On the relationship between tumor structure and complexity of the spatial distribution of cancer cell nuclei: A fractal geometrical model of prostate carcinoma

Waliszewski, Przemysław; Wagenlehner, Florian M. E.; Gattenlöhner, Stefan; Weidner, W.

doi:10.1002/pros.22926

Cited by 34 publications

(53 citation statements)

References 88 publications

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“…The values calculated for the nine GLCM parameters are listed in Tables I-IV. To obtain suitable parameters for the identification of MM, we defined the difference criterion (DIF) as follows: respectively. These values are much better than those obtained by fractal analysis [6][7][8][9][10][11].…”

Section: Discussioncontrasting

confidence: 51%

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Label-Free Imaging of Melanoma with Confocal Photothermal Microscopy: Differentiation between Malignant and Benign Tissue

Kobayashi

Nakata

Yajima

et al. 2018

Preprint

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Label-free confocal photothermal (CPT) microscopy was utilized for the first time to investigate malignancy in mouse skin cells. A laser diode (LD) with 405nm or 488nm was used as a pump and 638nm LD as a probe for the CPT microscope. The Grey Level Cooccurrence Matrix (GLCM) for texture analysis was applied to the CPT images. Nine parameters of GLCM were calculated for the intracellular super-resolved CPT images, and the parameters Entropy and Prominence were found to be most suited among the nine parameters to discriminate between healthy cells and MM cells in case pump wavelength of 488nm is used.

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Section: Discussioncontrasting

confidence: 51%

“…This method is based on calculation of the fractal dimension (FD) of the structure of MM cells and their distribution [6][7][8][9][10][11]). Although this is a very attractive idea, the method has the following three drawbacks.…”

Section: Introductionmentioning

confidence: 99%

Label-Free Imaging of Melanoma with Confocal Photothermal Microscopy: Differentiation between Malignant and Benign Tissue

Kobayashi

Nakata

Yajima

et al. 2018

Preprint

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“…The complexity of the tumor structure can be also described by fractal geometry. A few studies have applied multifractal (MF) analyses to breast12, colorectal1314, prostate15 and lung tumor collections (reviewed in ref. 16).…”

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confidence: 99%

Image analysis-derived metrics of histomorphological complexity predicts prognosis and treatment response in stage II-III colon cancer

Mezheyeuski

Hrynchyk

Karlberg

et al. 2016

Sci Rep

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The complexity of tumor histomorphology reflects underlying tumor biology impacting on natural course and response to treatment. This study presents a method of computer-aided analysis of tissue sections, relying on multifractal (MF) analyses, of cytokeratin-stained tumor sections which quantitatively evaluates of the morphological complexity of the tumor-stroma interface. This approach was applied to colon cancer collection, from an adjuvant treatment randomized study. Metrics obtained with the method acted as independent markers for natural course of the disease, and for benefit of adjuvant treatment. Comparative analyses demonstrated that MF metrics out-performed standard histomorphological features such as tumor grade, budding and configuration of invasive front. Notably, the MF analyses-derived “αmax” –metric constitutes the first response-predictive biomarker in stage II-III colon cancer showing significant interactions with treatment in analyses using a randomized trial-derived study population. Based on these results the method appears as an attractive and easy-to-implement tool for biomarker identification.

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“…Assuming that the structure of tumor tissue is reflected in the arrangement of cancer cell nuclei, Waliszewski et al calculated several different fractal dimensions as well as the Shannon entropy and lacunarity to characterize the spatial distribution of cancer cell nuclei in prostate tumor tissue and compared the results to the corresponding Gleason scores in an attempt to find a more objective way to classify prostate tumor tissue (37,38).…”

Section: Discussionmentioning

confidence: 99%

Fractal dimension, occupancy and hotspot analyses of B cell spatial distribution predict clinical outcome in breast cancer

Wortman¹,

Solomon

et al. 2019

Preprint

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While the density of tumor-infiltrating lymphocytes (TILs) is now well known to correlate with clinical outcome, the clinical significance of spatial distribution of TILs is not well characterized. We have developed novel statistical techniques (including fractal dimension differences, a hotspot analysis, a box counting method that we call 'occupancy' and a way to normalize cell density that we call 'thinning') to analyze the spatial distribution (at different length scales) of various types of TILs in triple negative breast tumors. Consistent with prior reports, the density of CD20 + B cells within tumors is not correlated with clinical outcome. However, we found that their spatial distribution differs significantly between good clinical outcome (no recurrence within at least 5 years of diagnosis) and poor clinical outcome (recurrence with 3 years of diagnosis). Furthermore, CD20 + B cells are more spatially dispersed in good outcome tumors and are more likely to infiltrate into cancer cell islands. Lastly, we found significant correlation between the spatial distributions of CD20 + B cells and CD8 + (cytotoxic) T cells (as well as CD3 + T cells), regardless of outcome. These results highlight the significance of the spatial distribution of TILs, especially B cells, within tumors. Significance StatementImmune cells can fight cancer. For example, a patient has a good prognosis when a high density of killer T cells, a type of immune cell that can kill cancer cells, infiltrates into a tumor. However, there is no clear association between prognosis and the density of B cells, another type of immune cell, in a tumor. We developed several statistical techniques to go beyond cell density and look at the spatial distribution, i.e., the pattern or arrangement of immune cells, in tumors that have been removed from patients with triple negative breast cancer. We find that B cells and killer T cells tend to be more spread out in the tumors of patients whose cancer did not recur.

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On the relationship between tumor structure and complexity of the spatial distribution of cancer cell nuclei: A fractal geometrical model of prostate carcinoma

Cited by 34 publications

References 88 publications

Label-Free Imaging of Melanoma with Confocal Photothermal Microscopy: Differentiation between Malignant and Benign Tissue

Label-Free Imaging of Melanoma with Confocal Photothermal Microscopy: Differentiation between Malignant and Benign Tissue

Image analysis-derived metrics of histomorphological complexity predicts prognosis and treatment response in stage II-III colon cancer

Fractal dimension, occupancy and hotspot analyses of B cell spatial distribution predict clinical outcome in breast cancer

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