scholarly journals Deep Neural Network for Differentiation of Brain Tumor Tissue Displayed by Confocal Laser Endomicroscopy

2021 ◽  
Vol 11 ◽  
Author(s):  
Andreas Ziebart ◽  
Denis Stadniczuk ◽  
Veronika Roos ◽  
Miriam Ratliff ◽  
Andreas von Deimling ◽  
...  
Keyword(s):  
Brain Tumor ◽  
Tumor Tissue ◽  
Ex Vivo ◽  
Confocal Laser Endomicroscopy ◽  
Confocal Laser ◽  
Site Classification ◽  
Histopathological Analysis ◽  
Test Accuracy ◽  
Tumor Type ◽  
Resected Tumor

BackgroundReliable on site classification of resected tumor specimens remains a challenge. Implementation of high-resolution confocal laser endoscopic techniques (CLEs) during fluorescence-guided brain tumor surgery is a new tool for intraoperative tumor tissue visualization. To overcome observer dependent errors, we aimed to predict tumor type by applying a deep learning model to image data obtained by CLE.MethodsHuman brain tumor specimens from 25 patients with brain metastasis, glioblastoma, and meningioma were evaluated within this study. In addition to routine histopathological analysis, tissue samples were stained with fluorescein ex vivo and analyzed with CLE. We trained two convolutional neural networks and built a predictive level for the outputs.ResultsMultiple CLE images were obtained from each specimen with a total number of 13,972 fluorescein based images. Test accuracy of 90.9% was achieved after applying a two-class prediction for glioblastomas and brain metastases with an area under the curve (AUC) value of 0.92. For three class predictions, our model achieved a ratio of correct predicted label of 85.8% in the test set, which was confirmed with five-fold cross validation, without definition of confidence. Applying a confidence rate of 0.999 increased the prediction accuracy to 98.6% when images with substantial artifacts were excluded before the analysis. 36.3% of total images met the output criteria.ConclusionsWe trained a residual network model that allows automated, on site analysis of resected tumor specimens based on CLE image datasets. Further in vivo studies are required to assess the clinical benefit CLE can have.

scholarly journals Confocal Laser Endomicroscopy Assessment of Pituitary Tumor Microstructure: A Feasibility Study

2020 ◽  
Vol 9 (10) ◽  
pp. 3146 ◽  
Author(s):  
Evgenii Belykh ◽  
Brandon Ngo ◽  
Dara S. Farhadi ◽  
Xiaochun Zhao ◽  
Michael A. Mooney ◽  
...  
Keyword(s):  
Pituitary Adenoma ◽  
Pituitary Tumor ◽  
Tumor Tissue ◽  
Ex Vivo ◽  
In Vivo Studies ◽  
Confocal Laser Endomicroscopy ◽  
Confocal Laser ◽  
Frozen Sections ◽  
Tuberculum Sellae

This is the first study to assess confocal laser endomicroscopy (CLE) use within the transsphenoidal approach and show the feasibility of obtaining digital diagnostic biopsies of pituitary tumor tissue after intravenous fluorescein injection. We confirmed that the CLE probe reaches the tuberculum sellae through the transnasal transsphenoidal corridor in cadaveric heads. Next, we confirmed that CLE provides images with identifiable histological features of pituitary adenoma. Biopsies from nine patients who underwent pituitary adenoma surgery were imaged ex vivo at various times after fluorescein injection and were assessed by a blinded board-certified neuropathologist. With frozen sections used as the standard, pituitary adenoma was diagnosed as “definitively” for 13 and as “favoring” in 3 of 16 specimens. CLE digital biopsies were diagnostic for pituitary adenoma in 10 of 16 specimens. The reasons for nondiagnostic CLE images were biopsy acquisition <1 min or >10 min after fluorescein injection (n = 5) and blood artifacts (n = 1). In conclusion, fluorescein provided sufficient contrast for CLE at a dose of 2 mg/kg, optimally 1–10 min after injection. These results provide a basis for further in vivo studies using CLE in transsphenoidal surgery.

scholarly journals Deep Learning Algorithm for the Confirmation of Mucosal Healing in Crohn’s Disease, Based on Confocal Laser Endomicroscopy Images

2021 ◽  
Vol 30 (1) ◽  
pp. 59-65
Author(s):  
Anca Loredana Udristoiu ◽  
Daniela Stefanescu ◽  
Gabriel Gruionu ◽  
Lucian Gheorghe Gruionu ◽  
Andreea Valentina Iacob ◽  
...  
Keyword(s):  
Crohn’S Disease ◽  
Deep Learning ◽  
Crohn's Disease ◽  
Colonic Mucosa ◽  
Mucosal Healing ◽  
Machine Learning Algorithms ◽  
Confocal Laser Endomicroscopy ◽  
Confocal Laser ◽  
Normal Colonic Mucosa ◽  
Test Accuracy

