The Semiquantitative Bone Scintigraphy Index Correlates With Serum Tartrate-Resistant Acid Phosphatase Activity in Breast Cancer Patients With Bone Metastasis

2007 ◽  
Vol 82 (8) ◽  
pp. 917-926 ◽  
Author(s):  
Shih-Hung Tsai ◽  
Ching-Yuan Chen ◽  
Chih-Hung Ku ◽  
Anthony J. Janckila ◽  
Lung T. Yam ◽  
...  
2004 ◽  
Vol 11 (4) ◽  
pp. 511-516 ◽  
Author(s):  
Tsu-Yi Chao ◽  
Ching-Liang Ho ◽  
Su-Huei Lee ◽  
Mary Mei-Ju Chen ◽  
Anthony Janckila ◽  
...  

Diagnostics ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 518
Author(s):  
Da-Chuan Cheng ◽  
Te-Chun Hsieh ◽  
Kuo-Yang Yen ◽  
Chia-Hung Kao

This study aimed to explore efficient ways to diagnose bone metastasis early using bone scintigraphy images through negative mining, pre-training, the convolutional neural network, and deep learning. We studied 205 prostate cancer patients and 371 breast cancer patients and used bone scintigraphy data from breast cancer patients to pre-train a YOLO v4 with a false-positive reduction strategy. With the pre-trained model, transferred learning was applied to prostate cancer patients to build a model to detect and identify metastasis locations using bone scintigraphy. Ten-fold cross validation was conducted. The mean sensitivity and precision rates for bone metastasis location detection and classification (lesion-based) in the chests of prostate patients were 0.72 ± 0.04 and 0.90 ± 0.04, respectively. The mean sensitivity and specificity rates for bone metastasis classification (patient-based) in the chests of prostate patients were 0.94 ± 0.09 and 0.92 ± 0.09, respectively. The developed system has the potential to provide pre-diagnostic reports to aid in physicians’ final decisions.


2002 ◽  
Vol 17 (4) ◽  
pp. 253-258 ◽  
Author(s):  
A. Martinetti ◽  
E. Seregni ◽  
C. Ripamonti ◽  
L. Ferrari ◽  
F. De Conno ◽  
...  

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e12594-e12594
Author(s):  
Ming-Shen Dai ◽  
Hao-Chan Lo ◽  
Li-Jia Chen ◽  
Shun-Fu Tseng

e12594 Background: Tartrate-resistant acid phosphatase (TRAP) is a metalloproteinase-like protein that is expressed in several primary and metastatic tumors, and its expression is positively correlated with the oncogenic process. Tartrate-resistant acid phosphatase is also a novel product of activated macrophage. We have previously demonstrated the clinical significance of TRAP expression in tumor-infiltrating macrophages and serum TRAP in patients with metastatic breast cancer (BC). Therefore, TRAP protein can potentially be a predictive and prognostic marker to evaluate disease progression and therapeutic response in breast cancer patients with bone metastasis. We aim to investigate the role of TRAP expression in breast cancer metastasis and survival. Methods: RNA-seq expression data were obtained from The Cancer Genome Atlas (TCGA) database. Estrogen receptor (ER)-positive, human epidermal growth factor receptor-2 (HER2)-negative, and TNBC subtypes were included in the analyses. The TRAP-overexpressed and -silenced breast cancer cells (MCF7, 4T1, MDA-MB-231) were used for validation. Survival data was also retrieved from the TCGA database to verify the prognostic biomarker. Results: Through TCGA database analysis, we found that TRAP expression correlated to the Ki-67 expression indicating the cancer cell proliferating activity. Additionally, TRAP expression positively correlated with mesenchymal markers (SNAIL, CDH1, MMP9, Fibronectin), and negatively correlated with epithelial markers (SMAD2, SOX10), implying that the TRAP expression is related to the breast cancer Epithelial-Mesenchymal-Transition process. This phenomenon was validated in TRAP-altered cell and confirmed inferior survival with TRAP-expressed breast cancer patients in TCGA database. Conclusions: Combining clinical TCGA data and cell-based analyses showed that TRAP expression was significantly associated with breast cancer proliferating activity, metastatic potential, and inferior survival. TRAP serves as a breast cancer prognostic biomarker and can be considered as a therapeutic target. Further investigation is warranted.


2005 ◽  
Vol 8 (1) ◽  
pp. 56
Author(s):  
Jeong Eon Lee ◽  
Hyuk Jai Shin ◽  
Wonshik Han ◽  
Seok Won Kim ◽  
Kyoung Sik Park ◽  
...  

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