Machine learning-based rapid diagnosis of human borderline ovarian cancer on second-harmonic generation images

Wang, Guangxing; Sun, Yang; Jiang, Shuisen; Wu, Guizhu; Liao, Wenliang; Chen, Yuwei; Zhang, Lin; Liu, Zhiyi; Zhuo, Shuangmu

doi:10.1364/boe.429918

Cited by 20 publications

(15 citation statements)

References 33 publications

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“…The second-order NLO process in which photons that interact with a nonlinear material “combine” effectively is known as second-harmonic generation (SHG) [ 181 ]. SHG, which depends on a second-order NLO difference system, permits specialists to perform non-checking and non-horrendous imaging of tissue structures at the cell level [ 182 ]. Currently, when relevant areas in SHG images are detected, further medical actions can be proposed [ 183 ].…”

Section: Nlo Processes Analyzed With MLmentioning

confidence: 99%

A Framework for Biosensors Assisted by Multiphoton Effects and Machine Learning

et al. 2022

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The ability to interpret information through automatic sensors is one of the most important pillars of modern technology. In particular, the potential of biosensors has been used to evaluate biological information of living organisms, and to detect danger or predict urgent situations in a battlefield, as in the invasion of SARS-CoV-2 in this era. This work is devoted to describing a panoramic overview of optical biosensors that can be improved by the assistance of nonlinear optics and machine learning methods. Optical biosensors have demonstrated their effectiveness in detecting a diverse range of viruses. Specifically, the SARS-CoV-2 virus has generated disturbance all over the world, and biosensors have emerged as a key for providing an analysis based on physical and chemical phenomena. In this perspective, we highlight how multiphoton interactions can be responsible for an enhancement in sensibility exhibited by biosensors. The nonlinear optical effects open up a series of options to expand the applications of optical biosensors. Nonlinearities together with computer tools are suitable for the identification of complex low-dimensional agents. Machine learning methods can approximate functions to reveal patterns in the detection of dynamic objects in the human body and determine viruses, harmful entities, or strange kinetics in cells.

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Section: Nlo Processes Analyzed With MLmentioning

confidence: 99%

A Framework for Biosensors Assisted by Multiphoton Effects and Machine Learning

et al. 2022

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“…By combining these results with AI, it is possible to predict the metastatic potential of cancer cells. Recent evidences report that diagnosis based on SHG images and ML can support the rapid and accurate detection of some kinds of cancer in clinical practice [ 121 ]. COC device hosting engineered tumoral tissue presenting native ECM (cell-synthesized or cell-derived) have been already used to be analyzed by SHG-MPM to detect pathological alterations to the ECM.…”

Section: Coupling Cancer On Chip and Artificial Intelligence For Future Cancer Managementmentioning

confidence: 99%

Organ on Chip Technology to Model Cancer Growth and Metastasis

2022

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Organ on chip (OOC) has emerged as a major technological breakthrough and distinct model system revolutionizing biomedical research and drug discovery by recapitulating the crucial structural and functional complexity of human organs in vitro. OOC are rapidly emerging as powerful tools for oncology research. Indeed, Cancer on chip (COC) can ideally reproduce certain key aspects of the tumor microenvironment (TME), such as biochemical gradients and niche factors, dynamic cell–cell and cell–matrix interactions, and complex tissue structures composed of tumor and stromal cells. Here, we review the state of the art in COC models with a focus on the microphysiological systems that host multicellular 3D tissue engineering models and can help elucidate the complex biology of TME and cancer growth and progression. Finally, some examples of microengineered tumor models integrated with multi-organ microdevices to study disease progression in different tissues will be presented.

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“…Although the growth pattern of ovarian cancer is different, Borderline OC normally falls between the benign cancer and malignant cancer, which is usually not dangerous but can produce malignancy. Therefore [20] quickly diagnosis of borderline tumor of these tissues. The data is taken from the-Cancer-hospital-of-Fujian-Medical-University which includes 6 ovarian tissues for normal, 7 for benign, 6 for borderline and 7 tissues for malignant from 20 patients, about 335 S-H-G images collected from these ovarian tissues as SHG imaging technique is mainly provides non-destructive and label free visualization of the structure of tissues which are at cellular level [20].…”

Section: Figure 1 Medical Imaging With ML and Dlmentioning

confidence: 99%

“…Therefore [20] quickly diagnosis of borderline tumor of these tissues. The data is taken from the-Cancer-hospital-of-Fujian-Medical-University which includes 6 ovarian tissues for normal, 7 for benign, 6 for borderline and 7 tissues for malignant from 20 patients, about 335 S-H-G images collected from these ovarian tissues as SHG imaging technique is mainly provides non-destructive and label free visualization of the structure of tissues which are at cellular level [20]. The area under the ROC curve for normal tissues is 0.97%, 0.99% for benign, 0.98% for borderline and 1.00% for malignant tissues.…”

Section: Figure 1 Medical Imaging With ML and Dlmentioning

confidence: 99%

Systematic Analysis of Ovarian Cancer Empowered with Machine and Deep Learning: A Taxonomy and Future Challenges

Sajjad

Khan

Nawaz

et al. 2022

JCBI

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Machine and Deep learning has witnessed an exceptional amount of admiration in recent years. ML has ability to learn data itself by predicting uncertain conditions or future and classify categories with minimum intervention of human. While in DL, computers are able to automatically, learn useful features and representation precisely from raw data. ML and DL potentially a disruptive technology in predictive healthcare analysis. A detailed understanding and evaluation of the applications and principles of radiomics, machine and deep learning is an important task to construct possible solutions that are capable of accomplish compulsory and ethical requirements, which can enhance efficiency, quality and outcomes. Machine and deep learning extensively being use in medical image analysis, medical diagnostics and medical image technology. The wide scope and sudden progress of ML and DL has remarkably change the ways of diagnosis, prediction, classification and analyzing ovarian, lungs, brain, skin and various other types of cancer. In view of multiple applications of ML and DL, in this article a review conducted on ovarian cancer. This review comprises the detailed analysis of OC (prognosis, diagnosis, classification, evaluation) by covering all the major contributions of machine and deep learning. Furthermore, a literature taxonomy of the research conferred and emerging aspects analyzed. A section of discussion also signified to elaborate the limitations and future challenges of machine and deep learning comprises with ovarian cancer.

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Machine learning-based rapid diagnosis of human borderline ovarian cancer on second-harmonic generation images

Cited by 20 publications

References 33 publications

A Framework for Biosensors Assisted by Multiphoton Effects and Machine Learning

A Framework for Biosensors Assisted by Multiphoton Effects and Machine Learning

Organ on Chip Technology to Model Cancer Growth and Metastasis

Systematic Analysis of Ovarian Cancer Empowered with Machine and Deep Learning: A Taxonomy and Future Challenges

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