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Cardiotoxicity refers to the damage induced by antineoplastic treatments, leading to various cardiovascular conditions. [18F]FDG PET radiomics analysis could provide relevant information on early onset changes occurring in cardiac metabolism of chemotherapy-induced cardiotoxicity. Patients’ sociodemographic data, cardiovascular risk factors, laboratory parameters, and left ventricle [18F]FDG PET radiomic features are analyzed. The HRad index for the quantification of the heterogeneity of the metabolic uptake patterns is proposed. Statistical analysis is performed by separating patients according to the diagnosis of cancer therapy-related cardiac dysfunction (CTRCD). Baseline, intermediate, and end-of-treatment scans are evaluated as separate groups. Overall, CTRCD+ patients show lower overall mean standardized uptake values (SUVmean) compared to CTRCD− patients, with statistically significant differences between groups only observed in the intermediate PET study (p = 0.025). A total of 34 radiomic features show statistically significant differences between the CTRCD+ and CTRCD− groups in the intermediate imaging studies. In the CTRCD− group, greater overall heterogeneity of metabolic uptake is observed in the intermediate PET image compared to the CTRCD+ groups (p = 0.025). The assessment of CTRCD through [18F]FDG PET radiomics analysis could be a potential tool for the identification of a predisposition to the later development of cardiac complications after cardiotoxic treatment.

Non-Hodgkin lymphoma is a heterogeneous group of cancers that triggers bone marrow infiltration in 20–40% of cases. Bone marrow biopsy in combination with a visual assessment of [18F]FDG PET/CT images is used to assess the marrow status. Despite the potential of both techniques, they still have limitations due to the subjectivity of visual assessment. The present study aims to develop models based on bone marrow uptake in [18F]FDG PET/CT images at the time of diagnosis to differentiate bone marrow status. For this purpose, a model trained for skeleton segmentation and based on the U-Net architecture is retrained for bone marrow segmentation from CT images. The mask obtained from this segmentation together with the [18F]FDG PET image is used to extract radiomics features with which 11 machine learning models for marrow status differentiation are trained. The segmentation model yields very satisfactory results with Jaccard and Dice index values of 0.933 and 0.964, respectively. As for the classification models, a maximum F1_score_weighted and F1_score_macro of 0.962 and 0.747, respectively, are achieved. This highlights the potential of these features for bone marrow assessment, laying the foundation for a new clinical decision support system.

Although there have been crucial advancements in the diagnostic and treatment approaches, the mortality rate of infective endocarditis is still an ongoing challenge in clinical practice. [18F]FDG PET/CT imaging has recently proven its potential role in the early identification of prosthetic valve endocarditis (PVE). Due to radiomics’ rising applicability, recent studies exhibit promising outcomes in the clinical setting. The aim of the present study is the evaluation of potential radiomic-based biomarkers of non-attenuation-corrected (NAC) [18F]FDG PET images for the diagnosis of PVE. An adequate pre-processing and segmentation of the prosthetic ring metabolic activity were performed. A reproducibility analysis prior to the image-based biomarkers’ identification was conducted in terms of the intraclass correlation coefficient (ICC) derived from the variations in the radiomic extraction configurations (bin number and voxel size). After the reliability analysis, statistical analysis was performed by means of the Mann–Whitney U Test to study the differences between the PVE groups. Only p values < 0.05 after the Benjamini Hochberg correction procedure for multiple comparisons were considered statistically significant. Eight ML classification models for PVE classification based on radiomic features were evaluated. Overall, 45.2% and 95.7% of the radiomic features showed a consistency ICC above 0.82, demonstrating great reproducibility against variations in the bin number and interpolation thickness, respectively. Variations in interpolation thickness demonstrated great reproducibility in absolute agreement with 80.0% robust features, proving a non-dependency relationship with radiomic values. In the present study, the utility of potential radiomic-based biomarkers in the diagnosis of PVE in NAC [18F]FDG PET/CT images has been evaluated. Future studies will be required to validate the use of this technology as a valuable tool to support the current PVE diagnostic criteria.

Evidence is emerging about the value of textural features as powerful outcome predictors in cancer lesions. The aim of this study is to evaluate the potential of [18F]FDG PET/CT conventional and textural parameters as survival predictors in patients with stage III and IV oropharyngeal cancer. The database includes 39 patients. Segmentation of the primary lesions was performed. A total of 48 features were extracted, comprising conventional parameters and textural features. A 2-year follow-up period to analyze the Overall Survival (OS) and Relapse-Free Survival (RFS) rates was defined. Kaplan–Meier and Cox proportional hazards regression analyses were computed. Higher TLG (p = 0.001) and Surface (p = 0.001) are significantly related to better OS in Cox regression analysis after multiple-testing correction. Higher GLZLM_ZLNU (p = 0.001) is significantly related to greater relapse rates in RFS Kaplan–Meier analysis after multiple-testing correction. Quantitative [18F]FDG PET/CT image features, especially the TLG, have been confirmed as predictors of OS and RFS. Textural features, such as GLZLM_ZLNU, demonstrated a potential predictive value for the OS and RFS of the patients. RFS analysis suggest stabilization of patients adhering to the treatment, showing no relapse events after 20 months of follow-up. [18F]FDG PET/CT is a useful tool for predicting prognosis after chemoradiation therapy of oropharyngeal cancer patients.

