Lung cancer-a fractal viewpoint

Kinetic modeling and parametric imaging with dynamic PET for oncological applications: general considerations, current clinical applications, and future perspectives

Dynamic PET (dPET) studies have been used until now primarily within research purposes. Although it is generally accepted that the information provided by dPET is superior to that of conventional static PET acquisitions acquired usually 60 min post injection of the radiotracer, the duration of dynamic protocols, the limited axial field of view (FOV) of current generation clinical PET systems covering a relatively small axial extent of the human body for a dynamic measurement, and the complexity of data evaluation have hampered its implementation into clinical routine. However, the development of new-generation PET/CT scanners with an extended FOV as well as of more sophisticated evaluation software packages that offer better segmentation algorithms, automatic retrieval of the arterial input function, and automatic calculation of parametric imaging, in combination with dedicated shorter dynamic protocols, will facilitate the wider use of dPET. This is expected to aid in oncological diagnostics and therapy assessment. The aim of this review is to present some general considerations about dPET analysis in oncology by means of kinetic modeling, based on compartmental and noncompartmental approaches, and parametric imaging. Moreover, the current clinical applications and future perspectives of the modality are outlined.

The Complexity and Fractal Geometry of Nuclear Medicine Images

Irregularity in shape and behavior is the main feature of every anatomical system, including human organs, tissues, cells, and sub-cellular entities. It has been shown that this property cannot be quantified by means of the classical Euclidean geometry, which is only able to describe regular geometrical objects. In contrast, fractal geometry has been widely applied in several scientific fields. This rapid growth has also produced substantial insights in the biomedical imaging. Consequently, particular attention has been given to the identification of pathognomonic patterns of "shape" in anatomical entities and their changes from natural to pathological states. Despite the advantages of fractal mathematics and several studies demonstrating its applicability to oncological research, many researchers and clinicians remain unaware of its potential. Therefore, this review aims to summarize the complexity and fractal geometry of nuclear medicine images.

Analysis of gene expression profiles of lung cancer subtypes with machine learning algorithms

Lung cancer is one of the most common cancer types worldwide and causes more than one million deaths annually. Lung adenocarcinoma (AC) and lung squamous cell cancer (SCC) are two major lung cancer subtypes and have different characteristics in several aspects. Identifying their differentially expressed genes and different gene expression patterns can deepen our understanding of these two subtypes at the transcriptomic level. In this work, we used several machine learning algorithms to investigate the gene expression profiles of lung AC and lung SCC samples retrieved from Gene Expression Omnibus. First, the profiles were analyzed by using a powerful feature selection method, namely, Monte Carlo feature selection. A feature list, ranking all features according to their importance, and some informative features were obtained. Then, the feature list was used in the incremental feature selection method to extract optimal features, which can allow the support vector machine (SVM) to yield the best performance for classifying lung AC and lung SCC samples. Some top genes (CSTA, TP63, SERPINB13, CLCA2, BICD2, PERP, FAT2, BNC1, ATP11B, FAM83B, KRT5, PARD6G, PKP1) were extensively analyzed to prove that they can be differentially expressed genes between lung AC and lung SCC. Meanwhile, a rule learning procedure was applied on informative features to construct the classification rules. These rules provide a clear procedure of classification and show some different gene expression patterns between lung AC and lung SCC.

Complex Network Characterization Using Graph Theory and Fractal Geometry: The Case Study of Lung Cancer DNA Sequences

This paper discusses an approach developed for exploiting the local elementary movements of evolution to study complex networks in terms of shared common embedding and, consequently, shared fractal properties. This approach can be useful for the analysis of lung cancer DNA sequences and their properties by using the concepts of graph theory and fractal geometry. The proposed method advances a renewed consideration of network complexity both on local and global scales. Several researchers have illustrated the advantages of fractal mathematics, as well as its applicability to lung cancer research. Nevertheless, many researchers and clinicians continue to be unaware of its potential. Therefore, this paper aims to examine the underlying assumptions of fractals and analyze the fractal dimension and related measurements for possible application to complex networks and, especially, to the lung cancer network. The strict relationship between the lung cancer network properties and the fractal dimension is proved. Results show that the fractal dimension decreases in the lung cancer network while the topological properties of the network increase in the lung cancer network. Finally, statistical and topological significance between the complexity of the network and lung cancer network is shown.

Magnetic resonance imaging-based 3-dimensional fractal dimension and lacunarity analyses may predict the meningioma grade

OBJECTIVE

To assess whether 3-dimensional (3D) fractal dimension (FD) and lacunarity features from MRI can predict the meningioma grade.

METHODS

This retrospective study included 131 patients with meningiomas (98 low-grade, 33 high-grade) who underwent preoperative MRI with post-contrast T1-weighted imaging. The 3D FD and lacunarity parameters from the enhancing portion of the tumor were extracted by box-counting algorithms. Inter-rater reliability was assessed with the intraclass correlation coefficient (ICC). Additionally, conventional imaging features such as location, heterogeneous enhancement, capsular enhancement, and necrosis were assessed. Independent clinical and imaging risk factors for meningioma grade were investigated using multivariable logistic regression. The discriminative value of the prediction model with and without fractal features was evaluated. The relationship of fractal parameters with the mitosis count and Ki-67 labeling index was also assessed.

RESULTS

The inter-reader reliability was excellent, with ICCs of 0.99 for FD and 0.97 for lacunarity. High-grade meningiomas had higher FD (p < 0.001) and higher lacunarity (p = 0.007) than low-grade meningiomas. In the multivariable logistic regression, the diagnostic performance of the model with clinical and conventional imaging features increased with 3D fractal features for predicting the meningioma grade, with AUCs of 0.78 and 0.84, respectively. The 3D FD showed significant correlations with both mitosis count and Ki-67 labeling index, and lacunarity showed a significant correlation with the Ki-67 labeling index (all p values < 0.05).

CONCLUSION

The 3D FD and lacunarity are higher in high-grade meningiomas and fractal analysis may be a useful imaging biomarker for predicting the meningioma grade.

KEY POINTS

• Fractal dimension (FD) and lacunarity are the two parameters used in fractal analysis to describe the complexity of a subject and may aid in predicting meningioma grade. • High-grade meningiomas had a higher fractal dimension and higher lacunarity than low-grade meningiomas, suggesting higher complexity and higher rotational variance. • The discriminative value of the predictive model using clinical and conventional imaging features improved when combined with 3D fractal features for predicting the meningioma grade.

Hounsfield units and fractal dimension (test HUFRA) for determining PET positive/negative lymph nodes in pediatric Hodgkin's lymphoma patients

Objectives

We had developed a method that can help detect and identify lymph nodes affected by the neoplastic process. Our group evaluated the fractal dimension (FD) and X-ray attenuation (XRA) of lymph nodes in HL and compared to their metabolic activity as measured by 18F-FDG-PET examination.

Methods

The training set included 72 lymph nodes from 31 consecutive patients, and the tested set of 71 lymph nodes from next 19 patients. The measurement of FD of each lymph node was performed before the start of therapy using original software. X-ray attenuation (XRA) expressed in HU (Hounsfield Units) from CT scans was compared with the metabolic activity of the lymphatic nodes, measured by 18F-FDG-PET examination.

Results

Significant differences were observed between XRA_max and FD_max values in assessing the PET(+) and PET(-) nodes. All nodes were scored from 0 to 2. The HUFRA test properly qualified 95% with a score of 2 and 0 points as PET(+) or PET(-).

