Prostate-specific membrane antigen-PET/CT may result in stage migration in prostate cancer: performances, quantitative analysis, and potential criticism in the clinical practice.

AIM: The early detection of prostate cancer (PCa) metastatic disease with PET imaging leads to stage migration and change of disease management. We aimed to assess the impact on clinical management deriving from prostate-specific membrane antigen (PSMA) imaging with a digital PET/CT during the routine application in the staging and restaging process of PCa. MATERIAL AND METHODS: Eighty consecutive PCa patients underwent 18F-PSMA-1007. Digital PET/CT were retrospectively evaluated and discussed with oncologists to evaluate the impact on clinical management. Performances analysis, correlation among variables also considering semiquantitative parameters have been conducted. RESULTS: In the whole group of 80 patients at staging (N = 31) and restaging (N = 49), the detection rate of PSMA PET was 85% for all lesions. At staging, the performance analysis resulted in sensitivity 77.6%, specificity 89.5%, negative predictive value (NPV) 77.6%, positive predictive value (PPV) 89.5%, accuracy 85.7%, and area under curve (AUC) 0.87%. The performance of restaging PET in the group of patients with PSA values <1 ng/ml resulted in the following values: sensitivity 66.7%, specificity 92.9%, NPV 85.7%, PPV 81.3%, accuracy 82.6%, and AUC 0.79. Semiquantitative analysis revealed a mean value of SUVmax, metabolic tumor volume, and total lesion PSMA expression with differences in patients with high risk compared to low intermediate. At restaging PET, semiquantitative values of patients with total prostate specific antigen (tPSA) ≤ 1 ng/ml were significantly less than those of the tPSA > 1 ng/ml. A significant impact on clinical management was reported in 46/80 patients (57.5%) based on PSMA PET findings at staging and restaging. CONCLUSION: Although PSMA-PET provides optimal performances, its current role in redefining a better staging should be translated in the current clinical scenario about potential improvement in clinical/survival outcomes.

Prostate-Specific Membrane Antigen Positron Emission Tomography Oncological Applications beyond Prostate Cancer in Comparison to Other Radiopharmaceuticals.

BACKGROUND: Prostate-specific membrane antigen (PSMA) is a type II transmembrane glycoprotein overexpressed on the surface of tumor cells in most of the patients affected by prostate adenocarcinoma (PCa). However, PSMA expression has also been demonstrated in the endothelial cells of newly formed vessels of various solid tumors, suggesting a role for PSMA in neoangiogenesis. In this scenario, gallium-68 (68Ga) or fluoro-18 (18F)-labeled PSMA positron emission tomography (PET) may play a role in tumors other than PCa, generally evaluated employing other radiopharmaceuticals targeting different pathways. This review aims to investigate the detection rate of PSMA-PET compared to other radiopharmaceuticals (especially [18F]FDG) in non-prostate tumors to identify patients who may benefit from the use of such a theragnostic agent. METHODS: We performed a bibliographic search on three different databases until February 2024 using the following terms: "positron emission tomography", "PET", "PET/CT", "Prostate-specific membrane antigen", "PSMA", "non-prostate", "not prostate cancer", "solid tumor", "FDG", "Fluorodeoxyglucose", "FAPi", "FET", "MET", "DOPA", "choline", "FCH", "FES", "DOTATOC", "DOTANOC", and "DOTATATE". Only original articles edited in English with at least 10 patients were included. RESULTS: Out of a total of 120 articles, only 25 original articles comparing PSMA with other radiotracers were included in this study. The main evidence was demonstrated in renal cell carcinoma, where PSMA showed a higher detection rate compared to [18F]FDG PET/CT, with implications for patient management. PSMA PET may also improve the assessment of other entities, such as gliomas, in defining regions of early neoangiogenesis. Further data are needed to evaluate the potential role of PSMA-PET in triple-negative breast cancer as a novel therapeutic vascular target. Finally, unclear applications of PSMA-PET include thyroid and gastrointestinal tumors. CONCLUSIONS: The present review shows the potential use of PSMA-labeled PET/CT in solid tumors beyond PCa, underlining its value over other radiopharmaceuticals (mainly [18F]FDG). Prospective clinical trials with larger sample sizes are crucial to further investigate these possible clinical applications.

