Attempt to predict early recurrence of prostate cancer following prostatectomy through machine learning
Editorial

Attempt to predict early recurrence of prostate cancer following prostatectomy through machine learning

Yan Gu1,2,3,4#, Xiaozeng Lin1,2,3,4#, Anil Kapoor2,3,5, Wenjuan Mei1,2,3,4,6, Damu Tang2,3,4

1Department of Medicine, McMaster University, Hamilton, Ontario, Canada; 2The Research Institute of St Joe’s Hamilton, Hamilton, Ontario, Canada; 3Hamilton Urologic Oncology Research Center (HUORC), Hamilton, Ontario, Canada; 4The Hamilton Center for Kidney Research, St. Joseph’s Hospital, Hamilton, Ontario, Canada; 5Department of Surgery, McMaster University, Hamilton, Ontario, Canada; 6Department of Nephrology, the First Affiliated Hospital of Nanchang University, Nanchang 330006, China

#These authors contributed equally to this work.

Correspondence to: Damu Tang. T3310, St. Joseph’s Hospital, 50 Charlton Ave East, Hamilton, Ontario, Canada. Email: damut@mcmaster.ca.

Provenance: This is an invited Editorial commissioned by the Section Editor Xiao Li (Department of Urologic Surgery, The Affiliated Cancer Hospital of Jiangsu Province of Nanjing Medical University, Nanjing, China).

Comment on: Wong NC, Lam C, Patterson L, et al. Use of machine learning to predict early biochemical recurrence after robot-assisted prostatectomy. BJU Int 2018. [Epub ahead of print].


Received: 06 September 2018; Accepted: 13 September 2018; Published: 28 September 2018.

doi: 10.21037/amj.2018.09.06


In the developed world, prostate cancer (PC) is the most common malignancy in men and the second major cause of cancer deaths (1). The disease progresses from high grade prostatic intra-epithelial neoplasia (HGPIN) to carcinoma. Primary PCs can be managed with a variety of options including watchful waiting, radical prostatectomy (RP), and radiation. The choice of different management plans depends on disease severity, patient age and preference. PCs are graded with Gleason score (GS) and GS-based World Health Organization (WHO) PC grading system (WHO grade group 1–5) or International Society of Urological Pathology (ISUP) grade (2-4). The disease evolves with a high degree of disparity. While GS6/WHO grade group 1 tumors are generally indolent, higher grade PCs are at risk of progression. Approximately 30% of tumors will relapse following RP; recurrent PCs are commonly detected by a rise in serum prostate-specific antigen (PSA), a process that is characterized as biochemical recurrence (BCR) (5). BCR is a major progression of PC (6): approximately 40% of PCs with BCR will progress to metastatic disease, which is mainly treated with androgen-deprivation therapy (ADT). This treatment is generally palliative, as progression to metastatic castration resistant prostate cancer (mCRPC) inevitably occurs (7). In the last decades, numerous agents have been developed to treat mCRPCs, such as taxane-based chemotherapy and androgen receptor (AR)-targeting therapy involving either abiraterone or enzalutamide (8,9). These therapies modestly extend patient’s overall survival (OS) for a few months before resistance develops (10). Under the current situation, one option to improve patient management is through intervention at the BCR stage, which will likely be more effective than treatment of metastatic PCs. However, this strategy will require effective prediction of BCR risk.

The importance of stratification of PCs with elevated risk of recurrence has been well recognized; there are 2,294 publications listed on PubMed on September 2, 2018 under the search term of “Prostate cancer, biochemical recurrence, and biomarkers”. This extensive research effort has yielded two commercially available multi-gene (mRNA) panels, Oncotype DX (Genomic Prostate Score/GPS) and Prolaris [cell cycle progression (CCP)]. The 17-gene Oncotype DX and the 31-gene Prolaris both improve the prediction of PCs at risk of recurrence at time of diagnosis (11-15) and after RP (16,17). Recently, a 15-gene signature (SigMuc1NW) had been reported that robustly predicts BCR following prostatectomy (18). Even with these developments, there remains a clear need to improve the current stratification of PCs with high risk of recurrence.

To meet this need, Wong et al. reported an attractive system to assess the risk of early BCR in a group patient treated with robot-assisted prostatectomy (n=338) (19). This was a single center-based investigation using clinical materials of 338 patients who have been treated by robot-assisted prostatectomy for local PC by a single surgeon during May 2012 to Dec 2015. A group of 19 clinical variables have been collected (Table 1). PC is in general a slowly progressive disease; PC evolves with a high level of disparity. BCR develops from several months to years after RP (18,20). With their cohort composed of patients with modest length of follow-up and relatively small size (Figure 1), the authors focused on the prediction of early BCR that was developed within one year after RP. In their cohort (n=338), 25 patients had BCR (Figure 1) (19). Wong et al. have randomly divided the cohort into a training and testing population in a 7:3 ratio (19), and trained the training set for classification of early BCR (Figure 1). Four statistical machine learning systems [K-nearest neighbors, random forest, logistic regression, and Cox proportional hazards (PH) regression] were used to model the contributions of the 19 clinical variables (Table 1) to early BCR. The resultant models were than analyzed using the testing population (Figure 1). The models produced by K-nearest neighbors, random forest, and logistic regression were quite robust in the discrimination of BCR with the respective area under curve (AUC) value of 0.903, 0.924, and 0.94 (Figure 1). In comparison, the Cox PH model stratified early BCR with the AUC value of 0.865 (Figure 1) (19).

