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Background: Meniscal preservation has been demonstrated to contribute to long-term knee health and has been a successful intervention in isolation and in patients with anterior cruciate ligament reconstruction (ACLR). The long-term results of meniscal repair in the setting of revision ACLR have yet to be documented. Purpose: To report the incidence of meniscal repair failures at the 6-year follow-up in a cohort of patients who underwent concurrent revision ACLR and primary meniscal repair. Study Design: Prospective cohort study; Level of evidence, 2. Methods: All revision ACLRs with concomitant primary meniscal repair cases from a multicenter group between 2006 and 2011 were selected. Six-year follow-up was obtained to determine whether any subsequent surgery had occurred since their initial revision ACLR. If so, operative reports were obtained, whenever possible, to verify pathological condition and treatment. Results: In total, 221 patients from 1234 revision ACLRs underwent concurrent primary meniscal repairs (18% of the cohort). There were 238 repairs performed: 173 medial and 65 lateral. The majority of these repairs (n = 181; 76%) were performed with an all-inside technique. Six-year surgical follow-up was obtained in 77% (171/221) of the cohort, or 189 of 238 (79%) of the repairs (136 medial, 53 lateral). The meniscal repair failure rate, defined as reoperation, was 16% (31/189) at 6 years. Of the 31 failures, 28 were medial (24 all-inside, 4 inside-out; 28/136 = 20.6% failure rate) and 3 were lateral (2 all-inside, 1 inside-out; 3/53 = 5.7% failure rate). Three medial failures were treated in conjunction with a subsequent repeat revision ACLR. Medial tears underwent reoperation for failure at a significantly higher rate than lateral tears (20.6% vs 5.7%; P = .01) and had a significantly shorter survival time compared with lateral tears ( P = .02). No difference was found between the failure and nonfailure groups when it came to tear type, tear length, repair technique utilized, suture/implant type, or number of sutures used between the 2 groups. Conclusion: Meniscal repair in the revision ACLR setting has a 16% failure rate at 6 years. Failure rates for medial tears (20.6%) were found to be higher than that for lateral tears (5.7%), which aligns with previous studies in both the revision and primary ACLR setting.

Abstract Recurrent or refractory IDH-mutant (mt) gliomas represent a population with significant unmet clinical need. The Notch ligand delta-like 3 (DLL3) is a tumor-associated antigen that is highly and homogeneously expressed in IDH-mt gliomas but absent in normal brain tissue, making it an attractive therapeutic target. Tarlatamab is a half-life extended bispecific T-cell engager that targets DLL3 and CD3 to redirect cytotoxic T cells toward DLL3-expressing tumor cells. Given the specific expression of DLL3 in IDH-mt gliomas and promising activity of tarlatamab in other DLL3-positive tumors, this study investigates the safety, efficacy, and intracranial activity of tarlatamab in this patient population. TARGID is an open-label, phase II trial enrolling adult patients with histologically confirmed IDH1 or IDH2-mt astrocytoma or oligodendroglioma following up to two prior lines of systemic therapies. The study comprises two cohorts. Cohort 1 (n=10) includes patients with resectable disease who will receive up to three cycles of tarlatamab prior to surgery; postoperative continuation is permitted. The primary endpoint for this cohort is the change in CD8+ T-cell infiltration in tumor tissue and blood. Cohort 2 (n=34) includes patients with unresectable disease and uses a Simon’s optimal two-stage design (H0: P ≤ 0.1 versus H1: P ≥ 0.25). In the 1st stage, 13 patients will be accrued. If 2 or more responses are observed, 21 additional patients will be enrolled. Tarlatamab is administered intravenously at 10 mg every two weeks, following a 1 mg priming dose on Cycle 1 Day 1. The primary endpoint is objective response rate by RANO 2.0. Exploratory endpoints include DLL3 expression, circulating biomarkers, and immune correlatives. The trial is currently in activation, with first patient enrollment anticipated in Q3 2025. The study is supported by the provision of study drug from Amgen as an investigator-initiated trial.

