Citations

We investigated the prognostic impact of blast counts, TP53 allelic state determinants (number of hits, del(17p), variant allele frequency, complex karyotype),and a novel karyotypic clonal cell fraction (≤50% clonal cells vs >50% clonal cells (termed ‘CK50’)) in 495 individuals with TP53- mutated ( TP53 MUT ) myeloid neoplasms. Outcome examined was 24-month survival (OS24). The cohort (median age 71) included 29% (144/495) myelodysplastic syndromes (MDS)/MDS-acute myeloid leukaemia (AML) (1%–19% blasts) and 71% (351/495) AML (≥20% blasts), with 18% (81/460) having low CK50. Overall, 83% received front-line hypomethylating agents. Higher blast counts (<20% vs ≥20%) were marginally associated with CK50 (p=0.08). In the OS24 analysis, blast count showed a marginal association with OS24 (HR 1.3 (95% CI 1.0 to 1.6); p=0.07), while CK50 predicted significantly inferior outcomes (HR=1.7 (95% CI 1.2 to 2.3); p=0.002). In a multivariable model including all TP53 allelic state determinants, only CK50 and complex karyotype remained relevant for predicting adverse outcomes.

Pediatric central nervous system tumors are the leading cause of cancer-related deaths in children. The five-year survival rate for high-grade glioma in children is less than 20%. The development of new treatments is dependent upon multi-institutional collaborative clinical trials requiring reproducible and accurate centralized response assessment. We present the results of the BraTS-PEDs 2023 challenge, the first Brain Tumor Segmentation (BraTS) challenge focused on pediatric brain tumors. This challenge utilized data acquired from multiple international consortia dedicated to pediatric neuro-oncology and clinical trials. BraTS-PEDs 2023 aimed to evaluate volumetric segmentation algorithms for pediatric brain gliomas from magnetic resonance imaging using standardized quantitative performance evaluation metrics employed across the BraTS 2023 challenges. The top-performing AI approaches for pediatric tumor analysis included ensembles of nnU-Net and Swin UNETR, Auto3DSeg, or nnU-Net with a self-supervised framework. The BraTS-PEDs 2023 challenge fostered collaboration between clinicians (neuro-oncologists, neuroradiologists) and AI/imaging scientists, promoting faster data sharing and the development of automated volumetric analysis techniques. These advancements could significantly benefit clinical trials and improve the care of children with brain tumors.

Abstract Severe aplastic anemia (SAA) is a rare and life-threatening bone marrow failure disorder. Immunosuppressive therapy (IST) with antithymocyte globulin and cyclosporine has long been a frontline treatment option in SAA; however, its limited durability and risk of long-term complications such as secondary malignancies remain a drawback in this treatment modality. Allogeneic hematopoietic cell transplantation (HCT) is a potentially curative option with significantly improved outcomes over the long term, particularly with HLA-matched related donors. However, the use of alternative donors, such as haploidentical, mismatched, or matched unrelated donors, has previously been limited due to increased transplant-related morbidity, particularly graft-versus-host disease (GVHD). HCTs have therefore been limited to young recipients and those with HLA-matched related donors, creating significant disparity for older adults and those who lack matched donor options. Nevertheless, more recent advances in HCT, such as posttransplant cyclophosphamide for GVHD prophylaxis, have led to improved outcomes of HCT with alternative donors; however, alternative donor HCT remains underused as up-front therapy, in part because of limited multicenter trial data. This review discusses current SAA treatment approaches, including both IST and HCT, and highlights remaining gaps. It also discusses how ongoing clinical trials such as CureAA and TransIT could help address these gaps. Furthermore, we discuss the importance of stakeholder engagement and implementation science in the integration of research-based evidence into clinical practice. Bridging these gaps is necessary for achieving equitable access for patients historically excluded from frontline HCT, including older adults and racially or ethnically diverse populations.

