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Positron emission tomography/computed tomography (PET/CT) is a standard procedure for imaging cancer commonly used in the clinical practice for several diseases, in particular for cancer staging, restaging, treatment monitoring and radiation therapy planning. Despite the availability of many radiotracers, 18F-fluoro-2-deoxy-2-d-glucose ([18F]FDG) is the most used. International PET/CT guidelines propose protocols for patients’ correct preparation before [18F]FDG injection, in particular with the regard of diabetic patients and therapy management. Hyperglycemic conditions and oral or insulin medication showed advantages and disadvantages on PET/CT scan accuracy: A correct knowledge of effects of these conditions on glucose metabolism assumes a fundamental role on patients management before [18F]FDG PET/CT scan.

High-resolution intraoperative PET/CT specimen imaging, coupled with prostate-specific membrane antigen (PSMA) molecular targeting, holds great potential for the rapid ex vivo identification of disease localizations in high-risk prostate cancer patients undergoing surgery. However, the accurate analysis of radiotracer uptake would require time-consuming manual volumetric segmentation of 3D images. The aim of this study was to test the feasibility of using machine learning to perform automatic nodal segmentation of intraoperative 68Ga-PSMA-11 PET/CT specimen images. Six (n = 6) lymph-nodal specimens were imaged in the operating room after an e.v. injection of 2.1 MBq/kg of 68Ga-PSMA-11. A machine learning-based approach for automatic lymph-nodal segmentation was developed using only open-source Python libraries (Scikit-learn, SciPy, Scikit-image). The implementation of a k-means clustering algorithm (n = 3 clusters) allowed to identify lymph-nodal structures by leveraging differences in tissue density. Refinement of the segmentation masks was performed using morphological operations and 2D/3D-features filtering. Compared to manual segmentation (ITK-SNAP v4.0.1), the automatic segmentation model showed promising results in terms of weighted average precision (97–99%), recall (68–81%), Dice coefficient (80–88%) and Jaccard index (67–79%). Finally, the ML-based segmentation masks allowed to automatically compute semi-quantitative PET metrics (i.e., SUVmax), thus holding promise for facilitating the semi-quantitative analysis of PET/CT images in the operating room.

The NETTER-1 study has proven peptide receptor radionuclide therapy (PRRT) to be one of the most effective therapeutic options for metastatic neuroendocrine tumors (NETs), improving progression-free survival and overall survival. However, PRRT response assessment is challenging and no consensus on methods and timing has yet been reached among experts in the field. This issue is owed to the suboptimal sensitivity and specificity of clinical biomarkers, limitations of morphological response criteria in slowly growing tumors and necrotic changes after therapy, a lack of standardized parameters and timing of functional imaging and the heterogeneity of PRRT protocols in the literature. The aim of this article is to review the most relevant current approaches for PRRT efficacy prediction and response assessment criteria in order to provide an overview of suitable tools for safe and efficacious PRRT.

The aim of this study was to investigate whether the favorable characteristics of novel digital PET/CT (dPET) scanners compared to analog systems (aPET) could translate into an improved disease localization in prostate cancer (PCa) patients with early biochemical recurrence/persistence (BCR/BCP). A retrospective analysis was conducted on 440 consecutive analog (n = 311) or digital (n = 129) 68Ga-PSMA-11 PET/CT scans performed in hormone-sensitive ADT-free PCa patients with early-BCR/BCP (PSA at PET ≤ 2.0 ng/mL), previously treated with radical intent (radical-prostatectomy/radiotherapy). dPET showed a higher positivity rate compared to aPET (48.8% [63/129] vs. 37.3% [116/311], p = 0.03), despite the slightly lower median PSA value of the dPET cohort (0.33 [IQR: 0.26–0.61] vs. 0.55 [IQR: 0.40–0.85] ng/mL, p < 0.01). dPET detection rate was higher in both PSA ranges 0.2–0.5 ng/mL (39.0% [32/82] vs. 25.2% [34/135], p = 0.03) and 0.5–1.0 ng/mL (63.2% [24/38] vs. 40.8% [53/130], p = 0.02), but not for PSA ≥ 1.0 ng/mL. dPET detected a higher per patient median number of pathologic findings (PSMA-RADS ≥ 3) and multi-metastatic cases (>3 lesions) among N1/M1-positive scans (21.7% [10/46] vs. 8.6% [9/105], p = 0.03). Moreover, the proportion of uncertain findings among pathological lesions was significantly lower for dPET than aPET (24.4% [39/160] vs. 38.5% [60/156], p = 0.008). Overall, 68Ga-PSMA-11 dPET showed a better performance compared to aPET, resulting in a higher scan-positivity rate, a higher number of detected pathological lesions, and a lower rate of uncertain findings.

