Machine learning methods can more efficiently predict prostate cancer compared with prostate-specific antigen density and prostate-specific antigen velocity

BackgroundProstate-specific antigen (PSA)–based screening for prostate cancer has been widely performed, but its accuracy is unsatisfactory. To improve accuracy, building an effective statistical model using machine learning methods (MLMs) is a promising approach.MethodsData on continuous changes in the PSA level over the past 2 years were accumulated from 512 patients who underwent prostate biopsy after PSA screening. The age of the patients, PSA level, prostate volumes, and white blood cell count in urinalysis were used as input data for the MLMs. As MLMs, we evaluated the efficacy of three different techniques: artificial neural networks (ANNs), random forest, and support vector machine. Model performance was evaluated using area under the receiver operating characteristic curve (AUC) and compared with the PSA level and the conventional PSA–based parameters: PSA density and PSA velocity.ResultsWhen using two annual PSA testing, all receiver operating characteristic curves of the three MLMs were above the curve for the PSA level, PSA density, and PSA velocity. The AUCs of ANNs, random forest, and support vector machine were 0.69, 0.64, and 0.63, respectively. Those values were higher than the AUCs of the PSA level, PSA density, and PSA velocity, 0.53, 0.41, and 0.55, respectively. The accuracies of the MLMs (71.6% to 72.1%) were also superior to those of the PSA level (39.1%), PSA density (49.7%), and PSA velocity (54.9%). Among the MLMs, ANNs showed the most favorable AUC. The MLMs showed higher sensitivity and specificity than conventional PSA–based parameters. The model performance did not improve when using three annual PSA testing.ConclusionThe present retrospective study results indicate that machine learning techniques can predict prostate cancer with significantly better AUCs than those of PSA density and PSA velocity

Adult genitourinary sarcoma: analysis using hospital-based cancer registry data in Japan

Abstract Background Genitourinary sarcomas are rare in adults and few large-scale studies on adult genitourinary sarcoma are reported. We aimed to elucidate the clinical characteristics, survival outcomes, and prognostic factors for overall survival of adult genitourinary sarcoma in Japan. Methods A hospital-based cancer registry data in Japan was used to identify and enroll patients diagnosed with genitourinary sarcoma in 2013. The datasets were registered from 121 institutions. Results A total of 116 men and 39 women were included, with a median age of 66 years. The most common primary site was the kidney in 47 patients, followed by the paratestis in 36 patients. The most common histological type was liposarcoma in 54 patients, followed by leiomyosarcoma in 25 patients. The 5-year overall survival rates were 57.6%. On univariate analysis, male gender, paratestis as primary organ, and histological subtype of liposarcoma were predictive of favorable survival while primary kidney, bladder, or prostate gland location were predictive of unfavorable survival. On multivariate analysis, primary paratestis was an independent predictor of favorable survival while primary kidney, bladder, or prostate gland were independent predictors of unfavorable survival. Conclusions This is the first report showing the clinical characteristics and survival outcomes of adult genitourinary sarcoma in Japan using a real-world large cohort database