TY - JOUR
T1 - Laser desorption tissue imaging with Differential Mobility Spectrometry
AU - Lepomäki, Maiju
AU - Anttalainen, Anna
AU - Vuorinen, Artturi
AU - Tolonen, Teemu
AU - Kontunen, Anton
AU - Karjalainen, Markus
AU - Vehkaoja, Antti
AU - Roine, Antti
AU - Oksala, Niku
N1 - Funding Information:
Maiju Lepomäki declares funding from the Doctoral School of Tampere University , The Finnish Medical Foundation [grant numbers 2167 , 4038 ], and Cancer Foundation of Finland . Anton Kontunen declares funding from the Doctoral School of Tampere University, The Finnish Foundation for Technology Promotion [grant number 7671 ], and Emil Aaltonen Foundation [grant number 210073K ]. This study has received funding from the ATTRACT project funded by the European Commission from the Horizon 2020 research and innovation programme [grant agreement 777222 ]. This study was also financially supported by Competitive State Research Financing of the Expert Responsibility Area of Tampere University Hospital and Pirkanmaa Hospital District [grant numbers 9AA057 , 9x040 , 9v044 , 9T044 , 9U042 , 9s045 , 150618 , 151B03 ]; Competitive funding to strengthen university research profiles funded by the Academy of Finland [decision number 292477 ]; and from the Tampere Tuberculosis Foundation . The study sponsors did not have any involvement in the study design; collection, analysis and interpretation of data; the writing of the manuscript; or the decision to submit the manuscript for publication.
Publisher Copyright:
© 2022 The Authors
PY - 2022/4
Y1 - 2022/4
N2 - Pathological gross examination of breast carcinoma samples is sometimes laborious. A tissue pre-mapping method could indicate neoplastic areas to the pathologist and enable focused sampling. Differential Mobility Spectrometry (DMS) is a rapid and affordable technology for complex gas mixture analysis. We present an automated tissue laser analysis system for imaging approaches (iATLAS), which utilizes a computer-controlled laser evaporator unit coupled with a DMS gas analyzer. The system is demonstrated in the classification of porcine tissue samples and three human breast carcinomas. Tissue samples from eighteen landrace pigs were classified with the system based on a pre-designed matrix (spatial resolution 1–3 mm). The smoke samples were analyzed with DMS, and tissue classification was performed with several machine learning approaches. Porcine skeletal muscle (n = 1030), adipose tissue (n = 1329), normal breast tissue (n = 258), bone (n = 680), and liver (n = 264) were identified with 86% cross-validation (CV) accuracy with a convolutional neural network (CNN) model. Further, a panel tissue that comprised all five tissue types was applied as an independent validation dataset. In this test, 82% classification accuracy with CNN was achieved. An analogous procedure was applied to demonstrate the feasibility of iATLAS in breast cancer imaging according to 1) macroscopically and 2) microscopically annotated data with 10-fold CV and SVM (radial kernel). We reached a classification accuracy of 94%, specificity of 94%, and sensitivity of 93% with the macroscopically annotated data from three breast cancer specimens. The microscopic annotation was applicable to two specimens. For the first specimen, the classification accuracy was 84% (specificity 88% and sensitivity 77%). For the second, the classification accuracy was 72% (specificity 88% and sensitivity 24%). This study presents a promising method for automated tissue imaging in an animal model and lays foundation for breast cancer imaging.
AB - Pathological gross examination of breast carcinoma samples is sometimes laborious. A tissue pre-mapping method could indicate neoplastic areas to the pathologist and enable focused sampling. Differential Mobility Spectrometry (DMS) is a rapid and affordable technology for complex gas mixture analysis. We present an automated tissue laser analysis system for imaging approaches (iATLAS), which utilizes a computer-controlled laser evaporator unit coupled with a DMS gas analyzer. The system is demonstrated in the classification of porcine tissue samples and three human breast carcinomas. Tissue samples from eighteen landrace pigs were classified with the system based on a pre-designed matrix (spatial resolution 1–3 mm). The smoke samples were analyzed with DMS, and tissue classification was performed with several machine learning approaches. Porcine skeletal muscle (n = 1030), adipose tissue (n = 1329), normal breast tissue (n = 258), bone (n = 680), and liver (n = 264) were identified with 86% cross-validation (CV) accuracy with a convolutional neural network (CNN) model. Further, a panel tissue that comprised all five tissue types was applied as an independent validation dataset. In this test, 82% classification accuracy with CNN was achieved. An analogous procedure was applied to demonstrate the feasibility of iATLAS in breast cancer imaging according to 1) macroscopically and 2) microscopically annotated data with 10-fold CV and SVM (radial kernel). We reached a classification accuracy of 94%, specificity of 94%, and sensitivity of 93% with the macroscopically annotated data from three breast cancer specimens. The microscopic annotation was applicable to two specimens. For the first specimen, the classification accuracy was 84% (specificity 88% and sensitivity 77%). For the second, the classification accuracy was 72% (specificity 88% and sensitivity 24%). This study presents a promising method for automated tissue imaging in an animal model and lays foundation for breast cancer imaging.
KW - Breast cancer
KW - Differential Mobility Spectrometry (DMS)
KW - Field asymmetric ion mobility spectrometry (FAIMS)
KW - Tissue imaging
KW - Tissue mapping
U2 - 10.1016/j.yexmp.2022.104759
DO - 10.1016/j.yexmp.2022.104759
M3 - Article
C2 - 35337806
AN - SCOPUS:85127125867
VL - 125
JO - EXPERIMENTAL AND MOLECULAR PATHOLOGY
JF - EXPERIMENTAL AND MOLECULAR PATHOLOGY
SN - 0014-4800
M1 - 104759
ER -
