Improving prediction of prostate cancer recurrence using chemical imaging.
Precise Outcome prediction is crucial to providing optimal cancer care across the spectrum of solid cancers. Clinically-useful tools to predict risk of adverse events (metastases, recurrence), however, remain deficient. Here, we report an approach to predict the risk of prostate cancer recurrence, at the time of initial diagnosis, using a combination of emerging chemical imaging, a diagnostic protocol that focuses simultaneously on the tumor and its microenvironment, and data analysis of frequent patterns in molecular expression. Fourier transform infrared (FT-IR) spectroscopic imaging was employed to record the structure and molecular content from tumors prostatectomy. We analyzed data from a patient cohort that is mid-grade dominant - which is the largest cohort of patients in the modern era and in whom prognostic methods are largely ineffective. Our approach outperforms the two widely used tools, Kattan nomogram and CAPRA-S score in a head-to-head comparison for predicting risk of recurrence. Importantly, the approach provides a histologic basis to the prediction that identifies chemical and morphologic features in the tumor microenvironment that is independent of conventional clinical information, opening the door to similar advances in other solid tumors.
1] Center for Interventional Oncology, National Institutes of Health, Bethesda, MD 20892, USA [2] Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA [3] Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.
Jin Tae
JT
Kwak
Department of Pathology, University of Illinois at Chicago, Chicago, IL 60612, USA.
André
A
Kajdacsy-Balla
Department of Pathology, University of Illinois at Chicago, Chicago, IL 60612, USA.
Virgilia
V
Macias
1] Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA [2] Department of Pathology, University of Illinois at Chicago, Chicago, IL 60612, USA.
Michael
M
Walsh
Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.
Saurabh
S
Sinha
1] Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA [2] Department of Bioengineering, Mechanical Science and Engineering, Electrical and Computer Engineering, Chemical and Biomolecular Engineering and University of Illinois Cancer Center, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.
Rohit
R
Bhargava
D000368:Aged; D000465:Algorithms; D006801:Humans; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical; D060787:Neoplasm Grading; D009364:Neoplasm Recurrence, Local; D009367:Neoplasm Staging; D017063:Outcome Assessment, Health Care / Q000379:methods / Q000706:statistics & numerical data; D011379:Prognosis; D011467:Prostate / Q000473:pathology*; D011471:Prostatic Neoplasms / Q000175:diagnosis* / Q000628:therapy; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D017550:Spectroscopy, Fourier Transform Infrared; D060388:Support Vector Machine