710 Optomagnetic imaging spectroscopy in characterisation of cervical tissue and cancer detection using unstained sample approach

Papic-Obradovic, Milena; Jeftić, Branislava; Dragičević, Aleksandra; Munćan, Jelena; Matija, Lidija; Koruga, Djuro

doi:10.1016/s0959-8049(16)30380-x

Cited by 3 publications

(3 citation statements)

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“…In our previous work, we have proposed new system for semi-automated classification of cervical samples by combining Optomagnetic Imaging Spectroscopy with machine learning algorithms [16,17,19]. In this paper, we have expanded our research to LBC samples.…”

Section: Resultsmentioning

confidence: 99%

“…Sample spectra have been obtained by Optomagnetic Imaging Spectroscopy (OMIS), which is easy to use, fast and inexpensive [15]. Previous studies reported high accuracy of OMIS in detection of abnormal cervical samples prepared according to conventional Papanicolaou procedure, as well as in detecting colon cancer [16][17][18][19]. Here, classification results for LBC samples, obtained by selected supervised learning algorithms have been compared, in order to investigate whether improved sample preparation method, i.e.…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning Classification of Cervical Tissue Liquid Based Cytology Smear Images by Optomagnetic Imaging Spectroscopy

et al. 2019

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Semi-automated system for classification of cervical smear images based on Optomagnetic Imaging Spectroscopy (OMIS) and machine learning is proposed. Optomagnetic Imaging Spectroscopy has been applied to screen 700 cervical samples prepared according to Liquid Based Cytology (LBC) principles and to record spectra of the samples. Peak intensities and peak shift frequencies from the spectra have been used as features in classification models. Several machine learning algorithms have been tested and results of classification have been compared. Results suggest that the presented approach can be used to improve standard LBC screening tests for cervical cancer detection. Developed system enables detection of pre-cancerous and cancerous states with sensitivity of 79% and specificity of 83% along with AUC (ROC) of 88% and could be used as an improved alternative procedure for cervical cancer screening. Moreover, this can be achieved via portable apparatus and with immediately available results.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Machine Learning Classification of Cervical Tissue Liquid Based Cytology Smear Images by Optomagnetic Imaging Spectroscopy

et al. 2019

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“…From these facts, it can be concluded that colorectal cancer is not brought under control in highly developed countries, that some of these countries even show a growth rate. All this proves a necessity of introducing new methods to improve screening [4].…”

Section: Introductionmentioning

confidence: 99%

Different Types of Colorectal Carcinoma Characterization Using Stained and Non Stained Plates by Opto-Magnetic Spectroscopy

Dragičević

Krivokapić

Marković

et al. 2014

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According to the World Health Organization cancer is the leading cause of death worldwide and on the third place is the most common and dangerous colorectal cancer with the rate of 17.3/100,000 people. Therefore, it is necessary to find effective solutions to improve the quality and speed of diagnostic tools so that they can respond to the challenges of prevention strategies which are an integral part of the rapid diagnosis of a large number of patients. The objectives of the research conducted in this study (in vitro) are: (1) examining the possibilities of application of opto-magnetic imaging spectroscopy for characterization between different types of colon cancer, stained and non-stained plates, (2) method validation, (3) parameters determination for research in in vivo conditions, and (4) application in clinical trial to increase the efficiency and give more reliable diagnosis. In this paper we present Opto-magnetic imaging spectroscopy (OMIS) as a novel optical method for differentiation of different types of colon cancer, significant similarity between stained and non-stained plates based on light-tissue interaction. Results have showed that OMIS can make a difference between colorectal carcinoma and other types of colon cancer, as well as prove that plates staining can be avoided.

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Classification of Healthy and Cancer States of Colon Epithelial Tissues Using Opto-magnetic Imaging Spectroscopy

et al. 2018

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710 Optomagnetic imaging spectroscopy in characterisation of cervical tissue and cancer detection using unstained sample approach

Cited by 3 publications

References 0 publications

Machine Learning Classification of Cervical Tissue Liquid Based Cytology Smear Images by Optomagnetic Imaging Spectroscopy

Machine Learning Classification of Cervical Tissue Liquid Based Cytology Smear Images by Optomagnetic Imaging Spectroscopy

Different Types of Colorectal Carcinoma Characterization Using Stained and Non Stained Plates by Opto-Magnetic Spectroscopy

Classification of Healthy and Cancer States of Colon Epithelial Tissues Using Opto-magnetic Imaging Spectroscopy

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