Conclusion

The HUFRA test can differentiate about 70–80% of lymph nodes as PET(+) or PET(-) based solely on the CT examination. It can be useful in patients who were not subjected to 18FFDG-PET/CT examination before the treatment, or who had an unreliable result of 18F-FDG-PET/CT with further research requirements.

Efficiency of Electromagnetic Navigation Bronchoscopy and Virtual Bronchoscopic Navigation

BACKGROUND

Image-guided bronchoscopy techniques have emerged as a means of improving pulmonary nodule biopsy yield. However comparisons of the diagnostic efficacy of electromagnetic navigation bronchoscopy (ENB) and virtual bronchoscopic navigation (VBN) have not reached a consensus. This meta-analysis evaluates the overall diagnostic yield and accuracy of ENB and VBN for pulmonary nodules.

METHODS

A systematic search was conducted to identify relevant articles. Meta-analysis was used to summarize the sensitivities, specificities, and area under the curve for ENB and VBN.

RESULTS

Thirty-two studies (1981 patients with pulmonary nodules) were included in this analysis. The pooled sensitivity, specificity, and area under the curve (95% confidence interval) of ENB were 0.80 (0.73-0.85), 0.81 (0.71-0.88), and 0.87 (0.84-0.90), respectively. Corresponding VBN values were 0.80 (0.76-0.83), 0.65 (0.56-0.73), and 0.81 (0.78-0.85), respectively. Comparison of the 2 techniques revealed that ENB had higher specificity and area under the curve but no difference in sensitivity.

CONCLUSIONS

Both ENB and VBN are valuable tools in the diagnosis of lung nodules. ENB achieved a higher specificity than VBN in the diagnose of lung nodules, whereas ENB performed better than VBN for pulmonary nodules. These results are due to the real-time positioning function of ENB.

Automated Machine Learning Diagnostic Support System as a Computational Biomarker for Detecting Drug-Induced Liver Injury Patterns in Whole Slide Liver Pathology Images

Drug-induced liver injury (DILI) is a challenging disease to diagnose, a leading cause of acute liver failure, and responsible for drug withdrawal from the market. There is no symptom, no biomarker or test for detection, no therapy, but discontinuation of the drug. Pharmaceutical companies spend huge money, time, and scientific research efforts to test DILI effects and drug efficacy. A preclinical diagnostic support system is designed and proposed for DILI detection and classification on liver biopsy histopathology images. Heterogeneity features and automated machine learning (AutoML) models were tested to classify DILI injury patterns on whole slide image. Fractal and lacunarity values were used to detect hepatocellular necrotic injury patterns caused on a rat liver (in vivo) by 10 drugs at four dose levels. Correlations between fractal and lacunarity values were statistically analyzed for the 10 drugs; the Pearson correlation (r = 0.9809), p-value (1.6612E-06), and R2 (0.9582) were found to be high in the case of carbon tetrachloride. The AutoML model was tested to understand the injury patterns on a subset of 1,277 histology images. The AutoML algorithm was able to classify necrotic injury patterns accurately with an average precision of 98.6% on a score threshold of 0.5.

Comparison of Diagnostic Performance of Two-Dimensional and Three-Dimensional Fractal Dimension and Lacunarity Analyses for Predicting the Meningioma Grade

BACKGROUND

To compare the diagnostic performance of two-dimensional (2D) and three-dimensional (3D) fractal dimension (FD) and lacunarity features from MRI for predicting the meningioma grade.

METHODS

This retrospective study included 123 meningioma patients [90 World Health Organization (WHO) grade I, 33 WHO grade II/III] with preoperative MRI including post-contrast T1-weighted imaging. The 2D and 3D FD and lacunarity parameters from the contrast-enhancing portion of the tumor were calculated. Reproducibility was assessed with the intraclass correlation coefficient. Multivariable logistic regression analysis using 2D or 3D fractal features was performed to predict the meningioma grade. The diagnostic ability of the 2D and 3D fractal models were compared.

RESULTS

The reproducibility between observers was excellent, with intraclass correlation coefficients of 0.97, 0.95, 0.98, and 0.96 for 2D FD, 2D lacunarity, 3D FD, and 3D lacunarity, respectively. WHO grade II/III meningiomas had a higher 2D and 3D FD (p=0.003 and p<0.001, respectively) and higher 2D and 3D lacunarity (p=0.002 and p=0.006, respectively) than WHO grade I meningiomas. The 2D fractal model showed an area under the curve (AUC), accuracy, sensitivity, and specificity of 0.690 [95% confidence interval (CI) 0.581-0.799], 72.4%, 75.8%, and 64.4%, respectively. The 3D fractal model showed an AUC, accuracy, sensitivity, and specificity of 0.813 (95% CI 0.733-0.878), 82.9%, 81.8%, and 70.0%, respectively. The 3D fractal model exhibited significantly better diagnostic performance than the 2D fractal model (p<0.001).

CONCLUSION

The 3D fractal analysis proved superiority in diagnostic performance to 2D fractal analysis in grading meningioma.

Cdh1 functions as an oncogene by inducing self-renewal of lung cancer stem-like cells via oncogenic pathways

The mortality rate of lung cancer remains the highest amongst all cancers despite of new therapeutic developments. While cancer stem cells (CSCs) may play a pivotal role in cancer, mechanisms underlying CSCs self-renewal and their relevance to cancer progression have not been clearly elucidated due to the lack of reliable and stable CSC cellular models. In the present study, we unveiled the novel oncogene function of cadherin 1 (Cdh1) via bioinformatic analysis in a broad spectrum of human cancers including lung adenocarcinoma (LUAD), adding a new dimension to the widely reported tumor suppressor function of Cdh1. Experimentally, we show for the first time that Cdh1 promotes the self-renewal of lung CSCs, consistent with its function in embryonic and normal stem cells. Using the LLC-Symmetric Division (LLC-SD) model, we have revealed an intricate cross-talk between the oncogenic pathway and stem cell pathway in which Cdh1 functions as an oncogene by promoting lung CSC renewal via the activation of the Phosphoinositide 3-kinase (PI3K) and inhibition of Mitogen-activated protein kinase (MAPK) pathways, respectively. In summary, this study has provided evidence demonstrating effective utilization of the normal stem cell renewal mechanisms by CSCs to promote oncogenesis and progression.

Monitoring and Determining Mitochondrial Network Parameters in Live Lung Cancer Cells

Mitochondria are dynamic organelles that constantly fuse and divide, forming dynamic tubular networks. Abnormalities in mitochondrial dynamics and morphology are linked to diverse pathological states, including cancer. Thus, alterations in mitochondrial parameters could indicate early events of disease manifestation or progression. However, finding reliable and quantitative tools for monitoring mitochondria and determining the network parameters, particularly in live cells, has proven challenging. Here, we present a 2D confocal imaging-based approach that combines automatic mitochondrial morphology and dynamics analysis with fractal analysis in live small cell lung cancer (SCLC) cells. We chose SCLC cells as a test case since they typically have very little cytoplasm, but an abundance of smaller mitochondria compared to many of the commonly used cell types. The 2D confocal images provide a robust approach to quantitatively measure mitochondrial dynamics and morphology in live cells. Furthermore, we performed 3D reconstruction of electron microscopic images and show that the 3D reconstruction of the electron microscopic images complements this approach to yield better resolution. The data also suggest that the parameters of mitochondrial dynamics and fractal dimensions are sensitive indicators of cellular response to subtle perturbations, and hence, may serve as potential markers of drug response in lung cancer.

