Annals of Nuclear Medicine最新文献

Objective: Using fluorine-18-fluorodeoxyglucose (¹⁸F-FDG) positron emission tomography/computed tomography (PET/CT), a multiparametric analysis will be performed in the differential diagnosis of patients with single solitary pulmonary lesion and extrapulmonary malignant tumor to discriminate between a second primary lung cancer (SPLC) and pulmonary metastasis (PM).

Methods: This study retrospectively studied 84 patients with preoperative exams utilizing ¹⁸F-FDG PET/CT. Using complementing PET/CT parameters, a composite model was developed. A receiver operating characteristic (ROC) analysis assessed the combined model and each independent parameter's differential diagnostic efficacies. Furthermore, this study investigated the improvement in diagnostic efficacy using other metrics, such as integrated discriminatory improvement (IDI) and net reclassification improvement (NRI).

Results: The highest discriminative diagnostic value was obtained by the independent parameters energy (1,039,358.1 [95126.2-1,965,032.2] vs. 92,011.0 [45916.3-365,322.9], P = 0.001). In comparison to peak standardized uptake value (SUVpeak), total lesion glycolysis (TLG), energy, lobulation, and spiculation alone, the combined model (addition of these factors) significantly improved the differential diagnostic efficacy of SPLCs and PMs (sensitivity = 76.2%, specificity = 83.8%, area under the curve [AUC] = 0.826) and permitted reclassification using IDI = 0.176 (P < 0.001), 0.169 (P < 0.001), 0.127 (P < 0.001), and categorical NRI = 0.678 (P < 0.001), 0.637 (P < 0.001), and 0.592 (P < 0.001) compared to SUVpeak, TLG and energy separately. DeLong's test revealed a statistically significant enhancement in ROC when compared to SUVpeak (Z = 2.372, P = 0.018), TLG (Z = 2.095, P = 0.036), and energy (Z = 2.318, P = 0.020).

Conclusion: Combining multiple parameters using ¹⁸F-FDG PET/CT may further improve distinguishing between SPLCs and PMs in patients with single solitary pulmonary lesion and extrapulmonary malignant tumor.

Purpose: The aim of this study was to develop a machine learning model (named V/P-mics) to identify pulmonary embolism based on lung ventilation/perfusion single-photon emission tomography (V/P-SPECT) images.

Methods: We retrospectively collected the data of 260 patients from one hospital who underwent V/P-SPECT. Patients were randomly assigned to training and testing groups in a 7:3 ratio. We created an internal further validation group using data of an additional 35 patients from the same hospital, and an external further validation group using data of 30 patients from another hospital. We constructed 35 models and selected one for further optimization. The generalizability of V/P-mics was proven by comparing the area under the curve (AUC) of the testing group, internal and external further validation groups. The diagnostic accuracy and efficiency of V/P-mics was compared with that of nuclear physicians.

Results: V/P-mics showed excellent generalizability, with no statistical difference in AUC among the testing, internal further validation, and external further validation groups (0.938 vs. 0.923 vs. 0.990, all P values > 0.05). The AUC of V/P-mics was close to that of the senior physician (0.923 vs. 0.975, P = 0.332), but significantly higher than the junior physician (0.923 vs. 0.725, P = 0.050). Furthermore, V/P-mics significantly shortened the diagnosis time as compared to the junior physician (100 ± 16 s vs. 240 ± 37 s, P = 0.001).

Conclusion: The V/P-mics had good discrimination and generalizability and significantly shortened the diagnosis time for patients with pulmonary embolism. Of note, the model showed excellent interpretability.

Objective: To explore the feasibility of a low-dose ¹⁸F-FDG protocol for the 30-cm standard axial field of view (SAFOV) PET/CT imaging in pediatric patients.

Methods: A retrospective analysis was conducted on 112 pediatric patients who underwent a full-dose (3.7 MBq/kg) ¹⁸F-FDG PET/CT imaging, and a prospective analysis was performed on 55 patients who received a low-dose (2.5 MBq/kg) imaging. PET images were reconstructed at 1.0-min/bed intervals, and labeled as G1.0, G2.0, G3.0 for the full-dose imaging and G1.0', G2.0', G3.0' for the low-dose imaging. Patients were categorized into three age groups, and the image quality was assessed using the Likert scale and signal-to-noise ratio (SNR); Lesion detectability was evaluated using lesion detection rates and the target-to-liver ratio (TLR).

Results: In G2.0 and G3.0, all cases (112/112) achieved an image score of ≥ 3 and a lesion detection rate of 100% (98/98). There were no significant differences in SNR between G2.0 and G3.0 (11.09 ± 2.31 vs. 11.88 ± 2.58, p = 0.39), nor between age groups ≤ 5 years and 6-10 years groups (9.52 ± 3.16 vs. 9.53 ± 3.19, p = 0.99). In G3.0', 98.2% of cases (54/55) had an image score ≥ 3 and a lesion detection rate of 100% (43/43). The SNR of every age group for G3.0' was comparable to that of G2.0, and no significant differences between ≤ 5 years and 6-10 years groups (9.32 ± 1.94 vs. 9.99 ± 2.28, p = 0.82).