Abstract BACKGROUND STELLAR was a phase III randomized, open-label trial of eflornithine (ornithine decarboxylase inhibitor) with lomustine versus lomustine alone in patients with recurrent anaplastic astrocytoma (AA). We previously reported an overall survival (OS) and progression free survival (PFS) of the combination in the patients with grade 3 IDH-mutant astrocytoma. Here we report updated OS and PFS outcomes based on additional molecular classification and blinded independent central review (BICR). METHOD Eligibility included: age ≥ 18 years, AA (2016 WHO criteria) with first recurrence ≥ 6 months after radiation, KPS ≥ 70, and no imaging findings consistent with grade 4 glioblastoma. Patients received eflornithine (2.8 g/m2 TID Q 2/3 weeks) with lomustine (90 mg/m2 Q 6 weeks) (Arm A) or lomustine alone (110 mg/m2 Q 6 weeks) (Arm B). Primary endpoint was OS. RESULTS Updated molecular data resulted in re-classification of the 343 AA patients enrolled into 3 WHO 2021 defined tumor subtypes, 196 tumors were IDHmut, CDKN2A/B intact, consistent with diagnosis of astrocytoma, IDHmut, WHO grade 3, 33 tumors had CDKN2A/B loss consistent with astrocytoma, IDHmut, WHO grade 4, 106 tumors were IDHwt consistent with diagnosis of glioblastoma, IDHwt, WHO grade 4. Updated results strengthen initial conclusion that eflornithine plus lomustine resulted in significant survival benefit in the astrocytoma, IDHmut, WHO grade 3 group (mOS 34.9 vs, 23.5 months, HR = 0.64, stratified log rank p =0.0136). PFS with eflornithine was also improved (15.8 vs, 7.2 months, HR = 0.57, stratified log rank p =0.0113). BICR demonstrated a high correlation between investigator and centrally determined PFS (p=0.0305), and high correlation between OS and PFS (p=.0001). CONCLUSION The clinically meaningful OS and PFS benefits observed with eflornithine in the molecularly defined 2021 WHO CNS grade 3 astrocytomas is further confirmed and expanded based on additional molecular classification and BICR of PFS.

Abstract INTRODUCTION Recurrent IDH-mutant gliomas pose a significant therapeutic challenge, with limited treatment options following progression after standard therapy. Combining PARP inhibitors with immune checkpoint blockade has been proposed as a synergistic strategy in IDH-mutant high-grade gliomas, leveraging vulnerabilities in homologous recombination repair and increased PD-L1 expression following PARP inhibition. OBJECTIVE This phase II trial (NCT03991832) evaluated the combination of the PARP inhibitor olaparib and the PD-L1 inhibitor durvalumab in patients with recurrent IDH-mutant glioma. We also investigated the potential of the plasma tumor methylome as a non-invasive biomarker of treatment response. METHODS Twenty-nine patients (median age 40.5 years; 41% female) were enrolled between January 2020 and February 2023. All patients received olaparib (300 mg twice daily) and durvalumab (1,500 mg IV every four weeks) until radiographic or clinical progression. Plasma samples were collected at baseline and monthly, and cell-free methylated DNA immunoprecipitation sequencing (cfMeDIP-seq) was performed. RESULTS The objective response rate was 10%, and median overall survival was 9.3 months. Longitudinal cfMeDIP-seq profiling enabled development of a circulating methylome classifier that accurately distinguished responders from non-responders. Integration with matched tumor transcriptomic and methylation data revealed enrichment of immune and DNA repair pathways in responders. Whole-exome sequencing identified no consistent mutational correlates. Spatial transcriptomic analysis demonstrated a more interactive, immune-rich tumor microenvironment and reduced malignant cell state diversity in responders. CONCLUSION This study supports the safety of combined PARP and PD-L1 blockade in recurrent IDH-mutant glioma and highlights the plasma methylome as a promising non-invasive biomarker of therapeutic response.

Abstract BACKGROUND Post-operative prognostication in glioma patients remains a clinical challenge, particularly given the heterogeneity in tumor biology and patient outcomes. We evaluated whether image-derived features from post-operative imaging improve survival modeling beyond established clinicopathologic factors. METHODS We analyzed a cohort of 71 post-surgical glioma patients treated at Princess Margaret Cancer Centre with known clinicopathologic features including age, histological classification, IDH mutation status, MGMT methylation status, extent of resection, and performance status. A baseline Cox proportional hazards model was fit to clinical variables. Radiomic features were extracted using PyRadiomics from post-operative CT and MRI across three regions of interest: whole brain, tumor bed, and peritumoral ring. Features underwent stepwise unsupervised reduction (variance filtering, exclusion of volume-correlated features, and removal of remaining collinear features), followed by dimensionality reduction via principal component analysis. Combined clinical and imaging models were evaluated in the overall cohort and stratified by IDH mutation status. RESULTS In the overall cohort, the clinical model was highly prognostic (C-index 0.74, p ~ 3.2×10⁻⁶). Adding imaging features improved performance (C-index 0.79) but was not significant (p ~ 0.31). Subset analysis revealed differential effects by IDH status. In IDH-mutant gliomas (n=24), neither clinical nor combined models were significant. In IDH wild-type glioblastoma (n=47), the clinical model achieved C-index 0.75 (p=0.04). The addition of imaging features significantly improved model performance (C-index 0.83, p=0.03), with selected imaging components contributing independent prognostic value after adjustment for clinical variables. Post-hoc analysis of radiomic feature importance (random forest ranking) revealed that top contributing features were drawn from both CT and MRI modalities, with most sourced from the whole brain region. CONCLUSIONS In post-surgical patients with IDH wild-type glioblastoma, image-derived features from post-operative imaging can significantly enhance prognostic modeling beyond known clinicopathologic factors. These preliminary findings support the potential role of radiomic biomarkers in refining risk stratification for this patient subgroup.

