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Objectives: To investigate the specific strengths of MRI and PET components in 68Ga-PSMA-11 PET/MRI for staging of patients with biochemically recurrent prostate cancer (PCa). Methods: Patients with biochemical recurrence of PCa and contrast-enhanced whole-body 68Ga-PSMA-11 PET/MRI including a dedicated pelvic multiparametric MRI were included in this retrospective study. Imaging datasets of MRI and PET were evaluated separately regarding local PCa recurrence (Tr), pelvic lymph node metastases (N1), distant lymph node metastases (M1a), bone metastases (M1b), and soft tissue metastases (M1c) according to PROMISE version 1. Data evaluation was performed patient- and region-/lesion-based. Cox regression revealed a PSA of 1.69 ng/mL as a cut-off for subgroup analysis. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy were evaluated for each image component. Differences in staging accuracy were assessed using the Wilcoxon and McNemar test. Results: Altogether 102 patients (mean aged 68 ± 8 years, median PSA 1.33 ng/mL) were included. PCa was found in 70/102 (68%) patients. Accuracy of MRI in the detection of Tr, N1, M + , M1a, and M1b was 100%, 79%, 90%, 97%, and 95% for PSA < 1.69 ng/mL and 100%, 87%, 87%, 91%, and 96% for PSA > 1.69 ng/mL. Accuracy of 68Ga-PSMA-11 PET was 93%, 97%, 93%, 98%, and 100% for PSA < 1.69 ng/mL and 87%, 91%, 96%, 100%, and 96% for PSA > 1.69 ng/mL. Conclusions: Combined assessment of 68Ga-PSMA-11 PET/MRI improves tumor localization in men with biochemical recurrence. The MRI detected local recurrence of PCa more often whereas 68 Ga-PSMA-11 PET detected lymph node metastases more often, especially for PSA < 1.69 ng/mL. Clinical relevance statement: This study gives a scientific baseline to improve the understanding and reading of 68Ga-PSMA-11 PET/MRI imaging in patients with biochemically recurrent PCa by showing the specific strength of each imaging component. Key Points: • Combining the individual modality strengths of 68Ga-PSMA-11 PET/MRI improves tumor localization in men with biochemical recurrence of prostate cancer. • MRI component of 68 Ga-PSMA-11 PET/MRI shows its strength in detecting local recurrence of prostate cancer, especially at PSA < 1.69 ng/mL. • 68 Ga-PSMA-11 PET component shows its strength in detecting local and distant lymph node metastases, especially at PSA < 1.69 ng/mL. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate if a machine learning prediction model based on clinical and easily assessable imaging features derived from baseline breast [18F]FDG-PET/MRI staging can predict pathologic complete response (pCR) in patients with newly diagnosed breast cancer prior to neoadjuvant system therapy (NAST). Methods: Altogether 143 women with newly diagnosed breast cancer (54 ± 12 years) were retrospectively enrolled. All women underwent a breast [18F]FDG-PET/MRI, a histopathological workup of their breast cancer lesions and evaluation of clinical data. Fifty-six features derived from positron emission tomography (PET), magnetic resonance imaging (MRI), sociodemographic / anthropometric, histopathologic as well as clinical data were generated and used as input for an extreme Gradient Boosting model (XGBoost) to predict pCR. The model was evaluated in a five-fold nested-cross-validation incorporating independent hyper-parameter tuning within the inner loops to reduce the risk of overoptimistic estimations. Diagnostic model-performance was assessed by determining the area under the curve of the receiver operating characteristics curve (ROC-AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy. Furthermore, feature importances of the XGBoost model were evaluated to assess which features contributed most to distinguish between pCR and non-pCR. Results: Nested-cross-validation yielded a mean ROC-AUC of 80.4 ± 6.0% for prediction of pCR. Mean sensitivity, specificity, PPV, and NPV of 54.5 ± 21.3%, 83.6 ± 4.2%, 63.6 ± 8.5%, and 77.6 ± 8.1% could be achieved. Histopathological data were the most important features for classification of the XGBoost model followed by PET, MRI, and sociodemographic/anthropometric features. Conclusion: The evaluated multi-source XGBoost model shows promising results for reliably predicting pathological complete response in breast cancer patients prior to NAST. However, yielded performance is yet insufficient to be implemented in the clinical decision-making process. [ABSTRACT FROM AUTHOR]

Our rationale was to investigate whether 18 F-FDG PET/MRI in addition to (guideline-recommended) conventional staging leads to changes in therapeutic management in patients with newly diagnosed breast cancer and compare the diagnostic accuracy of 18 F-FDG PET/MRI with that of conventional staging for determining the Union for International Cancer Control (UICC) stage. Methods: In this prospective, double-center study, 208 women with newly diagnosed, therapy-naïve invasive breast cancer were enrolled in accordance with the inclusion criteria. All patients underwent guideline-recommended conventional staging and whole-body 18 F-FDG PET/MRI with a dedicated breast examination. A multidisciplinary tumor board served to determine 2 different therapy recommendations for each patient, one based on conventional staging alone and another based on combined assessment of conventional staging and 18 F-FDG PET/MRI examinations. Major changes in therapy recommendations and differences between the conventional staging algorithm and 18 F-FDG PET/MRI for determining the correct UICC stage were reported and evaluated. Results: Major changes in therapeutic management based on combined assessment of conventional staging and 18 F-FDG PET/MRI were detected in 5 of 208 patients, amounting to changes in therapeutic management in 2.4% (95% CI, 0.78%-5.2%) of the study population. In determining the UICC stage, the guideline-based staging algorithm and 18 F-FDG PET/MRI were concordant in 135 of 208 (64.9%; 95% CI, 58%-71.4%) patients. The conventional guideline algorithm correctly determined the UICC stage in 130 of 208 (62.5%; 95% CI, 55.5%-69.1%) patients, and 18 F-FDG PET/MRI correctly determined the UICC stage in 170 of 208 (81.9%; 95% CI, 75.8%-86.7%) patients. Conclusion: Despite the diagnostic superiority of 18 F-FDG PET/MRI over conventional staging in determining the correct UICC stage, the current (guideline-recommended) conventional staging algorithm is sufficient for adequate therapeutic management of patients with newly diagnosed breast cancer, and 18 F-FDG PET/MRI does not have an impact on patient management., (© 2024 by the Society of Nuclear Medicine and Molecular Imaging.)