Purpose

To evaluate whether a three-phase dynamic contrast-enhanced CT protocol, when combined with a deep learning model, has similar accuracy in differentiating hepatocellular carcinoma (HCC) from other focal liver lesions (FLLs) compared with a four-phase protocol.

Methods

Three hundred and forty-two patients (mean age 49.1 ± 10.5 years, range 19–86 years, 65.8% male) scanned with a four-phase CT protocol (precontrast, arterial, portal-venous and delayed phases) were retrospectively enrolled. A total of 449 FLLs were categorized into HCC and non-HCC groups based on the best available reference standard. Three convolutional dense networks (CDNs) with the input of four-phase CT images (model A), three-phase images without portal-venous phase (model B) and three-phase images without precontrast phase (model C) were trained on 80% of lesions and evaluated in the other 20% by receiver operating characteristics (ROC) and confusion matrix analysis. The DeLong test was performed to compare the areas under the ROC curves (AUCs) of A with B, B with C, and A with C.

Results

The diagnostic accuracy in differentiating HCC from other FLLs on test sets was 83.3% for model A, 81.1% for model B and 85.6% for model C, and the AUCs were 0.925, 0.862 and 0.920, respectively. The AUCs of models A and C did not differ significantly (p = 0.765), but the AUCs of models A and B (p = 0.038) and of models B and C (p = 0.028) did.

Conclusions

When combined with a CDN, a three-phase CT protocol without precontrast showed similar diagnostic accuracy as a four-phase protocol in differentiating HCC from other FLLs, suggesting that the multiphase CT protocol for HCC diagnosis might be optimized by removing the precontrast phase to reduce radiation dose.

中文翻译：

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深度学习辅助将肝细胞癌与局灶性肝病变区分开：选择四相和三相CT成像方案。

目的

若要评估三相动态对比增强CT方案与深度学习模型相结合，与四阶段方案相比，在区分肝细胞癌（HCC）与其他局灶性肝病灶（FLL）方面是否具有相似的准确性。

方法

回顾性分析了接受四阶段CT检查（造影前，动脉，门静脉和延迟期）的342例患者（平均年龄49.1±10.5岁，范围19-86岁，男性65.8％）。根据最佳参考标准，总共将449个FLL分为HCC组和非HCC组。在80上训练了带有输入四相CT图像（模型A），无门静脉相的三相图像（模型B）和无预对比相的三相图像（模型C）的三个卷积密集网络（CDN）。 ％的病灶，其余20％的病灶通过接受者操作特征（ROC）和混淆矩阵分析进行评估。进行DeLong测试以比较A与B，B与C以及A与C的ROC曲线（AUC）下的面积。

结果

对于A组，在测试集上区分HCC与其他FLL的诊断准确性为83.3％，B组为81.1％，C组为85.6％，AUC分别为0.925、0.862和0.920。模型A和模型C的AUC没有显着差异（p  = 0.765），但是模型A和模型B（p  = 0.038）以及模型B和模型C（p  = 0.028）的AUC差异不大。

结论

当与CDN结合使用时，没有预对比的三相CT协议在区分HCC和其他FLL方面显示出与四阶段协议相似的诊断准确性，这表明可以通过删除预对比相来优化用于HCC诊断的多相CT协议，以减少辐射剂量。