Spectral complexity of 5-ALA induced PpIX fluorescence in guided surgery: a clinical study towards the discrimination of healthy tissue and margin boundaries in high and low grade gliomas

Abstract INTRODUCTION Fluorescence-guided surgery can improve tumor identification and extent of surgical resection. 5-ALA is the standard for GBM, but is limited by lack of quantitative fluorescence, a need to work in the dark, and a lack of sensitivity for low grade tumors. We have developed a novel instrument for dye-free tissue autofluorescence lifetime imaging (FLIm) to identify glioma tissue during resection. This approach utilizes time-resolved autofluorescence measurements in narrow-band channels to assess markers of tissue metabolism. Compared to intensity-based imaging of exogenous fluorophores, FLIm has greater sensitivity without dependence on background lighting. The advantages of FLIm include quantitative tissue analysis, the ability to work under full light conditions, sensitivity for high and low grade gliomas, and the potential ability to identify IDH mutational status. In this study, we validated the use of FLIm for identification of glioma tissue at tumor resection margins. METHODS FLIm was used to image tissue margins during glioma resections and compared to microbiopsies from imaged regions to correlate fluorescence with histopathology. RESULTS FLIm was applied intraoperatively to 11 GBM and 5 LGG patients (7 imaged biopsies per patient). In GBM, tumor infiltration of cortex was associated with significantly decreased fluorescence lifetime (FL) in channels 2 (470/28nm;p<0.05) and 3 (542/50nm;p<0.002). In subcortical margins, FL was inversely proportional to the density of tumor in channels 2,3 (p<0.05). When IDH wild-type GBMs were compared to IDH1-mutant tumors, FL was noted to be significantly longer in channel 1 (390/40nm;p<0.05), and trended towards longer FL in channel 2, shorter FL in channel 3. In LGG, FL was significantly correlated with tumor density in channel 2 (p<0.01). CONCLUSIONS FLIm is a dye-free, quantitative alternative to 5-ALA for fluorescence guided glioma resections with sensitivity to high and low-grade tumors, and the ability to predict IDH mutations in GBM. Further validation studies are on-going.

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Faculty Opinions recommendation of Low-grade gliomas in adults.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.13679956.15104064 ◽

2012 ◽

Author(s):

Zvi Ram ◽

Rachel Grossman

Keyword(s):

Low Grade ◽

Low Grade Gliomas

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Faculty Opinions recommendation of Decreased quality of life and depression as predictors for shorter survival among patients with low-grade gliomas: a follow-up from 1990 to 2003.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.718482634.793496837 ◽

2014 ◽

Author(s):

Rakesh Jalali

Keyword(s):

Quality Of Life ◽

Low Grade ◽

Low Grade Gliomas

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Domain Mapping and Deep Learning from Multiple MRI Clinical Datasets for Prediction of Molecular Subtypes in Low Grade Gliomas

Brain Sciences ◽

10.3390/brainsci10070463 ◽

2020 ◽

Vol 10 (7) ◽

pp. 463 ◽

Cited By ~ 1

Author(s):

Muhaddisa Barat Ali ◽

Irene Yu-Hua Gu ◽

Mitchel S. Berger ◽

Johan Pallud ◽

Derek Southwell ◽

...

Keyword(s):

