Automatic grading of prostate cancer in digitized histopathology images : Learning from multiple experts
Copyright © 2018 Elsevier B.V. All rights reserved..
Prostate cancer (PCa) is a heterogeneous disease that is manifested in a diverse range of histologic patterns and its grading is therefore associated with an inter-observer variability among pathologists, which may lead to an under- or over-treatment of patients. In this work, we develop a computer aided diagnosis system for automatic grading of PCa in digitized histopathology images using supervised learning methods. Our pipeline comprises extraction of multi-scale features that include glandular, cellular, and image-based features. A number of novel features are proposed based on intra- and inter-nuclei properties; these features are shown to be among the most important ones for classification. We train our classifiers on 333 tissue microarray (TMA) cores that were sampled from 231 radical prostatectomy patients and annotated in detail by six pathologists for different Gleason grades. We also demonstrate the TMA-trained classifier's performance on additional 230 whole-mount slides of 56 patients, independent of the training dataset, by examining the automatic grading on manually marked lesions and randomly sampled 10% of the benign tissue. For the first time, we incorporate a probabilistic approach for supervised learning by multiple experts to account for the inter-observer grading variability. Through cross-validation experiments, the overall grading agreement of the classifier with the pathologists was found to be an unweighted kappa of 0.51, while the overall agreements between each pathologist and the others ranged from 0.45 to 0.62. These results suggest that our classifier's performance is within the inter-observer grading variability levels across the pathologists in our study, which are also consistent with those reported in the literature.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2018 |
|---|---|
| Erschienen: |
2018 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:50 |
|---|---|
| Enthalten in: |
Medical image analysis - 50(2018) vom: 18. Dez., Seite 167-180 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Nir, Guy [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
Computer aided diagnosis |
|---|
| Anmerkungen: |
Date Completed 25.10.2019 Date Revised 25.10.2019 published: Print-Electronic Citation Status MEDLINE |
|---|
| doi: |
10.1016/j.media.2018.09.005 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM289744970 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM289744970 | ||
| 003 | DE-627 | ||
| 005 | 20231225063227.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231225s2018 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1016/j.media.2018.09.005 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n0965.xml |
| 035 | |a (DE-627)NLM289744970 | ||
| 035 | |a (NLM)30340027 | ||
| 035 | |a (PII)S1361-8415(18)30749-7 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Nir, Guy |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Automatic grading of prostate cancer in digitized histopathology images |b Learning from multiple experts |
| 264 | 1 | |c 2018 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Completed 25.10.2019 | ||
| 500 | |a Date Revised 25.10.2019 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright © 2018 Elsevier B.V. All rights reserved. | ||
| 520 | |a Prostate cancer (PCa) is a heterogeneous disease that is manifested in a diverse range of histologic patterns and its grading is therefore associated with an inter-observer variability among pathologists, which may lead to an under- or over-treatment of patients. In this work, we develop a computer aided diagnosis system for automatic grading of PCa in digitized histopathology images using supervised learning methods. Our pipeline comprises extraction of multi-scale features that include glandular, cellular, and image-based features. A number of novel features are proposed based on intra- and inter-nuclei properties; these features are shown to be among the most important ones for classification. We train our classifiers on 333 tissue microarray (TMA) cores that were sampled from 231 radical prostatectomy patients and annotated in detail by six pathologists for different Gleason grades. We also demonstrate the TMA-trained classifier's performance on additional 230 whole-mount slides of 56 patients, independent of the training dataset, by examining the automatic grading on manually marked lesions and randomly sampled 10% of the benign tissue. For the first time, we incorporate a probabilistic approach for supervised learning by multiple experts to account for the inter-observer grading variability. Through cross-validation experiments, the overall grading agreement of the classifier with the pathologists was found to be an unweighted kappa of 0.51, while the overall agreements between each pathologist and the others ranged from 0.45 to 0.62. These results suggest that our classifier's performance is within the inter-observer grading variability levels across the pathologists in our study, which are also consistent with those reported in the literature | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a Computer aided diagnosis | |
| 650 | 4 | |a Digital pathology | |
| 650 | 4 | |a Machine learning | |
| 650 | 4 | |a Prostate cancer | |
| 700 | 1 | |a Hor, Soheil |e verfasserin |4 aut | |
| 700 | 1 | |a Karimi, Davood |e verfasserin |4 aut | |
| 700 | 1 | |a Fazli, Ladan |e verfasserin |4 aut | |
| 700 | 1 | |a Skinnider, Brian F |e verfasserin |4 aut | |
| 700 | 1 | |a Tavassoli, Peyman |e verfasserin |4 aut | |
| 700 | 1 | |a Turbin, Dmitry |e verfasserin |4 aut | |
| 700 | 1 | |a Villamil, Carlos F |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Gang |e verfasserin |4 aut | |
| 700 | 1 | |a Wilson, R Storey |e verfasserin |4 aut | |
| 700 | 1 | |a Iczkowski, Kenneth A |e verfasserin |4 aut | |
| 700 | 1 | |a Lucia, M Scott |e verfasserin |4 aut | |
| 700 | 1 | |a Black, Peter C |e verfasserin |4 aut | |
| 700 | 1 | |a Abolmaesumi, Purang |e verfasserin |4 aut | |
| 700 | 1 | |a Goldenberg, S Larry |e verfasserin |4 aut | |
| 700 | 1 | |a Salcudean, Septimiu E |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Medical image analysis |d 1996 |g 50(2018) vom: 18. Dez., Seite 167-180 |w (DE-627)NLM096527188 |x 1361-8423 |7 nnns |
| 773 | 1 | 8 | |g volume:50 |g year:2018 |g day:18 |g month:12 |g pages:167-180 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1016/j.media.2018.09.005 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 50 |j 2018 |b 18 |c 12 |h 167-180 | ||