Pathol. Oncol. Res., 17 August 2022

Mid-Infrared Imaging Characterization to Differentiate Lung Cancer Subtypes

www.frontiersin.orgE. Kontsek1*, www.frontiersin.orgA. Pesti1, www.frontiersin.orgJ. Slezsák2, www.frontiersin.orgP. Gordon3, www.frontiersin.orgT. Tornóczki4, www.frontiersin.orgG. Smuk4, www.frontiersin.orgS. Gergely2 and www.frontiersin.orgA. Kiss1*
  • 12nd Department of Pathology, Semmelweis University, Budapest, Hungary
  • 2Department of Applied Biotechnology and Food Science, Budapest University of Technology and Economics, Budapest, Hungary
  • 3Department of Electronics Technology, Budapest University of Technology and Economics, Budapest, Hungary
  • 4Department of Pathology, Medical School and Clinical Center, University of Pécs, Pécs, Hungary

Introduction: Lung cancer is the most common malignancy worldwide. Squamous cell carcinoma (SQ) and adenocarcinoma (LUAD) are the two most frequent histological subtypes. Small cell carcinoma (SCLC) subtype has the worst prognosis. Differential diagnosis is essential for proper oncological treatment. Life science associated mid- and near-infrared based microscopic techniques have been developed exponentially, especially in the past decade. Vibrational spectroscopy is a potential non-destructive approach to investigate malignancies.

Aims: Our goal was to differentiate lung cancer subtypes by their label-free mid-infrared spectra using supervised multivariate analyses.

Material and Methods: Formalin-fixed paraffin-embedded (FFPE) samples were selected from the archives. Three subtypes were selected for each group: 10-10 cases SQ, LUAD and SCLC. 2 μm thick sections were cut and laid on aluminium coated glass slides. Transflection optical setup was applied on Perkin-Elmer infrared microscope. 250 × 600 μm areas were imaged and the so-called mid-infrared fingerprint region (1800-648cm−1) was further analysed with linear discriminant analysis (LDA) and support vector machine (SVM) methods.

Results: Both “patient-based” and “pixel-based” approaches were examined. Patient-based analysis by using 3 LDA models and 2 SVM models resulted in different separations. The higher the cut-off value the lower is the accuracy. The linear C-support vector classification (C-SVC) SVM resulted in the best (100%) accuracy for the three subtypes using a 50% cut-off value. The pixel-based analysis gave, similarly, the linear C-SVC SVM model to be the most efficient in the statistical indicators (SQ sensitivity 81.65%, LUAD sensitivity 82.89% and SCLC sensitivity 88.89%). The spectra cut-off, the kernel function and the algorithm function influence the accuracy.

Conclusion: Mid-Infrared imaging could be used to differentiate FFPE lung cancer subtypes. Supervised multivariate tools are promising to accurately separate lung tumor subtypes. The long-term perspective is to develop a spectroscopy-based diagnostic tool, revolutionizing medical differential diagnostics, especially cancer identification.


Lung Tumors

In 2018 lung cancer was the most commonly diagnosed tumor and the leading cause of death in both sexes worldwide [1]. Preoperative biopsy materials have particular importance and the volume of biopsy material is limited. The clinical sampling also determines the feasible pathological methods. Brush cytology is often performed, however, diagnostic methods on cells or cell groups are more limited than histological analysis on FFPE samples collected by bronchoscopic tissue sampling. There is a need for ancillary diagnostics to determine histological subtypes to save material for the upcoming molecular diagnostics. Infrared spectroscopy might be one of these tools [2]. Optical fibre-based techniques combined with bronchoscopes or transthoracic needles may also disrupt and improve the current diagnostic pathways [3].

For a long time, the differential diagnosis between small cell versus non-small cell lung cancer was the most important clinicopathological aspect. Squamous cell carcinoma (SQ) and adenocarcinoma (LUAD) are the two most frequent histological subtypes. Small cell carcinoma (SCLC) subtype has the worst prognosis. There are rare tumor types or mixed entities such as mesothelioma or adenosquamous carcinoma. The development of targeted therapies and their spread in the routine oncological treatment required subtype-specific differentiation and definition of the tissue of origin even in dedifferentiated tumors and cytological specimens. This can be achieved by immunohistochemical typing [4, 5]. The most targeted therapies are available for the adenocarcinoma subtype.

The routine histological subtypization is based on Hematoxylin and Eosin (H&E) staining and five to six immunohistochemical reactions. Despite the low number of thin sections, the feasibility of high-quality nucleic acid isolation is endangered due to the size and consistency of the tissue core.

