The fractal dimension of chromatin - a potential molecular marker for carcinogenesis, tumor progression and prognosis

Fractal Dimension, Circularity, and Solidity of Cell Clusters in Liquid-Based Endometrial Cytology Are Potentially Useful for Endometrial Cancer Detection and Prognosis Prediction

Endometrial cancer (EC) in women is increasing globally, necessitating improved diagnostic methods and prognosis prediction. While endometrial histology is the conventional approach, liquid-based endometrial cytology may benefit from novel analytical techniques for cell clusters. A clinical study was conducted at the University of Fukui Hospital from 2012 to 2018, involving 210 patients with endometrial cytology. The liquid-based cytology images were analyzed using cell cluster analysis with Image J software. Logistic regression, ROC analysis, and survival analysis were employed to assess the diagnostic accuracy and prognosis between cell cluster analysis and EC/atypical endometrial hyperplasia (AEH). Circularity and fractal dimension demonstrated significant associations with EC and AEH, regardless of age and cytology results. The ROC analysis revealed improved diagnostic accuracy when combining fractal dimension with cytology, particularly in menopausal age groups. Lower circularity and solidity were independently associated with poor overall survival, while higher fractal dimension values correlated with poorer overall survival in Grades 2 and 3 endometrial cancers. The combination of circularity and fractal dimension with cytology improved diagnostic accuracy for both EC and AEH. Moreover, circularity, solidity, and fractal dimension may serve as prognostic indicators for endometrial cancer, contributing to the development of more refined screening and diagnostic strategies.

Artificial Intelligence in Cardiology and Atherosclerosis in the Context of Precision Medicine: A Scoping Review

Cardiovascular diseases remain the main cause of death worldwide which makes it essential to better understand, diagnose, and treat atherosclerosis. Artificial intelligence (AI) and novel technological solutions offer us new possibilities and enable the practice of individually tailored medicine. The study was performed using the PRISMA protocol. As of January 10, 2023, the analysis has been based on a review of 457 identified articles in PubMed and MEDLINE databases. The search covered reviews, original articles, meta-analyses, comments, and editorials published in the years 2009-2023. In total, 123 articles met inclusion criteria. The results were divided into the subsections presented in the review (genome-wide association studies, radiomics, and other studies). This paper presents actual knowledge concerning atherosclerosis, in silico, and big data analyses in cardiology that affect the way medicine is practiced in order to create an individual approach and adjust the therapy of atherosclerosis.

An image analysis approach to characterize micronuclei differences in different subtypes of breast cancer

BACKGROUND

Micronuclei (MN) have been used as screening, diagnostic and prognostic markers in multiple cancer types, including breast cancer (BC). However, the question that the MN present in all subtypes of BC are similar or different remains unanswered. We thus hypothesized that MN present in different subtypes of BC may differ in their contents which may be visible as differences in their morphologic and morphometric features. This study was thus carried out with the aim to identify the differences between MN morphometry, complexity, and texture in different subtypes of BC, such as estrogen and progesterone receptor-positive (ER+/PR+; MCF-7, T-47D), human epidermal growth factor receptor-positive (Her2 +;SKBR3) and triple-negative BC (TNBC; MDA-MB-231, MDA-MB-468) cell lines (CLs) by ImageJ software.

METHODS

For analysis of MN dimensions, MN irregularity, and texture, we used morphometry and two mathematical computer-assisted algorithms, i.e., fractal dimension (FD) and grey level co-occurrence matrix (GLCM) of ImageJ software.

RESULTS

MN area and perimeter values showed differences in the size of MN in different subtypes of BC, with the largest MN in TNBC CLs. GLCM parameters (entropy, angular second moment, inverse difference moment, contrast, and correlation) showed highly heterogenous texture in case of TNBC MN as compared to the others. FD analysis also revealed more complexity and irregularity in MN found in TNBC cells.

CONCLUSION

The study for the first time showed morphometric, architectural and texture related differences amongst MN present in different subtypes of BC. The above may reflect differences in their composition and contents. Further, these differences may point towards the distinct mechanisms involved in the formation of MN in different subtypes of BC that need to be explored further.

