Improved nationwide survival of sarcoma patients with a network of reference centers.

BACKGROUND: We investigated the impact of the implementation of a network of reference centers for sarcomas (NETSARC) on the care and survival of sarcoma patients in France since 2010. PATIENTS AND METHODS: NETSARC (netsarc.org) is a network of 26 reference sarcoma centers with specialized multidisciplinary tumor boards (MDTBs), funded by the French National Cancer Institute (INCa) since 2010. Its aims are to improve the quality of diagnosis and care of sarcoma patients. Patients' characteristics, treatments, and outcomes are collected in a nationwide database. The objective of this analysis was to compare the survival of patients in three periods: 2010-2012 (non-exhaustive), 2013-2015, and 2016-2020. RESULTS: A total of 43 975 patients with sarcomas, gastrointestinal stromal tumors (GISTs), or connective tissue tumors of intermediate malignancy were included in the NETSARC+ database since 2010 (n = 9266 before 2013, n = 12 274 between 2013 and 2015, n = 22 435 in 2016-2020). Median age was 56 years, 50.5% were women, and 13.2% had metastasis at diagnosis. Overall survival was significantly superior in the period 2016-2020 versus 2013-2015 versus 2010-2012 for the entire population, for patients >18 years of age, and for both metastatic and non-metastatic patients in univariate and multivariate analyses (P < 0.0001). Over the three periods, we observed a significantly improved compliance to clinical practice guidelines (CPGs) nationwide: the proportion of patients biopsied before surgery increased from 62.9% to 72.6%; the percentage of patients presented to NETSARC MDTBs before first surgery increased from 31.7% to 44.4% (P < 0.0001). The proportion of patients with R0 resection on first surgery increased (from 36.1% to 46.6%), while R2 resection rate decreased (from 10.9% to 7.9%), with a better compliance and improvement in NETSARC centers. CONCLUSIONS: The implementation of the national reference network for sarcoma was associated with an improvement of overall survival and compliance to guidelines nationwide in sarcoma patients. Referral to expert networks for sarcoma patients should be encouraged, though a better compliance to CPGs can still be achieved.

Risk factors for adverse outcomes in children with diabetic ketoacidosis.

OBJECTIVE: Develop a multivariable model to identify children with diabetic ketoacidosis (DKA) and/or hyperglycemic hyperosmolar state (HHS) at increased risk of adverse outcomes, and apply it to analyze adverse outcomes during and after the COVID-19 pandemic. DESIGN: Retrospective review of clinical data from 4565 admissions (4284 with DKA alone, 31 [0.7%] only HHS, 250 [5.4%] hyperosmolar DKA) to a large academic children's hospital from January 2010-June 2023. 2010-2019 data (N=3004) were used as a training dataset, and 2020-2021 (N=903) and 2022-2023 (N=658) data for validation. Death or intensive care unit stays >48 hours comprised a composite "Adverse Outcome" group. Risks for this composite outcome were assessed using generalized estimating equations. RESULTS: There were 47 admissions with Adverse Outcomes (1.5%) in 2010-2019, 46 (5.0%) in 2020-2021, and 16 (2.4%) in 2022-2023. Eight patients died (0.18%). Maximum serum glucose, initial pH and diagnosis of type 2 diabetes most strongly predicted Adverse Outcomes. The proportion of patients with type 2 diabetes was highest in 2020-2021. A multivariable model incorporating these factors had excellent discrimination (area under receiver operator characteristic curve [AUC] of 0.948) for the composite outcome in the training dataset, and similar predictive power (AUC 0.960 and 0.873) in the 2020-2021 and 2022-2023 validation datasets, respectively. In the full dataset, AUC for death was 0.984. CONCLUSIONS: Type 2 diabetes and severity of initial hyperglycemia and acidosis are independent risk factors for Adverse Outcomes, and explain the higher frequency of Adverse Outcomes during the COVID-19 pandemic. Risks decreased in January 2022-June 2023.

Assessment of the current and emerging criteria for the histopathological classification of lung neuroendocrine tumours in the lungNENomics project.

BACKGROUND: Six thoracic pathologists reviewed 259 lung neuroendocrine tumours (LNETs) from the lungNENomics project, with 171 of them having associated survival data. This cohort presents a unique opportunity to assess the strengths and limitations of current World Health Organization (WHO) classification criteria and to evaluate the utility of emerging markers. PATIENTS AND METHODS: Patients were diagnosed based on the 2021 WHO criteria, with atypical carcinoids (ACs) defined by the presence of focal necrosis and/or 2-10 mitoses per 2 mm2. We investigated two markers of tumour proliferation: the Ki-67 index and phospho-histone H3 (PHH3) protein expression, quantified by pathologists and automatically via deep learning. Additionally, an unsupervised deep learning algorithm was trained to uncover previously unnoticed morphological features with diagnostic value. RESULTS: The accuracy in distinguishing typical from ACs is hampered by interobserver variability in mitotic counting and the limitations of morphological criteria in identifying aggressive cases. Our study reveals that different Ki-67 cut-offs can categorise LNETs similarly to current WHO criteria. Counting mitoses in PHH3+ areas does not improve diagnosis, while providing a similar prognostic value to the current criteria. With the advantage of being time efficient, automated assessment of these markers leads to similar conclusions. Lastly, state-of-the-art deep learning modelling does not uncover undisclosed morphological features with diagnostic value. CONCLUSIONS: This study suggests that the mitotic criteria can be complemented by manual or automated assessment of Ki-67 or PHH3 protein expression, but these markers do not significantly improve the prognostic value of the current classification, as the AC group remains highly unspecific for aggressive cases. Therefore, we may have exhausted the potential of morphological features in classifying and prognosticating LNETs. Our study suggests that it might be time to shift the research focus towards investigating molecular markers that could contribute to a more clinically relevant morpho-molecular classification.