Artificial intelligence-based measurements of PET/CT imaging biomarkers are associated with disease-specific survival of high-risk prostate cancer patients
Artikel i vetenskaplig tidskrift, 2021

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 SUVmax, 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 SUVmax 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 SUVmax (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 SUVmax and Gleason scores were not. The AI-based approach appears well-suited for clinically relevant patient stratification and monitoring of individual therapy.

prostate cancer

disease-specific survival

imaging biomarkers

Artificial intelligence

18 F-choline-PET/CT

Författare

Eirini Polymeri

Göteborgs universitet

Sahlgrenska universitetssjukhuset

Henrik Kjölhede

Sahlgrenska universitetssjukhuset

Göteborgs universitet

Olof Enqvist

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik, Digitala bildsystem och bildanalys

Johannes Ulén

Eigenvision AB

M. H. Poulsen

Odense Universitetshospital

J. Simonsen

Odense Universitetshospital

Pablo Borrelli

Sahlgrenska universitetssjukhuset

E. Tragardh

Skånes universitetssjukhus (SUS)

Åse (Allansdotter) Johnsson

Sahlgrenska universitetssjukhuset

Göteborgs universitet

Poul Flemming Høilund–Carlsen

Odense Universitetshospital

L. Edenbrandt

Göteborgs universitet

Sahlgrenska universitetssjukhuset

Scandinavian Journal of Urology

2168-1805 (ISSN) 2168-1813 (eISSN)

Vol. In Press

Ämneskategorier

Urologi och njurmedicin

Radiologi och bildbehandling

Cancer och onkologi

DOI

10.1080/21681805.2021.1977845

PubMed

34565290

Mer information

Senast uppdaterat

2021-10-06