Ensemble-based multi-tissue classification approach of colorectal cancer histology images using a novel hybrid deep learning framework

Colorectal cancer (CRC) is the second leading cause of cancer death in the world, so digital pathology is essential for assessing prognosis. Due to the increasing resolution and quantity of whole slide images (WSIs), as well as the lack of annotated information, previous methodologies cannot be generalized as effective decision-making systems. Since deep learning (DL) methods can handle large-scale applications, they can provide a viable alternative to histopathology image (HI) analysis. DL architectures, however, may not be sufficient to classify CRC tissues based on anatomical histopathology data. A dilated ResNet (dResNet) structure and attention module are used to generate deep feature maps in order to classify multiple tissues in HIs. In addition, neighborhood component analysis (NCA) overcomes the constraint of computational complexity. Data is fed into a deep support vector machine (SVM) based on an ensemble learning algorithm called DeepSVM after the features have been selected. CRC-5000 and NCT-CRC-HE-100 K datasets were analyzed to validate and test the hybrid procedure. We demonstrate that the hybrid model achieves 98.75% and 99.76% accuracy on CRC datasets. The results showed that only pathologists' labels could successfully classify unseen WSIs. Furthermore, the hybrid deep learning method outperforms state-of-the-art approaches in terms of computational efficiency and time. Using the proposed mechanism for tissue analysis, it will be possible to correctly predict CRC based on accurate pathology image classification.

The Fractal Viewpoint of Tumors and Nanoparticles

Even though the promising therapies against cancer are rapidly improved, the oncology patients population has seen exponential growth, placing cancer in 5th place among the ten deadliest diseases. Efficient drug delivery systems must overcome multiple barriers and maximize drug delivery to the target tumors, simultaneously limiting side effects. Since the first observation of the quantum tunneling phenomenon, many multidisciplinary studies have offered quantum-inspired solutions to optimized tumor mapping and efficient nanodrug design. The property of a wave function to propagate through a potential barrier offer the capability of obtaining 3D surface profiles using imaging of individual atoms on the surface of a material. The application of quantum tunneling on a scanning tunneling microscope offers an exact surface roughness mapping of tumors and pharmaceutical particles. Critical elements to cancer nanotherapeutics apply the fractal theory and calculate the fractal dimension for efficient tumor surface imaging at the atomic level. This review study presents the latest biological approaches to cancer management based on fractal geometry.

Machine Learning to Predict Risk of Relapse Using Cytologic Image Markers in Patients With Acute Myeloid Leukemia Posthematopoietic Cell Transplantation

PURPOSE

Allogenic hematopoietic stem-cell transplant (HCT) is a curative therapy for acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS). Relapse post-HCT is the most common cause of treatment failure and is associated with a poor prognosis. Pathologist-based visual assessment of aspirate images and the manual myeloblast counting have shown to be predictive of relapse post-HCT. However, this approach is time-intensive and subjective. The premise of this study was to explore whether computer-extracted morphology and texture features from myeloblasts' chromatin patterns could help predict relapse and prognosticate relapse-free survival (RFS) after HCT.

MATERIALS AND METHODS

In this study, Wright-Giemsa-stained post-HCT aspirate images were collected from 92 patients with AML/MDS who were randomly assigned into a training set (St = 52) and a validation set (Sv = 40). First, a deep learning-based model was developed to segment myeloblasts. A total of 214 texture and shape descriptors were then extracted from the segmented myeloblasts on aspirate slide images. A risk score on the basis of texture features of myeloblast chromatin patterns was generated by using the least absolute shrinkage and selection operator with a Cox regression model.

RESULTS

The risk score was associated with RFS in St (hazard ratio = 2.38; 95% CI, 1.4 to 3.95; P = .0008) and Sv (hazard ratio = 1.57; 95% CI, 1.01 to 2.45; P = .044). We also demonstrate that this resulting signature was predictive of AML relapse with an area under the receiver operating characteristic curve of 0.71 within Sv. All the relevant code is available at GitHub.