Is tumour sphericity an important prognostic factor in patients with lung cancer?

BACKGROUND AND PURPOSE

Quantitative tumour shape features extracted from radiotherapy planning scans have shown potential as prognostic markers. In this study, we investigated if sphericity of the gross tumour volume (GTV) on planning computed tomography (CT) is an independent predictor of overall survival (OS) in lung cancer patients treated with standard radiotherapy. In the analysis, we considered whether tumour sphericity is correlated with clinical prognostic factors or influenced by the inclusion of lymph nodes in the GTV.

MATERIALS AND METHODS

Sphericity of single GTV delineation was extracted for 457 lung cancer patients. Relationships between sphericity, and common patient and tumour characteristics were investigated via correlation analysis and multivariate Cox regression to assess prognostic value of GTV sphericity. A subset analysis was performed for 290 nodal stage N0 patients to determine prognostic value of primary tumour sphericity.

RESULTS

Sphericity is correlated with clinical variables: tumour volume, mean lung dose, N stage, and T stage. Sphericity is strongly associated with OS (p < 0.001, hazard ratio (HR) (95% confidence interval (CI)) = 0.13 (0.04-0.41)) in univariate analysis. However, this association did not remain significant in multivariate analysis (p = 0.826, HR (95% CI) = 0.83 (0.16-4.31), and inclusion of sphericity to a clinical model did not improve model performance. In addition, no significant relationship between sphericity and OS was detected in univariate (p = 0.072) or multivariate (p = 0.920) analysis of N0 subset.

CONCLUSION

Sphericity correlates clearly with clinical prognostic factors, which are often unaccounted for in radiomic studies. Sphericity is also influenced by the presence of nodal involvement within the GTV contour.

The DNA walk and its demonstration of deterministic chaos-relevance to genomic alterations in lung cancer

MOTIVATION

Advancements in cancer genetics have facilitated the development of therapies with actionable mutations. Although mutated genes have been studied extensively, their chaotic behavior has not been appreciated. Thus, in contrast to naïve DNA, mutated DNA sequences can display characteristics of unpredictability and sensitivity to the initial conditions that may be dictated by the environment, expression patterns and presence of other genomic alterations. Employing a DNA walk as a form of 2D analysis of the nucleotide sequence, we demonstrate that chaotic behavior in the sequence of a mutated gene can be predicted.

RESULTS

Using fractal analysis for these DNA walks, we have determined the complexity and nucleotide variance of commonly observed mutated genes in non-small cell lung cancer, and their wild-type counterparts. DNA walks for wild-type genes demonstrate varying levels of chaos, with BRAF, NTRK1 and MET exhibiting greater levels of chaos than KRAS, paxillin and EGFR. Analyzing changes in chaotic properties, such as changes in periodicity and linearity, reveal that while deletion mutations indicate a notable disruption in fractal 'self-similarity', fusion mutations demonstrate bifurcations between the two genes. Our results suggest that the fractals generated by DNA walks can yield important insights into potential consequences of these mutated genes.

AVAILABILITY AND IMPLEMENTATION

Introduction to Turtle graphics in Python is an open source article on learning to develop a script for Turtle graphics in Python, freely available on the web at https://docs.python.org/2/library/turtle.html. cDNA sequences were obtained through NCBI RefSeq database, an open source database that contains information on a large array of genes, such as their nucleotide and amino acid sequences, freely available at https://www.ncbi.nlm.nih.gov/refseq/. FracLac plugin for Fractal analysis in ImageJ is an open source plugin for the ImageJ program to perform fractal analysis, free to download at https://imagej.nih.gov/ij/plugins/fraclac/FLHelp/Introduction.html.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

The Promise of Digital Biopsy for the Prediction of Tumor Molecular Features and Clinical Outcomes Associated With Immunotherapy

Immunotherapy by immune checkpoint inhibitors has emerged as an effective treatment for a slight proportion of patients with aggressive tumors. Currently, some molecular determinants, such as the expression of the programmed cell death ligand-1 (PD-L1) or the tumor mutational burden (TMB) have been used in the clinical practice as predictive biomarkers, although they fail in consistency, applicability, or reliability to precisely identify the responding patients mainly because of their spatial intratumoral heterogeneity. Therefore, new biomarkers for early prediction of patient response to immunotherapy, that could integrate several approaches, are eagerly sought. Novel methods of quantitative image analysis (such as radiomics or pathomics) might offer a comprehensive approach providing spatial and temporal information from macroscopic imaging features potentially predictive of underlying molecular drivers, tumor-immune microenvironment, tumor-related prognosis, and clinical outcome (in terms of response or toxicity) following immunotherapy. Preliminary results from radiomics and pathomics analysis have demonstrated their ability to correlate image features with PD-L1 tumor expression, high CD3 cell infiltration or CD8 cell expression, or to produce an image signature concordant with gene expression. Furthermore, the predictive power of radiomics and pathomics can be improved by combining information from other modalities, such as blood values or molecular features, leading to increase the accuracy of these models. Thus, “digital biopsy,” which could be defined by non-invasive and non-consuming digital techniques provided by radiomics and pathomics, may have the potential to allow for personalized approach for cancer patients treated with immunotherapy.

Clinical impact of variability on CT radiomics and suggestions for suitable feature selection: a focus on lung cancer

Background

Radiomics suffers from feature reproducibility. We studied the variability of radiomics features and the relationship of radiomics features with tumor size and shape to determine guidelines for optimal radiomics study.

Methods

We dealt with 260 lung nodules (180 for training, 80 for testing) limited to 2 cm or less. We quantified how voxel geometry (isotropic/anisotropic) and the number of histogram bins, factors commonly adjusted in multi-center studies, affect reproducibility. First, features showing high reproducibility between the original and isotropic transformed voxel settings were identified. Second, features showing high reproducibility in various binning settings were identified. Two hundred fifty-two features were computed and features with high intra-correlation coefficient were selected. Features that explained nodule status (benign/malignant) were retained using the least absolute shrinkage selector operator. Common features among different settings were identified, and the final features showing high reproducibility correlated with nodule status were identified. The identified features were used for the random forest classifier to validate the effectiveness of the features. The properties of the uncalculated feature were inspected to suggest a tentative guideline for radiomics studies.

Results

Nine features showing high reproducibility for both the original and isotropic voxel settings were selected and used to classify nodule status (AUC 0.659–0.697). Five features showing high reproducibility among different binning settings were selected and used in classification (AUC 0.729–0.748). Some texture features are likely to be successfully computed if a nodule was larger than 1000 mm³.

Conclusions

Features showing high reproducibility among different settings correlated with nodule status were identified.

Electronic supplementary material

The online version of this article (10.1186/s40644-019-0239-z) contains supplementary material, which is available to authorized users.

The Mitochondrion as an Emerging Therapeutic Target in Cancer

Mitochondria have emerged as important pharmacological targets because of their key role in cellular proliferation and death. In tumor tissues, mitochondria can switch metabolic phenotypes to meet the challenges of high energy demand and macromolecular synthesis. Furthermore, mitochondria can engage in crosstalk with the tumor microenvironment, and signals from cancer-associated fibroblasts can impinge on mitochondria. Cancer cells can also acquire a hybrid phenotype in which both glycolysis and oxidative phosphorylation (OXPHOS) can be utilized. This hybrid phenotype can facilitate metabolic plasticity of cancer cells more specifically in metastasis and therapy-resistance. In light of the metabolic heterogeneity and plasticity of cancer cells that had until recently remained unappreciated, strategies targeting cancer metabolic dependency appear to be promising in the development of novel and effective cancer therapeutics.

