Development of a Preoperative Prediction Model Based on Spectral CT to Evaluate Axillary Lymph Node With Macrometastases in Clinical T1/2N0 Invasive Breast Cancer

IF 2.9 3区 医学 Q2 ONCOLOGY Clinical breast cancer Pub Date : 2024-06-18 DOI:10.1016/j.clbc.2024.06.010
Fang Zeng, Weifeng Cai, Lin Lin, Cong Chen, Xiaoxue Tang, Zheting Yang, Yilin Chen, Lihong Chen, Lili Chen, Jing Li, Suping Chen, Chuang Wang, Yunjing Xue
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Abstract

To develop a prediction model based on spectral computed tomography (CT) to evaluate axillary lymph node (ALN) with macrometastases in clinical T1/2N0 invasive breast cancer. A total of 217 clinical T1/2N0 invasive breast cancer patients who underwent spectral CT scans were retrospectively enrolled and categorized into a training cohort (n = 151) and validation cohort (n = 66). These patients were classified into ALN nonmacrometastases (stage pN0 or pN0 [i+] or pN1mi) and ALN macrometastases (stage pN1-3) subgroups. The morphologic criteria and quantitative spectral CT parameters of the most suspicious ALN were measured and compared. Least absolute shrinkage and selection operator (Lasso) was used to screen predictive indicators to build a logistic model. The receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were used to evaluate the models. The combined arterial-venous phase spectral CT model yielded the best diagnostic performance in discrimination of ALN nonmacrometastases and ALN macrometastases with the highest AUC (0.963 in the training cohort and 0.945 in validation cohorts). Among single phase spectral CT models, the venous phase spectral CT model showed the best performance (AUC = 0.960 in the training cohort and 0.940 in validation cohorts). There was no significant difference in AUCs among the 3 models (DeLong test, > .05 for each comparison). A Lasso-logistic model that combined morphologic features and quantitative spectral CT parameters based on contrast-enhanced spectral imaging potentially be used as a noninvasive tool for individual preoperative prediction of ALN status in clinical T1/2N0 invasive breast cancers.
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基于光谱 CT 的术前预测模型的开发,用于评估临床 T1/2N0 浸润性乳腺癌腋窝淋巴结大转移情况
建立基于光谱计算机断层扫描(CT)的预测模型,以评估临床T1/2N0浸润性乳腺癌患者腋窝淋巴结(ALN)的大转移情况。研究人员回顾性招募了217名接受光谱CT扫描的临床T1/2N0浸润性乳腺癌患者,并将其分为训练队列(151人)和验证队列(66人)。这些患者被分为 ALN 非大转移(pN0 期或 pN0 [i+] 期或 pN1mi 期)和 ALN 大转移(pN1-3 期)亚组。对最可疑 ALN 的形态学标准和定量频谱 CT 参数进行了测量和比较。采用最小绝对收缩和选择算子(Lasso)筛选预测指标,建立逻辑模型。采用接收者操作特征曲线(ROC)和决策曲线分析(DCA)对模型进行评估。动静脉联合相位频谱 CT 模型在鉴别 ALN 非大转移灶和 ALN 大转移灶方面的诊断性能最佳,AUC 最高(训练队列为 0.963,验证队列为 0.945)。在单相频谱 CT 模型中,静脉相频谱 CT 模型表现最佳(训练队列中的 AUC = 0.960,验证队列中的 AUC = 0.940)。3 个模型的 AUC 没有明显差异(DeLong 检验,每次比较均 > .05)。基于对比增强光谱成像的拉索逻辑模型结合了形态学特征和定量光谱 CT 参数,可作为一种无创工具,用于术前预测临床 T1/2N0 浸润性乳腺癌的 ALN 状态。
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来源期刊
Clinical breast cancer
Clinical breast cancer 医学-肿瘤学
CiteScore
5.40
自引率
3.20%
发文量
174
审稿时长
48 days
期刊介绍: Clinical Breast Cancer is a peer-reviewed bimonthly journal that publishes original articles describing various aspects of clinical and translational research of breast cancer. Clinical Breast Cancer is devoted to articles on detection, diagnosis, prevention, and treatment of breast cancer. The main emphasis is on recent scientific developments in all areas related to breast cancer. Specific areas of interest include clinical research reports from various therapeutic modalities, cancer genetics, drug sensitivity and resistance, novel imaging, tumor genomics, biomarkers, and chemoprevention strategies.
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