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1 Department of Radiology, Peking University First Hospital, Beijing, China, 100032 2 Center for Medical Device Evaluation, CFDA, Beijing, China, 100044 3 Department of Radiology, the First Affiliated Hospital with Nanjing Medical University, Nanjing, China, 210009 Please corresponding to: Yu-Dong Zhang, M.D., Department of Radiology, the First Affiliated Hospital with Nanjing Medical University 300, Guangzhou Road, Nanjing, Jiangsu Province, China, 210009 E-mail: [email protected] Tel: +86-158-0515-1704 Xiaoying Wang, M.D., Department of Radiology, Peking University First Hospital, No. 8, Xishenku St., Xicheng District Beijing, China, 100032 E-mail: [email protected] Tel: +86-135-1107-7396 Running title:
Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on January 31, 2017; DOI: 10.1158/1078-0432.CCR-16-2884
Image-based predictive nomogram in prostate cancer Relevant keywords: Prostate cancer; PSA; multi-parameter MRI; machine learning analysis; nomogram; support vector machine STATEMENT OF TRANSLATIONAL RELEVANCE 1. This is an prospective study with the largest patient capacity in China sought to answer the question that it is time to consider a role for MRI before prostate biopsy. 2. Featured with high accuracy and low false positive rate, the newly developed nomogram based on pre-biopsy clinical and imaging data can help to predict the clinical outcome accurately in 92.8% patients, even without biopsy results. This finding strongly supports the theory that prostate mp-MRI could be the first investigation of a man with a raised PSA before invasive biopsy, avoiding over-diagnosis and overtreatment.
Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on January 31, 2017; DOI: 10.1158/1078-0432.CCR-16-2884
Purpose: To investigate whether pre-biopsy multi-parametric (mp) MRI can help to improve predictive performance in prostate cancer (PCa). Experimental Design: Based on a support vector machine (SVM) analysis, we prospectively modeled clinical data (age, PSA, DRE, TRUS, PSA density and prostate volume) and mp-MRI findings (PI-RADS score and TNM stage) on 985 men to predict the risk of PCa. The new nomogram was validated on 493 patients treated at a same institution. Multivariable Cox regression analyses assessed the association between input variables and risk of PCa. And area under the receiver operating characteristic curve (Az) analyzed the predictive ability. Results: At 5-yr follow-up period, 34.3% of patients had systemic progression of PCa. Nomogram (SVM-MRI) predicting 5-yr PCa rate trained with clinical and mp-MRI data was accurate and discriminating with an externally validated Az of 0.938, positive predictive value (PPV) of 77.4% and negative predictive value of 91.5%. The improvement was significant (p < 0.001) comparing to the nomogram trained with clinical data. When stratified by PSA, SVM-MRI nomogram had high PPV (93.6%) in patients with PSA > 20 ng/ml, with intermediate to low PPV in PSA 10-20 ng/ml (64%), PSA 4-10 ng/ml (55.8%) and PSA 0-4 ng/ml (29%). PI-RADS score (Cox hazard ratio [HR]: 2.112; p < 0.001), PSA level (HR: 1.435; p
Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on January 31, 2017; DOI: 10.1158/1078-0432.CCR-16-2884
< 0.001) and age (HR: 1.012; p = 0.043) were independent predictors of PCa. CONCLUSION: Featured with low false positive rate, mp-MRI could be the first investigation of a man with a raised PSA before prostate biopsy. Key words: Prostate cancer; PSA; multi-parametric MRI; machine learning analysis; nomogram; support vector machine
Introduction
Prostate cancer (PCa) is the most commonly diagnosed cancer that affects elderly men and therefore
is a bigger health concern in developed countries (1), and now the incidence is also rapidly increasing
