Automated detection and classification of tumor histotypes on dynamic PET imaging data through machine-learning driven voxel classification.

2-deoxy-2-fluorine-(18F)fluoro-d-glucose Positron Emission Tomography/Computed Tomography (18F-FDG-PET/CT) is widely used in oncology mainly for diagnosis and staging of various cancer types, including lung cancer, which is the most common cancer worldwide. Since histopathologic subtypes of lung cancer show different degree of 18F-FDG uptake, to date there are some diagnostic limits and uncertainties, hindering an 18F-FDG-PET-driven classification of histologic subtypes of lung cancers. On the other hand, since activated macrophages, neutrophils, fibroblasts and granulation tissues also show an increased 18F-FDG activity, infectious and/or inflammatory processes and post-surgical and post-radiation changes may cause false-positive results, especially for lymph-nodes assessment. Here we propose a model-free, machine-learning based algorithm for the automated classification of adenocarcinoma, the most common type of lung cancer, and other types of tumors. Input for the algorithm are dynamic acquisitions of PET data (dPET), providing for a spatially and temporally resolved characterization of the uptake kinetic. The algorithm consists in a trained Random Forest classifier which, relying contextually on several spatial and temporal features of 18F-FDG uptake, generates as an outcome probability maps allowing to distinguish adenocarcinoma from other lung histotype and to identify metastatic lymph-nodes, ultimately increasing the specificity of the technique. Its performance, evaluated on a dPET dataset of 19 patients affected by primary lung cancer, provides a probability 0.943 ± 0.090 for the detection of adenocarcinoma. The use of this algorithm will guarantee an automatic and more accurate localization and discrimination of tumors, also providing a powerful tool for detecting at which extent tumor has spread beyond a primary tumor into lymphatic system.

The role of nuclear medicine in the diagnosis of spondylodiscitis.

BACKGROUND: The diagnosis of spondylodiscitis can be difficult, because the patients history, subjective symptoms and physical findings are often inconclusive, particularly in the early stages. AIM: To perform an overview on the role of nuclear medicine procedures with single photon emission tomography (SPET) and positron emission tomography (PET) tracers in the diagnosis of spondylodiscitis. MATERIALS AND METHODS: A literature review about bone scintigraphy, Gallium-67-citrate scintigraphy, labeled leukocytes scintigraphy and PET was performed. Main findings of the literature were reported. RESULTS: Bone scintigraphy is a sensitive and widely available nuclear medicine technique, but it is characterized by low specificity. Gallium-67-citrate scintigraphy is often used as a complement to bone scintigraphy to enhance the specificity of the study and to detect extra-osseous sites of infection. Labeled leukocytes scintigraphy is not a useful method in the diagnosis of spondylodiscitis. Fluorine-18-fluorodeoxyglucose positron emission tomography is a sensitive method and could potentially be useful in the diagnosis of spondylodiscitis and in the evaluation of treatment response. Nevertheless, scientific literature about this topic is still limited. CONCLUSIONS: Overall, nuclear medicine procedures play a useful role in the diagnosis of spondylodiscitis identifying functional abnormalities which precede morphological changes. Therefore, nuclear medicine procedures may complement or integrate morphological imaging findings in patients with suspected spondylodiscitis.