Application of breast MRI for prediction of lymph node metastases - systematic approach using 17 individual descriptors and a dedicated decision tree.
BACKGROUND
The presence of lymph node metastases (LNMs) is one of the most important prognostic factors in breast cancer.


OBJECTIVE
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.


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).


CONCLUSIONS
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.
Dietzel
Matthias
M
Institute of Diagnostic and Interventional Radiology, Friedrich-Schiller-University Jena, Jena, Germany. matthias.dietzel@med.uni-jena.de
Baltzer
Pascal A T
PA
Vag
Tibor
T
Gröschel
Tobias
T
Gajda
Mieczyslaw
M
Camara
Oumar
O
Kaiser
Werner A
WA
D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001943:Breast Neoplasms / Q000473:pathology*; D003663:Decision Trees; D005260:Female; D006801:Humans; D008198:Lymph Nodes / Q000473:pathology*; D008207:Lymphatic Metastasis; D008279:Magnetic Resonance Imaging; D008875:Middle Aged; D009361:Neoplasm Invasiveness; D009367:Neoplasm Staging; D011237:Predictive Value of Tests; D011379:Prognosis; D011446:Prospective Studies