Identification and Analysis of Imaging-Genomic Signatures to Study Recurrence in Breast Cancers.

One of the main causes of breast cancer related death is its recurrence. In this study, we investigate the association of gene expression and pathological image features to understand breast cancer recurrence. A total of 172 breast cancer patient data was downloaded from the TCGA-BRCA database. The dataset contained diagnostic whole slide images and RNA-seq data of 80 recurrent and 92 disease-free breast cancer patients. We performed genomic analysis on RNA-seq data to obtain the hub genes related to recurrent breast cancer. We extracted relevant pathomic features from histopathology images. The discriminative ability of the hub genes and pathomic features were evaluated using machine learning classifiers. We used Spearman rank correlation analysis to find statistically significant association between gene expression and pathomic features. We identified that, genes which are related to breast cancer progression is significantly associated (adjusted p-value<0.05) with several pathomic features.Clinical Relevance- Histopathology is the gold standard for cancer detection. It provides us with cellular level information. A strong association between a pathomic feature and a gene expression will help clinicians understand the cellular and molecular mechanism of cancer for better prognosis.

Identification of hub genes to determine drug-disease correlation in breast carcinomas.

The exact molecular mechanism underlying the heterogeneous drug response against breast carcinoma remains to be fully understood. It is urgently required to identify key genes that are intricately associated with varied clinical response of standard anti-cancer drugs, clinically used to treat breast cancer patients. In the present study, the utility of transcriptomic data of breast cancer patients in discerning the clinical drug response using machine learning-based approaches were evaluated. Here, a computational framework has been developed which can be used to identify key genes that can be linked with clinical drug response and progression of cancer, offering an immense opportunity to predict potential prognostic biomarkers and therapeutic targets. The framework concerned utilizes DeSeq2, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Cytoscape, and machine learning techniques to find these crucial genes. Total RNA extraction and qRT-PCR were performed to quantify relative expression of few hub genes selected from the networks. In our study, we have experimentally checked the expression of few key hub genes like APOA2, DLX5, APOC3, CAMK2B, and PAK6 that were predicted to play an immense role in breast cancer tumorigenesis and progression in response to anti-cancer drug Paclitaxel. However, further experimental validations will be required to get mechanistic insights of these genes in regulating the drug response and cancer progression which will likely to play pivotal role in cancer treatment and precision oncology.

Ventricle shape analysis using modified WKS for atrophy detection.

Brain ventricle is one of the biomarkers for detecting neurological disorders. Studying the shape of the ventricles will aid in the diagnosis process of atrophy and other CSF-related neurological disorders, as ventricles are filled with CSF. This paper introduces a spectral analysis algorithm based on wave kernel signature. This shape signature was used for studying the shape of segmented ventricles from the brain images. Based on the shape signature, the study groups were classified as normal subjects and atrophy subjects. The proposed algorithm is simple, effective, automated, and less time consuming. The proposed method performed better than the other methods heat kernel signature, scale invariant heat kernel signature, wave kernel signature, and spectral graph wavelet signature, which were used for validation purpose, by producing 94-95% classification accuracy by classifying normal and atrophy subjects correctly for CT, MR, and OASIS datasets.

Modified distance regularized level set evolution for brain ventricles segmentation.

Neurodegenerative disorders are commonly characterized by atrophy of the brain which is caused by neuronal loss. Ventricles are one of the prominent structures in the brain; their shape changes, due to their content, the cerebrospinal fluid. Analyzing the morphological changes of ventricles, aids in the diagnosis of atrophy, for which the region of interest needs to be separated from the background. This study presents a modified distance regularized level set evolution segmentation method, incorporating regional intensity information. The proposed method is implemented for segmenting ventricles from brain images for normal and atrophy subjects of magnetic resonance imaging and computed tomography images. Results of the proposed method were compared with ground truth images and produced sensitivity in the range of 65%-90%, specificity in the range of 98%-99%, and accuracy in the range of 95%-98%. Peak signal to noise ratio and structural similarity index were also used as performance measures for determining segmentation accuracy: 95% and 0.95, respectively. The parameters of level set formulation vary for different datasets. An optimization procedure was followed to fine tune parameters. The proposed method was found to be efficient and robust against noisy images. The proposed method is adaptive and multimodal.