Small Cell Lung Cancer Therapeutic Responses Through Fractal Measurements: From Radiology to Mitochondrial Biology

Small cell lung cancer (SCLC) is an aggressive neuroendocrine disease with an overall 5 year survival rate of ~7%. Although patients tend to respond initially to therapy, therapy-resistant disease inevitably emerges. Unfortunately, there are no validated biomarkers for early-stage SCLC to aid in early detection. Here, we used readouts of lesion image characteristics and cancer morphology that were based on fractal geometry, namely fractal dimension (FD) and lacunarity (LC), as novel biomarkers for SCLC. Scanned tumors of patients before treatment had a high FD and a low LC compared to post treatment, and this effect was reversed after treatment, suggesting that these measurements reflect the initial conditions of the tumor, its growth rate, and the condition of the lung. Fractal analysis of mitochondrial morphology showed that cisplatin-treated cells showed a discernibly decreased LC and an increased FD, as compared with control. However, treatment with mdivi-1, the small molecule that attenuates mitochondrial division, was associated with an increase in FD as compared with control. These data correlated well with the altered metabolic functions of the mitochondria in the diseased state, suggesting that morphological changes in the mitochondria predicate the tumor’s future ability for mitogenesis and motogenesis, which was also observed on the CT scan images. Taken together, FD and LC present ideal tools to differentiate normal tissue from malignant SCLC tissue as a potential diagnostic biomarker for SCLC.

Airway morphology and inspiratory flow features in the early stages of Chronic Obstructive Pulmonary Disease

BACKGROUND

Chronic Obstructive Pulmonary Disease (COPD) is among the leading causes of death worldwide. Inhaled pollutants are the prime risk factor, but the pathogenesis and progression of the diseased is poorly understood. Most studies on the disease onset and trajectory have focused on genetic and molecular biomarkers. Here we investigate the role of the airway anatomy and the consequent respiratory fluid mechanics on the development of COPD.

METHODS

We segmented CT scans from a five-year longitudinal study in three groups of smokers (18 subjects each) having: (i) minimal/mild obstruction at baseline with declining lung function at year five; (ii) minimal/mild obstruction at baseline with stable function, and (iii) normal and stable lung function over the five year period. We reconstructed the bronchial trees up to the 7th generation, and for one subject in each group we performed MRI velocimetry in 3D printed models.

FINDINGS

The subjects with airflow obstruction at baseline have smaller airway diameters, smaller child-to-parent diameter ratios, larger length-to-diameter ratios, and smaller fractal dimensions. The differences are more significant for subjects that develop severe decline in pulmonary function. The secondary flows that characterize lateral dispersion along the airways are found to be less intense in the subjects with airflow obstruction.

INTERPRETATION

These results indicate that morphology of the conducting airways and inspiratory flow features are correlated with the status and progression of COPD already at an early stage of the disease. This suggests that imaging-based biomarkers may allow a pre-symptomatic diagnosis of disease progression.

PEST-containing nuclear protein regulates cell proliferation, migration, and invasion in lung adenocarcinoma

Lung cancer is the leading cause of cancer-related mortality worldwide. PEST-containing nuclear protein (PCNP) has been found in the nucleus of cancer cells. Whether PCNP plays a role in the growth of lung adenocarcinoma is still unknown. In the present study, the results indicated that the level of PCNP in lung adenocarcinoma tissue was significantly higher than that in corresponding adjacent non-tumor tissue. Over-expression of PCNP promoted the proliferation, migration, and invasion of lung adenocarcinoma cells, while down-regulation of PCNP exhibited opposite effects. PCNP over-expression decreased apoptosis through up-regulating the expression levels of phospho (p)-signal transducers and activators of transcription (STAT) 3 and p-STAT5 in lung adenocarcinoma cells, whereas PCNP knockdown showed opposite trends. PCNP overexpression enhanced autophagy by increasing the expression levels of p-phosphatidylinositol 3-kinase (PI3K), p-Akt, and p-mammalian target of rapamycin (mTOR) in lung adenocarcinoma cells, however an opposite trend was observed in the sh-PCNP group. In addition, overexpression of PCNP showed the tumor-promoting effect on xenografted lung adenocarcinoma, while PCNP knockdown reduced the growth of lung adenocarcinoma via regulating angiogenesis. Our study elucidates that PCNP can regulate the procession of human lung adenocarcinoma cells via STAT3/5 and PI3K/Akt/mTOR signaling pathways. PCNP may be considered as a promising biomarker for the diagnosis and prognosis in patients with lung adenocarcinoma. Furthermore, PCNP can be a novel therapeutic target and potent PCNP inhibitors can be designed and developed in the treatment of lung adenocarcinoma.

Nr5a2 promotes cancer stem cell properties and tumorigenesis in nonsmall cell lung cancer by regulating Nanog

Lung cancer has the highest mortality rate due to late diagnosis and high incidence of metastasis. Cancer stem cells (CSCs) are a subgroup of cancer cells with self‐renewal capability similar to that of normal stem cells (NSCs). While CSCs may play an important role in cancer progression, mechanisms underlying CSC self‐renewal and the relationship between self‐renewal of the NSCs and CSCs remain elusive. The orphan nuclear receptor Nr5a2 is a transcriptional factor, and a regulator of stemness of embryonic stem cells and induced pluripotent stem cells. However, whether Nr5a2 regulates the self‐renewal of lung CSCs is unknown. Here, we showed the diagnostic and prognostic values of elevated Nr5a2 expression in human lung cancer. We generated the mouse LLC‐SD lung carcinoma CSC cellular model in which Nr5a2 expression was enhanced. Using the LLC‐SD model, through transient and stable siRNA interference of Nr5a2 expression, we provided convincing evidence for a regulatory role of Nr5a2 in the maintenance of lung CSC self‐renewal and stem cell properties in vitro. Further, using the syngeneic and orthotopic lung transplantation model, we elucidated augmented cancer biological properties associated with Nr5a2 promotion of LLC‐SD self‐renewal. More importantly, we revealed that Nr5a2’s regulatory role in promoting LLC‐SD self‐renewal is mediated by transcriptional activation of its direct target Nanog. Taken together, in this study, we have provided convincing evidence in vitro and in vivo demonstrating that Nr5a2 can induce lung CSC properties and promote tumorigenesis and progression through transcriptional up‐regulation of Nanog.

A universal dimensionality function for the fractal dimensions of Laplacian growth

Laplacian growth, associated to the diffusion-limited aggregation (DLA) model or the more general dielectric-breakdown model (DBM), is a fundamental out-of-equilibrium process that generates structures with characteristic fractal/non-fractal morphologies. However, despite diverse numerical and theoretical attempts, a data-consistent description of the fractal dimensions of the mass-distributions of these structures has been missing. Here, an analytical model of the fractal dimensions of the DBM and DLA is provided by means of a recently introduced dimensionality equation for the scaling of clusters undergoing a continuous morphological transition. Particularly, this equation relies on an effective information-function dependent on the Euclidean dimension of the embedding-space and the control parameter of the system. Numerical and theoretical approaches are used in order to determine this information-function for both DLA and DBM. In the latter, a connection to the Rényi entropies and generalized dimensions of the cluster is made, showing that DLA could be considered as the point of maximum information-entropy production along the DBM transition. The results are in good agreement with previous theoretical and numerical estimates for two- and three-dimensional DBM, and high-dimensional DLA. Notably, the DBM dimensions conform to a universal description independently of the initial cluster-configuration and the embedding-space.

