Advancing cervical cancer diagnosis and screening with spectroscopy and machine learning

Ramírez, Carlos; Greenop, Michael; Almoshawah, Yasser A; Hirsch, Pierre L. Martin; Rehman, Ihtesham Ur

doi:10.1080/14737159.2023.2203816

Cited by 9 publications

(2 citation statements)

References 95 publications

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“…Concurrently, the integration of machine learning and chemometrics with spectroscopy has gained interest not just for medical diagnostics, [33][34][35] but also for applications such as food quality control, detection of chloramphenicol in food products, 36 and the comparative study of chemometric challenges in food analysis. 37 The energy sector is similarly evolving with these methodologies.…”

Section: Introductionmentioning

confidence: 99%

Complete characterization of RNA biomarker fingerprints using a multi-modal ATR-FTIR and SERS approach for label-free early breast cancer diagnosis

Zhang,

Wu,

Hum

et al. 2024

RSC Adv.

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Section: Introductionmentioning

confidence: 99%

Complete characterization of RNA biomarker fingerprints using a multi-modal ATR-FTIR and SERS approach for label-free early breast cancer diagnosis

Zhang,

Wu,

Hum

et al. 2024

RSC Adv.

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“…It has the potential to detect minute morphological and biochemical changes that occur inside the cervical tissue structure during the initial stages of the disease, based on the interaction of different fluorophores (FAD, NADH, porphyrin, and collagen) present inside the tissue structure [13,14]. To enhance the efficiency and accuracy of disease detection, integrating machine learning (ML) with spectral data analysis is essential [15][16][17]. However, ML techniques typically demand an efficient feature extraction approach to effectively capture the intricate variability present in the spectral data.…”

Section: Introductionmentioning

confidence: 99%

Wavelet scattering transform and entropy features in fluorescence spectral signal analysis for cervical cancer diagnosis

Deo,

Nayak,

Pal

et al. 2024

Biomed. Phys. Eng. Express

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Cervical cancer is a prevalent malignant tumor within the female reproductive system and is regarded as a prominent cause of female mortality on a global scale. Timely and precise detection of various phases of cervical cancer holds the potential to substantially enhance both the rate of successful treatment and the duration of patient survival. Fluorescence spectroscopy is a highly sensitive method for detecting the biochemical changes that arise during cancer progression. In our study, fluorescence spectral data is collected from a diverse group of 110 subjects. The potential of the scattering transform technique for the purpose of cancer detection is explored. The processed signal undergoes an initial decomposition into scattering coefficients using the wavelet scattering transform (WST). Subsequently, the scattering coefficients are subjected to computation for fuzzy entropy, dispersion entropy, phase entropy, and spectral entropy, for effectively characterizing the fluorescence spectral signals. These combined features generated through the proposed approach are then fed to 1D convolutional neural network (CNN) classifier to classify them into normal, pre-cancerous, and cancerous categories, thereby evaluating the effectiveness of the proposed methodology. We obtained mean classification accuracy of 97% using 5-fold cross-validation. This demonstrates the potential of combining WST and entropic features for analyzing fluorescence spectroscopy signals using 1D CNN classifier that enables early cancer detection in contrast to prevailing diagnostic methods.

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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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Advancing cervical cancer diagnosis and screening with spectroscopy and machine learning

Cited by 9 publications

References 95 publications

Complete characterization of RNA biomarker fingerprints using a multi-modal ATR-FTIR and SERS approach for label-free early breast cancer diagnosis

Complete characterization of RNA biomarker fingerprints using a multi-modal ATR-FTIR and SERS approach for label-free early breast cancer diagnosis

Wavelet scattering transform and entropy features in fluorescence spectral signal analysis for cervical cancer diagnosis

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

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