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Radiogenomics analysis identifies correlations of digital mammography with clinical molecular signatures in breast cancer.

Radiogenomics analysis identifies correlations of digital mammography with clinical molecular signatures in breast cancer.
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Tamez-Peña JG, Rodriguez-Rojas JA, Gomez-Rueda H, Celaya-Padilla JM, Rivera-Prieto RA, Palacios-Corona R, Garza-Montemayor M, Cardona-Huerta S, Treviño V,


Tamez-Peña JG, Rodriguez-Rojas JA, Gomez-Rueda H, Celaya-Padilla JM, Rivera-Prieto RA, Palacios-Corona R, Garza-Montemayor M, Cardona-Huerta S, Treviño V, (click to view)

Tamez-Peña JG, Rodriguez-Rojas JA, Gomez-Rueda H, Celaya-Padilla JM, Rivera-Prieto RA, Palacios-Corona R, Garza-Montemayor M, Cardona-Huerta S, Treviño V,

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PloS one 2018 03 2913(3) e0193871 doi 10.1371/journal.pone.0193871

Abstract

In breast cancer, well-known gene expression subtypes have been related to a specific clinical outcome. However, their impact on the breast tissue phenotype has been poorly studied. Here, we investigate the association of imaging data of tumors to gene expression signatures from 71 patients with breast cancer that underwent pre-treatment digital mammograms and tumor biopsies. From digital mammograms, a semi-automated radiogenomics analysis generated 1,078 features describing the shape, signal distribution, and texture of tumors along their contralateral image used as control. From tumor biopsy, we estimated the OncotypeDX and PAM50 recurrence scores using gene expression microarrays. Then, we used multivariate analysis under stringent cross-validation to train models predicting recurrence scores. Few univariate features reached Spearman correlation coefficients above 0.4. Nevertheless, multivariate analysis yielded significantly correlated models for both signatures (correlation of OncotypeDX = 0.49 ± 0.07 and PAM50 = 0.32 ± 0.10 in stringent cross-validation and OncotypeDX = 0.83 and PAM50 = 0.78 for a unique model). Equivalent models trained from the unaffected contralateral breast were not correlated suggesting that the image signatures were tumor-specific and that overfitting was not a considerable issue. We also noted that models were improved by combining clinical information (triple negative status and progesterone receptor). The models used mostly wavelets and fractal features suggesting their importance to capture tumor information. Our results suggest that molecular-based recurrence risk and breast cancer subtypes have observable radiographic phenotypes. To our knowledge, this is the first study associating mammographic information to gene expression recurrence signatures.

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