CONCLUSION

The texture features extracted from chromatin patterns of myeloblasts can predict post-HCT relapse and prognosticate RFS of patients with AML/MDS.

The Fractal Dimension Suggests Two Chromatin Configurations in Small Cell Neuroendocrine Lung Cancer and Is an Independent Unfavorable Prognostic Factor for Overall Survival

Experimental studies have shown that in small cell neuroendocrine lung carcinomas (SCLC) global opening of the chromatin structure is associated with a higher transcription activity and increase of tumor aggressiveness and metastasis. The study of the fractal characteristics (FD) of nuclear chromatin has been widely used to describe the cell nuclear texture and its changes correspond to changes in nuclear metabolic and transcription activity. Hence, we investigated whether the nuclear fractal dimension could be a prognostic factor in SCLC. Hematoxylin-eosin stained brush cytology slides from 49 patients with SCLC were retrieved from our files. The chromatin (FD) was calculated in digitalized and interactively segmented nuclei using a differential box-counting method. The 3,575 nuclei studied showed a bimodal distribution (peaks at FD1 = 2.115 and FD2 = 2.180). The 75 percentile of the FD was an independent unfavorable prognostic factor for overall survival when tested together with ECOG (Eastern Cooperative Oncology Group) performance status, tumor extension, and therapy in a multivariate Cox regression. Our study corroborates the concept of two main chromatin configurations in small cell neuroendocrine carcinomas and that globally more open chromatin indicates a higher risk of metastasis and therefore a shorter survival of the patient.

The seen and the unseen: Molecular classification and image based-analysis of gastrointestinal cancers

Gastrointestinal cancers account for 22.5% of cancer related deaths worldwide and represent circa 20% of all cancers. In the last decades, we have witnessed a shift from histology-based to molecular-based classifications using genomic, epigenomic, and transcriptomic data. The molecular based classification revealed new prognostic markers and may aid the therapy selection. Because of the high-costs to perform a molecular classification, in recent years immunohistochemistry-based surrogate classification were developed which permit the stratification of patients, and in parallel multiple groups developed hematoxylin and eosin whole slide image analysis for sub-classifying these entities. Hence, we are witnessing a return to an image-based classification with the purpose to infer hidden information from routine histology images that would permit to detect the patients that respond to specific therapies and would be able to predict their outcome. In this review paper, we will discuss the current histological, molecular, and immunohistochemical classifications of the most common gastrointestinal cancers, gastric adenocarcinoma, and colorectal adenocarcinoma, and will present key aspects for developing a new artificial intelligence aided image-based classification of these malignancies.

Bifractal structure of chromatin in rat lymphocyte nuclei

The small-angle neutron scattering (SANS) on the rat lymphocyte nuclei demonstrates the bifractal nature of the chromatin structural organization. The scattering intensity from rat lymphocyte nuclei is described by power law Q^{-D} with fractal dimension approximately 2.3 on smaller scales and 3 on larger scales. The crossover between two fractal structures is detected at momentum transfer near 10^{-1}nm^{-1}. The use of contrast variation (D_{2}O-H_{2}O) in SANS measurements reveals clear similarity in the structural organizations of nucleic acids (NA) and proteins. Both chromatin components show bifractal behavior with logarithmic fractal structure on the large scale and volume fractal with slightly smaller than 2.5 structure on the small scale. Scattering intensities from chromatin, protein component, and NA component demonstrate an extremely extensive range of logarithmic fractal behavior (from 10^{-3} to approximately 10^{-1}nm^{-1}). We compare the fractal arrangement of rat lymphocyte nuclei with that of chicken erythrocytes and the immortal HeLa cell line. We conclude that the bifractal nature of the chromatin arrangement is inherent in the nuclei of all these cells. The details of the fractal arrangement-its range and correlation/interaction between nuclear acids and proteins are specific for different cells and is related to their functionality.

