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Deep learning models based on medical images play an increasingly important role for cancer outcome prediction. The standard approach involves usage of convolutional neural networks (CNNs) to automatically extract relevant features from the patient's image and perform a binary classification of the occurrence of a given clinical endpoint. In this work, a 2D-CNN and a 3D-CNN for the binary classification of distant metastasis (DM) occurrence in head and neck cancer patients were extended to perform time-to-event analysis. The newly built CNNs incorporate censoring information and output DM-free probability curves as a function of time for every patient. In total, 1037 patients were used to build and assess the performance of the time-to-event model. Training and validation was based on 294 patients also used in a previous benchmark classification study while for testing 743 patients from three independent cohorts were used. The best network could reproduce the good results from 3-fold cross validation [Harrell's concordance indices (HCIs) of 0.78, 0.74 and 0.80] in two out of three testing cohorts (HCIs of 0.88, 0.67 and 0.77). Additionally, the capability of the models for patient stratification into high and low-risk groups was investigated, the CNNs being able to significantly stratify all three testing cohorts. Results suggest that image-based deep learning models show good reliability for DM time-to-event analysis and could be used for treatment personalisation. [ABSTRACT FROM AUTHOR]

A novel strategy for 4D PET optimization in the sinogram domain is proposed, aiming at motion model application before image reconstruction ("sinogram warping" strategy). Compared to state-of-the-art 4D-MLEM reconstruction, the proposed strategy is able to optimize the image SNR, avoiding iterative direct and inverse warping procedures, which are typical of the 4D-MLEM algorithm. A full-count statistics sinogram of the motion-compensated 4D PET reference phase is generated by warping the sinograms corresponding to the different PET phases. This is achieved relying on a motion model expressed in the sinogram domain. The strategy was tested on the anthropomorphic 4D PET-CT NCAT phantom in comparison with the 4D-MLEM algorithm, with particular reference to robustness to PET-CT co-registrations artefacts. The MLEM reconstruction of the warped sinogram according to the proposed strategy exhibited better accuracy (up to +40.90 % with respect to the ideal value), whereas images reconstructed according to the 4D-MLEM reconstruction resulted in less noisy (down to -26.90 % with respect to the ideal value) but more blurred. The sinogram warping strategy demonstrates advantages with respect to 4D-MLEM algorithm. These advantages are paid back by introducing approximation of the deformation field, and further efforts are required to mitigate the impact of such an approximation in clinical 4D PET reconstruction. [ABSTRACT FROM AUTHOR]