A Spectral Library and Method for Sparse Unmixing of Hyperspectral Images in Fluorescence Guided Resection of Brain Tumors

Through spectral unmixing, hyperspectral imaging (HSI) in fluorescence-guided brain tumor surgery has enabled detection and classification of tumor regions invisible to the human eye. Prior unmixing work has focused on determining a minimal set of viable fluorophore spectra known to be present in the brain and effectively reconstructing human data without overfitting. With these endmembers, non-negative least squares regression (NNLS) was used to compute the abundances. However, HSI images are heterogeneous, so one small set of endmember spectra may not fit all pixels well. Additionally, NNLS is the maximum likelihood estimator only if the measurement is normally distributed, and it does not enforce sparsity, which leads to overfitting and unphysical results. Here, we analyzed 555666 HSI fluorescence spectra from 891 ex vivo measurements of patients with brain tumors to show that a Poisson distribution models the measured data 82% better than a Gaussian in terms of the Kullback-Leibler divergence and that the endmember abundance vectors are sparse. With this knowledge, we introduce (1) a library of 9 endmember spectra, (2) a sparse, non-negative Poisson regression algorithm to perform physics-informed unmixing with this library without overfitting, and (3) a highly realistic spectral measurement simulation with known endmember abundances. The new unmixing method was then tested on the human and simulated data and compared to four other candidate methods. It outperforms previous methods with 25% lower error in the computed abundances on the simulated data than NNLS, lower reconstruction error on human data, beUer sparsity, and 31 times faster runtime than state-of-the-art Poisson regression. This method and library of endmember spectra can enable more accurate spectral unmixing to beUer aid the surgeon during brain tumor resection.Comment: 17 pages, 4 tables, 6 figures; Under revie

Towards Machine Learning-based Quantitative Hyperspectral Image Guidance for Brain Tumor Resection

Complete resection of malignant gliomas is hampered by the difficulty in distinguishing tumor cells at the infiltration zone. Fluorescence guidance with 5-ALA assists in reaching this goal. Using hyperspectral imaging, previous work characterized five fluorophores' emission spectra in most human brain tumors. In this paper, the effectiveness of these five spectra was explored for different tumor and tissue classification tasks in 184 patients (891 hyperspectral measurements) harboring low- (n=30) and high-grade gliomas (n=115), non-glial primary brain tumors (n=19), radiation necrosis (n=2), miscellaneous (n=10) and metastases (n=8). Four machine learning models were trained to classify tumor type, grade, glioma margins and IDH mutation. Using random forests and multi-layer perceptrons, the classifiers achieved average test accuracies of 84-87%, 96%, 86%, and 93% respectively. All five fluorophore abundances varied between tumor margin types and tumor grades (p < 0.01). For tissue type, at least four of the five fluorophore abundances were found to be significantly different (p < 0.01) between all classes. These results demonstrate the fluorophores' differing abundances in different tissue classes, as well as the value of the five fluorophores as potential optical biomarkers, opening new opportunities for intraoperative classification systems in fluorescence-guided neurosurgery.Comment: 22 pages, 8 figure

Fluorescence-Guided Resection of Malignant Glioma with 5-ALA

Malignant gliomas are extremely difficult to treat with no specific curative treatment. On the other hand, photodynamic medicine represents a promising technique for neurosurgeons in the treatment of malignant glioma. The resection rate of malignant glioma has increased from 40% to 80% owing to 5-aminolevulinic acid-photodynamic diagnosis (ALA-PDD). Furthermore, ALA is very useful because it has no serious complications. Based on previous research, it is apparent that protoporphyrin IX (PpIX) accumulates abundantly in malignant glioma tissues after ALA administration. Moreover, it is evident that the mechanism underlying PpIX accumulation in malignant glioma tissues involves an abnormality in porphyrin-heme metabolism, specifically decreased ferrochelatase enzyme activity. During resection surgery, the macroscopic fluorescence of PpIX to the naked eye is more sensitive than magnetic resonance imaging, and the alert real time spectrum of PpIX is the most sensitive method. In the future, chemotherapy with new anticancer agents, immunotherapy, and new methods of radiotherapy and gene therapy will be developed; however, ALA will play a key role in malignant glioma treatment before the development of these new treatments. In this paper, we provide an overview and present the results of our clinical research on ALA-PDD

