Prognostic Efficacy of Nuclear Morphometry at Invasive Front of Oral Squamous Cell Carcinoma: An Image Analysis Microscopic Study

Vedam, Vaishnavi; Boaz, Karen; Natarajan, Srikant

doi:10.1155/2014/247853

Cited by 12 publications

(10 citation statements)

References 20 publications

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“…Vedam et al assessed nuclear diameter, area, perimeter, and shape in the invasive front of 32 oral cavity squamous cell carcinomas and found a correlation of these features with disease recurrence. 36 While our approach did evaluate features relating to nuclear shape, texture and appearance, it is different from the approaches in 18 and 36 in at least three important ways. Firstly, the goal of the new local co-occurrence of morphology feature approach was to evaluate and correlate diversity in nuclear morphologic features with disease recurrence, as opposed to correlating the absolute feature values with outcomes.…”

Section: Discussionmentioning

confidence: 99%

“…Firstly, the goal of the new local co-occurrence of morphology feature approach was to evaluate and correlate diversity in nuclear morphologic features with disease recurrence, as opposed to correlating the absolute feature values with outcomes. 36 Secondly, cellular diversity was captured via measurement of co-occurring second order statistics (eg entropy) of nuclear size, shape, and texture within local nuclear clusters. By first constructing local cell graphs, we were able to define local neighborhoods of proximal nuclei within which we were then able to compute entropy and diversity of cellular morphologic features.…”

Section: Discussionmentioning

confidence: 99%

“…It is becoming clear that markedly heterogeneous tumors with many sub-clones (at least to a point), are clinically more aggressive. 36,37 The five most discriminating local cooccurrence of morphology features were identified as (1) contrast entropy of nuclear mean intensity, (2) intensity average of nuclear shape (equivalent diameter) (3) nuclear mean intensity immediately outside the boundary (4) entropy of nuclear mean intensity immediately outside the boundary, and (5) nuclear size. The local co-occurrence of morphology features were found to have higher entropy values in worse prognosis tumors compared with the ones with better prognosis.…”

Section: Discussionmentioning

confidence: 99%

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An oral cavity squamous cell carcinoma quantitative histomorphometric-based image classifier of nuclear morphology can risk stratify patients for disease-specific survival

Lewis

Dupont

et al. 2017

Modern Pathology

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Oral cavity squamous cell carcinoma is the most common type of head and neck carcinoma. Its incidence is increasing worldwide, and it is associated with major morbidity and mortality. It is often unclear which patients have aggressive, treatment refractory tumors vs those whose tumors will be more responsive to treatment. Better identification of patients with high- vs low-risk cancers could help provide more tailored treatment approaches and could improve survival rates while decreasing treatment-related morbidity. This study investigates computer-extracted image features of nuclear shape and texture on digitized images of H&E-stained tissue sections for risk stratification of oral cavity squamous cell carcinoma patients compared with standard clinical and pathologic parameters. With a tissue microarray cohort of 115 retrospectively identified oral cavity squamous cell carcinoma patients, 50 were randomly chosen as the modeling set, and the remaining 65 constituted the test set. Following nuclear segmentation and feature extraction, the Wilcoxon rank sum test was used to identify the five most prognostic quantitative histomorphometric features from the modeling set. These top ranked features were then combined via a machine learning classifier to construct the oral cavity histomorphometric-based image classifier (OHbIC). The classifier was then validated for its ability to risk stratify patients for disease-specific outcomes on the test set. On the test set, the classifier yielded an area under the receiver operating characteristic curve of 0.72 in distinguishing disease-specific outcomes. In univariate survival analysis, high-risk patients predicted by the classifier had significantly poorer disease-specific survival (P = 0.0335). In multivariate analysis controlling for T/N-stage, resection margins, and smoking status, positive classifier results were independently predictive of poorer disease-specific survival: hazard ratio (95% confidence interval) = 11.023 (2.62–46.38) and P = 0.001. Our results suggest that quantitative histomorpho-metric features of local nuclear architecture derived from digitized H&E slides of oral cavity squamous cell carcinomas are independently predictive of patient survival.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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An oral cavity squamous cell carcinoma quantitative histomorphometric-based image classifier of nuclear morphology can risk stratify patients for disease-specific survival

Lewis

Dupont

et al. 2017

Modern Pathology

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“…Several studies aimed to evaluate different methods to analyze oral alterations in order to improve the diagnosis, prognosis and treatment in dental research. Some comparisons were performed to evaluate tumors [15] and lesions [16] sizes, mineralized hard tissue density [17] and tissue morphologies [18,19]. Regarding this, during the past few decades, our research group has been focusing in the comprehension of the many aspects involved in the etiopathogenesis of the endodontic infection, chronic periapical lesion development and pulp therapy.…”

