We present the first machine learning application to dynamic patient risk prediction and clustering, with full utilization of time-varying data, to improve personalized follow-up in early prostate cancer patients, and illustrate its utility in a clinical demonstrator web app
Predicting risk in a dynamic setting
The human body is a dynamic system, with innumerable biochemical reactions occurring continually, as well as high-level changes like growth and development, and of course individual actions, behaviors, life events and so on unfolding over time. It follows that a longitudinal (time-varying) perspective is therefore required to accurately model risks we face – especially so for a complex disease like cancer.
Despite this, many models and guidelines for disease progression and risk prediction in clinical use today rely primarily on baseline (fixed) characteristics of a patient , . It would be of great value to patients and clinicians to see progress in development of more powerful and flexible medical models, which incorporate time-varying patient data, especially since more such high-quality data is being collected. With this in mind, we introduce a novel, machine learning-based model for risk prediction and patient clustering in the setting of prostate cancer active surveillance.
Modelling prostate cancer progression
Prostate cancer is the most common male malignancy in the Western world ; while its prevalence is high, mortality is relatively low. However, it is a diverse disease known to take varied clinical trajectories in different patients . Around one in five men newly diagnosed are now placed under active surveillance (AS) , that is, the patient’s condition is closely monitored but no treatment is given unless tests indicate the condition is worsening.
We applied our model to a dataset of 585 men placed on AS with early prostate cancer, whose condition was categorized as Cambridge Prognostic Group (CPG) 1 or 2. Patient data consisted of baseline characteristics (age, ethnicity, family history etc.), but also, crucially, of the longitudinal measurements, such as repeat Prostate Specific Antigen (PSA) tests, multi-parametric Magnetic Resonance Imaging (MRI), as well as repeat prostate biopsies.
The goal of our model was twofold:
- To dynamically (that is, over time) estimate the risk of an adverse event: the progression of the patient’s condition to the more severe CPG3 category (or higher).
- To dynamically allocate each patient to clusters, corresponding to different levels of risk.
The data flow and an illustration of the prediction and clustering set-up is clarified in Figure 1.
Why and how is this useful? First, dynamic risk prediction allows for a “real-time” view of the patient’s condition, as whenever a new measurement is added to the patient history, the prediction is updated to incorporate this information. Second, the patient’s risk cluster assignment, which too, changes over time, provides a high-level indication of the disease trajectory. In short, such a model significantly enriches the consultation and planning management experience for both the patient and the clinician.
Interactive clinical demonstrator
To illustrate the potential utility of our work, we provide an interactive web app, demonstrating a possible future clinical platform, which we encourage the reader to try out. Two illustrative patients “Case A” (lower risk) and “Case B” (higher risk) are presented. The app consists of three tabs:
- “New Observation”, where the user can enter new measurements, such as PSA or biopsy information, and observe the impact on the risk prediction curve and cluster assignments.
- “Historic Risk”, where the estimated risk and cluster assignments, computed after each new measurement is taken, are shown.
- “Cluster Space”, where the patient’s trajectory across the risk clusters over time can be seen.
Figure 2 gives an overview of this demonstrator.
How does it work?
Our model is powered by cutting-edge machine learning (deep learning), a data-driven pattern recognition methodology. As such, it isn’t restricted by assumptions and constraints of traditional statistical methods. In particular, we term our predictive model Dynamic-DeepHit-Lite (DDHL), as it is derived from Dynamic-DeepHit . This makes use of a recurrent neural network, with the risk estimate computed using the methods from survival analysis. In turn, the temporal cluster assignment component, which “wraps around” this predictor, uses the Actor-Critic approach, and is thus termed Actor-Critic Temporal Predictive Clustering (AC-TPC). This powerful method of clustering is first described and then further enhanced in these innovative papers: , . For those visually oriented, and for the machine learners, Figure 3 shows a block diagram of the model.
We compare the prediction performance of our model with two commonly used methods: Cox proportional hazards with the baseline data, and a landmarking version of the Cox model at three- and five-year time points from the start of active surveillance. We use the concordance index (C-index) to evaluate the discriminative power of the model – the ability to correctly rank the individual risk scores. We find that the performance of DDHL is comparable with both Cox and landmarking Cox when using the baseline data alone, but as more longitudinal data is incorporated, DDHL significantly improves over the others: at the five-year time point, the C-index was 0.82 (± 0.08) for DDHL, 0.75 (± 0.08) for Cox and 0.73 (± 0.09) for landmarking Cox. Model calibration was good across all models tested.
We then compare the discriminative performance of the four clusters discovered by AC-TPC with four clusters of the Canary-PASS risk stratification method , on our dataset. Here again, we find that AC-TPC performance is superior, with the C-index of 0.92 vs 0.79. Another advantage of our temporal clustering approach is that it provides insights into the disease trajectory on the patient and population levels, both unpacked in Figure 4. In the left panel, two example patients’ trajectories over time are illustrated in the “cluster space” (PCA projection is used to show this in two dimensions), with clusters labelled 1 to 4 from lowest to highest risk. We observe that Patient A deteriorates over time, moving from cluster 3 to 4, while Patient B improves, transitioning from cluster 2 to 1. The right panel, in turn, displays the population-level dynamics in a form of a transition diagram: the probability of any patient staying in a particular cluster vs moving to an adjacent cluster are made clear.
We present the first machine learning application to dynamic risk prediction and temporal clustering on continuous data to inform personalized follow-up in prostate cancer active surveillance patients. We demonstrate that the algorithm outperforms standard statistical techniques and improves its predictive power over time. Finally, we illustrate how it can be developed into a clinical tool that can be used in practice.
Developing machine learning algorithms for dynamic estimation of progression during active surveillance for prostate cancer
Active Surveillance (AS) for prostate cancer is a management option that continually monitors early disease and considers intervention if progression occurs. A robust method to incorporate “live” updates of progression risk during follow-up has hitherto been lacking.
To address this, we developed a deep learning-based individualized longitudinal survival model using Dynamic-DeepHit-Lite (DDHL) that learns data-driven distribution of time-to-event outcomes. Further refining outputs, we used a reinforcement learning approach (Actor-Critic) for temporal predictive clustering (AC-TPC) to discover groups with similar time-to-event outcomes to support clinical utility.
We applied these methods to data from 585 men on AS with longitudinal and comprehensive follow-up (median 4.4 years). Time-dependent C-indices and Brier scores were calculated and compared to Cox regression and landmarking methods. Both Cox and DDHL models including only baseline variables showed comparable C-indices but the DDHL model performance improved with additional follow-up data. With 3 years of data collection and 3 years follow-up the DDHL model had a C-index of 0.79 (± 0.11) compared to 0.70 (± 0.15) for landmarking Cox and 0.67 (± 0.09) for baseline Cox only. Model calibration was good across all models tested. The AC-TPC method further discovered 4 distinct outcome-related temporal clusters with distinct progression trajectories. Those in the lowest risk cluster had negligible progression risk while those in the highest cluster had a 50% risk of progression by 5 years.
In summary we report a novel machine learning approach to inform personalised follow-up during active surveillance which improves predictive power with increasing data input over time.
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