Yi Xi, Wang Guiliang, Yang Yu, Che Yilei
Department of Radiology, Hunan Provincial People's Hospital (the First Affiliated Hospital of Hunan Normal University), Changsha 410016, China (X.Y., Y.C.).
Department of Radiology, the First Hospital of Hunan University of Chinese Medicine, Changsha 410007, China (Y.Y.).
Acad Radiol. 2025 Feb;32(2):664-680. doi: 10.1016/j.acra.2024.09.028. Epub 2024 Sep 24.
This study aims to develop and validate a new diagnostic model based on the Kaiser score for preoperative diagnosis of the malignancy probability of enhancing lesions on breast MRI.
This study collected consecutive inpatient data (including imaging data, clinical data, and pathological data) from two different institutions. All patients underwent preoperative breast Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) examinations and were found to have enhancing lesions. These lesions were confirmed as benign or malignant by surgical resection or biopsy pathology (all carcinomas in situ were confirmed by pathology after surgical resection). Data from one institution were used as the training set(284 cases), and data from the other institution were used as the validation set(107 cases). The Kaiser score was directly incorporated into the diagnostic model as a single predictive variable. Other predictive variables were screened using Least Absolute Shrinkage and Selection Operator (LASSO) regression. Multivariate logistic regression was employed to integrate the Kaiser score and other selected predictive variables to construct a new diagnostic model, presented in the form of a nomogram. Receiver operating characteristic (ROC) curve, DeLong test, net reclassification improvement (NRI), and integrated discrimination improvement (IDI) were adopted to evaluate and compare the discrimination of the diagnostic model for breast enhancing lesions based on Kaiser score (hereinafter referred to as the "breast lesion diagnostic model") and the Kaiser score alone. Calibration curves were used to assess the calibration of the breast lesion diagnostic model, and decision curve analysis (DCA) was used to evaluate the clinical efficacy of the diagnostic model and the Kaiser score.
LASSO regression indicated that, besides the indicators already included in the Kaiser score system, "age", "MIP sign", "associated imaging features", and "clinical breast examination (CBE) results" were other valuable diagnostic parameters for breast enhancing lesions. In the training set, the AUCs of the breast lesion diagnostic model and the Kaiser score were 0.948 and 0.869, respectively, with a statistically significant difference (p < 0.05). In the validation set, the AUCs of the breast lesion diagnostic model and the Kaiser score were 0.956 and 0.879, respectively, with a statistically significant difference (p < 0.05). The DeLong test, NRI, and IDI showed that the breast lesion diagnostic model had a higher discrimination ability for breast enhancing lesions compared to the Kaiser score alone, with statistically significant differences (p < 0.05). The calibration curves indicated good calibration of the breast lesion diagnostic model. DCA demonstrated that the breast lesion diagnostic model had higher clinical application value, with greater net clinical benefit over a wide range of diagnostic thresholds compared to the Kaiser score.
The Kaiser score-based breast lesion diagnostic model, which integrates "age," "MIP sign", "associated imaging features", and "CBE results", can be used for the preoperative diagnosis of the malignancy probability of breast enhancing lesions, and it outperforms the classic Kaiser score in terms of diagnostic performance for such lesions.
本研究旨在开发并验证一种基于凯泽评分的新诊断模型，用于术前诊断乳腺磁共振成像（MRI）上强化病灶的恶性概率。
本研究收集了来自两个不同机构的连续住院患者数据（包括影像数据、临床数据和病理数据）。所有患者均接受了术前乳腺动态对比增强磁共振成像（DCE-MRI）检查，且发现有强化病灶。这些病灶通过手术切除或活检病理确诊为良性或恶性（所有原位癌均在手术切除后经病理确诊）。来自一个机构的数据用作训练集（284例），来自另一个机构的数据用作验证集（107例）。凯泽评分作为单一预测变量直接纳入诊断模型。使用最小绝对收缩和选择算子（LASSO）回归筛选其他预测变量。采用多变量逻辑回归将凯泽评分和其他选定的预测变量整合，构建以列线图形式呈现的新诊断模型。采用受试者操作特征（ROC）曲线、德龙检验、净重新分类改善（NRI）和综合判别改善（IDI）来评估和比较基于凯泽评分的乳腺强化病灶诊断模型（以下简称“乳腺病灶诊断模型”）与单独的凯泽评分对乳腺强化病灶的判别能力。校准曲线用于评估乳腺病灶诊断模型的校准情况，决策曲线分析（DCA）用于评估诊断模型和凯泽评分的临床疗效。
LASSO回归表明，除了凯泽评分系统中已包含的指标外，“年龄”“最大强度投影（MIP）征象”“相关影像特征”和“临床乳腺检查（CBE）结果”是乳腺强化病灶的其他有价值的诊断参数。在训练集中，乳腺病灶诊断模型和凯泽评分的曲线下面积（AUC）分别为0.948和0.869，差异有统计学意义（p<0.05）。在验证集中，乳腺病灶诊断模型和凯泽评分的AUC分别为0.956和0.879，差异有统计学意义（p<0.05）。德龙检验以及NRI和IDI表明，与单独的凯泽评分相比，乳腺病灶诊断模型对乳腺强化病灶具有更高的判别能力，差异有统计学意义（p<0.05）。校准曲线表明乳腺病灶诊断模型校准良好。DCA表明，乳腺病灶诊断模型具有更高临床应用价值，在广泛的诊断阈值范围内，与凯泽评分相比，其净临床效益更大。
基于凯泽评分的乳腺病灶诊断模型整合了“年龄”“MIP征象”“相关影像特征”和“CBE结果”，可用于术前诊断乳腺强化病灶的恶性概率，且在对此类病灶的诊断性能方面优于经典的凯泽评分。
