Improvement of results by assigning adaptive weights opens a new line of investigation for the future work, in order to define better mechanisms to improve the performance of regressor. Additionally, regression based on GBR can be improved by re-defining new learners and putting better constraints to keep the learners weak enough to avoid overfitting. Besides, more investigations are needed to study the correlations between the features in order to provide more evidence to the hypothesis of poor performance of MOR. As an alternative solution, it is worth studying a combined methodology of predicting breast deformation with machine learning techniques followed by some final steps of the biomechanical models; hence the required computational time for the biomechanical models is decreased since the predecessor machine learning methodology has provided a partial solution. Last but not least, new learning methodologies such as convolution neural network regression, and deep geometric learning can be investigated, as well as performing the prediction of breast shape directly on the 3D surface data.