MR-determined metabolic phenotype of breast cancer in prediction of lymphatic spread, grade, and hormone status.
The purpose of the study was to evaluate the use of metabolic phenotype, described by high-resolution magic angle spinning magnetic resonance spectroscopy (HR MAS MRS), as a tool for prediction of histological grade, hormone status, and axillary lymphatic spread in breast cancer patients. Biopsies from breast cancer (n = 91) and adjacent non-involved tissue (n = 48) were excised from patients (n = 77) during surgery. HR MAS MR spectra of intact samples were acquired. Multivariate models relating spectral data to histological grade, lymphatic spread, and hormone status were designed. The multivariate methods applied were variable reduction by principal component analysis (PCA) or partial least-squares regression-uninformative variable elimination (PLS-UVE), and modelling by PLS, probabilistic neural network (PNN), or cascade correlation neural network. In the end, model verification by prediction of blind samples (n = 12) was performed. Validation of PNN training resulted in sensitivity and specificity ranging from 83 to 100% for all predictions. Verification of models by blind sample testing showed that hormone status was well predicted by both PNN and PLS (11 of 12 correct), lymphatic spread was best predicted by PLS (8 of 12), whereas PLS-UVE PNN was the best approach for predicting grade (9 of 12 correct). MR-determined metabolic phenotype may have a future role as a supplement for clinical decision-making-concerning adjuvant treatment and the adaptation to more individualised treatment protocols.
Bathen
Tone F
TF
Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, 7489 Trondheim, Norway. tone.f.bathen@ntnu.no
Jensen
Line R
LR
Sitter
Beathe
B
Fjösne
Hans E
HE
Halgunset
Jostein
J
Axelson
David E
DE
Gribbestad
Ingrid S
IS
Lundgren
Steinar
S
D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D014408:Biomarkers, Tumor / Q000378:metabolism; D001943:Breast Neoplasms / Q000378:metabolism* / Q000473:pathology*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005260:Female; D006801:Humans; D008198:Lymph Nodes / Q000473:pathology*; D008207:Lymphatic Metastasis; D009682:Magnetic Resonance Spectroscopy / Q000379:methods*; D008875:Middle Aged; D016571:Neural Networks, Computer; D010641:Phenotype; D011379:Prognosis; D011960:Receptors, Estrogen / Q000378:metabolism*; D011980:Receptors, Progesterone / Q000378:metabolism*; D013113:Spin Labels
D014408:Biomarkers, Tumor; D011960:Receptors, Estrogen; D011980:Receptors, Progesterone; D013113:Spin Labels