Label-free imaging of human brain tissue at subcellular resolution for potential rapid intra-operative assessment of glioma surgery

Chen, Defu; Nauen, David W.; Park, Hyeon Cheol; Li, Dawei; Yuan, Weihao; Li, Ang; Guan, Honghua; Kut, Carmen; Chaichana, Kaisorn L.; Bettegowda, Chetan; Quiñones‐Hinojosa, Alfredo; Li, Xingde

doi:10.7150/thno.59244

Cited by 20 publications

(11 citation statements)

References 41 publications

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“…In the study that employed multiphoton microscopy with deep learning techniques for the histopathological assessment of gliomas, control tissue was obtained from brain autopsies on individuals without neurologic diseases. [ 36 ] While this approach avoids the presence of tumor infiltration in control tissue, it does not provide a realistic representation of the reactive normal tissue in brain tumor cases. In contrast, the use of adjacent brain tissue from nondiffuse brain tumor samples as control tissue in our study avoids the risk of unwanted infiltration, but also addresses the challenge of reactive gliosis.…”

Section: Discussionmentioning

confidence: 99%

Detecting Tumor Infiltration in Diffuse Gliomas with Deep Learning

Prathaban,

Wu,

Tan

et al. 2023

Advanced Intelligent Systems

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Glioblastoma tumor recurrences often occur in brain tissue areas harboring infiltrating tumor cells, resembling healthy tissue in brain imaging. Demarcating infiltrative regions for aggressive resections is critical for improving prognostic outcomes but is challenging in neurosurgery. Herein, a multilayer sigmoid‐activated convolutional neural network (MLS‐CNN) is developed for rapidly distinguishing glioma tumor infiltration in brain tissue histology. Unlike conventional multiclass classifiers, the MLS‐CNN employs sigmoidal activation to accommodate coexisting classes within patch images. 59 811 image patches (25 807 infiltrating edge, 15 178 normal brain, 18 826 cellular tumor) from 73 brain tissue samples are extracted to train the classifier. The model achieves an accuracy of 91.70% (sensitivity: 91.62%; specificity: 91.78%) and area under the curve (AUC) of 0.964 in distinguishing infiltrating edges, outperforming the current state‐of‐the‐art Vision Transformer (ViT) (accuracy: 89.45; AUC: 0.947). The MLS‐CNN is computationally efficient, generating predictions within 11.5 s in comparison to 81.4 s for ViT. The predictions strongly correlate with In Situ Hybridization expression intensities, validating the utility of the MLS‐CNN model in spatial genomics investigations in gliomas. The robust model can therefore serve as an automatic and accurate classifier to help pathologists identify infiltrative glioma for better diagnosis and patient outcomes in brain oncology.

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Section: Discussionmentioning

confidence: 99%

Detecting Tumor Infiltration in Diffuse Gliomas with Deep Learning

Prathaban,

Wu,

Tan

et al. 2023

Advanced Intelligent Systems

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“…Human induced pluripotent stem cells were grown on Matrigel matrix coated six well plates at 10 µg cm −2 growth area in E8 medium in a humidified incubator at 37 °C with 5% CO 2 . [ 26 ] To label the cells with red fluorescent protein a PiggyBac transposon expressing mCherry under the control of the human phosphoglycerate kinase promoter was stably introduced.…”

Section: Methodsmentioning

confidence: 99%

Poly(acrylamide) Spheroids with Tunable Elasticity for Scalable Cell Culture Applications

Pieroth

Heras-Bautista

Hamad

et al. 2022

Macro Chemistry & Physics

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Many biological tissues resemble hydrogels and display Young's moduli below 50 kPa, corresponding to most cell types for the natural environmental conditions to grow. Contrastingly, conventional cell culture usually involves rigid substrates resulting in stiff priming effects, which are of increasing concern when it comes to scalable culturing of adhesive cells for regenerative purposes. As a solution to this problem, the employment of synthetic poly(acryl amide) (PAAm)-based hydrogel beads with tissue-matched mechanical properties are proposed as soft matrix for culture modes suitable for tidal bioreactor culture. Herein, technology is described to generate spherical, mm-scaled PAAm hydrogel beads with adjustable, soft elastic properties that are produced by a continuous microfluidic approach. A simple and robust method is demonstrated to functionalize the spheroids with protein ligands, and the suitability of the matrix for cell cultivation is successfully demonstrated with three different cell types (murine mesenchymal stem cells, renal carcinoma cells, and human induced pluripotent stem cells) in model experiments and in a tidal bioreactor system. This versatile approach will pave the way toward novel cell culture systems based on bioreactors that allow scalable, soft carrier-based expansion of cells on matrices with tissue-matched elasticity.