The above-mentioned aspect explains the need for new methods that can determine the origin of a single cell or group of cells without requiring immunohistochemical reactions, therefore, leaving more material for DNA or RNA isolation and consequent molecular pathological analysis. Infrared spectroscopy could do this and we aimed to build up classification models. We focused on the differential diagnostic application of FT-IR in the most frequent tumor types [6]. There are molecular, immunohistochemical markers to distinguish histological subtypes [7], however, these altogether small differences would not be detected by FT-IR since it is expected to reveal rather a spectral fingerprint characteristic of subtypes than a different quantitative compositional alteration on the level of specific proteins (e.g., p63, TTF1).

Infrared Spectroscopy

The spectral range over 780 nm is called infrared, which is conventionally divided into near-, mid- and far-infrared (NIR, MIR, FIR, respectively). The wavelength range of NIR is defined from 780 to 2500 nm (12,820-4000 cm−1—since due to the dispersed Fourier-transform (FT) spectrophotometers the wavenumber is typically measured in units of cm−1), the wavelengths of MIR are between 2500 and 25,000 nm (4000-400 cm−1) and the FIR range is between 25 and 1000 μm (400-10 cm−1). The higher the wavenumber, the higher the energy of the light. NIR and MIR photons elevate the chemical bonds to higher energy levels, causing deformation motions (e.g., angular changes). The FIR light has lower energy so it can excite the rotation of the atoms in the bonds. The quick and non-destructive NIR and MIR spectroscopy techniques are mostly used for investigating biological systems, while FIR is less relevant from this point of view and is not applied because of the shallow energy level. The mid-infrared area includes the so-called fingerprint region (1800-400 cm−1) where lipids, protein, amide I/II and nucleic acid peaks are highly representative [8].

One of the earliest MIR spectroscopic applications was to determine the cis/trans conformation of lipids [9]. MIR techniques were developed to analyse ingredients in milk [10, 11] and wine [12, 13]. Both NIR and MIR techniques are widespread in the field of biological matrix analysis. The qualitative and quantitative application of NIR in the agro-food sector began in the 1960s. Plants, animal products and processed foods from these are samples of complex biological origin, containing various contents of water, proteins, lipids and carbohydrates. Infrared analytics of grains and cereal-based products became a widespread technology, with the main focus being on the changes in protein content [14] and quality during ripening [15], and the monitoring of milling [16]. Additionally, in the field of pharmacology chemical structure of drug compounds [17] and polymers such as hydrogels [18] are proved by MIR spectroscopy as well.

MIR photons have less energy, therefore, the spatial penetration is shorter whereas the signal-to-noise ratio of the MIR spectra is about two orders of magnitude higher than in the case of NIR. There are fewer medical applications of MIR methods than that of NIR applications. MIR optical fibre, however, have been commercially available since 2016, whereas earlier only laboratory tools existed [1922]. In a study, the breast cancer imaging of 15 patients was carried out using mid- and long-wave infrared cameras [23]. In another study, urine samples from a small cohort of healthy women as well as female patients with gynaecological malignancies were investigated with MIR resulting in diagnoses with high accuracy [24]. The basic tissue processing of pathological specimens and an imaging protocol were created by Zahdi et al. [25]. A further study has highlighted the pitfalls and best practices of tissue preparation methods for FT-IR spectroscopic analysis [26]. The most common method is to use fluorescent dyes, however, there is also another approach which chooses marker-free FT-IR imaging as a tool with promising results on lung tumour subtyping [27]. Großerueschkamp et al. analysed fresh frozen samples using random forest algorithms. They set up a 3-level decision-making scheme and even at a more detailed level they were able to successfully recognize adenocarcinoma subtypes as well [27]. Gayoud et al. focused on squamous cell FFPE sample preneoplastic and neoplastic separations on 34 samples with PCA and PLS-DA tools [28]. Akalin et al. choose spectral pretreatment as first step using Mie scattering and extended multiplicative signal correction (EMSC) algorithms. They get rid of the low signal quality spectra gained from tissue microarrays (TMA) and then processed via hierarchic clustering and SVM [29]. Molecular expression patterns resulting also from mutated genes and proteins represent such a complexity that can not be expected to be precisely reflected by MIR spectra. MIR rather detects a fingerprint representative of the above-mentioned molecular complexity of a tumor. Mass spectrometry might focus either on specific molecular changes in the molecular composition or similarly detect a complex pattern. Spectral bands that can be assigned to chemical functions or to macromolcules are collected in a table published by Le Naour et al. [30]. The purpose of the present study was to differentiate lung tumor subtypes using label-free mid-infrared imaging.

