Benign and malignant phyllodes tumors of the breast (PTB) exhibit significant differences in surgical strategies, recurrence risks, and metastasis risks. Preoperative differentiation between the two subtypes is crucial for treatment decision-making. Conventional ultrasound has inherent limitations in diagnosis, while the diagnostic performance of ultrasound habitat imaging for distinguishing benign from malignant PTB remains not systematically validated.

To evaluate the diagnostic efficacy of ultrasound habitat imaging in differentiating benign from malignant PTB.

A retrospective analysis was performed on clinical and ultrasound data of 102 patients with pathologically confirmed PTB who underwent surgery at Daping Hospital, Army Medical University, from September 2014 to June 2024. Patients were divided into the benign group (n=54) and the borderline/malignant group (n=48, including 30 borderline cases and 18 malignant cases) based on pathological findings. Ultrasound images were recorded, and the tumor region of interest (ROI) was manually segmented using ITK-SNAP software. The ROI was divided into 3 habitat subregions via K-means clustering, and habitat features were extracted using PyRadiomics. Optimal features were selected using random forest (RF) algorithm, and a habitat score (Hab-score) was calculated to construct the habitat model. The conventional ultrasound model was established by incorporating conventional ultrasound variables with statistically significant differences in univariate analysis. A combined model was constructed by integrating conventional ultrasound features and Hab-score. Receiver operating characteristic (ROC) curves and Delong test were used to compare the diagnostic efficacy of the three models, and decision curve analysis (DCA) was employed to evaluate their clinical applicability.

Statistically significant differences were observed between the two groups regarding maximum tumor diameter, internal echo, boundary clarity, and cystic changes (all P<0.05). The conventional ultrasound model was built by including these 4 variables; 7 habitat features (including 3 first-order features and 4 texture features) were retained after RF selection for Hab-score calculation and habitat model construction; the combined model was established by adding Hab-score to the 4 conventional ultrasound variables. The areas under the ROC curve (AUC) of the conventional ultrasound model, habitat model, and combined model were 0.718, 0.725, and 0.799, respectively. Delong test results indicated that the AUC of the combined model was significantly higher than those of the other two models (both P<0.05). DCA curve analysis demonstrated that the combined model yielded the highest clinical net benefit for PTB differentiation within the threshold range of 0.4-0.9.

Ultrasound habitat imaging is effective for differentiating benign from malignant PTB. When combined with conventional ultrasound, it further improves diagnostic efficacy and reduces the risks of missed diagnosis and misdiagnosis associated with a single technical approach, thus holding substantial potential for clinical application.

乳腺叶状肿瘤（PTB）良恶性的手术策略及复发、转移风险差异显著，术前良恶性鉴别对治疗决策至关重要，传统超声诊断存在局限性，而超声生境成像在其良恶性鉴别中的效能尚未系统明确。

基于超声生境成像在鉴别PTB良恶性中的效能评估。

回顾性分析2014年9月—2024年6月于陆军军医大学大坪医院经手术病理证实的102例PTB患者的临床及超声影像资料，根据病理诊断结果分为良性组（54例），交界性/恶性组（48例，包括交界性30例、恶性18例）。记录超声图像并使用ITK-SNAP软件手动勾画肿瘤感兴趣区（ROI），通过K-means聚类将ROI划分为3个生境亚区，利用PyRadiomics提取生境特征；经随机森林（RF）筛选保留最优特征并计算生境组学评分（Hab-score）构建生境模型，纳入单因素分析差异有统计学意义的常规超声变量构建常规超声模型，纳入常规超声特征及Hab-score构建联合模型，采用受试者工作特征（ROC）曲线及Delong检验比较各模型的诊断效能，使用决策曲线分析（DCA）评估模型的临床适用性。

两组最大直径、内部回声、边界、囊变比较，差异具有统计学意义（P<0.05）。纳入上述4个超声变量，构建常规超声模型；经RF筛选，最终保留7个生境特征（3个一阶特征，4个纹理特征）用于计算Hab-score，并构建生境模型；在上述4个常规超声变量基础上，进一步纳入Hab-score构建联合模型。常规超声模型、生境模型与联合模型鉴别良恶性叶状肿瘤的ROC曲线下面积（AUC）分别为0.718、0.725、0.799，Delong检验结果显示，联合模型的AUC高于常规超声模型、生境模型（P<0.05）；DCA显示，联合模型在0.4~0.9阈值区间对PTB良恶性的鉴别具有最高的临床效益。

超声生境成像能有效应用于鉴别PTB良恶性，结合常规超声，能进一步提升诊断效能，降低单一技术路径的漏诊、误诊风险，具有潜在的临床应用价值。