Literature DB >> 30840739

PROSTATEx Challenges for computerized classification of prostate lesions from multiparametric magnetic resonance images.

Samuel G Armato1, Henkjan Huisman2, Karen Drukker1, Lubomir Hadjiiski3, Justin S Kirby4, Nicholas Petrick5, George Redmond6, Maryellen L Giger1, Kenny Cha3,5, Artem Mamonov7, Jayashree Kalpathy-Cramer7, Keyvan Farahani6.   

Abstract

Grand challenges stimulate advances within the medical imaging research community; within a competitive yet friendly environment, they allow for a direct comparison of algorithms through a well-defined, centralized infrastructure. The tasks of the two-part PROSTATEx Challenges (the PROSTATEx Challenge and the PROSTATEx-2 Challenge) are (1) the computerized classification of clinically significant prostate lesions and (2) the computerized determination of Gleason Grade Group in prostate cancer, both based on multiparametric magnetic resonance images. The challenges incorporate well-vetted cases for training and testing, a centralized performance assessment process to evaluate results, and an established infrastructure for case dissemination, communication, and result submission. In the PROSTATEx Challenge, 32 groups apply their computerized methods (71 methods total) to 208 prostate lesions in the test set. The area under the receiver operating characteristic curve for these methods in the task of differentiating between lesions that are and are not clinically significant ranged from 0.45 to 0.87; statistically significant differences in performance among the top-performing methods, however, are not observed. In the PROSTATEx-2 Challenge, 21 groups apply their computerized methods (43 methods total) to 70 prostate lesions in the test set. When compared with the reference standard, the quadratic-weighted kappa values for these methods in the task of assigning a five-point Gleason Grade Group to each lesion range from - 0.24 to 0.27; superiority to random guessing can be established for only two methods. When approached with a sense of commitment and scientific rigor, challenges foster interest in the designated task and encourage innovation in the field.

Entities:  

Keywords:  Gleason Grade Group; grand challenge; imaging biomarker; lesion classification; multiparametric magnetic resonance images; prostate cancer

Year:  2018        PMID: 30840739      PMCID: PMC6228312          DOI: 10.1117/1.JMI.5.4.044501

Source DB:  PubMed          Journal:  J Med Imaging (Bellingham)        ISSN: 2329-4302


  36 in total

1.  A UK-based investigation of inter- and intra-observer reproducibility of Gleason grading of prostatic biopsies.

Authors:  J Melia; R Moseley; R Y Ball; D F R Griffiths; K Grigor; P Harnden; M Jarmulowicz; L J McWilliam; R Montironi; M Waller; S Moss; M C Parkinson
Journal:  Histopathology       Date:  2006-05       Impact factor: 5.087

2.  Evaluation of prostate segmentation algorithms for MRI: the PROMISE12 challenge.

Authors:  Geert Litjens; Robert Toth; Wendy van de Ven; Caroline Hoeks; Sjoerd Kerkstra; Bram van Ginneken; Graham Vincent; Gwenael Guillard; Neil Birbeck; Jindang Zhang; Robin Strand; Filip Malmberg; Yangming Ou; Christos Davatzikos; Matthias Kirschner; Florian Jung; Jing Yuan; Wu Qiu; Qinquan Gao; Philip Eddie Edwards; Bianca Maan; Ferdinand van der Heijden; Soumya Ghose; Jhimli Mitra; Jason Dowling; Dean Barratt; Henkjan Huisman; Anant Madabhushi
Journal:  Med Image Anal       Date:  2013-12-25       Impact factor: 8.545

3.  Prospective assessment of prostate cancer aggressiveness using 3-T diffusion-weighted magnetic resonance imaging-guided biopsies versus a systematic 10-core transrectal ultrasound prostate biopsy cohort.

Authors:  Thomas Hambrock; Caroline Hoeks; Christina Hulsbergen-van de Kaa; Tom Scheenen; Jurgen Fütterer; Stefan Bouwense; Inge van Oort; Fritz Schröder; Henkjan Huisman; Jelle Barentsz
Journal:  Eur Urol       Date:  2011-08-27       Impact factor: 20.096

4.  The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository.

Authors:  Kenneth Clark; Bruce Vendt; Kirk Smith; John Freymann; Justin Kirby; Paul Koppel; Stephen Moore; Stanley Phillips; David Maffitt; Michael Pringle; Lawrence Tarbox; Fred Prior
Journal:  J Digit Imaging       Date:  2013-12       Impact factor: 4.056

5.  Prostate cancer localization with dynamic contrast-enhanced MR imaging and proton MR spectroscopic imaging.

Authors:  Jurgen J Fütterer; Stijn W T P J Heijmink; Tom W J Scheenen; Jeroen Veltman; Henkjan J Huisman; Pieter Vos; Christina A Hulsbergen-Van de Kaa; J Alfred Witjes; Paul F M Krabbe; Arend Heerschap; Jelle O Barentsz
Journal:  Radiology       Date:  2006-09-11       Impact factor: 11.105

6.  Computer-assisted analysis of peripheral zone prostate lesions using T2-weighted and dynamic contrast enhanced T1-weighted MRI.