Materials and Methods

Aluminium Coated Slides

Thin-film metal layers were deposited onto glass slides by vacuum evaporation to gain mid-infrared capable reflective surface. An electron-beam evaporation source was applied in a high-vacuum chamber, in which the glass slides were fastened onto the rotary sample holder. Aluminium was evaporated at 10−4 Pa for 20 min at an accelerating voltage of 7 kV and beam current of 200 mA, resulting in a layer thickness of ca. 150 nm.

Lung Cancer Samples

A total of 30 FFPE lung cancer biopsies were selected from the archive of 2nd Department of Pathology Semmelweis University and Department of Pathology University of Pécs 10 for each subtype. 2 μm slides were cut from paraffin-embedded blocks and were deparaffinized two times 10 min xylene. The use of human FFPE samples was approved by the Hungarian Medical Research Council, Budapest, Hungary (no. 61303-2/2018/EKU). In the routine workflow the sample preparation takes 1 day after grossing.

Infrared Imaging

Fourier transform mid-infrared imaging was used for collecting spectra with transflection optical setup. The feasibility of this approach has been tested on the separation of ethanol fixed cell lines, this has been reported previously [31]. Spotlight 400 microscope (Perkin Elmer Inc., Waltham, MA, United States) was connected to Spectrum 400 spectrophotometer used for scanning images. The Mercury Cadmium Tellurite (MCT) detector collected spectra with 32 scans with a resolution of 16 cm−1 and data interval of 8 cm−1 were recorded for each spectrum in the mid-infrared wavelength range between 4000 and 648 nm. The 250 μm × 600 μm images were scanned (Figure 1) by pixel size 6.25 μm × 6.25 μm. A single image contained 40 × 96 pixels and resulted in 3,840 spectra on a 0.15 mm2 area. The acquisition time for each selected area was 46 min.


FIGURE 1. Representative H&E stained region of a LUAD and multicolor average absorbance infrared image overlayed over the identical area (on the left). Representative spectrum (4000-650 cm−1) of LUAD of the location marked with a star (on the right).

Data Processing

Atmospheric Correction and Noise Reduction

The acquired images were treated with two built-in algorithms of the SpectrumIMAGE R1.6.5.0396 software (Perkin Elmer Inc., Waltham, Massachusetts, United States) for atmospheric correction and noise reduction. The atmospheric CO2/H2O suppression by the least square fitting of the algorithm affected the atmospheric correction of the spectra. (Patent No.: US 6,518,753 B1) The noise reduction was based on a 20-factor principal component analysis. Since the noise has lower weights, the 20-factor-based reconstructed spectrum is noise reduced. This method does not lead to the broadening of the spectrum peaks, unlike smoothing.

From the whole spectrum the so-called fingerprint subregion was analysed. In case of the fingerprint range of measured IR spectra there are 145 wavenumbers (1800-648 cm−1 wavenumber range) considered as variables. According to a large amount of data, it is difficult to interpret the data cube, therefore some information may partly stay hidden.

Support Vector Machine

Classification by SVM is a method based on statistical learning. The essence of SVM is to determine the hyperplane with the maximum margin for linearly separable data. The reason for the maximum margin is that decision boundaries with a large margin tend to have better generalization error than those with a small margin. The method has a wide range of applications in statistical analysis in almost all disciplines. The main advantage of the method is that it can be extended to non-linear data sets using kernel functions. In order to perform the analysis, the algorithm must be trained on a part of the data set, which contains a category variable, and then validated on the other part of the data. Once these operations have been performed, the accuracy of the method can be deduced from the results. Another advantage of the support vector machine is that it has good generalisation capabilities and can be easily applied to multidimensional data. The confusion matrix summarizes the prediction results on the classification problem. Unscrambler X 10.4 (CAMO Software AS, Oslo, Norway) software was applied to perform the SVMs.

Linear Discriminant Analysis

LDA is a method for separating two or more classes by considering several quantitative variables simultaneously. A prerequisite for its application is that objects are already divided into classes. We must, therefore, be familiar with the classes that are identified by this classifying variable.

LDA is a classification method in which n-dimensional patterns are transformed into an m-dimensional space (m < n) by linear transformation. Consequently, samples from the same class will be located close to each other, while samples from different classes will be located far in space. The method is a supervised classification method, unlike the unsupervised, e.g., Cluster and Principal component analyses. The purpose of LDA is to determine the best fitting parameters for grouping the samples in the constructed model. The already constructed model can be used to project unknown samples. LDA is an uncomplicated method to use and is approximated on Bayes’ formula [32]. The projected results can be also put in a confusion matrix as described earlier.

Unscrambler X 10.4 (CAMO Software AS, Oslo, Norway) software was applied to perform the classifications.


