| 李迎辞,吴东博,宫飞飞.基于PET/CT深度学习及其联合模型预测肺浸润性腺癌术后进展[J].中国医学影像技术,2024,40(8):1194~1198 |
| 基于PET/CT深度学习及其联合模型预测肺浸润性腺癌术后进展 |
| Deep learning model based on PET/CT and combination with Cox proportional hazard model for predicting progression of lung invasive adenocarcinoma after surgery |
| 投稿时间：2023-12-30 修订日期：2024-03-19 |
| DOI：10.13929/j.issn.1003-3289.2024.08.018 |
| 中文关键词: 肺腺癌 正电子发射断层显像和计算机体层摄影术 深度学习 疾病恶化 |
| 英文关键词:adenocarcinoma of lung positron-emission tomography and computed tomography deep learning disease progression |
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| 中文摘要: |
| 目的 观察基于PET/CT图像构建深度学习(DL)模型及其联合Cox比例风险回归模型预测肺浸润性腺癌术后5年内疾病进展(PD)的效能。方法 回顾性分析250例肺浸润性腺癌临床、PET/CT及术后5年随访资料,根据有无PD将其分为进展组(n=71)与无进展组(n=179),比较组间基本资料及PET/CT所见;通过受试者工作特征(ROC)曲线及相应最佳截断值将组间存在差异的计量变量转换为分类变量,以多因素Cox比例风险回归模型遴选肺浸润性腺癌术后5年内PD的独立预测因素。按照6∶2∶2比例将患者分为训练集、验证集及测试集,以训练集及验证集PET/CT数据训练模型并调整参数,得到PET/CT DL模型;以串联方式将其与上述因素相结合而得到联合模型。以曲线下面积(AUC)评估各模型预测测试集肺浸润性腺癌术后5年内PD的效能并加以比较。结果 组间患者性别、吸烟与否,基于PET 所测病灶长径、SUVmax及SUVmean,CT所示病灶长径、短径及其类型差异均有统计学意义(P均＜0.05)。吸烟 及病灶SUVmax＞4.15 可预测肺浸润性腺癌术后5年内PD。PET/CT DL模型预测测试集PD的AUC为0.847,联合模型的AUC为0.890,后者高于前者(P=0.036)。结论 PET/CT DL模型预测肺浸润性腺癌术后5年内PD效能较高;联合Cox比例风险回归模型可进一步提升其预测效能。 |
| 英文摘要: |
| Objective To observe the efficacy of deep learning (DL) model based on PET/CT and its combination with Cox proportional hazard model for predicting progressive disease (PD) of lung invasive adenocarcinoma within 5 years after surgery. Methods The clinical, PET/CT and 5-year follow-up data of 250 patients with lung invasive adenocarcinoma were retrospectively analyzed. According to PD or not, the patients were divided into the PD group (n=71) and non-PD group (n=179). The basic data and PET/CT findings were compared between groups, among which the quantitative variables being significant different between groups were transformed to categorical variables using receiver operating characteristic (ROC) curve and corresponding cut-off value. Multivariant Cox proportional hazard model was used to select independent predicting factors of PD of lung invasive adenocarcinoma within 5 years after surgery. The patients were divided into training, validation and test sets at the ratio of 6∶2∶2, and PET/CT data in training set and validation set were used to train model and tuning parameters to build the PET/CT DL model, and the combination model was built in serial connection of DL model and the predictive factors. In test set, the efficacy of each model for predicting PD of lung invasive adenocarcinoma within 5 years after surgery was assessed and compared using the area under the curve (AUC). Results Patients' gender and smoking status, as well as the long diameter, SUVmax and SUVmean of lesions measured on PET images, the long diameter, short diameter and type of lesions showed on CT were statistically different between groups (all P＜0.05). Smoking (HR=1.787, P=0.031) and lesion SUVmax ＞4.15 (HR=5.249, P=0.042) were both predictors of PD of lung invasive adenocarcinoma within 5 years after surgery. In test set, the AUC of PET/CT DL model for predicting PD was 0.847, of the combination model was 0.890, of the latter was higher than of the former (P=0.036). Conclusion DL model based on PET/CT had high efficacy for predicting PD of lung invasive adenocarcinoma within 5 years after surgery. Combining with Cox proportional hazard model could further improve its predicting efficacy. |
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