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Pediatric laboratory test results must be interpreted in the context of interindividual variation and age- and sex-dependent dynamics. Reference intervals as presently defined for separate age groups can only approximate the age-related dynamics encountered in pediatrics. Continuous reference intervals from birth to adulthood are not available for most laboratory analytes because of the ethical and practical constraints of defining reference intervals using a population of healthy community children. We applied an indirect method to generate continuous reference intervals for 22 hematologic and biochemical analytes by analyzing clinical laboratory data from blood samples taken during clinical care of patients. We included samples from 32 000 different inpatients and outpatients (167 000 samples per analyte) from a German pediatric tertiary care center. Measurements were performed on a Sysmex-XE 2100 and a Cobas Integra 800 during clinical care over a 6-year period. The distribution of samples considered normal was estimated with an established indirect statistical approach and used for the calculation of reference intervals. We provide continuous reference intervals from birth to adulthood for 9 hematology analytes (hemoglobin, hematocrit, red cell indices, red cell count, red cell distribution width, white cell count, and platelet count) and 13 biochemical analytes (sodium, chloride, potassium, calcium, magnesium, phosphate, creatinine, aspartate transaminase, alanine transaminase, γ-glutamyltransferase, alkaline phosphatase, lactate dehydrogenase, and total protein). Continuous reference intervals capture the population changes in laboratory analytes during pediatric development more accurately than age groups. After local validation, the reference intervals provided should allow a more precise consideration of these dynamics in clinical decision making.

Background Specialized pediatric oncology is typically concentrated in a few high-volume centers, creating tensions between the need for centralization and equitable spatial access. For regional health planning, robust methods are required to delineate hospital catchment areas and understand how structural site characteristics and accessibility shape patient-to-hospital travel flows. This study uses pediatric oncology in Bavaria, Germany, as a case to develop and test an extended, empirically calibrated Huff model for modeling hospital catchment areas. Methods We analyzed 3,320 incident cases of pediatric oncology recorded in the German Childhood Cancer Registry between 2014 and 2023, which were treated at the seven specialized hospitals in Bavaria. An extended Huff model was specified that integrates structural indicators of hospital capacity and quality (bed capacity, staffing, cancer center accreditation), a spatial clustering variable that captures proximity-related interactions among nearby hospital sites, and a logistic distance-decay function based on travel times. Model parameters were estimated using maximum likelihood, and competing specifications were compared primarily using mean absolute percentage error (MAPE). A scenario analysis was conducted to assess how a reduction of nurse staffing ratios at two Munich hospitals would affect patient-to-hospital travel flows and catchment areas. Results Our final baseline model, comprising four structural indicators, a clustering variable, and a logistic travel-time function, achieved a MAPE of 5.85% and an R² of 0.89. Capacity and quality indicators displayed positive effects on hospital choice, whereas the clustering parameter was negative, indicating proximity-related interaction effects among nearby hospitals. In the case scenario, a 20% reduction in the nursing staff ratio at the Munich sites led to declining modeled patient shares at both hospitals (− 2.0 and − 2.5% points, respectively) and corresponding gains primarily at Augsburg (+ 3.5% points) and Regensburg (+ 1.3% points), particularly in overlapping and transitional catchment zones. Conclusions Our extended Huff model, which combines multidimensional structural indicators, spatial clustering, and realistic travel-time effects, can accurately represent hospital catchment areas and patient-to-hospital travel flows in specialized pediatric oncology. The approach provides a transparent, empirically grounded framework for assessing accessibility, identifying spatial interdependencies between hospital sites, and conducting scenario-based simulations to inform regional health planning and workforce policy in specialized care settings.

Background Reference intervals represent the expected range of physiological test results in a healthy population and are essential to support medical decision making. Particularly in the context of pediatric reference intervals, where recruitment regulations make prospective studies challenging to conduct, indirect estimation strategies are becoming increasingly important. Established indirect methods enable robust identification of the distribution of “healthy” samples from laboratory databases, which include unlabeled pathologic cases, but are currently severely limited when adjusting for essential patient characteristics such as age. Here, we propose the use of mixture density networks (MDN) to overcome this problem and model all parameters of the mixture distribution in a single step. Results Estimated reference intervals from varying settings with simulated data demonstrate the ability to accurately estimate latent distributions from unlabeled data using different implementations of MDNs. Comparing the performance with alternative estimation approaches further highlights the importance of modeling the mixture component weights as a function of the input in order to avoid biased estimates for all other parameters and the resulting reference intervals. We also provide a strategy to generate partially customized starting weights to improve proper identification of the latent components. Finally, the application on real-world hemoglobin samples provides results in line with current gold standard approaches, but also suggests further investigations with respect to adequate regularization strategies in order to prevent overfitting the data. Conclusions Mixture density networks provide a promising approach capable of extracting the distribution of healthy samples from unlabeled laboratory databases while simultaneously and explicitly estimating all parameters and component weights as non-linear functions of the covariate(s), thereby allowing the estimation of age-dependent reference intervals in a single step. Further studies on model regularization and asymmetric component distributions are warranted to consolidate our findings and expand the scope of applications.

