Machine learning-based LIBS spectrum analysis of human blood plasma allows ovarian cancer diagnosis

Yue, Zengqi; Sun, Chen; Chen, Fengye; Zhang, Yuqing; Xu, Weijie; Shabbir, Sahar; Zou, Long; Lü, Weiguo; Wang, Wei; Xie, Zhenwei; Zhou, Lili; Lu, Yan; Yu, Jianhua

doi:10.1364/boe.421961

Cited by 51 publications

(20 citation statements)

References 36 publications

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“…Yue et al [35] used LIBS combined with machine learning techniques to diagnose and classify ovarian cancer. They also used the blood plasma of 176 patients, including ovarian cyst and normal samples.…”

Section: Libs Analysis For Different Cancers Ovarian Cancermentioning

confidence: 99%

A Review on Laser-Induced Breakdown Spectroscopy in Different Cancers Diagnosis and Classification

et al. 2022

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Due to the new demonstrations of Laser-induced breakdown spectroscopy (LIBS) applicability in a surprisingly wide variety of applications, the use of LIBS as a medical diagnostic tool is steadily gaining momentum. Especially in different cancer diseases, LIBS has the potential to become a fast and valuable analytical tool. We addressed LIBS equipment and quantitative analytical procedures, and signal enhancement techniques for improving element detection. For detailed aspects of applications, we reviewed the recent progress of LIBS in different cancer diseases diagnoses by using different tissues and medical fluid as samples. To fulfill the high demands in the medical industry and overcome the severe tissue sample problem, it is proposed that the chemometric and signal amplification techniques for quantitative analysis should be employed, and robust and effective LIBS devices should be developed. This overview of the different cancers by LIBS is meant to summarize the research performed to date and suggest some suitable advanced chemometrics techniques and effective LIBS devices, if successfully implemented, would be significantly beneficial to the medical field in the future.

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Section: Libs Analysis For Different Cancers Ovarian Cancermentioning

confidence: 99%

A Review on Laser-Induced Breakdown Spectroscopy in Different Cancers Diagnosis and Classification

et al. 2022

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“…A transfer learning model training algorithm was developed in this work on the basis of that used for machine learning model training presented in detail in our previous publication 13 and used for various application scenarios 12 , 29 – 33 . The flowchart of transfer learning model training is shown in Fig.…”

Section: Data Treatment Methodsmentioning

confidence: 99%

From machine learning to transfer learning in laser-induced breakdown spectroscopy analysis of rocks for Mars exploration

Sun

Tan

et al. 2021

Sci Rep

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With the ChemCam instrument, laser-induced breakdown spectroscopy (LIBS) has successively contributed to Mars exploration by determining the elemental compositions of soils, crusts, and rocks. The American Perseverance rover and the Chinese Zhurong rover respectively landed on Mars on February 18 and May 15, 2021, further increase the number of LIBS instruments on Mars. Such an unprecedented situation requires a reinforced research effort on the methods of LIBS spectral data analysis. Although the matrix effects correspond to a general issue in LIBS, they become accentuated in the case of rock analysis for Mars exploration, because of the large variation of rock compositions leading to the chemical matrix effect, and the difference in surface physical properties between laboratory standards (in pressed powder pellet, glass or ceramic) used to establish calibration models and natural rocks encountered on Mars, leading to the physical matrix effect. The chemical matrix effect has been tackled in the ChemCam project with large sets of laboratory standards offering a good representation of various compositions of Mars rocks. The present work more specifically deals with the physical matrix effect which is still lacking a satisfactory solution. The approach consists in introducing transfer learning in LIBS data treatment. For the specific application of total alkali-silica (TAS) classification of rocks (either with a polished surface or in the raw state), the results show a significant improvement in the ability to predict of pellet-based models when trained together with suitable information from rocks in a procedure of transfer learning. The correct TAS classification rate increases from 25% for polished rocks and 33.3% for raw rocks with a machine learning model, to 83.3% with a transfer learning model for both types of rock samples.

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“…A transfer learning model training algorithm was developed in this work on the basis of that used for machine learning model training presented in detail in our previous publication 13 and used in various application scenarios. 12,[26][27][28][29][30] The flowchart of transfer learning model training is shown in Fig. 5.…”

Section: Transfer Learning-based Calibration Model Trainingmentioning

confidence: 99%

From Machine Learning to Transfer Learning in Laser-Induced Breakdown Spectroscopy: the Case of Rock Analysis for Mars Exploration

Sun¹,

Xu²,

Tan³

et al. 2021

Preprint

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With the ChemCam instrument, laser-induced breakdown spectroscopy (LIBS) has successively contributed to Mars exploration by determining elemental compositions of soils, crusts and rocks. American Perseverance landed since Feb 18, 2021 on Mars and Chinese Tianwen 1 planned for landing soon, further increase the number of LIBS instruments on Mars. Such unprecedented situation requires a reinforced research effort on the methods of LIBS spectral data treatment. Although the matrix effects correspond to a general issue in LIBS, they become accentuated in the case of rock analysis for Mars exploration, because of the large variation of rock compositions leading to the chemical matrix effect, and the difference in surface physical properties between laboratory standards (in pressed powder pellet, glass or ceramic) used to establish calibration models and natural rocks encountered on Mars, leading to the physical matrix effect. The chemical matrix effect has been tackled in the ChemCam project with large sets of laboratory standards offering a good representation of various compositions of Mars rocks. The present work more specifically deals with the physical matrix effect which is still expecting a satisfactory solution. The approach consists in introducing transfer learning in LIBS data treatment. For the specific application of total alkali-silica (TAS) classification of rocks (either with a polished surface or in the raw state), the results show a significant improvement of the prediction ability of pellet-based models when trained together with suitable information from rocks in a procedure of transfer learning. The correct TAS classification rate increases from 25% for polished rocks and 33.3% for raw rocks with a machine learning model, to 83.3% with a transfer learning model for the both types of rock samples.

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Machine learning-based LIBS spectrum analysis of human blood plasma allows ovarian cancer diagnosis

Cited by 51 publications

References 36 publications

A Review on Laser-Induced Breakdown Spectroscopy in Different Cancers Diagnosis and Classification

A Review on Laser-Induced Breakdown Spectroscopy in Different Cancers Diagnosis and Classification

From machine learning to transfer learning in laser-induced breakdown spectroscopy analysis of rocks for Mars exploration

From Machine Learning to Transfer Learning in Laser-Induced Breakdown Spectroscopy: the Case of Rock Analysis for Mars Exploration

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