Authors:  Pieter C Vos; Thomas Hambrock; Jelle O Barenstz; Henkjan J Huisman
Journal:  Phys Med Biol       Date:  2010-03-02       Impact factor: 3.609

7.  Computer-aided detection of prostate cancer in MRI.

Authors:  Geert Litjens; Oscar Debats; Jelle Barentsz; Nico Karssemeijer; Henkjan Huisman
Journal:  IEEE Trans Med Imaging       Date:  2014-05       Impact factor: 10.048

8.  Reliable and computationally efficient maximum-likelihood estimation of "proper" binormal ROC curves.

Authors:  Lorenzo L Pesce; Charles E Metz
Journal:  Acad Radiol       Date:  2007-07       Impact factor: 3.173

9.  Prostate cancer: computer-aided diagnosis with multiparametric 3-T MR imaging--effect on observer performance.

Authors:  Thomas Hambrock; Pieter C Vos; Christina A Hulsbergen-van de Kaa; Jelle O Barentsz; Henkjan J Huisman
Journal:  Radiology       Date:  2012-11-30       Impact factor: 11.105

10.  Quantitative analysis of multiparametric prostate MR images: differentiation between prostate cancer and normal tissue and correlation with Gleason score--a computer-aided diagnosis development study.

Authors:  Yahui Peng; Yulei Jiang; Cheng Yang; Jeremy Bancroft Brown; Tatjana Antic; Ila Sethi; Christine Schmid-Tannwald; Maryellen L Giger; Scott E Eggener; Aytekin Oto
Journal:  Radiology       Date:  2013-02-07       Impact factor: 11.105

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  23 in total

1.  Selective identification and localization of indolent and aggressive prostate cancers via CorrSigNIA: an MRI-pathology correlation and deep learning framework.

Authors:  Indrani Bhattacharya; Arun Seetharaman; Christian Kunder; Wei Shao; Leo C Chen; Simon J C Soerensen; Jeffrey B Wang; Nikola C Teslovich; Richard E Fan; Pejman Ghanouni; James D Brooks; Geoffrey A Sonn; Mirabela Rusu
Journal:  Med Image Anal       Date:  2021-11-06       Impact factor: 8.545

2.  PROSTATE CANCER DIAGNOSIS WITH SPARSE BIOPSY DATA AND IN PRESENCE OF LOCATION UNCERTAINTY.

Authors:  Alireza Mehrtash; Tina Kapur; Clare M Tempany; Purang Abolmaesumi; William M Wells
Journal:  Proc IEEE Int Symp Biomed Imaging       Date:  2021-05-25

3.  Adversarial training for prostate cancer classification using magnetic resonance imaging.

Authors:  Lei Hu; Da-Wei Zhou; Xiang-Yu Guo; Wen-Hao Xu; Li-Ming Wei; Jun-Gong Zhao
Journal:  Quant Imaging Med Surg       Date:  2022-06

4.  Automatic zonal segmentation of the prostate from 2D and 3D T2-weighted MRI and evaluation for clinical use.

Authors:  Dimitri Hamzaoui; Sarah Montagne; Raphaële Renard-Penna; Nicholas Ayache; Hervé Delingette
Journal:  J Med Imaging (Bellingham)       Date:  2022-03-14

5.  Performance of Deep Learning and Genitourinary Radiologists in Detection of Prostate Cancer Using 3-T Multiparametric Magnetic Resonance Imaging.

Authors:  Ruiming Cao; Xinran Zhong; Sohrab Afshari; Ely Felker; Voraparee Suvannarerg; Teeravut Tubtawee; Sitaram Vangala; Fabien Scalzo; Steven Raman; Kyunghyun Sung
Journal:  J Magn Reson Imaging       Date:  2021-03-12       Impact factor: 4.813

6.  Federated learning improves site performance in multicenter deep learning without data sharing.

Authors:  Karthik V Sarma; Stephanie Harmon; Thomas Sanford; Holger R Roth; Ziyue Xu; Jesse Tetreault; Daguang Xu; Mona G Flores; Alex G Raman; Rushikesh Kulkarni; Bradford J Wood; Peter L Choyke; Alan M Priester; Leonard S Marks; Steven S Raman; Dieter Enzmann; Baris Turkbey; William Speier; Corey W Arnold
Journal:  J Am Med Inform Assoc       Date:  2021-06-12       Impact factor: 4.497

7.  Deep-Learning-Based Artificial Intelligence for PI-RADS Classification to Assist Multiparametric Prostate MRI Interpretation: A Development Study.

Authors:  Thomas Sanford; Stephanie A Harmon; Evrim B Turkbey; Deepak Kesani; Sena Tuncer; Manuel Madariaga; Chris Yang; Jonathan Sackett; Sherif Mehralivand; Pingkun Yan; Sheng Xu; Bradford J Wood; Maria J Merino; Peter A Pinto; Peter L Choyke; Baris Turkbey
Journal:  J Magn Reson Imaging       Date:  2020-06-01       Impact factor: 5.119

Review 8.  Applications of Artificial Intelligence to Prostate Multiparametric MRI (mpMRI): Current and Emerging Trends.

Authors:  Michelle D Bardis; Roozbeh Houshyar; Peter D Chang; Alexander Ushinsky; Justin Glavis-Bloom; Chantal Chahine; Thanh-Lan Bui; Mark Rupasinghe; Christopher G Filippi; Daniel S Chow
Journal:  Cancers (Basel)       Date:  2020-05-11       Impact factor: 6.639

9.  A Transfer Learning Approach for Malignant Prostate Lesion Detection on Multiparametric MRI.

Authors:  Quan Chen; Shiliang Hu; Peiran Long; Fang Lu; Yujie Shi; Yunpeng Li
Journal:  Technol Cancer Res Treat       Date:  2019-01-01

10.  Harnessing clinical annotations to improve deep learning performance in prostate segmentation.

Authors:  Karthik V Sarma; Alex G Raman; Nikhil J Dhinagar; Alan M Priester; Stephanie Harmon; Thomas Sanford; Sherif Mehralivand; Baris Turkbey; Leonard S Marks; Steven S Raman; William Speier; Corey W Arnold
Journal:  PLoS One       Date:  2021-06-25       Impact factor: 3.240

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