Comparison of Monofractal, Multifractal and gray level Co-occurrence matrix algorithms in analysis of Breast tumor microscopic images for prognosis of distant metastasis risk

A machine learning radiomics model based on bpMRI to predict bone metastasis in newly diagnosed prostate cancer patients

OBJECTIVES

To develop and evaluate a machine learning radiomics model based on biparametric magnetic resonance imaging MRI (bpMRI) to predict bone metastasis (BM) status in newly diagnosed prostate cancer (PCa) patients.

METHODS

We retrospectively analyzed bpMRI scans of PCa patients from multiple centers between January 2016 and October 2021. 348 PCa patients were recruited from two institutions for this study. The first institution contributed 284 patients, stratified and randomly divided into training and internal validation cohorts at a 7:3 ratio. The remaining 64 patients were sourced from the second institution and comprised the external validation cohort. Radiomics features were extracted from axial T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) tumor regions. We developed the radiomics prediction model for BM in the training cohort and validated it in the internal and external validation cohorts. As a benchmark, we trained the logistic regression model with lasso feature reduction (LFR-LRM) in the training cohort and further compared it with Naive Bayes, eXtreme Gradient Boosting (XGboost), Random Forest (RF), GBDT, SVM, Adaboost, and KNN algorithms and validated in both the internal and external cohorts. The performance of several predictive models was assessed by receiver operating characteristic (ROC).

RESULTS

The LFR-LRM model achieved an area under the receiver operating characteristic curve (AUC) of 0.89 (95% CI: 0.822-0.974) and an accuracy of 0.828 (95% CI: 0.713-0.911). The AUC and accuracy in external validation were 0.866 (95% CI: 0.784-0.948) and 0.769 (95% CI: 0.648-0.864), respectively. The RF and XGBoost models outperformed the LFR-LRM, with AUCs of 0.907 (95% CI: 0.863-0.949) and 0.928 (95% CI: 0.882-0.974) and accuracies of 0.831 (95% CI: 0.727-0.907) and 0.884 (95% CI: 0.792-0.946). External validation for these models yielded AUCs and accuracies of 0.911 (95% CI: 0.861-0.966), 0.921 (95% CI: 0.889-0.953), and 0.846 (95% CI: 0.735-0.923) and 0.876 (95% CI: 0.771-0.945), respectively.

CONCLUSIONS

The XGboost machine learning model is more accurate than LFR-LRM for predicting BM in patients with newly confirmed PCa.

Improved window adaptive gray level co-occurrence matrix for extraction and analysis of texture characteristics of pulmonary nodules

BACKGROUND AND OBJECTIVE

Identifying benign and malignant pulmonary nodules is essential for the early diagnosis of lung cancer and targeted surgical resection. This study aimed to differentiate benign from malignant pulmonary nodules based on computed tomography (CT) plain scan texture analysis technique.

METHODS

A total of 47 pulmonary nodules use the improved window adaptive gray level co-occurrence matrix (GLCM) algorithm to extract the texture characteristics of the area of interest. The Fisher coefficient (Fisher), classification error probability joint average correlation coefficient (POE+ACC), mutual information (MI), and the combination of above three methods joint (FPM) were used to select the best texture parameters set. After that, the analysis of the screened texture parameters was adopted. The B11 module provides four analytical methods, including raw data analysis (RDA), principal component analysis (PCA), linear discriminant analysis (LDA), and nonlinear discriminant analysis (NDA). The results were expressed in the form of misclassification rate (MCR). Region of curve (ROC) analysis was also performed on the selected optimal texture parameters.

RESULTS

The MCR of all the three texture feature extraction methods, Fisher, POE+ACC, and MI, were lower in differentiating benign from malignant pulmonary nodules. FPM method could further reduce the MCR. The NDA analysis had the lowest MCR for both of these three feature extraction methods. The MCR can be further reduced to 2.13% by the combination of NDA and FPM. The ROC curve showed that Perc.01% parameter had the highest AUC value and the most discriminative efficacy.

