TY - GEN
T1 - Automated classification of pancreatic neuroendocrine tumors using label-free multiphoton microscopy and deep learning
AU - Guan, Shuyuan
AU - Daigle, Noelle
AU - Sawyer, Travis W.
N1 - Publisher Copyright:
© COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.
PY - 2024
Y1 - 2024
N2 - Pancreatic neuroendocrine tumors (PNETs) present significant diagnostic and therapeutic challenges due to their heterogeneity and complex nature as a subtype of pancreatic cancer. The treatment approach varies considerably based on the tumor's location, grading, and focality. Accurate prognosis and management typically necessitate the expertise of a pathologist to evaluate histological slides of the tissue, a process that is often time-consuming and labor-intensive. Developing point-of-care techniques for automatic classification of PNETs would greatly improve the ability to treat and manage this disease by providing real-time decision-making information. In response to these challenges, our study introduces a highly efficient and versatile diagnostic strategy. This innovative approach synergistically integrates label-free multiphoton microscopy with finely adjusted, pre-trained deep learning models, optimized for performance even with limited data availability. We have meticulously optimized four pre-trained convolutional neural networks, utilizing a dataset comprising only 49 images, which includes both two-photon excitation fluorescence and second-harmonic generation imaging. This refined approach has resulted in an impressive average classification accuracy of over 95% for the development dataset and more than 90% for the test dataset. These results are significantly superior when compared to the preoperative misdiagnosis rates of conventional diagnostic modalities such as ultrasound (US) and computed tomography (CT), which stand at 81.8% and 61.5%, respectively. This methodology represents a significant advancement in the diagnostic process for PNETs, promising a more streamlined, rapid, and accurate approach to treatment. Furthermore, it opens substantial potential for the automated classification of various tumor types using multiphoton microscopic imaging, even in scenarios characterized by limited data availability.
AB - Pancreatic neuroendocrine tumors (PNETs) present significant diagnostic and therapeutic challenges due to their heterogeneity and complex nature as a subtype of pancreatic cancer. The treatment approach varies considerably based on the tumor's location, grading, and focality. Accurate prognosis and management typically necessitate the expertise of a pathologist to evaluate histological slides of the tissue, a process that is often time-consuming and labor-intensive. Developing point-of-care techniques for automatic classification of PNETs would greatly improve the ability to treat and manage this disease by providing real-time decision-making information. In response to these challenges, our study introduces a highly efficient and versatile diagnostic strategy. This innovative approach synergistically integrates label-free multiphoton microscopy with finely adjusted, pre-trained deep learning models, optimized for performance even with limited data availability. We have meticulously optimized four pre-trained convolutional neural networks, utilizing a dataset comprising only 49 images, which includes both two-photon excitation fluorescence and second-harmonic generation imaging. This refined approach has resulted in an impressive average classification accuracy of over 95% for the development dataset and more than 90% for the test dataset. These results are significantly superior when compared to the preoperative misdiagnosis rates of conventional diagnostic modalities such as ultrasound (US) and computed tomography (CT), which stand at 81.8% and 61.5%, respectively. This methodology represents a significant advancement in the diagnostic process for PNETs, promising a more streamlined, rapid, and accurate approach to treatment. Furthermore, it opens substantial potential for the automated classification of various tumor types using multiphoton microscopic imaging, even in scenarios characterized by limited data availability.
KW - convolutional neural networks
KW - deep learning
KW - image classification
KW - multiphoton microscopy
KW - Pancreatic neuroendocrine tumor
UR - http://www.scopus.com/inward/record.url?scp=85190989093&partnerID=8YFLogxK
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U2 - 10.1117/12.3018502
DO - 10.1117/12.3018502
M3 - Conference contribution
AN - SCOPUS:85190989093
T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE
BT - Label-free Biomedical Imaging and Sensing (LBIS) 2024
A2 - Shaked, Natan T.
A2 - Hayden, Oliver
PB - SPIE
T2 - 2024 Label-free Biomedical Imaging and Sensing, LBIS 2024
Y2 - 27 January 2024 through 30 January 2024
ER -