Insulin-like growth factor (IGF)-I obliterates the pregnancy-associated protection against mammary carcinogenesis in rats: evidence that IGF-I enhances cancer progression through estrogen receptor-α activation via the mitogen-activated protein kinase pathway

INTRODUCTION: Pregnancy protects against breast cancer development in humans and rats. Parous rats have persistently reduced circulating levels of growth hormone, which may affect the activity of the growth hormone/insulin-like growth factor (IGF)-I axis. We investigated the effects of IGF-I on parity-associated protection against mammary cancer. METHODS: Three groups of rats were evaluated in the present study: IGF-I-treated parous rats; parous rats that did not receive IGF-I treatment; and age-matched virgin animals, which also did not receive IGF-I treatment. Approximately 60 days after N-methyl-N-nitrosourea injection, IGF-I treatment was discontinued and all of the animal groups were implanted with a silastic capsule containing 17β-estradiol and progesterone. The 17β-estradiol plus progesterone treatment continued for 135 days, after which the animals were killed. RESULTS: IGF-I treatment of parous rats increased mammary tumor incidence to 83%, as compared with 16% in parous rats treated with 17β-estradiol plus progesterone only. Tumor incidence and average number of tumors per animal did not differ between IGF-I-treated parous rats and age-matched virgin rats. At the time of N-methyl-N-nitrosourea exposure, DNA content was lowest but the α-lactalbumin concentration highest in the mammary glands of untreated parous rats in comparison with age-matched virgin and IGF-I-treated parous rats. The protein levels of estrogen receptor-α in the mammary gland was significantly higher in the age-matched virgin animals than in untreated parous and IGF-I-treated parous rats. Phosphorylation (activation) of the extracellular signal-regulated kinase-1/2 (ERK1/2) and expression of the progesterone receptor were both increased in IGF-I-treated parous rats, as compared with those in untreated parous and age-matched virgin rats. Expressions of cyclin D(1 )and transforming growth factor-β(3 )in the mammary gland were lower in the age-matched virgin rats than in the untreated parous and IGF-I-treated parous rats. CONCLUSION: We argue that tumor initiation (transformation and fixation of mutations) may be similar in parous and age-matched virgin animals, suggesting that the main differences in tumor formation lie in differences in tumor progression caused by the altered hormonal environment associated with parity. Furthermore, we provide evidence supporting the notion that tumor growth promotion seen in IGF-I-treated parous rats is caused by activation of estrogen receptor-α via the Raf/Ras/mitogen-activated protein kinase cascade

Ovarian Tumor Characterization and Classification Using Ultrasound-A New Online Paradigm

Among gynecological malignancies, ovarian cancer is the most frequent cause of death. Image mining algorithms have been predominantly used to give the physicians a more objective, fast, and accurate second opinion on the initial diagnosis made from medical images. The objective of this work is to develop an adjunct computer-aided diagnostic technique that uses 3D ultrasound images of the ovary to accurately characterize and classify benign and malignant ovarian tumors. In this algorithm, we first extract features based on the textural changes and higher-order spectra information. The significant features are then selected and used to train and evaluate the decision tree (DT) classifier. The proposed technique was validated using 1,000 benign and 1,000 malignant images, obtained from ten patients with benign and ten with malignant disease, respectively. On evaluating the classifier with tenfold stratified cross validation, the DT classifier presented a high accuracy of 97 %, sensitivity of 94.3 %, and specificity of 99.7 %. This high accuracy was achieved because of the use of the novel combination of the four features which adequately quantify the subtle changes and the nonlinearities in the pixel intensity variations. The rules output by the DT classifier are comprehensible to the end-user and, hence, allow the physicians to more confidently accept the results. The preliminary results show that the features are discriminative enough to yield good accuracy. Moreover, the proposed technique is completely automated, accurate, and can be easily written as a software application for use in any compute