Stratification of tumour cell radiation response and metabolic signatures visualization with Raman spectroscopy and explainable convolutional neural network

Fuentes, Alejandra M.; Milligan, Kirsty; Wiebe, Mitchell; Narayan, Apurva; Lum, Julian J.; Brolo, Alexandre G.; Andrews, Jeffrey L.; Jirasek, Andrew

doi:10.1039/d3an01797d

Cited by 5 publications

(4 citation statements)

References 54 publications

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“…The article tackles the task of distinguishing between tumor-repopulating cells (TRCs) and parental control cells while determining the best combination of data type, dimension size, and classification technique to differentiate the cell types. 51 Raman spectra were collected from 13 samples: eight parental controls for the 37 spectra and five TRCs for the 14 spectra. An accuracy of 98% is obtained from SVM and kNN classifiers.…”

Section: A Pancreatic Cancermentioning

confidence: 99%

“…This proves that different or even multiple tasks could be performed at once, enabling parallel and multi-level analysis. Furthermore, several studies (e.g., [39], [45], [51], [56]) made efforts to provide explanations for their findings, illustrating when the outputs were useful for the implemented algorithms. This aspect holds significant importance as it can improve outcomes across various domains, particularly within healthcare.…”

Section: A Ai Analysis and Insightsmentioning

confidence: 99%

“…[51] explored the use of RS and CNN to characterize tumor response to radiotherapy, with a focus on identifying its degree of radioresistance with an explainable AI technique. Their dataset comprised Raman spectra gathered from three distinct human tumor cell lines (breast, lung, and prostate cancer) cultivated in vitro, classified as either radiosensitive or radioresistant across various treatment doses and time points.…”

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confidence: 99%

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Raman Spectroscopy and AI Applications in Cancer Grading: An Overview

Conforti,

Lazzini,

Russo

et al. 2024

IEEE Access

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Raman spectroscopy (RS) is a label-free molecular vibrational spectroscopy technique that is able to identify the molecular fingerprint of various samples making use of the inelastic scattering of monochromatic light. Because of its advantages of non-destructive and accurate detection, RS is finding more and more use for benign and malignant tissues, tumor differentiation, tumor subtype classification, and section pathology diagnosis, operating either in vivo or in vitro. However, the high specificity of RS comes at a cost. The acquisition rate is low, depth information cannot be directly accessed, and the sampling area is limited. Such limitations can be contained if data pre-and post-processing methods are combined with current methods of Artificial Intelligence (AI), essentially, Machine Learning (ML) and Deep Learning (DL). The latter is modifying the approach to cancer diagnosis currently used to automate many cancer data analyses, and it has emerged as a promising option for improving healthcare accuracy and patient outcomes by abiliting prediction diseases tools. In a very broad context, Artificial Intelligence applications in oncology include risk assessment, early diagnosis, patient prognosis estimation, and treatment selection based on deep knowledge. The application of autonomous methods to datasets generated by RS analysis of benign and malignant tissues could make RS a rapid and stand-alone technique to help pathologists diagnose cancer with very high accuracy. This review describes the current milestones achieved by applying AI-based algorithms to RS analysis, grouped according to seven major types of cancers (Pancreatic, Breast, Skin, Brain, Prostate, Ovarian and Oral cavity). Additionally, it provides a theoretical foundation to tackle both present and forthcoming challenges in this domain. By exploring the current achievements and discussing the relative methodologies, this review offers recapitulative insights on recent and ongoing efforts to position RS as a rapid and effective cancer screening tool for pathologists. Accordingly, we aim to encourage future research endeavors and to facilitate the realization of the full potential of RS and AI applications in cancer grading.

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Section: A Pancreatic Cancermentioning

confidence: 99%

Section: A Ai Analysis and Insightsmentioning

confidence: 99%

mentioning

confidence: 99%

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Raman Spectroscopy and AI Applications in Cancer Grading: An Overview

Conforti,

Lazzini,

Russo

et al. 2024

IEEE Access

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“…When explainable NN employing mathematical functions such as Gradient-Weighted Class Activation Mapping is utilized, one can extract the explanatory features to understand the model’s classification/prediction decisions. Adapted in part with permission from ref . Copyright 2024 Royal Society of Chemistry.…”

Section: Sers Imaging Of Brain Tissues and Surgeries With Nanoparticl...mentioning

confidence: 99%

Surface-Enhanced Raman Scattering Nanosensing and Imaging in Neuroscience

Boudries,

Williams,

Paquereau--Gaboreau

et al. 2024

ACS Nano

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Monitoring neurochemicals and imaging the molecular content of brain tissues in vitro, ex vivo, and in vivo is essential for enhancing our understanding of neurochemistry and the causes of brain disorders. This review explores the potential applications of surface-enhanced Raman scattering (SERS) nanosensors in neurosciences, where their adoption could lead to significant progress in the field. These applications encompass detecting neurotransmitters or brain disorders biomarkers in biofluids with SERS nanosensors, and imaging normal and pathological brain tissues with SERS labeling. Specific studies highlighting in vitro, ex vivo, and in vivo analysis of brain disorders using fit-for-purpose SERS nanosensors will be detailed, with an emphasis on the ability of SERS to detect clinically pertinent levels of neurochemicals. Recent advancements in designing SERS-active nanomaterials, improving experimentation in biofluids, and increasing the usage of machine learning for interpreting SERS spectra will also be discussed. Furthermore, we will address the tagging of tissues presenting pathologies with nanoparticles for SERS imaging, a burgeoning domain of neuroscience that has been demonstrated to be effective in guiding tumor removal during brain surgery. The review also explores future research applications for SERS nanosensors in neuroscience, including monitoring neurochemistry in vivo with greater penetration using surface-enhanced spatially offset Raman scattering (SESORS), near-infrared lasers, and 2-photon techniques. The article concludes by discussing the potential of SERS for investigating the effectiveness of therapies for brain disorders and for integrating conventional neurochemistry techniques with SERS sensing.

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