Deep Analysis of Mitochondria and Cell Health Using Machine Learning

There is a critical need for better analytical methods to study mitochondria in normal and diseased states. Mitochondrial image analysis is typically done on still images using slow manual methods or automated methods of limited types of features. MitoMo integrated software overcomes these bottlenecks by automating rapid unbiased quantitative analysis of mitochondrial morphology, texture, motion, and morphogenesis and advances machine-learning classification to predict cell health by combining features. Our pixel-based approach for motion analysis evaluates the magnitude and direction of motion of: (1) molecules within mitochondria, (2) individual mitochondria, and (3) distinct morphological classes of mitochondria. MitoMo allows analysis of mitochondrial morphogenesis in time-lapse videos to study early progression of cellular stress. Biological applications are presented including: (1) establishing normal phenotypes of mitochondria in different cell types; (2) quantifying stress-induced mitochondrial hyperfusion in cells treated with an environmental toxicant, (3) tracking morphogenesis in mitochondria undergoing swelling, and (4) evaluating early changes in cell health when morphological abnormalities are not apparent. MitoMo unlocks new information on mitochondrial phenotypes and dynamics by enabling deep analysis of mitochondrial features in any cell type and can be applied to a broad spectrum of research problems in cell biology, drug testing, toxicology, and medicine.

Effect of RIF1 on response of non-small-cell lung cancer patients to platinum-based chemotherapy by regulating MYC signaling pathway

Platinum-based chemotherapy is used as first-line therapy for advanced non-small-cell lung cancer (NSCLC). However, there is no effective indicator to predict whether the patient would be chemo-resistant or sensitive to the therapy. In addition, it is urgent to elucidate the mechanisms of cisplatin resistance. RIF1 plays important roles in DNA replication regulation and DNA repair pathway. However, the role of RIF1 in NSCLC progression and chemotherapy response is still unknown. In this study, we found that RIF1 expression was correlated with the response of NSCLC patients to platinum-based chemotherapy (n=89, P=0.002). Among patients who have been treated with platinum chemo-therapy, those with high levels of RIF1 expression had significantly shorter survival than those with low RIF1 expression (P<0.05). RIF1 knockdown increased sensitivity to cisplatin in NSCLC patients both in vitro and in vivo. Moreover, RIF1 knockdown induced G0/G1 phase arrest and increased cisplatin-induced apoptosis and DNA damage. Further investigation showed that RIF1 regulated the expression of MYC and MYC downstream targets, including the cell cycle and double-stranded break (DSB) repair genes which might mediate the effect of RIF1 on cellular response to cisplatin. Overexpression of MYC could reverse the inhibition of MYC targets by RIF1 knockdown. Taken together, these data revealed that RIF1 played an important role in regulating MYC and MYC-activated genes, which in turn contributes to cellular response to cisplatin and NSCLC patients' response to platinum-based chemotherapy. RIF1 might serve as a novel biomarker for predicting platinum-based chemo-sensitivity and the prognosis of NSCLC patients, so as to guide the chemotherapy regimen adjustment for individual patient with NSCLC and improve their clinical outcomes.

VALUE OF FRACTAL ANALYSIS OF OPTICAL COHERENCE TOMOGRAPHY ANGIOGRAPHY IN VARIOUS STAGES OF DIABETIC RETINOPATHY

PURPOSE

To use fractal dimensional analysis to investigate retinal vascular disease patterns in patients with diabetic retinopathy using spectral domain optical coherence tomography angiography.

METHODS

A retrospective study was conducted which included 49 eyes from 26 control subjects and 58 eyes from 35 patients known to have diabetic retinopathy. Of the 58 eyes with known retinopathy, 31 were categorized as nonproliferative diabetic retinopathy (13 mild, 9 moderate, and 9 severe) and 27 were categorized as proliferative diabetic retinopathy. Optical coherence tomography angiography images were acquired using the RTVue XR Avanti (Optovue, Inc). Automated segmentation was obtained through both the superficial and deep capillary plexuses for each eye. Grayscale optical coherence tomography angiography images were standardized and binarized using ImageJ (National Institutes of Health). Fractal box-counting analyses were conducted using Fractalyse (ThéMA). Fractal dimensions (FDs) and correlation coefficient of the superficial and deep capillary plexuses were compared between control eyes and those in various stages of diabetic retinopathy.

RESULTS

The superficial and deep capillary plexuses from diabetic and control eyes were analyzed. The average FD for diabetic eyes was significantly lower than in control eyes in the superficial plexus (P = 2.4 × 10) and in the deep capillary plexus (P = 1.87 × 10 ) with a more statistically significant difference noted in the deep capillary plexus. When analyzing diabetic patients without edema noted on optical coherence tomography, the FD was significantly reduced in the superficial (P = 0.001) and deep (P = 1.49 × 10) plexuses. When analyzing diabetic patients with edema noted on optical coherence tomography, the FD was significantly reduced in the superficial (P = 2.0 × 10) and deep (P = 1.85 × 10) plexuses.

CONCLUSION

The optical coherence tomography angiography FD is significantly lower in both superficial and deep capillary plexuses in eyes with all stages studied of diabetic retinopathy. The results were more often significant for the deep capillary plexus. The use of fractal analysis provides an objective criterion to assess microvascular disease burden in diabetic retinopathy.

Integer-dimensional fractals of nonlinear dynamics, control mechanisms, and physical implications

Fractal dimensionality is accepted as a measure of complexity for systems that cannot be described by integer dimensions. However, fractal control mechanisms, physical implications, and relations to nonlinear dynamics have not yet been fully clarified. Herein we explore these issues in a spacetime using a nonlinear integrated model derived by applying Newton’s second law into self-regulating systems. We discover that (i) a stochastic stable fixed point exhibits self-similarity and long-term memory, while a deterministic stable fixed point usually only exhibits self-similarity, if our observation scale is large enough; (ii) stochastic/deterministic period cycles and chaos only exhibit long-term memory, but also self-similarity for even restorative delays; (iii) fractal level of a stable fixed point is controlled primarily by the wave indicators that reflect the relative strength of extrinsic to intrinsic forces: a larger absolute slope (smaller amplitude) indicator leads to higher positive dependence (self-similarity), and a relatively large amplitude indicator or an even restorative delay could make the dependence oscillate; and (iv) fractal levels of period cycles and chaos rely on the intrinsic resistance, restoration, and regulative delays. Our findings suggest that fractals of self-regulating systems can be measured by integer dimensions.

Microvascular fractal dimension predicts prognosis and response to chemotherapy in glioblastoma: an automatic image analysis study

The microvascular profile has been included in the WHO glioma grading criteria. Nevertheless, microvessels in gliomas of the same WHO grade, e.g., WHO IV glioblastoma (GBM), exhibit heterogeneous and polymorphic morphology, whose possible clinical significance remains to be determined. In this study, we employed a fractal geometry-derived parameter, microvascular fractal dimension (mvFD), to quantify microvessel complexity and developed a home-made macro in Image J software to automatically determine mvFD from the microvessel-stained immunohistochemical images of GBM. We found that mvFD effectively quantified the morphological complexity of GBM microvasculature. Furthermore, high mvFD favored the survival of GBM patients as an independent prognostic indicator and predicted a better response to chemotherapy of GBM patients. When investigating the underlying relations between mvFD and tumor growth by deploying Ki67/mvFD as an index for microvasculature-normalized tumor proliferation, we discovered an inverse correlation between mvFD and Ki67/mvFD. Furthermore, mvFD inversely correlated with the expressions of a glycolytic marker, LDHA, which indicated poor prognosis of GBM patients. Conclusively, we developed an automatic approach for mvFD measurement, and demonstrated that mvFD could predict the prognosis and response to chemotherapy of GBM patients.

