Quantum Cascade Laser-Based Infrared Imaging as a Label-Free and Automated Approach to Determine Mutations in Lung Adenocarcinoma

Goertzen, Nina; Pappesch, Roberto; Fassunke, Jana; Brüning, Thomas; Ko, Yon‐Dschun; Schmidt, Joachim; Großerueschkamp, Frederik; Buettner, Reinhard; Gerwert, Klaus

doi:10.1016/j.ajpath.2021.04.013

Cited by 8 publications

(5 citation statements)

References 51 publications

(54 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This finding, however, cannot be transferred to arbitrary settings: our results do not exclude that in other settings, e.g. when dealing with classifying or localizing cancer subtypes, 8,42,43 performance could suffer significantly under dimensionality reduction. The same holds for neural network topologies other than the CompSegNet investigated here, which could potentially be more or less sensitive to dimensionality reduction.…”

Section: Discussioncontrasting

confidence: 55%

Dimensionality reduction for deep learning in infrared microscopy: a comparative computational survey

Müller,

Schuhmacher,

Schörner

et al. 2023

Analyst

View full text Add to dashboard Cite

show abstract

Section: Discussioncontrasting

confidence: 55%

Dimensionality reduction for deep learning in infrared microscopy: a comparative computational survey

Müller,

Schuhmacher,

Schörner

et al. 2023

Analyst

View full text Add to dashboard Cite

show abstract

“…For example, cytokeratin is present only in epithelial cells; hence, a first step was to segment tissue into epithelial cells and then analyze epithelial spectra to predict the expression level ( 119 ). Similarly, a five-step scheme was used ( 121 ) in a carefully designed study that could achieve not only histologic and disease classification but also a sensitivity and specificity of 95% for each of the three mutations important in lung cancer. The second approach to bridge the molecular and spectral domains is to use IR tags or labels, wherein an IR reporter is attached to a specific molecule(s) of interest and the amplification of a signal can be achieved by using probes with IR absorption that can be tuned to be high and away from common tissues’ vibrational modes ( 122 , 123 ).…”

Section: Infrared Spectroscopic Imaging Technologymentioning

confidence: 99%

Digital Histopathology by Infrared Spectroscopic Imaging

Bhargava

2023

Annual Rev. Anal. Chem.

View full text Add to dashboard Cite

Infrared (IR) spectroscopic imaging records spatially resolved molecular vibrational spectra, enabling a comprehensive measurement of the chemical makeup and heterogeneity of biological tissues. Combining this novel contrast mechanism in microscopy with the use of artificial intelligence can transform the practice of histopathology, which currently relies largely on human examination of morphologic patterns within stained tissue. First, this review summarizes IR imaging instrumentation especially suited to histopathology, analyses of its performance, and major trends. Second, an overview of data processing methods and application of machine learning is given, with an emphasis on the emerging use of deep learning. Third, a discussion on workflows in pathology is provided, with four categories proposed based on the complexity of methods and the analytical performance needed. Last, a set of guidelines, termed experimental and analytical specifications for spectroscopic imaging in histopathology, are proposed to help standardize the diversity of approaches in this emerging area. Expected final online publication date for the Annual Review of Analytical Chemistry, Volume 16 is June 2023. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

show abstract

“…One possible solution to this would be to implement artificial intelligence in order to guarantee better sensitivity, specificity and efficiency. For example, a model that was trained using deep neural networks could link gene expression with drug response and predict drug response and survival [137,138]. Furthermore, Yu et al, was able to determine the patient survival rate by combining NGS data with histopathology data in lung cancer cohort [139].…”

Section: Precision Oncology In Clinical Settingmentioning

confidence: 99%

Shifting Gears in Precision Oncology—Challenges and Opportunities of Integrative Data Analysis

2021

Self Cite

View full text Add to dashboard Cite

For decades, research relating to modification of host immunity towards antitumor response activation has been ongoing, with the breakthrough discovery of immune-checkpoint blockers. Several biomarkers with potential predictive value have been reported in recent studies for these novel therapies. However, with the plethora of therapeutic options existing for a given cancer entity, modern oncology is now being confronted with multifactorial interpretation to devise “the best therapy” for the individual patient. Into the bargain come the multiverse guidelines for established and emerging diagnostic biomarkers, as well as the complex interplay between cancer cells and tumor microenvironment, provoking immense challenges in the therapy decision-making process. Through this review, we present various molecular diagnostic modalities and techniques, such as genomics, immunohistochemistry and quantitative image analysis, which have the potential of becoming powerful tools in the development of an optimal treatment regime when analogized with patient characteristics. We will summarize the underlying complexities of these methods and shed light upon the necessary considerations and requirements for data integration. It is our hope to provide compelling evidence to emphasize on the need for inclusion of integrative data analysis in modern cancer therapy, and thereupon paving a path towards precision medicine and better patient outcomes.

show abstract

Quantum Cascade Laser-Based Infrared Imaging as a Label-Free and Automated Approach to Determine Mutations in Lung Adenocarcinoma

Cited by 8 publications

References 51 publications

Dimensionality reduction for deep learning in infrared microscopy: a comparative computational survey

Dimensionality reduction for deep learning in infrared microscopy: a comparative computational survey

Digital Histopathology by Infrared Spectroscopic Imaging

Shifting Gears in Precision Oncology—Challenges and Opportunities of Integrative Data Analysis

Contact Info

Product

Resources

About