Department of Radiology, Harbin Medical University Cancer Hospital, Harbin, 150081, China.
Department of PET/CT-MR Center, Harbin Medical University Cancer Hospital, Harbin, 150081, China.
BMC Cancer. 2024 Oct 5;24(1):1229. doi: 10.1186/s12885-024-13018-7.
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.
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.
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).
The deep learning model based on PET/CT images exhibits high diagnostic efficacy in distinguishing and diagnosing various pathological subtypes of invasive lung adenocarcinoma, demonstrating the significant potential of deep learning techniques in accurately identifying and predicting disease subgroups.
评估基于 PET/CT 图像的深度学习（DL）模型在区分和预测浸润性肺腺癌各种病理亚型方面的诊断效能。
本回顾性研究纳入了 250 名经病理诊断为浸润性肺癌的患者。记录了癌症的病理亚型。分析了 PET/CT 图像，包括 CT 图像最大横截面上的短径和长径、病变密度以及相关的影像学征象的测量和记录。还测量了 PET 图像上的 SUVmax、SUVmean 和病变的长径和短径。构建了一个手动诊断模型来分析其在不同病理亚型中的诊断性能。首先对采集的图像进行去噪，然后进行数据扩充以扩大数据集。然后采用 U-Net 网络架构进行特征提取和网络分割。分类网络基于 ResNet 残差网络，以解决深度网络中梯度消失的问题。应用批量归一化，以确保特征矩阵遵循均值为 0、方差为 1 的分布。将图像按 6:2:2 的比例分为训练集、验证集和测试集，以训练模型。然后构建深度学习模型来分析其在不同病理亚型中的诊断性能。
在 PET/CT 影像学表现方面，四种不同亚型之间存在统计学差异（P<0.05）。手动诊断模型对不同病理亚型的 AUC 和诊断准确率如下：APA：0.647，0.664；SPA：0.737，0.772；PPA：0.698，0.780；LPA：0.849，0.904。卡方检验表明这些亚型之间存在显著的统计学差异（P<0.05）。深度学习模型对不同病理亚型的 AUC 和诊断准确率如下：APA：0.854，0.864；SPA：0.930，0.936；PPA：0.878，0.888；LPA：0.900，0.920。卡方检验也表明这些亚型之间存在显著的统计学差异（P<0.05）。Delong 检验表明，深度学习模型的诊断性能优于手动诊断模型（P<0.05）。
基于 PET/CT 图像的深度学习模型在区分和诊断浸润性肺腺癌的各种病理亚型方面具有较高的诊断效能，表明深度学习技术在准确识别和预测疾病亚组方面具有显著潜力。
