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.
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 cancerpatients 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 cancerpatients, 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 cancerpatients, 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 cancerpatients 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: Gary H Lyman; Armando E Giuliano; Mark R Somerfield; Al B Benson; Diane C Bodurka; Harold J Burstein; Alistair J Cochran; Hiram S Cody; Stephen B Edge; Sharon Galper; James A Hayman; Theodore Y Kim; Cheryl L Perkins; Donald A Podoloff; Visa Haran Sivasubramaniam; Roderick R Turner; Richard Wahl; Donald L Weaver; Antonio C Wolff; Eric P Winer Journal: J Clin Oncol Date: 2005-09-12 Impact factor: 44.544
Authors: Giuseppe Viale; Patrizia Dell'Orto; Maria O Biasi; Viviana Stufano; Luciana N De Brito Lima; Giovanni Paganelli; Patrick Maisonneuve; Janet M Vargo; George Green; Wuxiong Cao; Ailsa Swijter; Giovanni Mazzarol Journal: Ann Surg Date: 2008-01 Impact factor: 12.969
Authors: Manon J Pepels; Maaike de Boer; Peter Bult; Jos A van Dijck; Carolien H van Deurzen; Marian B Menke-Pluymers; Paul J van Diest; George F Borm; Vivianne C G Tjan-Heijnen Journal: Ann Surg Date: 2012-01 Impact factor: 12.969
Authors: Umberto Veronesi; Giovanni Paganelli; Giuseppe Viale; Alberto Luini; Stefano Zurrida; Viviana Galimberti; Mattia Intra; Paolo Veronesi; Patrick Maisonneuve; Giovanna Gatti; Giovanni Mazzarol; Concetta De Cicco; Gianfranco Manfredi; Julia Rodríguez Fernández Journal: Lancet Oncol Date: 2006-12 Impact factor: 41.316
Authors: G Cserni; S Bianchi; V Vezzosi; H Peterse; A Sapino; R Arisio; A Reiner-Concin; P Regitnig; J-P Bellocq; C Marin; R Bori; J M Penuela; A Córdoba Iturriagagoitia Journal: J Clin Pathol Date: 2006-02-23 Impact factor: 3.411
Authors: A Nissan; D Jager; M Roystacher; D Prus; T Peretz; I Eisenberg; H R Freund; M Scanlan; G Ritter; L J Old; S Mitrani-Rosenbaum Journal: Br J Cancer Date: 2006-03-13 Impact factor: 7.640