TY - JOUR
T1 - Three-dimensional quantitative ultrasound for detecting lymph node metastases
AU - Saegusa-Beecroft, Emi
AU - Machi, Junji
AU - Mamou, Jonathan
AU - Hata, Masaki
AU - Coron, Alain
AU - Yanagihara, Eugene T.
AU - Yamaguchi, Tadashi
AU - Oelze, Michael L.
AU - Laugier, Pascal
AU - Feleppa, Ernest J.
N1 - Funding Information:
The authors thank Gregory K. Kobayashi, MD, FCAP, Clifford C. Wong, MD, FCAP, Patricia Kim, MD, FCAP, and Conway Fung, MT, at the Department of Pathology, Kuakini Medical Center, Honolulu, Hawaii, for pathology and histology expertise. Emi Saegusa-Beecroft thanks Professor Keisho Marumo, MD, PhD, Department of Orthopedic Surgery, Jikei University School of Medicine, Tokyo, Japan, for his mentorship. Research reported in this publication was supported by the National Institutes of Health under grant CA 100183 , awarded to Ernest J. Feleppa, PhD. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
PY - 2013/7
Y1 - 2013/7
N2 - Purpose: Detection of metastases in lymph nodes (LNs) is critical for cancer management. Conventional histological methods may miss metastatic foci. To date, no practical means of evaluating the entire LN volume exists. The aim of this study was to develop fast, reliable, operator-independent, high-frequency, quantitative ultrasound (QUS) methods for evaluating LNs over their entire volume to effectively detect LN metastases. Methods: We scanned freshly excised LNs at 26 MHz and digitally acquired echo-signal data over the entire three-dimensional (3D) volume. A total of 146 LNs of colorectal, 26 LNs of gastric, and 118 LNs of breast cancer patients were enrolled. We step-sectioned LNs at 50-μm intervals and later compared them with 13 QUS estimates associated with tissue microstructure. Linear-discriminant analysis classified LNs as metastatic or nonmetastatic, and we computed areas (Az) under receiver-operator characteristic curves to assess classification performance. The QUS estimates and cancer probability values derived from discriminant analysis were depicted in 3D images for comparison with 3D histology. Results: Of 146 LNs of colorectal cancer patients, 23 were metastatic; Az = 0.952 ± 0.021 (95% confidence interval [CI]: 0.911-0.993); sensitivity = 91.3% (specificity = 87.0%); and sensitivity = 100% (specificity = 67.5%). Of 26 LNs of gastric cancer patients, five were metastatic; Az = 0.962 ± 0.039 (95% CI: 0.807-1.000); sensitivity = 100% (specificity = 95.3%). A total of 17 of 118 LNs of breast cancer patients were metastatic; Az = 0.833 ± 0.047 (95% CI: 0.741-0.926); sensitivity = 88.2% (specificity = 62.5%); sensitivity = 100% (specificity = 50.5%). 3D cancer probability images showed good correlation with 3D histology. Conclusions: These results suggest that operator- and system-independent QUS methods allow reliable entire-volume LN evaluation for detecting metastases. 3D cancer probability images can help pathologists identify metastatic foci that could be missed using conventional methods.
AB - Purpose: Detection of metastases in lymph nodes (LNs) is critical for cancer management. Conventional histological methods may miss metastatic foci. To date, no practical means of evaluating the entire LN volume exists. The aim of this study was to develop fast, reliable, operator-independent, high-frequency, quantitative ultrasound (QUS) methods for evaluating LNs over their entire volume to effectively detect LN metastases. Methods: We scanned freshly excised LNs at 26 MHz and digitally acquired echo-signal data over the entire three-dimensional (3D) volume. A total of 146 LNs of colorectal, 26 LNs of gastric, and 118 LNs of breast cancer patients were enrolled. We step-sectioned LNs at 50-μm intervals and later compared them with 13 QUS estimates associated with tissue microstructure. Linear-discriminant analysis classified LNs as metastatic or nonmetastatic, and we computed areas (Az) under receiver-operator characteristic curves to assess classification performance. The QUS estimates and cancer probability values derived from discriminant analysis were depicted in 3D images for comparison with 3D histology. Results: Of 146 LNs of colorectal cancer patients, 23 were metastatic; Az = 0.952 ± 0.021 (95% confidence interval [CI]: 0.911-0.993); sensitivity = 91.3% (specificity = 87.0%); and sensitivity = 100% (specificity = 67.5%). Of 26 LNs of gastric cancer patients, five were metastatic; Az = 0.962 ± 0.039 (95% CI: 0.807-1.000); sensitivity = 100% (specificity = 95.3%). A total of 17 of 118 LNs of breast cancer patients were metastatic; Az = 0.833 ± 0.047 (95% CI: 0.741-0.926); sensitivity = 88.2% (specificity = 62.5%); sensitivity = 100% (specificity = 50.5%). 3D cancer probability images showed good correlation with 3D histology. Conclusions: These results suggest that operator- and system-independent QUS methods allow reliable entire-volume LN evaluation for detecting metastases. 3D cancer probability images can help pathologists identify metastatic foci that could be missed using conventional methods.
KW - Breast cancer
KW - Colorectal cancer
KW - Gastric cancer
KW - High-frequency ultrasound
KW - Lymph node metastases
KW - Lymph node micrometastases
KW - Prospective cohort study
KW - Step-sectioning histology
KW - Three-dimensional quantitative ultrasound
UR - http://www.scopus.com/inward/record.url?scp=84879125875&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84879125875&partnerID=8YFLogxK
U2 - 10.1016/j.jss.2012.12.017
DO - 10.1016/j.jss.2012.12.017
M3 - Article
C2 - 23333189
AN - SCOPUS:84879125875
SN - 0022-4804
VL - 183
SP - 258
EP - 269
JO - Journal of Surgical Research
JF - Journal of Surgical Research
IS - 1
ER -