Breast Tumor Heterogeneity Quantification using 3D Ultrasound Texture

被引:0
作者
Megha, R. [1 ]
Geethapriya [2 ]
Radhakrishna, Selvi [2 ]
Eranki, Avinash [1 ]
机构
[1] Indian Inst Technol Hyderabad, Dept Biomed Engn, Hyderabad, India
[2] Chennai Breast Ctr, Chennai, Tamil Nadu, India
来源
PROCEEDINGS OF THE 2024 IEEE SOUTH ASIAN ULTRASONICS SYMPOSIUM, SAUS 2024 | 2024年
关键词
breast cancer; tumor heterogeneity; ultrasound; texture analysis; CANCER;
D O I
10.1109/SAUS61785.2024.10563639
中图分类号
O42 [声学];
学科分类号
070206 ; 082403 ;
摘要
Breast tumors are heterogeneous disease that can vary among individuals (intertumor heterogeneity) and within a patient's tumor (intratumor heterogeneity). Intratumor heterogeneity indicates the existence of varied cellular groups within a tumor, potentially influencing tumor progression and impacting therapy response. Ultrasound, a primary imaging modality for breast tumors, can offer valuable insights into the subtle texture characteristics throughout the entire tumor, particularly when employed in a three-dimensional mode. In this study, our objective is to quantitatively assess the intratumor heterogeneity using three-dimensional B-mode ultrasound texture to enhance our understanding of tumor heterogeneity in treatment-naive breast tumor patients. The results of our study underscore the presence of varied textural characteristics within the tumor across different spatial locations. These observations suggest the potential for non-invasive biomarkers that can offer insights into the tumor microenvironment, behavior, and progression.
引用
收藏
页数:4
相关论文
共 50 条
  • [21] Nonlinear characterization of breast cancer using multi-compression 3D ultrasound elastography in vivo
    Sayed, Ahmed
    Layne, Ginger
    Abraham, Jame
    Mukdadi, Osama
    ULTRASONICS, 2013, 53 (05) : 979 - 991
  • [22] Development of a Multicellular 3D Tumor Model to Study Cellular Heterogeneity and Plasticity in NSCLC Tumor Microenvironment
    Arora, Leena
    Kalia, Moyna
    Dasgupta, Suman
    Singh, Navneet
    Verma, Anita K.
    Pal, Durba
    FRONTIERS IN ONCOLOGY, 2022, 12
  • [23] 3D Texture Features Mining for MRI Brain Tumor Identification
    Rahim, Mohd Shafry Mohd
    Saba, Tanzila
    Nayer, Fatima
    Syed, Afraz Zahra
    3D RESEARCH, 2014, 5 (01): : 1 - 8
  • [24] Quantification of Breast Cancer Cell Invasiveness Using a Three-dimensional (3D) Model
    Cvetkovic, Donna
    Goertzen, Cameron Glenn-Franklin
    Bhattacharya, Moshmi
    JOVE-JOURNAL OF VISUALIZED EXPERIMENTS, 2014, (88):
  • [25] Automatic 3D lesion segmentation on breast ultrasound images
    Kuo, Hsien-Chi
    Giger, Maryellen L.
    Reiser, Ingrid
    Drukker, Karen
    Edwards, Alexandra
    Sennett, Charlene A.
    MEDICAL IMAGING 2013: COMPUTER-AIDED DIAGNOSIS, 2013, 8670
  • [26] Breast Tumor Segmentation in Ultrasonography Based on 3D Region Growing Method
    Lin, Wan-Ting
    Huang, Yu-Len
    Chen, Dar-Ren
    MANUFACTURING, DESIGN SCIENCE AND INFORMATION ENGINEERING, VOLS I AND II, 2015, : 1226 - 1233
  • [27] Heralding a new paradigm in 3D tumor modeling
    Fong, Eliza L. S.
    Harrington, Daniel A.
    Farach-Carson, Mary C.
    Yu, Hanry
    BIOMATERIALS, 2016, 108 : 197 - 213
  • [28] 3D Frequency-Domain Ultrasound Wave form Tomography Breast Imaging
    Sandhu, Gursharan Yash
    West, Erik
    Li, Cuiping
    Roy, Olivier
    Duric, Neb
    MEDICAL IMAGING 2017: ULTRASONIC IMAGING AND TOMOGRAPHY, 2017, 10139
  • [29] Breast tumor parameter estimation and interactive 3D thermal tomography using discrete thermal sensor data
    Antony, Linta
    Arathy, K.
    Sudarsan, Nimmi
    Muralidharan, M. N.
    Ansari, Seema
    BIOMEDICAL PHYSICS & ENGINEERING EXPRESS, 2021, 7 (01)
  • [30] 2D and 3D texture analysis to predict lymphovascular invasion in lung adenocarcinoma
    Yang, Guangjie
    Nie, Pei
    Zhao, Lianzi
    Guo, Jian
    Xue, Wei
    Yan, Lei
    Cui, Jingjing
    Wang, Zhenguang
    EUROPEAN JOURNAL OF RADIOLOGY, 2020, 129