Real-time colorectal cancer diagnosis using PR-OCT with deep learning

Prior reports have shown optical coherence tomography (OCT) can differentiate normal colonic mucosa from neoplasia, potentially offering an alternative technique to endoscopic biopsy - the current gold-standard colorectal cancer screening and surveillance modality. To help clinical translation limit...

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Veröffentlicht in:Theranostics Jg. 10; H. 6; S. 2587 - 2596
Hauptverfasser: Zeng, Yifeng, Xu, Shiqi, Chapman, William C., Li, Shuying, Alipour, Zahra, Abdelal, Heba, Chatterjee, Deyali, Mutch, Matthew, Zhu, Quing
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Australia Ivyspring International Publisher Pty Ltd 01.01.2020
Ivyspring International Publisher
Schlagworte:
Aged
Aged, 80 and over
Biopsy
Classification
Colon - diagnostic imaging
Colorectal cancer
Colorectal Neoplasms - diagnostic imaging
Deep Learning
Diagnosis, Computer-Assisted - methods
Endoscopy
Humans
Image Processing, Computer-Assisted - methods
Middle Aged
Neural networks
Noise
Pattern recognition
Research Paper
Surveillance
Tomography, Optical Coherence - methods
Tumors
deep learning
colorectal cancer
optical biopsy
optical coherence tomography (OCT)
ISSN:1838-7640, 1838-7640
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Zusammenfassung:Prior reports have shown optical coherence tomography (OCT) can differentiate normal colonic mucosa from neoplasia, potentially offering an alternative technique to endoscopic biopsy - the current gold-standard colorectal cancer screening and surveillance modality. To help clinical translation limited by processing the large volume of generated data, we designed a deep learning-based pattern recognition (PR) OCT system that automates image processing and provides accurate diagnosis potentially in real-time. : OCT is an emerging imaging technique to obtain 3-dimensional (3D) "optical biopsies" of biological samples with high resolution. We designed a convolutional neural network to capture the structure patterns in human colon OCT images. The network is trained and tested using around 26,000 OCT images acquired from 20 tumor areas, 16 benign areas, and 6 other abnormal areas. : The trained network successfully detected patterns that identify normal and neoplastic colorectal tissue. Experimental diagnoses predicted by the PR-OCT system were compared to the known histologic findings and quantitatively evaluated. A sensitivity of 100% and specificity of 99.7% can be reached. Further, the area under the receiver operating characteristic (ROC) curves (AUC) of 0.998 is achieved. : Our results demonstrate that PR-OCT can be used to give an accurate real-time computer-aided diagnosis of colonic neoplastic mucosa. Future development of this system as an "optical biopsy" tool to assist doctors in real-time for early mucosal neoplasms screening and treatment evaluation following initial oncologic therapy is planned.
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Competing Interests: The authors have declared that no competing interest exists.
These authors contributed equally to this paper.
ISSN:1838-7640
1838-7640
DOI:10.7150/thno.40099