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SummaryPlasma concentrations of testosterone were estimated in normal men, in patients before treatment for prostatic cancer, and in patients who had had various forms of endocrine treatment for prostatic carcinoma. There was no decline in plasma testosterone levels with age. Patients with non-metastatic disease had levels similar to those of normal controls, but in advanced metastaticdisease the levels were low. After orchidectomy the plasma testosterone level fell to that found in normal women. In every patient stilboestrol in doses as small as 1 mg three times a day suppressed plasma testosterone at first to negligible amounts, irrespective of the clinical response. Subsequently a small but significant rise in the concentration was always observed over a period of six months' oestrogen therapy. Pituitary ablation with yttrium-90 lowered the plasma testosterone concentration again to negligible amounts in patients who had been on stilboestrol. In advanced metastatic disease this was often associated with relief of pain. Preliminary studies with aminoglutethimide indicate that it can produce biochemical and clinical effects similar to those of pituitary ablation. IntroductionOur purpose was to study the effect on plasma testosterone levels of the various forms of endocrine therapy for prostatic carcinoma. We therefore measured concentrations of plasma testosterone before and during treatment and compared them with those in normal men. The treatments included stilboestrol, orchidectomy, pituitary ablation, and "medical adrenalectomy"

There is a strong association between histologic grade and survival in patients with AJCC stage I or II oral cavity SCC. High histologic grade in early stage oral cavity cancer is associated with poorer survival and carries independent prognostic value in addition to tumor size, node status, and presence of distant metastasis (TNM) stage. Thus, histologic grade should be considered clinically when making treatment decisions, and multivariable models of survival should include grade as a covariate to improve prognostic accuracy.

We propose an extrinsic regression framework for modeling data with manifold valued responses and Euclidean predictors. Regression with manifold responses has wide applications in shape analysis, neuroscience, medical imaging and many other areas. Our approach embeds the manifold where the responses lie onto a higher dimensional Euclidean space, obtains a local regression estimate in that space, and then projects this estimate back onto the image of the manifold. Outside the regression setting both intrinsic and extrinsic approaches have been proposed for modeling i.i.d manifold-valued data. However, to our knowledge our work is the first to take an extrinsic approach to the regression problem. The proposed extrinsic regression framework is general, computationally efficient and theoretically appealing. Asymptotic distributions and convergence rates of the extrinsic regression estimates are derived and a large class of examples are considered indicating the wide applicability of our approach.

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