Conclusions: A 2.5 MBq/kg dose with a 3.0 min/bed acquisition protocol is feasible for ¹⁸F-FDG 30-cm SAFOV PET/CT imaging in pediatric patients, and SNR and TLR demonstrated age-dependent discrepancies.

Objective: Posterior cortical atrophy (PCA) is generally considered an atypical variant of Alzheimer's disease (AD) and is an important component of early-onset AD. Symptomatologic heterogeneity has led to a high rate of misdiagnosis or delayed diagnosis of early-onset AD. We sought to establish the phenotypic-specific metabolic patterns of PCA and early-onset typical AD (tAD) and to assess whether phenotype-specific neuroimaging biomarkers are more valuable for disease recognition.

Methods: Patients accepting ¹⁸F-FDG PET with an onset age younger than 65 years (PCA, n = 40; early-onset tAD, n = 37; behavioral variant frontotemporal dementia (bv-FTD), n = 35) and healthy controls (HCs, n = 30) were enrolled and divided into two cohorts for pattern establishment and validation, respectively. Similarities and differences between patterns were assessed by pattern topography, expression, classification performance and correlation with clinical severity.

Results: PCA-related pattern (PCARP) was characterized by extensively relative hypometabolism in the parietal lobe, occipital lobe, temporal lobe, cingulate gyrus, and relative hypermetabolism mainly in vermis, thalamus. Early-onset tAD-related pattern (EOtADRP) was characterized by relative hypometabolism mainly in the middle frontal gyrus, angular gyrus, precuneus, middle temporal gyrus, cingulate gyrus, caudate, and relative hypermetabolism mainly in vermis, thalamus, postcentral gyrus. PCARP and EOtADRP were closely related in topography (r = 0.909, P < 0.001) and expression (r = 0.862, P < 0.001). High accuracies in distinguishing corresponding patient group from HC were found in both, while only PCARP was capable of phenotype discrimination (PCA versus early-onset tAD; area under the receiver operating characteristic curve [AUC] = 0.84-0.88 for PCARP, AUC = 0.57-0.62 for EOtADRP) and distinguishment between PCA/early-onset tAD and bv-FTD (AUC = 1.00/0.91 for PCARP, AUC = 0.73/0.62 for EOtADRP). PCARP showed great potential in detecting clinical severity in both phenotypes whereas EOtADRP only worked in early-onset tAD.

Conclusion: PCARP outperformed EOtADRP in phenotype discrimination with better potential in severity assessment.

Objective: Compared with those of sporadic differentiated thyroid cancer (SDTC), the tumor characteristics of familial nonmedullary thyroid cancer (FNMTC) remain debatable. In addition, there is a paucity of data suggesting that their response to radioactive iodine (RAI) therapy differs from that of SDTC. We aimed to determine whether FNMTC is a homogenous biological entity that differs from SDTC in RAI therapy.

Methods: We retrospectively analyzed patients who underwent adjuvant ablative iodine radiotherapy between July 2016 and March 2020. We compared the differences in clinicopathological features and prognoses between the FNMTC and SDTC groups. The endpoint of the study was the rate of therapeutic response after 1 and 3 years of follow-up after first-line treatment. The response to therapy was classified into four categories: excellent response (ER), biochemical incomplete response (BIR), structural incomplete response (SIR) and indeterminate response (IDR). In addition, we conducted a meta-analysis of cohort studies to investigate the clinical outcomes.

Results: Of all the patients (n = 1758), 109 (6.2%) were classified as having FNMTC, whereas the remaining 1649 (93.8%) had SDTC. A greater proportion of FNMTC patients presented with extrathyroidal extension, capsular invasion, and multifocality (P = 0.01). In addition, patients with FNMTC were more likely to have advanced primary tumor stage and lymph node metastasis, but the differences were not statistically significant. At the end of follow-up (median follow-up of 3 years), the response to RAI therapy was significantly different between the two groups (ER: 66.1% vs 74.3%; IDR: 9.2% vs 12.1%; SIR: 14.7% vs 7.1%; BIR: 10.1% vs 6.5%). Moreover, patients with FNMTC more frequently presented evidence of disease than patients with SDTC did. However, the disease-free survival of patients with FNMTC was not shorter than that of patients with SDTC (P ≥ 0.05).

Conclusion: Patients with FNMTC more often have aggressive characteristics at presentation and relatively worse clinical outcomes after RAI therapy. However, there was no statistically significant difference in the recurrence rate in the short-term follow-up. Considering that, FNMTC patients may be required more aggressive treatment approaches and closer postoperative monitoring.

Background: This study aimed the role of volumetric and dissemination features of staging [18F]PSMA-1007 PET/CT in predicting progression-free survival (PFS) in patients with prostate cancer (PCa) and their relationship with the main clinical data (ISUP grade groups, number of lesions, PSA).

