Freely Available, Fully Automated AI-Based Analysis of Primary Tumour and Metastases of Prostate Cancer in Whole-Body [F-18]-PSMA-1007 PET-CT
Artikel i vetenskaplig tidskrift, 2022

Here, we aimed to develop and validate a fully automated artificial intelligence (AI)-based method for the detection and quantification of suspected prostate tumour/local recurrence, lymph node metastases, and bone metastases from [F-18]PSMA-1007 positron emission tomography-computed tomography (PET-CT) images. Images from 660 patients were included. Segmentations by one expert reader were ground truth. A convolutional neural network (CNN) was developed and trained on a training set, and the performance was tested on a separate test set of 120 patients. The AI method was compared with manual segmentations performed by several nuclear medicine physicians. Assessment of tumour burden (total lesion volume (TLV) and total lesion uptake (TLU)) was performed. The sensitivity of the AI method was, on average, 79% for detecting prostate tumour/recurrence, 79% for lymph node metastases, and 62% for bone metastases. On average, nuclear medicine physicians' corresponding sensitivities were 78%, 78%, and 59%, respectively. The correlations of TLV and TLU between AI and nuclear medicine physicians were all statistically significant and ranged from R = 0.53 to R = 0.83. In conclusion, the development of an AI-based method for prostate cancer detection with sensitivity on par with nuclear medicine physicians was possible. The developed AI tool is freely available for researchers.

artificial intelligence

convolutional neural network

PSMA

deep learning

prostate cancer

Författare

Elin Tragardh

Skånes universitetssjukhus (SUS)

Lunds universitet

Olof Enqvist

Digitala bildsystem och bildanalys

Johannes Ulen

Eigenvision AB

Jonas Jogi

Lunds universitet

Skånes universitetssjukhus (SUS)

Ulrika Bitzen

Skånes universitetssjukhus (SUS)

Fredrik Hedeer

Skånes universitetssjukhus (SUS)

Kristian Valind

Skånes universitetssjukhus (SUS)

Lunds universitet

Sabine Garpered

Skånes universitetssjukhus (SUS)

Erland Hvittfeldt

Lunds universitet

Skånes universitetssjukhus (SUS)

Pablo Borrelli

Sahlgrenska universitetssjukhuset

Lars Edenbrandt

Göteborgs universitet

Diagnostics

20754418 (eISSN)

Vol. 12 9 2101

Ämneskategorier

Urologi och njurmedicin

Radiologi och bildbehandling

Medicinsk bildbehandling

DOI

10.3390/diagnostics12092101

PubMed

36140502

Mer information

Senast uppdaterat

2023-04-21