Accelerated Post-contrast Wave-CAIPI T1 SPACE Achieves Equivalent Diagnostic Performance Compared With Standard T1 SPACE for the Detection of Brain Metastases in Clinical 3T MRI

Abstract Background Brain metastases occur in up to 40% of patients with cancer and are associated with poor prognosis and considerable levels of recurrence. Consequently, close follow-up with serial brain MRI is performed post-treatment to monitor for recurrent disease. Although conventional MRI (CE-T1-weighted and FLAIR/T2-weighted) is the recommended follow-up modality, it has poor specificity with limited ability to differentiate between true disease recurrence and treatment-related changes such as radiation necrosis. Therefore, alternative imaging options are sought in order to help physicians confidently diagnose treatment-related changes and thus reliably stratify the risk of continuation of a therapeutic regimen, especially given the morbidity associated with current treatments. Amino acid PET imaging agent, 18F-fluciclovine, has increased uptake in brain tumors relative to normal tissue and may be useful for detecting recurrent brain metastases. Methods NCT04410133 is a prospective, open-label, single-arm, single-dose (185 MBq ±20%) study with a primary objective to confirm the diagnostic performance of 18F-fluciclovine PET (read with conventional MRI for anatomical reference) for detection of recurrent brain metastases where MRI is equivocal. Approximately 150 subjects with solid tumor brain metastases who have undergone radiation therapy will be enrolled in this multicenter trial (~18 US sites) if they have a lesion considered equivocal on MRI that requires further confirmatory diagnostic procedures such as biopsy/neurosurgical intervention or clinical follow-up. Subjects will undergo 18F-fluciclovine PET <28 days after the equivocal MRI and 2–21 days pre-biopsy/neurosurgical intervention. Clinical follow-up will occur for 6m post-18F-fluciclovine PET. Secondary objectives include evaluation of subject- and lesion-level 18F-fluciclovine negative and positive percent agreement (equivalent to specificity and sensitivity respectively) for recurrent brain metastases, inter-reader and intra-reader agreement, and safety evaluations. Enrolment began in October 2020 and the trial is open at the time of submission.

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NEIM-02. TRIAL IN PROGRESS: A MULTICENTER PHASE 3 STUDY TO ESTABLISH THE DIAGNOSTIC PERFORMANCE OF 18F-FLUCICLOVINE PET IN DETECTING RECURRENT BRAIN METASTASES AFTER RADIATION THERAPY (REVELATE)

Neuro-Oncology Advances ◽

10.1093/noajnl/vdab112.023 ◽

2021 ◽

Vol 3 (Supplement_4) ◽

pp. iv6-iv7

Author(s):

Samuel T Chao ◽

Alain Chaglassian ◽

Nancy Tainer ◽

Eugene J Teoh

Keyword(s):

Radiation Therapy ◽

Brain Metastases ◽

Diagnostic Performance ◽

Brain Mri ◽

Primary Objective ◽

Open Label ◽

Conventional Mri ◽

Specificity And Sensitivity ◽

Neurosurgical Intervention

Abstract BACKGROUND Brain metastases occur in up to 40% of patients with cancer and are associated with poor prognosis and considerable levels of recurrence. Consequently, close follow-up with serial brain MRI is performed post-treatment to monitor for recurrent disease. Although conventional MRI (CE-T1-weighted and FLAIR/T2-weighted) is the recommended follow-up modality, it has poor specificity with limited ability to differentiate between true disease recurrence and treatment-related changes such as radiation necrosis. Therefore, alternative imaging options are sought in order to help physicians confidently diagnose treatment-related changes and thus reliably stratify the risk of continuation of a therapeutic regimen, especially given the morbidity associated with current treatments. Amino acid PET imaging agent, 18F-fluciclovine, has increased uptake in brain tumors relative to normal tissue and may be useful for detecting recurrent brain metastases. METHODS NCT04410133 is a prospective, open-label, single-arm, single-dose (185 MBq ±20%) study with a primary objective to confirm the diagnostic performance of 18F-fluciclovine PET (read with conventional MRI for anatomical reference) for detection of recurrent brain metastases where MRI is equivocal. Approximately 150 subjects with solid tumor brain metastases who have undergone radiation therapy will be enrolled in this multicenter trial (~18 US sites) if they have a lesion considered equivocal on MRI that requires further confirmatory diagnostic procedures such as biopsy/neurosurgical intervention or clinical follow-up. Subjects will undergo 18F-fluciclovine PET <42 days after the equivocal MRI and 1–21 days pre-biopsy/neurosurgical intervention. Clinical follow-up will occur for 6m post-18F-fluciclovine PET. Secondary objectives include evaluation of subject- and lesion-level 18F-fluciclovine negative and positive percent agreement (equivalent to specificity and sensitivity, respectively) for recurrent brain metastases, inter-reader and intra-reader agreement, and safety evaluations. Enrolment began in October 2020 and the trial is open at the time of submission.