10-10 specimens were selected from each of the three histological subtypes (Table 1). Cases 2 and 6 are resected tumours from the same patient operated on two different locations at different timepoints. After identifying the tumorous region on the H&E slides, the consecutive slides’ parallel area was imaged on the aluminium coated slides using the infrared microscope. The scheme of technology is visualized by a graphical workflow (Figure 2). The acquired spectra were collected and treated with atmospheric correction and noise reduction.


TABLE 1. Clinicopathological features of the patients.


FIGURE 2. The scheme of experimental setup.

The spectra of the acquired images were put into a table, from which every second spectra were selected and used as a training set. A total of 5 analyses were performed using three LDA (using linear, quadratic and Mahalanobis distances) and two SVM (linear nu-SVC SVM and linear C-SVC SVM settings) models. We ran the other part of the datasets on these models and the methods predicted the histological subtype.

Two approaches were used to examine the data. We examined both “patient-based” and “pixel-based” analyses. The “patient-based” classifications are shown first. This approach is based on majority projection, therefore, the lowest recommended cut-off value is 50%. Each spectrum was classified individually by the five above-mentioned mathematical models. We have sorted the results by sample into bias matrices. The proportion of correctly classified spectra from each patient sample (3,840 spectra per sample) was considered as the decisive factor for the correct classification. The different cut-off values were compared in order to rank the accuracy of the models (Table 2).


TABLE 2. Accuracy of the prediction models using different cut-off values.

Linear C-SVC SVM model with a 50% cut-off was the most successful regarding the separation of the subtypes. For interpretation of the table selecting e.g., 70% cut-off value and linear C-SVC SVM one could see that the cut-off value was not achieved in only 6 out of 30 cases: 2 samples in SQ, 1 SCLC and 3 samples in LUAD. However, these cases were also correctly predicted if we classify the samples based on the 50% cut-off value. Of course, the fine-tuning of the above-described method could improve the other models as well. It might be worthy of testing lower than 50% cut-off values, however, this approach would certainly require bigger cohorts and further testing of mixed differentiation such as adenosquamous carcinomas which feature both adenocarcinoma and squamous cell carcinoma subtypes.

By examining these five models on a pixel basis, we can obtain cumulative data for each subtype based on the prediction of each spectrum. The performance of the five models can be compared in terms of sensitivity, specificity, positive predictive value (ppv) and negative predictive value (npv) characteristics. Overall, consistent with the patient-based approach, the linear C-SVC SVM model proved to be the best again with sensitivity ranging from 81.645% to 88.885% and specificity from 90.484% to 94.784% regarding histological subtypes (Table 3). Quadratic LDA model achieved higher sensitivity for determining SQ predictions compared to linear C-SVC SVM but with lower specificity. Similarly, for spectra of LUAD samples, quadratic LDA gave 99% specificity but lagged behind SVM in sensitivity. The quadratic LDA has a 95.107% ppv for LUAD which is better than the 82.885% of the C-SVC SVM performance.


TABLE 3. The performance of the five models.

The classification by Mahalanobis LDA was completely wrong. The model considered virtually all spectra as adenocarcinomas. That is why a 100% sensitivity was obtained for the spectra of the LUAD samples, but the specificity is 0.008%. The SQ and SCLC specificities gave a vain value of 100% since the sensitivity values are close to zero.


In this paper, we present a marker-free and automated diagnostic FT-IR imaging-based tool for pathological decision support. Classification of histologically significant lung tumor subtypes was achieved. We also highlighted the differences between five multivariate data analysis models.

The accuracy of these models was calculated using several cut-off parameters. The higher the accuracy the lower the cut-off in general. A strong bias was observed regarding the Mahalanobis model because every sample was predicted into one class. The best separation was reached by Linear C-SVC SVM model combined with 50% cut-off value according to our findings. Further optimization of the cut-off value would require a larger cohort. The pixel-based predictions also proved to be successful. Overall the C-SVC SVM performed better than the other 4 models.

The discriminative power of Linear C-SVC SVM method outperformed the others, however, certain individual statistical metrics of the other methods—such as sensitivity, specificity, ppv, npv—were better regarding the histological subtypes. Experienced pathologists in their routine activity combine the advantages of several approaches, therefore, the above-mentioned parameters are excellent [33]. The Linear C-SVC SVM sensitivity and specificity values are in a comparable range with pathology-associated image analysis tools such as the PAPNET for cervical smear [34]. The infrared spectral analysis has a promising perspective to develop this method to assist intraoperative decision-making similar to mass spectrometry-assisted tools. The correct tissue classification by mass spectrometry was characterized by high accuracy with a sensitivity of 90.5% and specificity of 89.7% which is comparable with our method [35].

These approaches promise reproducibility, objectivity, and higher accuracy compared to current methodologies for lung tumor diagnostics. There is a growing need for personalization in medicine which requires a fast and accurate way of differential diagnosis. The approach has yet to be validated on a larger scale.