Observation of nucleic acid and protein correlation in chromatin of HeLa nuclei using small-angle neutron scattering with D_{2}O-H_{2}O contrast variation

The small-angle neutron scattering (SANS) on HeLa nuclei demonstrates the bifractal nature of the chromatin structural organization. The border line between two fractal structures is detected as a crossover point at Q_{c}≈4×10^{-2}nm^{-1} in the momentum transfer dependence Q^{-D}. The use of contrast variation (D_{2}O-H_{2}O) in SANS measurements reveals clear similarity in the large scale structural organizations of nucleic acids (NA) and proteins. Both NA and protein structures have a mass fractal arrangement with the fractal dimension of D≈2.5 at scales smaller than 150 nm down to 20 nm. Both NA and proteins show a logarithmic fractal behavior with D≈3 at scales larger than 150 nm up to 6000 nm. The combined analysis of the SANS and atomic force microscopy data allows one to conclude that chromatin and its constitutes (DNA and proteins) are characterized as soft, densely packed, logarithmic fractals on the large scale and as rigid, loosely packed, mass fractals on the smaller scale. The comparison of the partial cross sections from NA and proteins with one from chromatin as a whole demonstrates spatial correlation of two chromatin's components in the range up to 900 nm. Thus chromatin in HeLa nuclei is built as the unified structure of the NA and proteins entwined through each other. Correlation between two components is lost upon scale increases toward 6000 nm. The structural features at the large scale, probably, provide nuclei with the flexibility and chromatin-free space to build supercorrelations on the distance of 10^{3} nm resembling cycle cell activity, such as an appearance of nucleoli and a DNA replication.

Morphological, fractal, and textural features for the blood cell classification: the case of acute myeloid leukemia

Microscopic examination of stained peripheral blood smears is, nowadays, an indispensable tool in the evaluation of patients with hematological and non-hematological diseases. While a rapid automated quantification of the regular blood cells is available, recognition and counting of immature white blood cells (WBC) still relies mostly on the microscopic examination of blood smears by an experienced observer. Recently, there are efforts to improve the prediction by various machine learning approaches. An open dataset collection including the recently digitalized single-cell images for 200 patients, from peripheral blood smears at 100 × magnification, was used. We studied different morphological, fractal, and textural descriptors for WBC classification, with an aim to indicate the most reliable parameters for the recognition of certain cell types. Structural properties of both the mature and non-mature leukocytes obtained from (i) acute myeloid leukemia patients, or (ii) non-malignant controls, were studied in depth, with a sample size of about 25 WBC per group. We quantified structural and textural differences and, based on the statistical ranges of parameters for different WBC types, selected eight features for classification: Cell area, Nucleus-to-cell ratio, Nucleus solidity, Fractal dimension, Correlation, Contrast, Homogeneity, and Energy. Classification Precision of up to 100% (80% on average) was achieved.

Structural Fractal Analysis of the Active Site of Acetylcholinesterase in Complexes with Huperzine A, Galantamine, and Donepezil

The fractal dimension (D) of the active site of hAChE in the unliganded state and as part of complexes with hyperzine A, galantamine, and donepezil is calculated using molecular interatomic-distance histograms. Fractal matrices of structural changes (FMSCs) are formed by pairwise comparison of the values of D and by revealing the significance of their differences. FMSCs are found to be used to quantitatively estimate the changes in the structures of the molecules in various states. When analyzing FMSCs, we found that the most significant structural changes are related to the Glu202 amino acid residue. No structural perturbations are revealed in the case of Trp86, Gly122, Ala204, Phe338, Tyr341, Gly448, and Ile451.

