In this JCO Precision Oncology Article Insights episode, Natalie DelRocco summarizes "Digital Pathology–Based Multimodal Artificial Intelligence Scores and Outcomes in a Randomized Phase III Trial in Men With Nonmetastatic Castration-Resistant Prostate Cancer" by Felix Y. Feng, et al published January 31, 2025. Come back for the next episode where JCO Precision Oncology Conversations host, Dr. Rafeh Naqash interviews the author of the JCO PO article discussed, Dr. Tim Showalter.

TRANSCRIPT

Natalie DelRocco: Hello and welcome to JCO Precision Oncology Article Insights. I'm your host Natalie Del Rocco. Today, we'll be discussing the article, “Digital Pathology-Based Multimodal Artificial Intelligence Scores and Outcomes in a Randomized Phase III Trial in Men With Nonmetastatic Castration-Resistant Prostate Cancer.” We will also be discussing the accompanying editorial, “Leveraging Artificial Intelligence to Improve Risk Stratification in Nonmetastatic Castration-Resistant Prostate Cancer.” So, we're going to start by summarizing the original report and then we'll jump into a few of the high-level interpretations that were supplied by the editorial.  

The original report by Feng et. al. describes the application of multimodal artificial intelligence to data collected on a nonmetastatic castration-resistant prostate cancer. We will call this disease moving forward NMCRPC, a Clinical Trial. So, we're looking at data from an NMCRPC clinical trial. The SPARTAN trial was a randomized phase three trial and this study compared metastasis-free survival as the primary endpoint for those treated with traditional androgen deprivation therapy or ADT to those treated with androgen deprivation therapy plus apalutamide. Other secondary endpoints included progression-free survival and overall survival, but the primary endpoint there was metastasis-free survival or MFS. This study found that the addition of apalutamide resulted in a significantly longer median metastasis-free survival compared to androgen deprivation therapy alone. And we should note that this is a double-blind placebo-controlled trial. In the overall study, 1,207 patients participated and over the course of this study histopathology slides were collected and they were digitized for future use. And that future use is what we are going to be discussing today. 

The authors do note that there are currently no good biomarkers for use in NMCRPC. The authors seem to be inspired by the ArteraAI prostate test, which was a recent application of multimodal artificial intelligence models. But in localized prostate cancer as opposed to NMCRPC, the authors constructed a multimodal artificial intelligence model or an MMAI model. They applied this to the SPARTAN trial with the intention of developing a risk score that could be used for risk stratification in NMCRPC. And we should note here that multimodal artificial intelligence or MMAI is a broad class of artificial intelligence models, and they can analyze different types of data at one time, hence the term multimodal. So in this example, the author's primary data source of interest were those digitized histopathology images because histopathology tells you a lot about NMCRPC. The authors though also wanted their model to consider traditional clinical factors that are known to be prognostic such as Gleason score, tumor stage, PSA level, and age. So those two different types of data, those histopathology images and that traditional clinical data are the two different types of data that make this model multimodal. So we should note here importantly, after dropping missing data, 420 patients contribute to this model, the MMAI model. 

The authors generate a risk score from this MMAI model and they categorize that risk score into low, intermediate, and high risk groups using clinical knowledge. The authors found in their results that an increase in this MMAI risk score was associated with an increased hazard of metastasis-free survival event with a hazard ratio from a Cox proportional hazards model of 1.72. To summarize how the authors arrived here, they derived a risk score from this MMAI model which incorporates both imaging and regular data. They plugged this risk score into a Cox proportional hazards mode,l modeling metastasis-free survival and they found that an increase in that MMAI based risk score is associated with increased hazard of metastasis-free event with a hazard ratio of 1.72, which is quite large. Additionally, the risk score seemed to be associated with PFS2 and OS, which were two of the secondary endpoints from the SPARTAN clinical trial, though the effect sizes were more modest. Those are the highlights from the original report, the methods and the results. 

The accompanying editorial notes that both histopathology and Gleason score specifically are very critical to understanding prostate cancer, and Gleason score alone is not sufficient to summarize the complexity of the disease, although it is a well validated prognostic factor for prostate cancer. So this makes MMAI an excellent tool in the setting described by the authors. We have an existing prognostic factor that doesn't describe the entire picture of the disease by itself and so we can use those digitized histopathology slides to help bolster our understanding and provide the model more information. MMAI allows you to do this because it can take in different types of data. So that was the main conclusion of the editorial. 

They also summarize a number of recent validations of MMAI models in prostate cancer research, noting that it will be an important tool for risk stratification and has already been shown to outperform classical techniques. The editorial though does highlight a number of weaknesses of this paper, limitations and I think the most important one to highlight, and we touched on this earlier, is that 420 patients from the SPARTAN clinical trial contributed to the development of this MMAI score. That is a small proportion of the roughly 1200 patients that did participate in the SPARTAN clinical trial. So we have a small subgroup analysis that can be limiting and this model will need to be validated in a broader population in the future.

 

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