Badawy, Mohamed

Importance Advances in Alzheimer disease (AD) have shifted research focus to earlier disease stages, necessitating more scalable approaches to identify cognitively unimpaired individuals with amyloid β (Aβ) pathology. Objective To assess the utility of plasma phosphorylated tau 217 (p-tau217) for classifying Aβ status in cognitively unimpaired individuals, both as a stand-alone test and in a 2-step approach where positive plasma results were confirmed using a second modality (Aβ positron emission tomography [PET] or cerebrospinal fluid [CSF]). Design, Setting, and Participants This cross-sectional cohort study used data collected between June 2009 and March 2024. We included 2916 cognitively unimpaired participants from 12 international independent observational cohorts in the US, Europe, Australia, and Canada with available plasma p-tau217 levels and CSF or PET Aβ biomarkers. Performance comparisons between mass spectrometry and immunoassay-based p-tau217 measurements were also performed (n = 964). Exposures Plasma p-tau217 levels measured by immunoassay. Main Outcome and Measures Aβ status, determined by CSF or Aβ PET biomarkers. Results Participants had a mean (SD) age of 66.9 (9.9) years; 971 (33.3%) were Aβ positive by either CSF or PET, 1667 (57.2%) were women, and 1108 (38.1%) carried at least 1 APOE ε4 allele. As a stand-alone test, plasma p-tau217 achieved a positive predictive value (PPV) of 79% (95% CI, 74-84) and an overall accuracy of 81% (95% CI, 80-82). In a 2-step workflow, the PPV and accuracy significantly increased to 91% (95% CI, 86-95). While this approach required screening of 677 individuals with plasma p-tau217 to identify 100 Aβ-positive individuals, compared to 536 participants when using PET alone, it reduced the need for PET testing to 124. Immunoassays demonstrated comparable PPVs to mass spectrometry (80% [95% CI, 74-86] vs 85% [95% CI, 81-90]; P = .12) but significantly lower overall accuracy (82% [95% CI, 79-84]% vs 88 [95% CI, 86-90]; P < .001) and true Aβ-positive detection rate (49% [95% CI, 43-55] vs 69% [95% CI, 64-75]; P < .001). Conclusions and Relevance The findings highlight the potential of plasma p-tau217 as a stand-alone test—or when used in a sequential 2-step approach alongside PET or CSF testing—as a cost-effective, scalable, and minimally burdensome strategy for identifying preclinical AD. Tailored screening workflows that incorporate p-tau217 can improve efficiency in participant selection for preclinical AD trials and, in the future, help guide access to disease-modifying treatments.

The translation of AI-generated brain metastases (BM) segmentation into clinical practice relies heavily on diverse, high-quality annotated medical imaging datasets. The BraTS-METS 2023 challenge has gained momentum for testing and benchmarking algorithms using rigorously annotated internationally compiled real-world datasets. This study presents the results of the segmentation challenge and characterizes the challenging cases that impacted the performance of the winning algorithms. Untreated brain metastases on standard anatomic MRI sequences (T1, T2, FLAIR, T1PG) from eight contributed international datasets were annotated in stepwise method: published UNET algorithms, student, neuroradiologist, final approver neuroradiologist. Segmentations were ranked based on lesion-wise Dice and Hausdorff distance (HD95) scores. False positives (FP) and false negatives (FN) were rigorously penalized, receiving a score of 0 for Dice and a fixed penalty of 374 for HD95. The mean scores for the teams were calculated. Eight datasets comprising 1303 studies were annotated, with 402 studies (3076 lesions) released on Synapse as publicly available datasets to challenge competitors. Additionally, 31 studies (139 lesions) were held out for validation, and 59 studies (218 lesions) were used for testing. Segmentation accuracy was measured as rank across subjects, with the winning team achieving a LesionWise mean score of 7.9. The Dice score for the winning team was 0.65 ± 0.25. Common errors among the leading teams included false negatives for small lesions and misregistration of masks in space. The Dice scores and lesion detection rates of all algorithms diminished with decreasing tumor size, particularly for tumors smaller than 100 mm3. In conclusion, algorithms for BM segmentation require further refinement to balance high sensitivity in lesion detection with the minimization of false positives and negatives. The BraTS-METS 2023 challenge successfully curated well- annotated, diverse datasets and identified common errors, facilitating the translation of BM segmentation across varied environments and providing the tools for future development of personalized volumetric reports to patients undergoing BM treatment.

