Department of Radiation Oncology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China.
Department of Oncology, School of Clinical Medicine, Southwest Medical University, Luzhou, China.
J Magn Reson Imaging. 2024 Apr;59(4):1206-1217. doi: 10.1002/jmri.28900. Epub 2023 Aug 1.
Tertiary lymphoid structures (TLSs) are potential prognostic indicators. Radiomics may help reduce unnecessary invasive operations.
To analyze the association between TLSs and prognosis, and to establish a nomogram model to evaluate the expression of TLSs in breast cancer (BC) patients.
Retrospective.
Two hundred forty-two patients with localized primary BC (confirmed by surgery) were divided into BC + TLS group (N = 122) and BC - TLS group (N = 120).
FIELD STRENGTH/SEQUENCE: 3.0T; Caipirinha-Dixon-TWIST-volume interpolated breath-hold sequence for dynamic contrast-enhanced (DCE) MRI and inversion-recovery turbo spin echo sequence for T2-weighted imaging (T2WI).
Three models for differentiating BC + TLS and BC - TLS were developed: 1) a clinical model, 2) a radiomics signature model, and 3) a combined clinical and radiomics (nomogram) model. The overall survival (OS), distant metastasis-free survival (DMFS), and disease-free survival (DFS) were compared to evaluate the prognostic value of TLSs.
LASSO algorithm and ANOVA were used to select highly correlated features. Clinical relevant variables were identified by multivariable logistic regression. Model performance was evaluated by the area under the receiver operating characteristic (ROC) curve (AUC), and through decision curve analysis (DCA). The Kaplan-Meier method was used to calculate the survival rate.
The radiomics signature model (training: AUC 0.766; test: AUC 0.749) and the nomogram model (training: AUC 0.820; test: AUC 0.749) showed better validation performance than the clinical model. DCA showed that the nomogram model had a higher net benefit than the other models. The median follow-up time was 52 months. While there was no significant difference in 3-year OS (P = 0.22) between BC + TLS and BC - TLS patients, there were significant differences in 3-year DFS and 3-year DMFS between the two groups.
The nomogram model performs well in distinguishing the presence or absence of TLS. BC + TLS patients had higher long-term disease control rates and better prognoses than those without TLS.
2 TECHNICAL EFFICACY: Stage 2.
三级淋巴结构(TLSs)是潜在的预后指标。放射组学可能有助于减少不必要的侵入性操作。
分析 TLSs 与预后的关系,并建立列线图模型来评估 TLSs 在乳腺癌(BC)患者中的表达。
回顾性。
242 名局部原发性 BC 患者(手术证实)分为 BC+TLS 组(N=122)和 BC-TLS 组(N=120)。
磁场强度/序列:3.0T;Caipirinha-Dixon-TWIST 容积内插屏气序列用于动态对比增强(DCE)MRI 和反转恢复涡轮自旋回波序列用于 T2 加权成像(T2WI)。
建立了三种区分 BC+TLS 和 BC-TLS 的模型:1)临床模型,2)放射组学特征模型,3)临床和放射组学(列线图)联合模型。比较总生存期(OS)、远处无转移生存期(DMFS)和无病生存期(DFS),以评估 TLSs 的预后价值。
LASSO 算法和 ANOVA 用于选择高度相关的特征。多变量逻辑回归确定临床相关变量。通过受试者工作特征(ROC)曲线下面积(AUC)和决策曲线分析(DCA)评估模型性能。Kaplan-Meier 方法用于计算生存率。
放射组学特征模型(训练:AUC 0.766;测试:AUC 0.749)和列线图模型(训练:AUC 0.820;测试:AUC 0.749)的验证性能优于临床模型。DCA 显示列线图模型比其他模型具有更高的净获益。中位随访时间为 52 个月。虽然 BC+TLS 和 BC-TLS 患者的 3 年 OS 无显著差异(P=0.22),但两组的 3 年 DFS 和 3 年 DMFS 有显著差异。
列线图模型在区分 TLS 存在与否方面表现良好。与无 TLS 的患者相比,BC+TLS 患者具有更高的长期疾病控制率和更好的预后。
2 级技术功效。