Chih-Wei Tsao1, Ching-Yu Liu2, Tai-Lung Cha3, Sheng-Tang Wu3, Guang-Huan Sun3, Dah-Shyong Yu3, Hong-I Chen4, Sun-Yran Chang5, Shih-Chang Chen6, Chien-Yeh Hsu7. 1. Division of Urology, Department of Surgery, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, ROC; Graduate Institute of Biomedical Informatics, and Center of Excellence for Cancer Research, Taipei Medical University, Taipei, Taiwan, ROC. 2. Department of Nutritional Science, Fu Jen Catholic University, New Taipei City, Taiwan, ROC. 3. Division of Urology, Department of Surgery, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, ROC. 4. Division of Urology, Department of Surgery, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, ROC; Graduate Institute of Medical Sciences, Chang Jung Christian University, Tainan, Taiwan, ROC. 5. Division of Urology, Department of Surgery, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, ROC; Taipei City Hospital, Taipei, Taiwan, ROC. 6. Graduate Institute of Patent, National Taiwan University of Science and Technology, Taipei, Taiwan, ROC. 7. Graduate Institute of Biomedical Informatics, and Center of Excellence for Cancer Research, Taipei Medical University, Taipei, Taiwan, ROC. Electronic address: cyhsu@tmu.edu.tw.
Abstract
BACKGROUND: We developed an artificial neural network (ANN) model to predict prostate cancer pathological staging in patients prior to when they received radical prostatectomy as this is more effective than logistic regression (LR), or combined use of age, prostate-specific antigen (PSA), body mass index (BMI), digital rectal examination (DRE), trans-rectal ultrasound (TRUS), biopsy Gleason sum, and primary biopsy Gleason grade. METHODS: Our study evaluated 299 patients undergoing retro-pubic radical prostatectomy or robotic-assisted laparoscopic radical prostatectomy surgical procedures with pelvic lymph node dissection. The results were intended to predict the pathological stage of prostate cancer (T2 or T3) after radical surgery. The predictive ability of ANN was compared with LR and validation of the 2007 Partin Tables was estimated by the areas under the receiving operating characteristic curve (AUCs). RESULTS: Of the 299 patients we evaluated, 109 (36.45%) displayed prostate cancer with extra-capsular extension (ECE), and 190 (63.55%) displayed organ-confined disease (OCD). LR analysis showed that only PSA and BMI were statistically significant predictors of prostate cancer with capsule invasion. Overall, ANN outperformed LR significantly (0.795 ± 0.023 versus 0.746 ± 0.025, p = 0.016). Validation using the current Partin Tables for the participants of our study was assessed, and the predictive capacity of AUC for OCD was 0.695. CONCLUSION: ANN was superior to LR at predicting OCD in prostate cancer. Compared with the validation of current Partin Tables for the Taiwanese population, the ANN model resulted in larger AUCs and more accurate prediction of the pathologic stage of prostate cancer.
BACKGROUND: We developed an artificial neural network (ANN) model to predict prostate cancer pathological staging in patients prior to when they received radical prostatectomy as this is more effective than logistic regression (LR), or combined use of age, prostate-specific antigen (PSA), body mass index (BMI), digital rectal examination (DRE), trans-rectal ultrasound (TRUS), biopsy Gleason sum, and primary biopsy Gleason grade. METHODS: Our study evaluated 299 patients undergoing retro-pubic radical prostatectomy or robotic-assisted laparoscopic radical prostatectomy surgical procedures with pelvic lymph node dissection. The results were intended to predict the pathological stage of prostate cancer (T2 or T3) after radical surgery. The predictive ability of ANN was compared with LR and validation of the 2007 Partin Tables was estimated by the areas under the receiving operating characteristic curve (AUCs). RESULTS: Of the 299 patients we evaluated, 109 (36.45%) displayed prostate cancer with extra-capsular extension (ECE), and 190 (63.55%) displayed organ-confined disease (OCD). LR analysis showed that only PSA and BMI were statistically significant predictors of prostate cancer with capsule invasion. Overall, ANN outperformed LR significantly (0.795 ± 0.023 versus 0.746 ± 0.025, p = 0.016). Validation using the current Partin Tables for the participants of our study was assessed, and the predictive capacity of AUC for OCD was 0.695. CONCLUSION: ANN was superior to LR at predicting OCD in prostate cancer. Compared with the validation of current Partin Tables for the Taiwanese population, the ANN model resulted in larger AUCs and more accurate prediction of the pathologic stage of prostate cancer.
Authors: Georgina Cosma; Giovanni Acampora; David Brown; Robert C Rees; Masood Khan; A Graham Pockley Journal: PLoS One Date: 2016-06-03 Impact factor: 3.240
Authors: Jae Kwon Kim; Mun Joo Choi; Jong Sik Lee; Jun Hyuk Hong; Choung-Soo Kim; Seong Il Seo; Chang Wook Jeong; Seok-Soo Byun; Kyo Chul Koo; Byung Ha Chung; Yong Hyun Park; Ji Youl Lee; In Young Choi Journal: J Healthc Eng Date: 2018-03-19 Impact factor: 2.682