Molecular Subtypes ◽

Negative Impact ◽

Molecular Classification ◽

Tumor Segmentation ◽

Test Accuracy ◽

Low Grade ◽

Idh Mutation ◽

Molecular Type ◽

Low Grade Gliomas ◽

Domain Mapping

Brain tumors, such as low grade gliomas (LGG), are molecularly classified which require the surgical collection of tissue samples. The pre-surgical or non-operative identification of LGG molecular type could improve patient counseling and treatment decisions. However, radiographic approaches to LGG molecular classification are currently lacking, as clinicians are unable to reliably predict LGG molecular type using magnetic resonance imaging (MRI) studies. Machine learning approaches may improve the prediction of LGG molecular classification through MRI, however, the development of these techniques requires large annotated data sets. Merging clinical data from different hospitals to increase case numbers is needed, but the use of different scanners and settings can affect the results and simply combining them into a large dataset often have a significant negative impact on performance. This calls for efficient domain adaption methods. Despite some previous studies on domain adaptations, mapping MR images from different datasets to a common domain without affecting subtitle molecular-biomarker information has not been reported yet. In this paper, we propose an effective domain adaptation method based on Cycle Generative Adversarial Network (CycleGAN). The dataset is further enlarged by augmenting more MRIs using another GAN approach. Further, to tackle the issue of brain tumor segmentation that requires time and anatomical expertise to put exact boundary around the tumor, we have used a tight bounding box as a strategy. Finally, an efficient deep feature learning method, multi-stream convolutional autoencoder (CAE) and feature fusion, is proposed for the prediction of molecular subtypes (1p/19q-codeletion and IDH mutation). The experiments were conducted on a total of 161 patients consisting of FLAIR and T1 weighted with contrast enhanced (T1ce) MRIs from two different institutions in the USA and France. The proposed scheme is shown to achieve the test accuracy of 74 . 81 % on 1p/19q codeletion and 81 . 19 % on IDH mutation, with marked improvement over the results obtained without domain mapping. This approach is also shown to have comparable performance to several state-of-the-art methods.

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Neurodevelopmental outcomes of children with low-grade gliomas

Developmental Disabilities Research Reviews ◽

10.1002/ddrr.27 ◽

2008 ◽

Vol 14 (3) ◽

pp. 196-202 ◽

Cited By ~ 23

Author(s):

M. Douglas Ris ◽

Dean W. Beebe

Keyword(s):

Low Grade ◽

Neurodevelopmental Outcomes ◽

Low Grade Gliomas

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LGG-18. EVEROLIMUS TREATMENT IN PEDIATRIC PATIENTS AFFECTED BY LOW-GRADE GLIOMAS (pLGG) NON-TSC, BRAF v600-WT

Neuro-Oncology ◽

10.1093/neuonc/noaa222.400 ◽

2020 ◽

Vol 22 (Supplement_3) ◽

pp. iii369-iii369

Author(s):

Antonella Cacchione ◽

Evelina Miele ◽

Maria Chiara Lodi ◽

Andrea Carai ◽

Giovanna Stefania Colafati ◽

...

Keyword(s):

Adverse Event ◽

Disease Progression ◽

Mapk Pathway ◽

Brain Mri ◽

Mammalian Target Of Rapamycin ◽

Low Grade ◽

Duration Of Treatment ◽

Tumor Biopsy ◽

Low Grade Gliomas ◽

Personalized Approach

Abstract BACKGROUND MAPK pathway is the hallmark of pediatric low grade gliomas (pLGGs); hyperactivation of mTOR (mammalian target of rapamycin) might be a suitable biomarker for therapeutic response. We investigated the feasibility of Everolimus, mTOR inhibitor, in patients affected by pLGGs. METHODS Patients 1 to 18 years old, diagnosed with pLGG, with a positive tumor biopsy for mTOR/phospho-mTOR and radiological and / or clinical disease progression, treated at Bambino Gesù Children’s Hospital in Rome were evaluated. Tumor DNA methylation analysis was performed in 10 cases. Exclusion criteria included: Tuberous Sclerosis patients, Sub Ependymal Giant Astrocytoma. Everolimus was administered orally at a dose of 2.5 mg or 5 mg daily based on body weight. Patients were evaluated with brain MRI every 4, 8 and 12 months after treatment start and every six months thereafter. RESULTS 16 patients were enrolled from September 2014 and 2019. The median age was 7.5 years old. All patients had at least one adverse event. Events rated as severe (grade 3/4) were reported in 6 patients. Stomatitis was the most frequent adverse event. One patient discontinued treatment due to grade 4 toxicity (ulcerative stomatitis and fatigue). The median duration of treatment was 21 months (4–57 months). Brain MRI evaluations have showed disease stability in 11 patients, partial response in 2 patients and disease progression in 3 patients. CONCLUSIONS Everolimus has proven to be well tolerated and effective treatment in terms of disease stability in patients with pLGGs. It’s also an excellent example of chemo-free personalized approach.

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Hemispheric low-grade gliomas

Child s Nervous System ◽

10.1007/s00381-016-3192-1 ◽

2016 ◽

Vol 32 (10) ◽

pp. 1787-1787

Author(s):

Gianpiero Tamburrini ◽

Jose Hinojosa

Keyword(s):

Low Grade ◽

Low Grade Gliomas

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