TY - GEN
T1 - Comparing Immunofluorescent versus HE Glandular Architecture Features in Prognostic Models from Prostate Biopsies
AU - Khan, Faisal M.
AU - Scott, Richard
AU - Donovan, Michael
AU - Fernandez, Gerardo
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/10/26
Y1 - 2018/10/26
N2 - Determining the best treatment for prostate cancer patients with a newly diagnosed positive biopsy can be challenging. Multivariate prognostic models are often employed to stratify patients into risk populations. Many models leverage quantitative features derived from morphological analysis of the tumor architecture in the biopsy specimen. The vast majority of these features are derived from analyzing standard hematoxylin and eosin (HE) images. Immunofluorescence (IF) image analysis of tissue pathology has also recently been proven to be robust. In this work, we constructed multivariate models for prostate cancer prognosis comparing the usage of previously published IF vs HE features. In images from 304 patients, the IF features prognostically outperform the HE features. The IF feature model also exhibits consistent training vs validation performance, an important consideration when developing models subject to regulatory oversight. This paper presents the first evaluation of comparing previously published HE and IF morphological features head-to-head in prognostic models from prostate biopsies.
AB - Determining the best treatment for prostate cancer patients with a newly diagnosed positive biopsy can be challenging. Multivariate prognostic models are often employed to stratify patients into risk populations. Many models leverage quantitative features derived from morphological analysis of the tumor architecture in the biopsy specimen. The vast majority of these features are derived from analyzing standard hematoxylin and eosin (HE) images. Immunofluorescence (IF) image analysis of tissue pathology has also recently been proven to be robust. In this work, we constructed multivariate models for prostate cancer prognosis comparing the usage of previously published IF vs HE features. In images from 304 patients, the IF features prognostically outperform the HE features. The IF feature model also exhibits consistent training vs validation performance, an important consideration when developing models subject to regulatory oversight. This paper presents the first evaluation of comparing previously published HE and IF morphological features head-to-head in prognostic models from prostate biopsies.
UR - http://www.scopus.com/inward/record.url?scp=85056653478&partnerID=8YFLogxK
U2 - 10.1109/EMBC.2018.8512292
DO - 10.1109/EMBC.2018.8512292
M3 - Conference contribution
C2 - 30440522
AN - SCOPUS:85056653478
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 838
EP - 841
BT - 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018
Y2 - 18 July 2018 through 21 July 2018
ER -