Deep convolutional neural network based on CT images of pulmonary nodules in the lungs of adolescent and young adult patients with osteosarcoma

Abstract

The aim of the present study was to explore the diagnostic value of a deep convolutional neural network (DCNN) model for the diagnosis of pulmonary nodules in adolescent and young adult patients with osteosarcoma. For the present study, 675 chest CT images were retrospectively collected from 109 patients with clinically confirmed osteosarcoma who underwent chest CT examination at Hangzhou Third People's Hospital (Hangzhou, China) from March 2011 to February 2022. CT images were then evaluated using the DCNN and manual models. Subsequently, pulmonary nodules of osteosarcoma were divided into calcified nodules, solid nodules, partially solid nodules and ground glass nodules using the DCNN model. Those patients with osteosarcoma who were diagnosed and treated were followed up to observe dynamic changes in the pulmonary nodules. A total of 3,087 nodules were detected, while 278 nodules were missed compared with those determined using the reference standard given by the consensus of three Experienced radiologists., which was analyzed by two diagnostic radiologists. In the manual model group, 2,442 nodules were detected, while 657 nodules were missed. The DCNN model showed significantly higher sensitivity and specificity compared with the manual model (sensitivity, 0.923 vs. 0.908; specificity, 0.552 vs. 0.351; P<0.05). In addition, the DCNN model yielded an area under the curve (AUC) value of 0.795 [95% confidence interval (CI), 0.743-0.846], outperforming that of the manual model (AUC, 0.687; 95% CI, 0.629-0.732; P<0.05). The film reading time of the DCNN model was also significantly shorter compared with that of the manual model [mean ± standard deviation (SD); 173.25±24.10 vs. 328.32±22.72 sec; P<0.05)]. The AUC of calcified nodules, solid nodules, partially solid nodules and ground glass nodules was calculated to be 0.766, 0.771, 0.761 and 0.796, respectively, using the DCNN model. Using this model, the majority of the pulmonary nodules were detected in patients with osteosarcoma at the initial diagnosis (69/109, 62.3%), and the majority of these were found with multiple pulmonary nodules instead of a single nodule (71/109, 65.1% vs. 38/109, 34.9%). These data suggest that, compared with the manual model, the DCNN model proved to be beneficial for the detection of pulmonary nodules in adolescent and young adult patients with osteosarcoma, which may reduce the time of artificial radiograph reading. In conclusion, the proposed DCNN model, developed using data from 675 chest CT images retrospectively collected from 109 patients with clinically confirmed osteosarcoma, may be used as an effective tool to evaluate pulmonary nodules in patients with osteosarcoma.

Advancements in Image Enhancement and Attention based efficientdet Optimization Classifier for Precise Osteosarcoma Lung Nodule Detection

“…AI imaging technology can increase doctors' productivity and has a high accuracy rate, according to several research. (13) As the number of precautionary and early detection methods rises as predicted, researchers are developing automated solutions that minimize the workload of physicians, boost the accuracy of diagnostics by lowering subjectivity, expedite analysis, and lower clinical expenses.…”