Section: Discussionmentioning

confidence: 99%

Correlation Between Histomorphometric and Micro-computed Tomography Analysis of Periapical Lesions in Mice Model

Oliveira

Küchler

Queiroz

et al. 2015

Ultrastructural Pathology

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Periapical lesions are processes mediated from bacterial invasion on the pulp tissue, which results in inflammatory process and bone resorption. Animal models have been used for the study of cell interactions and inflammatory responses involved in periapical lesion. The use of different methods for morphometric evaluation of this lesion has been reported. However, due to the limitation of 2D methods, computerized microtomography (µCT) has been indicated. Therefore, the aim of this study was to evaluate the correlation between the histomorphometric measures of periapical lesions and the µCT analysis obtained in an animal experimental model. Briefly, the periapical lesions were induced by pulp exposal and after the experimental periods (7, 21 and 42 days) the mice were euthanized. The mandibles were evaluated under the µCT scanning and then were subjected to the routine histologic procedures, stained with hematoxilin and eosin. The periapical lesions were measured by the specific softwares of each technique. A weak correlation among the histomorphometric measurement and the 2D µCT analysis (r(2)= .204) and between the histomorphometric measurement and the 3D µCT analysis (r(2)= .237) was observed. A strong correlation between the 2D and 3D µCT analysis (r(2)= .867) was observed. Thereby, it can be concluded that the µCT is a method that allows a precise volumetric scanning and represents an important tool for morphometric evaluation.

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“…Circularity is a mathematical measure of compactness [14,15]. A circularity value of 1.0 is representative of a perfect circle.…”

Section: Nuclear Morphometrymentioning

confidence: 99%

Nuclear shape descriptors by automated morphometry may distinguish aggressive variants of squamous cell carcinoma from relatively benign skin proliferative lesions: a pilot study

2015

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We evaluated whether degrees of dysplasia may be consistently accessed in an automatic fashion, using different kinds of non-melanoma skin cancer (NMSC) as a validatory model. Namely, we compared Bowen disease, actinic keratosis, basal cell carcinoma, low-grade squamous cell carcinoma, and invasive squamous cell carcinoma. We hypothesized that characterizing the shape of nuclei may be important to consistently diagnose the aggressiveness of a skin tumor. While basal cell carcinoma is comparatively relatively benign, management of squamous cell carcinoma is controversial because of its potential to recur and intraoperative dilemma regarding choice of the margin or the depth for the excision. We provide evidence here that progressive nuclear dysplasia may be automatically estimated through the thresholded images of skin cancer and quantitative parameters estimated to provide a quasi-quantitative data, which can thenceforth guide the management of the particular cancer. For circularity, averaging more than 2500 nuclei in each group estimated the means ± SD as 0.8 ± 0.007 vs. 0.78 ± 0.0063 vs. 0.42 ± 0.014 vs. 0.63 ± 0.02 vs. 0.51 ± 0.02 (F = 318063.56, p < 0.0001, one-way analyses of variance). The mean aspect ratios were (means ± SD) 0.97 ± 0.0014 vs. 0.95 ± 0.002 vs. 0.38 ± 0.018 vs. 0.84 ± 0.0035 vs. 0.74 ± 0.019 (F = 1022631.931, p < 0.0001, one-way analyses of variance). The Feret diameters averaged over 2500 nuclei in each group were the following: 1 ± 0.0001 vs. 0.9 ± 0.002 vs. 5 ± 0.031 vs. 1.5 ± 0.01 vs. 1.9 ± 0.004 (F = 33105614.194, p < 0.0001, one-way analyses of variance). Multivariate analyses of composite parameters potentially detect aggressive variants of squamous cell carcinoma as the most dysplastic form, in comparison to locally occurring squamous cell carcinoma and basal cell carcinoma, or benign skin lesions.

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Prognostic Efficacy of Nuclear Morphometry at Invasive Front of Oral Squamous Cell Carcinoma: An Image Analysis Microscopic Study

Cited by 12 publications

References 20 publications

An oral cavity squamous cell carcinoma quantitative histomorphometric-based image classifier of nuclear morphology can risk stratify patients for disease-specific survival

An oral cavity squamous cell carcinoma quantitative histomorphometric-based image classifier of nuclear morphology can risk stratify patients for disease-specific survival

Correlation Between Histomorphometric and Micro-computed Tomography Analysis of Periapical Lesions in Mice Model

Nuclear shape descriptors by automated morphometry may distinguish aggressive variants of squamous cell carcinoma from relatively benign skin proliferative lesions: a pilot study

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