FAPi-Based Agents in Thyroid Cancer: A New Step towards Diagnosis and Therapy? A Systematic Review of the Literature.

(1) Background: Thyroid cancer (TC) is often treated with surgery followed by iodine-131. Up to 50% of the instances of TC lose their avidity to 131I, becoming more aggressive. In this scenario, [18F]FDG PET/CT imaging is used for evaluating the widespread nature of the disease, despite its low sensitivity and a false negative rate of 8-21.1%. A novel class of PET agents targeting the fibroblast activation protein inhibitor (FAPi) has emerged, studied particularly for their potential application to theranostics. (2) Methods: A search of the literature was performed by two independent authors (P.G. and L.E.) using the PubMed, Scopus, Web of Science, Cochrane Library, and EMBASE databases. The following terms were used: "FAP" or "FAPi" or "Fibroblast activating protein" and "thyroid" or "thyroid cancer", in different combinations. The included papers were original articles, clinical studies, and case reports in the English language. No time limits were used. Editorials, conference papers, reviews, and preclinical studies were excluded. (3) Results: There were 31 papers that were selected. Some studies reported a low or absent FAPi uptake in TC lesions; others reported promising findings for the detection of metastases. (4) Conclusions: The preliminary results are encouraging. FAPI agents are an alternative to [18F]FDG and a promising theranostic tool. However, further studies with a larger population are needed.

Artificial Intelligence Analysis Using MRI and PET Imaging in Gliomas: A Narrative Review.

The lack of early detection and a high rate of recurrence/progression after surgery are defined as the most common causes of a very poor prognosis of Gliomas. The developments of quantification systems with special regards to artificial intelligence (AI) on medical images (CT, MRI, PET) are under evaluation in the clinical and research context in view of several applications providing different information related to the reconstruction of imaging, the segmentation of tissues acquired, the selection of features, and the proper data analyses. Different approaches of AI have been proposed as the machine and deep learning, which utilize artificial neural networks inspired by neuronal architectures. In addition, new systems have been developed using AI techniques to offer suggestions or make decisions in medical diagnosis, emulating the judgment of radiologist experts. The potential clinical role of AI focuses on the prediction of disease progression in more aggressive forms in gliomas, differential diagnosis (pseudoprogression vs. proper progression), and the follow-up of aggressive gliomas. This narrative Review will focus on the available applications of AI in brain tumor diagnosis, mainly related to malignant gliomas, with particular attention to the postoperative application of MRI and PET imaging, considering the current state of technical approach and the evaluation after treatment (including surgery, radiotherapy/chemotherapy, and prognostic stratification).

Head-to-Head Comparison of FDG and Radiolabeled FAPI PET: A Systematic Review of the Literature.

FAPI-based radiopharmaceuticals are a novel class of tracers, mainly used for PET imaging, which have demonstrated several advantages over [18F]FDG, especially in the case of low-grade or well-differentiated tumors. We conducted this systematic review to evaluate all the studies where a head-to-head comparison had been performed to explore the potential utility of FAPI tracers in clinical practice. FAPI-based radiopharmaceuticals have shown promising results globally, in particular in detecting peritoneal carcinomatosis, but studies with wider populations are needed to better understand all the advantages of these new radiopharmaceuticals.

18F-FDG PET-Derived Volume-Based Parameters to Predict Disease-Free Survival in Patients with Grade III Breast Cancer of Different Molecular Subtypes Candidates to Neoadjuvant Chemotherapy.