CONCLUSION

The lowest MCR values were calculated by the FPM dimensionality reduction method and the NDA analysis method. The improved GLCM algorithm has a discriminative role in CT texture analysis of benign and malignant pulmonary nodules.

Computational analysis of MRIs predicts osteosarcoma chemoresponsiveness

Aim: This study aimed to improve osteosarcoma chemoresponsiveness prediction by optimization of computational analysis of MRIs. Patients & methods: Our retrospective predictive model involved osteosarcoma patients with MRI scans performed before OsteoSa MAP neoadjuvant cytotoxic chemotherapy. Results: We found that several monofractal and multifractal algorithms were able to classify tumors according to their chemoresponsiveness. The predictive clues were defined as morphological complexity, homogeneity and fractality. The monofractal feature CV for Λ'(G) provided the best predictive association (area under the ROC curve = 0.88; p <0.001), followed by   Y-axis intersection of the regression line  for  box fractal dimension, r²  for  FD_M and tumor circularity. Conclusion: This is the first full-scale study to indicate that computational analysis of pretreatment MRIs could provide imaging biomarkers for the classification of osteosarcoma according to their chemoresponsiveness.

Analysis of Spatial Distribution and Prognostic Value of Different Pan Cytokeratin Immunostaining Intensities in Breast Tumor Tissue Sections

Cancer risk prognosis could improve patient survival through early personalized treatment decisions. This is the first systematic analysis of the spatial and prognostic distribution of different pan cytokeratin immunostaining intensities in breast tumors. The prognostic model included 102 breast carcinoma patients, with distant metastasis occurrence as the endpoint. We segmented the full intensity range (0–255) of pan cytokeratin digitized immunostaining into seven discrete narrow grey level ranges: 0–130, 130–160, 160–180, 180–200, 200–220, 220–240, and 240–255. These images were subsequently examined by 33 major (GLCM), fractal and first-order statistics computational analysis features. Interestingly, while moderate intensities were strongly associated with metastasis outcome, high intensities of pan cytokeratin immunostaining provided no prognostic value even after an exhaustive computational analysis. The intense pan cytokeratin immunostaining was also relatively rare, suggesting the low differentiation state of epithelial cells. The observed variability in immunostaining intensities highlighted the intratumoral heterogeneity of the malignant cells and its association with a poor disease outcome. The prognostic importance of the moderate intensity range established by complex computational morphology analyses was supported by simple measurements of its immunostaining area which was associated with favorable disease outcome. This study reveals intratumoral heterogeneity of the pan cytokeratin immunostaining together with the prognostic evaluation and spatial distribution of its discrete intensities.

Size and Shape Filtering of Malignant Cell Clusters within Breast Tumors Identifies Scattered Individual Epithelial Cells as the Most Valuable Histomorphological Clue in the Prognosis of Distant Metastasis Risk

Survival and life quality of breast cancer patients could be improved by more aggressive chemotherapy for those at high metastasis risk and less intense treatments for low-risk patients. Such personalized treatment cannot be currently achieved due to the insufficient reliability of metastasis risk prognosis. The purpose of this study was therefore, to identify novel histopathological prognostic markers of metastasis risk through exhaustive computational image analysis of 80 size and shape subsets of epithelial clusters in breast tumors. The group of 102 patients had a follow-up median of 12.3 years, without lymph node spread and systemic treatments. Epithelial cells were stained by the AE1/AE3 pan-cytokeratin antibody cocktail. The size and shape subsets of the stained epithelial cell clusters were defined in each image by use of the circularity and size filters and analyzed for prognostic performance. Epithelial areas with the optimal prognostic performance were uniformly small and round and could be recognized as individual epithelial cells scattered in tumor stroma. Their count achieved an area under the receiver operating characteristic curve (AUC) of 0.82, total area (AUC = 0.77), average size (AUC = 0.63), and circularity (AUC = 0.62). In conclusion, by use of computational image analysis as a hypothesis-free discovery tool, this study reveals the histomorphological marker with a high prognostic value that is simple and therefore easy to quantify by visual microscopy.