Use of a Radiomics Model to Predict Tumor Invasiveness of Pulmonary Adenocarcinomas Appearing as Pulmonary Ground-Glass Nodules

Background

It is important to distinguish the classification of lung adenocarcinoma. A radiomics model was developed to predict tumor invasiveness using quantitative and qualitative features of pulmonary ground-glass nodules (GGNs) on chest CT.

Materials and Methods

A total of 599 GGNs [including 202 preinvasive lesions and 397 minimally invasive and invasive pulmonary adenocarcinomas (IPAs)] were evaluated using univariate, multivariate, and logistic regression analyses to construct a radiomics model that predicted invasiveness of GGNs. In primary cohort (comprised of patients scanned from August 2012 to July 2016), preinvasive lesions were distinguished from IPAs based on pure or mixed density (PM), lesion shape, lobulated border, and pleural retraction and 35 other quantitative parameters (P <0.05) using univariate analysis. Multivariate analysis showed that PM, lobulated border, pleural retraction, age, and fractal dimension (FD) were significantly different between preinvasive lesions and IPAs. After logistic regression analysis, PM and FD were used to develop a prediction nomogram. The validation cohort was comprised of patients scanned after Jan 2016.

Results

The model showed good discrimination between preinvasive lesions and IPAs with an area under curve (AUC) of 0.76 [95% CI: 0.71 to 0.80] in ROC curve for the primary cohort. The nomogram also demonstrated good discrimination in the validation cohort with an AUC of 0.79 [95% CI: 0.71 to 0.88].

Conclusions

For GGNs, PM, lobulated border, pleural retraction, age, and FD were features discriminating preinvasive lesions from IPAs. The radiomics model based upon PM and FD may predict the invasiveness of pulmonary adenocarcinomas appearing as GGNs.

Generalized time-independent correlation transport equation with static background: influence of anomalous transport on the field autocorrelation function

A generalized time-independent correlation transport equation (GCTE) is proposed for the field autocorrelation function. The GCTE generalizes various models for anomalous transport of photons and takes into account the possible presence of a static background. In a tutorial example, the GCTE is solved for a homogeneous semi-infinite medium in reflectance configuration through Monte Carlo simulations. The chosen anomalous photon transport model also includes the classic and the "generalized" Lambert-Beer's law (depending on the choice of parameters). A numerical algorithm allowing generation of the related anomalous random photon steps is also given. The clear influence of anomalous transport on the field autocorrelation function is shown and discussed for the proposed specific examples by comparing the general results with the classical case (Lambert-Beer's law).

Shell feature: a new radiomics descriptor for predicting distant failure after radiotherapy in non-small cell lung cancer and cervix cancer

Distant failure is the main cause of human cancer-related mortalities. To develop a model for predicting distant failure in non-small cell lung cancer (NSCLC) and cervix cancer (CC) patients, a shell feature, consisting of outer voxels around the tumor boundary, was constructed using pre-treatment positron emission tomography (PET) images from 48 NSCLC patients received stereotactic body radiation therapy and 52 CC patients underwent external beam radiation therapy and concurrent chemotherapy followed with high-dose-rate intracavitary brachytherapy. The hypothesis behind this feature is that non-invasive and invasive tumors may have different morphologic patterns in the tumor periphery, in turn reflecting the differences in radiological presentations in the PET images. The utility of the shell was evaluated by the support vector machine classifier in comparison with intensity, geometry, gray level co-occurrence matrix-based texture, neighborhood gray tone difference matrix-based texture, and a combination of these four features. The results were assessed in terms of accuracy, sensitivity, specificity, and AUC. Collectively, the shell feature showed better predictive performance than all the other features for distant failure prediction in both NSCLC and CC cohorts.

Elastic Backbone Defines a New Transition in the Percolation Model

The elastic backbone is the set of all shortest paths. We found a new phase transition at p_{eb} above the classical percolation threshold at which the elastic backbone becomes dense. At this transition in 2D, its fractal dimension is 1.750±0.003, and one obtains a novel set of critical exponents β_{eb}=0.50±0.02, γ_{eb}=1.97±0.05, and ν_{eb}=2.00±0.02, fulfilling consistent critical scaling laws. Interestingly, however, the hyperscaling relation is violated. Using Binder's cumulant, we determine, with high precision, the critical probabilities p_{eb} for the triangular and tilted square lattice for site and bond percolation. This transition describes a sudden rigidification as a function of density when stretching a damaged tissue.

Renal-Clearable Hollow Bismuth Subcarbonate Nanotubes for Tumor Targeted Computed Tomography Imaging and Chemoradiotherapy

Although metallic nanomaterials with high X-ray attenuation coefficients have been widely used as X-ray computed tomography (CT) contrast agents, their intrinsically poor biodegradability requires them to be cleared from the body to avoid any potential toxicity. On the other hand, extremely small-sized nanomaterials with outstanding renal clearance properties are not much effective for tumor targeting because of their too rapid clearance in vivo. To overcome this dilemma, here we report on the hollow bismuth subcarbonate nanotubes (BNTs) assembled from renal-clearable ultrasmall bismuth subcarbonate nanoclusters for tumor-targeted imaging and chemoradiotherapy. The BNTs could be targeted to tumors with high efficiency and exhibit a high CT contrast effect. Moreover, simultaneous radio- and chemotherapy using drug-loaded BNTs could significantly suppress tumor volumes, highlighting their potential application in CT imaging-guided therapy. Importantly, the elongated nanotubes could be disassembled into isolated small nanoclusters in the acidic tumor microenvironment, accelerating the payload release and kidney excretion. Such body clearable CT contrast agent with high imaging performance and multiple therapeutic functions shall have a substantial potential for biomedical applications.

Radiomic features of pretreatment MRI could identify T stage in patients with rectal cancer: Preliminary findings

BACKGROUND

Recent studies have shown that magnetic resonance (MR) radiomic analysis is feasible and has some value in identifying tumor characteristics, but there are few data regarding the role of MR-based radiomic features in rectal cancer.

PURPOSE

The aim of this study was to determine whether radiomic features extracted from T2 -weighted imaging (T2 WI) can identify pathological features in rectal cancer.

STUDY TYPE

Retrospective study.

POPULATION/SUBJECTS

A cohort comprising 119 rectal cancer patients who underwent surgery between January 2015 and November 2016.

FIELD STRENGTH/SEQUENCE

3.0T, axial high-resolution T2 -weighted turbo spin echo (TSE) sequence.

ASSESSMENT

Patients were classified according to pathological features such as T stage, N stage, perineural invasion, histological grade, lymph-vascular invasion, tumor deposits, and circumferential resection margin (CRM). The whole tumor volume (WTV) was distinguished, and segments were quantified on axial high-resolution T2 WI by a radiologist. A total of 256 radiomic features were extracted.

STATISTICAL TESTS

To achieve reliable results, cluster analysis and least absolute shrinkage and selection operator (LASSO) were implemented. In the cluster analysis, the patients were divided into two groups, and chi-square tests were performed to investigate the relationship between the pathological features and the radiomic-based clusters. The area under the curve (AUC) was calculated to evaluate the predictability of the model in the LASSO analysis.

