Biophotonics, which uses photons to interrogate cancer microenvironments and eliminate cancer-based cells and tissues, has paved new pathways for cancer research. As laser light can be focused onto diffraction-limited spots, the induced light scattering and absorption deliver the information on cellular and sub-cellular levels, available for early detection of cancers. Besides, ultra-short laser bursts allow for clear cutting and drilling in tissues, which reduce time spent on surgery, bleeding, and patient's pain. Nowadays, artificial intelligence (AI) incorporated with photonic technology have devised new systems to differentiate between normal from malignant tissues as well as to stage cancers. Providing innovative solutions for cancer diagnostics and treatment are indispensable to increase comfortability of patient care and survival rate.
In the past decade, the advances in photonics technology enable the visualization of detailed subcellular structures and the deeper imaging depth into living tissues. The revolution of machine learning further enhances the efficiency and accuracy of image qualification and quantification. We would like to explore the integrations of advanced microscopic techniques and machine learning methodologies for the phenotyping of cancer cells or tissues. In addition to cancer diagnostics, we aim to review the trajectories of anti-cancer biophotonic therapies and project the future direction of therapeutic innovations. These two goals will cohere with the proposed Research Topic entitled “Biophotonics for cancer diagnostics and treatment”.
The scope of this research topic includes, but is not limited to, the following fields:
• Development of label-free imaging systems for cancer diagnostics
• Rapid and precise data analysis for cancer diagnostics
• Advanced optical imaging techniques in cancer diagnostics
• Integrated (multimodality) photonic systems to address issues in cancer
• Applications of spectroscopy in cancer diagnostics
• Endoscopic-assisted surgery for cancer diseases
• Cancer treatment by photoablation, photothermal ablation, plasma-induced laser ablation, and photodisruption.
Biophotonics, which uses photons to interrogate cancer microenvironments and eliminate cancer-based cells and tissues, has paved new pathways for cancer research. As laser light can be focused onto diffraction-limited spots, the induced light scattering and absorption deliver the information on cellular and sub-cellular levels, available for early detection of cancers. Besides, ultra-short laser bursts allow for clear cutting and drilling in tissues, which reduce time spent on surgery, bleeding, and patient's pain. Nowadays, artificial intelligence (AI) incorporated with photonic technology have devised new systems to differentiate between normal from malignant tissues as well as to stage cancers. Providing innovative solutions for cancer diagnostics and treatment are indispensable to increase comfortability of patient care and survival rate.
In the past decade, the advances in photonics technology enable the visualization of detailed subcellular structures and the deeper imaging depth into living tissues. The revolution of machine learning further enhances the efficiency and accuracy of image qualification and quantification. We would like to explore the integrations of advanced microscopic techniques and machine learning methodologies for the phenotyping of cancer cells or tissues. In addition to cancer diagnostics, we aim to review the trajectories of anti-cancer biophotonic therapies and project the future direction of therapeutic innovations. These two goals will cohere with the proposed Research Topic entitled “Biophotonics for cancer diagnostics and treatment”.
The scope of this research topic includes, but is not limited to, the following fields:
• Development of label-free imaging systems for cancer diagnostics
• Rapid and precise data analysis for cancer diagnostics
• Advanced optical imaging techniques in cancer diagnostics
• Integrated (multimodality) photonic systems to address issues in cancer
• Applications of spectroscopy in cancer diagnostics
• Endoscopic-assisted surgery for cancer diseases
• Cancer treatment by photoablation, photothermal ablation, plasma-induced laser ablation, and photodisruption.