We investigated whether baseline [18F] Fluorodeoxyglucose (18F-FDG) positron emission tomography (PET)-derived semiquantitative parameters could predict disease-free survival (DFS) in patients with grade III breast cancer (BC) of different molecular subtypes candidate to neoadjuvant chemotherapy (NAC). For each 18F-FDG-PET/CT scan, the following parameters were calculated in the primary tumor (SUVmax, SUVmean, MTV, TLG) and whole-body (WB_SUVmax, WB_MTV, and WB_TLG). Receiver operating characteristic (ROC) analysis was used to determine the capability to predict DFS and find the optimal threshold for each parameter. Ninety-five grade III breast cancer patients with different molecular types were retrieved from the databases of the University Hospital of Padua and the University Hospital of Ferrara (luminal A: 5; luminal B: 34; luminal B-HER2: 22; HER2-enriched: 7; triple-negative: 27). In luminal B patients, WB_MTV (AUC: 0.75; best cut-off: WB_MTV > 195.33; SS: 55.56%, SP: 100%; p = 0.002) and WB_TLG (AUC: 0.73; best cut-off: WB_TLG > 1066.21; SS: 55.56%, SP: 100%; p = 0.05) were the best predictors of DFS. In luminal B-HER2 patients, WB_SUVmax was the only predictor of DFS (AUC: 0.857; best cut-off: WB_SUVmax > 13.12; SS: 100%; SP: 71.43%; p < 0.001). No parameter significantly affected the prediction of DFS in patients with grade III triple-negative BC. Volume-based parameters, extracted from baseline 18F-FDG PET, seem promising in predicting recurrence in patients with grade III luminal B and luminal B- HER2 breast cancer undergoing NAC.

Artificial Intelligence in Breast Cancer: A Systematic Review on PET Imaging Clinical Applications.

BACKGROUND: 18F-FDG PET/CT imaging represents the most important functional imaging method in oncology. European Society of Medical Oncology and the National Comprehensive Cancer Network guidelines defined a crucial role of 18F-FDG PET/CT imaging for local/locally advanced breast cancer. The application of artificial intelligence on PET images might potentially contributes in the field of precision medicine. OBJECTIVE: This review aims to summarize the clinical indications and limitations of PET imaging for comprehensive artificial intelligence in relation to breast cancer subtype, hormone receptor status, proliferation rate, and lymphonodal (LN)/distant metastatic spread, based on recent literature. METHODS: A literature search of the Pubmed/Scopus/Google Scholar/Cochrane/EMBASE databases was carried out, searching for articles on the use of artificial intelligence and PET in breast tumors. The search was updated from January 2010 to October 2021 and was limited to original articles published in English and about humans. A combination of the search terms "artificial intelligence", "breast cancer", "breast tumor", "PET", "Positron emission tomography", "PET/CT", "PET/MRI", "radiomic"," texture analysis", "machine learning", "deep learning" was used. RESULTS: Twenty-three articles were selected following the PRISMA criteria from 139 records obtained from the Pubmed/Scopus/Google Scholar/Cochrane/EMBASE databases according to our research strategy. The QUADAS of 30 full-text articles assessed reported seven articles that were excluded for not being relevant to population and outcomes and/or for lower level of evidence. The majority of papers were at low risk of bias and applicability. The articles were divided per topic, such as the value of PET in the staging and re-staging of breast cancer patients, including new radiopharmaceuticals and simultaneous PET/MRI. CONCLUSION: Despite the current role of AI in this field remains still undefined, several applications for PET/CT imaging are under development, with some preliminary interesting results particularly focused on the staging phase that might be clinically translated after further validation studies.

The Value of Semiquantitative Parameters Derived from 18F-FDG PET/CT for Predicting Response to Neoadjuvant Chemotherapy in a Cohort of Patients with Different Molecular Subtypes of Breast Cancer.

Pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) is a strong prognostic factor in breast cancer (BC). The aim of this study was to investigate whether semiquantitative parameters derived from baseline [18F]Fluorodeoxyglucose ([18F]FDG) positron emission computed tomography/computed tomography (PET/CT) could predict pCR after NAC and survival outcomes in patients affected by different molecular subtypes of BC. We retrospectively retrieved patients from the databases of two Italian hospitals (Centre A: University Hospital of Ferrara; Centre B: University of Padua) meeting the following inclusion criteria: (1) diagnosis of BC; (2) history of NAC; (3) baseline [18F]FDG PET/CT performed before the first cycle of NAC; (4) available follow-up data (response after NAC and survival information). For each [18F]FDG PET/CT scan, semiquantitative parameters (SUVmax, SUVmean, MTV and TLG) related to the primary tumor (B), to the reference lesion for both axillary (N) and distant lymph node (DN), and to the whole-body burden of disease (WB) were evaluated. Patients enrolled were 133: 34 from centre A and 99 from centre B. Patients' molecular subtypes were: 9 luminal A, 49 luminal B, 33 luminal B + HER-2, 10 HER-2 enriched, and 32 triple negative (TNBC). Luminal A and HER-2 enriched BC patients were excluded from the analysis due to the small sample size. pCR after NAC was achieved in 47 patients (41.2%). [18F]FDG PET/CT detected the primary tumor in 98.3% of patients and lymph node metastases were more frequently detected in Luminal B subgroup. Among Luminal B patients, median SUVmean_B values were significantly higher (p = 0.027) in responders (7.06 ± 5.9) vs. non-responders (4.4 ± 2.1) to NAC. Luminal B + HER-2 non-responders showed a statistically significantly higher median MTV_B (7.3 ± 4.2 cm3 vs. 3.5 ± 2.5 cm3; p = 0.003) and TLG_B (36.5 ± 24.9 vs. 18.9 ± 17.7; p = 0.025) than responders at baseline [18F]FDG PET/CT. None of the semiquantitative parameters predicted pCR after NAC in TNBC patients. However, among TNBC patients who achieved pCR after NAC, 4 volumetric parameters (MTV_B, TLG_B, MTV_WB and TLG_WB) were significantly higher in patients dead at follow-up. If confirmed in further studies, these results could open up a widespread use of [18F]FDG PET/CT as a baseline predictor of response to NAC in luminal B and luminal B + HER-2 patients and as a prognostic tool in TNBC.

The Added Value of [18F]Choline PET/CT in Low-Risk Prostate Cancer Staging: A Case Report.

In the management of prostate cancer (PCa), correct staging is crucial in order to assess the right therapeutic approach. [18F]Choline PET/CT has been shown to provide more accurate staging information than conventional imaging approaches. The aim of this paper is to provide a real practice demonstration of the impact of [18F]Choline PET/CT on low-risk prostate cancer staging and clinical management. We report a 64-year-old man with biochemical PCa recurrence diagnosis after transurethral resection of the prostate. The patient, after the detection of an increased level of PSA, underwent multi-parametric prostate magnetic resonance imaging (mpMRI) that did not show evidence of disease. The patient was admitted to perform [18F]Choline PET/CT that showed a macroscopic prostate recurrence. Patient underwent photon external beam radiation therapy (EBRT) treatment, and [18F]Choline PET/CT was also used to define treatment volumes. At 3- and 6-month clinical follow-up evaluations, no late toxicity was detected and a significant reduction in PSA value was shown. Therefore, our case highlights the potential usefulness of [18F]Choline PET/CT for the staging of low-risk prostate cancer and its impact on the management and quality of life of such patients. The presented case should urge the scientific community to enhance larger and multicentric studies, assessing more extensively the potential impact of [18F]Choline PET/CT in this clinical scenario.

Sporadic and von Hippel-Lindau Related Hemangioblastomas of Brain and Spinal Cord: Multimodal Imaging for Intraoperative Strategy.

Hemangioblastomas (HBs) are rare, benign tumors often related to von Hippel-Lindau disease. They represent the most frequent primary cerebellar tumors in adults. Neurosurgical procedures aim to obtain a gross-total resection of tumor nodules, avoiding intra-postoperative hemorrhage. The introduction of new intraoperative imaging techniques has considerably changed surgical strategies in neuro-oncology. We present an overview of clinical and radiological data of a mono-institutional retrospective cohort, focusing on the role of intraoperative multimodal imaging in surgical strategy. From 2015 to 2021, we identified 64 (81%) cranial (42 cerebellar, 8 supratentorial, and 14 of the brainstem) HBs and 15 (19%) spinal (4 cervical and 11 dorsal) HBs in 79 patients. Intraoperatively, indocyanine green videoangiography with FLOW800 was used in 62 cases (52 cranial and 10 spinal), intraoperative ultrasound and contrast-enhanced ultrasounds in 22 cases (18 cranial and 4 spinal HBs), and fluorescein in 10 cases (in 6 cranial and 2 spinal cases used as SF-VA). Gross total resection was achieved in 100% of the cases (53 mural nodule removal and 26 complete resections of the solid tumor). No side effects were reported following the combination of these tools. Multimodal intraoperative techniques provide valuable and reliable information to identify the tumor and its vasculature, guiding a more precise and safer resection and reducing the risk of recurrence.

