## 4 Discussion
[23], although this might be explained by a slightly different definition of Hr and H/WBr and by the lower number of analyzed patients (i.e., 55 among ATTRwt and ATTRm) [23]. Of note, the fact that no statistically significant difference was observed in terms of WBr suggests that the higher cardiac uptake in ATTRwt patients (proved by a large and statistically significant difference in Hr between the two populations) is balanced by a higher diffuse uptake in soft tissues and bones in ATTRm patients. Furthermore, the fact that the semiquantitative evaluation between the two populations (ATTRwt vs. ATTRm) is different could be explained not only by the etiology but also by the stage of the disease, since ATTRm patients are often identified in an early stage of the disease because they reach specialist cardiologist attention through family screening, in the absence of symptoms or laboratory/radiological findings suspicious for cardiac amyloidosis. Of note, a limitation of these results is that the calculation of the semi-quantitative cardiac indexes was processed on two numerically different ATTR populations, i.e., a lower cohort of ATTRm patients than ATTRwt patients (\(n=54\) vs. \(n=100\), respectively). Even in our population, nevertheless, the NM semiquantitative indexes are not sufficient alone to discriminate ATTRwt and ATTRm patients with acceptable accuracy; for this reason, different computational models have been developed simultaneously considering both clinical and NM-derived variables. In the last year, machine-learning approaches have been explored for the detection and classification of cardiac amyloidosis or patient risk stratification [24]; nevertheless, this is, to the best of our knowledge, the first case in which semiquantitative NM indexes derived from bone scintigraphy were used to develop a computational model which is able to discriminate ATTRwt and ATTRm patients, with the exception of the work of [PERSON] et al. that used the cardiac uptake in bone scintigraphy as a binary variable among various clinical variables in an unsupervised machine learning approach to cluster patients with suspected cardiac amyloidosis [25]. In detail, in the LASSO regression model, and similarly (in terms of specificity and sensitivity) in the RF and NN models as well, the most important variables identified were patient age_FE and Hr, which resulted in a higher discrimination ability than other clinical or echocardiography-derived variables. Overall, these data highlight the importance of bone scintigraphy in the diagnostic process. In any case, the development of machine learning-based computational models requires wide patient populations, for which the calculation of the semiquantitative NM indexes can be easy by the GM, which has been shown to be substantially equivalent to the CM, and by the development and use of assisted or semi-automated tools, as proposed in this work. Etiology is an important determinant of \({}^{99\text{m}}\)Tc-DPD scintigraphic variability in cardiac amyloidosis. Nevertheless, a further step would be the use of semiquantitative NM indexes to aid the classification of patients among ATTRm subtypes. Our analysis indicates that \({}^{99\text{m}}\)Tc-DPD scintigraphy could offer a useful first step in the workup of the differential diagnosis of ATTRm versus ATTRwt etiology in patients with documented cardiac amyloidosis. Unfortunately, this goal was not feasible with the available patient population as subtypes were under-represented. In detail, the largest ATTRm subgroup, Ile68 Leu, was reported in 27 out of 54 ATTRm patients, while all other ATTRm subgroups were represented by less than eight patients each. In this sense, only the development of multicentric datasets would enable this goal. According to our findings, \({}^{99\text{m}}\)Tc-DPD could offer a preliminary assessment of different etiologies in order to find out in advance if the patient with certain clinical variables and NM parameters could belong to the ATTRm or ATTRwt class.