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“…25 Ultimately, MPM represents an imaging modality that can acquire volumetric images at a sufficiently high resolution to enable the accurate assessment of the molecular features comprising a tissue specimen. 26 Developing models of DGAST multiphoton fluorescence is an initial step towards in vivo labelfree measurements of these lesions. This would bypass the need for fluorescent dyes that pose the risk of cytotoxicity while allowing for the screening, diagnosis, and staging of DGASTs.…”

Section: Autofluorescent Multiphoton Microscopy (Mpm)mentioning

confidence: 99%

“…This reduces the chance of inadvertent tissue damage or photobleaching during image acquisition and prevents fluorescence emission outside of the focal plane which could degrade image quality 25 . Ultimately, MPM represents an imaging modality that can acquire volumetric images at a sufficiently high resolution to enable the accurate assessment of the molecular features comprising a tissue specimen 26 . Developing models of DGAST multiphoton fluorescence is an initial step towards in vivo label‐free measurements of these lesions.…”

Section: Introductionmentioning

confidence: 99%

Quantitative characterization of duodenal gastrinoma autofluorescence using multiphoton microscopy

et al. 2022

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Background: Duodenal gastrinomas (DGASTs) are neuroendocrine tumors that develop in the submucosa of the duodenum and produce the hormone gastrin. Surgical resection of DGASTs is complicated by the small size of these tumors and the tendency for them to develop diffusely in the duodenum. Endoscopic mucosal resection of DGASTs is an increasingly popular method for treating this disease due to its low complication rate but suffers from poor rates of pathologically negative margins. Multiphoton microscopy can capture high-resolution images of biological tissue with contrast generated from endogenous fluorescence (autofluorescence [AF]) through two-photon excited fluorescence (2PEF). Second harmonic generation is another popular method of generating image contrast with multiphoton microscopy (MPM) and is a lightscattering phenomenon that occurs predominantly from structures such as collagen in biological samples. Some molecules that contribute to AF change in abundance from processes related to the cancer disease process (e.g., metabolic changes, oxidative stress, and angiogenesis). Study Design/Materials and Methods: MPM was used to image 12 separate patient samples of formalin-fixed and paraffin-embedded duodenal gastrinoma slides with a second-harmonic generation (SHG) channel and four 2PEF channels. The excitation and emission profiles of each 2PEF channel were tuned to capture signal dominated by distinct fluorophores with well-characterized fluorescent spectra and known connections to the physiologic changes that arise in cancerous tissue. Results: We found that there was a significant difference in the relative abundance of signal generated in the 2PEF channels for regions of DGASTs in comparison to the neighboring tissues of the duodenum. Data generated from texture feature extraction of the MPM images were used in linear discriminant analysis models to create classifiers for tumor versus all other tissue types before and after principal component analysis (PCA). PCA improved the classifier accuracy and reduced the number of features required to achieve maximum accuracy. The linear discriminant classifier after PCA distinguished between tumor and other tissue types with an accuracy of 90.6%−93.8%. Conclusions: These results suggest that multiphoton microscopy 2PEF and SHG imaging is a promising label-free method for discriminating between DGASTs and normal duodenal tissue which has implications for future applications of in vivo assessment of resection margins with endoscopic MPM.

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Label-free imaging of human brain tissue at subcellular resolution for potential rapid intra-operative assessment of glioma surgery

Cited by 20 publications

References 41 publications

Detecting Tumor Infiltration in Diffuse Gliomas with Deep Learning

Detecting Tumor Infiltration in Diffuse Gliomas with Deep Learning

Poly(acrylamide) Spheroids with Tunable Elasticity for Scalable Cell Culture Applications

Quantitative characterization of duodenal gastrinoma autofluorescence using multiphoton microscopy

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