Table 1
Table 1 Baseline clinical variables and their association with BCR1
Full table
Figure 1 Study design. Patients with primary PC treated with robot-assisted prostatectomy with BCR (n=25) and without BCR (n=313) are shown. Patients were randomly divided into a training and testing set at the indicated ratio. The training population was modeled to predict BCR on the 19 baseline clinical variables (Table 1) using the indicated machine learning tools. The resultant models discriminate BCR with the indicated values of AUC. PC, prostate cancer; BCR, biochemical recurrence; AUC, area under curve.

Machine learning has been rising as a powerful tool in classification and regression modeling of high dimensional variables in cancer recurrence and OS. For insistence, 150 clinical baseline variables have been modeled for prediction of OS in patients with mCRPC (21,22) and more than 600 differentially expressed genes have been selected to stratify BCR (18). The majority of these machine learning efforts were based on the Cox PH model. By formulating the response as with or without early BCR and ignoring the time-to-event component of BCR, Wong et al. used more flexible models K-nearest neighbors and random forest to model importance of the 19 baseline clinical variables (Table 1) in early BCR development (19). Both models require no hypothesis and no consideration of data distributions, are quite robust, and do not commonly produce overfitting models. In this regard, through simplification of PC recurrence by focusing on early BCR, both models and logistic regression can be robust. However, we should be cautious to conclude these models as superior to those of Cox PH-based; recurrence occurred at the first year is clearly not the same from those developed after 5 years. Even with recurrence within the first year, PCs that relapse within 6 months are likely different from those with recurrence progression at 12 months. Nonetheless, it can be envisaged that the kinetic issue can be minimized though more detailed division of recurrence timespan, for example 6, 12, 18 months and so on, following the growth in size and complexity of their patient population. Indeed, Wong et al. have proposed to expand their study with more patients and including additional clinical baseline factors. Additional patients can be recruited from other surgeons in their Institute. It will be more appealing if multiple centers can be involved in the future. The robustness of their model in the prediction of early BCR is calling this effort. Such efforts may lead to the generation of effective clinical systems to predict BCR. With today’s computing power and machine learning capacity, the days may not be too far for doctors to enter a set of baseline clinical variables at their terminals to come out with accurate prediction of PC recurrence. Clearly, the same principle can be applied to other clinical outcomes such as OS as well as other cancer types.

Despite the great potential discussed, this research is still at an early stage. One major limitation is the small sample size; the issue was compounded considering the random division of 25 recurrent tumors in a 7:3 ratio into a training and testing population. With the limited number of recurrent tumors in both the training and testing populations, the accuracy of the models will need to be confirmed using larger patient populations in the future.

It may shed light on the models with respect to their utility if more details of the model were provided. The models were built on 19 baseline clinical variables (Table 1), including those with well-established association with PC recurrence, such as Gleason scores, percentage of tumor involvement, extracapsular extension, seminal vesicle invasion, margin status, T-stage, and nodal involvement. Were all the 19 clinical variables essential or did those established clinical characteristics contribute more to the prediction? The feature (variable) importance derived from random forest modeling should provide an indication on this issue should this data be reported. Furthermore, lactate dehydrogenase (LDH), albumin, hemoglobin, alkaline phosphatase (ALP) (23) along with a set of clinical factors related to kidney function, haematology, and others (21,22) display predictive value toward OS in patients with mCRPC. Should these factors be relevant to the author’s models?

Progression to BCR is regulated by molecular networks; the complexity of these networks is clearly reflected by the number of publications (n=2,294) listed in PubMed (September 2, 2018) on this issue. The molecular alterations may also need to be included in the models reported in this study (19). A good starting point is to consider the genes reported in Oncotype DX (Genomic Prostate Score/GPS) (11-15), Prolaris (cell cycle progression/CCP) (16,17), and SigMuc1NW (18).


Acknowledgements

Funding: D.T. is supported by an Award from Teresa Cascioli Charitable Foundation Research Award in Women’s Health and grants from Canadian Cancer Society (grant #: 319412) and Cancer Research Society. Y.G. is supported by Studentship provided by Ontario Graduate Scholarships and Research Institute of St Joe’s Hamilton.


Footnote

Conflicts of Interest: The authors have no conflicts of interest to declare.


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doi: 10.21037/amj.2018.09.06
Cite this article as: Gu Y, Lin X, Kapoor A, Mei W, Tang D. Attempt to predict early recurrence of prostate cancer following prostatectomy through machine learning. AME Med J 2018;3:96.