ArticleBMC cancer2015
Fractal analysis of nuclear histology integrates tumor and stromal features into a single prognostic factor of the oral cancer microenvironment.
Article in BMC cancer, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed, 28 citations in OpenAlex.
- Deep learning-driven recognition of panoramic tumor microenvironment features in H&E sections and its application.Journal for immunotherapy of cancer · 2026Review
- Prognostic influence of chronic inflammation in patients with head and neck cancer based on sex differences.Clinical & experimental metastasis · 2025Review
- Higher density of compact B cell clusters in invasive front may contribute to better prognosis in pancreatic ductal adenocarcinoma.Discover oncology · 2025Article
- Histomorphometric Analysis of Oral Squamous Cell Carcinoma, Apparently Normal Adjacent Mucosa and Epithelial Dysplasia.Indian journal of otolaryngology and head and neck surgery : official publication of the Association of Otolaryngologists of India · 2024Article
- Computational methods and biomarker discovery strategies for spatial proteomics: a review in immuno-oncology.Briefings in bioinformatics · 2024Review
- Spatial analyses of immune cell infiltration in cancer: current methods and future directions: A report of the International Immuno-Oncology Biomarker Working Group on Breast Cancer.The Journal of pathology · 2023Review
- Spatial distribution of B cells and lymphocyte clusters as a predictor of triple-negative breast cancer outcome.NPJ breast cancer · 2021Article
- Feature-driven local cell graph (FLocK): New computational pathology-based descriptors for prognosis of lung cancer and HPV status of oropharyngeal cancers.Medical image analysis · 2021Article
- Reimagining T Staging Through Artificial Intelligence and Machine Learning Image Processing Approaches in Digital Pathology.JCO clinical cancer informatics · 2020Article
- Small Cell Lung Cancer Therapeutic Responses Through Fractal Measurements: From Radiology to Mitochondrial Biology.Journal of clinical medicine · 2019Article
- Nuclear shape and orientation features from H&E images predict survival in early-stage estrogen receptor-positive breast cancers.Laboratory investigation; a journal of technical methods and pathology · 2018Article
- An oral cavity squamous cell carcinoma quantitative histomorphometric-based image classifier of nuclear morphology can risk stratify patients for disease-specific survival.Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc · 2017Article
- Spatial Heterogeneity in the Tumor Microenvironment.Cold Spring Harbor perspectives in medicine · 2016Review
- Perspective of nuclear fractal dimension in diagnosis and prognosis of oral squamous cell carcinoma.Journal of oral and maxillofacial pathology : JOMFPArticle
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8 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe lack of prognostic biomarkers in oral squamous cell carcinoma (OSCC) has hampered treatment decision making and survival in OSCC remains poor. Histopathological features are used for prognostication in OSCC and, although useful for predicting risk, manual assessment of histopathology is subjective and labour intensive. In this study, we propose a method that integrates multiple histopathological features of the tumor microenvironment into a single, digital pathology-based biomarker using nuclear fractal dimension (nFD) analysis.
methodsOne hundred and seven consecutive OSCC patients diagnosed between 1998 and 2006 in Calgary, Canada were included in the study. nFD scores were generated from DAPI-stained images of tissue microarray (TMA) cores. Ki67 protein expression was measured in the tumor using fluorescence immunohistochemistry (IHC) and automated quantitative analysis (AQUA®). Lymphocytic infiltration (LI) was measured in the stroma from haematoxylin-eosin (H&E)-stained TMA slides by a pathologist.
resultsTwenty-five (23.4%) and 82 (76.6%) patients were classified as high and low nFD, respectively. nFD was significantly associated with pathological tumor-stage (pT-stage; P = 0.01) and radiation treatment (RT; P = 0.01). High nFD of the total tumor microenvironment (stroma plus tumor) was significantly associated with improved disease-specific survival (DSS; P = 0.002). No association with DSS was observed when nFD of either the tumor or the stroma was measured separately. pT-stage (P = 0.01), pathological node status (pN-status; P = 0.02) and RT (P = 0.03) were also significantly associated with DSS. In multivariate analysis, nFD remained significantly associated with DSS [HR 0.12 (95% CI 0.02-0.89, P = 0.04)] in a model adjusted for pT-stage, pN-status and RT. We also found that high nFD was significantly associated with high tumor proliferation (P < 0.0001) and high LI (P < 0.0001), factors that we and others have shown to be associated with improved survival in OSCC.
conclusionsWe provide evidence that nFD analysis integrates known prognostic factors from the tumor microenvironment, such as proliferation and immune infiltration, into a single digital pathology-based biomarker. Prospective validation of our results could establish nFD as a valuable tool for clinical decision making in OSCC.
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