Evaluating oral epithelial dysplasia classification system by near-infrared Raman spectroscopy

Label-Free Optical Spectroscopy for Early Detection of Oral Cancer

Oral cancer is the 16th most common cancer worldwide. It commonly arises from painless white or red plaques within the oral cavity. Clinical outcome is highly related to the stage when diagnosed. However, early diagnosis is complex owing to the impracticality of biopsying every potentially premalignant intraoral lesion. Therefore, there is a need to develop a non-invasive cost-effective diagnostic technique to differentiate non-malignant and early-stage malignant lesions. Optical spectroscopy may provide an appropriate solution to facilitate early detection of these lesions. It has many advantages over traditional approaches including cost, speed, objectivity, sensitivity, painlessness, and ease-of use in clinical setting for real-time diagnosis. This review consists of a comprehensive overview of optical spectroscopy for oral cancer diagnosis, epidemiology, and recent improvements in this field for diagnostic purposes. It summarizes major developments in label-free optical spectroscopy, including Raman, fluorescence, and diffuse reflectance spectroscopy during recent years. Among the wide range of optical techniques available, we chose these three for this review because they have the ability to provide biochemical information and show great potential for real-time deep-tissue point-based in vivo analysis. This review also highlights the importance of saliva-based potential biomarkers for non-invasive early-stage diagnosis. It concludes with the discussion on the scope of development and future demands from a clinical point of view.

Automated detection of premalignant oral lesions on whole slide images using convolutional neural networks

INTRODUCTION

Oral epithelial dysplasia (OED) is a precursor lesion to oral squamous cell carcinoma, a disease with a reported overall survival rate of 56 percent across all stages. Accurate detection of OED is critical as progression to oral cancer can be impeded with complete excision of premalignant lesions. However, previous research has demonstrated that the task of grading of OED, even when performed by highly trained experts, is subject to high rates of reader variability and misdiagnosis. Thus, our study aims to develop a convolutional neural network (CNN) model that can identify regions suspicious for OED whole-slide pathology images.

METHODS

During model development, we optimized key training hyperparameters including loss function on 112 pathologist annotated cases between the training and validation sets. Then, we compared OED segmentation and classification metrics between two well-established CNN architectures for medical imaging, DeepLabv3+ and UNet++. To further assess generalizability, we assessed case-level performance of a held-out test set of 44 whole-slide images.

RESULTS

DeepLabv3+ outperformed UNet++ in overall accuracy, precision, and segmentation metrics in a 4-fold cross validation study. When applied to the held-out test set, our best performing DeepLabv3+ model achieved an overall accuracy and F1-Score of 93.3 percent and 90.9 percent, respectively.

CONCLUSION

The present study trained and implemented a CNN-based deep learning model for identification and segmentation of oral epithelial dysplasia (OED) with reasonable success. Computer assisted detection was shown to be feasible in detecting premalignant/precancerous oral lesions, laying groundwork for eventual clinical implementation.

Prediction of malignant transformation in oral epithelial dysplasia using machine learning

A machine learning algorithm (MLA) has been applied to a Fourier transform infrared spectroscopy (FTIR) dataset previously analysed with a principal component analysis (PCA) linear discriminant analysis (LDA) model. This comparison has confirmed the robustness of FTIR as a prognostic tool for oral epithelial dysplasia (OED). The MLA is able to predict malignancy with a sensitivity of 84 ± 3% and a specificity of 79 ± 3%. It provides key wavenumbers that will be important for the development of devices that can be used for improved prognosis of OED.

Prediction of malignant transformation in oral epithelial dysplasia using infrared absorbance spectra

