title: Three-dimensional quantitative ultrasound for detecting lymph node metastases.
abstract: OBJECTIVE
  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.
authors: Department of Surgery, University of Hawaii and Kuakini Medical Center, Honolulu, Hawaii 96817, USA. esaegusa@hawaii.edu
authors: Emi
authors: E
authors: Saegusa-Beecroft
authors: Junji
authors: J
authors: Machi
authors: Jonathan
authors: J
authors: Mamou
authors: Masaki
authors: M
authors: Hata
authors: Alain
authors: A
authors: Coron
authors: Eugene T
authors: ET
authors: Yanagihara
authors: Tadashi
authors: T
authors: Yamaguchi
authors: Michael L
authors: ML
authors: Oelze
authors: Pascal
authors: P
authors: Laugier
authors: Ernest J
authors: EJ
authors: Feleppa
mesh_terms: D000230:Adenocarcinoma / Q000473:pathology*; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D005260:Female; D006801:Humans; D021621:Imaging, Three-Dimensional; D008198:Lymph Nodes / Q000000981:diagnostic imaging* / Q000473:pathology; D008207:Lymphatic Metastasis / Q000000981:diagnostic imaging* / Q000473:pathology; D008297:Male; D008875:Middle Aged; D009369:Neoplasms / Q000473:pathology*; D011446:Prospective Studies; D014463:Ultrasonography