Wavelet Transform and Texton based Analysis for Detection of Benign and Malignant Masses.

Cancer has affected the human community to a large extent due to its low survival rate towards the end stage of the disease. It is asymptomatic in many cases during the initial stage. Thus the dependency on early diagnosis and regular check up increases manifold. Computer Aided Diagnostic Model is the need of the hour which will increase the diagnostic efficiency. A total of 400 images acquired from the Digital Database for Screening Mammography have been used here for analysis. This paper proposes a novel technique to differentiate benign and malignant breast lesions in mammograms using multiresolution analysis and Schmid Filter Bank, which were not reported earlier. A three level Haar wavelet decomposed image(L1, L2, L3) is obtained for each Region of Interest. In each level Texton based analysis is further investigated through Schmid filter bank. Statistical features and Haralick's Features are obtained from filter response and Gray Level Cooccurence Matrix respectively. Partition Membership Filter is further applied to the feature matrix for feature partitioning. The method shows maximum accuracy of 98.63% and Area under Curve of 0.981 using Random Forest Classifier and ten fold cross validation.

Efficient automated detection of mitotic cells from breast histological images using deep convolution neutral network with wavelet decomposed patches.

In medical practice, the mitotic cell count from histological images acts as a proliferative marker for cancer diagnosis. Therefore, an accurate method for detecting mitotic cells in histological images is essential for cancer screening. Manual evaluation of clinically relevant image features that might reflect mitotic cells in histological images is time-consuming and error prone, due to the heterogeneous physical characteristics of mitotic cells. Computer-assisted automated detection of mitotic cells could overcome these limitations of manual analysis and act as a useful tool for pathologists to make cancer diagnoses efficiently and accurately. Here, we propose a new approach for mitotic cell detection in breast histological images that uses a deep convolution neural network (CNN) with wavelet decomposed image patches. In this approach, raw image patches of 81 × 81 pixels are decomposed to patches of 21 × 21 pixels using Haar wavelet and subsequently used in developing a deep CNN model for automated detection of mitotic cells. The decomposition step reduces convolution time for mitotic cell detection relative to the use of raw image patches in conventional CNN models. The proposed deep network was tested using the MITOS (ICPR2012) and MITOS-ATYPIA-14 breast cancer histological datasets and shown to outperform existing algorithms for mitotic cell detection. Overall, our method improves the performance and reduces the computational burden of conventional deep CNN approaches for mitotic cell detection.

Automatic identification of clinically relevant regions from oral tissue histological images for oral squamous cell carcinoma diagnosis.

Identification of various constituent layers such as epithelial, subepithelial, and keratin of oral mucosa and characterization of keratin pearls within keratin region as well, are the important and mandatory tasks for clinicians during the diagnosis of different stages in oral cancer (such as precancerous and cancerous). The architectural variations of epithelial layers and the presence of keratin pearls, which can be observed in microscopic images, are the key visual features in oral cancer diagnosis. The computer aided tool doing the same identification task would certainly provide crucial aid to clinicians for evaluation of histological images during diagnosis. In this paper, a two-stage approach is proposed for computing oral histology images, where 12-layered (7 × 7×3 channel patches) deep convolution neural network (CNN) are used for segmentation of constituent layers in the first stage and in the second stage the keratin pearls are detected from the segmented keratin regions using texture-based feature (Gabor filter) trained random forests. The performance of the proposed computing algorithm is tested in our developed oral cancer microscopic image database. The proposed texture-based random forest classifier has achieved 96.88% detection accuracy for detection of keratin pearls.

Modulation-demodulation hypothesis of periodic breathing in human respiration.

Periodic breathing (PB) is a diseased condition of the cardiorespiratory system, and mathematically it is modelled as an oscillation. Modeling approaches replicate periodic oscillation in the minute ventilation due to a higher than normal gain of the feedback signals from the chemoreceptors coupled with a longer than normal latency in feedback, and do not consider the waxing-waning pattern of the oronasal airflow. In this work, a noted regulation model is extended by integrating respiratory mechanics and respiratory central pattern generator (rCPG) model, using modulation-demodulation1 hypothesis. This is a top-down modeling approach, and it is assumed that the sensory feedback signal from the chemoreceptors modulates the output of the rCPG model. It is also assumed that the brainstem network is responsible for the demodulation process. The respiratory mechanics is modeled as a multi-input multi-output (MIMO) system, where modulated and demodulated neural signals are applied as input and the minute ventilation and the oronasal airflow are specified as output. The minute ventilation signal drives the regulation model, completing the feedback loop. The proposed model is validated by comparing the model output with the clinical data. Using the modulation-demodulation hypothesis, a respiratory mechanics model is formulated in the form of a linear state-space model, which can be useful for providing assisted ventilation in clinical conditions.

