Predicting lesion malignancy accurately and reliably in digital breast tomosynthesis is critically important for breast cancer screening. Tumor shape and interactive effect between the tumor and surrounding normal tissue are two of the most important indicators in radiologists’ reading. On the other hand, the density and texture of region within the tumor also play an important role in malignancy classification. Inspired by the above observations, shell and kernel descriptors were proposed in this work for breast lesion malignancy prediction, in which the shell descriptor is used for describing the tumor shape and surrounding normal tissue while the kernel descriptor is used to describe the internal tumor region. A joint deep learning model based on the AlexNet was designed to learn and fuse features from shell and kernel. Additionally, to obtain more reliable predictive results, a multi-objective optimization algorithm and a reliable classifier fusion strategy were used to train the predictive model and optimally combine outputs from both shell and kernel descriptors. In this study, 278 malignant and 685 benign cases were used through 2-fold cross validation. Compared with the single descriptor based models using either shell or kernel, the experimental results demonstrated that the combined shell and kernel descriptors can capture the most important features and the corresponding predictive model achieved the best performance as well.