Ga-PSMA PET outperforms planar BS for the detection of affected bone regions as well as determination of overall bone involvement in PC patients. Our results indicate that BS in patients who have received PSMA PET for staging only rarely offers additional information; however, prospective studies, including a standardized integrated x-ray computed tomography (SPECT/CT) protocol, should be performed in order to confirm the presented results.

Glioblastoma is a highly invasive brain tumor, whose cells infiltrate surrounding normal brain tissue beyond the lesion outlines visible in the current medical scans. These infiltrative cells are treated mainly by radiotherapy. Existing radiotherapy plans for brain tumors derive from population studies and scarcely account for patient-specific conditions. Here we provide a Bayesian machine learning framework for the rational design of improved, personalized radiotherapy plans using mathematical modeling and patient multimodal medical scans. Our method, for the first time, integrates complementary information from high resolution MRI scans and highly specific FET-PET metabolic maps to infer tumor cell density in glioblastoma patients. The Bayesian framework quantifies imaging and modeling uncertainties and predicts patient-specific tumor cell density with credible intervals. The proposed methodology relies only on data acquired at a single time point and thus is applicable to standard clinical settings. An initial clinical population study shows that the radiotherapy plans generated from the inferred tumor cell infiltration maps spare more healthy tissue thereby reducing radiation toxicity while yielding comparable accuracy with standard radiotherapy protocols. Moreover, the inferred regions of high tumor cell densities coincide with the tumor radioresistant areas, providing guidance for personalized doseescalation. The proposed integration of multimodal scans and mathematical modeling provides a robust, non-invasive tool to assist personalized radiotherapy design.

BackgroundTextural features in FDG-PET have been shown to provide prognostic information in a variety of tumor entities. Here we evaluate their predictive value for recurrence and prognosis in NSCLC patients receiving primary stereotactic radiation therapy (SBRT).Methods45 patients with early stage NSCLC (T1 or T2 tumor, no lymph node or distant metastases) were included in this retrospective study and followed over a median of 21.4 months (range 3.1–71.1). All patients were considered non-operable due to concomitant disease and referred to SBRT as the primary treatment modality. Pre-treatment FDG-PET/CT scans were obtained from all patients. SUV and volume-based analysis as well as extraction of textural features based on neighborhood gray-tone difference matrices (NGTDM) and gray-level co-occurence matrices (GLCM) were performed using InterView Fusion™ (Mediso Inc., Budapest). The ability to predict local recurrence (LR), lymph node (LN) and distant metastases (DM) was measured using the receiver operating characteristic (ROC). Univariate and multivariate analysis of overall and disease-specific survival were executed.Results7 out of 45 patients (16%) experienced LR, 11 (24%) LN and 11 (24%) DM. ROC revealed a significant correlation of several textural parameters with LR with an AUC value for entropy of 0.872. While there was also a significant correlation of LR with tumor size in the overall cohort, only texture was predictive when examining T1 (tumor diameter < = 3 cm) and T2 (>3 cm) subgroups. No correlation of the examined PET parameters with LN or DM was shown.In univariate survival analysis, both heterogeneity and tumor size were predictive for disease-specific survival, but only texture determined by entropy was determined as an independent factor in multivariate analysis (hazard ratio 7.48, p = .016). Overall survival was not significantly correlated to any examined parameter, most likely due to the high comorbidity in our cohort.ConclusionsOur study adds to the growing evidence that tumor heterogeneity as described by FDG-PET texture is associated with response to radiation therapy in NSCLC. The results may be helpful into identifying patients who might profit from an intensified treatment regime, but need to be verified in a prospective patient cohort before being incorporated into routine clinical practice.

In prostate cancer (PC) patients, the differentiation between lung metastases and lesions of different origin, for example, primary lung cancer, is a common clinical question. Herein, we investigated the use of Glu-NH-CO-NH-Lys(Ahx)-HBED-CC ( 68 Ga-PSMA-HBED-CC) for this purpose. Methods: PC patients (n 5 1,889) undergoing 68 Ga-PSMA PET/CT or PET/MR scans were evaluated retrospectively for suggestive lung lesions. For up to 5 lesions per patient, location, CT diameter, CT morphology, and SUV max were determined. The standard for classification was either histopathologic evaluation or, in the case of PC metastases, responsivity to antihormone therapy. A comparison of the different classes was executed by Student t test. Prostate-specific antigen and prostate-specific membrane antigen (PSMA) immunohistochemistry were performed if histologic samples were available; 68 Ga-PSMA autoradiography was performed on an exemplary case of PET-positive lung cancer. Results: Eighty-nine lesions in 45 patients were identified, of which 76 were classified as PC (39 proven, 37 highly probable), 7 as primary lung cancer, and 2 as activated tuberculosis; 4 lesions remained unclear. The mean SUV max was 4.4 ± 3.9 for PC metastases and 5.6 ± 1.6 for primary lung cancer (P 5 0.408). Additionally, substantial differences in SUV max intraindividually were detected. The 2 tuberculous lesions showed an SUV max of 7.8 and 2.5. Using immunohistochemistry, we could demonstrate PSMA expression in the neovasculature of several PSMA PET-positive lung cancers as well as in tuberculous lesions from our histologic database. Conclusion: Quantitative (SUV) analysis of 68 Ga-PSMA PET was not able to discriminate reliably between pulmonary metastases and primary lung cancer in PC patients. The reason for the unexpectedly high tracer uptake in non-PC lesions is not completely clear. PSMA expression in neovasculature provides a possible explanation for this finding; however, other contributing factors, such as tracer binding to proteins other than PSMA, cannot be excluded at present.