{"title":"基于光声的瘤内和瘤周放射计量学提名图,用于乳腺恶性肿瘤 Ki-67 表达的术前预测","authors":"Zhibin Huang, Mengyun Wang, Yao Kong, Guoqiu Li, Hongtian Tian, Huaiyu Wu, Jing Zheng, Sijie Mo, Jinfeng Xu, Fajin Dong","doi":"10.1016/j.acra.2024.10.036","DOIUrl":null,"url":null,"abstract":"<p><strong>Rationale and objectives: </strong>This study investigated the preoperative predictive efficiency of radiomics derived from photoacoustic (PA) imaging, integrated with the clinical features of Ki-67 expression in malignant breast cancer (BC), with a focus on both intratumoral and peritumoral regions.</p><p><strong>Methods: </strong>This study involved 359 patients, divided into a training set (n = 251) and a testing set (n = 108). Radiomic features were extracted from intratumoral and peritumoral regions using PA imaging. Multivariate logistic regression was employed to identify significant clinical factors. LASSO regression was used to select the features extracted from the training set. The selected radiomics features were combined with clinical features to develop a radiomics nomogram. The predictive efficiency of the model was assessed using the area under the receiver operating characteristic curve (AUC), and its clinical utility and accuracy were evaluated through decision curve analysis and calibration curves, respectively.</p><p><strong>Results: </strong>The developed nomogram combined 6 mm peritumoral data with intratumoral and clinical features and showed excellent diagnostic performance, achieving an AUC of 0.899 in the testing set. They both showed good calibrations. The outperformed models based solely on clinical features or other radiomics methods, with the 6 mm surrounding tumor area proving most effective in identifying Ki-67 status in BC patients.</p><p><strong>Conclusion: </strong>Integrating PA radiomics with clinical features offers a robust preoperative tool for predicting Ki-67 status in BC, optimizing the delineation of peritumoral regions for enhanced diagnostic precision. The model's strong performance supports its potential as a non-invasive adjunct to traditional biopsy methods, aiding in the personalized management of BC treatment.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8000,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Photoacoustic-Based Intra- and Peritumoral Radiomics Nomogram for the Preoperative Prediction of Expression of Ki-67 in Breast Malignancy.\",\"authors\":\"Zhibin Huang, Mengyun Wang, Yao Kong, Guoqiu Li, Hongtian Tian, Huaiyu Wu, Jing Zheng, Sijie Mo, Jinfeng Xu, Fajin Dong\",\"doi\":\"10.1016/j.acra.2024.10.036\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Rationale and objectives: </strong>This study investigated the preoperative predictive efficiency of radiomics derived from photoacoustic (PA) imaging, integrated with the clinical features of Ki-67 expression in malignant breast cancer (BC), with a focus on both intratumoral and peritumoral regions.</p><p><strong>Methods: </strong>This study involved 359 patients, divided into a training set (n = 251) and a testing set (n = 108). Radiomic features were extracted from intratumoral and peritumoral regions using PA imaging. Multivariate logistic regression was employed to identify significant clinical factors. LASSO regression was used to select the features extracted from the training set. The selected radiomics features were combined with clinical features to develop a radiomics nomogram. The predictive efficiency of the model was assessed using the area under the receiver operating characteristic curve (AUC), and its clinical utility and accuracy were evaluated through decision curve analysis and calibration curves, respectively.</p><p><strong>Results: </strong>The developed nomogram combined 6 mm peritumoral data with intratumoral and clinical features and showed excellent diagnostic performance, achieving an AUC of 0.899 in the testing set. They both showed good calibrations. The outperformed models based solely on clinical features or other radiomics methods, with the 6 mm surrounding tumor area proving most effective in identifying Ki-67 status in BC patients.