## Materials and Methods
### Evaluation  
To evaluate the quality of registration outcomes, an experienced breast radiologist annotated eight to twelve internal anatomical landmarks in the source image and their corresponding locations in the target image. These pairs of annotations were distributed as uniformly as possible throughout the breast volume in order to obtain a representative overall estimation of the registration accuracy.  
We calculated the mean TRE as the mean Euclidean distance between the internal landmark locations in the target image and their locations in the transformed source image.  
To assess the quality of the learned class solutions, we compared them to the solutions obtained by our previously-introduced patient-specific multi-objective optimization approach [10], where for each individual patient, the optimal weight configurations that yield a non-dominated front of DIR outcomes were found. These results were considered the best possible outcomes for these individual DIR cases. For this, we used the inverted generational distance (IGD) [26; 27], a well-known performance indicator that describes how close one non-dominated front (called the reference front) is approximated by another. Given a set of class solutions \(A=\{a_{0},a_{1},\ldots,a_{K-1}\}\) and a reference front (here, the non-dominated front obtained by patient-specific optimization) \(P=\{p_{0},p_{1},\ldots,p_{L-1}\}\), the \(IGD_{objectives}\) is defined as follows:  
\[IGD_{objectives}(A,P)=\frac{1}{L}\sum_{i=0}^{L-1}\min_{a_{j}\in A}\left\{d(p_{ i},a_{j})\right\} \tag{2}\]  
where \(d\) is the Euclidean distance in objective space. IGD is a common descriptor in the multi-objective EA literature, with a value around 0.001 to be considered a very good approximation of a non-dominated front that is ranging between zero and one in every objective and has 5000 points on its front [24]. Here, after normalizing every objective to [0, 1] and adjusting for the size of our non-dominated fronts, which was on average of 500 points, we considered a value of \(IGD_{objectives}\leq 0.1\) to be a very good approximation.  
From a clinical standpoint, however, it is important to observe whether a solution in the reference front is covered by the existence of a class solution with similar TRE. For this reason, in Equation (2), we replaced the Euclidean distance in objective space with the distance (in mm) between points in TRE space, by calculating additionally \(IGD_{TRE}\) (we denote with \(Mean\)\(TRE(a)\) the mean TRE associated with solution \(a\)):  
\[IGD_{TRE}(A,P)=\frac{1}{L}\sum_{i=0}^{L-1}\min_{a_{j}\in A}\left\{d(Mean\)\(TRE(p_{i}),Mean\)\(TRE(a_{j}))\right\} \tag{3}\]  
Moreover, in recent work, it was observed that, when provided a potentially navigable patient-specific front [10] and the right user interface, decision makers can actually easily identify preferred solutions that have the near best TRE [28]. Following from this, we are interested in the TRE difference between the potentially best solution found by the patient-specific approach (i.e., the solution with the lowest mean TRE) and the best solution among the class solutions:  
\[TRE_{diff}=\left\|\min_{p_{i}\in P}\left\{Mean\)\(TRE(p_{i})\right\}-\min_{a_{j}\in A}\left\{Mean\)\(TRE(a_{j})\right\}\right\| \tag{4}\]  
Finally, we measured computation time for each DIR outcome on a six-core PC (Intel Xeon E5-1650). In patient-specific optimization, 100 generations were used to compute results, where the DIR method elastix was called 100 times in each generation (see [10]). In the case of prone-prone DIR, performing DIR on one core took approximately 20 s, whereas for prone-supine DIR, this was 60 s. For patient-specific optimization, this resulted in a total computation time of approximately 12 h for prone-prone and 27 h for prone-supine cases, making use of the parallelization feature of elastix[29].