Thyroglobulin Value Predict Iodine-123 Imaging Result in Differentiated Thyroid Cancer Patients.

Background: In differentiated thyroid cancer (DTC) patients, the response to initial treatments is evaluated 6-12 months after radioiodine therapy (RIT) according to the 2015 American Thyroid Association (2015 ATA) criteria. In selected patients, diagnostic 131-radioiodine whole-body scintigraphy (Dx-WBS) is recommended. We evaluated the diagnostic performance of 123I-Dx-WBS-SPECT/CT imaging in detecting incomplete structural responses in the early follow-up of DTC patients and, additionally, derived optimized basal-Tg value as a yardstick for scintigraphic imaging. Methods: We reviewed the records of 124 low or intermediate-risk DTC patients with negative anti-thyroglobulin antibody. All patients had undergone (near)-total-thyroidectomy followed by RIT. The response to initial treatments was evaluated 6-12 months after RIT. Results: According to the 2015 ATA criteria, 87, 19 and 18 DTC patients were classified to have excellent response (ER), indeterminate/incomplete biochemical response (BIndR/BIR) or structural incomplete response (SIR), respectively. Among patients with less than ER, 18 had a positive 123I-Dx-WBS-SPECT/CT. Metastatic disease at 123I-Dx-WBS-SPECT/CT mainly involved lymph nodes within the central compartment, and corresponding neck ultrasound examinations were negative. The ROC curve analysis was performed to define the best basal-Tg cut-off (i.e., 0.39 ng/mL; AUC = 0.852) able to discriminate patients with and without positive 123I-Dx-WBS-SPECT/CT, respectively. The overall sensitivity, specificity, accuracy, PPV and NPV were 77.8%, 89.6%, 87.9%, 56.0% and 95.9%, respectively. Basal-Tg cut-off was an independent risk factor for having a positive 123I-Dx-WBS-SPECT/CT. Conclusion:123I-Dx-WBS-SPECT/CT identified lymph node metastases in 14/37 patients with less than ER and a negative neck ultrasound, thus modifying the management of such patients. The diagnostic performance of 123I-Dx-WBS-SPECT/CT significantly increased in patients with basal-Tg values ≥ 0.39 ng/mL.

Preliminary Findings of the Role of FAPi in Prostate Cancer Theranostics.

Prostate cancer (PCa) is the most frequently diagnosed cancer worldwide and the second most common cause of cancer-related deaths among men. Progress in molecular imaging has magnified its clinical management; however, an unmet clinical need involves the identification of new imaging biomarkers that complement the gold standard of prostate-specific membrane antigen (PSMA) positron emission tomography (PET) in cases of clinically significant PCa that do not express PSMA. Fibroblast activation protein (FAP) is a type II transmembrane serine overexpressed in many solid cancers that can be imaged through quinoline-based PET tracers derived from an FAP inhibitor (FAPi). Preliminary results of FAPi application in PCa (in PSMA-negative lesions, and in comparison with fluorodeoxyglucose-FDG) are now available in the literature. FAP-targeting ligands for PCa are not limited to detection, but could also include therapeutic applications. In this preliminary review, we provide an overview of the clinical applications of FAPi ligands in PCa, summarising the main results and highlighting contemporary strengths and limitations.

Prostate Cancer Biochemical Recurrence Resulted Negative on [68Ga]Ga-PSMA-11 but Positive on [18F]Fluoromethylcholine PET/CT.

For prostate cancer (PCa) biochemical recurrence (BCR), the primarily suggested imaging technique by the European Association of Urology (EAU) guidelines is prostate-specific membrane antigen (PSMA) positron emission tomography/computer tomography (PET/CT). Indeed, the increased detection rate of PSMA PET/CT for early BCR has led to a fast and wide acceptance of this novel technology. However, PCa is a very heterogeneous disease, not always easily assessable with the highly specific PSMA PET with around 10% of cases occuring without PSMA expression. In this paper, we present the case of a patient with PCa BCR that resulted negative on [68Ga]Ga-PSMA-11 PET/CT, but positive on [18F]Fluoromethylcholine (Choline) PET/CT.

[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.

Artificial Neural Networks in Cardiovascular Diseases and its Potential for Clinical Application in Molecular Imaging.

In medical imaging, Artificial Intelligence is described as the ability of a system to properly interpret and learn from external data, acquiring knowledge to achieve specific goals and tasks through flexible adaptation. The number of possible applications of Artificial Intelligence is also huge in clinical medicine and cardiovascular diseases. To describe for the first time in literature, the main results of articles about Artificial Intelligence potential for clinical applications in molecular imaging techniques, and to describe its advancements in cardiovascular diseases assessed with nuclear medicine imaging modalities. A comprehensive search strategy was used based on SCOPUS and PubMed databases. From all studies published in English, we selected the most relevant articles that evaluated the technological insights of AI in nuclear cardiology applications. Artificial Intelligence may improve patient care in many different fields, from the semi-automatization of the medical work, through the technical aspect of image preparation, interpretation, the calculation of additional factors based on data obtained during scanning, to the prognostic prediction and risk-- group selection. Myocardial implementation of Artificial Intelligence algorithms in nuclear cardiology can improve and facilitate the diagnostic and predictive process, and global patient care. Building large databases containing clinical and image data is a first but essential step to create and train automated diagnostic/prognostic models able to help the clinicians to make unbiased and faster decisions for precision healthcare.