Neurodegenerative parkinsonisms affect mainly cognitive and motor functions and are syndromes of overlapping symptoms and clinical manifestations such as tremor, rigidness, and bradykinesia. These include idiopathic Parkinson’s disease (PD) and the atypical parkinsonisms, namely progressive supranuclear palsy (PSP), corticobasal degeneration (CBD), multiple system atrophy (MSA) and dementia with Lewy body (DLB). Differences in the striatal metabolism among these syndromes are evaluated using [18F]FDG PET, caused by alterations to the dopaminergic activity and neuronal loss. A study cohort of three patients with PD, 29 with atypical parkinsonism (10 PSP, 6 CBD, 2 MSA, 7 DLB, and 4 non-classifiable), and a control group of 25 patients with normal striatal metabolism is available. Standardized uptake value ratios (SUVR) are extracted from the striatum, and the caudate and the putamen separately. SUVRs are compared among the study groups. In addition, hemispherical and caudate-putamen differences are evaluated in atypical parkinsonisms. Striatal hypermetabolism is detected in patients with PD, while atypical parkinsonisms show hypometabolism, compared to the control group. Hemispherical differences are observed in CBD, MSA and DLB, with the latter also showing statistically significant caudate–putamen asymmetry (p = 0.018). These results indicate disease-specific metabolic uptake patterns in the striatum that can support the differential diagnosis.

Dynamic early-phase PET images acquired with radiotracers binding to fibrillar amyloid-beta (Aβ) have shown to correlate with [18F]fluorodeoxyglucose (FDG) PET images and provide perfusion-like information. Perfusion information of static PET scans acquired during the first minute after radiotracer injection (FMF, first-minute-frame) is compared to [18F]FDG PET images. FMFs of 60 patients acquired with [18F]florbetapir (FBP), [18F]flutemetamol (FMM), and [18F]florbetaben (FBB) are compared to [18F]FDG PET images. Regional standardized uptake value ratios (SUVR) are directly compared and intrapatient Pearson’s correlation coefficients are calculated to evaluate the correlation of FMFs to their corresponding [18F]FDG PET images. Additionally, regional interpatient correlations are calculated. The intensity profiles of mean SUVRs among the study cohort (r = 0.98, p < 0.001) and intrapatient analyses show strong correlations between FMFs and [18F]FDG PET images (r = 0.93 ± 0.05). Regional VOI-based analyses also result in high correlation coefficients. The FMF shows similar information to the cerebral metabolic patterns obtained by [18F]FDG PET imaging. Therefore, it could be an alternative to the dynamic imaging of early phase amyloid PET and be used as an additional neurodegeneration biomarker in amyloid PET studies in routine clinical practice while being acquired at the same time as amyloid PET images.

Manual segmentation of muscle and adipose compartments from computed tomography (CT) axial images is a potential bottleneck in early rapid detection and quantification of sarcopenia. A prototype deep learning neural network was trained on a multi-center collection of 3413 abdominal cancer surgery subjects to automatically segment truncal muscle, subcutaneous adipose tissue and visceral adipose tissue at the L3 lumbar vertebral level. Segmentations were externally tested on 233 polytrauma subjects. Although after severe trauma abdominal CT scans are quickly and robustly delivered, with often motion or scatter artefacts, incomplete vertebral bodies or arms that influence image quality, the concordance was generally very good for the body composition indices of Skeletal Muscle Radiation Attenuation (SMRA) (Concordance Correlation Coefficient (CCC) = 0.92), Visceral Adipose Tissue index (VATI) (CCC = 0.99) and Subcutaneous Adipose Tissue Index (SATI) (CCC = 0.99). In conclusion, this article showed an automated and accurate segmentation system to segment the cross-sectional muscle and adipose area L3 lumbar spine level on abdominal CT. Future perspectives will include fine-tuning the algorithm and minimizing the outliers.

Amyloid positron emission tomography (PET) brain imaging with radiotracers like [18F]florbetapir (FBP) or [18F]flutemetamol (FMM) is frequently used for the diagnosis of Alzheimer’s disease. Quantitative analysis is usually performed with standardized uptake value ratios (SUVR), which are calculated by normalizing to a reference region. However, the reference region could present high variability in longitudinal studies. Texture features based on the grey-level co-occurrence matrix, also called Haralick features (HF), are evaluated in this study to discriminate between amyloid-positive and negative cases. A retrospective study cohort of 66 patients with amyloid PET images (30 [18F]FBP and 36 [18F]FMM) was selected and SUVRs and 6 HFs were extracted from 13 cortical volumes of interest. Mann–Whitney U-tests were performed to analyze differences of the features between amyloid positive and negative cases. Receiver operating characteristic (ROC) curves were computed and their area under the curve (AUC) was calculated to study the discriminatory capability of the features. SUVR proved to be the most significant feature among all tests with AUCs between 0.692 and 0.989. All HFs except correlation also showed good performance. AUCs of up to 0.949 were obtained with the HFs. These results suggest the potential use of texture features for the classification of amyloid PET images.

Chronic thromboembolic pulmonary hypertension (CTEPH) is confirmed by visual analysis of single-photon emission computer tomography (SPECT) ventilation and perfusion (V/Q) images. Defects in the perfusion image discordant with the ventilation image indicate obstructed segments and the positive diagnosis of CTEPH. A quantitative metric and classification algorithm are proposed based on volumetric data from SPECT V/Q images. The difference in ventilation and perfusion volumes (VV-P) is defined as a quantitative metric to identify discordant defects in the SPECT images. The algorithm was validated with 22 patients grouped according to their diagnosis: (1) CTEPH and (2) respiratory pathology. Volumetric data from SPECT perfusion images was also compared before and after treatment for CTEPH. CTEPH was detected with a sensitivity of 0.67 and specificity of 0.80. The performance of volumetric data from SPECT perfusion images for the evaluation of treatment response was studied for two cases and improvement of pulmonary perfusion was observed in one case. This study uses volumetric data from SPECT V/Q images for the diagnosis of CTEPH and its differentiation from respiratory pathologies. The results indicate that the defined metric is a viable option for a quantitative analysis of SPECT V/Q images.