Automated detection and classification of tumor histotypes on dynamic PET imaging data through machine-learning driven voxel classification. (June 2022)

• Record Type: Journal Article
• Authors: Bianchetti, G.; Taralli, S.; Vaccaro, M.; Indovina, L.; Mattoli, M.V.; Capotosti, A.; Scolozzi, V.; Calcagni, M.L.; Giordano, A.; De Spirito, M.; Maulucci, G.
• Is Part Of: Computers in biology and medicine. Volume 145(2022)
• Publication Date: 2022-06
• Subjects: Dynamic PET imaging -- Machine learning -- Automated classification -- Lung histotypes -- Metastasis; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2022.105423
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: 2-deoxy-2-fluorine-( 18 F)fluoro-d -glucose Positron Emission Tomography/Computed Tomography ( 18 F-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 18 F-FDG uptake, to date there are some diagnostic limits and uncertainties, hindering an 18 F-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 18 F-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 18 F-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. Highlights: Dynamic PET (dPET) data provide metabolic characterization of tissues FDG uptake. Machine-learning (ML) based tools for dPET analysis still require kinetic modeling. A model-free ML-based workflow provides automated classification of lung histotypes. The algorithm can also identify tumor's spread into lymphatic system.