Mid-Infrared Imaging Is Able to Characterize and Separate Cancer Cell Lines

Infrared Spectroscopy in Gynecological Oncology: A Comprehensive Review of Diagnostic Potentials and Challenges

The early detection of gynecological cancers, which is critical for improving patient survival rates, is challenging because of the vague early symptoms and the diagnostic limitations of current approaches. This comprehensive review delves into the game-changing potential of infrared (IR) spectroscopy, a noninvasive technology used to transform the landscape of cancer diagnosis in gynecology. By collecting the distinctive vibrational frequencies of chemical bonds inside tissue samples, Fourier-transform infrared (FTIR) spectroscopy provides a 'molecular fingerprint' that outperforms existing diagnostic approaches. We highlight significant advances in this field, particularly the identification of discrete biomarker bands in the mid- and near-IR spectra. Proteins, lipids, carbohydrates, and nucleic acids exhibited different absorption patterns. These spectral signatures not only serve to distinguish between malignant and benign diseases, but also provide additional information regarding the cellular changes associated with cancer. To underscore the practical consequences of these findings, we examined studies in which IR spectroscopy demonstrated exceptional diagnostic accuracy. This review supports the use of IR spectroscopy in normal clinical practice, emphasizing its capacity to detect and comprehend the intricate molecular underpinnings of gynecological cancers.

Hyperspectral Mapping of Human Primary and Stem Cells at Cell-Matrix Interfaces

Extracellular matrices interface with cells to promote cell growth and tissue development. Given this critical role, matrix mimetics are introduced to enable biomedical materials ranging from tissue engineering scaffolds and tumor models to organoids for drug screening and implant surface coatings. Traditional microscopy methods are used to evaluate such materials in their ability to support exploitable cell responses, which are expressed in changes in cell proliferation rates and morphology. However, the physical imaging methods do not capture the chemistry of cells at cell-matrix interfaces. Herein, we report hyperspectral imaging to map the chemistry of human primary and embryonic stem cells grown on matrix materials, both native and artificial. We provide the statistical analysis of changes in lipid and protein content of the cells obtained from infrared spectral maps to conclude matrix morphologies as a major determinant of biochemical cell responses. The study demonstrates an effective methodology for evaluating bespoke matrix materials directly at cell-matrix interfaces.

Deep learning analysis of mid-infrared microscopic imaging data for the diagnosis and classification of human lymphomas

The present study presents an alternative analytical workflow that combines mid-infrared (MIR) microscopic imaging and deep learning to diagnose human lymphoma and differentiate between small and large cell lymphoma. We could show that using a deep learning approach to analyze MIR hyperspectral data obtained from benign and malignant lymph node pathology results in high accuracy for correct classification, learning the distinct region of 3900 to 850 cm-1 . The accuracy is above 95% for every pair of malignant lymphoid tissue and still above 90% for the distinction between benign and malignant lymphoid tissue for binary classification. These results demonstrate that a preliminary diagnosis and subtyping of human lymphoma could be streamlined by applying a deep learning approach to analyze MIR spectroscopic data.

Mid-Infrared Imaging Characterization to Differentiate Lung Cancer Subtypes

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.

Mid-infrared spectral classification of endometrial cancer compared to benign controls in serum or plasma samples

This study demonstrates a discrimination of endometrial cancer versus (non-cancerous) benign controls based on mid-infrared (MIR) spectroscopy of dried plasma or serum liquid samples. A detailed evaluation was performed using four discriminant methods (LDA, QDA, kNN or SVM) to execute the classification task. The discriminant methods used in the study comprised methods that are widely used in the statistics (LDA and QDA) and machine learning literature (kNN and SVM). Of particular interest, is the impact of discrimination when presented with spectral data from a section of the bio-fingerprint region (1430 cm-1 to 900 cm-1) in contrast to the more extended bio-fingerprint region used here (1800 cm-1 to 900 cm-1). Quality metrics used were the misclassification rate, sensitivity, specificity, and Matthew's correlation coefficient (MCC). For plasma (with spectral data ranging from 1430 cm-1 to 900 cm-1), the best performing classifier was kNN, which achieved a sensitivity, specificity and MCC of 0.865 ± 0.043, 0.865 ± 0.023 and 0.762 ± 0.034, respectively. For serum (in the same wavenumber range), the best performing classifier was LDA, achieving a sensitivity, specificity and MCC of 0.899 ± 0.023, 0.763 ± 0.048 and 0.664 ± 0.067, respectively. For plasma (with spectral data ranging from 1800 cm-1 to 900 cm-1), the best performing classifier was SVM, with a sensitivity, specificity and MCC of 0.993 ± 0.010, 0.815 ± 0.000 and 0.815 ± 0.010, respectively. For serum (in the same wavenumber range), QDA performed best achieving a sensitivity, specificity and MCC of 0.852 ± 0.023, 0.700 ± 0.162 and 0.557 ± 0.012, respectively. Our findings demonstrate that even when a section of the bio-fingerprint region has been removed, good classification of endometrial cancer versus non-cancerous controls is still maintained. These findings suggest the potential of a MIR screening tool for endometrial cancer screening.