OBJECTIVE: The aim of this study was to assess the performance of experimental software (Qontraxt) intended to provide automated quantification of sonographic signal intensity, which is related to the contrast enhancement of lymph node tissue, to differentiate benign from malignant lymph nodes. SUBJECTS AND METHODS: In 31 patients (age range, 24-86 years; mean age +/- SD, 53.6 +/- 14.4 years) a single lymph node per patient was evaluated on sonography after the administration of sulfur hexafluoride-filled microbubbles. The stored sonographic images were analyzed and processed into chromatic maps that had numeric values related to the amount of contrast. The lymph node regions in which the increase of signal intensity values with respect to baseline were highest (maximum signal intensity value [SImax]) and lowest (minimum signal intensity value [SImin]) were identified, and the corresponding numeric data were stored. Statistical analyses were performed by means of the Student's t test; a p value of less than 0.05 was considered to be statistically significant. RESULTS: Histopathologic analysis revealed metastatic lesions in 12 of the 31 lymph nodes; the remaining 19 were benign (16 reactive lymph nodes, two cases of granulomatous lymphadenitis, and one case of tubercular lymphadenitis). Values obtained from the SImax regions showed no consistent difference between benign and malignant lymph nodes; on the other hand, values from the SImin regions comparing baseline and maximal contrast-enhanced values were significantly different in the two groups (p < 0.001). Confidence for characterization of malignancy was significant using the difference between values from SImax and SImin regions, with the higher diagnostic value from 24 to 31 inclusive: sensitivity, 92% (11/12); specificity, 89% (17/19); positive predictive value, 85% (11/13); and accuracy, 90% (28/31). CONCLUSION: The software being tested proved to be useful in differentiating benign from metastatic lymph nodes on the basis of the quantitative data it provided.
OBJECTIVE: The aim of this study was to assess the performance of experimental software (Qontraxt) intended to provide automated quantification of sonographic signal intensity, which is related to the contrast enhancement of lymph node tissue, to differentiate benign from malignant lymph nodes. SUBJECTS AND METHODS: In 31 patients (age range, 24-86 years; mean age +/- SD, 53.6 +/- 14.4 years) a single lymph node per patient was evaluated on sonography after the administration of sulfur hexafluoride-filled microbubbles. The stored sonographic images were analyzed and processed into chromatic maps that had numeric values related to the amount of contrast. The lymph node regions in which the increase of signal intensity values with respect to baseline were highest (maximum signal intensity value [SImax]) and lowest (minimum signal intensity value [SImin]) were identified, and the corresponding numeric data were stored. Statistical analyses were performed by means of the Student's t test; a p value of less than 0.05 was considered to be statistically significant. RESULTS: Histopathologic analysis revealed metastatic lesions in 12 of the 31 lymph nodes; the remaining 19 were benign (16 reactive lymph nodes, two cases of granulomatous lymphadenitis, and one case of tubercular lymphadenitis). Values obtained from the SImax regions showed no consistent difference between benign and malignant lymph nodes; on the other hand, values from the SImin regions comparing baseline and maximal contrast-enhanced values were significantly different in the two groups (p < 0.001). Confidence for characterization of malignancy was significant using the difference between values from SImax and SImin regions, with the higher diagnostic value from 24 to 31 inclusive: sensitivity, 92% (11/12); specificity, 89% (17/19); positive predictive value, 85% (11/13); and accuracy, 90% (28/31). CONCLUSION: The software being tested proved to be useful in differentiating benign from metastatic lymph nodes on the basis of the quantitative data it provided.
Authors: Dionysios Dellaportas; Andreas Koureas; John Contis; Panagis M Lykoudis; Irene Vraka; Dimitrios Psychogios; Agatha Kondi-Pafiti; Dionysios K Voros Journal: Breast Care (Basel) Date: 2015-07-10 Impact factor: 2.860
Authors: Xin-Wu Cui; Christian Jenssen; Adrian Saftoiu; Andre Ignee; Christoph F Dietrich Journal: World J Gastroenterol Date: 2013-08-14 Impact factor: 5.742
Authors: M-X Tang; H Mulvana; T Gauthier; A K P Lim; D O Cosgrove; R J Eckersley; E Stride Journal: Interface Focus Date: 2011-05-18 Impact factor: 3.906