Gold nanorod embedded reduction responsive block copolymer micelle-triggered drug delivery combined with photothermal ablation for targeted cancer therapy.

BACKGROUND: Gold nanorods, by virtue of surface plasmon resonance, convert incident light energy (NIR) into heat energy which induces hyperthermia. We designed unique, multifunctional, gold nanorod embedded block copolymer micelle loaded with GW627368X for targeted drug delivery and photothermal therapy. METHODS: Glutathione responsive diblock co-polymer was synthesized by RAFT process forming self-assembled micelle on gold nanorods prepared by seed mediated method and GW627368X was loaded on to the reduction responsive gold nanorod embedded micelle. Photothermal therapy was administered using cwNIR laser (808nm; 4W/cm2). Efficacy of nanoformulated GW627368X, photothermal therapy and combination of both were evaluated in vitro and in vivo. RESULTS: In response to photothermal treatment, cells undergo regulated, patterned cell death by necroptosis. Combining GW627368X with photothermal treatment using single nanoparticle enhanced therapeutic outcome. In addition, these nanoparticles are effective X-ray CT contrast agents, thus, can help in monitoring treatment. CONCLUSION: Reduction responsive nanorod embedded micelle containing folic acid and lipoic acid when treated on cervical cancer cells or tumour bearing mice, aggregate in and around cancer cells. Due to high glutathione concentration, micelles degrade releasing drug which binds surface receptors inducing apoptosis. When incident with 808nm cwNIR lasers, gold nanorods bring about photothermal effect leading to hyperthermic cell death by necroptosis. Combination of the two modalities enhances therapeutic efficacy by inducing both forms of cell death. GENERAL SIGNIFICANCE: Our proposed treatment strategy achieves photothermal therapy and targeted drug delivery simultaneously. It can prove useful in overcoming general toxicities associated with chemotherapeutics and intrinsic/acquired resistance to chemo and radiotherapy.

Oxygen therapy for management of periodic breathing : A theoretical approach.

A generalized framework for the generation of Periodic Breathing (PB), caused by delay variation, hypocapnia and sleep along with its management with oxygen therapy is presented. For this, a minimal model of respiratory regulation with cardiovascular component and two delays is proposed. This model is linearized and analyzed for stability by the proposed algorithms using Lyapunov-Krasovskii functional. Oscillation in this model is produced by the increase of delays, an increase of chemoreceptor gains (hypocapnia) and a decrease in minute ventilation (sleep induced PB). For delay variation, it is established that both the delays are responsible for oscillation in the system. However, maximum tolerable delay limit for the peripheral chemoreceptors is lower (0.3  min) compared to the central chemoreceptors (5.2  min). Stability analysis shows that application of additional oxygen is capable of suppressing the oscillation in the system by increasing the tolerable delay limit. Hypocapnia caused by hyperventilation is modeled by increased chemoreceptor gain. 50% increase in chemoreceptor gain along with 46% decrease in lung carbon dioxide storage makes the system oscillatory, which increases average minute ventilation by 19.42%. Application of additional oxygen makes the system stable. For sleep induced PB, it is shown that lowering minute ventilation causes oscillation in the system. A parameter is introduced to limit the minute ventilation in sleep, and its upper limit is calculated (8.7% drop in minute ventilation) for producing oscillation in the system. Application of higher oxygen makes the system stable by compensating for the reduction. Finally, two simulation studies are presented, showing the delay limits in hyperventilation and sleep condition. In these conditions, as the gains increase or minute ventilation decreases, tolerable delay limits become smaller.

Learning of speckle statistics for in vivo and noninvasive characterization of cutaneous wound regions using laser speckle contrast imaging.

