Diffusion-Weighted Imaging for Predicting and Monitoring Primary Central Nervous System Lymphoma Treatment Response

Huang, Weiyuan; Wen, Junmiao; Wu, Gang; Yin, Bo; Li, J.-J.; Geng, Dao Ying

doi:10.3174/ajnr.a4867

Cited by 40 publications

(33 citation statements)

References 21 publications

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“…A high ADC corresponds to increased water mobility towards free diffusion, and conversely a low ADC corresponds to restricted diffusion. In a densely cellular homogeneous tumour, such as lymphoma, treatment-related ADC changes may be as high as 50% 4 , however treatment responses may be heterogeneous due to regional micro environmental factors or genetic variation 5 – 7 . A recent animal model study of ovarian tumours showed an average 7.5% increase in mean ADC after treatment but identified significant spatial heterogeneity due to variations in tumour response 7 .…”

Section: Introductionmentioning

confidence: 99%

A data-driven statistical model that estimates measurement uncertainty improves interpretation of ADC reproducibility: a multi-site study of liver metastases

Pathak

Ragheb

Thacker

et al. 2017

Sci Rep

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Apparent Diffusion Coefficient (ADC) is a potential quantitative imaging biomarker for tumour cell density and is widely used to detect early treatment changes in cancer therapy. We propose a strategy to improve confidence in the interpretation of measured changes in ADC using a data-driven model that describes sources of measurement error. Observed ADC is then standardised against this estimation of uncertainty for any given measurement. 20 patients were recruited prospectively and equitably across 4 sites, and scanned twice (test-retest) within 7 days. Repeatability measurements of defined regions (ROIs) of tumour and normal tissue were quantified as percentage change in mean ADC (test vs. re-test) and then standardised against an estimation of uncertainty. Multi-site reproducibility, (quantified as width of the 95% confidence bound between the lower confidence interval and higher confidence interval for all repeatability measurements), was compared before and after standardisation to the model. The 95% confidence interval width used to determine a statistically significant change reduced from 21.1 to 2.7% after standardisation. Small tumour volumes and respiratory motion were found to be important contributors to poor reproducibility. A look up chart has been provided for investigators who would like to estimate uncertainty from statistical error on individual ADC measurements.

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Section: Introductionmentioning

confidence: 99%

A data-driven statistical model that estimates measurement uncertainty improves interpretation of ADC reproducibility: a multi-site study of liver metastases

Pathak

Ragheb

Thacker

et al. 2017

Sci Rep

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show abstract

“…Our results showed that the ADC min -post and the percentage change of ADC min could predict the local control. Another study conducted by our team demonstrated that pre-therapeutic ADC min failed to predict the primary central nervous system lymphoma outcomes, but ADC min changes and percentage changes after one cycle of chemotherapy could more precisely predict treatment responses 18 . A possible explanation is that pre-therapeutic intratumoral cell density and stromal space cannot precisely reflect the treatment response.…”

Section: Discussionmentioning

confidence: 97%

In Vivo Imaging Markers for Prediction of Radiotherapy Response in Patients with Nasopharyngeal Carcinoma: RESOLVE DWI versus DKI

Huang

Lin

et al. 2018

Sci Rep

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In this prospective study, we compared the performance of readout segmentation of long variable echo trains of diffusion-weighted imaging (RESOLVE DWI) and diffusion kurtosis imaging (DKI) for the prediction of radiotherapy response in patients with nasopharyngeal carcinoma (NPC). Forty-one patients with NPC were evaluated. All patients underwent conventional MRI, RESOLVE DWI and DKI, before and after radiotherapy. All patients underwent conventional MRI every 3 months until 1 year after radiotherapy. The patients were divided into response group (RG; 36/41 patients) and no-response group (NRG; 5/41 patients) based on follow-up results. DKI (the mean of kurtosis coefficient, Kmean and the mean of diffusion coefficient, Dmean) and RESOLVE DWI (the minimum apparent diffusion coefficient, ADCmin) parameters were calculated. Parameter values at the pre-treatment period, post-treatment period, and the percentage change between these 2 periods were obtained. All parameters differed between the RG and NRG groups except for the pretreatment Dmean and ADCmin. Kmean-post was considered as an independent predictor of local control, with 87.5% sensitivity and 91.3% specificity (optimal threshold = 0.30, AUC: 0.924; 95% CI, 0.83–1.00). Kmean-post values of DKI have the potential to be used as imaging biomarkers for the early evaluation of treatment effects of radiotherapy on NPC.

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“…9 Specifically, apparent diffusion coefficient values derived from DWI have been shown to correlate with tumor cellularity, glioma grade, and treatment response. [9][10][11][12][13] Moreover, recent studies have shown that DWI parameters may differentiate the histology of BM from lung cancer. 14,15 However, the association between DWI parameters and the gene mutation status has not been assessed for BM from lung cancer, to our knowledge.…”

Section: Discussionmentioning

confidence: 99%

“…Such patients are expected to respond well to noninvasive TKI treatment, and clinicians can plan this treatment accordingly if the mutation status is known. Moreover, Huang et al 13 recently reported that ADC min has the potential to predict and monitor the response of primary CNS lymphoma to chemotherapy. Therefore, through further study, we can apply serial ADC values to predict the response of BM from lung adenocarcinoma with EGFR mutations to TKI treatment.…”

Section: Discussionmentioning

confidence: 99%

Diffusion-Weighted Imaging of Brain Metastasis from Lung Cancer: Correlation of MRI Parameters with the Histologic Type and Gene Mutation Status

Jung

Park²,

Hong

et al. 2018

AJNR Am J Neuroradiol

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Diffusion-Weighted Imaging for Predicting and Monitoring Primary Central Nervous System Lymphoma Treatment Response

Cited by 40 publications

References 21 publications

A data-driven statistical model that estimates measurement uncertainty improves interpretation of ADC reproducibility: a multi-site study of liver metastases

A data-driven statistical model that estimates measurement uncertainty improves interpretation of ADC reproducibility: a multi-site study of liver metastases

In Vivo Imaging Markers for Prediction of Radiotherapy Response in Patients with Nasopharyngeal Carcinoma: RESOLVE DWI versus DKI

Diffusion-Weighted Imaging of Brain Metastasis from Lung Cancer: Correlation of MRI Parameters with the Histologic Type and Gene Mutation Status

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