in China (2). The use of prostate-specific antigen (PSA) in combination with digital rectal examination
(DRE) is a widely adopted, population-based screening program in clinical practice that can effectively
detect PCa at earlier, asymptomatic stages (3, 4). However, this traditional screening plan has recently
come in for a lot of criticism that due to its noticeable limitations for cancer detecting, disease
monitoring and patient management (5). For its natural feature of variability, PSA-based screening
lead to an increase in the number of unnecessary biopsies and high risk of overtreatment (6, 7). Now
there is a lack of evidence to support PSA-based screening can influence PCa mortality. And a prostate
lung colorectal & ovarian cancer screening (PLCO) program has been discredited because of poor
survival advantage (8). Therefore, there is an imminent need for simplified predictive tools that
Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on January 31, 2017; DOI: 10.1158/1078-0432.CCR-16-2884
Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on January 31, 2017; DOI: 10.1158/1078-0432.CCR-16-2884
Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on January 31, 2017; DOI: 10.1158/1078-0432.CCR-16-2884
Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on January 31, 2017; DOI: 10.1158/1078-0432.CCR-16-2884
Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on January 31, 2017; DOI: 10.1158/1078-0432.CCR-16-2884
Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on January 31, 2017; DOI: 10.1158/1078-0432.CCR-16-2884
Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on January 31, 2017; DOI: 10.1158/1078-0432.CCR-16-2884
Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on January 31, 2017; DOI: 10.1158/1078-0432.CCR-16-2884
Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on January 31, 2017; DOI: 10.1158/1078-0432.CCR-16-2884
Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on January 31, 2017; DOI: 10.1158/1078-0432.CCR-16-2884
Last, this is a single center study, an external validation cohort should be included to test the
reproducibility of established model in future.
Conclusions
We first investigated the systemic outcome of a crowd underwent PSA-based screening and pre-biopsy
mp-MRI, and demonstrated the predictive role of pre-biopsy mp-MRI for PCa by using an advanced
machine learning-based approach. Here we answer two important questions at the beginning of the
paper: (1) Machine learning analysis of mp-MRI findings can help to improve the predictive
performance in PCa, by which, the outcome of 92.8% patients could be accurately predicted in the
first 1-yr follow-up period by combining clinical and mp-MRI findings. (2) Advanced in high PPVs and
NPVs, prostate mp-MRI could be the first investigation of a man with a raised PSA before invasive
biopsy, avoiding over-diagnosis and overtreatment.
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Tables and legends: Table 1: Clinical and MR imaging characteristics of patients in training and validation cohorts
Variable Training data (n = 985)
Validation data (n = 493) p
Clinical characteristics Median age (IQR) 70 (65-75) 70 (65-75) 0.967 Median follow-up (IQR) 40 (1-80) 38 (1-78) 0.936 Median ng/ml PSA (IQR) 10.3 (6.2-19.1) 9.8 (6.2-18.5) 0.981 Median prostate volume (IQR) 54.5 (35.6-73.1) 51.9 (32.5-63.7) 0.463 Median PSAD 0.19 (0.10-0.40) 0.20 (0.11-0.44) 0.897 DRE positive, n (%) 82 (8.3%) 58 (11.8%) 0.033 TRUS positive, n (%) 73 (7.4%) 30 (6.1%) 0.387 Initial biopsy positive, n (%) 232 (42.6%) 107 (48.0%) 0.175
Diagnosis of PCa Total detecting rate, n (%) 343 (34.8%) 164 (33.3%) 0.255 Detected by histopathology, n (%) 294 (29.8%) 107 (21.7%) Diagnosed by clinical analysis, n (%) 49 (5.0%) 57 (11.6)
