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Proceedings Paper

Automatic high-grade cancer detection on prostatectomy histopathology images
Author(s): W. Han; C. Johnson; M. Gaed; J. A. Gomez; M. Moussa; J. L. Chin; S. E. Pautler; G. Bauman; A. D. Ward
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Paper Abstract

Automatic cancer grading and high-grade cancer detection for radical prostatectomy (RP) specimens can benefit pathological assessment for prognosis and post-surgery treatment decision making. We developed and validated an automatic system which grades cancerous tissue as high-grade (Gleason grade 4 and higher) vs. low-grade (Gleason grade 3) on digital histopathology whole-slide images (WSIs). We combined this grading system with our previouslyreported cancer detection system to build a high-grade cancer detection system which automatically finds high-grade cancerous foci on WSIs. The system was tuned on a 3-patient data set and cross-validated against expert-drawn contours on a separate 68-patient data set comprising 286 mid-gland whole-slide images of RP specimens. The system uses machine learning techniques to classify each region of interest (ROI) on the slide as cancer or non-cancer and each cancerous ROI as high-grade or low-grade cancer. We used leave-one-patient-out cross-validation to measure the performance of cancer grading for classified ROIs with three different classifiers and the performance of the high-grade cancer detection system on a per tumor focus basis. The best performing (Fisher) classifier yielded an area under the receiver-operating characteristic curve of 0.87 for cancer grading. The system yielded error rates of 19.5% and 23.4% for pure high-grade (Gleason 4+4, 5+5) and high-grade (Gleason Score ≥ 7) cancer detection, respectively. The system demonstrated potential for practical computation speeds. Upon successful multi-centre validation, this system has the potential to assist the pathologist to find high-grade cancer more efficiently, which benefits the selection and guidance of adjuvant therapy and prognosis post RP.

Paper Details

Date Published: 18 March 2019
PDF: 9 pages
Proc. SPIE 10956, Medical Imaging 2019: Digital Pathology, 109560D (18 March 2019); doi: 10.1117/12.2512916
Show Author Affiliations
W. Han, Western Univ. (Canada)
Lawson Health Research Institute (Canada)
C. Johnson, Western Univ. (Canada)
M. Gaed, Western Univ. (Canada)
J. A. Gomez, Western Univ. (Canada)
M. Moussa, Western Univ. (Canada)
J. L. Chin, Western Univ. (Canada)
S. E. Pautler, Western Univ. (Canada)
G. Bauman, Western Univ. (Canada)
Lawson Health Research Institute (Canada)
A. D. Ward, Western Univ. (Canada)
Lawson Health Research Institute (Canada)

Published in SPIE Proceedings Vol. 10956:
Medical Imaging 2019: Digital Pathology
John E. Tomaszewski; Aaron D. Ward, Editor(s)

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