Abstract B059: Cancer Recurrence and Anxiety/Depression: High Accuracy Artificial Intelligence Predictive Modeling

Abstract A. Purpose: Predicting recurrence and survival in individual patients with localized breast cancer will identify circumstance-specific events and interventions that either predispose or prevent recurrence. B. Methods and Procedures: We used the SEER-Medicare linked dataset to investigate women diagnosed with stage I-IV breast cancer who were enrolled at 65 years or older for age eligibility. We collected time-fixed data on patients and cancers at diagnosis and time-varying covariates after diagnosis (e.g., treatments, comorbidities, age, frailty index, adverse events, anxiety, and depression). Our longitudinal data comprised hundreds of thousands of patients with hundreds of millions of records spanning over 20 years. We identified the recurrence of stage I-III disease by documenting new diagnoses of recurrent, contralateral, new chemo-, bio, hormone, or radiotherapy after 4 months following completion of initial therapy, and for all patients, recorded date of death. We have previously demonstrated that combining time-varying with time-fixed covariates into DL modeling of survival for individual patients at all stages results in significant improvements in the model's prediction accuracy (from around 65% to >90%) for stage I-IV patients and considers the impact of subsequent lines of potential individually tailored therapy on survival of stage IV patients. In an earlier study, we extended four deep learning models (DL) to deal with combined time-fixed and time-varying patient covariates to predict survival for individual patients at all stages. The results showed improvements in the model's prediction accuracy (from around 65% to >90%). In this study, we applied four deep learning models (DL) to predict individual patients' recurrence-free survival probabilities in different patient and cancer categories distributed according to race, stage, and hormone receptor status. Results: The predictive accuracy of the models was greater than 95%. Patient recurrence-free survival curves generated by the DL models reveal a vast variability in predicted survival within each broad patient grouping (stage, race, hormone status). Our results show that approximately 36% of the patient population had a diagnosis of anxiety and/or depression, with a higher prevalence in White (W) patients. Our results also demonstrate that the adrenergic stressors, anxiety and depression, previously suspected factors in recurrence, increase the population recurrence rate of dormant breast cancer by 27%. Conclusions: Our modeling confirms the exceptional circumstance-specific variability in interpatient recurrence and survival probabilities. It demonstrates the capacity to model individual patient recurrence and the impact of anxiety/depression as proof of the principle of life events that can affect recurrence from localized breast cancer. The application of these models will serve as a vital tool for testing relevant hypotheses for recurrence-inducing or recurrence-preventing events in individual circumstances that can be tested in clinical trials with a high likelihood of success. Citation Format: Nabil R. Adam, Tarek R. Adam, Robert Wieder. Cancer Recurrence and Anxiety/Depression: High Accuracy Artificial Intelligence Predictive Modeling [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Artificial Intelligence and Machine Learning; 2025 Jul 10-12; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(13_Suppl):Abstract nr B059.

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