Artificial intelligence-based measurements of PET/CT imaging biomarkers are associated with disease-specific survival of high-risk prostate cancer patients

Abstract

Objective

Artificial intelligence (AI) offers new opportunities for objective quantitative measurements of imaging biomarkers from positron-emission tomography/computed tomography (PET/CT). Clinical image reporting relies predominantly on observer-dependent visual assessment and easily accessible measures like SUV_max, representing lesion uptake in a relatively small amount of tissue. Our hypothesis is that measurements of total volume and lesion uptake of the entire tumour would better reflect the disease`s activity with prognostic significance, compared with conventional measurements.

Methods

An AI-based algorithm was trained to automatically measure the prostate and its tumour content in PET/CT of 145 patients. The algorithm was then tested retrospectively on 285 high-risk patients, who were examined using ¹⁸F-choline PET/CT for primary staging between April 2008 and July 2015. Prostate tumour volume, tumour fraction of the prostate gland, lesion uptake of the entire tumour, and SUV_max were obtained automatically. Associations between these measurements, age, PSA, Gleason score and prostate cancer-specific survival were studied, using a Cox proportional-hazards regression model.

Results

Twenty-three patients died of prostate cancer during follow-up (median survival 3.8 years). Total tumour volume of the prostate (p = 0.008), tumour fraction of the gland (p = 0.005), total lesion uptake of the prostate (p = 0.02), and age (p = 0.01) were significantly associated with disease-specific survival, whereas SUV_max (p = 0.2), PSA (p = 0.2), and Gleason score (p = 0.8) were not.

Conclusion

AI-based assessments of total tumour volume and lesion uptake were significantly associated with disease-specific survival in this patient cohort, whereas SUV_max and Gleason scores were not. The AI-based approach appears well-suited for clinically relevant patient stratification and monitoring of individual therapy.

Keywords:

• Artificial intelligence
• prostate cancer
• ¹⁸F-choline-PET/CT
• imaging biomarkers
• disease-specific survival

Acknowledgements

We would like to thank Anna Grimby-Ekman for her valuable input on the appropriate statistical method used in our manuscript.

Ethical considerations

The following ethical approvals were obtained: Research Ethical Review Board at the University of Lund (EPN LU 552/2007 and 2016/61) and the Regional Ethics Review Boards of Sweden (295-08 and 2016/103) and Denmark (3-3013-1692/1).

Disclosure statement

LE was employed as Scientific Director by EXINI Diagnostics AB (Lund, Sweden).

Data availability statement

The data that support the findings of this study are available from the corresponding author, [EP], upon reasonable request.

Additional information

Funding

The study was supported by grants from the Swedish state under the agreement between Swedish government and the county councils, the ALF-agreement (ALFGBG-720751 and ALFGBG-873181) and EXINI Diagnostics AB, Lund Sweden. The funding bodies had no influence on the design of the study, data collection, data analysis or the writing of the manuscript.