Abstract
Pathological estimation of tumor necrosis after chemotherapy is essential for patients with osteosarcoma. This study reports the first fully automated tool to assess viable and necrotic tumor in osteosarcoma, employing advances in histopathology digitization and automated learning. We selected 40 digitized whole slide images representing the heterogeneity of osteosarcoma and chemotherapy response. With the goal of labeling the diverse regions of the digitized tissue into viable tumor, necrotic tumor, and non-tumor, we trained 13 machine-learning models and selected the top performing one (a Support Vector Machine) based on reported accuracy. We also developed a deep-learning architecture and trained it on the same data set. We computed the receiver-operator characteristic for discrimination of non-tumor from tumor followed by conditional discrimination of necrotic from viable tumor and found our models performing exceptionally well. We then used the trained models to identify regions of interest on image-tiles generated from test whole slide images. The classification output is visualized as a tumor-prediction map, displaying the extent of viable and necrotic tumor in the slide image. Thus, we lay the foundation for a complete tumor assessment pipeline from original histology images to tumor-prediction map generation. The proposed pipeline can also be adopted for other types of tumor.
| Original language | English (US) |
|---|---|
| Article number | e0210706 |
| Journal | PloS one |
| Volume | 14 |
| Issue number | 4 |
| DOIs | |
| State | Published - Apr 1 2019 |
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ASJC Scopus subject areas
- Biochemistry, Genetics and Molecular Biology(all)
- Agricultural and Biological Sciences(all)
Cite this
Viable and necrotic tumor assessment from whole slide images of osteosarcoma using machine-learning and deep-learning models. / Arunachalam, Harish Babu; Mishra, Rashika; Daescu, Ovidiu; Cederberg, Kevin B; Rakheja, Dinesh; Sengupta, Anita L; Leonard, David; Hallac, Rami Robert; Leavey, Patrick J.
In: PloS one, Vol. 14, No. 4, e0210706, 01.04.2019.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Viable and necrotic tumor assessment from whole slide images of osteosarcoma using machine-learning and deep-learning models
AU - Arunachalam, Harish Babu
AU - Mishra, Rashika
AU - Daescu, Ovidiu
AU - Cederberg, Kevin B
AU - Rakheja, Dinesh
AU - Sengupta, Anita L
AU - Leonard, David
AU - Hallac, Rami Robert
AU - Leavey, Patrick J
PY - 2019/4/1
Y1 - 2019/4/1
N2 - Pathological estimation of tumor necrosis after chemotherapy is essential for patients with osteosarcoma. This study reports the first fully automated tool to assess viable and necrotic tumor in osteosarcoma, employing advances in histopathology digitization and automated learning. We selected 40 digitized whole slide images representing the heterogeneity of osteosarcoma and chemotherapy response. With the goal of labeling the diverse regions of the digitized tissue into viable tumor, necrotic tumor, and non-tumor, we trained 13 machine-learning models and selected the top performing one (a Support Vector Machine) based on reported accuracy. We also developed a deep-learning architecture and trained it on the same data set. We computed the receiver-operator characteristic for discrimination of non-tumor from tumor followed by conditional discrimination of necrotic from viable tumor and found our models performing exceptionally well. We then used the trained models to identify regions of interest on image-tiles generated from test whole slide images. The classification output is visualized as a tumor-prediction map, displaying the extent of viable and necrotic tumor in the slide image. Thus, we lay the foundation for a complete tumor assessment pipeline from original histology images to tumor-prediction map generation. The proposed pipeline can also be adopted for other types of tumor.
AB - Pathological estimation of tumor necrosis after chemotherapy is essential for patients with osteosarcoma. This study reports the first fully automated tool to assess viable and necrotic tumor in osteosarcoma, employing advances in histopathology digitization and automated learning. We selected 40 digitized whole slide images representing the heterogeneity of osteosarcoma and chemotherapy response. With the goal of labeling the diverse regions of the digitized tissue into viable tumor, necrotic tumor, and non-tumor, we trained 13 machine-learning models and selected the top performing one (a Support Vector Machine) based on reported accuracy. We also developed a deep-learning architecture and trained it on the same data set. We computed the receiver-operator characteristic for discrimination of non-tumor from tumor followed by conditional discrimination of necrotic from viable tumor and found our models performing exceptionally well. We then used the trained models to identify regions of interest on image-tiles generated from test whole slide images. The classification output is visualized as a tumor-prediction map, displaying the extent of viable and necrotic tumor in the slide image. Thus, we lay the foundation for a complete tumor assessment pipeline from original histology images to tumor-prediction map generation. The proposed pipeline can also be adopted for other types of tumor.
UR - http://www.scopus.com/inward/record.url?scp=85064441641&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85064441641&partnerID=8YFLogxK
U2 - 10.1371/journal.pone.0210706
DO - 10.1371/journal.pone.0210706
M3 - Article
C2 - 30995247
AN - SCOPUS:85064441641
VL - 14
JO - PLoS One
JF - PLoS One
SN - 1932-6203
IS - 4
M1 - e0210706
ER -