Name
Abstract | ||
|---|---|---|
Automatic localization of cancer on whole-slide histology images from radical prostatectomy specimens would support quantitative, graphical pathology reporting and research studies validating in vivo imaging against gold-standard histopathology. There is an unmet need for such a system that is robust to staining variability, is sufficiently fast and parallelizable as to be integrated into the clinical pathology workflow, and is validated using whole-slide images. We developed and validated such a system, with tuning occurring on an 8-patient data set and cross-validation occurring on a separate 41-patient data set comprising 703,745 480 mu m x 480 mu m sub-images from 166 whole-slide images. Our system computes tissue component maps from pixel data using a technique that is robust to staining variability, showing the loci of nuclei, luminal areas, and areas containing other tissue including stroma. Our system then computes first- and second-order texture features from the tissue component maps and uses machine learning techniques to classify each sub-image on the slide as cancer or non-cancer. The system was validated against expert-drawn contours that were verified by a genitourinary pathologist. We used leave-one-patient-out, 5-fold, and 2-fold cross-validation to measure performance with three different classifiers. The best performing support vector machine classifier yielded an area under the receiver operating characteristic curve of 0.95 from leave-one-out cross-validation. The system demonstrated potential for practically useful computation speeds, with further optimization and parallelization of the implementation. Upon successful multi-centre validation, this system has the potential to enable quantitative surgical pathology reporting and accelerate imaging validation studies using histopathologic reference standards |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1117/12.2292450 | Proceedings of SPIE |
Keywords | DocType | Volume |
Automatic prostate cancer detection,Surgical pathology,Whole-slide cancer mapping,Tissue component mapping,Staining variability,Color deconvolution,Machine learning | Conference | 10581 |
ISSN | Citations | PageRank |
0277-786X | 0 | 0.34 |
References | Authors | |
0 | 9 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Han, W. | 1 | 0 | 1.01 |
| C. Johnson | 2 | 0 | 0.68 |
| Mena Gaed | 3 | 25 | 8.08 |
| J. A. Gomez | 4 | 0 | 0.68 |
| Madeleine Moussa | 5 | 27 | 8.83 |
| Joseph L. Chin | 6 | 1 | 3.08 |
| Stephen E. Pautler | 7 | 24 | 8.13 |
| Glenn Bauman | 8 | 4 | 4.07 |
| Aaron D Ward | 9 | 89 | 22.61 |