Objectives Somatostatin receptor (SSTR) PET imaging is utilized with increasing frequency in the clinical management of neuroendocrine tumors. Incidental PET-avid CNS lesions are commonly noted and presumed to be meningiomas. However, SSTR PET lacks specificity for meningioma identification. This study aimed to clarify the role of SSTR-based imaging in the classification of incidental CNS lesions based on current clinical practice. Methods Patients who underwent both Ga-68-DOTATATE PET and brain MRI and had an incidental CNS lesion identified with a radiographic prediction of meningioma via one (discordant prediction) or both (concordant prediction) imaging modalities were retrospectively analyzed. Imaging indication, semiquantitative measures, and clinical history were recorded. Results Among 48 patients with a CNS lesion identified on both imaging modalities, most scans were performed for a history of neuroendocrine tumor (64.6%). Cases with concordant lesion-type prediction of meningioma between imaging modalities (N = 24) displayed a significantly higher SUV max (median 7.9 vs. 4.0; P = 0.008) and Krenning score (median 3.0 vs. 2.0; P = 0.005) on Ga-68-DOTATATE PET compared with cases with a discordant prediction of meningioma (N = 24). In cases with lower SUV max values, Ga-68-DOTATATE was more likely to discordantly predict meningioma without agreement by the corresponding MRI. Prior cranial radiation or use of somatostatin mimetics did not affect quantitative radiographic measures, and MRI-based tumor size was similar across groups. Conclusion Lesions with increased avidity may be more confidently predicted as meningioma in Ga-68-DOTATATE PET scans, whereas there is more discrepancy in prediction among low SUV cases.

Metastatic neuroendocrine tumour (NET) to brain has been reported in 1.5–5% of patients with NETs. Differentiation between intracranial NET metastasis and meningiomas can cause a diagnostic dilemma. We present a symptomatic case of a 66-year-old male with a history of left-sided skull base mass. The diagnosis of a meningioma was made based on the MRI findings and clinical presentation. The patient received radiation and the mass remained stable on serial MRI images at follow-up visits. Five years after his initial presentation, the patient’s mass showed further growth. He also complained of worsening of his recent diagnosis of irritable bowel syndrome and fluctuations in his blood pressure. Surgical resection was performed, and histopathological features were consistent with moderately differentiated neuroendocrine tumour. Further evaluation with 68 Gallium-DOTATATE positron emission-computed tomography (Ga-68 PET/CT) showed metastatic disease involving the bones, lymph nodes, and liver without convincing evidence of the location of primary malignancy within the bowel loops or the pancreas. The patient was started on combination of capecitabine and temozolomide with partial response and significant improvement of his symptoms. This case highlights the clinical and radiological behaviour of intracranial NET that can mimic the diagnosis of meningioma.

Abstract BACKGROUND Somatostatin receptor (SSTR) PET/CT is utilized with increasing frequency in the clinical treatment of neuroendocrine tumors. Incidental CNS lesions are commonly noted and presumed to be meningiomas, however 68Gallium (Ga-68)-DOTATATE radionuclide use lacks specificity for meningioma identification. Currently there is no established SSTR-based imaging criteria for classification of incidental CNS lesions. METHODS We retrospectively analyzed patients across clinical and radiological databases at Mayo Clinic who had undergone both Ga-68-DOTATATE PET/CT and brain MR imaging who had an incidental CNS lesion identified with clinical or radiographic prediction of meningioma. Known meningioma diagnoses prior to imaging were excluded. Imaging indication, semi-quantitative measures, and corresponding clinical history were analyzed to identify metrics impacting both the predictive and descriptive utility of SSTR-based imaging as compared to corresponding MRI prediction in incidental CNS lesions. RESULTS Among 59 patients with a CNS lesion identified on both imaging modalities, most scans were performed for a history of neuroendocrine tumor (71%). Cases with concordant lesion type prediction of meningioma between imaging modalities (N = 25) displayed a higher median SUV max and Krenning score on Ga-68-DOTATATE PET compared to cases with concordant prediction of other lesions (N=10) or discordant prediction of meningioma (N=24). Ga-68-DOTATATE was more likely to discordantly predict meningioma as compared to the corresponding patient MRI in cases with lower SUV max values. The addition of Qclear technology was significantly associated with diagnostic clarification in cases of imaging discordant tumor type prediction. Prior cranial radiation or use of a somatostatin mimetic did not affect quantitative radiographic measures in lesion characterization, and MRI-based tumor size was similar across groups. CONCLUSION The continued development of SSTR-based imaging biomarker signatures across incidental CNS lesion subtypes will further define the role of this imaging modality in the clinical diagnosis and management of presumed meningiomas.