RESULTS

The cluster results revealed that patients could be stratified into two groups, and the chi-square test results indicated that the pT stage was correlated with the radiomic feature cluster results (P = 0.002). The prediction model AUC for the diagnostic T stage was 0.852 (95% confidence interval: 0.677-1; sensitivity: 79.0%, specificity: 82.0%).

DATA CONCLUSION

The use of MRI-derived radiomic features to identify the T stage is feasible in rectal cancer.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018.

Fractal analysis of plaque border, a novel method for the quantification of atherosclerotic plaque contour irregularity, is associated with pro-atherogenic plasma lipid profile in subjects with non-obstructive carotid stenoses

BACKGROUND AND AIMS

Plaque border irregularity is a known imaging characteristic of vulnerable plaques, but its evaluation heavily relies on subjective evaluation and operator expertise. Aim of the present work is to propose a novel fractal-analysis based method for the quantification of atherosclerotic plaque border irregularity and assess its relation with cardiovascular risk factors.

METHODS AND RESULTS

Forty-two asymptomatic subjects with carotid stenosis underwent ultrasound evaluation and assessment of cardiovascular risk factors. Total, low-density lipoprotein (LDL), high-density lipoprotein (HDL) plasma cholesterol and triglycerides concentrations were measured for each subject. Fractal analysis was performed in all the carotid segments affected by atherosclerosis, i.e. 147 segments. The resulting fractal dimension (FD) is a measure of irregularity of plaque profile on long axis view of the plaque. FD in the severest stenosis (main plaque FD,mFD) was 1.136±0.039. Average FD per patient (global FD,gFD) was 1.145±0.039. FD was independent of other plaque characteristics. mFD significantly correlated with plasma HDL (r = -0.367,p = 0.02) and triglycerides-to-HDL ratio (r = 0.480,p = 0.002).

CONCLUSIONS

Fractal analysis is a novel, readily available, reproducible and inexpensive technique for the quantitative measurement of plaque irregularity. The correlation between low HDL levels and plaque FD suggests a role for HDL in the acquisition of morphologic features of plaque instability. Further studies are needed to validate the prognostic value of fractal analysis in carotid plaques evaluation.

Marginally compact fractal trees with semiflexibility

We study marginally compact macromolecular trees that are created by means of two different fractal generators. In doing so, we assume Gaussian statistics for the vectors connecting nodes of the trees. Moreover, we introduce bond-bond correlations that make the trees locally semiflexible. The symmetry of the structures allows an iterative construction of full sets of eigenmodes (notwithstanding the additional interactions that are present due to semiflexibility constraints), enabling us to get physical insights about the trees' behavior and to consider larger structures. Due to the local stiffness, the self-contact density gets drastically reduced.

CT Radiomics in Thoracic Oncology: Technique and Clinical Applications

Precision medicine offers better treatment options and improved survival for cancer patients based on individual variability. As the success of precision medicine depends on robust biomarkers, the requirement for improved imaging biomarkers that reflect tumor biology has grown exponentially. Radiomics, the field of study in which high-throughput data are generated and large amounts of advanced quantitative features are extracted from medical images, has shown great potential as a source of quantitative biomarkers in the field of oncology. Radiomics provides quantitative information about the morphology, texture, and intratumoral heterogeneity of the tumor itself as well as features related to pulmonary function. Hence, radiomics data can be used to build descriptive and predictive clinical models that relate imaging characteristics to tumor biology phenotypes. In this review, we describe the workflow of CT radiomics, types of CT radiomics, and its clinical application in thoracic oncology.

Symmetry and symmetry breaking in cancer: a foundational approach to the cancer problem

Symmetry and symmetry breaking concepts from physics and biology are applied to the problem of cancer. Three categories of symmetry breaking in cancer are examined: combinatorial, geometric, and functional. Within these categories, symmetry breaking is examined for relevant cancer features, including epithelial-mesenchymal transition (EMT); tumor heterogeneity; tensegrity; fractal geometric and information structure; functional interaction networks; and network stabilizability and attack tolerance. The new cancer symmetry concepts are relevant to homeostasis loss in cancer and to its origin, spread, treatment and resistance. Symmetry and symmetry breaking could provide a new way of thinking and a pathway to a solution of the cancer problem.

Morphological and functional properties of the conducting human airways investigated by in vivo computed tomography and in vitro MRI

The accurate representation of the human airway anatomy is crucial for understanding and modeling the structure-function relationship in both healthy and diseased lungs. The present knowledge in this area is based on morphometric studies of excised lung casts, partially complemented by in vivo studies in which computed tomography (CT) was used on a small number of subjects. In the present study, we analyzed CT scans of a cohort of healthy subjects and obtained comprehensive morphometric information down to the seventh generation of bronchial branching, including airway diameter, length, branching angle, and rotation angle. Although some of the geometric parameters (such as the child-to-parent branch diameter ratio) are found to be in line with accepted values, for others (such as the branch length-to-diameter ratio) our findings challenge the common assumptions. We also evaluated several metrics of self-similarity, including the fractal dimension of the airway tree. Additionally, we used phase-contrast magnetic resonance imaging (MRI) to obtain the volumetric flow field in the three-dimensional-printed airway model of one of the subjects during steady inhalation. This is used to relate structural and functional parameters and, in particular, to close the power-law relationship between branch flow rate and diameter. The diameter exponent is found to be significantly lower than in the usually assumed Poiseuille regime, which we attribute to the strong secondary (i.e., transverse) velocity component. The strength of the secondary velocity with respect to the axial component exceeds the levels found in idealized airway models and persists within the first seven generations. NEW & NOTEWORTHY We performed a comprehensive computed tomography-based study of the conductive airway morphology in normal human subjects, including branch diameter, length, and mutual angles. We found significant departure from classic homothetic relationships. We also carried out MRI measurements of the three-dimensional inspiratory flow in an anatomy-based model and directly assessed structure-function relationships that have so far been assumed. We found that strong secondary flows (i.e., transverse velocity components) persist through the first seven generations of bronchial branching.

Relationship between necrotic patterns in glioblastoma and patient survival: fractal dimension and lacunarity analyses using magnetic resonance imaging

Necrosis is a hallmark feature of glioblastoma (GBM). This study investigated the prognostic role of necrotic patterns in GBM using fractal dimension (FD) and lacunarity analyses of magnetic resonance imaging (MRI) data and evaluated the role of lacunarity in the biological processes leading to necrosis. We retrospectively reviewed clinical and MRI data of 95 patients with GBM. FD and lacunarity of the necrosis on MRI were calculated by fractal analysis and subjected to survival analysis. We also performed gene ontology analysis in 32 patients with available RNA-seq data. Univariate analysis revealed that FD < 1.56 and lacunarity > 0.46 significantly correlated with poor progression-free survival (p = 0.006 and p = 0.012, respectively) and overall survival (p = 0.008 and p = 0.005, respectively). Multivariate analysis revealed that both parameters were independent factors for unfavorable progression-free survival (p = 0.001 and p = 0.015, respectively) and overall survival (p = 0.002 and p = 0.007, respectively). Gene ontology analysis revealed that genes positively correlated with lacunarity were involved in the suppression of apoptosis and necrosis-associated biological processes. We demonstrate that the fractal parameters of necrosis in GBM can predict patient survival and are associated with the biological processes of tumor necrosis.

