Treatment Response Assessment Maps to Delineate Necrosis From Tumor After Stereotactic Radiation in Brain Metastases

Author(s):  
2021 ◽  
Vol 23 (Supplement_4) ◽  
pp. iv22-iv23
Author(s):  
Markand Patel ◽  
Dilina Rajapakse ◽  
Jian Ping Jen ◽  
Sara Meade ◽  
Helen Benghiat ◽  
...  

Abstract Aims Following stereotactic radiosurgery (SRS), brain metastases can increase in size in up to a third of cases. Conventional magnetic resonance imaging (MRI) has a limited role to distinguish between tumour recurrence and SRS-induced changes, which can impact patient management. Delayed contrast MRI treatment response assessment maps (TRAM) use the principle of contrast clearance seen in other tumours, where high vascularity shows a rapid rise in contrast as well as rapid clearance, whereas areas of damaged or low vascularity show accumulation of contrast. We aimed to assess the ability of delayed contrast MRI and multiparametric MRI techniques of diffusion-weighted imaging (DWI), perfusion-weighted imaging (PWI) and MR spectroscopy (MRS) to distinguish between radiation-related effects and tumour tissue, as these techniques assess tissue physiological and metabolic information. Method A retrospective review was performed on 23 patients who had delayed contrast and multiparametric MRI between October 2018 to April 2020. Studies were restricted to cases with brain metastases enlarging post-SRS with uncertainty at the MDT meeting regarding progression or treatment-related change, impacting the patient’s management. MRI was performed at 3T including DWI, PWI, MRS with short and intermediate echo times, and 3D T1 MPRAGE at 3-5, 20-30 and 70-90 minutes after administration of intravenous contrast. Contrast clearance analysis was performed by selecting an enhancing region of interest (ROI), measuring signal intensities at the three different timepoints and taking apparent diffusion coefficient (ADC) and relative cerebral blood volume (rCBV) values from the ROI. Choline/Creatine values were calculated from a single-voxel (10 mm isotropic) encompassing the entire contrast-enhancing lesion. Outcome was established from MRI follow-up at 6 months, with a stable or responding lesion considered treatment-related changes and increase considered progression. Results Across 23 patients, 24 metastases were assessed. Two patients were excluded as appropriate follow-up was not available. Sites of primary tumours included breast (n=8), lung (n=6), melanoma (n=4), neuroendocrine tumour from the lung (n=2) and renal cell carcinoma (n=2). Mean age was 56 years and 50% were female. In this cohort, 59% (n=13) were classified as having radiation-related changes on follow-up. Delayed MRI contrast clearance between the 3-5 and 70-90 minute imaging was significantly higher in cases of progression (23.6% vs. 2.5% decrease, p<0.05), as were the rCBV and Cho/Cr ratio (rCBV 3.1 vs. 1.5 and Cho/Cr ratio 2.3 vs. 1.4, p<0.05). Accuracy, sensitivity and specificity of using TRAM alone (contrast clearance decrease of >0%) for progression was 63%/100%/38%, PWI alone (rCBV cut-off 2.0) yielded results of 77%/75%/79% and for both Cho/Cr ratio alone (cut-off 1.8) and combined with TRAM, it was 90%/88%/92%. Neuroradiologist assessment of all techniques was 95%/100%/92%. Conclusion This study shows the effectiveness of delayed contrast and multiparametric MRI for treatment response assessment in patients with brain metastases treated by SRS in clinical practice. Although a delayed contrast MRI study is a very sensitive tool for detecting tumour progression, it lacks specificity. The accuracy of differentiating between tumour and treatment-related effects increases when delayed contrast MRI is used in combination with other advanced techniques such as MRS. By combining all these techniques, neuroradiologists had the highest accuracy, sensitivity and specificity for detecting progression in post-SRS brain metastases.


2021 ◽  
Vol 11 ◽  
Author(s):  
Jungheum Cho ◽  
Young Jae Kim ◽  
Leonard Sunwoo ◽  
Gi Pyo Lee ◽  
Toan Quang Nguyen ◽  
...  

BackgroundAlthough accurate treatment response assessment for brain metastases (BMs) is crucial, it is highly labor intensive. This retrospective study aimed to develop a computer-aided detection (CAD) system for automated BM detection and treatment response evaluation using deep learning.MethodsWe included 214 consecutive MRI examinations of 147 patients with BM obtained between January 2015 and August 2016. These were divided into the training (174 MR images from 127 patients) and test datasets according to temporal separation (temporal test set #1; 40 MR images from 20 patients). For external validation, 24 patients with BM and 11 patients without BM from other institutions were included (geographic test set). In addition, we included 12 MRIs from BM patients obtained between August 2017 and March 2020 (temporal test set #2). Detection sensitivity, dice similarity coefficient (DSC) for segmentation, and agreements in one-dimensional and volumetric Response Assessment in Neuro-Oncology Brain Metastases (RANO-BM) criteria between CAD and radiologists were assessed.ResultsIn the temporal test set #1, the sensitivity was 75.1% (95% confidence interval [CI]: 69.6%, 79.9%), mean DSC was 0.69 ± 0.22, and false-positive (FP) rate per scan was 0.8 for BM ≥ 5 mm. Agreements in the RANO-BM criteria were moderate (κ, 0.52) and substantial (κ, 0.68) for one-dimensional and volumetric, respectively. In the geographic test set, sensitivity was 87.7% (95% CI: 77.2%, 94.5%), mean DSC was 0.68 ± 0.20, and FP rate per scan was 1.9 for BM ≥ 5 mm. In the temporal test set #2, sensitivity was 94.7% (95% CI: 74.0%, 99.9%), mean DSC was 0.82 ± 0.20, and FP per scan was 0.5 (6/12) for BM ≥ 5 mm.ConclusionsOur CAD showed potential for automated treatment response assessment of BM ≥ 5 mm.


2021 ◽  
Author(s):  
Chunhao Wang ◽  
Kyle R. Padgett ◽  
Min‐Ying Su ◽  
Eric A. Mellon ◽  
Danilo Maziero ◽  
...  

Radiology ◽  
2021 ◽  
Vol 299 (2) ◽  
pp. 346-348
Author(s):  
Mustafa R. Bashir ◽  
Mishal Mendiratta-Lala

2019 ◽  
Vol 7 (4) ◽  
pp. 285-294
Author(s):  
Daniela Salvatore ◽  
Alessia Lo Dico ◽  
Cristina Martelli ◽  
Cecilia Diceglie ◽  
Luisa Ottobrini

Sign in / Sign up

Export Citation Format

Share Document