Background: [ ¹⁸ F]-Fluorodeoxyglucose (FDG)-avid thyroid lesions incidentally detected on positron emission tomography/computed tomography (PET/CT) scans represent a tumor lesion in about 30% of cases. The present study evaluated the ability of PET metrics and radiomics features to predict final diagnosis of [ ¹⁸ F]FDG thyroid incidentalomas (TIs). Methods: A total of 104 patients with 107 TIs were retrospectively studied; 30 nodules (28%) were diagnosed as malignant. After volumetric segmentation of each thyroid lesion, metabolic tumor volume (MTV), total lesion glycolysis (TLG), standardized uptake values (SUVs), and metabolic heterogeneity were estimated, and 107 radiomics features were extracted following a standard protocol. Results: MTV, TLG, SUVmax, SUVmean, and SUVpeak among functional PET parameters, and gray-level co-occurrence matrix (GLCM)_InverseDifferenceMoment, shape_Sphericity, GLCM_SumSquares, firstorder_Maximum2DDiameterSlice, firstorder_Energy, and GLCM_Contrast among nonredundant radiomics features, showed significantly different values between malignant and benign TIs (Mann–Whitney U-test, p < 0.01 for all). Univariate logistic regression revealed that these parameters demonstrated good ability to predict final diagnosis of TIs ( p < 0.02 for all). Shape_Sphericity was the best predictor classifying 82% of TIs correctly ( p < 0.0001). Only TLG, SUVmax, and shape_Sphericity retained significance ( p < 0.0001) by multivariate analysis. Malignant lesion prevalence increased from 7% to 100% in accordance with the number (score, 0–3) of the three positive parameters present (χ ² trend, p < 0.0001). A score of 0 excludes malignant TIs with a negative predictive value of 93%, while a score of 3 predicted malignancy with a positive predictive value of 100%. Conclusions: PET metrics and radiomics analysis can improve identification of [ ¹⁸ F]FDG-avid TIs at high risk of malignancy. A model based on TLG, SUVmax, and shape_Sphericity may allow prediction of a final diagnosis, providing useful information for the management of TIs.

Accurate estimation of the progression risk after first-line therapy represents an unmet clinical need in diffuse large B-cell lymphoma (DLBCL). Baseline (18)F-fluorodeoxyglucose positron emission tomography/computed tomography (PET/CT) parameters, together with genetic analysis of lymphoma cells, could refine the prediction of treatment failure. We evaluated the combined impact of mutation profiling and baseline PET/CT functional parameters on the outcome of DLBCL patients treated with the R-CHOP14 regimen in the SAKK38/07 clinical trial (NCT00544219). The concomitant presence of mutated SOCS1 with wild-type CREBBP and EP300 defined a group of patients with a favorable prognosis and 2-year progression-free survival (PFS) of 100%. Using an unsupervised recursive partitioning approach, we generated a classification-tree algorithm that predicts treatment outcomes. Patients with elevated metabolic tumor volume (MTV) and high metabolic heterogeneity (MH) (15%) had the highest risk of relapse. Patients with low MTV and favorable mutational profile (9%) had the lowest risk, while the remaining patients constituted the intermediate-risk group (76%). The resulting model stratified patients among three groups with 2-year PFS of 100%, 82%, and 42%, respectively (p < 0.001).

Artificial intelligence (AI) and machine learning (ML) in the medical field have the potential to revolutionize the way in which diseases are detected and treated. Exploiting advanced algorithms and techniques, AI-based systems can analyze a very large amount of medical data and identify patterns that may not be detectable to human experts. This can lead to more accurate and efficient diagnoses, as well as the development of new diagnostic/prognostic methods. Integrated diagnostics combines multiple diagnostic modalities and data sources to provide a more comprehensive understanding of a patient’s health status. Due to a large amount of information collectable in this field, the application of AI may provide significant and cost-effective advancements in the next years. In this chapter, the current state of AI in integrated diagnostic medicine will be explored, including its applications, challenges, and future prospects.

Magnetic resonance (MR)-based synthetic computed tomography (sCT) can improve target and organs-of-interest delineation in brain radiotherapy. However, metallic implants may cause anatomical inconsistencies, dose inaccuracies, and distortions. This study evaluates the impact of dental metal artifacts on MR-only workflow. Ninety-six patients underwent MR and CT for radiotherapy planning; fifty-two with dental implants undergoing standard or stereotactic treatments were eligible. Dose-volume metrics (D_mean, D_2%, and D_98%) and γ-index analyses (1%/1 mm and 3%/3 mm) were used to compare dose for targets and organs. For a subgroup of 15 patients, further analyses were performed. Image quality of sCT was evaluated against planning CT using Mean Absolute Error (MAE) of Hounsfield units (HU) within targets and organs. Offline sCT-cone beam CT (CBCT) registrations were compared with online CT-CBCT matches. Sequences for B0-map, the map of main static magnetic field, quantified geometric distortion. Median target dose differences remained within ±0.3%, with maximum of 3.7% for D_98% due to artifact-induced body contour changes. Median organ dose deviations were ±0.3%. Passing rates for γ-index were 97.8% and 100% for 1%/1 mm and 3%/3 mm, respectively. Average MAE was below 10 HU for brain, reaching 40 HU in bone-target regions. No differences were observed between CT-CBCT and sCT-CBCT registration. Geometric distortions remained within 1 mm, satisfying radiotherapy requirements. Despite dental metal artifacts, sCT demonstrate dose accuracy comparable to standard CT. The results support clinical use of sCT in brain radiotherapy, with caution when artifacts alter the body near targets.