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Prediction of prostate cancer recurrence using quantitative phase imaging
Shamira Sridharan1, Virgilia Macias2, Krishnarao Tangella3
1Quantitative Light Imaging Laboratory, Department of Bioengineering, Beckman Institute of Advanced Science and Technology, University of Illinois at Urbana-Champaign, 405 N. Matthews Avenue, Urbana, IL 61801, USA.
Scientific Reports
|May 16, 2015
Summary
A new microscopy technique accurately predicts prostate cancer recurrence risk. Lower light scattering anisotropy in adjacent stroma indicates higher risk, outperforming current clinical tools for better patient stratification.
Area of Science:
- Biomedical Optics
- Cancer Biology
- Pathology
Background:
- Prostate cancer recurrence after radical prostatectomy affects approximately 25% of patients.
- Current methods struggle to accurately predict recurrence risk, especially in intermediate-risk cases.
- Improved prediction tools are crucial for personalized treatment strategies.
Purpose of the Study:
- To evaluate the potential of spatial light interference microscopy (SLIM) for predicting biochemical recurrence of prostate cancer.
- To investigate the relationship between light scattering anisotropy in prostatectomy tissue and cancer recurrence.
- To compare the efficacy of this novel method against the established CAPRA-S score.
Main Methods:
- Utilized label-free spatial light interference microscopy (SLIM) for localized light scattering measurements.
- Analyzed anisotropy of light scattering in the stroma adjacent to cancerous glands in prostatectomy tissue microarrays.
- Compared the predictive performance of SLIM-based anisotropy with CAPRA-S, Gleason grade, PSA levels, and pTNM stage.
Main Results:
- Lower anisotropy values in stromal light scattering correlate with a higher risk of prostate cancer recurrence.
- The stroma adjacent to glands in recurrent patients exhibits greater fractionation compared to non-recurrent patients.
- The SLIM-based method demonstrated superior predictive performance compared to CAPRA-S across various clinical parameters.
Conclusions:
- Light scattering anisotropy measured by SLIM is a promising biomarker for predicting prostate cancer recurrence.
- This quantitative phase imaging (QPI) approach offers a novel tool to aid pathologists in risk stratification.
- The findings suggest potential for improved clinical decision-making and patient management for prostate cancer.
