ABSTRACT
OBJECTIVE: To review somatic genetic changes in nephrogenic rests (NR), which are considered to be precursor lesions of Wilms tumors (WT). METHODS: This systematic review is written according to the PRISMA statement. PubMed and EMBASE were systematically searched for articles in the English language studying somatic genetic changes in NR between 1990 and 2022. RESULTS: Twenty-three studies were included in this review, describing 221 NR of which 119 were pairs of NR and WT. Single gene studies showed mutations in WT1 and WTX, but not CTNNB1 to occur in both NR and WT. Studies investigating chromosomal changes showed loss of heterozygosity of 11p13 and 11p15 to occur in both NR and WT, but loss of 7p and 16q occurred in WT only. Methylome-based studies found differential methylation patterns between NR, WT, and normal kidney (NK). CONCLUSIONS: Over a 30-year time frame, few studies have addressed genetic changes in NR, likely hampered by technical and practical limitations. A limited number of genes and chromosomal regions have been implicated in the early pathogenesis of WT, exemplified by their occurrence in NR, including WT1, WTX, and genes located at 11p15. Further studies of NR and corresponding WT are urgently needed.
ABSTRACT
BACKGROUND: Histopathological classification of Wilms tumors determines treatment regimen. Machine learning has been shown to contribute to histopathological classification in various malignancies but requires large numbers of manually annotated images and thus specific pathological knowledge. This study aimed to assess whether trained, inexperienced observers could contribute to reliable annotation of Wilms tumor components for classification performed by machine learning. METHODS: Four inexperienced observers (medical students) were trained in histopathology of normal kidneys and Wilms tumors by an experienced observer (pediatric pathologist). Twenty randomly selected scanned Wilms tumor-slides (from n = 1472 slides) were annotated, and annotations were independently classified by both the inexperienced observers and two experienced pediatric pathologists. Agreement between the six observers and for each tissue element was measured using kappa statistics (κ). RESULTS: Pairwise interobserver agreement between all inexperienced and experienced observers was high (range: 0.845-0.950). The interobserver variability for the different histological elements, including all vital tumor components and therapy-related effects, showed high values for all κ-coefficients (> 0.827). CONCLUSIONS: Inexperienced observers can be trained to recognize specific histopathological tumor and tissue elements with high interobserver agreement with experienced observers. Nevertheless, supervision by experienced pathologists remains necessary. Results of this study can be used to facilitate more rapid progress for supervised machine learning-based algorithm development in pediatric pathology and beyond.
