PDF(Pubmed)
Abstract：
OBJECTIVE: 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).
METHODS: 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).
RESULTS: 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.
CONCLUSIONS: 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.
CONCLUSIONS: 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.
CONCLUSIONS: 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.
METHODS: 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).
RESULTS: 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.
CONCLUSIONS: 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.
CONCLUSIONS: 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.
CONCLUSIONS: 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.
摘要：
目的：探讨肿瘤和多个肿瘤周围区域在动态对比增强磁共振成像（MRI）上的预测性能，确定用于开发微血管侵犯（MVI）等级的术前预测模型的最佳感兴趣区域。
方法：共147例经手术诊断为肝细胞癌的患者，我们招募了最大肿瘤直径≤5cm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).我们利用预先训练的AlexNet从各种MRI序列图像中的肿瘤最大横截面的七个不同区域中提取深度学习特征。随后，采用极端梯度提升(XGBoost)分类器构建MVI等级预测模型，基于曲线下面积(AUC)进行评估。
结果:用来自20-mm肿瘤周围区域的数据训练的XGBoost分类器显示出比单独的肿瘤区域更高的AUC。AUC值在使用5毫米的数据时持续增加，10-mm,和20毫米的肿瘤周围区域。结合动脉和延迟阶段数据产生了最高的预测性能，微观和宏观平均AUC分别为0.78和0.74。临床数据的整合进一步将AUC值改善至0.83和0.80。
结论：与肿瘤区域相比，瘤周区域的深度学习特征为预测MVI等级提供了更重要的信息。结合肿瘤区域和20-mm肿瘤周围区域产生相对理想和准确的区域,在该区域内可以预测MVI的等级。
结论：在预测MVI分级方面，20-mm肿瘤周围区域比肿瘤区域更重要。深度学习特征可以通过从肿瘤区域提取信息并直接从瘤周区域捕获MVI信息来间接预测MVI。
结论：我们研究了肿瘤和不同的肿瘤周围区域，以及它们的融合。MVI主要发生在肿瘤周围区域，与肿瘤区域相比，这是一个更好的预测指标。瘤周20mm区域对于准确预测三级MVI是合理的。
方法：共147例经手术诊断为肝细胞癌的患者，我们招募了最大肿瘤直径≤5cm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).我们利用预先训练的AlexNet从各种MRI序列图像中的肿瘤最大横截面的七个不同区域中提取深度学习特征。随后，采用极端梯度提升(XGBoost)分类器构建MVI等级预测模型，基于曲线下面积(AUC)进行评估。
结果:用来自20-mm肿瘤周围区域的数据训练的XGBoost分类器显示出比单独的肿瘤区域更高的AUC。AUC值在使用5毫米的数据时持续增加，10-mm,和20毫米的肿瘤周围区域。结合动脉和延迟阶段数据产生了最高的预测性能，微观和宏观平均AUC分别为0.78和0.74。临床数据的整合进一步将AUC值改善至0.83和0.80。
结论：与肿瘤区域相比，瘤周区域的深度学习特征为预测MVI等级提供了更重要的信息。结合肿瘤区域和20-mm肿瘤周围区域产生相对理想和准确的区域,在该区域内可以预测MVI的等级。
结论：在预测MVI分级方面，20-mm肿瘤周围区域比肿瘤区域更重要。深度学习特征可以通过从肿瘤区域提取信息并直接从瘤周区域捕获MVI信息来间接预测MVI。
结论：我们研究了肿瘤和不同的肿瘤周围区域，以及它们的融合。MVI主要发生在肿瘤周围区域，与肿瘤区域相比，这是一个更好的预测指标。瘤周20mm区域对于准确预测三级MVI是合理的。