Importance Treatment for cognitive dysfunction due to postacute sequelae of long COVID (ie, symptoms of fatigue, malaise, weakness, confusion that persist beyond 12 weeks after an initial COVID infection) remains a significant unmet need. Objective To test evidence-based rehabilitation strategies for improving cognitive symptoms in persons with long COVID. Design, Setting, and Participants This was a 5-arm, multicenter, randomized clinical trial of 3 remotely delivered interventions conducted between August 17, 2023, and June 10, 2024. The study took place at 22 trial sites and included the screening of individuals with cognitive long COVID. Interventions Participants were randomized to 1 of 5 arms: adaptive computerized cognitive training (BrainHQ [Posit Science]), cognitive-behavioral rehabilitation involving both group and individual counseling sessions (PASC-Cognitive Recovery [PASC-CoRE]) paired with BrainHQ, and transcranial direct current stimulation (tDCS) paired with BrainHQ. Two comparator arms were included as follows: unstructured computer puzzles and games (active comparator) and sham tDCS paired with BrainHQ. The interventions occurred 5 times per week over 10 weeks. Main Outcomes and Measures Cognitive and behavioral in-person assessments were performed at baseline, midintervention, at the end of intervention, and 3 months after the end of the intervention. The primary outcome measure was the modified Everyday Cognition Scale 2 (ECog2) completed at the end of the intervention compared to the baseline visit based on participant self-report looking back over the prior 7 days. Results A total of 378 individuals were screened, from which there were 328 participants (median [IQR] age, 48.0 [37.0-58.0] years; 241 female [73.5%]; race: 15 Asian [4.6%], 47 Black [14.3%], and 235 White [71.6%]; ethnicity: 52 Hispanic [15.9%]). None of the 3 active interventions demonstrated benefits on the modified ECog2 in the intention-to-treat population by the end of the intervention period. The adjusted differences in mean change were 0.0 (95% CI, −0.2 to 0.2) for BrainHQ vs active comparator, 0.1 (95% CI, −0.1 to 0.3) for PASC-CoRE + BrainHQ vs active comparator, 0.0 (95% CI, −0.2 to 0.2) for tDCS-active + BrainHQ vs tDCS-sham + BrainHQ, and 0.1 (95% CI, −0.1 to 0.3) for PASC-CoRE + BrainHQ vs BrainHQ alone. Secondary participant-reported outcomes and neuropsychological tests showed no differential benefits for any treatment arm. All 5 arms demonstrated some improvements over time on the modified ECog2 and on secondary outcomes. There were no serious adverse events attributable to the interventions. Conclusions and Relevance This phase 2 randomized clinical trial failed to demonstrate differential benefits for online cognitive training, a structured cognitive rehabilitation program, and tDCS for cognitive long COVID. Trial Registration ClinicalTrials.gov Identifier: NCT05965739 .

