## 3 Results
### Quantitative Bone Marrow Assessment
#### 3.2.2 Machine Learning Models  
Tables 4 and 5 show the F1_score metrics of the machine learning models with the best F1_macro * and F1_weighted, respectively, for the PET, MFC, and pGT labeling for NHL, FL, and DLBCL patients. The lack of significant features should be noted for the NHL and DLBCL groups in MFC labeling. Both the FL group in MFC labeling and the DLBCL group in pGT labeling have significant features only for BW3, BW2, and BW3, respectively. As can be seen in Table 5, the best F1_macro * results are obtained for the PET labeling with maximums of 0.822, 0.836, and 0.759 for NHL, FL, and DLBCL, respectively, since the features studied are directly extracted from the [18]FFDG PET image without any clinical aspect. Similarly, pGT labeling obtains better performances of 0.685, 0.739, and 0.744 than MFC with 0.659, 0.619, and 0.714 since it partially takes into account the PET labeling. \begin{table}
\begin{tabular}{c c c c c c|c c c c|c c c} \hline \hline \multirow{2}{*}{**F1\_macro *} & \multicolumn{4}{c}{NHLL} & \multicolumn{4}{c}{FL} & \multicolumn{4}{c}{DLBCL} \\ \cline{2-13}  & **ORIG** & **RED** & **OS** & **OS\_R** & **ORIG** & **RED** & **OS** & **OS\_R** & **ORIG** & **RED** & **OS** & **OS\_R** \\ \hline \multirow{9}{*}{**PET**} & F1\_0 & 0.900 & 0.852 & **0.918** & 0.873 & 0.903 & _1.000_ & 0.815 & 0.897 & 0.897 & 0.897 & 0.857 & **0.929** \\  & F1\_1 & 0.778 & **0.842** & 0.824 & 0.700 & 0.727 & _0.923_ & 0.667 & 0.727 & **0.500** & **0.500** & **0.500** & **0.500** \\  & F1\_2 & 0.500 & 0.222 & 0.500 & **0.571** & 0.000 & 0.000 & _1.000_ & 0.500 & **0.667** & **0.667** & 0.500 & 0.500 \\  & F1\_m & 0.726 & 0.639 & **0.747** & 0.715 & 0.543 & 0.641 & _0.827_ & 0.708 & **0.688** & **0.688** & 0.619 & 0.643 \\  & F1\_m * & 0.803 & 0.748 & **0.822** & 0.792 & 0.710 & 0.812 & _0.836_ & 0.797 & 0.757 & 0.757 & 0.706 & **0.759** \\  & F1\_w & 0.818 & 0.792 & **0.843** & 0.803 & 0.815 & _0.962_ & 0.768 & 0.835 & 0.741 & 0.741 & 0.690 & 0.807 \\ \hline \multirow{9}{*}{**MFC**} & F1\_0 & 0.727 & & **0.744** & & 0.500 & 0.421 & **0.727** & 0.455 & 0.750 & & _0.842_ & \\  & F1\_1 & **0.444** & & 0.167 & & 0.421 & 0.471 & **0.625** & 0.353 & _0.667_ & & _0.667_ & \\  & F1\_2 & 0.500 & & _0.667_ & & _0.667_ & 0.333 & 0.000 & _0.667_ & **0.444** & & 0.333 & \\  & F1\_m & **0.557** & & 0.526 & & **0.529** & 0.408 & 0.451 & 0.492 & _0.620_ & & 0.614 & \\  & F1\_m * & **0.659** & & 0.652 & & 0.605 & 0.546 & **0.620** & 0.569 & 0.705 & & _0.715_ & \\  & F1\_w & **0.644** & & 0.637 & & 0.532 & 0.517 & **0.683** & 0.476 & 0.699 & & _0.724_ & \\ \hline \multirow{9}{*}{**pGT**} & F1\_0 & 0.682 & 0.682 & _0.846_ & 0.698 & 0.667 & 0.696 & 0.727 & **0.737** & **0.833** & 0.727 & 0.727 & 0.700 \\  & F1\_1 & 0.615 & 0.615 & _0.720_ & 0.710 & 0.667 & 0.632 & 0.696 & _0.720_ & **0.500** & **0.500** & **0.500** & **0.500** \\  & F1\_2 & **0.333** & **0.333** & 0.000 & 0.000 & 0.333 & 0.333 & 0.000 & _0.500** & _0.667_ & 0.500 & 0.500 & 0.600 \\  & F1\_m & **0.543** & **0.543** & 0.522 & 0.469 & 0.556 & 0.554 & 0.474 & **0.652** & **0.667** & 0.576 & 0.576 & 0.600 \\  & F1\_m * & 0.662 & 0.662 & **0.686** & 0.632 & 0.673 & 0.674 & 0.641 & **0.740** & **0.744** & 0.668 & 0.668 & 0.690 \\  & F1\_w & 0.672 & 0.672 & **0.754** & 0.682 & 0.694 & 0.696 & 0.707 & **0.745** & **0.732** & 0.643 & 0.643 & 0.666 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Metrics for the models with the best performance (maximum F1_m *) for each group (ORIG, OS, RED, or OS_R), group of patients (NHL, FL, and DLBCL), and labeling (PET, MFC, and pGT). (F1_0) F1_score for BMI\(-\) class; (F1_1) F1_score for BMI\(+\) class; (F1_2) F1_score for rBM class; (F1_m) F1_score_macro, calculated as the mean F1_score of the three previous metrics; (F1_m *) F1_score_macro*, calculated as the mean F1_score considered as binary problem for any class; (F1_w) F1_score_weighted. Grey cells represent the lack of those groups, i.e., no significant features after reduction. Green cells represent crossover between the model with the best F1_m* and the best F1_w.