Extracellular vesicles (EVs) are abundantly released into the systemic circulation, where they show remarkable stability and harbor molecular constituents that provide biochemical information about their cells of origin. Due to this characteristic, EVs are attracting increasing attention as a source of circulating biomarkers for cancer liquid biopsy and personalized medicine. Despite this potential, none of the discovered biomarkers has entered the clinical practice so far, and novel approaches for the label-free characterization of EVs are highly demanded. In this regard, Fourier Transform Infrared Spectroscopy (FTIR) has great potential as it provides a quick, reproducible, and informative biomolecular fingerprint of EVs. In this pilot study, we investigated, for the first time in the literature, the capability of FTIR spectroscopy to distinguish between EVs extracted from sera of cancer patients and controls based on their mid-IR spectral response. For this purpose, EV-enriched suspensions were obtained from the serum of patients diagnosed with Hepatocellular Carcinoma (HCC) of nonviral origin and noncancer subjects. Our data point out the presence of statistically significant differences in the integrated intensities of major mid-IR absorption bands, including the carbohydrate and nucleic acids band, the protein amide I and II bands, and the lipid CH stretching band. Additionally, we used Principal Component Analysis combined with Linear Discriminant Analysis (PCA-LDA) for the automated classification of spectral data according to the shape of specific mid-IR spectral signatures. The diagnostic performances of the proposed spectral biomarkers, alone and combined, were evaluated using multivariate logistic regression followed by a Receiving Operator Curve analysis, obtaining large Areas Under the Curve (AUC = 0.91, 95% CI 0.81–1.0). Very interestingly, our analyses suggest that the discussed spectral biomarkers can outperform the classification ability of two widely used circulating HCC markers measured on the same groups of subjects, namely alpha-fetoprotein (AFP), and protein induced by the absence of vitamin K or antagonist-II (PIVKA-II).

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.

(1) Background: In ⁹⁰Y-TARE treatments, lung-absorbed doses should be calculated according to the manufacturer’s instructions, using the MIRD-scheme. This scheme is derived from the assumption that ⁹⁰Y-microspheres deliver the dose in a water-equivalent medium. Since the density of the lungs is quite different from that of the liver, the absorbed dose to the lungs could vary considerably, especially at the liver/lungs interface. The aim of this work is to compare the dosimetric results obtained by two dedicated software packages implementing a water-equivalent dose calculation and a Monte Carlo (MC) simulation, respectively. (2) Methods: An anthropomorphic IEC phantom and a retrospective selection of 24 patients with a diagnosis of HCC were taken into account. In the phantom study, starting from a ⁹⁰Y-PET/CT acquisition, the liver cavity was manually fixed with a uniform activity concentration on PET series, while the lung compartment was manually expanded on a CT series to simulate a realistic situation in which the liver and lungs are adjacent. These steps were performed by using MIM ⁹⁰Y SurePlan. Then, a first simulation was carried out with only the liver cavity filled, while a second one was carried out, in which the lung compartment was also manually fixed with a uniform activity concentration corresponding to 10% lung shunt fraction. MIM ⁹⁰Y SurePlan was used to obtain Voxel S-Value (VSV) approach dose values; instead, Torch was used to obtain MC approach dose values for both the phantom and the patients. (3) Results: In the phantom study, the percentage mean dose differences (∆D%) between VSV and MC in the first and second simulation, respectively were found to be 1.2 and 0.5% (absolute dose variation, ∆D, of 0.7 and 0.3 Gy) for the liver, −56 and 70% (∆D of −0.3 and −16.2 Gy) for the lungs, and −48 and −60% (∆D of −4.3 and −16.5 Gy) for the Liver/Lungs Edge region. The patient study reports similar results with ∆D% between VSV and MC of 7.0%, 4.1% and 6.7% for the whole liver, healthy liver, and tumor, respectively, while the result was −61.2% for the left lung and −61.1% for both the right lung and lungs. (4) Conclusion: Both VSV and MC allowed accurate radiation dose estimation with small differences (50%) were observed for air-equivalent regions in the phantom simulation and the patient study.