Machine learning model to forecast patient availability for oncology clinical trials

This was a retrospective study based on data drawn from the ConcertAI Oncology Research database, enriched by key variables derived from unstructured data. Line of therapy was derived from expert rules applied to structured medications data. Our cohort consisted of patients with confirmed diagnosis of solid cancers without a second malignancy. Patient follow-up period started on the date of diagnosis of metastasis and ended on the earlier of last date of activity / date of death. Random observation date was set between start and end dates to label patients. Patients administered a new treatment after the random observation date were labelled evet, else censored (no new treatment began). Label date is start of new treatment and end dates for event & censored cases respectively. The time to event (TTE) was defined as the duration between the random observation and the label dates. In the event cases, this duration is the time to next treatment (TTNT). Over 2000 features based on variables broadly grouped as tumor-specific biomarkers (PTEN, KRAS, etc.), ECOG, staging, disease status, medications, and imaging (evidence of image, not report) were employed to build multiple ML models. Temporal validation of the models was performed by setting up a simulated index date and predicting the probability of patient beginning a new treatment within 60 days of the simulated index date. Patients receiving new treatment within the 60 days were true positives.

TTE models were trained on a cohort comprised of 90K patients across 12 cancer indications with 54% patients starting a new treatment. Median age and overall survival (OS) of the cohort was 73 years and 703 days respectively. Temporal validation was performed on 25K patients with similar demographics/OS and 58% patients starting new treatment. Multiple ML methods were used to train models, with boosted gradient model demonstrating highest c-index of 0.73 based on 87 features. Temporal validation demonstrated AUC and weighted F1 of 87% and 67% respectively. True positive cases were assigned high predicted probability in 75% of the cases

AI models supporting 12 solid cancer indications accurately predicted patient availability. These models can be integrated into real-time clinical workflows alongside patient eligibility models to provide clinicians and patients visibility in ascertaining a patient’s likelihood of being eligible for a clinical trial.