Altogether, the overall training time using half of the spectra for different models took between 2 and 12 h. However, the prediction time using these models was tremendously shortened: 5–10 min. The major delay in the current routine workflow of pathology is the sample preparation which needs 1 day after grossing. Based on the H&E image the infrared acquisition time of the selected area takes only 46 min. Analysis of native surgical specimens would reduce diagnostic intervals and enable on-site measurements. An intraoperative approach could also be executed with different detection e.g., Raman spectroscopy works better in an aqueous medium like unfixed, on-site specimens.

These in vitro approaches might serve as the basis to develop a dye-free intraoperative technique to facilitate surgical decision-making [27, 36]. Altogether, our in vitro results project the feasibility of infrared imaging to identify different cancer subtypes. Optional other ancillary methods would be the detection of tumor type specific tumor-associated DNA, however, there are no clinically reliable markers available so far.

The infrared technique would have an advantage as non-destructive and even on-site. We developed our method on histologically verified tissue sections. Long-standing problem is the adequacy of the sampling for the diagnostic needs. The oncoteam decision-making requires steadily increasing tissue-related diagnostic information. Certain genetic data need next generation sequencing, the other histological, immunohistochemical or fluorescent in situ hybridization information. Infrared spectroscopy might provide necessary data from less or minimal amounts of tissue. That means that biopsy material will be saved for other necessary methods, therefore, less or minimal invasive sampling would be enough [37].

The selection of the proper cut-off value secures the specificity of the analysis. Therefore, tumors featuring areas of different histological subtypes such as adenosquamous carcinomas of the lung could be used to test the feasibility of possibly lower cut-off values of the methods. This could improve the power of separation. Adenosquamous carcinomas are a rare mixed differentiation subtype of non–small cell carcinoma of the lung, constituting 0.4%–4% of cases. p63 IHC reaction is the tool to identify the squamous component. The lowest cut-off value could be determined on a large number of mixed entities.

Infrared imaging might be suitable to identify the lung cancer subtypes, therefore, fewer slides would be necessary for IHC and more tissue would be preserved for molecular pathology to select potential novel therapies. Larger datasets must be analyzed to further support our results.

In our study, label-free mid-infrared imaging was used to acquire spectra from three lung cancer subtypes. LDAs and SVMs were performed on all the investigated subgroups in the fingerprint mid-infrared region. Based on our results SVM models performed better although spectral pretreatments might further increase the accuracy, therefore, it could be an additional option. We successfully demonstrated the feasibility of our infrared method to separate cancer subtypes types by their label-free mid-infrared spectra with highlighted range. In conclusion, our data suggest the usage of transflective optical set and 1800-648 cm−1 spectral range to gain spectra.

Data Availability Statement

The raw data supporting the conclusion of this article will be made available by the authors, without undue reservation.

Ethics Statement

The studies involving human participants were reviewed and approved by the Medical Research Council (ETT TUKEB) 7-8 Széchenyi István tér, Budapest, H-1051, Hungary. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements. Written informed consent was not obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.

Author Contributions

Experiment design: EK, AK, TT, and GS; aluminium coated slide production: PG, EK, and JS; infrared imaging: EK and AP; data processing: EK, AP, and SG. All authors were involved in writing the manuscript.


This work was supported by the K_18 128881 Grant of the National Research, Development and Innovation Office (Hungary) and Thematic Excellence Program (2020-4.1.1.-TKP2020 Grant within the framework of the DigitalBiomarker thematic program of the Semmelweis University) of the Ministry for Innovation and Technology (Hungary). This work was supported by the Higher Education Excellence Program of the Ministry of Human Capacities in the frame of Biotechnology research area of Budapest University of Technology and Economics (BME FIKP-BIO). The research reported in this paper is part of project no. TKP2021-EGA-02, implemented with the support provided by the Ministry for Innovation and Technology of Hungary from the National Research, Development and Innovation Fund, financed under the TKP2021 funding scheme.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


1. Bray, F, Ferlay, J, Soerjomataram, I, Siegel, RL, Torre, LA, and Jemal, A. Global Cancer Statistics 2018: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin (2018) 68(6):394–424. doi:10.3322/caac.21492

PubMed Abstract | CrossRef Full Text | Google Scholar

2. Finlayson, D, Rinaldi, C, and Baker, MJ. Is Infrared Spectroscopy Ready for the Clinic? Anal Chem (2019) 91(19):12117–28. doi:10.1021/acs.analchem.9b02280

PubMed Abstract | CrossRef Full Text | Google Scholar

3. Fernandes, S, Williams, G, Williams, E, Ehrlich, K, Stone, J, Finlayson, N, et al. Solitary Pulmonary Nodule Imaging Approaches and the Role of Optical Fibre-Based Technologies. Eur Respir J (2021) 57(3):2002537. doi:10.1183/13993003.02537-2020

