Previous studies have suggested elevated estrogen production in tumour-bearing breast quadrants as well as in breast cancers versus benign tissue. Using highly sensitive assays, we determined breast cancer tissue estrogen concentrations together with plasma and benign tissue estrogen concentrations in each quadrant obtained from mastectomy specimens (34 postmenopausal and 13 premenopausal women). We detected similar concentrations of each of the three major estrogens estradiol (E(2)), estrone (E(1)) and E(1)S in tumour-bearing versus non-tumour-bearing quadrants. Considering malignant tumours, intratumour E(1) levels were reduced in cancer tissue obtained from pre- as well as postmenopausal women independent of tumour ER status (average ratio E(1) cancer: benign tissue of 0.2 and 0.3, respectively; p<0.001 for both groups), suggesting intratumour aromatization to be of minor importance. The most striking finding was a significant (4.1-8.6-fold) increased E(2) concentration in ER positive tumours versus normal tissue (p<0.05 and <0.001 for pre- and postmenopausal patients, respectively), contrasting low E(2) concentrations in ER- tumours (p<0.01 and <0.001 comparing E(2) levels between ER+ and ER- tumours in pre- and postmenopausals, respectively). A possible explanation to our finding is increased ligand receptor binding capacity for E(2) in receptor positive tumours but alternative factors influencing intratumour estrogen disposition cannot be excluded.
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http://dx.doi.org/10.1016/j.jsbmb.2009.06.005 | DOI Listing |
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United European Gastroenterol J
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"Carol Davila" University of Medicine and Pharmacy, Bucharest, Romania.
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Reproductive Health, Nursing and Midwifery Care Research Center, Midwifery Group, Mashhad University of Medical Sciences, Mashhad, Iran.
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Deep-learning models have shown promise in differentiating between benign and malignant lesions. Previous studies have primarily focused on specific anatomical regions, overlooking tumors occurring throughout the body with highly heterogeneous whole-body backgrounds. Using neurofibromatosis type 1 (NF1) as an example, this study developed highly accurate MRI-based deep-learning models for the early automated screening of malignant peripheral nerve sheath tumors (MPNSTs) against complex whole-body background.
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