Background Medical decision making based on quantitative test results depends on reliable reference intervals, which represent the range of physiological test results in a healthy population. Current methods for the estimation of reference limits focus either on modelling the age-dependent dynamics of different analytes directly in a prospective setting or the extraction of independent distributions from contaminated data sources, e.g. data with latent heterogeneity due to unlabeled pathologic cases. In this article, we propose a new method to estimate indirect reference limits with non-linear dependencies on covariates from contaminated datasets by combining the framework of mixture models and distributional regression. Results Simulation results based on mixtures of Gaussian and gamma distributions suggest accurate approximation of the true quantiles that improves with increasing sample size and decreasing overlap between the mixture components. Due to the high flexibility of the framework, initialization of the algorithm requires careful considerations regarding appropriate starting weights. Estimated quantiles from the extracted distribution of healthy hemoglobin concentration in boys and girls provide clinically useful pediatric reference limits similar to solutions obtained using different approaches which require more samples and are computationally more expensive. Conclusions Latent class distributional regression models represent the first method to estimate indirect non-linear reference limits from a single model fit, but the general scope of applications can be extended to other scenarios with latent heterogeneity.

Primary thyroid teratomas are exceedingly rare. Mature and immature variants recapitulate their gonadal counterparts (predilection for infants/children, triphasic germ layer differentiation, and favorable outcome). On the other hand, the so-called malignant teratomas affect predominantly adults and elderly, are highly aggressive, and, according to a few published cases, harbor DICER1 mutations. We describe three highly aggressive sporadic malignant teratoid thyroid tumors in 2 females (17 and 45 years) and one male (17 years). Histology showed triphasic neoplasms composed of solid nests of small primitive monomorphic cells embedded in a cellular stroma with primitive immature rhabdomyosarcoma-like (2) or pleomorphic sarcoma-like (1) phenotype. The third component was represented by TTF1+/PAX8+ primitive teratoid epithelial tubules reminiscent of primitive thyroid follicles and/or Wilms tumor, admixed with scattered respiratory- or enteric-type tubules, neuroepithelial rosettes, and fetal-type squamoid nests. Foci of cartilage were seen in two cases, but none contained mature organoid adult-type tissue or skin adnexa. SALL4 was expressed in the small cell (2) and stromal (1) component. Other germ cell markers were negative. Molecular testing revealed a known “hotspot” pathogenic DICER1 mutation in two cases. In addition, case 1 had a missense TP53 variant. This type of thyroid malignancy is distinct from genuine teratomas. The immunoprofile suggests primitive thyroid- or branchial cleft-like differentiation. Given that “blastoma” is a well-accepted terminology in the spectrum of DICER1-associated malignancies, the term “thyroblastoma” might be more convenient for these malignant teratoid tumors of the thyroid gland. Relationship of thyroblastoma to the DICER1 syndrome remains to be addressed.

Oral antitumour therapeutics (OAT) are increasingly used due to improvements in outcomes and their convenient application. However, complex intake regimens pose several challenges. The randomised AMBORA trial (Medication Safety With Oral Antitumour Drugs) demonstrated highly positive outcomes of a clinical pharmacological/pharmaceutical care program for adults treated with numerous OAT, but comparable concepts in paediatrics are lacking so far. We used a parallel mixed-methods approach to develop a tailored pharmacological/pharmaceutical care program for OAT in paediatrics (youngAMBORA). We combined a quantitative analysis of tumour entities and used OAT in a paediatric cancer centre with a qualitative survey for patients, caregivers, and healthcare professionals to identify particular demands and educational needs (e.g., application problems, side effects). Leukaemia (77/315) and antimetabolites (95/151) were the most frequently observed entity and OAT, respectively. Of 22 surveyed patients, 81.8% wanted to be involved in oral medication education. Compared to caregivers, significantly more healthcare professionals graded the three most common application problems to be challenging ('Smell/taste': 32/36 vs. 23/42, p = 0.001; 'Refusal of intake': 31/36 vs. 16/42, p<0.001; 'Swallowing problems': 28/36 vs. 21/42, p = 0.011). We identified nine relevant side effects, of which two ('Skin dryness', 'Taste changes') were not included in 15 previously published core side effects of the Common Terminology Criteria of Adverse Events (CTCAE) item library. Based on the present findings, the tailored youngAMBORA care program will include: 1) counselling sessions for classic and targeted OAT, 2) child-friendly support with drug application, and 3) systematic evaluation of 17 relevant side effects from patients' and caregivers' points of view including age-appropriate information material.