PubMed Abstract | CrossRef Full Text | Google Scholar

4. Loo, PS, Thomas, SC, Nicolson, MC, Fyfe, MN, and Kerr, KM. Subtyping of Undifferentiated Non-small Cell Carcinomas in Bronchial Biopsy Specimens. J Thorac Oncol (2010) 5(4):442–7. doi:10.1097/JTO.0b013e3181d40fac

PubMed Abstract | CrossRef Full Text | Google Scholar

5. Rossi, G, Pelosi, G, Graziano, P, Barbareschi, M, and Papotti, M. A Reevaluation of the Clinical Significance of Histological Subtyping of Non-small-cell Lung Carcinoma: Diagnostic Algorithms in the Era of Personalized Treatments. Int J Surg Pathol (2009) 17(3):206–18. doi:10.1177/1066896909336178

PubMed Abstract | CrossRef Full Text | Google Scholar

6. Su, KY, and Lee, WL. Fourier Transform Infrared Spectroscopy as a Cancer Screening and Diagnostic Tool: A Review and Prospects. Cancers (2020) 12(1):E115. doi:10.3390/cancers12010115

PubMed Abstract | CrossRef Full Text | Google Scholar

7. Yatabe, Y, Dacic, S, Borczuk, AC, Warth, A, Russell, PA, Lantuejoul, S, et al. Best Practices Recommendations for Diagnostic Immunohistochemistry in Lung Cancer. J Thorac Oncol (2019) 14(3):377–407. doi:10.1016/j.jtho.2018.12.005

PubMed Abstract | CrossRef Full Text | Google Scholar

8. Balan, V, Mihai, CT, Cojocaru, FD, Uritu, CM, Dodi, G, Botezat, D, et al. Vibrational Spectroscopy Fingerprinting in Medicine: From Molecular to Clinical Practice. Materials (2019) 12(18):E2884. doi:10.3390/ma12182884

PubMed Abstract | CrossRef Full Text | Google Scholar

9. Baumann, WJ, Madson, TH, and Weseman, BJ. “Plasmalogen-type” Cyclic Acetals: Formation and Conformation of the 1, 3-dioxanes and 1, 3-dioxolanes from 1-O-Cis-Alk-1′-Enyl-Sn-Glycerols. J Lipid Res (1972) 13(5):640–50. doi:10.1016/s0022-2275(20)39369-x

PubMed Abstract | CrossRef Full Text | Google Scholar

10. O'Sullivan, A, O'Connor, B, Kelly, A, and McGrath, MJ. The Use of Chemical and Infrared Methods for Analysis of Milk and Dairy Products. Int J Dairy Technol (1999) 52(4):139–48. doi:10.1111/j.1471-0307.1999.tb02856.x

CrossRef Full Text | Google Scholar

11. Goulden, JDS. Analysis of Milk by Infra-red Absorption. J Dairy Res (1964) 31(03):273–84. doi:10.1017/s0022029900018203

CrossRef Full Text | Google Scholar

12. GarciaJares, CM, and Medina, B. Application of Multivariate Calibration to the Simultaneous Routine Determination of Ethanol, Glycerol, Fructose, Glucose and Total Residual Sugars in Botrytized-Grape Sweet Wines by Means of Near-Infrared Reflectance Spectroscopy. Fresenius' J Anal Chem (1997) 357(1):86–91. doi:10.1007/s002160050117

CrossRef Full Text | Google Scholar

13. Nieuwoudt, HH, Prior, BA, Pretorius, IS, Manley, M, and Bauer, FF. Principal Component Analysis Applied to Fourier Transform Infrared Spectroscopy for the Design of Calibration Sets for Glycerol Prediction Models in Wine and for the Detection and Classification of Outlier Samples. J Agric Food Chem (2004) 52(12):3726–35. doi:10.1021/jf035431q

PubMed Abstract | CrossRef Full Text | Google Scholar

14. Kays, SE, Barton, FE, and Windham, WR. Predicting Protein Content by Near Infrared Reflectance Spectroscopy in Diverse Cereal Food Products. J Near Infrared Spectrosc (2000) 8(1):35–43. doi:10.1255/jnirs.262

CrossRef Full Text | Google Scholar

15. Gergely, S, and Salgo, A. Changes in Carbohydrate Content during Wheat Maturation - what Is Measured by Near Infrared Spectroscopy? J Near Infrared Spectrosc (2005) 13(1):9–17. doi:10.1255/jnirs.452