Advances in the In Vivo Quantitative and Qualitative Imaging Characterization of Gliomas.

Gliomas are the most common and aggressive intra-axial primary tumours of the central nervous system (CNS), arising from glial cells [...].

A Systematic Review Comparing Lymphoscintigraphy and Magnetic Resonance Imaging Techniques in the Assessment of Peripheral Lymphedema.

BACKGROUND: Peripheral lymphedema represents a debilitating condition affecting the lymphatic system of the limbs resulting from impaired drainage and excessive lymphatic fluid accumulation in the interstitial spaces. Lymphoscintigraphy is the imaging modality of first choice to investigate patients with peripheral lymphedema. Nevertheless, in recent times, magnetic resonance imaging (MRI) techniques have also been applied to assess patients with lymphedema. OBJECTIVE: The present systematic review aims to appraise the evidence by providing a head-to-head comparison between lymphoscintigraphy and MRI techniques in peripheral lymphedema. METHODS: A systematic literature search was performed using the PubMed database and Cochrane Central Register of Controlled Trials (CENTRAL). The eligibility criteria for the articles to be included in the qualitative synthesis were: 1) a study cohort or a subset of patients with a clinical diagnosis of peripheral lymphedema (either upper or lower limb); 2) execution of both MR imaging and lymphoscintigraphy in the same subset of patients. The methodological quality of the studies was assessed by an investigator using the "Quality Assessment of Diagnostic Accuracy Studies" tool, v. 2 (QUADAS-2). RESULTS: Overall, 11 studies were ultimately included in the quantitative analysis. No meta-analysis was performed due to the heterogeneous patient samples, the different study aims of the retrieved literature, and the limited number of available articles. In the diagnosis of upper limb extremity lymphedema, the sensitivity of MRI techniques appears superior to that of lymphoscintigraphy. Comparative studies in the lower limbs are still scarce but suggest that MRI may increase the diagnostic accuracy for lymphedema. CONCLUSION: The available literature on patients with lymphedema evaluated with both lymphoscintigraphy and MRI does not allow definite conclusions on the superiority of one imaging technique over the other. Further studies, including well-selected patient samples, are still necessary to compare the accuracy of these imaging modalities. Since MRI techniques seem to provide complementary findings to lymphoscintigraphy, it would be conceivable to acquire both imaging exams in patients with peripheral lymphedema. Furthermore, studies evaluating the clinical impact of adding MRl to the diagnostic workup are warranted.

Radiomics Analysis of Brain [18F]FDG PET/CT to Predict Alzheimer's Disease in Patients with Amyloid PET Positivity: A Preliminary Report on the Application of SPM Cortical Segmentation, Pyradiomics and Machine-Learning Analysis.