BACKGROUND: Intracranial atherosclerosis (ICAS) is associated with an increased risk of early recurrent ischemic stroke. We evaluated whether biomarkers of impaired distal perfusion—specifically, anterior circulation borderzone infarct, and hypoperfusion mismatch volume—were associated with recurrent ischemic stroke within 90 days. METHODS: The BIORISK ICAS (Biomarkers and Recurrence Risk in Symptomatic Intracranial Atherosclerosis) is a multicenter retrospective international study (35 sites) that included hospitalized patients with symptomatic ICAS (50%–99% luminal stenosis of the intracranial vertebral, basilar, distal internal carotid, or proximal middle cerebral artery) from January 2019 to June 2024. The primary outcome was recurrent ischemic stroke in the territory of the symptomatic artery within 90 days. In the primary analysis, the exposure was an acute anterior circulation borderzone infarct. In secondary analysis of the subset with presentation within 72 hours of last known normal and perfusion imaging completed, the exposure of interest was prespecified as hypoperfusion mismatch volume at T max (time to maximum) threshold of 6 seconds, dichotomized at the Youden index. We performed multivariable Cox regression to test associations between exposure variables and the outcome, adjusting for clinically relevant variables and those associated with the outcome ( P <0.1). RESULTS: Of 2050 patients with symptomatic ICAS, 1737 (84.7%) presented within 72 hours of symptom onset, among whom 509 (29.3%) underwent perfusion imaging. The primary analysis included 1891 patients; 174 (9.2%) patients had recurrent ischemic stroke in the symptomatic arterial territory. In adjusted Cox regression models, there was an association between anterior circulation borderzone infarct and recurrent ischemic stroke at 90 days (adjusted hazard ratio, 1.40 [95% CI, 1.02–1.93]). In the perfusion imaging analysis, hypoperfusion mismatch of ≥10 mL was associated with recurrent ischemic stroke (adjusted hazard ratio, 1.83 [95% CI, 1.03–3.28]). CONCLUSIONS: Biomarkers of impaired distal perfusion, anterior circulation borderzone infarct, and hypoperfusion mismatch were associated with increased risk of recurrent ischemic stroke. These findings support the use of perfusion imaging in ICAS as well as future trials investigating early reperfusion in high-risk patients with ICAS.

ABSTRACT Background Patients with cancer undergoing cardiac implantable electronic device (CIED) implantation face unique challenges. This study evaluates the outcomes and implications of CIED implantation in this population. Methods Using data from the National Readmission Database (NRD) from 2016 to 2019, we analyzed CIED implantations in patients aged ≥18 years, categorized by cancer status: no cancer, historical cancer, and active cancer. Multivariable logistic and linear regression models assessed associations with in‐hospital, 30‐day, and 90‐day outcomes, adjusting for various patient characteristics. Results A total of 748,957 CIED procedures were analyzed among which 13.3% were in patients with historical cancer and 3.8% with active cancer. Active cancer was linked to higher odds of pacemaker implantation but lower odds for ICD and CRT devices. Patients with active cancer experienced higher index hospitalization mortality (aOR 1.68; 1.50–1.89; p < 0.001), major adverse cardiovascular events (aOR 1.07; 1.02–1.14; p = 0.01), bleeding (aOR 1.36; 1.27–1.46; p < 0.01), and sepsis‐related (aOR 1.31; 1.19–1.44; p < 0.01) complications, with increased readmission rates at 30 (aOR 1.37; 1.30–1.44; p < 0.01) and 90 days (aOR 1.37; 1.31–1.44; p < 0.01) compared to those without cancer. Historical cancer was associated with better outcomes. Key predictors of adverse outcomes included chronic heart failure, kidney disease, cerebrovascular disease, and protein‐calorie malnutrition. Conclusions Patients with active cancer undergoing CIED implantation have an increased risk of in‐hospital complications and readmissions as compared to those without cancer.

Abstract Medical imaging is undergoing a transformation driven by the advent of new, highly effective, machine learning techniques paired with increases in computational capabilities (Cheng et al. 2021; Gilson et al. 2023; Almeida et al. 2024; Krishna et al. 2024). These advanced algorithms have the potential to improve disease detection, diagnosis, prognosis, and treatment outcomes. However, the complexity of machine learning models, the large amounts of curated and annotated data required by some methods, and the potential for bias and error make it challenging for individuals to safely and effectively leverage these methods (Lin et al. 2024; Guo et al. 2024; Xu et al. 2024; Linguraru et al. 2024; Wood et al. 2019). To address these challenges, the American Association of Physicists in Medicine (AAPM), American College of Radiology (ACR), Radiological Society of North America (RSNA), and Society for Imaging Informatics in Medicine (SIIM) have worked together to develop a syllabus detailing a recommended set of competencies for medical imaging professionals interacting with these systems. This guide is aimed at four different personas: users of AI systems, purchasers of AI systems, individuals who provide clinical expertise during the development of AI systems (“clinical collaborators”), and developers of AI systems.1 This is a syllabus, not a curriculum, and is intentional in this scope. Recognizing that individuals may benefit from different presentations of the same material, this work enumerates a series of relevant competencies but does not prescribe, nor offer, a method of instruction (Schuur, Rezazade Mehrizi, and Ranschaert 2021; Garin et al. 2023). By addressing the task‐specific demands of each role, this guide will enable medical imaging professionals to utilize machine learning systems more safely and effectively, ultimately improving patient care and outcomes.