Tag Archive for: predictive model


Article of the Month: Use of machine learning to predict early biochemical recurrence after robot‐assisted prostatectomy

Every month, the Editor-in-Chief selects an Article of the Month from the current issue of BJUI. The abstract is reproduced below and you can click on the button to read the full article, which is freely available to all readers for at least 30 days from the time of this post.

In addition to the article itself, there is an accompanying editorial written by a prominent member of the urological community. This blog is intended to provoke comment and discussion and we invite you to use the comment tools at the bottom of each post to join the conversation.

If you only have time to read one article this week, it should be this one.

Use of machine learning to predict early biochemical recurrence after robot‐assisted prostatectomy

Nathan C. Wong , Cameron Lam, Lisa Patterson and Bobby Shayegan
Division of Urology, Department of Surgery, McMaster University, Hamilton, ON, Canada

Visual abstract created Rebecca Fisher @beckybeckyfish



To train and compare machine‐learning algorithms with traditional regression analysis for the prediction of early biochemical recurrence after robot‐assisted prostatectomy.

Patients and Methods

A prospectively collected dataset of 338 patients who underwent robot‐assisted prostatectomy for localized prostate cancer was examined. We used three supervised machine‐learning algorithms and 19 different training variables (demographic, clinical, imaging and operative data) in a hypothesis‐free manner to build models that could predict patients with biochemical recurrence at 1 year. We also performed traditional Cox regression analysis for comparison.

= 0.686) and with a univariate regression model (AUC = 0.865).


K‐nearest neighbour, logistic regression and random forest classifier were used as machine‐learning models. Classic Cox regression analysis had an area under the curve (AUC) of 0.865 for the prediction of biochemical recurrence. All three of our machine‐learning models (K‐nearest neighbour (AUC 0.903), random forest tree (AUC 0.924) and logistic regression (AUC 0.940) outperformed the conventional statistical regression model. Accuracy prediction scores for K‐nearest neighbour, random forest tree and logistic regression were 0.976, 0.953 and 0.976, respectively.


Machine‐learning techniques can produce accurate disease predictability better that traditional statistical regression. These tools may prove clinically useful for the automated prediction of patients who develop early biochemical recurrence after robot‐assisted prostatectomy. For these patients, appropriate individualized treatment options can improve outcomes and quality of life.

Editorial: Can machine‐learning algorithms replace conventional statistics?

Wong et al. [1] evaluate 19 clinical variables (training data) and three supervised machine‐learning algorithms to predict early biochemical recurrence after robot‐assisted prostatectomy. They further compare the areas under the curve (AUCs) resulting from these algorithms with the AUC of a conventional Cox regression model and conclude that the machine‐learning algorithms can produce accurate disease prognosis, perhaps better than a traditional Cox regression model. As the authors state, predictive models have the potential to better individualize care to patients at highest risk of prostate cancer recurrence and progression.

The authors should be commended for their adoption of machine‐learning algorithms to better interpret the vast volumes of clinical data and assess prognosis after robot‐assisted prostatectomy. This should represent another step forward for the management of prostate cancer, where tailored treatment is now largely based on the clinical risk stratification of the disease [2]. Incidentally, we are also in an era where we are seeing aspects of artificial intelligence (machine learning being a subset of it) vastly transform how we view and process data in everyday life. This has been true in medicine as well, particularly for prostate cancer [3].

While our own research group has also evaluated machine‐learning algorithms to process surgeon performance metrics and predict clinical outcomes after robot‐assisted prostatectomy [4], I want to express a word of caution. Utilization of machine learning does not in itself imply automatic superiority over conventional statistics [5] despite literature that has demonstrated so [3]. The success of predictive models in machine learning still relies on the quality of data introduced and careful execution of the analysis. In our experience, it works best when highly experienced clinicians and data scientists are working hand in hand.

Furthermore, I would argue that the results of this present study do not necessarily show that machine learning is superior to conventional statistics, but rather it highlights an inherent advantage of machine learning. While traditional analyses require the a priori selection of a model based on the available data, machine learning has more flexibility for model fitting [6]. Additionally, inclusion of variables in traditional analyses is constrained by the sample size. In contrast, by design, machine learning models thrive on their ability to consider many variables concurrently, and as such, have the potential to detect underlying patterns that may otherwise be undetectable when data are examined effectively in individual silos.

We look forward to the external validation of the methodology described in the present article. Big and diverse data are critical requirements of machine learning. A multi‐institutional, multi‐surgeon cohort is necessary to confirm the findings in this report. A further step from there is the adoption of such prediction models into clinical use. The ultimate question is how improved prognostic data may influence surgeon and patient decisions.

Conflict of Interest

Dr Hung reports personal fees from Ethicon, Inc, outside the submitted work.


  1. Wong NC, Lam C, Patterson L, Shayegan B. Use of machine learning to predict early biochemical recurrence following robotic prostatectomy. BJU Int 2019; 123: 51–7
  2. D’Amico AV, Whittington R, Malkowicz SB et al. Biochemical outcome after radical prostatectomy, external beam radiation therapy or interstitial radiation therapy for clinically localized prostate cancer. JAMA 1998; 280: 969–74
  3. Hung AJ, Chen J, Che Z et al. Utilizing machine learning and automated performance metrics to evaluate robot‐assisted radical prostatectomy performance and predict outcomes. J Endourol 2018; 32: 438–445
  4. Kattan MW. Comparison of Cox regression with other methods for determining prediction models and nomograms. J Urol 2003; 170 (6 Pt 2): S6–9
  5. Hung AJ, Chen J, Gill IS. Automated performance metrics and machine learning algorithms to measure surgeon performance and anticipate clinical outcomes in robotic surgery. JAMA Surg 2018; 153: 770–1
© 2024 BJU International. All Rights Reserved.