BACKGROUND: The presence of lymph node metastases (LNMs) is one of the most important prognostic factors in breast cancer. PURPOSE: To correlate a detailed catalog of 17 descriptors in breast MRI (bMRI) with the presence of LNMs and to identify useful combinations of such descriptors for the prediction of LNMs using a dedicated decision tree. MATERIAL AND METHODS: A standardized protocol and study design was applied in this IRB-approved study (T1-weighted FLASH; 0.1 mmol/kg body weight Gd-DTPA; T2-weighted TSE; histological verification after bMRI). Two experienced radiologists performed prospective evaluation of the previously acquired examination in consensus. In every lesion 17 previously published descriptors were assessed. Subgroups of primary breast cancers with (N+: 97) and without LNM were created (N-: 253). The prevalence and diagnostic accuracy of each descriptor were correlated with the presence of LNM (chi-square test; diagnostic odds ratio/DOR). To identify useful combinations of descriptors for the prediction of LNM a chi-squared automatic interaction detection (CHAID) decision tree was applied. RESULTS: Seven of 17 descriptors were significantly associated with LNMs. The most accurate were "Skin thickening" (P < 0.001; DOR 5.9) and "Internal enhancement" (P < 0.001; DOR <or=13.7). The CHAID decision tree identified useful combinations of descriptors: "Skin thickening" plus "Destruction of nipple line" raised the probability of N+ by 40% (P< 0.05). In case of absence of "Skin thickening", "Edema", and "Irregular margins", the likelihood of N+ was 0% (P<0.05). CONCLUSION: Our data demonstrate the close association of selected breast MRI descriptors with nodal status. If present, such descriptors can be used - as stand alone or in combination - to accurately predict LNM and to stratify the patient's prognosis.
BACKGROUND: The presence of lymph node metastases (LNMs) is one of the most important prognostic factors in breast cancer. PURPOSE: To correlate a detailed catalog of 17 descriptors in breast MRI (bMRI) with the presence of LNMs and to identify useful combinations of such descriptors for the prediction of LNMs using a dedicated decision tree. MATERIAL AND METHODS: A standardized protocol and study design was applied in this IRB-approved study (T1-weighted FLASH; 0.1 mmol/kg body weight Gd-DTPA; T2-weighted TSE; histological verification after bMRI). Two experienced radiologists performed prospective evaluation of the previously acquired examination in consensus. In every lesion 17 previously published descriptors were assessed. Subgroups of primary breast cancers with (N+: 97) and without LNM were created (N-: 253). The prevalence and diagnostic accuracy of each descriptor were correlated with the presence of LNM (chi-square test; diagnostic odds ratio/DOR). To identify useful combinations of descriptors for the prediction of LNM a chi-squared automatic interaction detection (CHAID) decision tree was applied. RESULTS: Seven of 17 descriptors were significantly associated with LNMs. The most accurate were "Skin thickening" (P < 0.001; DOR 5.9) and "Internal enhancement" (P < 0.001; DOR <or=13.7). The CHAID decision tree identified useful combinations of descriptors: "Skin thickening" plus "Destruction of nipple line" raised the probability of N+ by 40% (P< 0.05). In case of absence of "Skin thickening", "Edema", and "Irregular margins", the likelihood of N+ was 0% (P<0.05). CONCLUSION: Our data demonstrate the close association of selected breast MRI descriptors with nodal status. If present, such descriptors can be used - as stand alone or in combination - to accurately predict LNM and to stratify the patient's prognosis.
Authors: D Leithner; G J Wengert; T H Helbich; S Thakur; R E Ochoa-Albiztegui; E A Morris; K Pinker Journal: Clin Radiol Date: 2017-12-09 Impact factor: 2.350
Authors: Piotr Andrzejewski; Georg Wengert; Thomas H Helbich; Heinrich Magometschnigg; Dietmar Georg; Marcus Hacker; Pascal Baltzer; Paola Clauser; Panagiotis Kapetas; Petra Georg; Wolfgang Wadsak; Katja Pinker Journal: Contrast Media Mol Imaging Date: 2019-01-08 Impact factor: 3.161