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PurposeEarly discontinuation affects over one-third of patients enrolled in early-phase oncology clinical trials. Early discontinuation is deleterious both for the patient and for the study, by inflating its duration and associated costs. We aimed at predicting the successful screening and dose-limiting toxicity period completion (SSD) from automatic analysis of consultation reports.Materials and methodsWe retrieved the consultation reports of patients included in phase I and/or phase II oncology trials for any tumor type at Gustave Roussy, France. We designed a pre-processing pipeline that transformed free-text into numerical vectors and gathered them into semantic clusters. These document-based semantic vectors were then fed into a machine learning model that we trained to output a binary prediction of SSD status.ResultsBetween September, 2012 and July, 2020, 56,924 consultation reports were used to build the dictionary, and 1,858 phase I/II inclusion reports were used to train (75%), validate (15%) and test (15%) a Random Forest model. Pre-processing could efficiently cluster words with semantic proximity. On the unseen test cohort of 264 consultation reports, the performances of the model reached: F1 score 0.80, recall 0.81 and AUC 0.88. Using this model, we could have reduced the screen fail rate (including DLT period) from 39.8% to 12.8% (RR=0.322, 95%CI[0.209-0.498], pConclusionMachine learning with semantic conservation is a promising tool to assist physicians in selecting patients prone to achieve SSD in early-phase oncology clinical trials. |
