Literature DB >> 32125775

Adaptive Boosting (AdaBoost)-based multiwavelength spatial frequency domain imaging and characterization for ex vivo human colorectal tissue assessment.

Shuying Li1, Yifeng Zeng1, William C Chapman2, Mohsen Erfanzadeh3, Sreyankar Nandy1, Matthew Mutch2, Quing Zhu1,4.   

Abstract

The current gold standard diagnostic test for colorectal cancer remains histological inspections of endoluminal neoplasia in biopsy specimens. However, biopsy site selection requires visual inspection of the bowel, typically with a white-light endoscope. Therefore, this technique is poorly suited to detect small or innocuous-appearing lesions. We hypothesize that an alternative modality-multiwavelength spatial frequency domain imaging (SFDI)-would be able to differentiate various colorectal neoplasia from normal tissue. In this ex vivo study of human colorectal tissues, we report the optical absorption and scattering signatures of normal, adenomatous polyp and cancer specimens. An abnormal vs. normal adaptive boosting (AdaBoost) classifier is trained to dichotomize tissue based on SFDI imaging characteristics, and an area under the receiver operating characteristic (ROC) curve (AUC) of 0.95 is achieved. We conclude that AdaBoost-based multiwavelength SFDI can differentiate abnormal from normal colorectal tissues, potentially improving endoluminal screening of the distal gastrointestinal tract in the future.
© 2020 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Entities:  

Keywords:  AdaBoost; colorectal cancer; spatial frequency domain imaging

Mesh:

Year:  2020        PMID: 32125775      PMCID: PMC7593835          DOI: 10.1002/jbio.201960241

Source DB:  PubMed          Journal:  J Biophotonics        ISSN: 1864-063X            Impact factor:   3.207


  19 in total

1.  Low-cost compact multispectral spatial frequency domain imaging prototype for tissue characterization.

Authors:  Mohsen Erfanzadeh; Sreyankar Nandy; Patrick D Kumavor; Quing Zhu
Journal:  Biomed Opt Express       Date:  2018-10-17       Impact factor: 3.732

2.  Deep learning model for ultrafast multifrequency optical property extractions for spatial frequency domain imaging.

Authors:  Yanyu Zhao; Yue Deng; Feng Bao; Hannah Peterson; Raeef Istfan; Darren Roblyer
Journal:  Opt Lett       Date:  2018-11-15       Impact factor: 3.776

3.  Predicting locally advanced rectal cancer response to neoadjuvant therapy with 18F-FDG PET and MRI radiomics features.

Authors:  V Giannini; S Mazzetti; I Bertotto; C Chiarenza; S Cauda; E Delmastro; C Bracco; A Di Dia; F Leone; E Medico; A Pisacane; D Ribero; M Stasi; D Regge
Journal:  Eur J Nucl Med Mol Imaging       Date:  2019-01-13       Impact factor: 9.236

4.  Discrimination of breast tumors in ultrasonic images using an ensemble classifier based on the AdaBoost algorithm with feature selection.

Authors:  Atsushi Takemura; Akinobu Shimizu; Kazuhiko Hamamoto
Journal:  IEEE Trans Med Imaging       Date:  2010-03       Impact factor: 10.048

5.  A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data.

Authors:  Bjoern H Menze; B Michael Kelm; Ralf Masuch; Uwe Himmelreich; Peter Bachert; Wolfgang Petrich; Fred A Hamprecht
Journal:  BMC Bioinformatics       Date:  2009-07-10       Impact factor: 3.169

6.  Fluorescence-guided optical coherence tomography imaging for colon cancer screening: a preliminary mouse study.

Authors:  Nicusor Iftimia; Arun K Iyer; Daniel X Hammer; Niyom Lue; Mircea Mujat; Martha Pitman; R Daniel Ferguson; Mansoor Amiji
Journal:  Biomed Opt Express       Date:  2011-12-19       Impact factor: 3.732

7.  How commonly is upper gastrointestinal cancer missed at endoscopy? A meta-analysis.

Authors:  Shyam Menon; Nigel Trudgill
Journal:  Endosc Int Open       Date:  2014-05-07

8.  Diagnostic miss rate for colorectal cancer: an audit.

Authors:  Mary Than; Jolene Witherspoon; Javed Shami; Prachi Patil; Avanish Saklani
Journal:  Ann Gastroenterol       Date:  2015 Jan-Mar

9.  The Angular Spectrum of the Scattering Coefficient Map Reveals Subsurface Colorectal Cancer.

Authors:  Yifeng Zeng; Bin Rao; William C Chapman; Sreyankar Nandy; Rehan Rais; Iván González; Deyali Chatterjee; Matthew Mutch; Quing Zhu
Journal:  Sci Rep       Date:  2019-02-28       Impact factor: 4.379

10.  Spectral discrimination of breast pathologies in situ using spatial frequency domain imaging.

Authors:  Ashley M Laughney; Venkataramanan Krishnaswamy; Elizabeth J Rizzo; Mary C Schwab; Richard J Barth; David J Cuccia; Bruce J Tromberg; Keith D Paulsen; Brian W Pogue; Wendy A Wells
Journal:  Breast Cancer Res       Date:  2013       Impact factor: 6.466

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  3 in total

1.  Machine learning model with physical constraints for diffuse optical tomography.

Authors:  Yun Zou; Yifeng Zeng; Shuying Li; Quing Zhu
Journal:  Biomed Opt Express       Date:  2021-08-23       Impact factor: 3.562

2.  Diagnosing colorectal abnormalities using scattering coefficient maps acquired from optical coherence tomography.

Authors:  Yifeng Zeng; William C Chapman; Yixiao Lin; Shuying Li; Matthew Mutch; Quing Zhu
Journal:  J Biophotonics       Date:  2020-10-22       Impact factor: 3.207

Review 3.  Deep learning in macroscopic diffuse optical imaging.

Authors:  Jason T Smith; Marien Ochoa; Denzel Faulkner; Grant Haskins; Xavier Intes
Journal:  J Biomed Opt       Date:  2022-02       Impact factor: 3.758

  3 in total

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