Background and Aims: Mucosal healing (MH) is associated with a stable course of Crohn’s disease (CD) which can be assessed by confocal laser endomicroscopy (CLE). To minimize the operator’s errors and automate assessment of CLE images, we used a deep learning (DL) model for image analysis. We hypothesized that DL combined with convolutional neural networks (CNNs) and long short-term memory (LSTM) can distinguish between normal and inflamed colonic mucosa from CLE images. Methods: The study included 54 patients, 32 with known active CD, and 22 control patients (18 CD patients with MH and four normal mucosa patients with no history of inflammatory bowel diseases). We designed and trained a deep convolutional neural network to detect active CD using 6,205 endomicroscopy images classified as active CD inflammation (3,672 images) and control mucosal healing or no inflammation (2,533 images). CLE imaging was performed on four colorectal areas and the terminal ileum. Gold standard was represented by the histopathological evaluation. The dataset was randomly split in two distinct training and testing datasets: 80% data from each patient were used for training and the remaining 20% for testing. The training dataset consists of 2,892 images with inflammation and 2,189 control images. The testing dataset consists of 780 images with inflammation and 344 control images of the colon. We used a CNN-LSTM model with four convolution layers and one LSTM layer for automatic detection of MH and CD diagnosis from CLE images. Results: CLE investigation reveals normal colonic mucosa with round crypts and inflamed mucosa with irregular crypts and tortuous and dilated blood vessels. Our method obtained a 95.3% test accuracy with a specificity of 92.78% and a sensitivity of 94.6%, with an area under each receiver operating characteristic curves of 0.98. Conclusions: Using machine learning algorithms on CLE images can successfully differentiate between inflammation and normal ileocolonic mucosa and can be used as a computer aided diagnosis for CD. Future clinical studies with a larger patient spectrum will validate our results and improve the CNN-SSTM model.

Novel Techniques for Diagnosis of Serous Cystadenoma: Fern Pattern of Vascularity Confirmed by In- and ex-vivo Confocal Laser Endomicroscopy

2016 ◽  
Vol 111 ◽  
pp. S185
Author(s):  
Rohan M. Modi ◽  
Amrit K. Kamboj ◽  
Benjamin J. Swanson ◽  
Peter Muscarella ◽  
Darwin L. Conwell ◽  
...  
Keyword(s):  
Ex Vivo ◽  
Serous Cystadenoma ◽  
Confocal Laser Endomicroscopy ◽  
Confocal Laser

Cystic Pancreatic Neuroendocrine Tumors: Validation of In Vivo Needle-Based Confocal Laser Endomicroscopy Findings by Ex Vivo Analysis

2016 ◽  
Vol 111 ◽  
pp. S739-S740
Author(s):  
Amrit K. Kamboj ◽  
Rohan M. Modi ◽  
Benjamin J. Swanson ◽  
Mary E. Dillhoff ◽  
Darwin L. Conwell ◽  
...  
Keyword(s):  
Neuroendocrine Tumors ◽  
Ex Vivo ◽  
Confocal Laser Endomicroscopy ◽  
Confocal Laser ◽  
Pancreatic Neuroendocrine Tumors

scholarly journals A comprehensive examination of the novel techniques used for in vivo and ex vivo confocal laser endomicroscopy of pancreatic cystic lesions

VideoGIE ◽  
2016 ◽  
Vol 1 (1) ◽  
pp. 6-7 ◽  
Author(s):  
Amrit K. Kamboj ◽  
Rohan M. Modi ◽  
Benjamin Swanson ◽  
Darwin L. Conwell ◽  
Somashekar G. Krishna
Keyword(s):  
Ex Vivo ◽  
Confocal Laser Endomicroscopy ◽  
Confocal Laser ◽  
Cystic Lesions ◽  
The Novel ◽  
Comprehensive Examination

scholarly journals Prospective evaluation of the utility of intraoperative confocal laser endomicroscopy in patients with brain neoplasms using fluorescein sodium: experience with 74 cases

2016 ◽  
Vol 40 (3) ◽  
pp. E11 ◽  
Author(s):  
Nikolay L. Martirosyan ◽  
Jennifer M. Eschbacher ◽  
M. Yashar S. Kalani ◽  
Jay D. Turner ◽  
Evgenii Belykh ◽  
...  
Keyword(s):  
Brain Tumors ◽  
Ex Vivo ◽  
Confocal Laser Endomicroscopy ◽  
Confocal Laser ◽  
Fluorescein Sodium ◽  
Frozen Sections ◽  
Diagnostic Image ◽  
Tissue Samples ◽  
Specificity And Sensitivity

