BACKGROUND Enzalutamide is an oral androgen-receptor inhibitor that prolongs survival in men with metastatic castration-resistant prostate cancer in whom the disease has progressed after chemotherapy. New treatment options are needed for patients with metastatic prostate cancer who have not received chemotherapy, in whom the disease has progressed despite androgen-deprivation therapy. METHODS In this double-blind, phase 3 study, we randomly assigned 1717 patients to receive either enzalutamide (at a dose of 160 mg) or placebo once daily. The coprimary end points were radiographic progression-free survival and overall survival. RESULTS The study was stopped after a planned interim analysis, conducted when 540 deaths had been reported, showed a benefit of the active treatment. The rate of radiographic progression-free survival at 12 months was 65% among patients treated with enzalutamide, as compared with 14% among patients receiving placebo (81% risk reduction; hazard ratio in the enzalutamide group, 0.19; 95% confidence interval [CI], 0.15 to 0.23; P<0.001). A total of 626 patients (72%) in the enzalutamide group, as compared with 532 patients (63%) in the placebo group, were alive at the data-cutoff date (29% reduction in the risk of death; hazard ratio, 0.71; 95% CI, 0.60 to 0.84; P<0.001). The benefit of enzalutamide was shown with respect to all secondary end points, including the time until the initiation of cytotoxic chemotherapy (hazard ratio, 0.35), the time until the first skeletal-related event (hazard ratio, 0.72), a complete or partial soft-tissue response (59% vs. 5%), the time until prostate-specific antigen (PSA) progression (hazard ratio, 0.17), and a rate of decline of at least 50% in PSA (78% vs. 3%) (P<0.001 for all comparisons). Fatigue and hypertension were the most common clinically relevant adverse events associated with enzalutamide treatment. CONCLUSIONS Enzalutamide significantly decreased the risk of radiographic progression and death and delayed the initiation of chemotherapy in men with metastatic prostate cancer. (Funded by Medivation and Astellas Pharma; PREVAIL ClinicalTrials.gov number, NCT01212991.)

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Enzalutamide significantly improved radiographic progression-free survival (rPFS) and overall survival (OS) among men with chemotherapy-naïve metastatic castration-resistant prostate cancer at the prespecified interim analysis of PREVAIL, a phase 3, double-blind, randomized study. We evaluated the longer-term efficacy and safety of enzalutamide up to the prespecified number of deaths in the final analysis, which included an additional 20 mo of follow-up for investigator-assessed rPFS, 9 mo of follow-up for OS, and 4 mo of follow-up for safety. Enzalutamide reduced the risk of radiographic progression or death by 68% (hazard ratio [HR] 0.32, 95% confidence interval [CI] 0.28–0.37; p < 0.0001) and the risk of death by 23% (HR 0.77, 95% CI 0.67–0.88; p = 0.0002). Median investigator-assessed rPFS was 20.0 mo (95% CI 18.9–22.1) in the enzalutamide arm and 5.4 mo (95% CI 4.1–5.6) in the placebo arm. Median OS was 35.3 mo (95% CI 32.2–not yet reached) in the enzalutamide arm and 31.3 mo (95% CI 28.8–34.2) in the placebo arm. At the time of the OS analysis, 167 patients in the placebo arm had crossed over to receive enzalutamide. The most common adverse events in the enzalutamide arm were fatigue, back pain, constipation, and arthralgia. This final analysis of PREVAIL provides more complete assessment of the clinical benefit of enzalutamide.

Deep learning algorithms have been successfully used in medical image classification. In the next stage, the technology of acquiring explainable knowledge from medical images is highly desired. Here we show that deep learning algorithm enables automated acquisition of explainable features from diagnostic annotation-free histopathology images. We compare the prediction accuracy of prostate cancer recurrence using our algorithm-generated features with that of diagnosis by expert pathologists using established criteria on 13,188 whole-mount pathology images consisting of over 86 billion image patches. Our method not only reveals findings established by humans but also features that have not been recognized, showing higher accuracy than human in prognostic prediction. Combining both our algorithm-generated features and human-established criteria predicts the recurrence more accurately than using either method alone. We confirm robustness of our method using external validation datasets including 2276 pathology images. This study opens up fields of machine learning analysis for discovering uncharted knowledge.