Analysis of the layering pattern of the apparent diffusion coefficient (ADC) for differentiation of radiation necrosis from tumour progression

Cha, Jihoon; Kim, Sung Tae; Kim, Hyung Jin; Kim, Hye Jeong; Kim, Byung Joon; Jeon, Pyoung; Kim, Keon Ha; Byun, Hong Sik

doi:10.1007/s00330-012-2638-4

Cited by 38 publications

(44 citation statements)

References 24 publications

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“…12,23 Other articles have applied ADC histogram analysis 24 and 3-layer patterns of ADC analysis, 25 though these techniques are not commercially available and have not been incorporated into clinical practice. ADC may be decreased with treatment-related changes, possibly due to cellular inflammatory factors and abundant polymorphonuclear lymphocytes.…”

Section: Discussionmentioning

confidence: 99%

Diffusion and Perfusion MRI to Differentiate Treatment-Related Changes Including Pseudoprogression from Recurrent Tumors in High-Grade Gliomas with Histopathologic Evidence

Prager¹,

Martinez

Beal

et al. 2015

AJNR Am J Neuroradiol

164

122

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BACKGROUND AND PURPOSE Treatment-related changes and recurrent tumors often have overlapping features on conventional MR imaging. The purpose of this study was to assess the utility of DWI and DSC perfusion imaging alone and in combination to differentiate treatment-related effects and recurrent high-grade gliomas. MATERIALS AND METHODS We retrospectively identified 68 consecutive patients with high-grade gliomas treated by surgical resection followed by radiation therapy and temozolomide, who then developed increasing enhancing mass lesions indeterminate for treatment-related changes versus recurrent tumor. All lesions were diagnosed by histopathology at repeat surgical resection. ROI analysis was performed of the enhancing lesion on the ADC and DSC maps. Measurements made by a 2D ROI of the enhancing lesion on a single slice were recorded as ADCLesion and rCBVLesion, and measurements made by the most abnormal small fixed diameter ROI as ADCROI and rCBVROI. Statistical analysis was performed with Wilcoxon rank sum tests with P = .05. RESULTS Ten of the 68 patients (14.7%) had treatment-related changes, while 58 patients (85.3%) had recurrent tumor only (n = 19) or recurrent tumor mixed with treatment effect (n = 39). DWI analysis showed higher ADCLesion in treatment-related changes than in recurrent tumor (P = .003). DSC analysis revealed lower relative cerebral blood volume (rCBV)Lesion and rCBVROI in treatment-related changes (P=.003 andP=.011, respectively). Subanalysis of patients with suspected pseudoprogression also revealed higher ADCLesion (P = .001) and lower rCBVLesion (P = .028) and rCBVROI (P = .032) in treatment-related changes. Applying a combined ADCLesion and rCBVLesion model did not outperform either the ADC or rCBV metric alone. CONCLUSIONS Treatment-related changes showed higher diffusion and lower perfusion than recurrent tumor. Similar correlations were found for patients with suspected pseudoprogression.

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Section: Discussionmentioning

confidence: 99%

Diffusion and Perfusion MRI to Differentiate Treatment-Related Changes Including Pseudoprogression from Recurrent Tumors in High-Grade Gliomas with Histopathologic Evidence

Prager¹,

Martinez

Beal

et al. 2015

AJNR Am J Neuroradiol

164

122

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“…They concluded that if rCBV larger than 2.6 was used to differentiate radiation necrosis and tumor progression, the sensitivity was 100% but specificity was only 56%. If the lesions with moderately increased rCBV (2.6–4.1) were excluded from tumor progression, the sensitivity and specificity increased to 100% [88]. …”

Section: Investigational Imaging Techniques For Distinguishing Progrementioning

confidence: 99%

Postradiation Imaging Changes in the CNS: How can we Differentiate Between Treatment Effect and Disease Progression?

