# Machine Learning Predicts Outcomes of Phase III Clinical Trials for Prostate Cancer
###### Abstract  
The ability to predict the individual outcomes of clinical trials could support the development of tools for precision medicine and improve the efficiency of clinical-stage drug development. However, there are no published attempts to predict individual outcomes of clinical trials for cancer. We used machine learning (ML) to predict individual responses to a two-year course of bicaltamide, a standard treatment for prostate cancer, based on data from three Phase III clinical trials (\(n=3653\)). We developed models that used a merged dataset from all three studies. The best performing models using merged data from all three studies had an accuracy of 76%. The performance of these models was confirmed by further modeling using a merged dataset from two of the three studies, and a separate study for testing. Together, our results indicate the feasibility of ML-based tools for predicting cancer treatment outcomes, with implications for precision oncology and improving the efficiency of clinical-stage drug development. clinical trials; machine learning; classification; prostate cancer; precision medicine; drug development +
Footnote †: journal: _Algorithms_  
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In 2014, IBM launched 'Watson for Oncology,' which aimed to use ML to recommend treatment plans for cancer, based on combined inputs from research, a patient's clinical notes, and the clinician [11]. However, this project has so far failed to deliver the kinds of commercial products which had been envisioned [12]. Other reports have used ML to predict treatment outcomes for cancer. One study used ML to predict patient survival based on microscopy images of cancer biopsy tissue and genomic markers [13]. Another study used ML to predict response to treatment in patients with cervical and head cancers based on PET images [14]. Despite such advances, there are currently no ML-based tools approved by regulators. A significant limitation of these kinds of exploratory approaches to ML-based tools for clinical practice is that they tend to rely on types of data that are expensive to collect and may require specialist skills to analyze (e.g., genomics or MRI/PET imaging). This limitation can be a barrier to translating systems into tools for routine clinical practice. One possible way to address this is to base such systems on data from Phase III clinical trials: studies that provide the pivotal data for regulators to assess whether a new drug should be commercially approved. Phase III clinical trials are typically large enough for ML (usually 1000+ subjects) and include a wide range of data (e.g., demographic, clinical, and biochemical), which can be easily stored in tabular format. Moreover, the current trend is towards making clinical trial datasets publicly available. Thus, Phase III clinical trial data may be a good source for developing practical ML-based tools for precision medicine. This approach is relatively untried: a literature search revealed only one prior study that used clinical trial data to predict treatment responses [15]. This study used ML to predict responses to an anti-depressant after 12 weeks. This was an important first step, however the accuracy level was modest (65%). It is possible that, with different data, and with a different approach to modeling, a level of accuracy could be achieved which could lead to the development of tools for real-world clinical practice. In addition to aiding precision medicine, the ability to predict individual treatment responses in clinical trials could improve the efficiency of clinical-stage drug development. This is an important issue because the average development cost of a new drug has become extremely expensive in recent decades, estimated at $2.6 billion (2013 U.S. dollars; [16]). In addition, investigational drugs in clinical trials have a significant rate of rejection by regulators [17]. Together, these factors represent a significant threat to existing models of drug development.