Owing to the expansion of treatment options for metastatic hormone-sensitive prostate cancer (mHSPC) and an appreciation of clinical subgroups with differential prognosis and treatment responses, prognostic and predictive biomarkers are needed to personalize care in this setting. Our aim was to evaluate a multimodal artificial intelligence (MMAI) biomarker for prognostic ability in mHSPC.
We used data from the phase 3 CHAARTED trial; 456/790 patients with mHSPC had evaluable digital histopathology images and requisite clinical variables to generate MMAI scores for inclusion in our analysis. We assessed the association of MMAI score with overall survival (OS), clinical progression (CP), and castration-resistant PC (CRPC) via univariable Cox proportional-hazards and Fine-Gray models.
In the analysis cohort, 370 patients (81.1%) were classified as MMAI-high and 86 (18.9%) as MMAI-intermediate/low risk. Estimated 5-yr OS was 39% for the MMAI-high, 58% for the MMAI-intermediate, and 83% for the MMAI-low groups (log-rank p < 0.001). The MMAI score was associated with OS (hazard ratio [HR] 1.51, 95% confidence interval [CI] 1.33-1.73; p < 0.001), CP (subdistribution HR 1.54, 95% CI 1.36-1.74; p < 0.001), and CRPC (subdistribution HR 1.63, 95% CI 1.45-1.83; p < 0.001). The proportion of MMAI-high cases was 50.0%, 83.7%, 66.7%, and 92.1% in the subgroups with low-volume metachronous (n = 74), low-volume synchronous (n = 80), high-volume metachronous (n = 48), and high-volume synchronous (n = 254) mHSPC, respectively. The MMAI biomarker remained prognostic after adjustment for treatment, volume status, and diagnosis stage.
Our findings show that the MMAI biomarker is prognostic for OS, CP, and CRPC among patients with mHSPC, regardless of clinical subgroup or treatment received. Further investigations of MMAI biomarkers in advanced PC are warranted.
We looked at the performance of an artificial intelligence (AI) tool that interprets images of samples of prostate cancer tissue in a group of men whose cancer had spread beyond the prostate. The AI tool was able to identify patients at higher risk of worse outcomes. These results show the potential benefit of AI tools in helping patients and their health care team in making treatment decisions.
European urology oncology. 2024 Dec 10 [Epub ahead of print]
Mark C Markowski, Yi Ren, Meghan Tierney, Trevor J Royce, Rikiya Yamashita, Danielle Croucher, Huei-Chung Huang, Tamara Todorovic, Emmalyn Chen, Timothy N Showalter, Michael A Carducci, Yu-Hui Chen, Glenn Liu, Charles T A Parker, Andre Esteva, Felix Y Feng, Gerhardt Attard, Christopher J Sweeney
John Hopkins University, Baltimore, MD, USA. Electronic address: ., Artera Inc, Los Altos, CA, USA., John Hopkins University, Baltimore, MD, USA., Dana Farber Cancer Institute, Boston, MA, USA., Carbone Cancer Center, University of Wisconsin, Madison, WI, USA., UCL Cancer Institute, University College London, London, UK., University of California-San Francisco, San Francisco, CA, USA., South Australian Immunogenomics Cancer Institute, University of Adelaide, Adelaide, Australia.