Methods: We included 164 patients with high-risk PCa who underwent baseline [18F]PSMA-1007 PET/CT. With the help of LIFEx version 7.7, the main volumetric and dissemination PET parameters were semi-automatically extracted: PSMA-prostate tumor volume (PSMA-TV), PSMA-prostate total lesion (PSMA-TL), PSMA total TV (PSMA-TTV), PSMA total TL (PSMA-TTL) and Dmax corrected for body-surface-area (Dmax_bsa). Spearman rank correlations between semiquantitative PET features and the clinical variables were analyzed. PFS estimates were plotted with the Kaplan-Meier method.

Results: A high correlation was seen between the number of lesions and both PSMA-TTL (r 0.725), and Dmaxbsa (r 0.935). A moderate correlation was registered between PSA and PSMA-TTV (r 0.333), PSMA-TTL (r 0.441), Dmax_bsa (r 0.333), as well as between number of lesions and PSMA-TTV (r 0.342). After a median follow-up of 17 months (range 2-45), relapse/progression happened in 17 patients (10%). PSA level, presence of distant metastases at staging, PSMA-TV, PSMA-TL, PSMA-TTL and Dmax_bsa were significantly associated with PFS at univariate analysis, but only the presence of distant metastases, PSMA-TTL and Dmax_bsa were confirmed to be independent prognostic factors.

Conclusion: Volumetric and dissemination features derived by staging [18F]PSMA-1007 PET/CT were significantly correlated with PSA and number of lesions. The combination of PSMA-TTL and Dmax_bsa was the best predictor of PFS and may help to better stratify PCa patients.

Objective: Bone scintigraphy is widely employed for detecting bone metastases, with the bone scan index (BSI) gaining traction as a quantitative tool in this domain. VSBONE BSI, an automated image analysis software, identifies abnormal hyperaccumulation areas in bone scintigraphy and computes BSI scores. The software, originally developed using data from conventional gamma cameras (C-Camera), has undergone two upgrades. This study hypothesized that the upgrades enhance the diagnostic accuracy for bone metastases and assessed the software's applicability to images obtained using a cadmium-zinc-telluride detector gamma camera (CZT-Camera). The aim was to compare the diagnostic accuracy of VSBONE BSI across software versions using both conventional and CZT detectors and to evaluate its utility.

Methods: A total of 287 patients with breast or prostate carcinoma who underwent whole-body bone scintigraphy were included. VSBONE BSI automatically analyzed and calculated the BSI. The analysis results were compared with the presence or absence of metastases for each software version by using detector type of camera. The diagnostic agreement was evaluated.

Results: Receiver operating characteristic analysis showed an area under the curve (AUC) exceeding 0.7 across all groups, indicating good diagnostic performance. AUC values significantly increased with version upgrades for all patients and for breast carcinoma patients. In metastasis-negative cases, BSI values decreased with each software version upgrade, with the reduction being more pronounced in breast carcinoma patients scanned with the CZT-Camera.

Conclusions: Using the VSBONE BSI, version 2 or 3 had a higher rate of diagnostic concordance with the clinical prognosis than version 1. In metastasis-negative patients, newer software versions yielded lower BSI values, especially for breast carcinoma patients scanned using the CZT-Camera, highlighting the improved diagnostic accuracy of the updated software.

Aim: This study aimed to analyze the clinicopathologic and metabolic parameters derived from staging ¹⁸F-FDG PET/CT that can predict recurrence patterns in non-small-cell lung cancer (NSCLC) after curative surgery.

Material and methods: Retrospective study included stage I-III NSCLC patients with a baseline ¹⁸F-FDG PET/CT scan. Relapse patterns were analyzed based on location, lesion, and organ-specific recurrence. Clinicopathologic variables were recorded. Three distinct categories of variables were obtained: standardized uptake value (SUV)-based metrics, heterogeneity parameters, and morphological features. The relation of relapse patterns with clinicopathologic and metabolic parameters was analyzed using the uni-multivariate logistic regression.

Results: Out of 173 patients, 104 experienced recurrences, with 66% presenting distant involvement and 56.7% exhibiting polymetastatic disease at initial recurrence. Patient age, pathologic lymphovascular invasion, and normalized SUVmax perimeter distance (nSPD) were considered as risk factors for early recurrence. Adenocarcinoma histology was identified as an independent variable for distant recurrence. Patient age, number of metastatic mediastinal lymph nodes at staging (nN), sphericity, normalized SUVpeak to centroid distance (nSCD), entropy, low gray-level run emphasis, and high gray-level run emphasis were independent variables for polymetastatic disease. Certain variables were correlated with organ-specific recurrence. Bone recurrence was related to nN and SUVmean. Brain recurrence was related to adenocarcinoma histology. Lung recurrence was associated with coefficient of variation and nSPD.

Conclusion: The metabolic profile of lung primary tumors obtained from ¹⁸F-FDG PET/CT seems to be predictive of recurrence patterns that are closely linked to the overall survival of NSCLC patients. These findings could help in the development of personalized follow-up strategies based on an individual's recurrence pattern.