Image-based cell subpopulation identification through automated cell tracking, principal component analysis, and partitioning around medoids clustering

In vitro cell culture model systems often employ monocultures, despite the fact that cells generally exist in a diverse, heterogeneous microenvironment in vivo. In response, heterogeneous cultures are increasingly being used to study how cell phenotypes interact. However, the ability to accurately identify and characterize distinct phenotypic subpopulations within heterogeneous systems remains a major challenge. Here, we present the use of a computational, image analysis-based approach-comprising automated contour-based cell tracking for feature identification, principal component analysis for feature reduction, and partitioning around medoids for subpopulation characterization-to non-destructively and non-invasively identify functionally distinct cell phenotypic subpopulations from live-cell microscopy image data. Using a heterogeneous model system of endothelial and smooth muscle cells, we demonstrate that this approach can be applied to both mono and co-culture nuclear morphometric and motility data to discern cell phenotypic subpopulations. Morphometric clustering identified minimal difference in mono- versus co-culture, while motility clustering revealed that a portion of endothelial cells and smooth muscle cells adopt increased motility rates in co-culture that are not observed in monoculture. We anticipate that this approach using non-destructive and non-invasive imaging can be applied broadly to heterogeneous cell culture model systems to advance understanding of how heterogeneity alters cell phenotype. This work presents a computational, image-analysis-based approach-comprising automated contour-based cell tracking for feature identification, principle component analysis for feature reduction, and partitioning around medoids for subpopulation characterization-to non-destructively and non-invasively identify functionally distinct cell phenotypic subpopulations from live-cell microscopy image data.

Morphological Changes in the Motor Endplate and in the Belly Muscle Induced by Previous Static Stretching to the Climbing Protocol

Static stretching provides benefits to the range of motion, modulates intramuscular connective tissue, and is incorporated into warm-up exercises. In this study, we present the effects in the motor endplate and belly muscle resulting from previous static stretching to climbing training. Twenty-four adult male Wistar rats were divided into four groups (n = 6 each): Sedentary (Sed), Climbing (Clb), Static stretching (Ss), and Static stretching prior to climbing (Ssc). The animals (Clb, Ss, and Ssc groups) were subjected to a training protocol 3×/week for 8 weeks, and the Ssc group was subjected to the Ss and Clb protocols in the same session. Samples from the animals were processed for immunostaining, histochemistry, and light microscopy. The Clb group presented a higher motor endplate; the Ss group presented no changes in the motor endplate; and the Ssc group demonstrated a higher compactness. We concluded that static stretching prior to the climbing protocol maintained the density of the motor endplate and increased the compactness of the neuromuscular junction structure. Also, there was a reduction in the myofibers’ diameter (Type I and IIa), an increase in myofibrillar densities (Type I and IIx, and total), and the reorganization of the myonuclei and the interstitium.

Valproic acid-induced changes of 4D nuclear morphology in astrocyte cells

Histone deacetylase inhibitors, such as valproic acid (VPA), have important clinical therapeutic and cellular reprogramming applications. They induce chromatin reorganization that is associated with altered cellular morphology. However, there is a lack of comprehensive characterization of VPA-induced changes of nuclear size and shape. Here, we quantify 3D nuclear morphology of primary human astrocyte cells treated with VPA over time (hence, 4D). We compared volumetric and surface-based representations and identified seven features that jointly discriminate between normal and treated cells with 85% accuracy on day 7. From day 3, treated nuclei were more elongated and flattened and then continued to morphologically diverge from controls over time, becoming larger and more irregular. On day 7, most of the size and shape descriptors demonstrated significant differences between treated and untreated cells, including a 24% increase in volume and 6% reduction in extent (shape regularity) for treated nuclei. Overall, we show that 4D morphometry can capture how chromatin reorganization modulates the size and shape of the nucleus over time. These nuclear structural alterations may serve as a biomarker for histone (de-)acetylation events and provide insights into mechanisms of astrocytes-to-neurons reprogramming.

Cancer: A turbulence problem

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Application of nonlinear dynamics to cancer ecosystems.

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Chemical turbulence and strange attractor models in tumor growth, invasion and pattern formation are investigated.

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Computational algorithms for detecting such structures are proposed.

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Complex systems applications to cancer dynamics.