OBJECTIVE This study evaluated the utility, specificity, and sensitivity of intraoperative confocal laser endomicroscopy (CLE) to provide diagnostic information during resection of human brain tumors. METHODS CLE imaging was used in the resection of intracranial neoplasms in 74 consecutive patients (31 male; mean age 47.5 years; sequential 10-month study period). Intraoperative in vivo and ex vivo CLE was performed after intravenous injection of fluorescein sodium (FNa). Tissue samples from CLE imaging–matched areas were acquired for comparison with routine histological analysis (frozen and permanent sections). CLE images were classified as diagnostic or nondiagnostic. The specificities and sensitivities of CLE and frozen sections for gliomas and meningiomas were calculated using permanent histological sections as the standard. RESULTS CLE images were obtained for each patient. The mean duration of intraoperative CLE system use was 15.7 minutes (range 3–73 minutes). A total of 20,734 CLE images were correlated with 267 biopsy specimens (mean number of images/biopsy location, in vivo 84, ex vivo 70). CLE images were diagnostic for 45.98% in vivo and 52.97% ex vivo specimens. After initiation of CLE, an average of 14 in vivo images and 7 ex vivo images were acquired before identification of a first diagnostic image. CLE specificity and sensitivity were, respectively, 94% and 91% for gliomas and 93% and 97% for meningiomas. CONCLUSIONS CLE with FNa provided intraoperative histological information during brain tumor removal. Specificities and sensitivities of CLE for gliomas and meningiomas were comparable to those for frozen sections. These data suggest that CLE could allow the interactive identification of tumor areas, substantially improving intraoperative decisions during the resection of brain tumors.

Diagnostic accuracy of confocal laser endomicroscopy for the ex vivo characterization of peritoneal nodules during laparoscopic surgery

2016 ◽  
Vol 31 (4) ◽  
pp. 1974-1981 ◽  
Author(s):  
Angelo Pierangelo ◽  
David Fuks ◽  
Abdelali Benali ◽  
Pierre Validire ◽  
Brice Gayet
Keyword(s):  
Laparoscopic Surgery ◽  
Diagnostic Accuracy ◽  
Ex Vivo ◽  
Confocal Laser Endomicroscopy ◽  
Confocal Laser

scholarly journals Mo1536 Detection of Neoplasia in Pancreatic Cysts Using Probe-Based Confocal Laser Endomicroscopy: A Prospective Ex-Vivo Study

2011 ◽  
Vol 73 (4) ◽  
pp. AB378-AB379 ◽  
Author(s):  
Muhammad W. Shahid ◽  
Murli Krishna ◽  
Horacio J. Asbun ◽  
Massimo Raimondo ◽  
Timothy A. Woodward ◽  
...  
Keyword(s):  
Ex Vivo ◽  
Confocal Laser Endomicroscopy ◽  
Confocal Laser ◽  
Pancreatic Cysts ◽  
Ex Vivo Study

VHM12 Confocal Laser Endomicroscopy (CLE): Evaluation of Pancreatic Cystic Lesions In Vivo and Ex Vivo

2011 ◽  
Vol 73 (4) ◽  
pp. AB111 ◽  
Author(s):  
Victoria Gomez ◽  
Muhammad Waseem Shahid ◽  
Murli Krishna ◽  
Horacio J. Asbun ◽  
Michael B. Wallace
Keyword(s):  
Ex Vivo ◽  
Confocal Laser Endomicroscopy ◽  
Confocal Laser ◽  
Cystic Lesions

scholarly journals OMIC-04. IDENTIFICATION AND CHARACTERIZATION OF CIRCULATING RNAS (CODING AND NONCODING) AND METABOLITES IN CEREBROSPINAL FLUID IN MEDULLOBLASTOMA PATIENTS

2021 ◽  
Vol 23 (Supplement_1) ◽  
pp. i37-i37
Author(s):  
Bongyong Lee ◽  
Stacie Stapleton ◽  
Rudramani Pokhrel ◽  
Chetan Bettegowda ◽  
George Jallo ◽  
...  
Keyword(s):  
Cerebrospinal Fluid ◽  
Brain Tumor ◽  
Metabolic Pathways ◽  
Tumor Tissue ◽  
Tumor Type ◽  
Molecular Signatures ◽  
Rna Seq ◽  
Neuro Imaging ◽  
Identification And Characterization

Abstract Medulloblastoma (MB) is the most common malignant brain tumor in children, and monitoring patients for treatment response and recurrence can be challenging with available current technologies in neuro-imaging and performing a biopsy to confirm response or recurrence carries risks, whereas cerebrospinal fluid (CSF) can be obtained with a little invasiveness. MB has altered cellular metabolism due to changes in gene expression, therefore, we hypothesized that any changes in MB cells lead to changes in cell-free transcripts and metabolites in CSF. To test this, we applied RNA-sequencing and mass spectrometry to analyze transcripts and metabolites including lipid in CSF from patients with different sub-groups of MB tumors (i.e., WNT, SHH, G3/4, G4, and unknown) and compared them to non-cancerous CSF. Tumor and sub-group specific transcriptomic and metabolic signatures were shown by unsupervised hierarchical clustering facilitating tumor type differentiation. By comparison with previously published tumor tissue RNA-seq data, we were able to identify a group of upregulated molecular signatures in both tumor tissue and CSF. We also identified a group of lipids that differentiate each MB sub-group from normal CSF, and Pathway analysis confirmed alterations in multiple metabolic pathways. Finally, we attempted to integrate RNA-seq data with lipidomics data, and results depict that the combinatorial analysis of CSF RNAs and metabolites can be useful in diagnosing and monitoring patients with MB tumors. (This research was conducted using samples made available by The Children’s Brain Tumor Network.)

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