Journal
LIFE-BASEL
Volume 12, Issue 9, Pages -Publisher
MDPI
DOI: 10.3390/life12091307
Keywords
magnetic resonance imaging; breast neoplasms; receptors; estrogen; quantitative values
Categories
Funding
- Japan Society for the Promotion of Science KAKENHI [JP19K08131]
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This study aimed to correlate quantitative T1, T2, and proton density (PD) values with breast cancer subtypes using synthetic MRI. The results showed that T2 relaxation time was significantly higher in ER-negative cancers compared to ER-positive cancers. T1, T2, and PD values were strongly positively correlated with Ki-67. Among ER-positive cancers, Luminal A cancers had significantly lower T1, T2, and PD values compared to Luminal B cancers. Synthetic MRI-derived quantitative values show potential for subtyping invasive breast cancers.
The purpose of this study is to correlate quantitative T1, T2, and proton density (PD) values with breast cancer subtypes. Twenty-eight breast cancer patients underwent MRI of the breast including synthetic MRI. T1, T2, and PD values were correlated with Ki-67 and were compared between ER-positive and ER-negative cancers, and between Luminal A and Luminal B cancers. The effectiveness of T1, T2, and PD in differentiating the ER-negative from the ER-positive group and Luminal A from Luminal B cancers was evaluated using receiver operating characteristic analysis. Mean T2 relaxation of ER-negative cancers was significantly higher than that of ER-positive cancers (p < 0.05). The T1, T2, and PD values exhibited a strong positive correlation with Ki-67 (Pearson's r = 0.75, 0.69, and 0.60 respectively; p < 0.001). Among ER-positive cancers, T1, T2, and PD values of Luminal A cancers were significantly lower than those of Luminal B cancers (p < 0.05). The area under the curve (AUC) of T2 for discriminating ER-negative from ER-positive cancers was 0.87 (95% CI: 0.69-0.97). The AUC of T1 for discriminating Luminal A from Luminal B cancers was 0.83 (95% CI: 0.61-0.95). In conclusion, quantitative values derived from synthetic MRI show potential for subtyping of invasive breast cancers.
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