OBJECTIVE: To evaluate the usefulness of MR computer-aided detection (CAD) in patients undergoing neoadjuvant chemotherapy for prediction of the pathological complete response of tumours. METHODS: 148 patients with breast cancer (mean age, 47.3 years; range, 29-72 years) who underwent neoadjuvant chemotherapy were included in our study. They underwent MRI before and after neoadjuvant chemotherapy, and we reviewed the pathological result as the gold standard. The computer-generated kinetic features for each lesion were recorded, and the features analysed included "threshold enhancement" at 50% and 100% minimum thresholds; degree of initial peak enhancement; and enhancement profiles comprising lesion percentages of washout, plateau and persistent enhancement. The final pathological size and character of tumours were correlated with post-chemotherapy mammography, ultrasonography and MR CAD findings. Kruskal-Wallis test and intraclass correlation coefficient were used to analyse the findings. RESULTS: We divided the 148 patients into complete pathological response and non-complete pathological response groups. A complete pathological response was defined as no histopathological evidence of any residual invasive cancer cells in the breast or axillary lymph nodes. 39 patients showed complete pathological response, and 109 patients showed non-complete pathological response. Between enhancement profiles of MR CAD, plateau proportion of tumours was significantly correlated with the pathological response of tumours (mean proportion of plateau on complete pathological response group was 27%, p = 0.007). CONCLUSION: When plateau proportion of tumours is high, we can predict non-complete pathological response of neoadjuvant chemotherapy. ADVANCES IN KNOWLEDGE: MR CAD can be a useful tool for the assessment of response to neoadjuvant chemotherapy and prediction of pathological results.
OBJECTIVE: To evaluate the usefulness of MR computer-aided detection (CAD) in patients undergoing neoadjuvant chemotherapy for prediction of the pathological complete response of tumours. METHODS: 148 patients with breast cancer (mean age, 47.3 years; range, 29-72 years) who underwent neoadjuvant chemotherapy were included in our study. They underwent MRI before and after neoadjuvant chemotherapy, and we reviewed the pathological result as the gold standard. The computer-generated kinetic features for each lesion were recorded, and the features analysed included "threshold enhancement" at 50% and 100% minimum thresholds; degree of initial peak enhancement; and enhancement profiles comprising lesion percentages of washout, plateau and persistent enhancement. The final pathological size and character of tumours were correlated with post-chemotherapy mammography, ultrasonography and MR CAD findings. Kruskal-Wallis test and intraclass correlation coefficient were used to analyse the findings. RESULTS: We divided the 148 patients into complete pathological response and non-complete pathological response groups. A complete pathological response was defined as no histopathological evidence of any residual invasive cancer cells in the breast or axillary lymph nodes. 39 patients showed complete pathological response, and 109 patients showed non-complete pathological response. Between enhancement profiles of MR CAD, plateau proportion of tumours was significantly correlated with the pathological response of tumours (mean proportion of plateau on complete pathological response group was 27%, p = 0.007). CONCLUSION: When plateau proportion of tumours is high, we can predict non-complete pathological response of neoadjuvant chemotherapy. ADVANCES IN KNOWLEDGE: MR CAD can be a useful tool for the assessment of response to neoadjuvant chemotherapy and prediction of pathological results.
Authors: T A Buchholz; B S Hill; S L Tucker; D K Frye; H M Kuerer; A U Buzdar; M D McNeese; S E Singletary; N T Ueno; L Pusztai; V Valero; G N Hortobagyi Journal: Cancer J Date: 2001 Sep-Oct Impact factor: 3.360
Authors: Savannah C Partridge; Jessica E Gibbs; Ying Lu; Laura J Esserman; Dan Sudilovsky; Nola M Hylton Journal: AJR Am J Roentgenol Date: 2002-11 Impact factor: 3.959
Authors: C Balu-Maestro; C Chapellier; A Bleuse; I Chanalet; C Chauvel; R Largillier Journal: Breast Cancer Res Treat Date: 2002-03 Impact factor: 4.872
Authors: Jean-Paul Delille; Priscilla J Slanetz; Eren D Yeh; Elkan F Halpern; Daniel B Kopans; Leoncio Garrido Journal: Radiology Date: 2003-05-29 Impact factor: 11.105