Recently, thyroid (99m)Tc-methoxyisobutylisonitrile ((99m)Tc-MIBI) scintiscanning has been proposed in an attempt to preoperatively identify thyroid malignancies, but discrepant results have been reported for oncocytic lesions. The aim of this study was to investigate the usefulness of visual and semiquantitative analyses of (99m)Tc-MIBI scintigraphy for preoperatively characterizing thyroid nodules with indeterminate cytologic diagnoses, segregating in advance nononcocytic variants from those that are oncocytic. This study also aimed to analyze the relationship between (99m)Tc-MIBI images and P-glycoprotein (P-gp)/multidrug resistance-associated protein-1 (MRP1) immunohistochemical expression. Fifty-one consecutive patients with cold thyroid nodules cytologically diagnosed as nononcocytic or oncocytic follicular neoplasm were prospectively studied. Visual and semiquantitative (99m)Tc-MIBI scanning was performed and the diagnoses of the lesions were histologically proven by subsequent thyroidectomy. Immunohistochemical evaluation of P-gp and MRP1 was also performed on surgical samples. Visual and semiquantitative (99m)Tc-MIBI scintiscans showed a low specificity in preoperatively discriminating malignant oncocytic lesions. In nononcocytic nodules, the semiquantitative method was more accurate than the visual (94.44% and 77.78%, respectively). P-gp protein expression was negative in all thyroid lesions, whereas apical plasma membrane MRP1 expression was found in 78% of the lesions with a negative (99m)Tc-MIBI retention index, compared with 11% of lesions with a positive retention index, correlating most strongly with a negative (99m)Tc-MIBI RI in those cases with strong MRP1 apical expression. Semiquantitative (99m)Tc-MIBI scintigraphy is an adjunctive method to predict preoperatively the malignant behavior of nononcocytic follicular thyroid nodules indeterminate at fine-needle aspiration biopsy, with a potential impact on the definition of their clinical management. Moreover, the good correlation found between immunohistochemical apical expression of MRP1 and the scintigraphic findings supports the (99m)Tc-MIBI results and provides tissue information on the molecular mechanisms responsible for (99m)Tc-MIBI images in thyroid lesions.

High-resolution intraoperative PET/CT specimen imaging, coupled with prostate-specific membrane antigen (PSMA) molecular targeting, holds great potential for the rapid ex vivo identification of disease localizations in high-risk prostate cancer patients undergoing surgery. However, the accurate analysis of radiotracer uptake would require time-consuming manual volumetric segmentation of 3D images. The aim of this study was to test the feasibility of using machine learning to perform automatic nodal segmentation of intraoperative [sup.68]Ga-PSMA-11 PET/CT specimen images. Six (n = 6) lymph-nodal specimens were imaged in the operating room after an e.v. injection of 2.1 MBq/kg of [sup.68]Ga-PSMA-11. A machine learning-based approach for automatic lymph-nodal segmentation was developed using only open-source Python libraries (Scikit-learn, SciPy, Scikit-image). The implementation of a k-means clustering algorithm (n = 3 clusters) allowed to identify lymph-nodal structures by leveraging differences in tissue density. Refinement of the segmentation masks was performed using morphological operations and 2D/3D-features filtering. Compared to manual segmentation (ITK-SNAP v4.0.1), the automatic segmentation model showed promising results in terms of weighted average precision (97–99%), recall (68–81%), Dice coefficient (80–88%) and Jaccard index (67–79%). Finally, the ML-based segmentation masks allowed to automatically compute semi-quantitative PET metrics (i.e., SUVmax), thus holding promise for facilitating the semi-quantitative analysis of PET/CT images in the operating room.

The aim of this study was to investigate whether the favorable characteristics of novel digital PET/CT (dPET) scanners compared to analog systems (aPET) could translate into an improved disease localization in prostate cancer (PCa) patients with early biochemical recurrence/persistence (BCR/BCP). A retrospective analysis was conducted on 440 consecutive analog (n = 311) or digital (n = 129) [sup.68]Ga-PSMA-11 PET/CT scans performed in hormone-sensitive ADT-free PCa patients with early-BCR/BCP (PSA at PET ≤ 2.0 ng/mL), previously treated with radical intent (radical-prostatectomy/radiotherapy). dPET showed a higher positivity rate compared to aPET (48.8% [63/129] vs. 37.3% [116/311], p = 0.03), despite the slightly lower median PSA value of the dPET cohort (0.33 [IQR: 0.26–0.61] vs. 0.55 [IQR: 0.40–0.85] ng/mL, p < 0.01). dPET detection rate was higher in both PSA ranges 0.2–0.5 ng/mL (39.0% [32/82] vs. 25.2% [34/135], p = 0.03) and 0.5–1.0 ng/mL (63.2% [24/38] vs. 40.8% [53/130], p = 0.02), but not for PSA ≥ 1.0 ng/mL. dPET detected a higher per patient median number of pathologic findings (PSMA-RADS ≥ 3) and multi-metastatic cases (>3 lesions) among N1/M1-positive scans (21.7% [10/46] vs. 8.6% [9/105], p = 0.03). Moreover, the proportion of uncertain findings among pathological lesions was significantly lower for dPET than aPET (24.4% [39/160] vs. 38.5% [60/156], p = 0.008). Overall, [sup.68]Ga-PSMA-11 dPET showed a better performance compared to aPET, resulting in a higher scan-positivity rate, a higher number of detected pathological lesions, and a lower rate of uncertain findings.