A healthy dose of chaos: Using fractal frameworks for engineering higher-fidelity biomedical systems

Optimal levels of chaos and fractality are distinctly associated with physiological health and function in natural systems. Chaos is a type of nonlinear dynamics that tends to exhibit seemingly random structures, whereas fractality is a measure of the extent of organization underlying such structures. Growing bodies of work are demonstrating both the importance of chaotic dynamics for proper function of natural systems, as well as the suitability of fractal mathematics for characterizing these systems. Here, we review how measures of fractality that quantify the dose of chaos may reflect the state of health across various biological systems, including: brain, skeletal muscle, eyes and vision, lungs, kidneys, tumours, cell regulation, skin and wound repair, bone, vasculature, and the heart. We compare how reports of either too little or too much chaos and fractal complexity can be damaging to normal biological function, and suggest that aiming for the healthy dose of chaos may be an effective strategy for various biomedical applications. We also discuss rising examples of the implementation of fractal theory in designing novel materials, biomedical devices, diagnostics, and clinical therapies. Finally, we explain important mathematical concepts of fractals and chaos, such as fractal dimension, criticality, bifurcation, and iteration, and how they are related to biology. Overall, we promote the effectiveness of fractals in characterizing natural systems, and suggest moving towards using fractal frameworks as a basis for the research and development of better tools for the future of biomedical engineering.

Computational quantitative MR image features - a potential useful tool in differentiating glioblastoma from solitary brain metastasis

PURPOSE

Glioblastomas (GBM) and metastases are the most frequent malignant brain tumors in the adult population. Their presentation on conventional MRI is quite similar, but treatment strategy and prognosis are substantially different. Even with advanced MR techniques, in some cases diagnostic uncertainty remains. The main objective of this study was to determine whether fractal, texture, or both MR image analyses could aid in differentiating glioblastoma from solitary brain metastasis.

METHOD

In a retrospective study of 55 patients (30 glioblastomas and 25 solitary metastases) who underwent T2W/SWI/CET1 MRI, quantitative parameters of fractal and texture analysis were estimated, using box-counting and gray level co-occurrence matrix (GLCM) methods.

RESULTS

All five GLCM parameters obtained from T2W images showed significant difference between glioblastomas and solitary metastases, as well as on CET1 images except correlation (S_COR), contrary to SWI images which showed different values of two parameters (angular second moment-S_ASM and contrast-S_CON). Only three fractal features (binary box dimension-D_bin, normalized box dimension-D_norm and lacunarity-λ) measured on T2W and D_norm measured on CET1 images significantly differed GBMs from solitary metastases. The highest sensitivity and specificity were obtained from inverse difference moment (S_IDM) on T2W and S_IDM on CET1 images, respectively. Combination of several GLCM parameters yielded better results. The processing of T2W images provided the most significantly different parameters between the groups, followed by CET1 and SWI images.

CONCLUSIONS

Computational-aided quantitative image analysis may potentially improve diagnostic accuracy. According to our results texture features are more significant than fractal-based features in differentiation glioblastoma from solitary metastasis.

Application of Gray Level Co-Occurrence Matrix Analysis as a New Method for Enzyme Histochemistry Quantification

Enzyme histochemistry is a valuable histological method which provides a connection between morphology, activity, and spatial localization of investigated enzymes. Even though the method relies purely on arbitrary evaluations performed by the human eye, it is still wildly accepted and used in histo(patho)logy. Texture analysis emerged as an excellent tool for image quantification of subtle differences reflected in both spatial discrepancies and gray level values of pixels. The current study of texture analysis utilizes the gray-level co-occurrence matrix as a method for quantification of differences between ecto-5'-nucleotidase activities in healthy hippocampal tissue and tissue with marked neurodegeneration. We used the angular second moment, contrast (CON), correlation, inverse difference moment (INV), and entropy for texture analysis and receiver operating characteristic analysis with immunoblot and qualitative assessment of enzyme histochemistry as a validation. Our results strongly argue that co-occurrence matrix analysis could be used for the determination of fine differences in the enzyme activities with the possibility to ascribe those differences to regions or specific cell types. In addition, it emerged that INV and CON are especially useful parameters for this type of enzyme histochemistry analysis. We concluded that texture analysis is a reliable method for quantification of this descriptive technique, thus removing biases and adding it a quantitative dimension.

