Discrimination of malignant and benign breast masses using automatic segmentation and features extracted from dynamic contrast-enhanced and diffusion-weighted MRI

Oncology Letters
Xinhua JiangLi Li

Abstract

Magnetic resonance imaging exhibits high sensitivity but low specificity for breast cancer. The present study aimed to investigate whether combining morphology, texture features and kinetic features with diffusion-weighted imaging using quantitative analysis improves the accuracy of discriminating malignant from benign breast masses. In total, 104 and 171 malignant lesions in 205 women were included. Additionally, 13 texture and 11 morphology features were computed from each lesion using a semi-automated segmentation method. To increase prediction accuracy, a newly designed classification model, difference-weighted local hyperplane, was used for statistical analysis of the combined effects of the features for predicting lesion type. The mean apparent diffusion coefficient (ADC) value for each lesion was calculated. Diagnostic performances of morphology and texture features, kinetic features and ADC alone and the combination of them were evaluated using receiver operating characteristics analysis. Malignant lesions had lower mean ADCs than benign lesions. By using 10-fold cross validation scheme, combined morphological and kinetic features achieved a diagnostic average accuracy of 0.87. Adding an ADC threshold of 1.37×10-3 mm2/s...Continue Reading

References

Jan 19, 1999·Academic Radiology·Y JiangK Doi
Feb 9, 2000·European Radiology·C K Kuhl
Aug 15, 2002·Journal of Clinical Oncology : Official Journal of the American Society of Clinical Oncology·Gayle F TillmanLawrence J Solin
Aug 31, 2002·Journal of Magnetic Resonance Imaging : JMRI·Yong GuoJia-Hong Gao
May 12, 2004·European Journal of Surgical Oncology : the Journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology·K SchelfoutA De Schepper
Sep 24, 2005·AJR. American Journal of Roentgenology·Ansgar MalichWerner A Kaiser
Oct 14, 2005·IEEE Transactions on Nanobioscience·Kai-Bo DuanFrancisco Azuaje
Nov 19, 2005·Journal of Clinical Oncology : Official Journal of the American Society of Clinical Oncology·Christiane K KuhlHans H Schild
Jun 21, 2006·Journal of Magnetic Resonance Imaging : JMRI·Erika RubesovaMarc Lemort
Feb 16, 2008·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·C Xu, J L Prince
May 8, 2008·Magnetic Resonance in Medical Sciences : MRMS : an Official Journal of Japan Society of Magnetic Resonance in Medicine·Masamitsu HatakenakaHiroshi Honda
Sep 23, 2009·AJR. American Journal of Roentgenology·Riham H El KhouliDavid A Bluemke
Nov 26, 2009·AJR. American Journal of Roentgenology·Savannah C PartridgeConstance D Lehman
Feb 27, 2010·Journal of Magnetic Resonance Imaging : JMRI·Savannah C PartridgeConstance D Lehman
Mar 23, 2010·European Journal of Cancer : Official Journal for European Organization for Research and Treatment of Cancer (EORTC) [and] European Association for Cancer Research (EACR)·Francesco SardanelliRobin Wilson
May 22, 2010·AJR. American Journal of Roentgenology·Savannah C PartridgeConstance D Lehman
Jan 22, 2011·Magnetic Resonance in Medicine : Official Journal of the Society of Magnetic Resonance in Medicine·S C PartridgeC D Lehman
Jun 13, 2015·Journal of the National Cancer Institute. Monographs·Vladimir Semiglazov
Feb 2, 2016·Current Problems in Diagnostic Radiology·Kyungmin ShinGary J Whitman

❮ Previous
Next ❯

Citations

Jul 3, 2021·Current Oncology·Filippo PesapaneEnrico Cassano

❮ Previous
Next ❯

Methods Mentioned

BETA
imaging techniques
biopsies
biopsy

Software Mentioned

MATLAB
DWLH

Related Concepts

Related Feeds

Cancer Imaging

Imaging techniques, including CT and MR, have become essential to tumor detection, diagnosis, and monitoring. Here is the latest research on cancer imaging.

© 2022 Meta ULC. All rights reserved