Classification of squamous cell carcinoma from FF-OCT images: Data selection and progressive model construction

Ho, Chi-Jui; Calderon-Delgado, Manuel; Lin, Ming-Yi; Tjiu, Jeng-Wei; Huang, Sheng-Lung; Chen, Homer H.

doi:10.1016/j.compmedimag.2021.101992

Cited by 6 publications

(9 citation statements)

References 21 publications

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“…A CNN classification model was built on top of the U-Net with symmetric down-and upsampling results for nBCC detection [17,24,25] (Figure S1). During the CNN training phase, 1253 image patches with nBCC were used.…”

Section: Deep-learning Algorithmmentioning

confidence: 99%

“…Although the emergent cellular-resolution OCT could significantly accelerate the clinical adoption of OCT to assist physicians in interpreting images [15][16][17][18], interpretation of OCT images often requires an expert with extensive training in reading these images, posing a major barrier to integrating OCT in clinics [6,9]. Thus, deep learning algorithms, in particular convolutional neural networks (CNNs), have become a powerful tool for analyzing medical images to assist physicians in detecting, classifying, segmenting, and even diagnosing tissue images [19].…”

Section: Introductionmentioning

confidence: 99%

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High‐resolution full‐field optical coherence tomography microscope for the evaluation of freshly excised skin specimens during Mohs surgery: A feasibility study

Jain,

Chang,

Singh

et al. 2023

Journal of Biophotonics

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Histopathology for tumor margin assessment is time‐consuming and expensive. High‐resolution full‐field optical coherence tomography (FF‐OCT) images fresh tissues rapidly at cellular resolution and potentially facilitates evaluation. Here, we define FF‐OCT features of normal and neoplastic skin lesions in fresh ex vivo tissues and assess its diagnostic accuracy for malignancies. For this, normal and neoplastic tissues were obtained from Mohs surgery, imaged using FF‐OCT, and their features were described. Two expert OCT readers conducted a blinded analysis to evaluate their diagnostic accuracies, using histopathology as the ground truth. A convolutional neural network was built to distinguish and outline normal structures and tumors. Of the 113 tissues imaged, 95 (84%) had a tumor (75 basal cell carcinomas [BCCs] and 17 squamous cell carcinomas [SCCs]). The average reader diagnostic accuracy was 88.1%, with a sensitivity of 93.7%, and a specificity of 58.3%. The artificial intelligence (AI) model achieved a diagnostic accuracy of 87.6 ± 5.9%, sensitivity of 93.2 ± 2.1%, and specificity of 81.2 ± 9.2%. A mean intersection‐over‐union of 60.3 ± 10.1% was achieved when delineating the nodular BCC from normal structures. Limitation of the study was the small sample size for all tumors, especially SCCs. However, based on our preliminary results, we envision FF‐OCT to rapidly image fresh tissues, facilitating surgical margin assessment. AI algorithms can aid in automated tumor detection, enabling widespread adoption of this technique.

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Section: Deep-learning Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

High‐resolution full‐field optical coherence tomography microscope for the evaluation of freshly excised skin specimens during Mohs surgery: A feasibility study

Jain,

Chang,

Singh

et al. 2023

Journal of Biophotonics

Self Cite

View full text Add to dashboard Cite

show abstract

“…Although the emergent cellular-resolution optical coherence tomography (OCT) could signi cantly accelerate the clinical adoption of OCT to assist physicians in interpreting images [15][16][17][18] , interpretation of OCT images often requires an expert with extensive training in reading these images, posing a major barrier to integrating OCT in clinics 6,9 . Thus, deep learning algorithms, in particular convolutional networks (CNN), have become a powerful tool for analyzing medical images to assist physicians in detecting, classifying, segmenting, and even diagnosing tissue images 19 .…”

Section: Ex Vivo Optical Imaging Devices Including Confocal Microscop...mentioning

confidence: 99%

High resolution full-field optical coherence tomography microscope for the evaluation of freshly excised skin specimens during Mohs surgery: A feasibility study

Jain

Chang

Singh

et al. 2023

Preprint

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Histopathology for tumor margin assessment is time-consuming and expensive. High-resolution full-field optical coherence tomography (FF-OCT) images fresh tissues rapidly at cellular resolution and potentially facilitates evaluation. Here, we define FF-OCT features of normal and neoplastic skin lesions in fresh ex vivo tissues and assess its diagnostic accuracy for malignancies. For this, normal and neoplastic tissues were obtained from Mohs surgery, imaged using FF-OCT, and their features were described. Two expert OCT readers conducted a blinded analysis to evaluate their diagnostic accuracies, using histopathology as the ground truth. A convolutional neural network was built to distinguish and outline normal structures and tumors. Of the 113 tissues imaged, 95 (84%) had a tumor (75 BCCs and 17 SCCs). The average reader diagnostic accuracy was 88.1%, with, a sensitivity of 93.7%, and a specificity of 58.3%. The AI model achieved a diagnostic accuracy of 87.6%±5.9%, sensitivity of 93.2%±2.1%, and specificity of 81.2%±9.2%. A mean intersection-over-union of 60.3%±10.1% was achieved when delineating the nodular BCC from normal structures. Limitation of the study was the small sample size for all tumors, especially SCCs. However, based on our preliminary results, we envision FF-OCT to rapidly image fresh tissues, facilitating surgical margin assessment. AI algorithms can aid in automated tumor detection, enabling widespread adoption of this technique.

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“…This procedure requires mechanically shifting the reference mirror, thereby changing the depth of the tissue being scanned ( 31 , 32 ). However, with the advancement of technology and technology and for different needs, TD-OCT has emerged many variants, such as line-field confocal OCT (LC-OCT) ( 33 , 34 ), full-field OCT (FF-OCT) ( 35 ), polarization-sensitive OCT (PS-OCT) ( 36 ), etc., to achieve more efficient and wide applications in the clinic.…”

Section: Development Of the Octmentioning

confidence: 99%

“…based on a convolutional neural network (CNN) developed a mouse skin SCC classification model that integrates a FF-OCT device. This model provides a rapid, non-invasive, and accurate SCC classification, achieving 87.12% and 90.10% classification accuracy at the image level and tomography image level, respectively ( 35 ).…”

Section: Application Of Oct In Oncologymentioning

confidence: 99%

Research progress on the application of optical coherence tomography in the field of oncology

et al. 2022

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Optical coherence tomography (OCT) is a non-invasive imaging technique which has become the “gold standard” for diagnosis in the field of ophthalmology. However, in contrast to the eye, nontransparent tissues exhibit a high degree of optical scattering and absorption, resulting in a limited OCT imaging depth. And the progress made in the past decade in OCT technology have made it possible to image nontransparent tissues with high spatial resolution at large (up to 2mm) imaging depth. On the one hand, OCT can be used in a rapid, noninvasive way to detect diseased tissues, organs, blood vessels or glands. On the other hand, it can also identify the optical characteristics of suspicious parts in the early stage of the disease, which is of great significance for the early diagnosis of tumor diseases. Furthermore, OCT imaging has been explored for imaging tumor cells and their dynamics, and for the monitoring of tumor responses to treatments. This review summarizes the recent advances in the OCT area, which application in oncological diagnosis and treatment in different types: (1) superficial tumors:OCT could detect microscopic information on the skin’s surface at high resolution and has been demonstrated to help diagnose common skin cancers; (2) gastrointestinal tumors: OCT can be integrated into small probes and catheters to image the structure of the stomach wall, enabling the diagnosis and differentiation of gastrointestinal tumors and inflammation; (3) deep tumors: with the rapid development of OCT imaging technology, it has shown great potential in the diagnosis of deep tumors such in brain tumors, breast cancer, bladder cancer, and lung cancer.

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Classification of squamous cell carcinoma from FF-OCT images: Data selection and progressive model construction

Cited by 6 publications

References 21 publications

High‐resolution full‐field optical coherence tomography microscope for the evaluation of freshly excised skin specimens during Mohs surgery: A feasibility study

High‐resolution full‐field optical coherence tomography microscope for the evaluation of freshly excised skin specimens during Mohs surgery: A feasibility study

High resolution full-field optical coherence tomography microscope for the evaluation of freshly excised skin specimens during Mohs surgery: A feasibility study

Research progress on the application of optical coherence tomography in the field of oncology

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