Automated lung cancer assessment on 18F-PET/CT using Retina U-Net and anatomical region segmentation.

ABSTRACT:

Objectives

To develop and test a Retina U-Net algorithm for the detection of primary lung tumors and associated metastases of all stages on FDG-PET/CT.

Methods

A data set consisting of 364 FDG-PET/CTs of patients with histologically confirmed lung cancer was used for algorithm development and internal testing. The data set comprised tumors of all stages. All lung tumors (T), lymphatic metastases (N), and distant metastases (M) were manually segmented as 3D volumes using whole-body PET/CT series. The data set was split into a training (n = 216), validation (n = 74), and internal test data set (n = 74). Detection performance for all lesion types at multiple classifier thresholds was evaluated and false-positive-findings-per-case (FP/c) calculated. Next, detected lesions were a

SUBMITTER: Weikert T 

PROVIDER: S-EPMC10182147 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature