术前利用T2WI和DWI MR序列的肿瘤内和肿瘤周围放射组学特征预测乳腺癌组织学分级。

IF 3.4 4区 医学 Q2 ONCOLOGY
Breast Cancer : Targets and Therapy Pub Date : 2024-12-19 eCollection Date: 2024-01-01 DOI:10.2147/BCTT.S487988
Yaxin Guo, Jun Liao, Shunian Li, Yiyan Shang, Yunxia Wang, Qingxia Wu, Yaping Wu, Meiyun Wang, Fengshan Yan, Hongna Tan
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引用次数: 0

摘要

背景:组织学分级是乳腺癌公认的预后因素,对确定临床治疗策略和预后评估至关重要。我们的研究旨在利用T2WI和DWI MR序列建立肿瘤内和肿瘤周围放射组学模型来预测乳腺癌的组织学分级。方法:选取术前行MRI扫描的700例乳腺癌患者为研究对象。人工圈定感兴趣的肿瘤内区域(ITR),而通过将ITR扩大3 mm自动获得肿瘤周围区域(ptr - 3mm)。利用乳腺MRI T2WI和DWI序列的肿瘤内和肿瘤周围图像提取放射组学特征。然后,选择对组织学分级预测能力最强的关键特征。最后,基于T2WI-ITR、T2WI-3mmPTR、DWI-ITR、DWI-3mmPTR、T2WI-ITR + 3mmPTR、DWI-ITR + 3mmPTR、(T2WI + DWI)-ITR、(T2WI + DWI)-3mmPTR和(T2WI + DWI)-ITR + 3mmPTR建立9个预测放射组学模型。结果:(T2WI + DWI)-ITR + 3mmPTR包含13个DWI特征,其中形状特征1个,纹理特征1个,滤波特征11个,T2WI特征10个,均为滤波特征。在9个模型中,无论是训练集还是测试集,组合模型的表现都优于单一模型,尤其是(T2WI + DWI)-ITR + 3mmPTR放射组学模型。(T2WI + DWI)-ITR + 3mmPTR放射组学模型在训练集的灵敏度、特异性、准确度和AUC分别为80.4%、72.4%、75.0%和0.860,在测试集的灵敏度、特异性、准确度和AUC分别为68.9%、70.5%、70.0%和0.781。决策曲线分析(Decision curve analysis, DCA)显示(T2WI + DWI)-ITR + 3mmPTR模型与其他模型相比具有最大的临床净获益。结论:使用T2WI和DWI MR序列的肿瘤内和肿瘤周围放射组学方法可用于评估乳腺癌的组织学分级,特别是(T2WI + DWI)- itr + 3mmPTR放射组学模型具有显著的临床应用潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Preoperative Prediction of Breast Cancer Histological Grade Using Intratumoral and Peritumoral Radiomics Features from T2WI and DWI MR Sequences.

Preoperative Prediction of Breast Cancer Histological Grade Using Intratumoral and Peritumoral Radiomics Features from T2WI and DWI MR Sequences.

Preoperative Prediction of Breast Cancer Histological Grade Using Intratumoral and Peritumoral Radiomics Features from T2WI and DWI MR Sequences.

Preoperative Prediction of Breast Cancer Histological Grade Using Intratumoral and Peritumoral Radiomics Features from T2WI and DWI MR Sequences.

Background: Histological grade is an acknowledged prognostic factor for breast cancer, essential for determining clinical treatment strategies and prognosis assessment. Our study aims to establish intra- and peritumoral radiomics models using T2WI and DWI MR sequences for predicting the histological grade of breast cancer.

Methods: 700 breast cancer cases who had MRI scans before surgery were included. The intratumoral region (ITR) of interest was manually delineated, while the peritumoral region (PTR-3 mm) was automatically obtained by expanding the ITR by 3 mm. Radiomics features were extracted using the intra- and peritumoral images from T2WI and DWI sequences on breast MRI. Then, the key features with the strongest predictivity of histological grade were selected. Finally, 9 predictive radiomics models were established based on T2WI-ITR, T2WI-3mmPTR, DWI-ITR, DWI-3mmPTR, T2WI-ITR + 3mmPTR, DWI-ITR + 3mmPTR, (T2WI + DWI)-ITR, (T2WI + DWI)-3mmPTR and (T2WI + DWI)-ITR + 3mmPTR.

Results: The (T2WI + DWI)-ITR + 3mmPTR contained 13 DWI features which included a shape feature, a texture feature, and 11 filtered features, as well as 10 T2WI features, all of which were filtered features. Among the 9 models, the combined models showed better performance than the single models in both the training and test sets, especially for the (T2WI + DWI)-ITR + 3mmPTR radiomics model. The (T2WI + DWI)-ITR + 3mmPTR radiomics model achieved a sensitivity, specificity, accuracy, and AUC of 80.4%, 72.4%, 75.0%, and 0.860 in the training set, and 68.9%, 70.5%, 70.0%, and 0.781 in the test set. Decision curve analysis (DCA) showed that the (T2WI + DWI)-ITR + 3mmPTR model had the greatest net clinical benefit compared to the other models.

Conclusion: The intra- and peritumoral radiomics methodologies using T2WI and DWI MR sequences could be utilized to assess histological grade for breast cancer, particularly with the (T2WI + DWI)-ITR + 3mmPTR radiomics model demonstrating significant potential for clinical application.

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