External and Internal Validation of a Computer Assisted Diagnostic Model for Detecting Multi-Organ Mass Lesions in CT images.
- Author:
Lian-Yan XU
1
;
Ke YAN
2
;
Le LU
2
;
Wei-Hong ZHANG
1
;
Xu CHEN
3
;
Xiao-Fei HUO
3
;
Jing-Jing LU
3
Author Information
- Publication Type:Journal Article
- Keywords: computer-aided diagnosis; convolutional neural network; deep learning; lesion detection
- MeSH: Computer Simulation; Computers; Neural Networks, Computer; Tomography, X-Ray Computed
- From: Chinese Medical Sciences Journal 2021;36(3):210-217
- CountryChina
- Language:English
- Abstract: Objective We developed a universal lesion detector (ULDor) which showed good performance in in-lab experiments. The study aims to evaluate the performance and its ability to generalize in clinical setting via both external and internal validation. Methods The ULDor system consists of a convolutional neural network (CNN) trained on around 80K lesion annotations from about 12K CT studies in the DeepLesion dataset and 5 other public organ-specific datasets. During the validation process, the test sets include two parts: the external validation dataset which was comprised of 164 sets of non-contrasted chest and upper abdomen CT scans from a comprehensive hospital, and the internal validation dataset which was comprised of 187 sets of low-dose helical CT scans from the National Lung Screening Trial (NLST). We ran the model on the two test sets to output lesion detection. Three board-certified radiologists read the CT scans and verified the detection results of ULDor. We used positive predictive value (PPV) and sensitivity to evaluate the performance of the model in detecting space-occupying lesions at all extra-pulmonary organs visualized on CT images, including liver, kidney, pancreas, adrenal, spleen, esophagus, thyroid, lymph nodes, body wall, thoracic spine,