</p><p><strong>Conclusion: </strong>Integrating PA radiomics with clinical features offers a robust preoperative tool for predicting Ki-67 status in BC, optimizing the delineation of peritumoral regions for enhanced diagnostic precision. The model's strong performance supports its potential as a non-invasive adjunct to traditional biopsy methods, aiding in the personalized management of BC treatment.</p>\",\"PeriodicalId\":50928,\"journal\":{\"name\":\"Academic Radiology\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":3.8000,\"publicationDate\":\"2024-11-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Academic Radiology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1016/j.acra.2024.10.036\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Academic Radiology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1016/j.acra.2024.10.036","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
引用次数: 0
摘要
依据和目的:本研究调查了光声(PA)成像得出的放射组学与恶性乳腺癌(BC)Ki-67表达的临床特征相结合的术前预测效率,重点关注瘤内和瘤周区域:本研究涉及 359 例患者,分为训练集(251 例)和测试集(108 例)。使用 PA 成像从瘤内和瘤周区域提取放射学特征。多变量逻辑回归用于识别重要的临床因素。LASSO 回归用于选择从训练集中提取的特征。所选的放射组学特征与临床特征相结合,形成放射组学提名图。利用接收者操作特征曲线下面积(AUC)评估模型的预测效率,并分别通过决策曲线分析和校准曲线评估其临床实用性和准确性:结果:所开发的提名图将 6 毫米瘤周数据与瘤内特征和临床特征相结合,显示出卓越的诊断性能,在测试集中的 AUC 达到 0.899。它们都显示出良好的校准性。它们的表现优于仅基于临床特征或其他放射组学方法的模型,其中肿瘤周围6毫米区域在确定BC患者的Ki-67状态方面最为有效:将 PA 放射组学与临床特征相结合,为预测 BC 患者的 Ki-67 状态提供了强有力的术前工具,优化了瘤周区域的划分,提高了诊断的精确性。该模型的强大性能支持其作为传统活检方法的无创辅助工具的潜力,有助于对 BC 治疗进行个性化管理。
Photoacoustic-Based Intra- and Peritumoral Radiomics Nomogram for the Preoperative Prediction of Expression of Ki-67 in Breast Malignancy.
Rationale and objectives: This study investigated the preoperative predictive efficiency of radiomics derived from photoacoustic (PA) imaging, integrated with the clinical features of Ki-67 expression in malignant breast cancer (BC), with a focus on both intratumoral and peritumoral regions.
Methods: This study involved 359 patients, divided into a training set (n = 251) and a testing set (n = 108). Radiomic features were extracted from intratumoral and peritumoral regions using PA imaging. Multivariate logistic regression was employed to identify significant clinical factors. LASSO regression was used to select the features extracted from the training set. The selected radiomics features were combined with clinical features to develop a radiomics nomogram. The predictive efficiency of the model was assessed using the area under the receiver operating characteristic curve (AUC), and its clinical utility and accuracy were evaluated through decision curve analysis and calibration curves, respectively.
Results: The developed nomogram combined 6 mm peritumoral data with intratumoral and clinical features and showed excellent diagnostic performance, achieving an AUC of 0.899 in the testing set. They both showed good calibrations. The outperformed models based solely on clinical features or other radiomics methods, with the 6 mm surrounding tumor area proving most effective in identifying Ki-67 status in BC patients.
Conclusion: Integrating PA radiomics with clinical features offers a robust preoperative tool for predicting Ki-67 status in BC, optimizing the delineation of peritumoral regions for enhanced diagnostic precision. The model's strong performance supports its potential as a non-invasive adjunct to traditional biopsy methods, aiding in the personalized management of BC treatment.
期刊介绍:
Academic Radiology publishes original reports of clinical and laboratory investigations in diagnostic imaging, the diagnostic use of radioactive isotopes, computed tomography, positron emission tomography, magnetic resonance imaging, ultrasound, digital subtraction angiography, image-guided interventions and related techniques. It also includes brief technical reports describing original observations, techniques, and instrumental developments; state-of-the-art reports on clinical issues, new technology and other topics of current medical importance; meta-analyses; scientific studies and opinions on radiologic education; and letters to the Editor.