Journal of Pathology Informatics

RESEARCH ARTICLE
Year
: 2021  |  Volume : 12  |  Issue : 1  |  Page : 27-

Analysis on the characterization of multiphoton microscopy images for malignant neoplastic colon lesion detection under deep learning methods


Elena Terradillos1, Cristina L Saratxaga1, Sara Mattana2, Riccardo Cicchi2, Francesco S Pavone2, Nagore Andraka3, Benjamin J Glover4, Nagore Arbide5, Jacques Velasco5, Mª Carmen Etxezarraga5, Artzai Picon6 
1 ECNALIA, Basque Research and Technology Alliance, Derio, Spain
2 European Laboratory for Non-Linear Spectroscopy, Sesto Fiorentino, Italy
3 Basque Foundation for Health Innovation and Research, Barakaldo, Spain
4 Department of Surgery and Cancer, Imperial College London, London, UK
5 Department of Pathological Anatomy, Osakidetza Basque Health Service, Basurto University Hospital, Bilbao, Spain
6 University of the Basque Country UPV/EHU, Bilbao, Spain

Correspondence Address:
Elena Terradillos
Parque Científico y Tecnológico de Bizkaia, Astondo Bidea, Edificio 700, Derio E-48160, Bizkaia
Spain

Background: Colorectal cancer has a high incidence rate worldwide, with over 1.8 million new cases and 880,792 deaths in 2018. Fortunately, its early detection significantly increases the survival rate, reaching a cure rate of 90% when diagnosed at a localized stage. Colonoscopy is the gold standard technique for detection and removal of colorectal lesions with potential to evolve into cancer. When polyps are found in a patient, the current procedure is their complete removal. However, in this process, gastroenterologists cannot assure complete resection and clean margins which are given by the histopathology analysis of the removed tissue, which is performed at laboratory. Aims: In this paper, we demonstrate the capabilities of multiphoton microscopy (MPM) technology to provide imaging biomarkers that can be extracted by deep learning techniques to identify malignant neoplastic colon lesions and distinguish them from healthy, hyperplastic, or benign neoplastic tissue, without the need for histopathological staining. Materials and Methods: To this end, we present a novel MPM public dataset containing 14,712 images obtained from 42 patients and grouped into 2 classes. A convolutional neural network is trained on this dataset and a spatially coherent predictions scheme is applied for performance improvement. Results: We obtained a sensitivity of 0.8228 ± 0.1575 and a specificity of 0.9114 ± 0.0814 on detecting malignant neoplastic lesions. We also validated this approach to estimate the self-confidence of the network on its own predictions, obtaining a mean sensitivity of 0.8697 and a mean specificity of 0.9524 with the 18.67% of the images classified as uncertain. Conclusions: This work lays the foundations for performing in vivo optical colon biopsies by combining this novel imaging technology together with deep learning algorithms, hence avoiding unnecessary polyp resection and allowing in situ diagnosis assessment.


How to cite this article:
Terradillos E, Saratxaga CL, Mattana S, Cicchi R, Pavone FS, Andraka N, Glover BJ, Arbide N, Velasco J, Etxezarraga MC, Picon A. Analysis on the characterization of multiphoton microscopy images for malignant neoplastic colon lesion detection under deep learning methods.J Pathol Inform 2021;12:27-27


How to cite this URL:
Terradillos E, Saratxaga CL, Mattana S, Cicchi R, Pavone FS, Andraka N, Glover BJ, Arbide N, Velasco J, Etxezarraga MC, Picon A. Analysis on the characterization of multiphoton microscopy images for malignant neoplastic colon lesion detection under deep learning methods. J Pathol Inform [serial online] 2021 [cited 2021 Jul 25 ];12:27-27
Available from: https://www.jpathinformatics.org/article.asp?issn=2153-3539;year=2021;volume=12;issue=1;spage=27;epage=27;aulast=Terradillos;type=0