Machine Learning to Predict the Individual Risk of Treatment-Relevant Toxicity for Patients With Breast Cancer Undergoing Neoadjuvant Systemic Treatment.

purposeToxicity to systemic cancer treatment represents a major anxiety for patients and a challenge to treatment plans. We aimed to develop machine learning algorithms for the upfront prediction of an individual's risk of experiencing treatment-relevant toxicity during the course of treatment.

methodsClinical records were retrieved from a single-center, consecutive cohort of patients who underwent neoadjuvant treatment for early breast cancer. We developed and validated machine learning algorithms to predict grade 3 or 4 toxicity (anemia, neutropenia, deviation of liver enzymes, nephrotoxicity, thrombopenia, electrolyte disturbance, or neuropathy). We used 10-fold cross-validation to develop two algorithms (logistic regression with elastic net penalty [GLM] and support vector machines [SVMs]). Algorithm predictions were compared with documented toxicity events and diagnostic performance was evaluated via area under the curve (AUROC).

resultsA total of 590 patients were identified, 432 in the development set and 158 in the validation set. The median age was 51 years, and 55.8% (329 of 590) experienced grade 3 or 4 toxicity. The performance improved significantly when adding referenced treatment information (referenced regimen, referenced summation dose intensity product) in addition to patient and tumor variables: GLM AUROC 0.59 versus 0.75,

conclusionThe individual risk of treatment-relevant toxicity can be predicted using machine learning algorithms. We demonstrate a promising way to improve efficacy and facilitate proactive toxicity management of systemic cancer treatment.