Background Computed tomography (CT) is often used to investigate muscle and fat mass in adult patients with cancer. However, this method has rarely been used in the pediatric cancer population. The... Show moreBackground Computed tomography (CT) is often used to investigate muscle and fat mass in adult patients with cancer. However, this method has rarely been used in the pediatric cancer population. The present retrospective study aimed to investigate changes in body composition using CT during treatment in children with neuroblastoma. Procedure CT images of 29 patients with high-risk neuroblastoma were retrospectively analyzed at diagnosis and longitudinally during treatment. The cross-sectional area of skeletal muscle, intermuscular adipose tissue (IMAT), visceral adipose tissue (VAT), and subcutaneous adipose tissue (SAT) and skeletal muscle density at the level of the third lumbar vertebra were examined. To correct for height, cross-sectional areas were divided by height in meters squared. A linear mixed model was estimated to investigate changes in body composition over time. Results A small increase in skeletal muscle (p = .029), skeletal muscle density (p = .002), and IMAT (p < .001) was found. Furthermore, a rapid increase in VAT (p < .001) and SAT (p = .001) was seen early during treatment with the highest volumes after six cycles of chemotherapy. Conclusions CT scans obtained during standard care provide insight into the direction and timing of changes in skeletal muscle and different types of adipose tissue in childhood cancer patients. Future research is needed regarding the consequences of the rapid increase of VAT and SAT early during treatment. Show less
Early diagnosis of pediatric cancer is key for adequate patient management and improved outcome. Although multiparameter flow cytometry (MFC) has proven of great utility in the diagnosis and... Show moreEarly diagnosis of pediatric cancer is key for adequate patient management and improved outcome. Although multiparameter flow cytometry (MFC) has proven of great utility in the diagnosis and classification of hematologic malignancies, its application to non-hematopoietic pediatric tumors remains limited. Here we designed and prospectively validated a new single eight-color antibody combination-solid tumor orientation tube, STOT-for diagnostic screening of pediatric cancer by MFC. A total of 476 samples (139 tumor mass, 138 bone marrow, 86 lymph node, 58 peripheral blood, and 55 other body fluid samples) from 296 patients with diagnostic suspicion of pediatric cancer were analyzed by MFC vs. conventional diagnostic procedures. STOT was designed after several design-test-evaluate-redesign cycles based on a large panel of monoclonal antibody combinations tested on 301 samples. In its final version, STOT consists of a single 8-color/12-marker antibody combination (CD99-CD8/(nu)myogenin/CD4-EpCAM/CD56/GD2/(sm)CD3-CD19/(cy)CD3-CD271/CD45). Prospective validation of STOT in 149 samples showed concordant results with the patient WHO/ICCC-3 diagnosis in 138/149 cases (92.6%). These included: 63/63 (100%) reactive/disease-free samples, 43/44 (98%) malignant and 4/4 (100%) benign non-hematopoietic tumors together with 28/38 (74%) leukemia/lymphoma cases; the only exception was Hodgkin lymphoma that required additional markers to be stained.& nbsp;In addition, STOT allowed accurate discrimination among the four most common subtypes of malignant CD45(-) CD56(++) non-hematopoietic solid tumors: 13/13 (GD2(++) (nu)myogenin(-) CD271(-/+) (nu)MyoD1(-) CD99(-) EpCAM(-)) neuroblastoma samples, 5/5 (GD2(-) (nu)myogenin(++) CD271(++) (nu)MyoD1(++) CD99(-/+) EpCAM(-)) rhabdomyosarcomas, 2/2 (GD2(-/+) (nu)myogenin(-) CD271(+) (nu)MyoD1(-) CD99(+) EpCAM(-)) Ewing sarcoma family of tumors, and 7/7 (GD2(-) (nu)myogenin(-) CD271(+) (nu)MyoD1(-) CD99(-) EpCAM(+)) Wilms tumors. In summary, here we designed and validated a new standardized antibody combination and MFC assay for diagnostic screening of pediatric solid tumors that might contribute to fast and accurate diagnostic orientation and classification of pediatric cancer in routine clinical practice. Show less
The adult mammalian kidney is a poorly regenerating organ that lacks the stem cells that could replenish functional homeostasis similarly to, e.g., skin or the hematopoietic system. Unlike a mature... Show moreThe adult mammalian kidney is a poorly regenerating organ that lacks the stem cells that could replenish functional homeostasis similarly to, e.g., skin or the hematopoietic system. Unlike a mature kidney, the embryonic kidney hosts at least three types of lineage-specific stem cells that give rise to (a) a ureter and collecting duct system, (b) nephrons, and (c) mesangial cells together with connective tissue of the stroma. Extensive interest has been raised towards these embryonic progenitor cells, which are normally lost before birth in humans but remain part of the undifferentiated nephrogenic rests in the pediatric renal cancer Wilms tumor. Here, we discuss the current understanding of kidney-specific embryonic progenitor regulation in the innate environment of the developing kidney and the types of disruptions in their balanced regulation that lead to the formation of Wilms tumor. Show less
Virgolin, M.; Wang, Z.Y.; Alderliesten, T.; Bosman, P.A.N. 2020
Purpose: Current phantoms used for the dose reconstruction of long-term childhood cancer survivors lack individualization. We design a method to predict highly individualized abdominal three... Show morePurpose: Current phantoms used for the dose reconstruction of long-term childhood cancer survivors lack individualization. We design a method to predict highly individualized abdominal three-dimensional (3-D) phantoms automatically.Approach: We train machine learning (ML) models to map (2-D) patient features to 3-D organat-risk (OAR) metrics upon a database of 60 pediatric abdominal computed tomographies with liver and spleen segmentations. Next, we use the models in an automatic pipeline that outputs a personalized phantom given the patient's features, by assembling 3-D imaging from the database. A step to improve phantom realism (i.e., avoid OAR overlap) is included. We compare five ML algorithms, in terms of predicting OAR left-right (LR), anterior-posterior (AP), inferior-superior (IS) positions, and surface Dice-Sorensen coefficient (sDSC). Furthermore, two existing human-designed phantom construction criteria and two additional control methods are investigated for comparison.Results: Different ML algorithms result in similar test mean absolute errors: similar to 8 mm for liver LR, IS, and spleen AP, IS; similar to 5 mm for liver AP and spleen LR; similar to 80% for abdomen sDSC; and similar to 60% to 65% for liver and spleen sDSC. One ML algorithm (GP-GOMEA) significantly performs the best for 6/9 metrics. The control methods and the human-designed criteria in particular perform generally worse, sometimes substantially (+5-mm error for spleen IS, -10% sDSC for liver). The automatic step to improve realism generally results in limited metric accuracy loss, but fails in one case (out of 60).Conclusion: Our ML-based pipeline leads to phantoms that are significantly and substantially more individualized than currently used human-designed criteria. (C) 2020 Society of Photo Optical Instrumentation Engineers (SPIE) Show less
Ojha, R.P.; Asdahl, P.H.; Steyerberg, E.; Schroeder, H. 2018
