Abstract：
OBJECTIVE: To evaluate lymphovascular invasion (LVI) in breast cancer by comparing the diagnostic performance of preoperative multimodal magnetic resonance imaging (MRI)-based radiomics and deep-learning (DL) models.
METHODS: This retrospective study included 262 patients with breast cancer-183 in the training cohort (144 LVI-negative and 39 LVI-positive cases) and 79 in the validation cohort (59 LVI-negative and 20 LVI-positive cases). Radiomics features were extracted from the intra- and peritumoral breast regions using multimodal MRI to generate gross tumor volume (GTV)_radiomics and gross tumor volume plus peritumoral volume (GPTV)_radiomics. Subsequently, DL models (GTV_DL and GPTV_DL) were constructed based on the GTV and GPTV to determine the LVI status. Finally, the most effective radiomics and DL models were integrated with imaging findings to establish a hybrid model, which was converted into a nomogram to quantify the LVI risk.
RESULTS: The diagnostic efficiency of GPTV_DL was superior to that of GTV_DL (areas under the curve [AUCs], 0.771 and 0.720, respectively). Similarly, GPTV_radiomics outperformed GTV_radiomics (AUC, 0.685 and 0.636, respectively). Univariate and multivariate logistic regression analyses revealed an association between imaging findings, such as MRI-axillary lymph nodes and peritumoral edema (AUC, 0.665). The hybrid model achieved the highest accuracy by combining GPTV_DL, GPTV_radiomics, and imaging findings (AUC, 0.872).
CONCLUSIONS: The diagnostic efficiency of the GPTV-derived radiomics and DL models surpassed that of the GTV-derived models. Furthermore, the hybrid model, which incorporated GPTV_DL, GPTV_radiomics, and imaging findings, demonstrated the effective determination of LVI status prior to surgery in patients with breast cancer.
METHODS: This retrospective study included 262 patients with breast cancer-183 in the training cohort (144 LVI-negative and 39 LVI-positive cases) and 79 in the validation cohort (59 LVI-negative and 20 LVI-positive cases). Radiomics features were extracted from the intra- and peritumoral breast regions using multimodal MRI to generate gross tumor volume (GTV)_radiomics and gross tumor volume plus peritumoral volume (GPTV)_radiomics. Subsequently, DL models (GTV_DL and GPTV_DL) were constructed based on the GTV and GPTV to determine the LVI status. Finally, the most effective radiomics and DL models were integrated with imaging findings to establish a hybrid model, which was converted into a nomogram to quantify the LVI risk.
RESULTS: The diagnostic efficiency of GPTV_DL was superior to that of GTV_DL (areas under the curve [AUCs], 0.771 and 0.720, respectively). Similarly, GPTV_radiomics outperformed GTV_radiomics (AUC, 0.685 and 0.636, respectively). Univariate and multivariate logistic regression analyses revealed an association between imaging findings, such as MRI-axillary lymph nodes and peritumoral edema (AUC, 0.665). The hybrid model achieved the highest accuracy by combining GPTV_DL, GPTV_radiomics, and imaging findings (AUC, 0.872).
CONCLUSIONS: The diagnostic efficiency of the GPTV-derived radiomics and DL models surpassed that of the GTV-derived models. Furthermore, the hybrid model, which incorporated GPTV_DL, GPTV_radiomics, and imaging findings, demonstrated the effective determination of LVI status prior to surgery in patients with breast cancer.
摘要：
目的：通过比较术前基于多模态磁共振成像（MRI）的影像组学和深度学习（DL）模型的诊断性能，评估乳腺癌的淋巴血管侵犯（LVI）。
方法：这项回顾性研究纳入了262例乳腺癌患者—183例（144例LVI阴性和39例LVI阳性）和79例验证队列（59例LVI阴性和20例LVI阳性）。使用多模态MRI从瘤内和瘤周乳腺区域提取影像组学特征，以生成大体肿瘤体积（GTV）_影像组学和大体肿瘤体积加瘤周体积（GPTV）_影像组学。随后，基于GTV和GPTV构建DL模型（GTV_DL和GPTV_DL）以确定LVI状态。最后,将最有效的影像组学和DL模型与影像学发现相结合，建立混合模型，将其转换为列线图以量化LVI风险。
结果：GPTV_DL的诊断效率优于GTV_DL（曲线下面积[AUCs]，分别为0.771和0.720）。同样，GPTV_放射学优于GTV_放射学(AUC，分别为0.685和0.636）。单变量和多变量逻辑回归分析揭示了影像学发现之间的关联，如MRI-腋窝淋巴结和瘤周水肿(AUC，0.665)。混合模型通过结合GPTV_DL，GPTV_影像组学，和影像学发现(AUC，0.872).
结论：GPTV衍生的放射组学和DL模型的诊断效率超过了GTV衍生的模型。此外，混合模型，合并了GPTV_DL，GPTV_影像组学，和影像学发现，证明了乳腺癌患者手术前LVI状态的有效测定。
方法：这项回顾性研究纳入了262例乳腺癌患者—183例（144例LVI阴性和39例LVI阳性）和79例验证队列（59例LVI阴性和20例LVI阳性）。使用多模态MRI从瘤内和瘤周乳腺区域提取影像组学特征，以生成大体肿瘤体积（GTV）_影像组学和大体肿瘤体积加瘤周体积（GPTV）_影像组学。随后，基于GTV和GPTV构建DL模型（GTV_DL和GPTV_DL）以确定LVI状态。最后,将最有效的影像组学和DL模型与影像学发现相结合，建立混合模型，将其转换为列线图以量化LVI风险。
结果：GPTV_DL的诊断效率优于GTV_DL（曲线下面积[AUCs]，分别为0.771和0.720）。同样，GPTV_放射学优于GTV_放射学(AUC，分别为0.685和0.636）。单变量和多变量逻辑回归分析揭示了影像学发现之间的关联，如MRI-腋窝淋巴结和瘤周水肿(AUC，0.665)。混合模型通过结合GPTV_DL，GPTV_影像组学，和影像学发现(AUC，0.872).
结论：GPTV衍生的放射组学和DL模型的诊断效率超过了GTV衍生的模型。此外，混合模型，合并了GPTV_DL，GPTV_影像组学，和影像学发现，证明了乳腺癌患者手术前LVI状态的有效测定。
