Use of a Radiomics Model to Predict Tumor Invasiveness of Pulmonary Adenocarcinomas Appearing as Pulmonary Ground-Glass Nodules
المؤلفون المشاركون
Xue, Xing
Yang, Yong
Huang, Qiang
Cui, Feng
Lian, Yuqing
Zhang, Siying
Yao, Linpeng
Peng, Wei
Li, Xin
Pang, Peipei
Yan, Jianhua
Chen, Feng
المصدر
العدد
المجلد 2018، العدد 2018 (31 ديسمبر/كانون الأول 2018)، ص ص. 1-9، 9ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2018-06-13
دولة النشر
مصر
عدد الصفحات
9
التخصصات الرئيسية
الملخص EN
Background.
It is important to distinguish the classification of lung adenocarcinoma.
A radiomics model was developed to predict tumor invasiveness using quantitative and qualitative features of pulmonary ground-glass nodules (GGNs) on chest CT.
Materials and Methods.
A total of 599 GGNs [including 202 preinvasive lesions and 397 minimally invasive and invasive pulmonary adenocarcinomas (IPAs)] were evaluated using univariate, multivariate, and logistic regression analyses to construct a radiomics model that predicted invasiveness of GGNs.
In primary cohort (comprised of patients scanned from August 2012 to July 2016), preinvasive lesions were distinguished from IPAs based on pure or mixed density (PM), lesion shape, lobulated border, and pleural retraction and 35 other quantitative parameters (P <0.05) using univariate analysis.
Multivariate analysis showed that PM, lobulated border, pleural retraction, age, and fractal dimension (FD) were significantly different between preinvasive lesions and IPAs.
After logistic regression analysis, PM and FD were used to develop a prediction nomogram.
The validation cohort was comprised of patients scanned after Jan 2016.
Results.
The model showed good discrimination between preinvasive lesions and IPAs with an area under curve (AUC) of 0.76 [95% CI: 0.71 to 0.80] in ROC curve for the primary cohort.
The nomogram also demonstrated good discrimination in the validation cohort with an AUC of 0.79 [95% CI: 0.71 to 0.88].
Conclusions.
For GGNs, PM, lobulated border, pleural retraction, age, and FD were features discriminating preinvasive lesions from IPAs.
The radiomics model based upon PM and FD may predict the invasiveness of pulmonary adenocarcinomas appearing as GGNs.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Xue, Xing& Yang, Yong& Huang, Qiang& Cui, Feng& Lian, Yuqing& Zhang, Siying…[et al.]. 2018. Use of a Radiomics Model to Predict Tumor Invasiveness of Pulmonary Adenocarcinomas Appearing as Pulmonary Ground-Glass Nodules. BioMed Research International،Vol. 2018, no. 2018, pp.1-9.
https://search.emarefa.net/detail/BIM-1128092
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Xue, Xing…[et al.]. Use of a Radiomics Model to Predict Tumor Invasiveness of Pulmonary Adenocarcinomas Appearing as Pulmonary Ground-Glass Nodules. BioMed Research International No. 2018 (2018), pp.1-9.
https://search.emarefa.net/detail/BIM-1128092
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Xue, Xing& Yang, Yong& Huang, Qiang& Cui, Feng& Lian, Yuqing& Zhang, Siying…[et al.]. Use of a Radiomics Model to Predict Tumor Invasiveness of Pulmonary Adenocarcinomas Appearing as Pulmonary Ground-Glass Nodules. BioMed Research International. 2018. Vol. 2018, no. 2018, pp.1-9.
https://search.emarefa.net/detail/BIM-1128092
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1128092
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر