Generalizability of deep learning based fluence map prediction as an inverse planning approach

Abstract

Deep learning-based fluence map prediction(DL-FMP) method has been reported in the literature, which generated fluence maps for desired dose by deep neural network(DNN)-based inverse mapping. We hypothesized that DL-FMP is similar to general fluence map optimization(FMO) because it's theoretically based on a general inverse mapping. We designed four experiments to validate the generalizability of DL-FMP to other types of plans apart from the training data, which contained only clinical head and neck(HN) full-arc VMAT plans. The first three experiments quantified the generalizability of DL-FMP to multiple anatomical sites, different delivery modalities, and various degree of modulation(DOM), respectively. The fourth experiment explored the generalizability and stability to infeasible dose inputs. Results of the first experiment manifested that DL-FMP can generalize to lung, liver, esophagus and prostate, with gamma passing rates (GPR) higher than 95%(2%/2mm). The second experiment showed that DL-FMP can generalize to partial-arc plans and predict partial-arc fluences. GPR(3mm/3%) ranged from 96% to 99%. DL-FMP cannot generate fluence maps in discrete beam angles for IMRT input. But the predicted dose still agreed with ground truth dose with 93% GPR(5%/5mm). The third experiment demonstrated that DL-FMP can generalize to various DOMs, with GPRs(3%/3mm) ranged in 94%-98%. Moreover, the DOM of predicted fluence maps correlated to the optimality of the input dose accordingly. The fourth experiment exemplified that DL-FMP can make stable predictions for infeasible dose input. In conclusion, we validated that DL-FMP can generalize to plans for multiple anatomical sites, plans of different delivery modalities and plans with various DOM. It can also make stable prediction for infeasible input.