BACKGROUND: Early in-vivo diagnosis of Alzheimer's disease (AD) is crucial for accurate management of patients, in particular, to select subjects with mild cognitive impairment (MCI) that may evolve into AD, and to define other types of MCI non-AD patients. The application of artificial intelligence to functional brain [18F]fluorodeoxyglucose (FDG) positron emission tomography (PET)/computed tomography(CT) aiming to increase diagnostic accuracy in the diagnosis of AD is still undetermined. In this field, we propose a radiomics analysis on advanced imaging segmentation method Statistical Parametric Mapping (SPM)-based completed with a Machine-Learning (ML) application to predict the diagnosis of AD, also by comparing the results with following Amyloid-PET and final clinical diagnosis. METHODS: From July 2016 to September 2017, 43 patients underwent PET/CT scans with FDG and Florbetaben brain PET/CT and at least 24 months of clinical/instrumental follow-up. Patients were retrospectively evaluated by a multidisciplinary team (MDT = Neurologist, Psychologist, Radiologist, Nuclear Medicine Physician, Laboratory Clinic) at the G. Giglio Institute in Cefalù, Italy. Starting from the cerebral segmentations applied by SPM on the main cortical macro-areas of each patient, Pyradiomics was used for the feature extraction process; subsequently, an innovative descriptive-inferential mixed sequential approach and a machine learning algorithm (i.e., discriminant analysis) were used to obtain the best diagnostic performance in prediction of amyloid deposition and the final diagnosis of AD. RESULTS: A total of 11 radiomics features significantly predictive of cortical beta-amyloid deposition (n = 6) and AD (n = 5) were found. Among them, two higher-order features (original_glcm_Idmn and original_glcm_Id), extracted from the limbic enthorinal cortical area (ROI-1) in the FDG-PET/CT images, predicted the positivity of Amyloid-PET/CT scans with maximum values of sensitivity (SS), specificity (SP), precision (PR) and accuracy (AC) of 84.92%, 75.13%, 73.75%, and 79.56%, respectively. Conversely, for the prediction of the clinical-instrumental final diagnosis of AD, the best performance was obtained by two higher-order features (original_glcm_MCC and original_glcm_Maximum Probability) extracted from ROI-2 (frontal cortex) with a SS, SP, PR and AC of 75.16%, 80.50%, 77.68%, and 78.05%, respectively, and by one higher-order feature (original_glcm_Idmn) extracted from ROI-3 (medial Temporal cortex; SS = 80.88%, SP = 76.85%, PR = 75.63%, AC = 78.76%. CONCLUSIONS: The results obtained in this preliminary study support advanced segmentation of cortical areas typically involved in early AD on FDG PET/CT brain images, and radiomics analysis for the identification of specific high-order features to predict Amyloid deposition and final diagnosis of AD.

PSMA and Choline PET for the Assessment of Response to Therapy and Survival Outcomes in Prostate Cancer Patients: A Systematic Review from the Literature.

The aims of this systematic review were to (1) assess the utility of PSMA-PET and choline-PET in the assessment of response to systemic and local therapy, and to (2) determine the value of both tracers for the prediction of response to therapy and survival outcomes in prostate cancer. We performed a systematic literature search in PubMed/Scopus/Google Scholar/Cochrane/EMBASE databases (between January 2010 and October 2021) accordingly. The quality of the included studies was evaluated following the "Quality Assessment of Prognostic Accuracy Studies" tool (QUAPAS-2). We selected 40 articles: 23 articles discussed the use of PET imaging with [68Ga]PSMA-11 (16 articles/1123 patients) or [11C]/[18F]Choline (7 articles/356 patients) for the prediction of response to radiotherapy (RT) and survival outcomes. Seven articles (three with [68Ga]PSMA-11, three with [11C]Choline, one with [18F]Choline) assessed the role of PET imaging in the evaluation of response to docetaxel (as neoadjuvant therapy in one study, as first-line therapy in five studies, and as a palliative regimen in one study). Seven papers with radiolabeled [18F]Choline PET/CT (n = 121 patients) and three with [68Ga]PSMA-11 PET (n = 87 patients) were selected before and after enzalutamide/abiraterone acetate. Finally, [18F]Choline and [68Ga]PSMA-11 PET/CT as gatekeepers for the treatment of metastatic prostate cancer with Radium-223 were assessed in three papers. In conclusion, in patients undergoing RT, radiolabeled choline and [68Ga]PSMA-11 have an important prognostic role. In the case of systemic therapies, the role of such new-generation imaging techniques is still controversial without sufficient data, thus requiring additional in this scenario.

[68Ga]DOTATOC PET/CT Radiomics to Predict the Response in GEP-NETs Undergoing [177Lu]DOTATOC PRRT: The "Theragnomics" Concept.

