[A proposal for the use of tridimensional reconstruction in oncology to better assess tumor stage and response to therapy].

Besides radiation therapy, diagnostic imaging has always been considered the main medical application of three-dimensional (3D) reconstruction. On the contrary, our study focused mainly on the use of 3D reconstruction for both spatial characterization and morphometric evaluation of the reconstructed objects. We aimed at assisting physicians to solve clinical and therapeutic problems. In particular, in oncology, 3D reconstruction may allow the objective and accurate quantification of the volume of neoplastic lesions. Therefore, we decided to focus our attention on the spatial characterization and morphometric assessment of the examined neoplastic masses. Volumetric measurements based on 3D reconstruction may be of great value to assess volume changes after irradiation and/or chemotherapy of neoplastic lesions. This might also allow to compare, on the basis of such changes, the role of different treatment protocols on similar neoplastic lesions and, possibly, to lead to a new TNM staging system no longer based on 2D measurements but on volumes. To meet these clinical requirements, we developed a software system for accurate volume measurements. We believed 3D reconstruction to be suited to this purpose and therefore we implemented a software incorporating 3D reconstruction capabilities of abnormal anatomical structures from 2D images, the rotation of the volume of interest for better assessment of spatial relationships, and finally morphometric evaluation, for accurate volume measurements. Instead of calculating the volume of a neoplastic lesion by means of a 3D reconstruction algorithm considering voxels as indivisible (voxel-based approach), we implemented a surface rendering algorithm using a cell-based approach, because it allowed voxels to be represented as small volume units, which could be further divided by means of linear interpolation. Thus, great flexibility was possible in the determination of surfaces, together with a good approximation of the volume of the neoplastic lesions. To assess the reliability of the developed software system, we used a real phantom. Its known actual volume was compared with the one measured by our system and the difference, expressed as a percentage of the actual volume itself, was compared with the one obtained by using reconstruction algorithms with a voxel-based approach (1.4% vs 4.4%). The error produced by the latter is three times greater than the one produced by our algorithm. This is a major result for the physician: better approximation of the actual volume of a neoplastic lesion means better evaluation of the number of neoplastic cells in the lesion. This may be useful for the clinical management of the patient. In the paper, the first clinical applications of our algorithm are reported.