CrossRef Full Text | Google Scholar

16. Huang, H, Yu, H, Xu, H, and Ying, Y. Near Infrared Spectroscopy for On/in-Line Monitoring of Quality in Foods and Beverages: A Review. J Food Eng (2008) 87(3):303–13. doi:10.1016/j.jfoodeng.2007.12.022

CrossRef Full Text | Google Scholar

17. Juriga, D, Laszlo, I, Ludanyi, K, Klebovich, I, Chae, CH, and Zrinyi, M. Kinetics of Dopamine Release from Poly(aspartamide)-Based Prodrugs. Acta Biomater (2018) 76:225–38. doi:10.1016/j.actbio.2018.06.030

PubMed Abstract | CrossRef Full Text | Google Scholar

18. Juriga, D, Kalman, EE, Toth, K, Barczikai, D, Szollosi, D, Foldes, A, et al. Analysis of Three-Dimensional Cell Migration in Dopamine-Modified Poly(aspartic Acid)-Based Hydrogels. Gels (2022) 8(2):65. doi:10.3390/gels8020065

PubMed Abstract | CrossRef Full Text | Google Scholar

19. Bogomolov, A, Belikova, V, Zabarylo, UJ, Bibikova, O, Usenov, I, Sakharova, T, et al. Synergy Effect of Combining Fluorescence and Mid Infrared Fiber Spectroscopy for Kidney Tumor Diagnostics. Sensors (Basel) (2017) 17(11):E2548. doi:10.3390/s17112548

PubMed Abstract | CrossRef Full Text | Google Scholar

20. Sojka, L, Tang, Z, Furniss, D, Sakr, H, Fang, Y, Beres-Pawlik, E, et al. Mid-infrared Emission in Tb3+-Doped Selenide Glass Fiber. J Opt Soc Am B (2017) 34(3):A70–A79. doi:10.1364/josab.34.000a70

CrossRef Full Text | Google Scholar

21. Nallala, J, Lloyd, GR, Hermes, M, Shepherd, N, and Stone, N. Enhanced Spectral Histology in the colon Using High-Magnification Benchtop FTIR Imaging. Vibrational Spectrosc (2017) 91:83–91. doi:10.1016/j.vibspec.2016.08.013

CrossRef Full Text | Google Scholar

22. Seddon, AB, Benson, TM, Sujecki, S, Abdel-Moneim, N, Tang, ZQ, Furniss, D, et al. Towards the Mid-infrared Optical Biopsy. In: Optical Biopsy Xiv: Toward Real-Time Spectroscopic Imaging and Diagnosis; 2016 7 Mar; San Francisco, CA (2016). p. 9703.

Google Scholar

23. Joro, R, Laaperi, AL, Dastidar, P, Soimakallio, S, Kuukasjarvi, T, Toivonen, T, et al. Imaging of Breast Cancer with Mid- and Long-Wave Infrared Camera. J Med Eng Technol (2008) 32(3):189–97. doi:10.1080/03091900701234358

PubMed Abstract | CrossRef Full Text | Google Scholar

24. Paraskevaidi, M, Morais, CLM, Lima, KMG, Ashton, KM, Stringfellow, HF, Martin-Hirsch, PL, et al. Potential of Mid-infrared Spectroscopy as a Non-invasive Diagnostic Test in Urine for Endometrial or Ovarian Cancer. Analyst (2018) 143(13):3156–63. doi:10.1039/c8an00027a

PubMed Abstract | CrossRef Full Text | Google Scholar

25. Sreedhar, H, Varma, VK, Nguyen, PL, Davidson, B, Akkina, S, Guzman, G, et al. High-definition Fourier Transform Infrared (FT-IR) Spectroscopic Imaging of Human Tissue Sections towards Improving Pathology. J Vis Exp (2015)(95) 52332. doi:10.3791/52332

PubMed Abstract | CrossRef Full Text | Google Scholar

26. Zohdi, V, Whelan, DR, Wood, BR, Pearson, JT, Bambery, KR, and Black, MJ. Importance of Tissue Preparation Methods in FTIR Micro-spectroscopical Analysis of Biological Tissues: 'Traps for New Users. PLoS One (2015) 10(2):e0116491. doi:10.1371/journal.pone.0116491

PubMed Abstract | CrossRef Full Text | Google Scholar

27. Grosserueschkamp, F, Kallenbach-Thieltges, A, Behrens, T, Bruning, T, Altmayer, M, Stamatis, G, et al. Marker-free Automated Histopathological Annotation of Lung Tumour Subtypes by FTIR Imaging. Analyst (2015) 140(7):2114–20. doi:10.1039/c4an01978d

