{"title":"结合ABVS冠状面和应变弹性成像的乳腺病变放射组学分析。","authors":"Qianqing Ma, Chunyun Shen, Yankun Gao, Yayang Duan, Wanyan Li, Gensheng Lu, Xiachuan Qin, Chaoxue Zhang, Junli Wang","doi":"10.2147/BCTT.S410356","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Breast cancer is the most common tumor globally. Automated Breast Volume Scanner (ABVS) and strain elastography (SE) can provide more useful breast information. The use of radiomics combined with ABVS and SE images to predict breast cancer has become a new focus. Therefore, this study developed and validated a radiomics analysis of breast lesions in combination with coronal plane of ABVS and SE to improve the differential diagnosis of benign and malignant breast diseases.</p><p><strong>Patients and methods: </strong>620 pathologically confirmed breast lesions from January 2017 to August 2021 were retrospectively analyzed and randomly divided into a training set (n=434) and a validation set (n=186). Radiomic features of the lesions were extracted from ABVS, B-ultrasound, and strain elastography (SE) images, respectively. These were then filtered by Gradient Boosted Decision Tree (GBDT) and multiple logistic regression. The ABVS model is based on coronal plane features for the breast, B+SE model is based on features of B-ultrasound and SE, and the multimodal model is based on features of three examinations. The evaluation of the predicted performance of the three models used the receiver operating characteristic (ROC) and decision curve analysis (DCA).</p><p><strong>Results: </strong>The area under the curve, accuracy, specificity, and sensitivity of the multimodal model in the training set are 0.975 (95% CI:0.959-0.991),93.78%, 92.02%, and 96.49%, respectively, and 0.946 (95% CI:0.913 -0.978), 87.63%, 83.93%, and 93.24% in the validation set, respectively. The multimodal model outperformed the ABVS model and B+SE model in both the training (<i>P</i> < 0.001, <i>P</i> = 0.002, respectively) and validation sets (<i>P</i> < 0.001, <i>P</i> = 0.034, respectively).</p><p><strong>Conclusion: </strong>Radiomics from the coronal plane of the breast lesion provide valuable information for identification. A multimodal model combination with radiomics from ABVS, B-ultrasound, and SE could improve the diagnostic efficacy of breast masses.</p>","PeriodicalId":9106,"journal":{"name":"Breast Cancer : Targets and Therapy","volume":null,"pages":null},"PeriodicalIF":3.3000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/56/d8/bctt-15-381.PMC10228588.pdf","citationCount":"1","resultStr":"{\"title\":\"Radiomics Analysis of Breast Lesions in Combination with Coronal Plane of ABVS and Strain Elastography.\",\"authors\":\"Qianqing Ma, Chunyun Shen, Yankun Gao, Yayang Duan, Wanyan Li, Gensheng Lu, Xiachuan Qin, Chaoxue Zhang, Junli Wang\",\"doi\":\"10.2147/BCTT.S410356\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Breast cancer is the most common tumor globally. Automated Breast Volume Scanner (ABVS) and strain elastography (SE) can provide more useful breast information. The use of radiomics combined with ABVS and SE images to predict breast cancer has become a new focus. Therefore, this study developed and validated a radiomics analysis of breast lesions in combination with coronal plane of ABVS and SE to improve the differential diagnosis of benign and malignant breast diseases.</p><p><strong>Patients and methods: </strong>620 pathologically confirmed breast lesions from January 2017 to August 2021 were retrospectively analyzed and randomly divided into a training set (n=434) and a validation set (n=186). Radiomic features of the lesions were extracted from ABVS, B-ultrasound, and strain elastography (SE) images, respectively. These were then filtered by Gradient Boosted Decision Tree (GBDT) and multiple logistic regression. The ABVS model is based on coronal plane features for the breast, B+SE model is based on features of B-ultrasound and SE, and the multimodal model is based on features of three examinations. The evaluation of the predicted performance of the three models used the receiver operating characteristic (ROC) and decision curve analysis (DCA).</p><p><strong>Results: </strong>The area under the curve, accuracy, specificity, and sensitivity of the multimodal model in the training set are 0.975 (95% CI:0.959-0.991),93.78%, 92.02%, and 96.49%, respectively, and 0.946 (95% CI:0.913 -0.978), 87.63%, 83.93%, and 93.24% in the validation set, respectively. The multimodal model outperformed the ABVS model and B+SE model in both the training (<i>P</i> < 0.001, <i>P</i> = 0.002, respectively) and validation sets (<i>P</i> < 0.001, <i>P</i> = 0.034, respectively).</p><p><strong>Conclusion: </strong>Radiomics from the coronal plane of the breast lesion provide valuable information for identification. A multimodal model combination with radiomics from ABVS, B-ultrasound, and SE could improve the diagnostic efficacy of breast masses.</p>\",\"PeriodicalId\":9106,\"journal\":{\"name\":\"Breast Cancer : Targets and Therapy\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.3000,\"publicationDate\":\"2023-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/56/d8/bctt-15-381.PMC10228588.pdf\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Breast Cancer : Targets and Therapy\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.2147/BCTT.S410356\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ONCOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Breast Cancer : Targets and Therapy","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.2147/BCTT.S410356","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ONCOLOGY","Score":null,"Total":0}
引用次数: 1
摘要
背景:乳腺癌是全球最常见的肿瘤。自动乳腺体积扫描仪(ABVS)和应变弹性成像(SE)可以提供更多有用的乳房信息。放射组学结合ABVS和SE影像预测乳腺癌已成为新的研究热点。因此,本研究发展并验证了结合ABVS和SE冠状面对乳腺病变的放射组学分析,以提高乳腺良恶性疾病的鉴别诊断。患者和方法:回顾性分析2017年1月至2021年8月病理证实的620例乳腺病变,随机分为训练组(n=434)和验证组(n=186)。分别从ABVS, b超和应变弹性成像(SE)图像中提取病变的放射学特征。然后通过梯度提升决策树(GBDT)和多元逻辑回归对这些数据进行过滤。ABVS模型基于乳腺冠状面特征,B+SE模型基于B超和SE特征,多模态模型基于三次检查的特征。采用受试者工作特征(ROC)和决策曲线分析(DCA)对三种模型的预测性能进行评价。结果:多模态模型在训练集中的曲线下面积、准确度、特异度和灵敏度分别为0.975 (95% CI:0.959-0.991)、93.78%、92.02%和96.49%;在验证集中的曲线下面积、准确度、特异度和灵敏度分别为0.946 (95% CI:0.913 -0.978)、87.63%、83.93%和93.24%。多模态模型在训练集(P < 0.001, P = 0.002)和验证集(P < 0.001, P = 0.034)上均优于ABVS模型和B+SE模型。结论:乳腺病变冠状面放射组学为鉴别提供了有价值的信息。多模态模型结合ABVS、b超、SE放射组学可提高乳腺肿块的诊断效能。
Radiomics Analysis of Breast Lesions in Combination with Coronal Plane of ABVS and Strain Elastography.
Background: Breast cancer is the most common tumor globally. Automated Breast Volume Scanner (ABVS) and strain elastography (SE) can provide more useful breast information. The use of radiomics combined with ABVS and SE images to predict breast cancer has become a new focus. Therefore, this study developed and validated a radiomics analysis of breast lesions in combination with coronal plane of ABVS and SE to improve the differential diagnosis of benign and malignant breast diseases.
Patients and methods: 620 pathologically confirmed breast lesions from January 2017 to August 2021 were retrospectively analyzed and randomly divided into a training set (n=434) and a validation set (n=186). Radiomic features of the lesions were extracted from ABVS, B-ultrasound, and strain elastography (SE) images, respectively. These were then filtered by Gradient Boosted Decision Tree (GBDT) and multiple logistic regression. The ABVS model is based on coronal plane features for the breast, B+SE model is based on features of B-ultrasound and SE, and the multimodal model is based on features of three examinations. The evaluation of the predicted performance of the three models used the receiver operating characteristic (ROC) and decision curve analysis (DCA).
Results: The area under the curve, accuracy, specificity, and sensitivity of the multimodal model in the training set are 0.975 (95% CI:0.959-0.991),93.78%, 92.02%, and 96.49%, respectively, and 0.946 (95% CI:0.913 -0.978), 87.63%, 83.93%, and 93.24% in the validation set, respectively. The multimodal model outperformed the ABVS model and B+SE model in both the training (P < 0.001, P = 0.002, respectively) and validation sets (P < 0.001, P = 0.034, respectively).
Conclusion: Radiomics from the coronal plane of the breast lesion provide valuable information for identification. A multimodal model combination with radiomics from ABVS, B-ultrasound, and SE could improve the diagnostic efficacy of breast masses.