Deep learning-based characterization of pathological subtypes in lung invasive adenocarcinoma utilizing 18F-deoxyglucose positron emission tomography imaging

• Published in: BMC cancer 2024-10, Vol.24 (1), p.1-10, Article 1229
• Main Authors: Wu, Dongbo, Li, Yingci, Zhou, Mingyan, Gong, Feifei, Li, Jiankun
• Format: Article
• Language: English
• Subjects: Accuracy; Adenocarcinoma; Adenocarcinoma of lung; Analysis; Artificial intelligence; Care and treatment; Computed tomography; Deep learning; Demographic aspects; Deoxyglucose; Diagnosis; Health aspects; Image processing; Invasiveness; Lung cancer; Lung diseases; Machine learning; Medical imaging; Methods; Observational learning; Pathology; Patients; PET imaging; Positron emission tomography; Positron-emission tomography and computed tomography; Statistical analysis; Statistical models; Tomography; Work stations
• ISSN: 1471-2407; 1471-2407
• DOI: 10.1186/s12885-024-13018-7

Summary Objective To evaluate the diagnostic efficacy of a deep learning (DL) model based on PET/CT images for distinguishing and predicting various pathological subtypes of invasive lung adenocarcinoma. Methods A total of 250 patients diagnosed with invasive lung cancer were included in this retrospective study. The pathological subtypes of the cancer were recorded. PET/CT images were analyzed, including measurements and recordings of the short and long diameters on the maximum cross-sectional plane of the CT image, the density of the lesion, and the associated imaging signs. The SUVmax, SUVmean, and the lesion's long and short diameters on the PET image were also measured. A manual diagnostic model was constructed to analyze its diagnostic performance across different pathological subtypes. The acquired images were first denoised, followed by data augmentation to expand the dataset. The U-Net network architecture was then employed for feature extraction and network segmentation. The classification network was based on the ResNet residual network to address the issue of gradient vanishing in deep networks. Batch normalization was applied to ensure the feature matrix followed a distribution with a mean of 0 and a variance of 1. The images were divided into training, validation, and test sets in a ratio of 6:2:2 to train the model. The deep learning model was then constructed to analyze its diagnostic performance across different pathological subtypes. Results Statistically significant differences (P < 0.05) were observed among the four different subtypes in PET/CT imaging performance. The AUC and diagnostic accuracy of the manual diagnostic model for different pathological subtypes were as follows: APA: 0.647, 0.664; SPA: 0.737, 0.772; PPA: 0.698, 0.780; LPA: 0.849, 0.904. Chi-square tests indicated significant statistical differences among these subtypes (P < 0.05). The AUC and diagnostic accuracy of the deep learning model for the different pathological subtypes were as follows: APA: 0.854, 0.864; SPA: 0.930, 0.936; PPA: 0.878, 0.888; LPA: 0.900, 0.920. Chi-square tests also indicated significant statistical differences among these subtypes (P < 0.05). The Delong test showed that the diagnostic performance of the deep learning model was superior to that of the manual diagnostic model (P < 0.05). Conclusions The deep learning model based on PET/CT images exhibits high diagnostic efficacy in distinguishing and diagnosing various pathological subtypes of in