Laser speckle contrast imaging (LSCI) provides a noninvasive and cost effective solution for in vivo monitoring of blood flow. So far, most of the researches consider changes in speckle pattern (i.e. correlation time of speckle intensity fluctuation), account for relative change in blood flow during abnormal conditions. This paper introduces an application of LSCI for monitoring wound progression and characterization of cutaneous wound regions on mice model. Speckle images are captured on a tumor wound region at mice leg in periodic interval. Initially, raw speckle images are converted to their corresponding contrast images. Functional characterization begins with first segmenting the affected area using k-means clustering, taking wavelet energies in a local region as feature set. In the next stage, different regions in wound bed are clustered based on progressive and non-progressive nature of tissue properties. Changes in contrast due to heterogeneity in tissue structure and functionality are modeled using LSCI speckle statistics. Final characterization is achieved through supervised learning of these speckle statistics using support vector machine. On cross evaluation with mice model experiment, the proposed approach classifies the progressive and non-progressive wound regions with an average sensitivity of 96.18%, 97.62% and average specificity of 97.24%, 96.42% respectively. The clinical information yield with this approach is validated with the conventional immunohistochemistry result of wound to justify the ability of LSCI for in vivo, noninvasive and periodic assessment of wounds.

Wavelet-based multiscale analysis of bioimpedance data measured by electric cell-substrate impedance sensing for classification of cancerous and normal cells.

The paper presents a study to differentiate normal and cancerous cells using label-free bioimpedance signal measured by electric cell-substrate impedance sensing. The real-time-measured bioimpedance data of human breast cancer cells and human epithelial normal cells employs fluctuations of impedance value due to cellular micromotions resulting from dynamic structural rearrangement of membrane protrusions under nonagitated condition. Here, a wavelet-based multiscale quantitative analysis technique has been applied to analyze the fluctuations in bioimpedance. The study demonstrates a method to classify cancerous and normal cells from the signature of their impedance fluctuations. The fluctuations associated with cellular micromotion are quantified in terms of cellular energy, cellular power dissipation, and cellular moments. The cellular energy and power dissipation are found higher for cancerous cells associated with higher micromotions in cancer cells. The initial study suggests that proposed wavelet-based quantitative technique promises to be an effective method to analyze real-time bioimpedance signal for distinguishing cancer and normal cells.

Review of laser speckle-based analysis in medical imaging.

Speckle pattern forms when a rough object is illuminated with coherent light (laser) and the backscattered radiation is imaged on a screen. The pattern changes over time due to movement in the object. Such time-integrate speckle pattern can be statistically analyzed to reveal the flow profile. For higher velocity the speckle contrast gets reduced. This theory can be utilized for tissue perfusion in capillaries of human skin tissue and cerebral blood flow mapping in rodents. Early, the technique was suffered from low resolution and computational intricacies for real-time monitoring purpose. However, modern engineering has made it feasible for real-time monitoring in microcirculation imaging with improved resolution. This review illustrates several modifications over classical technique done by many researchers. Recent advances in speckle contrast methods gain major interest, leading towards practical implementation of this technique. The review also brings out the scopes of laser speckle-based analysis in various medical applications.

Quantitative dimensions of histopathological attributes and status of GSTM1-GSTT1 in oral submucous fibrosis.

Oral submucous fibrosis (OSF) is a precancerous condition of the oral cavity and oropharynx and a significant number of such cases transform into oral squamous cell carcinoma (OSCC). Presently, diagnosis of OSF is done mainly through qualitative histopathological techniques and in the level of diagnostic molecular biology no specific genetic marker is evident. Keeping these facts in mind this study evaluates histopathological changes in the epithelium and subepithelial connective tissue of OSF through quantitative digital image analysis in respect to specific candidate features and analyses null mutations in the GSTM1 and GSTT1 by PCR amplification. The analysis revealed that there are subtle quantitative differences in the histological images of OSF compared to NOM. The thickness of the epithelium and cell population in its different zones, radius of curvature of rete-ridges and connective tissue papillae were decreased but length of rete-ridges and connective tissue papillae, fibrocity and the number of cellular components (predominantly inflammatory cells) in the subepithelial connective tissue were increased in OSF. The PCR study revealed that there is no significant difference in the allelic variants in GSTM1 between OSF and normal, while GSTT1 null gene showed significantly higher frequencies in this precancerous condition. This study establishes a distinct quantitative difference between normal oral mucosa (NOM) and OSF in respect to their histological features and GST null gene frequencies.