MRL is easy and safe to use and combines extensive information on the anatomy and functionality of lymphatic vessels and veins in a single process; therefore, it could be useful in LVA treatment planning and evaluating possible super-microsurgical treatment complications in patients with lymphedema.

In the field of mortality, the Lee–Carter based approach can be considered the milestone to forecast mortality rates among stochastic models. We could define a “Lee–Carter model family” that embraces all developments of this model, including its first formulation (1992) that remains the benchmark for comparing the performance of future models. In the Lee–Carter model, the κ t parameter, describing the mortality trend over time, plays an important role about the future mortality behavior. The traditional ARIMA process usually used to model κ t shows evident limitations to describe the future mortality shape. Concerning forecasting phase, academics should approach a more plausible way in order to think a nonlinear shape of the projected mortality rates. Therefore, we propose an alternative approach the ARIMA processes based on a deep learning technique. More precisely, in order to catch the pattern of κ t series over time more accurately, we apply a Recurrent Neural Network with a Long Short-Term Memory architecture and integrate the Lee–Carter model to improve its predictive capacity. The proposed approach provides significant performance in terms of predictive accuracy and also allow for avoiding the time-chunks’ a priori selection. Indeed, it is a common practice among academics to delete the time in which the noise is overflowing or the data quality is insufficient. The strength of the Long Short-Term Memory network lies in its ability to treat this noise and adequately reproduce it into the forecasted trend, due to its own architecture enabling to take into account significant long-term patterns.

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Aim. To investigate the role of maximum tumour diameter (D-max) reduction rate at CT examination in predicting histopathological tumour regression grade (TRG according to the Becker grade), after neoadjuvant chemotherapy (NAC), in patients with resectable advanced gastric cancer (AGC). Materials and Methods. Eighty-six patients (53 M, mean age 62.1 years) with resectable AGC (≥T3 or N+), treated with NAC and radical surgery, were enrolled from 5 centres of the Italian Research Group for Gastric Cancer (GIRCG). Staging and restaging CT and histological results were retrospectively reviewed. CT examinations were contrast enhanced, and the stomach was previously distended. The D-max was measured using 2D software and compared with Becker TRG. Statistical data were obtained using "R" software. Results. The interobserver agreement was good/very good. Becker TRG was predicted by CT with a sensitivity and specificity, respectively, of 97.3% and 90.9% for Becker 1 (D-max reduction rate > 65.1%), 76.4% and 80% for Becker 3 (D-max reduction rate < 29.9%), and 70.8% and 83.9% for Becker 2. Correlation between radiological and histological D-max measurements was strongly confirmed by the correlation index (c.i.= 0.829). Conclusions. D-max reduction rate in AGC patients may be helpful as a simple and reproducible radiological index in predicting TRG after NAC.

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