Yafei Wang, Fang Wang, Yue Ma, Aidi Liu, Mengran Zhao, Keyi Bian, Yueqiang Zhu, Lu Yin, Hong Lu, Zhaoxiang Ye
{"title":"术前增强锥形束乳腺CT (CE-CBBCT)与MRI鉴别乳腺癌病理完全缓解与微小残留病变的比较分析","authors":"Yafei Wang, Fang Wang, Yue Ma, Aidi Liu, Mengran Zhao, Keyi Bian, Yueqiang Zhu, Lu Yin, Hong Lu, Zhaoxiang Ye","doi":"10.1186/s12880-025-01926-4","DOIUrl":null,"url":null,"abstract":"<p><strong>Rationale and objectives: </strong>To evaluate the performance of contrast-enhanced cone-beam breast CT (CE-CBBCT) using visual, quantitative, and combined models in distinguishing pathological complete response (pCR) from minimal residual disease (MRD) after neoadjuvant therapy (NAT), and to compare its diagnostic efficacy with MRI.</p><p><strong>Materials and methods: </strong>This study enrolled 65 female patients who underwent both CE-CBBCT and MRI after NAT and were classified as having either pCR or MRD. Univariate and multivariate logistic regression analyses were performed to identify independent visual and quantitative features from CE-CBBCT and MRI associated with pCR. Model performance was assessed and compared using the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), DeLong's test, and McNemar's test. The bootstrap method was employed to assess the stability of each model.</p><p><strong>Results: </strong>Multivariate analysis identified fine and branched calcification morphology on CE-CBBCT (visual model: odds ratio [OR] = 4.500; combined model: OR = 4.527), enhanced degree (ΔHU, quantitative model: OR = 1.036; combined model: OR = 1.035), radiographic complete response (rCR; visual model: OR = 0.103; combined model: OR = 0.097), and delayed-phase MRI enhancement ratio (ER<sub>dpMRI</sub>; quantitative model: OR = 5.048; combined model: OR = 5.583) as independent predictors of pCR. The CE-CBBCT combined model demonstrated a significantly higher AUC than the visual model (0.805 vs. 0.698, p = 0.017) and performed comparably to the MRI combined model (0.805 vs. 0.819, p = 0.811). In the HER2-enriched subgroup, the CE-CBBCT combined model exhibited higher specificity than MRI (0.857 vs. 0.714, p = 0.011) for identifying pCR.</p><p><strong>Conclusion: </strong>The combination of calcification morphology and ΔHU on CE-CBBCT improved accuracy in discriminating pCR from MRD, achieving performance comparable to MRI. Notably, the CE-CBBCT combined model showed superior specificity to MRI within the HER2-enriched subgroup, suggesting its potential utility in reducing overtreatment in this patient population.</p><p><strong>Clinical trial number: </strong>Not applicable.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"25 1","pages":"390"},"PeriodicalIF":3.2000,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12482609/pdf/","citationCount":"0","resultStr":"{\"title\":\"Comparative analysis of preoperative contrast-enhanced cone beam breast CT (CE-CBBCT) and MRI for differentiating pathological complete response from minimal residual disease in breast cancer.\",\"authors\":\"Yafei Wang, Fang Wang, Yue Ma, Aidi Liu, Mengran Zhao, Keyi Bian, Yueqiang Zhu, Lu Yin, Hong Lu, Zhaoxiang Ye\",\"doi\":\"10.1186/s12880-025-01926-4\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Rationale and objectives: </strong>To evaluate the performance of contrast-enhanced cone-beam breast CT (CE-CBBCT) using visual, quantitative, and combined models in distinguishing pathological complete response (pCR) from minimal residual disease (MRD) after neoadjuvant therapy (NAT), and to compare its diagnostic efficacy with MRI.</p><p><strong>Materials and methods: </strong>This study enrolled 65 female patients who underwent both CE-CBBCT and MRI after NAT and were classified as having either pCR or MRD. Univariate and multivariate logistic regression analyses were performed to identify independent visual and quantitative features from CE-CBBCT and MRI associated with pCR. Model performance was assessed and compared using the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), DeLong's test, and McNemar's test. The bootstrap method was employed to assess the stability of each model.</p><p><strong>Results: </strong>Multivariate analysis identified fine and branched calcification morphology on CE-CBBCT (visual model: odds ratio [OR] = 4.500; combined model: OR = 4.527), enhanced degree (ΔHU, quantitative model: OR = 1.036; combined model: OR = 1.035), radiographic complete response (rCR; visual model: OR = 0.103; combined model: OR = 0.097), and delayed-phase MRI enhancement ratio (ER<sub>dpMRI</sub>; quantitative model: OR = 5.048; combined model: OR = 5.583) as independent predictors of pCR. The CE-CBBCT combined model demonstrated a significantly higher AUC than the visual model (0.805 vs. 0.698, p = 0.017) and performed comparably to the MRI combined model (0.805 vs. 0.819, p = 0.811). In the HER2-enriched subgroup, the CE-CBBCT combined model exhibited higher specificity than MRI (0.857 vs. 0.714, p = 0.011) for identifying pCR.