基于超声的放射组学和栖息地分析用于乳腺癌症中Ki-67过度表达的无创评估
Ultrasound-based radiomics and habitat analysis for noninvasive assessment of Ki-67 overexpression in breast cancer.
摘要(中文译文)
乳腺癌管理中，术前准确评估Ki-67增殖指数仍是一项临床挑战。传统超声影像组学往往无法充分捕捉瘤内异质性，存在过拟合问题，并包含降低泛化能力的冗余特征。在这项回顾性研究中，我们分析了288例经病理确诊为乳腺癌的女性的术前超声图像和免疫组化结果。我们提取了传统影像组学特征和瘤内生境特征，计算风险评分，并将其与临床病理变量（如孕激素受体状态、淋巴结受累）整合，构建列线图。通过受试者工作特征曲线下面积（AUC）、校准曲线和决策曲线分析（DCA）评估模型性能。Clinics_Habitat_Radiomics模型在训练队列中达到AUC为0.877（95% CI：0.826-0.929），在验证队列中模型达到AUC为0.830，灵敏度为60.3%，特异度为91.7%，显著优于其他模型。校准曲线表明预测概率与观察结果高度一致（Hosmer-Lemeshow检验：= 0.14[训练集]，= 0.19[验证集]）。DCA显示，与单模态方法相比，该模型在多个阈值概率范围内均具有更优的净临床获益。将生境分析与基于超声的影像组学相结合，能够构建一个协同整合多模态影像特征和临床病理参数的列线图，为乳腺癌Ki-67表达提供无创预测工具。该模型不仅提高了肿瘤生物学评估的精确性，还为优化治疗方案、监测治疗反应和分层预后风险提供了可操作的见解，从而弥合了影像组学诊断与个性化肿瘤诊疗之间的差距。
Abstract
Accurate preoperative assessment of Ki-67 proliferation index remains a clinical challenge in breast cancer management. Conventional ultrasound radiomics often fails to fully capture intratumoral heterogeneity, suffers from overfitting, and includes redundant features that limit generalizability. In this retrospective study, we analyzed preoperative ultrasound images and immunohistochemical results from 288 women with pathologically confirmed breast cancer. We extracted both conventional radiomic features and intratumoral habitat features, computed risk scores, and integrated them with clinicopathological variables (e.g., progesterone receptor status, lymph node involvement) to construct a nomogram. Model performance was assessed by area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCA). The Clinics_Habitat_Radiomics model achieved AUCs of 0.877 (95% CI: 0.826-0.929) in the training cohort and in the validation cohort, the model achieved an AUC of 0.830, with a sensitivity of 60.3% and specificity of 91.7%, significantly outperforming other models. Calibration curves indicated close agreement between predicted probabilities and observed outcomes (Hosmer-Lemeshow test: = 0.14 [training], = 0.19 [validation]). DCA demonstrated superior net clinical benefit across a range of threshold probabilities compared with single-modality approaches. The integration of habitat analysis with ultrasound-based radiomics enables the development of a nomogram that synergistically incorporates multimodal imaging features and clinicopathological parameters, offering a non-invasive predictive tool for Ki-67 expression in breast cancer. This model not only enhances the precision of tumor biology assessment but also provides actionable insights for optimizing therapeutic regimens, monitoring treatment responses, and stratifying prognostic risks, thereby bridging the gap between radiomic diagnostics and personalized oncology care.
如何引用
AMA
Li Zhu, Shanni Dong, Xushuang Qin, Xiaoying Mi, Jiaqi Zhang, Xiaoshu Zhu, et al. Ultrasound-based radiomics and habitat analysis for noninvasive assessment of Ki-67 overexpression in breast cancer.. Frontiers in oncology. 2026; doi:10.3389/fonc.2026.1803826.
APA
Li Zhu, Shanni Dong, Xushuang Qin, Xiaoying Mi, Jiaqi Zhang, Xiaoshu Zhu, et al (2026). Ultrasound-based radiomics and habitat analysis for noninvasive assessment of Ki-67 overexpression in breast cancer.. Frontiers in oncology. https://doi.org/10.3389/fonc.2026.1803826
GB/T 7714
Li Zhu, Shanni Dong, Xushuang Qin, Xiaoying Mi, Jiaqi Zhang, Xiaoshu Zhu, et al. Ultrasound-based radiomics and habitat analysis for noninvasive assessment of Ki-67 overexpression in breast cancer.[J]. Frontiers in oncology, 2026 doi:10.3389/fonc.2026.1803826.
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