title: Entropy-based adaptive nuclear texture features are independent prognostic markers in a total population of uterine sarcomas.
abstract: Nuclear texture analysis measures the spatial arrangement of the pixel gray levels in a digitized microscopic nuclear image and is a promising quantitative tool for prognosis of cancer. The aim of this study was to evaluate the prognostic value of entropy-based adaptive nuclear texture features in a total population of 354 uterine sarcomas. Isolated nuclei (monolayers) were prepared from 50 µm tissue sections and stained with Feulgen-Schiff. Local gray level entropy was measured within small windows of each nuclear image and stored in gray level entropy matrices, and two superior adaptive texture features were calculated from each matrix. The 5-year crude survival was significantly higher (P < 0.001) for patients with high texture feature values (72%) than for patients with low feature values (36%). When combining DNA ploidy classification (diploid/nondiploid) and texture (high/low feature value), the patients could be stratified into three risk groups with 5-year crude survival of 77, 57, and 34% (Hazard Ratios (HR) of 1, 2.3, and 4.1, P < 0.001). Entropy-based adaptive nuclear texture was an independent prognostic marker for crude survival in multivariate analysis including relevant clinicopathological features (HR = 2.1, P = 0.001), and should therefore be considered as a potential prognostic marker in uterine sarcomas.
authors.affiliation: Institute for Cancer Genetics and Informatics, Oslo University Hospital, Oslo, Norway; Centre for Cancer Biomedicine, University of Oslo, Oslo, Norway.
authors.forename: Birgitte
authors.initials: B
authors.lastname: Nielsen
authors.forename: Tarjei Sveinsgjerd
authors.initials: TS
authors.lastname: Hveem
authors.forename: Wanja
authors.initials: W
authors.lastname: Kildal
authors.forename: Vera M
authors.initials: VM
authors.lastname: Abeler
authors.forename: Gunnar B
authors.initials: GB
authors.lastname: Kristensen
authors.forename: Fritz
authors.initials: F
authors.lastname: Albregtsen
authors.forename: Håvard E
authors.initials: HE
authors.lastname: Danielsen
mesh_terms: D000465:Algorithms; D014408:Biomarkers, Tumor / Q000032:analysis*; D002467:Cell Nucleus / Q000502:physiology*; D019277:Entropy; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D007890:Leiomyosarcoma / Q000401:mortality* / Q000473:pathology; D015999:Multivariate Analysis; D011379:Prognosis; D012189:Retrospective Studies; D018203:Sarcoma, Endometrial Stromal / Q000401:mortality* / Q000473:pathology; D013194:Staining and Labeling; D014594:Uterine Neoplasms / Q000401:mortality* / Q000473:pathology
chemical_list: D014408:Biomarkers, Tumor
keywords: adaptive features; endometrial stromal sarcoma; entropy; leiomyosarcoma; nuclear texture analysis; nucleotyping; prognostic markers; uterine sarcomas