The Pan-Cytokeratin Staining Intensity and Fractal Computational Analysis of Breast Tumor Malignant Growth Patterns Prognosticate the Occurrence of Distant Metastasis

Improved prognosis of breast cancer outcome could prolong patient survival by reliable identification of patients at high risk of metastasis occurrence which could benefit from more aggressive treatments. Based on such clinical need, we prognostically evaluated the malignant cells in breast tumors, as the obvious potential source of unexploited prognostic information. The patient group was homogeneous, without any systemic treatments or lymph node spread, with smaller tumor size (pT1/2) and a long follow-up. Epithelial cells were labeled with AE1/AE3 pan-cytokeratin antibody cocktail and comprehensively analyzed. Monofractal and multifractal analyses were applied for quantification of distribution, shape, complexity and texture of malignant cell clusters, while mean pixel intensity and total area were measures of the pan-cytokeratin immunostaining intensity. The results surprisingly indicate that simple binary images and monofractal analysis provided better prognostic information then grayscale images and multifractal analysis. The key findings were that shapes and distribution of malignant cell clusters (by binary fractal dimension; AUC = 0.29), their contour shapes (by outline fractal dimension; AUC = 0.31) and intensity of the pan-cytokeratin immunostaining (by mean pixel intensity; AUC = 0.30) offered significant performance in metastasis risk prognostication. The results reveal an association between the lower pan-cytokeratin staining intensity and the high metastasis risk. Another interesting result was that multivariate analysis could confirm the prognostic independence only for fractal but not for immunostaining intensity features. The obtained results reveal several novel and unexpected findings highlighting the independent prognostic efficacy of malignant cell cluster distribution and contour shapes in breast tumors.

Effects of different preprocessing algorithms on the prognostic value of breast tumour microscopic images

The purpose of this study was to improve the prognostic value of tumour histopathology image analysis methodology by image preprocessing. Key image qualities were modified including contrast, sharpness and brightness. The texture information was subsequently extracted from images of haematoxylin/eosin-stained tumour tissue sections by GLCM, monofractal and multifractal algorithms without any analytical limitation to predefined structures. Images were derived from patient groups with invasive breast carcinoma (BC, 93 patients) and inflammatory breast carcinoma (IBC, 51 patients). The prognostic performance was indeed significantly enhanced by preprocessing with the average AUCs of individual texture features improving from 0.68 ± 0.05 for original to 0.78 ± 0.01 for preprocessed images in the BC group and 0.75 ± 0.01 to 0.80 ± 0.02 in the IBC group. Image preprocessing also improved the prognostic independence of texture features as indicated by multivariate analysis. Surprisingly, the tonal histogram compression by the nonnormalisation preprocessing has prognostically outperformed the tested contrast normalisation algorithms. Generally, features without prognostic value showed higher susceptibility to prognostic enhancement by preprocessing whereas IDM texture feature was exceptionally susceptible. The obtained results are suggestive of the existence of distinct texture prognostic clues in the two examined types of breast cancer. The obtained enhancement of prognostic performance is essential for the anticipated clinical use of this method as a simple and cost-effective prognosticator of cancer outcome.

Magnetic Resonance Image Texture Analysis of the Periaqueductal Gray Matter in Episodic Migraine Patients without T2-Visible Lesions

Objective

The periaqueductal gray matter (PAG), a small midbrain structure, presents dysfunction in migraine. However, the precise neurological mechanism is still not well understood. Herein, the aim of this study was to investigate the texture characteristics of altered PAG in episodic migraine (EM) patients based on high resolution brain structural magnetic resonance (MR) images.

Materials and Methods

The brain structural MR images were obtained from 18 normal controls (NC), 18 EM patients and 16 chronic migraine (CM) patients using a 3T MR system. A PAG template was created using the International Consortium Brain Mapping 152 gray matter model, and the individual PAG segment was developed by applying the deformation field from the structural image segment to the PAG template. A grey level co-occurrence matrix was used to calculate the texture parameters including the angular second moment (ASM), contrast, correlation, inverse difference moment (IDM) and entropy.