ABSTRACT Objective Despite high stand‐alone performance, studies demonstrate that artificial intelligence (AI)‐supported endoscopic diagnostics often fall short in clinical applications due to human‐AI interaction factors. This video‐based trial on Barrett's esophagus aimed to investigate how examiner behavior, their levels of confidence, and system usability influence the diagnostic outcomes of AI‐assisted endoscopy. Methods The present analysis employed data from a multicenter randomized controlled tandem video trial involving 22 endoscopists with varying degrees of expertise. Participants were tasked with evaluating a set of 96 endoscopic videos of Barrett's esophagus in two distinct rounds, with and without AI assistance. Diagnostic confidence levels were recorded, and decision changes were categorized according to the AI prediction. Additional surveys assessed user experience and system usability ratings. Results AI assistance significantly increased examiner confidence levels ( p < 0.001) and accuracy. Withdrawing AI assistance decreased confidence ( p < 0.001), but not accuracy. Experts consistently reported higher confidence than non‐experts ( p < 0.001), regardless of performance. Despite improved confidence, correct AI guidance was disregarded in 16% of all cases, and 9% of initially correct diagnoses were changed to incorrect ones. Overreliance on AI, algorithm aversion, and uncertainty in AI predictions were identified as key factors influencing outcomes. The System Usability Scale questionnaire scores indicated good to excellent usability, with non‐experts scoring 73.5 and experts 85.6. Conclusions Our findings highlight the pivotal function of examiner behavior in AI‐assisted endoscopy. To fully realize the benefits of AI, implementing explainable AI, improving user interfaces, and providing targeted training are essential. Addressing these factors could enhance diagnostic accuracy and confidence in clinical practice.

ABSTRACT Background Over the years, prognostic indexes have been developed to help clinicians stratify patients with biliary tract cancers (BTC) into risk groups. This study aims to identify a new prognostic index for patients with BTC treated with cisplatin, gemcitabine and durvalumab (CGD) in the first‐line setting. Patients and Methods The study population consisted of patients with BTC from 11 Eastern and Western Countries. Using multivariate analysis for overall survival (OS), we identified 5 baseline statistically significant variables: stage, carcinoembryonic antigen (CEA) levels, albumin levels, gamma glutamyl transferase (GGT) levels, neutrophil‐to‐lymphocyte ratio (NLR). Metastatic disease is a prognostic factor with a superior weight considering the HR of 3.62, while all the others can be considered prognostic factors with equivalent weight as the HRs are quite similar (HRs between 1.55 and 1.92). Based on these reasons, we developed a prognostic model called the MAGIC‐D index by assigning a score of 2 for metastatic disease, and a score of 1 for CEA increased levels, albumin decreased levels, GGT increased levels, NLR ≥ 3. Patients were stratified into three risk groups as follows: low‐risk group (Score from 0 to 2), intermediate‐risk group (Score from 3 to 4) and high‐risk group (Score from 5 to 6). At the first data cutoff (April 2024), these data were available for 319 patients that composed the training cohort used for the analysis. At the second data cutoff (May 2025), 79 patients were further enrolled and composed the validation cohort. Results Median progression‐free survival was 11.7 months in low‐risk group (20.7%), 8.7 months in intermediate‐risk group (46.4%) and 5.4 months in high‐risk group (32.9%) [low‐risk hazard ratio (HR): 0.27, intermediate‐risk HR: 0.55, high‐risk HR: 1, p < 0.0001]. Median OS was 18.4 months in low‐risk group,15.9 months in intermediate‐risk group and 7.8 months in high‐risk group (low‐risk HR: 0.17, intermediate‐risk HR: 0.43, high‐risk HR: 1, p < 0.0001). There was no difference in overall response rate (low‐risk: 31.8%, intermediate‐risk: 36.5% and high‐risk: 25.7%; p = 0.0718), while disease control rate was significantly different across the three risk groups (low‐risk: 83.3%, intermediate‐risk: 70.9% and high‐risk: 60.9%; p < 0.0001) as well as the rate of patients receiving a second‐line therapy (low‐risk: 54.5%, intermediate‐risk: 48.6% and high‐risk: 25.7%; p = 0.0061). Finally, the prognostic role in terms of OS and PFS of the MAGIC‐D index was confirmed in a validation cohort of 79 patients. Conclusion The MAGIC‐D index is an easy‐to‐use tool able to stratify patients with BTC with different prognoses undergoing first‐line therapy with CGD.