The aim of this study was to investigate whether the favorable characteristics of novel digital PET/CT (dPET) scanners compared to analog systems (aPET) could translate into an improved disease localization in prostate cancer (PCa) patients with early biochemical recurrence/persistence (BCR/BCP). A retrospective analysis was conducted on 440 consecutive analog ( = 311) or digital ( = 129) Ga-PSMA-11 PET/CT scans performed in hormone-sensitive ADT-free PCa patients with early-BCR/BCP (PSA at PET ≤ 2.0 ng/mL), previously treated with radical intent (radical-prostatectomy/radiotherapy). dPET showed a higher positivity rate compared to aPET (48.8% [63/129] vs. 37.3% [116/311], = 0.03), despite the slightly lower median PSA value of the dPET cohort (0.33 [IQR: 0.26-0.61] vs. 0.55 [IQR: 0.40-0.85] ng/mL, < 0.01). dPET detection rate was higher in both PSA ranges 0.2-0.5 ng/mL (39.0% [32/82] vs. 25.2% [34/135], = 0.03) and 0.5-1.0 ng/mL (63.2% [24/38] vs. 40.8% [53/130], = 0.02), but not for PSA ≥ 1.0 ng/mL. dPET detected a higher per patient median number of pathologic findings (PSMA-RADS ≥ 3) and multi-metastatic cases (>3 lesions) among N1/M1-positive scans (21.7% [10/46] vs. 8.6% [9/105], = 0.03). Moreover, the proportion of uncertain findings among pathological lesions was significantly lower for dPET than aPET (24.4% [39/160] vs. 38.5% [60/156], = 0.008). Overall, Ga-PSMA-11 dPET showed a better performance compared to aPET, resulting in a higher scan-positivity rate, a higher number of detected pathological lesions, and a lower rate of uncertain findings.

Recently, thyroid ^sup 99m^Tc-methoxyisobutylisonitrile (^sup 99m^Tc-MIBI) scintiscanning has been proposed in an attempt to preoperatively identify thyroid malignancies, but discrepant results have been reported for oncocytic lesions. The aim of this study was to investigate the usefulness of visual and semiquantitative analyses of ^sup 99m^Tc-MIBI scintigraphy for preoperatively characterizing thyroid nodules with indeterminate cytologic diagnoses, segregating in advance nononcocytic variants from those that are oncocytic. This study also aimed to analyze the relationship between ^sup 99m^Tc-MIBI images and P-glycoprotein (P-gp)/multidrug resistance-associated protein-1 (MRP1) immunohistochemical expression. Methods: Fifty-one consecutive patients with cold thyroid nodules cytologically diagnosed as nononcocytic or oncocytic follicular neoplasm were prospectively studied. Visual and semiquantitative ^sup 99m^Tc-MIBI scanning was performed and the diagnoses of the lesions were histologically proven by subsequent thyroidectomy. Immunohistochemical evaluation of P-gp and MRP1 was also performed on surgical samples. Results: Visual and semiquantitative ^sup 99m^Tc-MIBI scintiscans showed a low specificity in preoperatively discriminating malignant oncocytic lesions. In nononcocytic nodules, the semiquantitative method was more accurate than the visual (94.44% and 77.78%, respectively). P-gp protein expression was negative in all thyroid lesions, whereas apical plasma membrane MRP1 expression was found in 78% of the lesions with a negative ^sup 99m^Tc-MIBI retention index, compared with 11% of lesions with a positive retention index, correlating most strongly with a negative ^sup 99m^Tc-MIBI RI in those cases with strong MRP1 apical expression. Conclusion: Semiquantitative ^sup 99m^Tc-MIBI scintigraphy is an adjunctive method to predict preoperatively the malignant behavior of nononcocytic follicular thyroid nodules indeterminate at fine-needle aspiration biopsy, with a potential impact on the definition of their clinical management. Moreover, the good correlation found between immunohistochemical apical expression of MRP1 and the scintigraphic findings supports the ^sup 99m^Tc-MIBI results and provides tissue information on the molecular mechanisms responsible for ^sup 99m^Tc-MIBI images in thyroid lesions. [PUBLICATION ABSTRACT]