Results

There was a significant difference for ASM, IDM and entropy in the EM group (998.629 ± 0.162 × 10⁻³, 999.311 ± 0.073 × 10⁻³, 916.354 ± 0.947 × 10⁻⁵) compared to that found in the NC group (998.760 ± 0.110 × 10⁻³, 999.358 ± 0.037 × 10⁻³ and 841.198 ± 0.575 × 10⁻⁵) (p < 0.05). The entropy was significantly lower among the patients with CM (864.116 ± 0.571 × 10⁻⁵) than that found among patients with EM (p < 0.05). The area under the receiver operating characteristic curve was 0.776 and 0.750 for ASM and entropy in the distinction of the EM from NC groups, respectively. ASM was negatively related to disease duration (DD) and the Migraine Disability Assessment Scale (MIDAS) scores in the EM group, and entropy was positively related to DD and MIDAS in the EM group (p < 0.05).

Conclusion

The present study identified altered MR image texture characteristics of the PAG in EM. The identified texture characteristics could be considered as imaging biomarkers for EM.

Fractal and Gray Level Cooccurrence Matrix Computational Analysis of Primary Osteosarcoma Magnetic Resonance Images Predicts the Chemotherapy Response

The prediction of induction chemotherapy response at the time of diagnosis may improve outcomes in osteosarcoma by allowing for personalized tailoring of therapy. The aim of this study was thus to investigate the predictive potential of the so far unexploited computational analysis of osteosarcoma magnetic resonance (MR) images. Fractal and gray level cooccurrence matrix (GLCM) algorithms were employed in retrospective analysis of MR images of primary osteosarcoma localized in distal femur prior to the OsteoSa induction chemotherapy. The predicted and actual chemotherapy response outcomes were then compared by means of receiver operating characteristic (ROC) analysis and accuracy calculation. Dbin, Λ, and SCN were the standard fractal and GLCM features which significantly associated with the chemotherapy outcome, but only in one of the analyzed planes. Our newly developed normalized fractal dimension, called the space-filling ratio (SFR) exerted an independent and much better predictive value with the prediction significance accomplished in two of the three imaging planes, with accuracy of 82% and area under the ROC curve of 0.20 (95% confidence interval 0-0.41). In conclusion, SFR as the newly designed fractal coefficient provided superior predictive performance in comparison to standard image analysis features, presumably by compensating for the tumor size variation in MR images.

Texture features of periaqueductal gray in the patients with medication-overuse headache

BACKGROUND

Periaqueductal gray (PAG) is the descending pain modulatory center, and PAG dysfunction had been recognized in migraine. Here we propose to investigate altered PAG texture features (quantitative approach for extracting texture descriptors for images) in the patients with medication-overuse headache (MOH) based on high resolution brain structural image to understand the MOH pathogenesis.

METHODS

The brain structural images were obtained from 32 normal controls (NC) and 44 MOH patients on 3.0 T MR system. PAG template was created based on the ICBM152 gray matter template, and the individual PAG segment was performed by applying the deformation field to the PAG template after structural image segment. Grey-level co-occurrence matrix (GLCM) was performed to measure the texture parameters including angular second moment (ASM), Contrast, Correlation, inverse difference moment (IDM) and Entropy.

RESULTS

Contrast was increased in MOH patients (9.28 ± 3.11) compared with that in NC (7.94 ± 0.65) (P < 0.05), and other texture features showed no significant difference between MOH and NC (P > 0.05). The area under the ROC curve was 0.697 for Contrast in the distinction of MOH from NC, and the cut-off value of Contrast was 8.11 with sensitivity 70.5% and specificity 62.5%. The contrast was negatively with the sleep scores (r = -0.434, P = 0.003).

CONCLUSION

Texture Contrast could be used to identify the altered MR imaging characteristics in MOH in understanding the MOH pathogenesis, and it could also be considered as imaging biomarker in for MOH diagnosis.