Ceacam1L as a new GIC regulator

Glioblastoma-initiating cells (GIC) are a tumorigenic cell subpopulation resistant to radiotherapy and chemotherapy, and are a likely source of recurrence. However, the basis through which GICs are maintained has yet to be elucidated in detail. We herein demonstrated that the carcinoembryonic antigen-related cell adhesion molecule Ceacam1L acts as a crucial factor in GIC maintenance and tumorigenesis by activating c-Src/STAT3 signaling. Furthermore, we showed that monomers of the cytoplasmic domain of Ceacam1L bound to c-Src and STAT3 and induced their phosphorylation, whereas oligomerization of this domain ablated this function. Our results suggest that Ceacam1L-dependent adhesion between GIC and surrounding cells play an essential role in GIC maintenance and proliferation, as mediated by signals transmitted by monomeric forms of the Ceacam1L cytoplasmic domain

Novel risk factors and management of brain sag after brain tumor surgery

Objective: Lumbar drains (LD) have been identified to cause brain sag (BS), which is a serious intracranial hypotension that causes coma; however, other contributing risk factors should also be considered. Therefore, this study aims to identify the BS risk factors and determine the proper treatment plan after brain tumor surgery for patients with BS. Methods: This retrospective study included patients who underwent brain tumor surgery between 2011 and 2015. BS was diagnosed based on the clinical signs of cerebrospinal fluid hypovolemia along with the radiological findings of the basal cistern effacement. Based on the data on the clinical features, the symptoms were used to determine the BS risk factors using logistic regression. Results: Overall, among the 412 patients included in this study, 12 (2.91%) were found to develop BS, 10 experienced altered consciousness, and 2 experienced orthostatic headaches. In all cases, radiological images at onset showed epidural/subdural fluid collection, along with basal cistern effacement. LD was inserted just before surgery in 11 cases, while a ventriculoperitoneal shunt was placed in one patient. The treatment strategies used are as follows: Ringer's solution infusion (n = 4), Trendelenburg position (n = 2), intrathecal injection of saline (n = 1), or no care due to misdiagnosis (n = 5). Moreover, the significant BS risk factors were determined to be the LD (odds ratio [OR] 30.70, CI 2.77-339.86), supratentorial skull base approach (SBA) (OR 35.71, CI 9.1922-138.7250), surgical time (1693.07, CI 69.5621-41207.448), and postoperative hyperosmotic diuretic use (OR 8.092, CI 2.485-26.345) after surgery. Conclusions: In the brain tumor surgery, aside from the LD, the application of supratentorial SBA, use of HD, and lengthy surgery are also found to be contributing risk factors for BS. Therefore, BS should be considered in the differential diagnosis of high-risk patients with postoperative altered consciousness

Towards machine learning-based quantitative hyperspectral image guidance for brain tumor resection

Abstract Background Complete resection of malignant gliomas is hampered by the difficulty in distinguishing tumor cells at the infiltration zone. Fluorescence guidance with 5-ALA assists in reaching this goal. Using hyperspectral imaging, previous work characterized five fluorophores’ emission spectra in most human brain tumors. Methods In this paper, the effectiveness of these five spectra was explored for different tumor and tissue classification tasks in 184 patients (891 hyperspectral measurements) harboring low- (n = 30) and high-grade gliomas (n = 115), non-glial primary brain tumors (n = 19), radiation necrosis (n = 2), miscellaneous (n = 10) and metastases (n = 8). Four machine-learning models were trained to classify tumor type, grade, glioma margins, and IDH mutation. Results Using random forests and multilayer perceptrons, the classifiers achieve average test accuracies of 84–87%, 96.1%, 86%, and 91% respectively. All five fluorophore abundances vary between tumor margin types and tumor grades (p < 0.01). For tissue type, at least four of the five fluorophore abundances are significantly different (p < 0.01) between all classes. Conclusions These results demonstrate the fluorophores’ differing abundances in different tissue classes and the value of the five fluorophores as potential optical biomarkers, opening new opportunities for intraoperative classification systems in fluorescence-guided neurosurgery