Utility of PSMA PET/CT in Staging and Restaging of Renal Cell Carcinoma: A Systematic Review and Metaanalysis.

Prostate-specific membrane antigen (PSMA) is expressed in the neovasculature of multiple solid tumors, including renal cell carcinoma (RCC). Studies have demonstrated promising results on the utility of PSMA-targeted PET/CT imaging in RCC. This report aims to provide a systematic review and metaanalysis on the utility and detection rate of PSMA PET/CT imaging in staging or evaluation of primary RCC and restaging of metastatic or recurrent RCC. Methods: Searches were performed in PubMed, Embase, and abstract proceedings (last updated, August 2023). Studies that provided a lesion-level detection rate of PSMA radiotracers in staging or restaging of RCC were included in the metaanalysis. The overall pooled detection rate with a 95% CI was estimated, and subgroup analysis was performed when feasible. Results: Nine studies comprising 152 patients (133 clear cell RCC [ccRCC], 19 other RCC subtypes) were included in the metaanalysis. The pooled detection rate of PSMA PET/CT in evaluation of primary or metastatic RCC was estimated to be 0.83 (95% CI, 0.67-0.92). Subgroup analysis showed a pooled PSMA detection rate of 0.74 (95% CI, 0.57-0.86) in staging or evaluation of primary RCC lesions and 0.87 (95% CI, 0.73-0.95) in restaging of metastatic or recurrent RCC. Analysis based on the type of radiotracer showed a pooled detection rate of 0.85 (95% CI, 0.62-0.95) for 68Ga-based PSMA tracers and 0.92 (95% CI, 0.76-0.97) for 18F-DCFPyL PET/CT. Furthermore, in metastatic ccRCC, the available data support a significantly higher detection rate for 18F-DCFPyL PET/CT than for conventional imaging modalities (2 studies). Conclusion: Our preliminary results show that PSMA PET/CT could be a promising alternative imaging modality for evaluating RCC, particularly metastatic ccRCC. Large prospective studies are warranted to confirm clinical utility in the staging and restaging of RCC.

Deep Semisupervised Transfer Learning for Fully Automated Whole-Body Tumor Quantification and Prognosis of Cancer on PET/CT.

Automatic detection and characterization of cancer are important clinical needs to optimize early treatment. We developed a deep, semisupervised transfer learning approach for fully automated, whole-body tumor segmentation and prognosis on PET/CT. Methods: This retrospective study consisted of 611 18F-FDG PET/CT scans of patients with lung cancer, melanoma, lymphoma, head and neck cancer, and breast cancer and 408 prostate-specific membrane antigen (PSMA) PET/CT scans of patients with prostate cancer. The approach had a nnU-net backbone and learned the segmentation task on 18F-FDG and PSMA PET/CT images using limited annotations and radiomics analysis. True-positive rate and Dice similarity coefficient were assessed to evaluate segmentation performance. Prognostic models were developed using imaging measures extracted from predicted segmentations to perform risk stratification of prostate cancer based on follow-up prostate-specific antigen levels, survival estimation of head and neck cancer by the Kaplan-Meier method and Cox regression analysis, and pathologic complete response prediction of breast cancer after neoadjuvant chemotherapy. Overall accuracy and area under the receiver-operating-characteristic (AUC) curve were assessed. Results: Our approach yielded median true-positive rates of 0.75, 0.85, 0.87, and 0.75 and median Dice similarity coefficients of 0.81, 0.76, 0.83, and 0.73 for patients with lung cancer, melanoma, lymphoma, and prostate cancer, respectively, on the tumor segmentation task. The risk model for prostate cancer yielded an overall accuracy of 0.83 and an AUC of 0.86. Patients classified as low- to intermediate- and high-risk had mean follow-up prostate-specific antigen levels of 18.61 and 727.46 ng/mL, respectively (P < 0.05). The risk score for head and neck cancer was significantly associated with overall survival by univariable and multivariable Cox regression analyses (P < 0.05). Predictive models for breast cancer predicted pathologic complete response using only pretherapy imaging measures and both pre- and posttherapy measures with accuracies of 0.72 and 0.84 and AUCs of 0.72 and 0.76, respectively. Conclusion: The proposed approach demonstrated accurate tumor segmentation and prognosis in patients across 6 cancer types on 18F-FDG and PSMA PET/CT scans.

Quantitative brain 18F-FDG PET/CT analysis in seronegative autoimmune encephalitis.

OBJECTIVE: Brain 18F-FDG PET/CT is a useful diagnostic in evaluating patients with suspected autoimmune encephalitis (AE). Specific patterns of brain dysmetabolism have been reported in anti-NMDAR and anti-LGI1 AE, and the degree of dysmetabolism may correlate with clinical functional status.18FDG-PET/CT abnormalities have not yet been described in seronegative AE. METHODS: We conducted a cross-sectional analysis of brain18FDG-PET/CT data in people with seronegative AE treated at the Johns Hopkins Hospital. Utilizing NeuroQ™ software, the Z-scores of 47 brain regions were calculated relative to healthy controls, then visually and statistically compared for probable and possible AE per clinical consensus diagnostic criteria to previous data from anti-NMDAR and anti-LGI1 cohorts. RESULTS: Eight probable seronegative AE and nine possible seronegative AE were identified. The group only differed in frequency of abnormal brain MRI, which was seen in all of the probable seronegative AE patients. Both seronegative groups had similar overall patterns of brain dysmetabolism. A common pattern of frontal lobe hypometabolism and medial temporal lobe hypermetabolism was observed in patients with probable and possible seronegative AE, as well as anti-NMDAR and anti-LGI1 AE as part of their respective characteristic patterns of dysmetabolism. Four patients had multiple brain18FDG-PET/CT scans, with changes in number and severity of abnormal brain regions mirroring clinical status. CONCLUSIONS: A18FDG-PET/CT pattern of frontal lobe hypometabolism and medial temporal lobe hypermetabolism could represent a common potential biomarker of AE, which along with additional clinical data may facilitate earlier diagnosis and treatment.