基于多模态PET/CT图像深度学习模型的局灶性肝脏病变检测与诊断初探

IF 1.8 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

European Journal of Radiology Open Pub Date : 2024-12-17 DOI:10.1016/j.ejro.2024.100624

Yingqi Luo , Qingqi Yang , Jinglang Hu , Xiaowen Qin , Shengnan Jiang , Ying Liu

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引用次数: 0

摘要

目的：开发并验证一种使用多模态PET/CT成像检测和分类局灶性肝脏病变（FLL）的深度学习模型。方法：本研究纳入了从2022年3月至2023年2月在我院接受18F-FDG PET/CT成像的185例患者。我们分析血清学资料和影像。在PET和CT上对肝脏病变进行分割，作为“参考标准”。使用PET和CT图像训练深度学习模型，生成预测分割并对病变性质进行分类。通过使用Dice、Precision、Recall、F1-score、ROC和AUC等指标将预测分割与参考分割进行比较，并将其与医生诊断进行比较，从而评估模型的性能。结果：本研究最终纳入150例患者，其中良性肝结节46例，恶性肝结节51例，无fll患者53例。年龄、AST、ALP、GGT、AFP、ca19 -9、CEA组间差异有统计学意义。在验证集上，模型的Dice系数为0.740。正常组的召回率为0.918，精密度为0.904，f1评分为0.909，AUC为0.976。良性组的召回率为0.869，精密度为0.862，f1评分为0.863，AUC为0.928。恶性组的召回率为0.858，准确率为0.914，f1评分为0.883，AUC为0.979。该模型的整体诊断性能介于初级和高级医师之间。结论：该深度学习模型对fll的检测灵敏度高，能有效区分良恶性病变。

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Preliminary study on detection and diagnosis of focal liver lesions based on a deep learning model using multimodal PET/CT images

Objectives

To develop and validate a deep learning model using multimodal PET/CT imaging for detecting and classifying focal liver lesions (FLL).

Methods

This study included 185 patients who underwent ¹⁸F-FDG PET/CT imaging at our institution from March 2022 to February 2023. We analyzed serological data and imaging. Liver lesions were segmented on PET and CT, serving as the "reference standard". Deep learning models were trained using PET and CT images to generate predicted segmentations and classify lesion nature. Model performance was evaluated by comparing the predicted segmentations with the reference segmentations, using metrics such as Dice, Precision, Recall, F1-score, ROC, and AUC, and compared it with physician diagnoses.

Results

This study finally included 150 patients, comprising 46 patients with benign liver nodules, 51 patients with malignant liver nodules, and 53 patients with no FLLs. Significant differences were observed among groups for age, AST, ALP, GGT, AFP, CA19–9and CEA. On the validation set, the Dice coefficient of the model was 0.740. For the normal group, the recall was 0.918, precision was 0.904, F1-score was 0.909, and AUC was 0.976. For the benign group, the recall was 0.869, precision was 0.862, F1-score was 0.863, and AUC was 0.928. For the malignant group, the recall was 0.858, precision was 0.914, F1-score was 0.883, and AUC was 0.979. The model's overall diagnostic performance was between that of junior and senior physician.