AI-Assisted <i>In Situ</i> Detection of Human Glioma Infiltration Using a Novel Computational Method for Optical Coherence Tomography

Juarez-Chambi, Ronald M.; Kut, Carmen; Rico-Jimenez, Jose J.; Chaichana, Kaisorn L.; Xi, Jiefeng; Campos-Delgado, Daniel U.; Rodríguez, Fausto J.; Quiñones‐Hinojosa, Alfredo; Li, Xingde; Jo, Javier A.

doi:10.1158/1078-0432.ccr-19-0854

Cited by 33 publications

(34 citation statements)

References 48 publications

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“…In addition, our research team is investigating the possibilities of a cross-polarization OCT method in determining the morphology and boundaries of brain tumors 4 . These studies illustrate the capabilities of optical methods (including OCT-based ones) for producing high-resolution visualization of tissue structure in places where the use of biopsy studies is completely impossible 59,60 .…”

Section: Discussionmentioning

confidence: 89%

Histological validation of in vivo assessment of cancer tissue inhomogeneity and automated morphological segmentation enabled by Optical Coherence Elastography

Plekhanov

Sirotkina

Sovetsky

et al. 2020

Sci Rep

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We present a non-invasive (albeit contact) method based on optical coherence elastography (oce) enabling the in vivo segmentation of morphological tissue constituents, in particular, monitoring of morphological alterations during both tumor development and its response to therapies. the method uses compressional OCE to reconstruct tissue stiffness map as the first step. Then the OCE-image is divided into regions, for which the Young's modulus (stiffness) falls in specific ranges corresponding to the morphological constituents to be discriminated. These stiffness ranges (characteristic "stiffness spectra") are initially determined by careful comparison of the "gold-standard" histological data and the OCE-based stiffness map for the corresponding tissue regions. After such pre-calibration, the results of morphological segmentation of oce-images demonstrate a striking similarity with the histological results in terms of percentage of the segmented zones. to validate the sensitivity of the oce-method and demonstrate its high correlation with conventional histological segmentation we present results obtained in vivo on a murine model of breast cancer in comparative experimental study of the efficacy of two antitumor chemotherapeutic drugs with different mechanisms of action. The new technique allowed in vivo monitoring and quantitative segmentation of (1) viable, (2) dystrophic, (3) necrotic tumor cells and (4) edema zones very similar to morphological segmentation of histological images. numerous applications in other experimental/clinical areas requiring rapid, nearly real-time, quantitative assessment of tissue structure can be foreseen.

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Section: Discussionmentioning

confidence: 89%

Histological validation of in vivo assessment of cancer tissue inhomogeneity and automated morphological segmentation enabled by Optical Coherence Elastography

Plekhanov

Sirotkina

Sovetsky

et al. 2020

Sci Rep

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“…It is observed that the least experienced clinicians gain the most from AI-based support. Chambi et al ( 2019 ) worked on the volumetric Optical coherence tomography datasets acquired from resected cerebrum tissue example of 21 patients with glioma tumours of various stages. They were marked as either non-destructive or limo-invaded based on histopathology assessment of the tissue examples.…”

Section: Reported Workmentioning

confidence: 99%

Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda

Kumar

Koul

Singla³

et al. 2022

J Ambient Intell Human Comput

273

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Artificial intelligence can assist providers in a variety of patient care and intelligent health systems. Artificial intelligence techniques ranging from machine learning to deep learning are prevalent in healthcare for disease diagnosis, drug discovery, and patient risk identification. Numerous medical data sources are required to perfectly diagnose diseases using artificial intelligence techniques, such as ultrasound, magnetic resonance imaging, mammography, genomics, computed tomography scan, etc. Furthermore, artificial intelligence primarily enhanced the infirmary experience and sped up preparing patients to continue their rehabilitation at home. This article covers the comprehensive survey based on artificial intelligence techniques to diagnose numerous diseases such as Alzheimer, cancer, diabetes, chronic heart disease, tuberculosis, stroke and cerebrovascular, hypertension, skin, and liver disease. We conducted an extensive survey including the used medical imaging dataset and their feature extraction and classification process for predictions. Preferred reporting items for systematic reviews and Meta-Analysis guidelines are used to select the articles published up to October 2020 on the Web of Science, Scopus, Google Scholar, PubMed, Excerpta Medical Database, and Psychology Information for early prediction of distinct kinds of diseases using artificial intelligence-based techniques. Based on the study of different articles on disease diagnosis, the results are also compared using various quality parameters such as prediction rate, accuracy, sensitivity, specificity, the area under curve precision, recall, and F1-score.

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“…OCT imaging has some benefits in comparison with other intraoperative technologies in neurosurgery: high resolution, high speed, low cost, no need to use contrast agents, non-invasiveness and convenient performance. Currently, OCT is available for intraoperative use in neurosurgery as a special microscope module [ 20 , 21 ], or using various optical probes [ 22 , 23 ].…”

Section: Introductionmentioning

confidence: 99%

OCT-Guided Surgery for Gliomas: Current Concept and Future Perspectives

et al. 2022

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Optical coherence tomography (OCT) has been recently suggested as a promising method to obtain in vivo and real-time high-resolution images of tissue structure in brain tumor surgery. This review focuses on the basics of OCT imaging, types of OCT images and currently suggested OCT scanner devices and the results of their application in neurosurgery. OCT can assist in achieving intraoperative precision identification of tumor infiltration within surrounding brain parenchyma by using qualitative or quantitative OCT image analysis of scanned tissue. OCT is able to identify tumorous tissue and blood vessels detection during stereotactic biopsy procedures. The combination of OCT with traditional imaging such as MRI, ultrasound and 5-ALA fluorescence has the potential to increase the safety and accuracy of the resection. OCT can improve the extent of resection by offering the direct visualization of tumor with cellular resolution when using microscopic OCT contact probes. The theranostic implementation of OCT as a part of intelligent optical diagnosis and automated lesion localization and ablation could achieve high precision, automation and intelligence in brain tumor surgery. We present this review for the increase of knowledge and formation of critical opinion in the field of OCT implementation in brain tumor surgery.

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AI-Assisted In Situ Detection of Human Glioma Infiltration Using a Novel Computational Method for Optical Coherence Tomography

Cited by 33 publications

References 48 publications

Histological validation of in vivo assessment of cancer tissue inhomogeneity and automated morphological segmentation enabled by Optical Coherence Elastography

Histological validation of in vivo assessment of cancer tissue inhomogeneity and automated morphological segmentation enabled by Optical Coherence Elastography

Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda

OCT-Guided Surgery for Gliomas: Current Concept and Future Perspectives

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