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Article

Feedforward Artificial Neural Network-Based Colorectal Cancer Detection Using Hyperspectral Imaging: A Step towards Automatic Optical Biopsy

1
Department of Visceral, Transplant, Thoracic and Vascular Surgery, University Hospital of Leipzig, 04103 Leipzig, Germany
2
Institute for Research against Digestive Cancer (IRCAD), 67091 Strasbourg, France
3
Department of General Surgery, Hospital Card. G. Panico, 73039 Tricase, Italy
4
Innovation Center Computer-Assisted Surgery (ICCAS), University of Leipzig, 04103 Leipzig, Germany
5
Institute of Pathology, University Hospital Leipzig, 04103 Leipzig, Germany
*
Author to whom correspondence should be addressed.
Academic Editor: Leticia Moreira
Cancers 2021, 13(5), 967; https://doi.org/10.3390/cancers13050967
Received: 25 January 2021 / Revised: 18 February 2021 / Accepted: 20 February 2021 / Published: 25 February 2021
(This article belongs to the Special Issue New Insights into Colorectal Cancer)
Detection of colorectal carcinoma is performed visually by investigators and is confirmed pathologically. With hyperspectral imaging, an expanded spectral range of optical information is now available for analysis. The acquired recordings were analyzed with a neural network, and it was possible to differentiate tumor from healthy mucosa in colorectal carcinoma by automatic classification with high reliability. Classification and visualization were performed based on a four-layer perceptron neural network. Based on a neural network, the classification of CA or AD resulted in a sensitivity of 86% and a specificity of 95%, by means of leave-one-patient-out cross-validation. Additionally, significant differences in terms of perfusion parameters (e.g., oxygen saturation) related to tumor staging and neoadjuvant therapy were observed. This is a step towards optical biopsy.
Currently, colorectal cancer (CRC) is mainly identified via a visual assessment during colonoscopy, increasingly used artificial intelligence algorithms, or surgery. Subsequently, CRC is confirmed through a histopathological examination by a pathologist. Hyperspectral imaging (HSI), a non-invasive optical imaging technology, has shown promising results in the medical field. In the current study, we combined HSI with several artificial intelligence algorithms to discriminate CRC. Between July 2019 and May 2020, 54 consecutive patients undergoing colorectal resections for CRC were included. The tumor was imaged from the mucosal side with a hyperspectral camera. The image annotations were classified into three groups (cancer, CA; adenomatous margin around the central tumor, AD; and healthy mucosa, HM). Classification and visualization were performed based on a four-layer perceptron neural network. Based on a neural network, the classification of CA or AD resulted in a sensitivity of 86% and a specificity of 95%, by means of leave-one-patient-out cross-validation. Additionally, significant differences in terms of perfusion parameters (e.g., oxygen saturation) related to tumor staging and neoadjuvant therapy were observed. Hyperspectral imaging combined with automatic classification can be used to differentiate between CRC and healthy mucosa. Additionally, the biological changes induced by chemotherapy to the tissue are detectable with HSI. View Full-Text
Keywords: hyperspectral imaging (HSI); colorectal cancer (CRC); machine learning; deep learning; optical biopsy; optical imaging hyperspectral imaging (HSI); colorectal cancer (CRC); machine learning; deep learning; optical biopsy; optical imaging
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MDPI and ACS Style

Jansen-Winkeln, B.; Barberio, M.; Chalopin, C.; Schierle, K.; Diana, M.; Köhler, H.; Gockel, I.; Maktabi, M. Feedforward Artificial Neural Network-Based Colorectal Cancer Detection Using Hyperspectral Imaging: A Step towards Automatic Optical Biopsy. Cancers 2021, 13, 967. https://doi.org/10.3390/cancers13050967

AMA Style

Jansen-Winkeln B, Barberio M, Chalopin C, Schierle K, Diana M, Köhler H, Gockel I, Maktabi M. Feedforward Artificial Neural Network-Based Colorectal Cancer Detection Using Hyperspectral Imaging: A Step towards Automatic Optical Biopsy. Cancers. 2021; 13(5):967. https://doi.org/10.3390/cancers13050967

Chicago/Turabian Style

Jansen-Winkeln, Boris, Manuel Barberio, Claire Chalopin, Katrin Schierle, Michele Diana, Hannes Köhler, Ines Gockel, and Marianne Maktabi. 2021. "Feedforward Artificial Neural Network-Based Colorectal Cancer Detection Using Hyperspectral Imaging: A Step towards Automatic Optical Biopsy" Cancers 13, no. 5: 967. https://doi.org/10.3390/cancers13050967

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