Breast cancer margin detection with a single frequency terahertz imaging system

2016 ◽  
Author(s):  
Sigfrid K. Yngvesson ◽  
Andrew Karellas ◽  
Stephen Glick ◽  
Ashraf Khan ◽  
Paul R. Siqueira ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Imaging System ◽  
Terahertz Imaging ◽  
Single Frequency ◽  
Margin Detection

scholarly journals Development and Testing of a Single Frequency Terahertz Imaging System for Breast Cancer Detection

2013 ◽  
Vol 3 (4) ◽  
pp. 374-386 ◽  
Author(s):  
Benjamin St. Peter ◽  
Sigfrid Yngvesson ◽  
Paul Siqueira ◽  
Patrick Kelly ◽  
Ashraf Khan ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Detection ◽  
Imaging System ◽  
Terahertz Imaging ◽  
Breast Cancer Detection ◽  
Single Frequency

scholarly journals Development and Testing of a Single Frequency Terahertz Imaging System for Breast Cancer Detection

2013 ◽  
Vol 17 (4) ◽  
pp. 785-797 ◽  
Author(s):  
Benjamin St. Peter ◽  
Sigfrid Yngvesson ◽  
Paul Siqueira ◽  
Patrick Kelly ◽  
Ashraf Khan ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Detection ◽  
Imaging System ◽  
Terahertz Imaging ◽  
Breast Cancer Detection ◽  
Single Frequency

Low-cost high-performance CMOS terahertz imaging system

2015 ◽  
Author(s):  
Chung Hsing Li ◽  
Tzu-Chao Yan ◽  
Yuhsin Chang ◽  
Chyong Chen ◽  
Chien-Nan Kuo
Keyword(s):  
High Performance ◽  
Imaging System ◽  
Low Cost ◽  
Terahertz Imaging

Reflection type of terahertz imaging system using a high-Tc superconducting oscillator

2014 ◽  
Vol 104 (2) ◽  
pp. 022601 ◽  
Author(s):  
T. Kashiwagi ◽  
K. Nakade ◽  
B. Marković ◽  
Y. Saiwai ◽  
H. Minami ◽  
...  
Keyword(s):  
Imaging System ◽  
Terahertz Imaging ◽  
High Tc

scholarly journals Performance of a novel protease-activated fluorescent imaging system for intraoperative detection of residual breast cancer during breast conserving surgery

2021 ◽  
Vol 187 (1) ◽  
pp. 145-153
Author(s):  
Conor R. Lanahan ◽  
Bridget N. Kelly ◽  
Michele A. Gadd ◽  
Michelle C. Specht ◽  
Carson L. Brown ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Real Time ◽  
Imaging System ◽  
Ex Vivo ◽  
Menopausal Status ◽  
Fluorescent Imaging ◽  
Cancer Subtypes ◽  
Surgical Workflow ◽  
Tumor Types

Abstract Purpose Safe breast cancer lumpectomies require microscopically clear margins. Real-time margin assessment options are limited, and 20–40% of lumpectomies have positive margins requiring re-excision. The LUM Imaging System previously showed excellent sensitivity and specificity for tumor detection during lumpectomy surgery. We explored its impact on surgical workflow and performance across patient and tumor types. Methods We performed IRB-approved, prospective, non-randomized studies in breast cancer lumpectomy procedures. The LUM Imaging System uses LUM015, a protease-activated fluorescent imaging agent that identifies residual tumor in the surgical cavity walls. Fluorescent cavity images were collected in real-time and analyzed using system software. Results Cavity and specimen images were obtained in 55 patients injected with LUM015 at 0.5 or 1.0 mg/kg and in 5 patients who did not receive LUM015. All tumor types were distinguished from normal tissue, with mean tumor:normal (T:N) signal ratios of 3.81–5.69. T:N ratios were 4.45 in non-dense and 4.00 in dense breasts (p = 0.59) and 3.52 in premenopausal and 4.59 in postmenopausal women (p = 0.19). Histopathology and tumor receptor testing were not affected by LUM015. Falsely positive readings were more likely when tumor was present < 2 mm from the adjacent specimen margin. LUM015 signal was stable in vivo at least 6.5 h post injection, and ex vivo at least 4 h post excision. Conclusions Intraoperative use of the LUM Imaging System detected all breast cancer subtypes with robust performance independent of menopausal status and breast density. There was no significant impact on histopathology or receptor evaluation.

Supervised Bayesian learning for breast cancer detection in terahertz imaging

2021 ◽  
Vol 70 ◽  
pp. 102949 ◽  
Author(s):  
Tanny Chavez ◽  
Nagma Vohra ◽  
Keith Bailey ◽  
Magda El-Shenawee ◽  
Jingxian Wu
Keyword(s):  
Breast Cancer ◽  
Cancer Detection ◽  
Bayesian Learning ◽  
Terahertz Imaging ◽  
Breast Cancer Detection
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