Show simple item record

dc.contributor.authorTerradillos, Elena
dc.contributor.authorSaratxaga, CristinaL
dc.contributor.authorMattana, Sara
dc.contributor.authorCicchi, Riccardo
dc.contributor.authorPavone, FrancescoS
dc.contributor.authorAndraka, Nagore
dc.contributor.authorGlover, BenjaminJ
dc.contributor.authorArbide, Nagore
dc.contributor.authorVelasco, Jacques
dc.contributor.authorEtxezarraga, MªCarmen
dc.contributor.authorPicon, Artzai
dc.date.accessioned2021-07-09T09:30:24Z
dc.date.available2021-07-09T09:30:24Z
dc.date.issued2021
dc.identifier.citationTerradillos, Elena, CristinaL Saratxaga, Sara Mattana, Riccardo Cicchi, FrancescoS Pavone, Nagore Andraka, BenjaminJ Glover, et al. “Analysis on the Characterization of Multiphoton Microscopy Images for Malignant Neoplastic Colon Lesion Detection Under Deep Learning Methods.” Journal of Pathology Informatics 12, no. 1 (2021): 27. doi:10.4103/jpi.jpi_113_20.en
dc.identifier.issn2229-5089en
dc.identifier.urihttp://hdl.handle.net/11556/1164
dc.description.abstractColorectal 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.en
dc.description.sponsorshipThe authors would like to thank Roberto Bilbao, director of the Basque Biobank, Ainara Egia Bizkarralegorra and biobank technicians from Basurto University Hospital (Spain) and pathologist Prof. Rob Goldin from Imperial College London (UK).en
dc.language.isoengen
dc.publisherWolters Kluwer Healthen
dc.rightsAttribution-NonCommercial-ShareAlike 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/*
dc.titleAnalysis on the characterization of multiphoton microscopy images for malignant neoplastic colon lesion detection under deep learning methodsen
dc.typearticleen
dc.identifier.doi10.4103/jpi.jpi_113_20en
dc.rights.accessRightsopenAccessen
dc.subject.keywordsColorectal polypsen
dc.subject.keywordsConvolutional neural networken
dc.subject.keywordsDataseten
dc.subject.keywordsMultiphoton microscopyen
dc.subject.keywordsOptical biopsyen
dc.identifier.essn2153-3539en
dc.issue.number1en
dc.journal.titleJournal of Pathology Informaticsen
dc.volume.number12en


Files in this item

Thumbnail

    Show simple item record

    Attribution-NonCommercial-ShareAlike 4.0 InternationalExcept where otherwise noted, this item's license is described as Attribution-NonCommercial-ShareAlike 4.0 International