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Use of radiomics for the prediction of local control of brain metastases after stereotactic radiosurgery

Neuro-Oncology ◽

10.1093/neuonc/noaa007 ◽

2020 ◽

Vol 22 (6) ◽

pp. 797-805 ◽

Cited By ~ 5

Author(s):

Andrei Mouraviev ◽

Jay Detsky ◽

Arjun Sahgal ◽

Mark Ruschin ◽

Young K Lee ◽

...

Keyword(s):

Brain Metastases ◽

Stereotactic Radiosurgery ◽

Clinical Features ◽

Characteristic Curve ◽

Response Prediction ◽

Response Assessment ◽

Local Failure ◽

Post Contrast ◽

Axial Diameter ◽

Fluid Attenuated Inversion Recovery

Abstract Background Local response prediction for brain metastases (BM) after stereotactic radiosurgery (SRS) is challenging, particularly for smaller BM, as existing criteria are based solely on unidimensional measurements. This investigation sought to determine whether radiomic features provide additional value to routinely available clinical and dosimetric variables to predict local recurrence following SRS. Methods Analyzed were 408 BM in 87 patients treated with SRS. A total of 440 radiomic features were extracted from the tumor core and the peritumoral regions, using the baseline pretreatment volumetric post-contrast T1 (T1c) and volumetric T2 fluid-attenuated inversion recovery (FLAIR) MRI sequences. Local tumor progression was determined based on Response Assessment in Neuro-Oncology‒BM criteria, with a maximum axial diameter growth of >20% on the follow-up T1c indicating local failure. The top radiomic features were determined based on resampled random forest (RF) feature importance. An RF classifier was trained using each set of features and evaluated using the area under the receiver operating characteristic curve (AUC). Results The addition of any one of the top 10 radiomic features to the set of clinical features resulted in a statistically significant (P < 0.001) increase in the AUC. An optimized combination of radiomic and clinical features resulted in a 19% higher resampled AUC (mean = 0.793; 95% CI = 0.792–0.795) than clinical features alone (0.669, 0.668–0.671). Conclusions The increase in AUC of the RF classifier, after incorporating radiomic features, suggests that quantitative characterization of tumor appearance on pretreatment T1c and FLAIR adds value to known clinical and dosimetric variables for predicting local failure.

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NIMG-43. LONGITUDINAL TRACKING AND GROWTH RATE CHARACTERIZATION OF BRAIN METASTASES ON MAGNETIC RESONANCE IMAGING

Neuro-Oncology ◽

10.1093/neuonc/noz175.713 ◽

2019 ◽

Vol 21 (Supplement_6) ◽

pp. vi170-vi171

Author(s):

Jay Patel ◽

Andrew Beers ◽

Ken Chang ◽

James Brown ◽

Katharina Hoebel ◽

...

Keyword(s):

Growth Rate ◽

Brain Metastases ◽

Brain Regions ◽

Brain Atlas ◽

Post Contrast ◽

Average Growth ◽

Imaging Protocol ◽

Time Points ◽

Lesion Growth

Abstract PURPOSE Measuring treatment response is vital for assessing efficacy of treatment regimen for patients with brain metastases (BM). Unfortunately, manual delineation of all lesions on MRI across time-points is prohibitively time-consuming, making it infeasible to track individual lesion growth/shrinkage rates as part of the clinical workflow. To overcome this challenge, we propose a deep learning approach to segment all BM, and furthermore, show that certain brain regions are more prone to high-growth rate lesions. METHODS 163 longitudinal MRIs from 77 patients with MPRAGE-post contrast imaging protocol were prospectively obtained from Massachusetts General Hospital (MGH). An expert neuro-oncologist provided ground truth segmentations for all patients. A 3D U-Net architecture was trained to automatically segment BM; training was stopped when validation set Dice score plateaued to prevent overfitting. To enable lesion tracking, all time-points per patient were affinely registered to each other. Every lesion was subsequently classified based on its growth rate (responder: overall lesion shrinkage; inconclusive: 0% to 40% lesion growth; non-responder: more than 40% lesion growth). Characterization of global lesion growth rate patterns was accomplished by affinely registering all time-points to the MNI brain atlas. Segmented lesions were projected onto the atlas, which was qualitatively analyzed to identify spatial regions composed primarily of one class of lesion. RESULTS For automatic segmentation, we report a mean dice score of 0.778, 0.737, and 0.704 on training, validation, and testing sets respectively. Furthermore, we find that the largest BM with the highest average growth rate (non-responders) tend to be located in the posterior frontal/parietal lobes, while smaller, lower growth rate lesions (responders) tend to be localized in the frontal lobes. The posterior fossa was found to be heterogeneous in lesion size and growth rate. CONCLUSION We developed automatic metastatic lesion tracking over time-points and identified brain regions associated with differing growth rate lesions.

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