  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.