MR findings Median PI-RADS score (IQR) 2 (1-4) 2 (1-4) 0.721 ECE positive, n (%) 188 (19.1%) 98 (19.9%) 0.727 SVI positive, n (%) 117 (11.9%) 20 (4.1%) 0.001 Local LN invasion, n (%) 49 (5.0%) 24 (4.9%) 0.929 Local bone metastasis, n (%) 84 (8.5%) 29 (5.9%) 0.073 Final Gleason score
≤ 3+3, n (%) 42 (14.3%) 10 (9.3%) 0.415 3+4, n (%) 56 (19.0%) 23 (21.5%) 0.876 4+3, n (%) 26 (8.8%) 10 (9.3%) 0.924 ≥ 4+4, n (%) 170 (57.8%) 64 (59.8%) 0.931
Treatment* and death rate ET, n (%) 183 (18.6%) 56 (11.3%) ADT, n (%) 54 (5.5%) 13 (2.6%) RP, n (%) 65 (6.6%) 27 (5.5%) RT, n (%) 50 (5.1%) 27 (5.5%) Chemotherapy, n (%) 5 (0.5%) 6 (1.2%) Total death rate, n (%) 73 (7.4%) 28 (5.6%) 0.087 Death by PCa, n (%) 25 (2.5%) 11 (2.2%) 0.488
Note. IQR = interquartile-range, DRE = digital rectal examination, PSA = prostate-specific antigen, PSAD = PSA density, TRUS = transrectal ultrasound, PCa = prostate cancer, PI-RADS = Prostate Imaging and Reporting and Data System, ECE = extracapsular extension; SVI = seminal vesicle invasion, LN = local lymph node (LN), ET = endocrine therapy, ADT = androgen deprivation therapy, RP = radical prostatectomy, RT = radiation therapy. *n is the therapy frequency (one patient may receive more than one therapy).
Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on January 31, 2017; DOI: 10.1158/1078-0432.CCR-16-2884
Note. -the cutoff level is the level of predictive probability (Pi). There are the points (the cutoff levels) over the receiver operator curve. For example, accuracy of 80% for a cutoff level of 0.5 means that the accuracy of the predictive model with a 50% probability or more for prediction of PCa is 80%. *using the cutoff value of 0.5, SVM-MRI has significantly (p < 0.001) higher SEN, PPV, NPV and ACC than SVM-clinical model. Table 3: Distribution of the 1478 patients according to PI-RADS score, with actual recorded events of PCa for each score PI-RADS Distribution, N (%) Actual PCa, N (%) SEN, % SPE, % PPV, % NPV, % Cutoff
Note. SEN = sensitivity, SPE= specificity, PPV = positive predictive value, NPV = negative predictive value. Figure 1: the performance of two predictive nomograms, i.e., SVM-clinical vs. SVM-MRI, in prediction
of PCa in training data (a) and validation data (b). Figure 2: 1-yr (a), 3-yr (b) and 5-yr (c) prediction rate functions (PRFs) of PCa by SVM-clinical and
SVM-MRI model in 493 validation data. It shows that SVM-MRI is dominantly superior to SVM-clinical
model in determination of patients’ short-term outcome (less than 32 mo, red arrow). Figure 3: the predictive performance of SVM-MRI model in four groups stratified by PSA level. PPV =
Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on January 31, 2017; DOI: 10.1158/1078-0432.CCR-16-2884
Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on January 31, 2017; DOI: 10.1158/1078-0432.CCR-16-2884
Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on January 31, 2017; DOI: 10.1158/1078-0432.CCR-16-2884
Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on January 31, 2017; DOI: 10.1158/1078-0432.CCR-16-2884
Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on January 31, 2017; DOI: 10.1158/1078-0432.CCR-16-2884
Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on January 31, 2017; DOI: 10.1158/1078-0432.CCR-16-2884
Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on January 31, 2017; DOI: 10.1158/1078-0432.CCR-16-2884
Published OnlineFirst January 31, 2017.Clin Cancer Res Rui Wang, Jing Wang, Ge Gao, et al. consecutive patientsperformance in prostate cancer: a prospective study in 1478 Pre-biopsy mp-MRI can help to improve the predictive
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Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on January 31, 2017; DOI: 10.1158/1078-0432.CCR-16-2884