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Research Domains
Biomedical and Clinical Sciences
Oncology and Carcinogenesis
Predictive and prognostic markers
Digital Pathology-based Artificial Intelligence Biomarker Validation in Metastatic Prostate Cancer

Digital Pathology-based Artificial Intelligence Biomarker Validation in Metastatic Prostate Cancer

Mark C Markowski ¹, Yi Ren ², Meghan Tierney ², Trevor J Royce ², Rikiya Yamashita ², Danielle Croucher ², Huei-Chung Huang ², Tamara Todorovic ², Emmalyn Chen ², Timothy N Showalter ², Michael A Carducci ¹, Yu-Hui Chen ³, Glenn Liu ⁴, Charles T A Parker ⁵, Andre Esteva ², Felix Y Feng ⁶, Gerhardt Attard ⁵, Christopher J Sweeney ⁷

Mark C Markowski ¹, Yi Ren ², Meghan Tierney ²

¹John Hopkins University, Baltimore, MD, USA.
²Artera Inc, Los Altos, CA, USA.
³Dana Farber Cancer Institute, Boston, MA, USA.
⁴Carbone Cancer Center, University of Wisconsin, Madison, WI, USA.
⁵UCL Cancer Institute, University College London, London, UK.
⁶University of California-San Francisco, San Francisco, CA, USA.
⁷South Australian Immunogenomics Cancer Institute, University of Adelaide, Adelaide, Australia.

⁰John Hopkins University, Baltimore, MD, USA.

European Urology Oncology +

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December 12, 2024

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December 12, 2024

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View abstract on PubMed

Summary

This summary is machine-generated.

A new multimodal artificial intelligence (MMAI) biomarker shows prognostic ability in metastatic hormone-sensitive prostate cancer (mHSPC). This AI tool can identify patients at higher risk for worse outcomes, aiding treatment decisions.

Area Of Science

Oncology
Artificial Intelligence in Medicine
Biomarker Discovery

Background

Personalized medicine for metastatic hormone-sensitive prostate cancer (mHSPC) requires prognostic and predictive biomarkers.
Treatment options and clinical subgroups in mHSPC necessitate tailored therapeutic strategies.

Purpose Of The Study

To evaluate a multimodal artificial intelligence (MMAI) biomarker for its prognostic capability in mHSPC.
To assess the association of MMAI scores with overall survival (OS), clinical progression (CP), and castration-resistant prostate cancer (CRPC).

Main Methods

Utilized data from the phase 3 CHAARTED trial, including digital histopathology images and clinical variables from 456 patients with mHSPC.
Generated MMAI scores and assessed their association with OS, CP, and CRPC using Cox proportional-hazards and Fine-Gray models.

Main Results

The MMAI biomarker demonstrated significant prognostic ability for OS, CP, and CRPC.
Patients classified as MMAI-high risk had significantly worse 5-year OS (39%) compared to MMAI-intermediate (58%) and MMAI-low (83%) groups.
The MMAI score remained prognostic across different clinical subgroups and after adjusting for treatment and clinical variables.

Conclusions

The MMAI biomarker is a valuable prognostic tool for patients with mHSPC, irrespective of clinical subgroup or treatment.
Further research into MMAI biomarkers is warranted for advanced prostate cancer management.
AI-driven tools show potential in enhancing clinical decision-making for prostate cancer patients.

Keywords:

Artificial intelligence Biomarker Digital pathology Prognosis Prostate cancer

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