Annals of Nuclear Medicine最新文献

Objectives: The identification of metastatic disease in patients with biochemical failure of prostate cancer (PCa) after radical prostatectomy (RP) determines subsequent treatment management. Our objectives were (i) to assess the prevalence of metastatic PCa in patients with biochemical failure following RP and PSA levels ≤ 1 ng/mL, as detected by ¹⁸F-PSMA-1007 PET/CT (PSMA-PET), and (ii) to identify predictors of metastatic disease.

Methods: Fifty-five patients with biochemical recurrence (BCR, n = 47) or persistent disease (n = 8) following RP as their primary and only prior treatment were retrospectively included if presenting with PSA levels ≤ 1 ng/mL. Patients who received any other anticancer treatment were excluded. PSMA-PET findings were categorized as either local recurrence (i.e., prostatic fossa and seminal vesicles) or metastases. Predictors of PSMA-PET positivity were assessed with univariate and multivariate regression analyses both in the whole sample and in two subgroups defined according to PSA levels (Group A = PSA < 0.5 ng/mL; Group B = PSA between 0.5 and 1 ng/mL).

Results: Median PSA at the time of PET/CT was 0.37 ng/mL (range: 0.13-1.0). PSMA-PET was positive in 22/55 (40%) patients, 14/55 (25%) patients had metastatic disease. Overall, 31 PSMA-positive lesions were identified: 12/31 (39%) and 19/31 (61%) were local recurrences and metastases, respectively. In the whole cohort, ISUP grade > 3 (p = 0.003), pN1 status after surgery (p = 0.011), time to BCR ≤ 26 months (p = 0.03), and persistent disease (p = 0.003) were significantly associated with a higher rate of PSMA-positive metastases. At multivariate analysis, ISUP grade > 3 (p = 0.016) was the only independent predictor of metastases. Metastatic disease was detected in 8/38 (21%) patients in Group A and in 6/17 (35%) patients in Group B, respectively. In Group A, pN1 (p = 0.043) and persistent disease (p = 0.040) were significant predictors of metastases. In Group B, ISUP grade > 3 was the only predictor (p = 0.028).

Conclusions: ISUP grade > 3, time to BCR ≤ 26 months, pN1 status, and persistent disease after surgery indicated a higher likelihood of PSMA-positive metastatic disease in a homogeneous cohort of patients with biochemical failure and low PSA values. pN1 status and persistent disease were significant predictors of metastatic disease also in patients with PSA levels < 0.5 ng/mL.

Purpose: This study aimed to develop deep learning (DL) models for CT-free attenuation correction and Monte Carlo-based scatter correction in ^99mTc-macroagregated albumin (^99mTc-MAA) SPECT imaging, with the goal of enhancing quantitative accuracy for improved treatment planning and pre-therapy dosimetry in ⁹⁰Y-selctive internal radiation therapy (SIRT).

Materials and methods: Data from 222 patients who underwent ^99mTc-MAA SPECT imaging prior to ⁹⁰Y-SIRT were included in this study. Uncorrected SPECT images (without attenuation and/or scatter correction) were used as input to a modified 3D shifted-window UNet Transformer (Swin UNETR) architecture. Three separate models were trained to predict attenuation corrected (AC), scatter corrected (SC), and joint attenuation and scatter corrected (ASC) SPECT images. The dataset was split into a training set (~ 80%) and an independent test set (~ 20%). Model training was performed using a five-fold cross-validation framework, with final evaluation conducted on the blind test set. To clinically assess model performance, 3D voxel-wise dosimetry was performed on the test set using the local energy deposition method, assuming ^99mTc-MAA as a surrogate for ⁹⁰Y distribution. Quantitative evaluation included organ- and voxel-level metrics, along with Gamma analysis using three combinations of distance-to-agreement (DTA, mm) and dose-difference (DD, %) criteria.

Results: The average (± SD) of the voxel-wise mean error (ME) was ≤ 0.003 Gy for all tasks. The Relative Error (RE (%)) for AC, SC, and ASC tasks were 4.64 ± 7.52%, 8.99 ± 26.35%, and 16.45 ± 25.83%, respectively. Voxel-level Gamma evaluations within the whole body using three different criteria sets, including "DTA: 4.79 mm, DD: 1%"; "DTA: 10 mm, DD: 5%"; and "DTA: 15 mm, DD: 10%" yielded pass rates of over 99.60%. The mean absolute error (MAE) for lesions, normal liver and lungs across all tasks were 3.16 ± 3.39, 0.35 ± 0.36, 0.41 ± 0.47 Gy for AC, 1.97 ± 2.79, 0.19 ± 0.16, 0.22 ± 0.20 Gy, for SC and 5.16 ± 7.10, 0.45 ± 0.51, and 0.34 ± 0.37 Gy for ASC, respectively.

Conclusion: Multiple models were developed for key SPECT quantification tasks, with potential value in clinical setting lacking reliable CT data or sufficient computational resources for Monte Carlo simulations. The models look promising for potential clinical translation and integration into commercial reconstruction software.

To evaluate the diagnostic performance, methodological quality, and clinical feasibility of ¹⁸F-FDG PET/CT-based radiomics machine learning models for predicting PD-L1 expression in non-small cell lung cancer (NSCLC). Systematic searches of PubMed, Scopus, Web of Science, Embase, and IEEE Xplore were conducted up to July 2025. Eligible studies developed radiomics-only models from ¹⁸F-FDG PET/CT for pre-biopsy or pre-operative PD-L1 prediction, with immunohistochemistry (IHC) as the reference standard (tumor proportion score ≥ 1%). Study quality was assessed using QUADAS-2 and METRICS. Pooled area under the curve (AUC), sensitivity, and specificity, with 95% confidence intervals (CI), were measured via a bivariate random-effects model. Eleven studies met the inclusion criteria; eight were included in the meta-analysis (n = 1,053). The pooled AUC was 0.83 (95% CI: 0.79-0.86), sensitivity 0.75 (95% CI: 0.64-0.84), and specificity 0.77 (95% CI: 0.64-0.87). Subgroup analyses revealed higher accuracy with semi-automatic segmentation, larger training cohorts, and biopsy-only specimens. QUADAS-2 identified high bias risk in the index test domain, mainly owing to the absence of segmentation validation and unclear blinding. METRICS scores averaged 58.04% (range: 41-64.7%), indicating moderate methodological quality. ¹⁸F-FDG PET/CT-based radiomics models show promise for non-invasive PD-L1 prediction in NSCLC, but their clinical translation is limited by methodological heterogeneity, absence of multi-center design, lack of external validation, and variable segmentation practices. Future work should focus on multi-center datasets, standardized workflows, and rigorous validation to enable reliable real-world applications.