Wei Guangya, Fang Guoxu, Guo Pengfei, Fang Peng, Wang Tongming, Lin Kecan, Liu Jingfeng
Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, 350014, China.
Department of Hepatopancreatobiliary Surgery, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, China.
Insights Imaging. 2024 Aug 1;15(1):188. doi: 10.1186/s13244-024-01760-2.
To explore the predictive performance of tumor and multiple peritumoral regions on dynamic contrast-enhanced magnetic resonance imaging (MRI), to identify optimal regions of interest for developing a preoperative predictive model for the grade of microvascular invasion (MVI).
A total of 147 patients who were surgically diagnosed with hepatocellular carcinoma, and had a maximum tumor diameter ≤ 5 cm were recruited and subsequently divided into a training set (n = 117) and a testing set (n = 30) based on the date of surgery. We utilized a pre-trained AlexNet to extract deep learning features from seven different regions of the maximum transverse cross-section of tumors in various MRI sequence images. Subsequently, an extreme gradient boosting (XGBoost) classifier was employed to construct the MVI grade prediction model, with evaluation based on the area under the curve (AUC).
The XGBoost classifier trained with data from the 20-mm peritumoral region showed superior AUC compared to the tumor region alone. AUC values consistently increased when utilizing data from 5-mm, 10-mm, and 20-mm peritumoral regions. Combining arterial and delayed-phase data yielded the highest predictive performance, with micro- and macro-average AUCs of 0.78 and 0.74, respectively. Integration of clinical data further improved AUCs values to 0.83 and 0.80.
Compared with those of the tumor region, the deep learning features of the peritumoral region provide more important information for predicting the grade of MVI. Combining the tumor region and the 20-mm peritumoral region resulted in a relatively ideal and accurate region within which the grade of MVI can be predicted.
The 20-mm peritumoral region holds more significance than the tumor region in predicting MVI grade. Deep learning features can indirectly predict MVI by extracting information from the tumor region and directly capturing MVI information from the peritumoral region.
We investigated tumor and different peritumoral regions, as well as their fusion. MVI predominantly occurs in the peritumoral region, a superior predictor compared to the tumor region. The peritumoral 20 mm region is reasonable for accurately predicting the three-grade MVI.
探讨肿瘤及多个瘤周区域在动态对比增强磁共振成像（MRI）上的预测性能，以确定用于建立微血管侵犯（MVI）分级术前预测模型的最佳感兴趣区域。
共纳入147例经手术诊断为肝细胞癌且最大肿瘤直径≤5 cm的患者，随后根据手术日期将其分为训练集（n = 117）和测试集（n = 30）。我们利用预训练的AlexNet从各种MRI序列图像中肿瘤最大横截面积的七个不同区域提取深度学习特征。随后，采用极端梯度提升（XGBoost）分类器构建MVI分级预测模型，并基于曲线下面积（AUC）进行评估。
与仅使用肿瘤区域数据训练的XGBoost分类器相比，使用距肿瘤边缘20 mm瘤周区域数据训练的分类器具有更高的AUC。当使用距肿瘤边缘5 mm、10 mm和20 mm瘤周区域的数据时，AUC值持续增加。结合动脉期和延迟期数据可获得最高的预测性能，微观和宏观平均AUC分别为0.78和0.74。纳入临床数据后，AUC值进一步提高至0.83和0.80。
与肿瘤区域相比，瘤周区域的深度学习特征为预测MVI分级提供了更重要的信息。结合肿瘤区域和距肿瘤边缘20 mm的瘤周区域可得到一个相对理想且准确的区域，用于预测MVI分级。
在预测MVI分级方面，距肿瘤边缘20 mm的瘤周区域比肿瘤区域更具意义。深度学习特征可通过从肿瘤区域提取信息间接预测MVI，并直接从瘤周区域捕获MVI信息。
我们研究了肿瘤及不同瘤周区域及其融合情况。MVI主要发生在瘤周区域，与肿瘤区域相比是更好的预测指标。距肿瘤边缘20 mm的瘤周区域对于准确预测三级MVI是合理的。
