Cancer Diagnosis using LIBS and Machine Learning Tools: Progress and Challenges

Despite numerous research and development efforts that provide important tools to fight cancer, this disease still poses great challenges to diagnosis and treatment, and it remains one of the leading causes of death worldwide. Early diagnosis is crucial to increase the survival rate and quality of life of cancer patients. Thus, developing non-invasive screening methods would represent a key step towards point-of-care large scale screening and prevention of asymptomatic tumors such as Epithelian Ovarian Cancer (EOC) and others. Our group has developed two experimental strategies to pursue early cancer diagnosis through Laser-Induced Breakdown Spectroscopy (LIBS), a versatile atomic spectroscopy technique whose main advantages are: little or no sample preparation required; real-time multi-elemental response; virtually no limitation about the kind of sample that can be analyzed. The first is a LIBS-based immunoassay (Tag-LIBS), where a cancer biomarker is tagged with a suitably functionalized inorganic microparticles, which are in turn quantitatively and sensitively detected by LIBS. The second is based on the direct analysis of biological fluids through the combined use of LIBS and machine learning algorithms. By combining femtosecond LIBS with unsupervised classification techniques, we have shown that it is possible to discriminate blood samples extracted from healthy and diseased mice with an accuracy that approaches 80%. We will present our most recent results obtained with both approaches, and in particular we will report about the effects of various substrates used for LIBS measurements on the classification accuracy of blood samples extracted from cancerous and healthy mice

Diagnosis of Alzheimer's disease using laser-induced breakdown spectroscopy and machine learning

Alzheimer's disease (AD) is a progressive incurable neurodegenerative disease and a major health problem in aging population. We show that the combined use of Laser-Induced Breakdown Spectroscopy (LIBS) and machine learning applied for the analysis of micro-drops of plasma samples of AD and healthy controls (HC) yields robust classification. Following the acquisition of LIBS spectra of 67 plasma samples from a cohort of 31 AD patients and 36 healthy controls (HC), we successfully diagnose late-onset AD (> 65 years old), with a total classification accuracy of 80%, a specificity of 75% and a sensitivity of 85%

Age-specific discrimination of blood plasma samples of healthy and ovarian cancer prone mice using laser-induced breakdown spectroscopy

International audienceEpithelial ovarian cancer (EOC) mortality rates are strongly correlated with the stage at which it is diagnosed. Detection of EOC prior to its dissemination from the site of origin is known to significantly improve the patient outcome. However, there are currently no effective methods for early detection of the most common and lethal subtype of EOC. We sought to determine whether laser-induced breakdown spectroscopy (LIBS) and classification techniques such as linear discriminant analysis (LDA) and random forest (RF) could classify and differentiate blood plasma specimens from transgenic mice with ovarian carcinoma and wild type control mice. Herein we report results using this approach to distinguish blood plasma samples obtained from serially bled (at 8, 12, and 16 weeks) tumor-bearing TgMISIIR-TAg transgenic and wild type cancer-free littermate control mice. We have calculated the age-specific accuracy of classification using 18,000 laser-induced breakdown spectra of the blood plasma samples from tumor-bearing mice and wild type controls. When the analysis is performed in the spectral range 250 nm to 680 nm using LDA, these are 76.7 (± 2.6)%, 71.2 (± 1.3)%, and 73.1 (± 1.4)%, for the 8, 12 and 16 weeks. When the RF classifier is used, we obtain values of 78.5 (± 2.3)%, 76.9 (± 2.1)% and 75.4 (± 2.0)% in the spectral range of 250 nm to 680 nm, and 81.0 (± 1.8)%, 80.4 (± 2.1)% and 79.6 (± 3.5)% in 220 nm to 850 nm. In addition, we report, the positive and negative predictive values of the classification of the two classes of blood plasma samples. The approach used in this study is rapid, requires only 5 μL of blood plasma, and is based on the use of unsupervised and widely accepted multivariate analysis algorithms. These findings suggest that LIBS and multivariate analysis may be a novel approach for detecting EOC

Using LIBS to diagnose melanoma in biomedical fluids deposited on solid substrates: Limits of direct spectral analysis and capability of machine learning

Diagnosis is crucial to increase the success rate of cancer treatments as well as the survival rate and life quality of patients, in particular for forms of cancer that remain largely asymptomatic until metastasis. Methodologies that allow the diagnosis of early-stage tumors as well as the detection of residual disease have the potential to improve cancer control and help monitor therapeutic outcomes. In this work, we report a Laser-Induced Breakdown Spectroscopy (LIBS) approach to early diagnosis of a form of skin cancer, melanoma, based on the analysis of biological fluids (blood and tissue homogenates) harvested from diseased mice and healthy controls. We acquired femtosecond LIBS spectra and used two different approaches for the analysis: through comparison of the emission intensity of selected analytes in healthy and diseased samples; and by using machine learning classification algorithms (LDA, Linear Discriminant Analysis; FDA, Fisher Discriminant Analysis; SVM, Support Vector Machines; and Gradient Boosting). We also addressed the effect of substrates on the analysis of liquid samples, by using four different substrates (PVDF, Cu, Al, Si) and comparing their performance. We show that with a combination of the most appropriate substrate and algorithm, we are able to discriminate between healthy and diseased mice with accuracy up to 96% while direct analysis of LIBS spectra did not provide any conclusive results. These series of results demonstrate that carefully designed LIBS measurements combined with machine learning can be a powerful and practical approach for the diagnosis of cancer

Laser induced breakdown spectroscopy for the rapid detection of SARS-CoV-2 immune response in plasma

As the SARS-CoV-2 pandemic persists, methods that can quickly and reliably confirm infection and immune status is extremely urgently and critically needed. In this contribution we show that combining laser induced breakdown spectroscopy (LIBS) with machine learning can distinguish plasma of donors who previously tested positive for SARS-CoV-2 by RT-PCR from those who did not, with up to 95% accuracy. The samples were also analyzed by LIBS-ICP-MS in tandem mode, implicating a depletion of Zn and Ba in samples of SARS-CoV-2 positive subjects that inversely correlate with CN lines in the LIBS spectra

Optical response of silver clusters and their hollow shells from linear-response TDDFT

We present a study of the optical response of compact and hollow icosahedral clusters containing up to 868 silver atoms by means of time-dependent density functional theory. We have studied the dependence on size and morphology of both the sharp plasmonic resonance at 3-4 eV (originated mainly from $sp$-electrons), and the less studied broader feature appearing in the 6-7 eV range (interband transitions). An analysis of the effect of structural relaxations, as well as the choice of exchange correlation functional (local density versus generalized gradient approximations) both in the ground state and optical response calculations is also presented. We have further analysed the role of the different atom layers (surface versus inner layers) and the different orbital symmetries on the absorption cross-section for energies up to 8 eV. We have also studied the dependence on the number of atom layers in hollow structures. Shells formed by a single layer of atoms show a pronounced red shift of the main plasmon resonances that, however, rapidly converge to those of the compact structures as the number of layers is increased. The methods used to obtain these results are also carefully discussed. Our methodology is based on the use of localized basis (atomic orbitals, and atom-centered- and dominant- product functions), which bring several computational advantages related to their relatively small size and the sparsity of the resulting matrices. Furthermore, the use of basis sets of atomic orbitals also brings the possibility to extend some of the standard population analysis tools (e.g., Mulliken population analysis) to the realm of optical excitations. Some examples of these analyses are described in the present work.Prédiction par calcul numérique intensif du potentiel à circuit ouvert au sein de cellules photovoltaïques organiques