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    • Assessing Algorithmic Fairness with a Multimodal Artificial Intelligence Model in Men of African and Non-African Origin on NRG Oncology Prostate Cancer Phase III Trials.

    Mack Roach, Jingbin Zhang， Phuoc T Tran,Felix Y Feng

    JCO clinical cancer informatics（2025）

    UCSF Medical Center | Artera | University of California San Francisco | Northwestern University | University of Michigan Comprehensive Cancer Center | Hematology-Oncology Medical Group of Fresno Inc | Horizon Health Network-Saint John Regional Hospital | Ingalls Memorial Hospital | CHUM-Centre Hospitalier de l'Universite de Montreal | Nova Scotia Cancer CentreNova Scotia HealthQEII Health Sciences Centre | VA Boston Healthcare System | University of Missouri-Ellis Fischel | The Research Institute of the McGill University Health Centre (MUHC) | Keck School of Medicine of USC | Cedars-Sinai Medical Center | University Hospitals Seidman Cancer Center | NRG Oncology Statistics and Data Management Center | Johns Hopkins UniversitySidney Kimmel Cancer Center

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    Abstract
    PURPOSE:Artificial intelligence (AI) tools could improve clinical decision making or exacerbate inequities because of bias. African American (AA) men reportedly have a worse prognosis for prostate cancer (PCa) and are underrepresented in the development genomic biomarkers. We assess the generalizability of tools developed using a multimodal AI (MMAI) deep learning system using digital histopathology and clinical data from NRG/Radiation Therapy Oncology Group PCa trials across racial subgroups. METHODS:In total, 5,708 patients from five randomized phase III trials were included. Two MMAI algorithms were evaluated: (1) the distant metastasis (DM) MMAI model optimized to predict risk of DM, and (2) the PCa-specific mortality (PCSM) MMAI model optimized to focus on prediction death in the presence of DM (DDM). The prognostic performance of the MMAI algorithms was evaluated in AA and non-AA subgroups using time to DM (primary end point) and time to DDM (secondary end point). Exploratory end points included time to biochemical failure and overall survival with Fine-Gray or Cox proportional hazards models. Cumulative incidence estimates were computed for time-to-event end points and compared using Gray's test. RESULTS:There were 948 (16.6%) AA patients, 4,731 non-AA patients (82.9%), and 29 (0.5%) patients with unknown or missing race status. The DM-MMAI algorithm showed a strong prognostic signal for DM in the AA (subdistribution hazard ratio [sHR], 1.2 [95% CI, 1.0 to 1.3]; P = .007) and non-AA subgroups (sHR, 1.4 [95% CI, 1.3 to 1.5]; P < .001). Similarly, the PCSM-MMAI score showed a strong prognostic signal for DDM in both AA (sHR, 1.3 [95% CI, 1.1 to 1.5]; P = .001) and non-AA subgroups (sHR, 1.5 [95% CI, 1.4 to 1.6]; P < .001), with similar distributions of risk. CONCLUSION:Using cooperative group data sets with a racially diverse population, the MMAI algorithm performed well across racial subgroups without evidence of algorithmic bias.
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    【要点】：本研究评估了基于多模态人工智能（MMAI）的算法在预测非洲和非非洲血统男性前列腺癌患者的预后中的公平性，发现MMAI算法在两个种族亚组中表现良好且未见算法偏见。
    

    【方法】：研究使用数字病理学和临床数据，基于NRG/Radiation Therapy Oncology Group前列腺癌随机三期试验中的5708名患者，评估了两种MMAI算法：一种针对远处转移风险预测，另一种针对在远处转移存在下的前列腺癌特异性死亡率预测。
    

    【实验】：研究纳入了948名非洲血统患者和4731名非非洲血统患者，利用Fine-Gray或Cox比例风险模型评估了算法在预测两个亚组患者的远处转移时间和远处转移存在下的死亡时间方面的预后性能，并通过Gray's test比较了事件发生时间的累积发生率估计。结果显示，MMAI算法在非洲和非非洲亚组中对远处转移和前列腺癌特异性死亡均显示出强烈的预后信号。