Repository logo
  • English
  • 中文
Log In
Have you forgotten your password?
  1. Home
  2. College of Electrical Engineering and Computer Science / 電機資訊學院
  3. Computer Science and Information Engineering / 資訊工程學系
  4. Solid Breast Masses: Neural Network Analysis of Vascular Features at Three-dimensional Power Doppler US for Benign or Malignant Classification 1
 
  • Details

Solid Breast Masses: Neural Network Analysis of Vascular Features at Three-dimensional Power Doppler US for Benign or Malignant Classification 1

Journal
Radiology
Journal Volume
243
Journal Issue
1
Pages
56--62
Date Issued
2007-04
Author(s)
Ruey-Feng Chang
Sheng-Fang Huang
Woo Kyung Moon
Yu-Hau Lee
Dar-Ren Chen
RUEY-FENG CHANG  
DOI
10.1148/radiol.2431060041
URI
http://scholars.lib.ntu.edu.tw/handle/123456789/331059
Abstract
Purpose: To retrospectively evaluate the accuracy of neural network analysis of tumor vascular features at three-dimensional (3D) power Doppler ultrasonography (US) for classification of breast tumors as benign or malignant, with histologic findings as the reference standard. Materials and Methods: This study was approved by the local ethics committee; informed consent was waived. Three-dimensional power Doppler US images of 221 solid breast masses (110 benign, 111 malignant) were obtained in 221 women (mean age, 46 years; range, 25-71 years). After narrowing down vessels to skeletons with a 3D thinning algorithm, six vascular feature values-vessel-to-volume ratio, number of vascular trees, total vessel length, longest path length, number of bifurcations, and vessel diameter-were computed. A neural network was used to classify tumors by using these features. Independent-samples t test and receiver operating characteristic (ROC) curve analysis were used. Results: Mean values of vessel-to-volume ratio, number of vascular trees, total vessel length, longest path length, number of bifurcations, and vessel diameter were 0.0089 ± 0.0073 (standard deviation), 26.41 ± 14.73, 23.02 cm ± 19.53, 8.44 cm ± 10.38, 36.31 ± 37.06, and 0.088 cm ± 0.021 in malignant tumors, respectively, and 0.0028 ± 0.0021, 9.69 ± 6.75, 5.17 cm ± 4.78, 1.68 cm ± 1.79, 6.05 ± 7.55, and 0.064 cm ± 0.028 in benign tumors, respectively (P < .001 for all six features). Area under ROC curve (Az) values of the six features were 0.84, 0.87, 0.87, 0.82, 0.84, and 0.75, respectively. Accuracy, sensitivity, specificity, and positive and negative predictive values were 85% (187 of 221), 83% (96 of 115), 86% (91 of 106), 86% (96 of 111), and 83% (91 of 110), respectively, with Az of 0.92 based on all six feature values. Conclusion: Three-dimensional power Doppler US images and neural network analysis of features can aid in classification of breast tumors as benign or malignant. ? RSNA, 2007.
SDGs

[SDGs]SDG3

Other Subjects
adult; aged; algorithm; article; artificial neural network; benign tumor; breast cancer; breast tumor; cancer diagnosis; diagnostic accuracy; differential diagnosis; Doppler echography; female; histopathology; human; human tissue; major clinical study; malignant neoplastic disease; prediction; priority journal; sensitivity and specificity; solid tumor; standard; three dimensional imaging; tumor vascularization; Adult; Aged; Blood Vessels; Breast; Breast Neoplasms; Female; Humans; Imaging, Three-Dimensional; Middle Aged; Neural Networks (Computer); Retrospective Studies; ROC Curve; Sensitivity and Specificity; Ultrasonography, Doppler
Type
journal article

臺大位居世界頂尖大學之列,為永久珍藏及向國際展現本校豐碩的研究成果及學術能量,圖書館整合機構典藏(NTUR)與學術庫(AH)不同功能平台,成為臺大學術典藏NTU scholars。期能整合研究能量、促進交流合作、保存學術產出、推廣研究成果。

To permanently archive and promote researcher profiles and scholarly works, Library integrates the services of “NTU Repository” with “Academic Hub” to form NTU Scholars.

總館學科館員 (Main Library)
醫學圖書館學科館員 (Medical Library)
社會科學院辜振甫紀念圖書館學科館員 (Social Sciences Library)

開放取用是從使用者角度提升資訊取用性的社會運動,應用在學術研究上是透過將研究著作公開供使用者自由取閱,以促進學術傳播及因應期刊訂購費用逐年攀升。同時可加速研究發展、提升研究影響力,NTU Scholars即為本校的開放取用典藏(OA Archive)平台。(點選深入了解OA)

  • 請確認所上傳的全文是原創的內容,若該文件包含部分內容的版權非匯入者所有,或由第三方贊助與合作完成,請確認該版權所有者及第三方同意提供此授權。
    Please represent that the submission is your original work, and that you have the right to grant the rights to upload.
  • 若欲上傳已出版的全文電子檔,可使用Open policy finder網站查詢,以確認出版單位之版權政策。
    Please use Open policy finder to find a summary of permissions that are normally given as part of each publisher's copyright transfer agreement.
  • 網站簡介 (Quickstart Guide)
  • 使用手冊 (Instruction Manual)
  • 線上預約服務 (Booking Service)
  • 方案一:臺灣大學計算機中心帳號登入
    (With C&INC Email Account)
  • 方案二:ORCID帳號登入 (With ORCID)
  • 方案一:定期更新ORCID者,以ID匯入 (Search for identifier (ORCID))
  • 方案二:自行建檔 (Default mode Submission)
  • 方案三:學科館員協助匯入 (Email worklist to subject librarians)

Built with DSpace-CRIS software - Extension maintained and optimized by 4Science