Despite impressive results, almost 30% of NET do not respond to PRRT and no well-established criteria are suitable to predict response. Therefore, we assessed the predictive value of radiomics [68Ga]DOTATOC PET/CT images pre-PRRT in metastatic GEP NET. We retrospectively analyzed the predictive value of radiomics in 324 SSTR-2-positive lesions from 38 metastatic GEP-NET patients (nine G1, 27 G2, and two G3) who underwent restaging [68Ga]DOTATOC PET/CT before complete PRRT with [177Lu]DOTATOC. Clinical, laboratory, and radiological follow-up data were collected for at least six months after the last cycle. Through LifeX, we extracted 65 PET features for each lesion. Grading, PRRT number of cycles, and cumulative activity, pre- and post-PRRT CgA values were also considered as additional clinical features. [68Ga]DOTATOC PET/CT follow-up with the same scanner for each patient determined the disease status (progression vs. response in terms of stability/reduction/disappearance) for each lesion. All features (PET and clinical) were also correlated with follow-up data in a per-site analysis (liver, lymph nodes, and bone), and for features significantly associated with response, the Δradiomics for each lesion was assessed on follow-up [68Ga]DOTATOC PET/CT performed until nine months post-PRRT. A statistical system based on the point-biserial correlation and logistic regression analysis was used for the reduction and selection of the features. Discriminant analysis was used, instead, to obtain the predictive model using the k-fold strategy to split data into training and validation sets. From the reduction and selection process, HISTO_Skewness and HISTO_Kurtosis were able to predict response with an area under the receiver operating characteristics curve (AUC ROC), sensitivity, and specificity of 0.745, 80.6%, 67.2% and 0.722, 61.2%, 75.9%, respectively. Moreover, a combination of three features (HISTO_Skewness; HISTO_Kurtosis, and Grading) did not improve the AUC significantly with 0.744. SUVmax, however, could not predict the response to PRRT (p = 0.49, AUC 0.523). The presented preliminary "theragnomics" model proved to be superior to conventional quantitative parameters to predict the response of GEP-NET lesions in patients treated with complete [177Lu]DOTATOC PRRT, regardless of the lesion site.

PET-based artificial intelligence applications in cardiac nuclear medicine.

In the recent years, artificial intelligence (AI) applications have gained interest in the field of cardiovascular medical imaging, including positron emission tomography (PET). The use of AI in cardiac PET imaging is to date limited, although first, important results have been shown, overcoming technical issues, improving diagnostic accuracy and providing prognostic information. In this review we aimed to summarize the state-of-the-art regarding AI applications in cardiovascular PET.

Choline PET/CT features to predict survival outcome in high-risk prostate cancer restaging: a preliminary machine-learning radiomics study.

BACKGROUND: Radiomic features are increasingly utilized to evaluate tumor heterogeneity in PET imaging but to date its role has not been investigated for Cho-PET in prostate cancer. The potential application of radiomics features analysis using a machine-learning radiomics algorithm was evaluated to select 18F-Cho PET/CT imaging features to predict disease progression in PCa. METHODS: We retrospectively analyzed high-risk PCa patients who underwent restaging 18F-Cho PET/CT from November 2013 to May 2018. 18F-Cho PET/CT studies and related structures containing volumetric segmentations were imported in the "CGITA" toolbox to extract imaging features from each lesion. A Machine-learning model has been adapted using NCA for feature selection, while DA was used as a method for feature classification and performance analysis. RESULTS: One hundred and six imaging features were extracted for 46 lesions for a total of 4876 features analyzed. No significant differences between the training and validating sets in terms of age, sex, PSA values, lesion location and size (P>0.05) were demonstrated by the machine-learning model. Thirteen features were able to discriminate FU disease status after NCA selection. Best performance in DA classification was obtained using the combination of the 13 selected features (sensitivity 74%, specificity 58% and accuracy 66%) compared to the use of all features (sensitivity 40%, specificity 52%, and accuracy 51%). Per-site performance of the 13 selected features in DA classification were as follows: T = sensitivity 63%, specificity 83%, accuracy 71%; N = sensitivity 87%, specificity 91% of and accuracy 90%; bone-M = sensitivity 33%, specificity 77% and accuracy 66%. CONCLUSIONS: An artificial intelligence model demonstrated to be feasible and able to select a panel of 18F-Cho PET/CT features with valuable association with PCa patients' outcome.

[18F] Fluorothymidine Positron Emission Tomography Imaging in Primary Brain Tumours: A Systematic Review.