Oral epithelial dysplasia (OED) is a histopathologically-defined, potentially premalignant condition of the oral cavity. The rate of transformation to frank carcinoma is relatively low (12% within 2 years) and prediction based on histopathological grade is unreliable, leading to both over- and under-treatment. Alternative approaches include infrared (IR) spectroscopy, which is able to classify cancerous and non-cancerous tissue in a number of cancers, including oral. The aim of this study was to explore the capability of FTIR (Fourier-transform IR) microscopy and machine learning as a means of predicting malignant transformation of OED. Supervised, retrospective analysis of longitudinally-collected OED biopsy samples from 17 patients with high risk OED lesions: 10 lesions transformed and 7 did not over a follow-up period of more than 3 years. FTIR spectra were collected from routine, unstained histopathological sections and machine learning used to predict malignant transformation, irrespective of OED classification. PCA-LDA (principal component analysis followed by linear discriminant analysis) provided evidence that the subsequent transforming status of these 17 lesions could be predicted from FTIR data with a sensitivity of 79 ± 5% and a specificity of 76 ± 5%. Six key wavenumbers were identified as most important in this classification. Although this pilot study used a small cohort, the strict inclusion criteria and classification based on known outcome, rather than OED grade, make this a novel study in the field of FTIR in oral cancer and support the clinical potential of this technology in the surveillance of OED.

Optical diagnosis of oral cavity lesions by label-free Raman spectroscopy

Oral squamous cell carcinoma (OSCC) is one of the most prevalent cancers and frequently preceded by non-malignant lesions. Using Shifted-Excitation Raman Difference Spectroscopy (SERDS), principal component and linear discriminant analysis in native tissue specimens, 9500 raw Raman spectra of OSCC, 4300 of non-malignant lesions and 4200 of physiological mucosa were evaluated. Non-malignant lesions were distinguished from physiological mucosa with a classification accuracy of 95.3% (95.4% sensitivity, 95.2% specificity, area under the curve (AUC) 0.99). Discriminating OSCC from non-malignant lesions showed an accuracy of 88.4% (93.7% sensitivity, 76.7% specificity, AUC 0.93). OSCC was identified against physiological mucosa with an accuracy of 89.8% (93.7% sensitivity, 81.0% specificity, AUC 0.90). These findings underline the potential of SERDS for the diagnosis of oral cavity lesions.

Raman spectroscopy and chemometrics: A potential universal method for diagnosing cancer

Cancer is the second-leading cause of death worldwide. It affects an unfathomable number of people, with almost 16 million Americans currently living with it. While many cancers can be detected, current diagnostic efforts exhibit definite room for improvement. It is imperative that a person be diagnosed with cancer as early on in its progression as possible. An earlier diagnosis allows for the best treatment and intervention options available to be presented. Unfortunately, existing methods for diagnosing cancer can be expensive, invasive, inconclusive or inaccurate, and are not always made during initial stages of the disease. As such, there is a crucial unmet need to develop a singular universal method that is reliable, cost-effective, and non-invasive and can diagnose all forms of cancer early-on. Raman spectroscopy in combination with advanced statistical analysis is offered here as a potential solution for this need. This review covers recently published research in which Raman spectroscopy was used for the purpose of diagnosing cancer. The benefits and the risks of the methodology are presented; however, there is overwhelming evidence that suggests Raman spectroscopy is highly suitable for becoming the first universal method to be used for diagnosing cancer.

Chemometric analysis of integrated FTIR and Raman spectra obtained by non-invasive exfoliative cytology for the screening of oral cancer

FTIR spectroscopy and Raman spectroscopy of biological analytes are increasingly explored as screening tools for early detection of cancer.

Surface-enhanced Raman spectroscopy of blood serum based on gold nanoparticles for tumor stages detection and histologic grades classification of oral squamous cell carcinoma

Background

Tumor stages detection and histologic grades classification are essential for the diagnosis and prognosis of oral squamous cell carcinoma (OSCC). In this research, we apply surface-enhanced Raman spectroscopy (SERS) of blood serum to detect the tumor stages and histologic classification of OSCC.

Methods

According to TNM classification and World Health Organization histologic grading system, the blood serum samples were collected from a total of 135 OSCC patients in the different tumor stages and histologic grades. Then the SERS spectra of serum samples from OSCC patients were diagnosed and classified into different groups using principal component analysis (PCA) and linear discriminant analysis (LDA) based on the tumor sizes, lymph node metastasis and histologic grades.

Results

The SERS spectra of blood serum samples have shown the distinct changes and differences compared with each other, which were assigned to the biomolecule alterations (nucleic acids, proteins, lipids, and so on) in blood serums. And all accuracies of detection and classification reached above 85%.

Conclusion

This study demonstrated that the SERS based on blood serum test had an enormous potential to carry out the preoperative assessment and prediction of the OSCC patients in different tumor stages and histologic classification.