In the treatment of childhood acute lymphoblastic leukemia (ALL), current protocols combine initial high-dose multiagent chemotherapy with prolonged oral therapy with 6-mercaptopurine (6MP) and low-dose methotrexate (MTX) maintenance therapy. Decades of research on ALL treatment have resulted in survival rates of approximately 90%. However, dose-response relationships vary widely between patients and insight into the influencing factors, that would allow for improved personalized treatment management, is insufficient. We use a detailed data set with measurements of thioguanine nucleotides and MTX in red blood cells and absolute neutrophil count (ANC) to develop pharmacokinetic models for 6MP and MTX, as well as a pharmacokinetic–pharmacodynamic (PKPD) model capable of predicting individual ANC levels and thus contributing to the development of personalized treatment strategies. Here, we show that integrating metabolite measurements in red blood cells into the full PKPD model improves results when less data is available, but that model predictions are comparable to those of a fixed pharmacokinetic model when data availability is not limited, providing further evidence of the quality of existing models. With this comprehensive model development leading to dynamics similar to simpler models, we validate the suitability of this model structure and provide a foundation for further exploration of maintenance therapy strategies through simulation and optimization.

Response to tyrosine kinase inhibitor (TKI) therapy in patients with chronic myeloid leukemia (CML) is monitored by quantification of BCR::ABL1 transcript levels. Milestones for assessing optimal treatment response have been defined in adult CML patients and are applied to children and adolescents although it is questionable whether transferability to pediatric patients is appropriate regarding genetic and clinical differences. Therefore, we analyzed the molecular response kinetics to TKI therapy in 129 pediatric CML patients and investigated whether response assessment based on continuous references can support an early individual therapy adjustment. We applied a moving quantiles approach to establish a high-resolution response target curve and contrasted the median responses in all patients with the median of the ideal target curve obtained from a subgroup of optimal responders. The high-resolution response target curve of the optimal responder group presents a valuable tool for continuous therapy monitoring of individual pediatric CML patients in addition to the fixed milestones. By further comparing BCR::ABL1 transcript levels with BCR::ABL1 fusion gene copy numbers, it is also possible to model the differential dynamics of BCR::ABL1 expression and cell number under therapy. The developed methodology can be transferred to other biomarkers for continuous therapy monitoring.

The cellular reconstitution after childhood cancer therapy is associated with the risk of infection and efficacy of revaccination. Many studies have described the reconstitution after stem cell transplantation (SCT). The recovery after cancer treatment in children who have not undergone SCT has mainly been investigated in acute lymphoblastic leukemia (ALL), less for solid tumors. Here, we have examined the temporal evolution of total leukocyte, neutrophil and lymphocyte counts as surrogate parameters for the post-therapeutic immune recovery in a cohort of n = 52 patients with ALL in comparison to n = 58 patients with Hodgkin’s disease (HD) and n = 22 patients with Ewing sarcoma (ES). Patients with ALL showed an efficient increase in blood counts reaching the age-adjusted lower limits of normal between 4 and 5 months after the end of maintenance therapy. The two groups of patients with HD and ES exhibited a comparably delayed recovery of total leukocytes due to a protracted post-therapeutic lymphopenia which was most pronounced in patients with HD after irradiation. Overall, we observed a clearly more efficient resurgence of total lymphocyte counts in patients aged below 12 years compared to patients aged 12 to 18 years. Our results underline that the kinetics of cellular reconstitution after therapy for HD and ES differ significantly from ALL and depend on treatment regimens and modalities as well as on patient age. This suggests a need for disease, treatment, and age specific recommendations concerning the duration of infection prophylaxis and the timing of revaccination.

Chronic myeloid leukemia (CML) typically occurs in late adulthood. Pediatric CML is a rare form of leukemia. In all age groups, the characteristic genetic driver of the disease is the BCR :: ABL1 fusion gene. However, additional genomic events contribute to leukemic transformation, which is not yet well-characterized in pediatric CML. We investigated the mutational landscape of pediatric CML to determine whether predisposing germline variants may play a role in early-age disease development. Whole exome sequencing and targeted sequencing were performed in pediatric and adult CML samples to identify age-related germline and somatic variants in addition to the BCR::ABL1 translocation. Germline variants were detected in about 60% of pediatric patients with CML, with predominantly hematopoietic genes affected, most frequently ASXL1 , NOTCH1 , KDM6B , and TET2 . The number of germline variants was significantly lower in adult patients with CML. If only confirmed pathogenic variants were regarded as cancer-predisposing variants, the occurrence was ~ 10% of pediatric CML, which is comparable to other hematological malignancies and most childhood cancer entities in general. We hypothesize that the interaction with the strong oncogene BCR::ABL1 may also favor the development of leukemia by weaker variants in the same genes. In pediatric patients, the germline variants of genes associated with clonal hematopoiesis may increase the likelihood that an incidental BCR::ABL1 translocation triggers the early manifestation of CML.