PubMed Abstract | CrossRef Full Text | Google Scholar

28. Gaydou, V, Polette, M, Gobinet, C, Kileztky, C, Angiboust, JF, Birembaut, P, et al. New Insights into Spectral Histopathology: Infrared-Based Scoring of Tumour Aggressiveness of Squamous Cell Lung Carcinomas. Chem Sci (2019) 10(15):4246–58. doi:10.1039/c8sc04320e

PubMed Abstract | CrossRef Full Text | Google Scholar

29. Akalin, A, Mu, X, Kon, MA, Ergin, A, Remiszewski, SH, Thompson, CM, et al. Classification of Malignant and Benign Tumors of the Lung by Infrared Spectral Histopathology (SHP). Lab Invest (2015) 95(4):697–21. doi:10.1038/labinvest.2015.55

PubMed Abstract | CrossRef Full Text | Google Scholar

30. Le Naour, F, Bralet, MP, Debois, D, Sandt, C, Guettier, C, Dumas, P, et al. Chemical Imaging on Liver Steatosis Using Synchrotron Infrared and ToF-SIMS Microspectroscopies. Plos One (2009) 4(10):e7408. doi:10.1371/journal.pone.0007408

PubMed Abstract | CrossRef Full Text | Google Scholar

31. Kontsek, E, Pesti, A, Bjornstedt, M, Uveges, T, Szabo, E, Garay, T, et al. Mid-Infrared Imaging is Able to Characterize and Separate Cancer Cell Lines. Pathol Oncol Res (2020) 26(4):2401–7. doi:10.1007/s12253-020-00825-z

PubMed Abstract | CrossRef Full Text | Google Scholar

32. James, G, Witten, D, Hastie, T, and Tibshirani, R. Classification. In: An Introduction to Statistical Learning: With Applications in R. New York, NY: Springer US (2021). p. 129–95.

CrossRef Full Text | Google Scholar

33. Paech, DC, Weston, AR, Pavlakis, N, Gill, A, Rajan, N, Barraclough, H, et al. A Systematic Review of the Interobserver Variability for Histology in the Differentiation between Squamous and Nonsquamous Non-small Cell Lung Cancer. J Thorac Oncol (2011) 6(1):55–63. doi:10.1097/JTO.0b013e3181fc0878

PubMed Abstract | CrossRef Full Text | Google Scholar

34. Irwig, L, Macaskill, P, Farnsworth, A, Wright, RG, McCool, J, Barratt, A, et al. A Randomized Crossover Trial of PAPNET for Primary Cervical Screening. J Clin Epidemiol (2004) 57(1):75–81. doi:10.1016/S0895-4356(03)00259-2

PubMed Abstract | CrossRef Full Text | Google Scholar

35. Phelps, DL, Balog, J, Gildea, LF, Bodai, Z, Savage, A, El-Bahrawy, MA, et al. The Surgical Intelligent Knife Distinguishes normal, Borderline and Malignant Gynaecological Tissues Using Rapid Evaporative Ionisation Mass Spectrometry (REIMS). Br J Cancer (2018) 118(10):1349–58. doi:10.1038/s41416-018-0048-3

PubMed Abstract | CrossRef Full Text | Google Scholar

36. Petersen, D, Mavarani, L, Niedieker, D, Freier, E, Tannapfel, A, Kotting, C, et al. Virtual Staining of colon Cancer Tissue by Label-free Raman Micro-spectroscopy. Analyst (2017) 142(8):1207–15. doi:10.1039/c6an02072k

PubMed Abstract | CrossRef Full Text | Google Scholar

37. Tian, P, Wang, Y, Li, L, Zhou, Y, Luo, W, and Li, W. CT-Guided Transthoracic Core Needle Biopsy for Small Pulmonary Lesions: Diagnostic Performance and Adequacy for Molecular Testing. J Thorac Dis (2017) 9(2):333–43. doi:10.21037/jtd.2017.02.16

PubMed Abstract | CrossRef Full Text | Google Scholar

Keywords: lung cancer, fingerprint region, transflectance, SVM, LDA, FTIR, infrared

Citation: Kontsek E, Pesti A, Slezsák J, Gordon P, Tornóczki T, Smuk G, Gergely S and Kiss A (2022) Mid-Infrared Imaging Characterization to Differentiate Lung Cancer Subtypes. Pathol. Oncol. Res. 28:1610439. doi: 10.3389/pore.2022.1610439

Received: 11 March 2022; Accepted: 20 July 2022;
Published: 17 August 2022.

Edited by:

Krenács Tibor, Semmelweis University, Hungary

Copyright © 2022 Kontsek, Pesti, Slezsák, Gordon, Tornóczki, Smuk, Gergely and Kiss. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: E. Kontsek, kontsek.endre@med.semmelweis-univ.hu; A. Kiss, kiss.andras@med.semmelweis-univ.hu