</p><p><strong>Conclusion: </strong>The combination of calcification morphology and ΔHU on CE-CBBCT improved accuracy in discriminating pCR from MRD, achieving performance comparable to MRI. Notably, the CE-CBBCT combined model showed superior specificity to MRI within the HER2-enriched subgroup, suggesting its potential utility in reducing overtreatment in this patient population.</p><p><strong>Clinical trial number: </strong>Not applicable.</p>\",\"PeriodicalId\":9020,\"journal\":{\"name\":\"BMC Medical Imaging\",\"volume\":\"25 1\",\"pages\":\"390\"},\"PeriodicalIF\":3.2000,\"publicationDate\":\"2025-09-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12482609/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"BMC Medical Imaging\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1186/s12880-025-01926-4\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"BMC Medical Imaging","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1186/s12880-025-01926-4","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
引用次数: 0
摘要
理由和目的:利用视觉、定量和联合模型评估对比增强锥形束乳腺CT (CE-CBBCT)在新辅助治疗(NAT)后病理完全缓解(pCR)和微小残留病(MRD)的诊断效果,并将其与MRI的诊断效果进行比较。材料和方法:本研究招募了65例女性患者,这些患者在NAT后接受了CE-CBBCT和MRI检查,并分为pCR或MRD。进行单因素和多因素logistic回归分析,以确定CE-CBBCT和MRI与pCR相关的独立视觉和定量特征。采用受试者工作特征曲线下面积(AUC)、敏感性、特异性、阳性预测值(PPV)、阴性预测值(NPV)、DeLong检验和McNemar检验对模型性能进行评估和比较。采用自举法评估各模型的稳定性。结果:多因素分析发现CE-CBBCT上细小和支状钙化形态(视觉模型:比值比[OR] = 4.500,联合模型:OR = 4.527)、增强程度(ΔHU,定量模型:OR = 1.036,联合模型:OR = 1.035)、影像学完全缓解(rCR,视觉模型:OR = 0.103,联合模型:OR = 0.097)、延迟期MRI增强比(ERdpMRI,定量模型:OR = 5.048,联合模型:OR = 5.583)是pCR的独立预测因子。CE-CBBCT联合模型的AUC显著高于视觉模型(0.805 vs. 0.698, p = 0.017),与MRI联合模型的AUC相当(0.805 vs. 0.819, p = 0.811)。在her2富集亚组中,CE-CBBCT联合模型识别pCR的特异性高于MRI (0.857 vs. 0.714, p = 0.011)。结论:CE-CBBCT结合钙化形态和ΔHU提高了区分pCR和MRD的准确性,达到了与MRI相当的性能。值得注意的是,CE-CBBCT联合模型在her2富集亚组中显示出优于MRI的特异性,这表明其在减少该患者群体的过度治疗方面具有潜在的实用性。临床试验号:不适用。
Comparative analysis of preoperative contrast-enhanced cone beam breast CT (CE-CBBCT) and MRI for differentiating pathological complete response from minimal residual disease in breast cancer.
Rationale and objectives: To evaluate the performance of contrast-enhanced cone-beam breast CT (CE-CBBCT) using visual, quantitative, and combined models in distinguishing pathological complete response (pCR) from minimal residual disease (MRD) after neoadjuvant therapy (NAT), and to compare its diagnostic efficacy with MRI.
Materials and methods: This study enrolled 65 female patients who underwent both CE-CBBCT and MRI after NAT and were classified as having either pCR or MRD. Univariate and multivariate logistic regression analyses were performed to identify independent visual and quantitative features from CE-CBBCT and MRI associated with pCR. Model performance was assessed and compared using the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), DeLong's test, and McNemar's test. The bootstrap method was employed to assess the stability of each model.
Results: Multivariate analysis identified fine and branched calcification morphology on CE-CBBCT (visual model: odds ratio [OR] = 4.500; combined model: OR = 4.527), enhanced degree (ΔHU, quantitative model: OR = 1.036; combined model: OR = 1.035), radiographic complete response (rCR; visual model: OR = 0.103; combined model: OR = 0.097), and delayed-phase MRI enhancement ratio (ERdpMRI; quantitative model: OR = 5.048; combined model: OR = 5.583) as independent predictors of pCR. The CE-CBBCT combined model demonstrated a significantly higher AUC than the visual model (0.805 vs. 0.698, p = 0.017) and performed comparably to the MRI combined model (0.805 vs. 0.819, p = 0.811). In the HER2-enriched subgroup, the CE-CBBCT combined model exhibited higher specificity than MRI (0.857 vs. 0.714, p = 0.011) for identifying pCR.
Conclusion: The combination of calcification morphology and ΔHU on CE-CBBCT improved accuracy in discriminating pCR from MRD, achieving performance comparable to MRI. Notably, the CE-CBBCT combined model showed superior specificity to MRI within the HER2-enriched subgroup, suggesting its potential utility in reducing overtreatment in this patient population.
期刊介绍:
BMC Medical Imaging is an open access journal publishing original peer-reviewed research articles in the development, evaluation, and use of imaging techniques and image processing tools to diagnose and manage disease.