A survival prediction model via interpretable machine learning for patients with oropharyngeal cancer following radiotherapy.

PURPOSE: To explore interpretable machine learning (ML) methods, with the hope of adding more prognosis value, for predicting survival for patients with Oropharyngeal-Cancer (OPC). METHODS: A cohort of 427 OPC patients (Training 341, Test 86) from TCIA database was analyzed. Radiomic features of gross-tumor-volume (GTV) extracted from planning CT using Pyradiomics, and HPV p16 status, etc. patient characteristics were considered as potential predictors. A multi-level dimension reduction algorithm consisting of Least-Absolute-Selection-Operator (Lasso) and Sequential-Floating-Backward-Selection (SFBS) was proposed to effectively remove redundant/irrelevant features. The interpretable model was constructed by quantifying the contribution of each feature to the Extreme-Gradient-Boosting (XGBoost) decision by Shapley-Additive-exPlanations (SHAP) algorithm. RESULTS: The Lasso-SFBS algorithm proposed in this study finally selected 14 features, and our prediction model achieved an area-under-ROC-curve (AUC) of 0.85 on the test dataset based on this feature set. The ranking of the contribution values calculated by SHAP shows that the top predictors that were most correlated with survival were ECOG performance status, wavelet-LLH_firstorder_Mean, chemotherapy, wavelet-LHL_glcm_InverseVariance, tumor size. Those patients who had chemotherapy, with positive HPV p16 status, and lower ECOG performance status, tended to have higher SHAP scores and longer survival; who had an older age at diagnosis, heavy drinking and smoking pack year history, tended to lower SHAP scores and shorter survival. CONCLUSION: We demonstrated predictive values of combined patient characteristics and imaging features for the overall survival of OPC patients. The multi-level dimension reduction algorithm can reliably identify the most plausible predictors that are mostly associated with overall survival. The interpretable patient-specific survival prediction model, capturing correlations of each predictor and clinical outcome, was developed to facilitate clinical decision-making for personalized treatment.

A planning and delivery study of a rotational IMRT technique with burst delivery.

PURPOSE: A novel rotational IMRT (rIMRT) technique using burst delivery (continuous gantry rotation with beam off during MLC repositioning) is investigated. The authors evaluate the plan quality and delivery efficiency and accuracy of this dynamic technique with a conventional flat 6 MV photon beam. METHODS: Burst-delivery rIMRT was implemented in a planning system and delivered with a 160-MLC linac. Ten rIMRT plans were generated for five anonymized patient cases encompassing head and neck, brain, prostate, and prone breast. All plans were analyzed retrospectively and not used for treatment. Among the varied plan parameters were the number of optimization points, number of arcs, gantry speed, and gantry angle range (alpha) over which the beam is turned on at each optimization point. Combined rotational/step-and-shoot rIMRT plans were also created by superimposing multiple-segment static fields at several optimization points. The rIMRT trial plans were compared with each other and with plans generated using helical tomotherapy and VMAT. Burst-mode rotational IMRT plans were delivered and verified using a diode array, ionization chambers, thermoluminescent dosimeters, and film. RESULTS: Burst-mode rIMRT can achieve plan quality comparable to helical tomotherapy, while the former may lead to slightly better OAR sparing for certain cases and the latter generally achieves slightly lower hot spots. Few instances were found in which increasing the number of optimization points above 36, or superimposing step-and-shoot IMRT segments, led to statistically significant improvements in OAR sparing. Using an additional rIMRT partial arc yielded substantial OAR dose improvements for the brain case. Measured doses from the rIMRT plan delivery were within 4% of the plan calculation in low dose gradient regions. Delivery time range was 228-375 s for single-arc rIMRT 200-cGy prescription with a 300 MU/min dose rate, comparable to tomotherapy and VMAT. CONCLUSIONS: Rotational IMRT with burst delivery, whether combined with static fields or not, yields clinically acceptable and deliverable treatment plans.