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Using Machine Learning to Predict GI Toxicity in Unresectable Pancreatic Cancer Patients Treated with Stereotactic Body Radiation Therapy

T Wu*, S Liauw , University of Chicago Hospitals, Chicago, IL


(Sunday, 7/29/2018) 3:00 PM - 6:00 PM

Room: Exhibit Hall

Purpose: We applied machine learning techniques to predict the occurrence of late GI toxicity among patients in a Phase I/II trial of SBRT for unresectable pancreatic cancer.

Methods: Dosimetric data for 15 patients who received 30-45Gy in 3 fractions were included (4 developed late Grade 3+ bowel toxicity). The tumor size, prescription dose, PTV V105%-V125%, and Duodenum D0.01-D5.0cc, V15-30Gy were selected as training features for machine learning using scikit-learn in Python. A feature selector was employed to rank the relative importances based on univariate statistics. Linear binary classifiers, i.e., k-nearest neighbour (kNN), logistic regression (LR) and support vector machine (SVM) models, were trained with a variety of leading features. Model hyper-parameters and selected number of features were cross validated by the area under the receiver operating characteristic curve (AUROC). The best model (greater AUROC and lower variance) was re-trained to derive a decision boundary where the severe toxicity probability was 0.5.

Results: The features with leading importances were PTV:V120%, Duodenum: V26Gy, GTV and PTV size, Duodenum: D0.1cc, followed by others. With the first three features, both LR and SVM models accurately predicted toxicity (mean±standard deviation AUROC 1.0±0.0). The AUROC decreased to 0.95±0.5 when 5+ features were used. Using single neighbor with 10 features was found best for kNN but the model underperformed (AUROC 0.86±0.14). In a 3D feature space, the re-trained LR model constructed a planar decision boundary that clearly separated patients with observed events.

Conclusion: Clinical data analysis with machine learning provided useful insights on the risk of GI toxicity in this small cohort of patients. As a supplement to other clinical knowledge, the learned model is potentially useful for identifying patients with high risks based their tumor size and treatment plan dose distributions.


Linear Regression Analysis, Statistical Analysis, Stereotactic Radiosurgery


TH- Dataset analysis/biomathematics: Machine learning techniques

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