PURPOSE: This review aimed to summarize the available literature on the clinical application of [18F] FLT PET imaging in primary brain tumours. METHODS: A comprehensive search strategy based on Pubmed/Medline, Scopus, Web of Science, Cochrane Library, Google Scholar, and the Embase databases was carried on using the following search string: ('3` Fluorothymidine'/exp OR 'FLT' OR '[81F]-FLT' OR '[18F] Fluorothymidine') AND ('pet'/exp OR 'pet' OR 'positron emission tomography') AND ('glioma'/exp OR 'glioma' OR 'brain tumour'/exp OR 'brain tumour'). The search was updated till March 2021 and only articles in English and studies investigating the clinical applications of [18F] FLT PET and PET/CT in primary brain tumours were considered eligible for inclusion. RESULTS: The literature search ultimately yielded 52 studies included in the systematic review, with main results as follows: a) the uptake of [18F] FLT may guide stereotactic biopsy but does not discriminate between grade II and III glioma. b) [18F] FLT uptake and texture parameters correlate with overall survival (OS) in newly diagnosed gliomas. c) In patients with recurrent glioma, proliferative volume (PV) and tumour-to-normal brain (T/N) uptake ratio are independent predictors of survival. d) Patients demonstrating response to therapy at [18F] FLT PET scan show longer OS compared to non-responders. e) [18F] FLT PET demonstrated good performance in discriminating tumour recurrence from radionecrosis. However, controversial results exist in comparative literature examining the performance of [18F] FLT vs. other radiotracers in the assessment of recurrence. CONCLUSION: [18F] FLT PET imaging has demonstrated potential benefits for grading, diagnostic and prognostic purposes, despite the small sample size studies due to the relatively low availability of the radiotracer.

Feasibility on the Use of Radiomics Features of 11[C]-MET PET/CT in Central Nervous System Tumours: Preliminary Results on Potential Grading Discrimination Using a Machine Learning Model.

BACKGROUND/AIM: Nowadays, Machine Learning (ML) algorithms have demonstrated remarkable progress in image-recognition tasks and could be useful for the new concept of precision medicine in order to help physicians in the choice of therapeutic strategies for brain tumours. Previous data suggest that, in the central nervous system (CNS) tumours, amino acid PET may more accurately demarcate the active disease than paramagnetic enhanced MRI, which is currently the standard method of evaluation in brain tumours and helps in the assessment of disease grading, as a fundamental basis for proper clinical patient management. The aim of this study is to evaluate the feasibility of ML on 11[C]-MET PET/CT scan images and to propose a radiomics workflow using a machine-learning method to create a predictive model capable of discriminating between low-grade and high-grade CNS tumours. MATERIALS AND METHODS: In this retrospective study, fifty-six patients affected by a primary brain tumour who underwent 11[C]-MET PET/CT were selected from January 2016 to December 2019. Pathological examination was available in all patients to confirm the diagnosis and grading of disease. PET/CT acquisition was performed after 10 min from the administration of 11C-Methionine (401-610 MBq) for a time acquisition of 15 min. 11[C]-MET PET/CT images were acquired using two scanners (24 patients on a Siemens scan and 32 patients on a GE scan). Then, LIFEx software was used to delineate brain tumours using two different semi-automatic and user-independent segmentation approaches and to extract 44 radiomics features for each segmentation. A novel mixed descriptive-inferential sequential approach was used to identify a subset of relevant features that correlate with the grading of disease confirmed by pathological examination and clinical outcome. Finally, a machine learning model based on discriminant analysis was used in the evaluation of grading prediction (low grade CNS vs. high-grade CNS) of 11[C]-MET PET/CT. RESULTS: The proposed machine learning model based on (i) two semi-automatic and user-independent segmentation processes, (ii) an innovative feature selection and reduction process, and (iii) the discriminant analysis, showed good performance in the prediction of tumour grade when the volumetric segmentation was used for feature extraction. In this case, the proposed model obtained an accuracy of ~85% (AUC ~79%) in the subgroup of patients who underwent Siemens tomography scans, of 80.51% (AUC 65.73%) in patients who underwent GE tomography scans, and of 70.31% (AUC 64.13%) in the whole patients' dataset (Siemens and GE scans). CONCLUSIONS: This preliminary study on the use of an ML model demonstrated to be feasible and able to select radiomics features of 11[C]-MET PET with potential value in prediction of grading of disease. Further studies are needed to improve radiomics algorithms to personalize predictive and prognostic models and potentially support the medical decision process.