Improving Diagnosis of Cervical Pre-Cancer: Combination of PCA and SVM Applied on Fluorescence Lifetime Images

Sahoo, Goutam Kumar; Singh, Paritosh; Pandey, Kiran; Kala, Chayanika; Pradhan, Asima

doi:10.3390/photonics5040057

Cited by 19 publications

(15 citation statements)

References 43 publications

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“…We used 20 principal components of each observation as input features for the SVM algorithm to reduce the dimensionality of the TOF‐SIMS spectra. The combined application of PCA and SVM was reported in previous studies for the classification of groups with high dimensionality 23,24 …”

Section: Resultsmentioning

confidence: 99%

“…The combined application of PCA and SVM was reported in previous studies for the classification of groups with high dimensionality. 23,24 The prepared SVM model was evaluated for its ability to classify the spectra from tumor and normal tissues based on several parameters, including a ROC curve. Table S2 provides a summary of the evaluation results.…”

Section: Optimization and Classification Accuracy Of The Svm Model mentioning

confidence: 99%

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Screening of important metabolites and KRAS genotypes in colon cancer using secondary ion mass spectrometry

Cho

Choi

Lee

et al. 2020

Bioengineering & Transla Med

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Time‐of‐flight secondary ion mass spectrometry (TOF‐SIMS) is an imaging‐based analytical technique that can characterize the surfaces of biomaterials. We used TOF‐SIMS to identify important metabolites and oncogenic KRAS mutation expressed in human colorectal cancer (CRC). We obtained 540 TOF‐SIMS spectra from 180 tissue samples by scanning cryo‐sections and selected discriminatory molecules using the support vector machine (SVM) algorithm. Each TOF‐SIMS spectrum contained nearly 860,000 ion profiles and hundreds of spectra were analyzed; therefore, reducing the dimensionality of the original data was necessary. We performed principal component analysis after preprocessing the spectral data, and the principal components (20) of each spectrum were used as the inputs of the SVM algorithm using the R package. The performance of the algorithm was evaluated using the receiver operating characteristic (ROC) area under the curve (AUC) (0.9297). Spectral peaks (m/z) corresponding to discriminatory molecules used to classify normal and tumor samples were selected according to p‐value and were assigned to arginine, α‐tocopherol, and fragments of glycerophosphocholine. Pathway analysis using these discriminatory molecules showed that they were involved in gastrointestinal disease and organismal abnormalities. In addition, spectra were classified according to the expression of KRAS somatic mutation, with 0.9921 AUC. Taken together, TOF‐SIMS efficiently and simultaneously screened metabolite biomarkers and performed KRAS genotyping. In addition, a machine learning algorithm was provided as a diagnostic tool applied to spectral data acquired from clinical samples prepared as frozen tissue slides, which are commonly used in a variety of biomedical tests.

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Section: Resultsmentioning

confidence: 99%

Section: Optimization and Classification Accuracy Of The Svm Model mentioning

confidence: 99%

Screening of important metabolites and KRAS genotypes in colon cancer using secondary ion mass spectrometry

Cho

Choi

Lee

et al. 2020

Bioengineering & Transla Med

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“…Most recently, FLIM classifiers have been applied in vitro for label-free assessment of microglia 86 and T-cell activation, 87 as well as for exogenous labeling of intracellular components 88 and monitoring of intracellular pharmacokinetics. 89 In addition, ML classifiers have been used for FLIM-based tissue discrimination and characterization in applications including diagnosis of cervical precancer, 90 breast cancer resection 91 [ Figs. 3(a) – 3(e) ], and oropharyngeal margin assessment.…”

Section: Deep Learning For Macroscopic Fluorescence Lifetime Imagingmentioning

confidence: 99%

Deep learning in macroscopic diffuse optical imaging

et al. 2022

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. Significance : Biomedical optics system design, image formation, and image analysis have primarily been guided by classical physical modeling and signal processing methodologies. Recently, however, deep learning (DL) has become a major paradigm in computational modeling and has demonstrated utility in numerous scientific domains and various forms of data analysis. Aim : We aim to comprehensively review the use of DL applied to macroscopic diffuse optical imaging (DOI). Approach : First, we provide a layman introduction to DL. Then, the review summarizes current DL work in some of the most active areas of this field, including optical properties retrieval, fluorescence lifetime imaging, and diffuse optical tomography. Results : The advantages of using DL for DOI versus conventional inverse solvers cited in the literature reviewed herein are numerous. These include, among others, a decrease in analysis time (often by many orders of magnitude), increased quantitative reconstruction quality, robustness to noise, and the unique capability to learn complex end-to-end relationships. Conclusions : The heavily validated capability of DL’s use across a wide range of complex inverse solving methodologies has enormous potential to bring novel DOI modalities, otherwise deemed impractical for clinical translation, to the patient’s bedside.

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“…In fluorescence spectroscopy, various native fluorophores like NADH, FAD, collagen, and porphyrins, present in different layers of tissue, are targeted with a particular excitation wavelength (ultraviolet or visible) and their respective fluorescence emission is observed in the long wavelength region (visible or near-infrared) [32,33]. The biochemical and morphological changes occurring in the layered tissue structure with the progression of abnormality [34][35][36][37] lead to changes in fluorescence spectral characteristics of different fluorophores. The native fluorescence of the fluorophores is also modulated by various wavelength dependent absorption and scattering present inside the biological tissue.…”

Section: Introductionmentioning

confidence: 99%

Cervical pre‐cancer classification using entropic features and CNN: In vivo validation with a handheld fluorescence probe

Deo,

Sah,

Shukla

et al. 2023

Journal of Biophotonics

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Cervical cancer is one of the most prevalent forms of cancer, with a lengthy latent period and a gradual onset phase. Conventional techniques are found to be severely lacking in real time detection of disease progression which can greatly enhance the cure rate. Due to their high sensitivity and specificity, optical techniques are emerging as reliable tools, particularly in case of cancer. It has been seen that biochemical changes are better highlighted through intrinsic fluorescence devoid of interference from absorption and scattering. Its effectiveness in in‐vivo conditions is affected by the fact that the intrinsic spectral signatures vary from patient to patient, as well as in different population groups. Here, we overcome this limitation by collectively enumerating the subtle changes in the spectral profiles and correlations through an information theory based entropic approach, which significantly amplifies the minute spectral variations. In conjunction with artificial intelligence (AI)/machine learning (ML) tools, it yields high specificity and sensitivity with a small dataset from patients in clinical conditions, without artificial augmentation. We have used an in‐house developed handheld probe (i‐HHP) for extracting intrinsic fluorescence spectra of human cervix from 110 different subjects drawn from diverse population groups. The average classification accuracy of the proposed methodology using 10‐fold cross validation is 93.17%. A combination of polarised fluorescence spectra from i‐HHP and the proposed classifier is proven to be minimally invasive with the ability to diagnose patients in real time. This paves the way for effective use of relatively smaller sized sensitive fluorescence data with advanced AI/ML tools for early cervical cancer detection in clinics.

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Improving Diagnosis of Cervical Pre-Cancer: Combination of PCA and SVM Applied on Fluorescence Lifetime Images

Cited by 19 publications

References 43 publications

Screening of important metabolites and KRAS genotypes in colon cancer using secondary ion mass spectrometry

Screening of important metabolites and KRAS genotypes in colon cancer using secondary ion mass spectrometry

Deep learning in macroscopic diffuse optical imaging

Cervical pre‐cancer classification using entropic features and CNN: In vivo validation with a handheld fluorescence probe

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