  Accurately predicting the treatment outcome plays a greatly important role in
tailoring and adapting a treatment planning in cancer therapy. Although the
development of different modalities and personalized medicine can greatly
improve the accuracy of outcome prediction, they also bring the three mainly
simultaneous challenges including multi-modality, multi-classifier and
multi-criteria, which are summarized as multifactorial outcome prediction
(MFOP) in this paper. Compared with traditional outcome prediction, MFOP is a
more generalized problem. To handle this novel problem, based on the recent
proposed radiomics, we propose a new unified framework termed as multifaceted
radiomics (M-radiomics). M-radiomics trains multiple modality-specific
classifiers first and then optimally combines the output from the outputs of
different classifiers which are trained according to multiple different
criteria such as sensitivity and specificity. It considers multi-modality,
multi-classifier and multi-criteria into a unified framework, which makes the
prediction more accurate. Furthermore, to obtain the more reliable predictive
performance which is to maximize the similarity between predicted output and
labelled vector, a new validation set based reliable fusion (VRF) strategy and
reliable optimization models as well as a new recursive two stage hybrid
optimization algorithm (RTSH) were also developed. Two clinical problems for
predicting distant metastasis and locoregional recurrence in head & neck cancer
were investigated to validate the performance and reliability of the proposed
M-radiomics. By using the proposed RF strategy and RTSH optimization algorithm,
the experimental results demonstrated that M-radiomics performed better than
current radiomic models that rely on a single objective, modality or
classifier.