Cancers are complex, adaptive ecosystems. They remain the leading cause of disease-related death among children in North America. As we approach computational oncology and Deep Learning Healthcare, our mathematical models of cancer dynamics must be revised. Recent findings support the perspective that cancer-microenvironment interactions may consist of chaotic gene expressions and turbulent protein flows during pattern formation. As such, cancer pattern formation, protein-folding and metastatic invasion are discussed herein as processes driven by chemical turbulence within the framework of complex systems theory. To conclude, cancer stem cells are presented as strange attractors of the Waddington landscape.

Switch of fractal properties of DNA in chicken erythrocytes nuclei by mechanical stress

The small-angle neutron scattering (SANS) on the chicken erythrocyte nuclei demonstrates the bifractal nature of the chromatin structural organization. Use of the contrast variation (D_{2}O-H_{2}O) in SANS measurements reveals the differences in the DNA and protein arrangements inside the chromatin substance. It is the DNA that serves as a framework that constitutes the bifractal behavior showing the mass fractal properties with D=2.22 at a smaller scale and the logarithmic fractal behavior with D≈3 at a larger scale. The protein spatial organization shows the mass fractal properties with D≈2.34 throughout the whole nucleus. The borderline between two fractal levels can be significantly shifted toward smaller scales by centrifugation of the nuclei disposed on the dry substrate, since nuclei suffer from mechanical stress transforming them to a disklike shape. The height of this disk measured by atomic force microscopy (AFM) coincides closely with the fractal borderline, thus characterizing two types of the chromatin with the soft (at larger scale) and rigid (at smaller scale) properties. The combined SANS and AFM measurements demonstrate the stress induced switch of the DNA fractal properties from the rigid, but loosely packed, mass fractal to the soft, but densely packed, logarithmic fractal.

The Fractal and GLCM Textural Parameters of Chromatin May Be Potential Biomarkers of Papillary Thyroid Carcinoma in Hashimoto's Thyroiditis Specimens

Occasionally, Hashimoto's thyroiditis (HT) and papillary thyroid carcinoma (PTC) share similar nuclear features. The current study aims to quantify the differences between the investigated specimens of HT-associated PTC versus the HT alone, to reduce the subjective experience of an observer, by the use of fractal parameters as well as gray-level co-occurrence matrix (GLCM) textural parameters. We have analyzed 250 segmented nuclei per group (nn = 25 per patient and np = 10 patients per group) using the ImageJ software (NIH, Bethesda, MD, USA) as well as an in-house written code for the GLCM analysis. The mean values of parameters were calculated for each patient. The results demonstrated that the malignant cells from the HT-associated PTC specimens showed lower chromatin fractal dimension (p = 0.0321) and higher lacunarity (p = 0.0038) compared with the corresponding cells from the HT specimens. Also, there was a statistically significant difference between the investigated specimens, in the contrast, correlation, angular second moment, and homogeneity, of the GLCM corresponding to the visual texture of follicular cell chromatin. The differences in chromatin fractal and GLCM parameters could be integrated with other diagnostic methods for the improved evaluation of distinctive features of the HT-associated PTC versus the HT in cytology and surgical pathology specimens.

Role of fractal analysis in detection of dysplasia in potentially malignant disorders

Background:

Fractal analysis is, a noninvasive method, used to determine the intricate characteristics of the matter. Oral leukoplakia (OL), a potential malignant disorder, has definite propensity to turn in to malignancy. In such lesions, fractal dimension analysis (FDA) could be helpful in the early detection of malignant transformation.

Objectives:

To determine the efficacy of fractal dimension analysis in detecting malignancy potential of oral leukoplakia.

Materials and Methods:

After ethical clearance, we enrolled 121 patients in our study. Lesions were photographed before and after toluidine staining. Image J software was used to analyze fractal dimensions (FDs) of digital image and results were compared with biopsy.

Results:

Fractal dimension value is significantly higher in leukoplakia with dysplastic changes. FD values increase as age of patients increases. FD value in leukoplakia with different tobacco products showed more positive correlation with surti/khaini abusers.

Conclusion:

Fractal dimension analysis is a useful method in determination of complication in OL cases and can be used as an effective, noninvasive screening tool at primary healthcare centers for early intervention.