Universal fractality of morphological transitions in stochastic growth processes

Stochastic growth processes give rise to diverse and intricate structures everywhere in nature, often referred to as fractals. In general, these complex structures reflect the non-trivial competition among the interactions that generate them. In particular, the paradigmatic Laplacian-growth model exhibits a characteristic fractal to non-fractal morphological transition as the non-linear effects of its growth dynamics increase. So far, a complete scaling theory for this type of transitions, as well as a general analytical description for their fractal dimensions have been lacking. In this work, we show that despite the enormous variety of shapes, these morphological transitions have clear universal scaling characteristics. Using a statistical approach to fundamental particle-cluster aggregation, we introduce two non-trivial fractal to non-fractal transitions that capture all the main features of fractal growth. By analyzing the respective clusters, in addition to constructing a dynamical model for their fractal dimension, we show that they are well described by a general dimensionality function regardless of their space symmetry-breaking mechanism, including the Laplacian case itself. Moreover, under the appropriate variable transformation this description is universal, i.e., independent of the transition dynamics, the initial cluster configuration, and the embedding Euclidean space.

Early and delayed evaluation of solid tumours with 64Cu-ATSM PET/CT: a pilot study on semiquantitative and computer-aided fractal geometry analysis

OBJECTIVE

The aim of this study was to analyse early and delayed acquisition on copper-64 diacetyl-bisN4-methylthiosemicarbazone (Cu-ATSM) PET/CT in a small cohort of patients by comparing semiquantitative and computer-aided fractal geometry analyses.

PATIENTS AND METHODS

Five cancer patients, including non-small-cell lung cancer and head and neck cancer, were investigated with Cu-ATSM PET/CT. Participants received an intravenous injection of Cu-ATSM according to body size and were imaged 60 min (early) and 16 h (delayed) later on hybrid PET/CT. Reconstructed images were visualized on advanced workstations for the definition of semiquantitative parameters: standardized uptake value (SUV)max, SUVratio-to-muscle, SUVmean, hypoxic volume (HV) and hypoxic burden (HB=HV×SUVmean). DICOM data retrieved from both scans were analysed using an ad-hoc computer program to determine the mean intensity value, SD, relative dispersion, three-dimensional histogram fractal dimension and three-dimensional fractal dimension.

RESULTS

All tumour lesions showed increased uptake of Cu-ATSM at early evaluation, with a median SUVratio-to-muscle of 4.42 (range: 1.58-5.62), a median SUVmax of 5.3 (range: 1.9-7.3), a median SUVmean of 2.8 (range: 1.5-3.9), a median HV of 41.6 cm (range: 2.8-453.7) and a median HB of 161.5 cm (range: 4.4-1112.5). All semiquantitative data obtained at 1 h were consistent with the parameters obtained on delayed imaging (P>0.05). A borderline statistically significant difference was found only for SUVmax of the muscle (P=0.045). Fractal geometry analysis on DICOM images showed that all parameters at early imaging showed no statistically significant difference with late acquisition (P>0.05).

CONCLUSION

Our findings support the consistency of Cu-ATSM PET/CT images obtained at early and delayed acquisition for the assessment of tumour lesions.

Fourier transform infrared spectroscopy microscopic imaging classification based on multifractal methods

Multifractal analysis (MFA) based on generalized concepts of fractals has been applied to biological tissues composed of complex structures. In this paper, a new MFA methodology based on the neighborhood spatial correlation (NSC) is proposed for an extracting texture feature. NSC is used to extract spatial features, and the obtained spatial features are combined with spectral features of characteristic absorption peaks (CAPs) to promote more feature information. This spatial-spectral structure is used as a feature to differentiate cholesterol from Fourier transform infrared spectroscopy microscopic imaging of a rabbit artery by a support vector machine classifier. The dataset was collected between 4000 and 720  cm^-1 on rabbit arteries as research objects. The experimental results show that the accuracy of the proposed spatial-spectral structure is higher than that of other multivariate analysis methods (PCA and 2DPCA). The NSC method, compared to the bottom interface method, new bottom interface method, variance method multi-weight method, and neighborhood spatial correlation method, could effectively reduce the influence of speckle noise, and the convergence rate of the weight factor q is not increased.

Tumor Heterogeneity and Therapeutic Resistance

The rapidly changing landscape of oncology has brought new light, and with it, new challenges to optimizing therapeutic strategies for patients. Although the concept of patient heterogeneity is well known to any practicing clinician, a more detailed understanding of the biologic changes that underscore the clinical picture is beginning to emerge. Thus, tumor heterogeneity has come to encompass more than just the clinical picture and can represent both intratumor and intertumor differences. Within the fields of thoracic oncology and melanoma, the discovery of key molecular drivers has resulted in landmark breakthroughs in therapy. However, the complexities of tumor genetics and the interaction within the environment continue to drive the search for better therapies. Ongoing challenges include the accurate and timely assessment of genetic changes as well as the development of resistance and the resultant compensatory mechanisms. Novel technologies, including commercially available next-generation sequencing, have allowed for a greater breadth and depth of information to be gained from a single pathologic specimen, and it is now being incorporated into routine clinical practice. Translational advances have subsequently provided valuable insight into mechanisms of resistance, with the development of novel treatment strategies. Future work will focus on novel diagnostic techniques and adaptive mechanisms that can ultimately drive the development of the next generation of cancer therapy.

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

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 "α_max" -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.

INO80 is required for oncogenic transcription and tumor growth in non-small cell lung cancer

Epigenetic regulators are attractive targets for the development of new cancer therapies. Among them, the ATP-dependent chromatin remodeling complexes control the chromatin architecture and have important roles in gene regulation. They are often found to be mutated and de-regulated in cancers, but how they influence the cancer gene expression program during cancer initiation and progression is not fully understood. Here we show that the INO80 chromatin remodeling complex is required for oncogenic transcription and tumor growth in non-small-cell lung cancer (NSCLC). Ino80, the SWI/SNF ATPase in the complex, is highly expressed in NSCLC cells compared with normal lung epithelia cells. Further, its expression, as well as that of another subunit Ino80B, negatively correlates with disease prognosis in lung cancer patients. Functionally, INO80 silencing inhibits NSCLC cell proliferation and anchorage-independent growth in vitro and tumor formation in mouse xenografts. It occupies enhancer regions near lung cancer-associated genes, and its occupancy correlates with increased genome accessibility and enhanced expression of downstream genes. Together, our study defines a critical role of INO80 in promoting oncogenic transcription and NSCLC tumorigenesis, and reveals a potential treatment strategy for inhibiting the cancer transcription network by targeting the INO80 chromatin remodeling complex.

Radiomics and its emerging role in lung cancer research, imaging biomarkers and clinical management: State of the art

With the development of functional imaging modalities we now have the ability to study the microenvironment of lung cancer and its genomic instability. Radiomics is defined as the use of automated or semi-automated post-processing and analysis of large amounts of quantitative imaging features that can be derived from medical images. The automated generation of these analytical features helps to quantify a number of variables in the imaging assessment of lung malignancy. These imaging features include: tumor spatial complexity, elucidation of the tumor genomic heterogeneity and composition, subregional identification in terms of tumor viability or aggressiveness, and response to chemotherapy and/or radiation. Therefore, a radiomic approach can help to reveal unique information about tumor behavior. Currently available radiomic features can be divided into four major classes: (a) morphological, (b) statistical, (c) regional, and (d) model-based. Each category yields quantitative parameters that reflect specific aspects of a tumor. The major challenge is to integrate radiomic data with clinical, pathological, and genomic information to decode the different types of tissue biology. There are many currently available radiomic studies on lung cancer for which there is a need to summarize the current state of the art.