response in HNC.

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“…To enable the derivation of analytical formulas, we will therefore restrict ourselves to the consideration of 𝛿 > 0 and the event (18). It is nevertheless possible to circumvent this simplification by numerical inversion of the relationship given in (16). But here, we will approximate…”

Section: Quantiles Of the Distribution Of P Esementioning

confidence: 99%

A statistical framework for planning and analysing test–retest studies of repeatability

Danzer,

Eveslage,

Görlich

et al. 2024

Stat Methods Med Res

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There is an increasing number of potential quantitative biomarkers that could allow for early assessment of treatment response or disease progression. However, measurements of such biomarkers are subject to random variability. Hence, differences of a biomarker in longitudinal measurements do not necessarily represent real change but might be caused by this random measurement variability. Before utilizing a quantitative biomarker in longitudinal studies, it is therefore essential to assess the measurement repeatability. Measurement repeatability obtained from test–retest studies can be quantified by the repeatability coefficient, which is then used in the subsequent longitudinal study to determine if a measured difference represents real change or is within the range of expected random measurement variability. The quality of the point estimate of the repeatability coefficient, therefore, directly governs the assessment quality of the longitudinal study. Repeatability coefficient estimation accuracy depends on the case number in the test–retest study, but despite its pivotal role, no comprehensive framework for sample size calculation of test–retest studies exists. To address this issue, we have established such a framework, which allows for flexible sample size calculation of test–retest studies, based upon newly introduced criteria concerning assessment quality in the longitudinal study. This also permits retrospective assessment of prior test–retest studies.

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Diffusion-weighted MRI for predicting treatment response in patients with nasopharyngeal carcinoma: a systematic review and meta-analysis

Cited by 14 publications

References 47 publications

The Diagnostic Performance of Maximum Uptake Value and Apparent Diffusion Coefficient in Differentiating Benign and Malignant Ovarian or Adnexal Masses: A Meta-Analysis

The Diagnostic Performance of Maximum Uptake Value and Apparent Diffusion Coefficient in Differentiating Benign and Malignant Ovarian or Adnexal Masses: A Meta-Analysis

Interval changes of histogram-derived diffusion indices predict treatment response to induction chemotherapy in head and neck cancer: a feasibility study

A statistical framework for planning and analysing test–retest studies of repeatability

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