et al. 2014

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A familiar challenge for neuroradiologists and neuro-oncologists is differentiating between radiation treatment effect and disease progression in the CNS. Both entities are characterized by an increase in contrast enhancement on MRI and present with similar clinical signs and symptoms that may occur either in close temporal proximity to the treatment or later in the disease course. When radiation-related imaging changes or clinical deterioration are mistaken for disease progression, patients may be subject to unnecessary surgery and/or a change from otherwise effective therapy. Similarly, when disease progression is mistaken for treatment effect, a potentially ineffective therapy may be continued in the face of progressive disease. Here we describe the three types of radiation injury to the brain based on the time to development of signs and symptoms – acute, subacute and late – and then review specific imaging changes after intensity-modulated radiation therapy, stereotactic radiosurgery and brachytherapy. We provide an overview of these phenomena in the treatment of a wide range of malignant and benign CNS illnesses. Finally, we review the published data regarding imaging techniques under investigation to address this well-known problem.

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“…Subtraction maps were first developed two decades ago 22,23 , but their application in HGG has not been adopted. More recent advances, such as PWI, have improved surveillance and continue to be investigated with multiparametric approaches showing particular promise 24,25 . PWI was not acquired routinely on patients included in this study, but future analyses would benefit from the addition of this modality.…”

Section: Discussionmentioning

confidence: 99%

A Simple Automated Method for Detecting Recurrence in High-Grade Glioma

Yanagihara¹,

Grinband²,

Rowley³

et al. 2016

AJNR Am J Neuroradiol

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BACKGROUND AND PURPOSE To develop an automated multiparametric MRI analysis of routinely acquired imaging sequences to identify areas of focally recurrent high-grade glioma. MATERIALS AND METHODS Data from 141 patients treated with radiotherapy with a diagnosis of high-grade glioma were reviewed. Strict inclusion/exclusion criteria identified a homogenous cohort of 12 patients with a nodular recurrence of high-grade glioma that was amenable to focal re-irradiation (cohort 1). T1WI, FLAIR and DWI data were used to create subtraction maps across time points. Linear regression was performed to identify the pattern of change in these three imaging sequences that best correlates with recurrence. The ability of these parameters to guide treatment decisions in individual patients was assessed in a separate cohort of four patients who were treated with radiosurgery for recurrent high-grade glioma (cohort 2). RESULTS A leave-one-out analysis of cohort 1 revealed that automated subtraction maps consistently predicted the radiologist-identified area of recurrence (median area under the receiver operating characteristic curve = 0.91). The regression model was tested in pre-radiosurgery MRIs in cohort 2 and identified eight recurrent lesions. Consistent with the model’s predictions, six lesions were treated with radiosurgery and were controlled on follow up imaging, but the remaining two lesions were not treated and progressed. CONCLUSIONS Multiparametric subtraction maps can predict areas of nodular progression in patients with previously treated high-grade glioma. This automated method based on routine imaging sequences presents a valuable tool to be prospectively validated in subsequent studies of treatment planning and post-treatment surveillance.

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Analysis of the layering pattern of the apparent diffusion coefficient (ADC) for differentiation of radiation necrosis from tumour progression

Abstract: The three-layer pattern of ADC shows high specificity in diagnosing radiation necrosis; therefore, combined analysis of the ADC pattern with rCBV may have added value in the correct differentiation of tumour progression from radiation necrosis.

Cited by 38 publications

References 24 publications

Diffusion and Perfusion MRI to Differentiate Treatment-Related Changes Including Pseudoprogression from Recurrent Tumors in High-Grade Gliomas with Histopathologic Evidence

Diffusion and Perfusion MRI to Differentiate Treatment-Related Changes Including Pseudoprogression from Recurrent Tumors in High-Grade Gliomas with Histopathologic Evidence

Postradiation Imaging Changes in the CNS: How can we Differentiate Between Treatment Effect and Disease Progression?

A Simple Automated Method for Detecting Recurrence in High-Grade Glioma

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