MOTIVATION: Time-lapse microscopy is a powerful technique that relies on images of live cells cultured ex vivo that are captured at regular intervals of time to describe and quantify their behavior under certain experimental conditions. This imaging method has great potential in advancing the field of precision oncology by quantifying the response of cancer cells to various therapies and identifying the most efficacious treatment for a given patient. Digital image processing algorithms developed so far require high-resolution images involving very few cells originating from homogeneous cell line populations. We propose a novel framework that tracks cancer cells to capture their behavior and quantify cell viability to inform clinical decisions in a high-throughput manner. RESULTS: The brightfield microscopy images a large number of patient-derived cells in an ex vivo reconstruction of the tumor microenvironment treated with 31 drugs for up to 6 days. We developed a robust and user-friendly pipeline CancerCellTracker that detects cells in co-culture, tracks these cells across time and identifies cell death events using changes in cell attributes. We validated our computational pipeline by comparing the timing of cell death estimates by CancerCellTracker from brightfield images and a fluorescent channel featuring ethidium homodimer. We benchmarked our results using a state-of-the-art algorithm implemented in ImageJ and previously published in the literature. We highlighted CancerCellTracker's efficiency in estimating the percentage of live cells in the presence of bone marrow stromal cells. AVAILABILITY AND IMPLEMENTATION: https://github.com/compbiolabucf/CancerCellTracker. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Lung cancer is the leading cause of cancer-related deaths globally, which makes early detection and diagnosis a high priority. Computed tomography (CT) is the method of choice for early detection and diagnosis of lung cancer. Radiomics features extracted from CT-detected lung nodules provide a good platform for early detection, diagnosis, and prognosis. In particular when using low dose CT for lung cancer screening, effective use of radiomics can yield a precise non-invasive approach to nodule tracking. Lately, with the advancement of deep learning, convolutional neural networks (CNN) are also being used to analyze lung nodules. In this study, our own trained CNNs, a pre-trained CNN and radiomics features were used for predictive analysis. Using subsets of participants from the National Lung Screening Trial, we investigated if the prediction of nodule malignancy could be further enhanced by an ensemble of classifiers using different feature sets and learning approaches. We extracted probability predictions from our different models on an unseen test set and combined them to generate better predictions. Ensembles were able to yield increased accuracy and area under the receiver operating characteristic curve (AUC). The best-known AUC of 0.96 and accuracy of 89.45% were obtained, which are significant improvements over the previous best AUC of 0.87 and accuracy of 76.79%.

Semantic features are common radiological traits used to characterize a lesion by a trained radiologist. These features have been recently formulated, quantified on a point scale in the context of lung nodules by our group. Certain radiological semantic traits have been shown to extremely predictive of malignancy [26]. Semantic traits observed by a radiologist at examination describe the nodules and the morphology of the lung nodule shape, size, border, attachment to vessel or pleural wall, location and texture etc. Deep features are numeric descriptors often obtained from a convolutional neural network (CNN) which are widely used for classification and recognition. Deep features may contain information about texture and shape, primarily. Lately, with the advancement of deep learning, convolutional neural networks (CNN) are also being used to analyze lung nodules. In this study, we relate deep features to semantic features by looking for similarity in ability to classify. Deep features were obtained using a transfer learning approach from both an ImageNet pre-trained CNN and our trained CNN architecture. We found that some of the semantic features can be represented by one or more deep features. In this process, we can infer that some deep feature(s) have similar discriminatory ability as semantic features.

Computed tomography (CT) is widely used during diagnosis and treatment of Non-Small Cell Lung Cancer (NSCLC). Current computer-aided diagnosis (CAD) models, designed for the classification of malignant and benign nodules, use image features, selected by feature selectors, for making a decision. In this paper, we investigate automated selection of different image features informed by different nodule size ranges to increase the overall accuracy of the classification. The NLST dataset is one of the largest available datasets on CT screening for NSCLC. We used 261 cases as a training dataset and 237 cases as a test dataset. The nodule size, which may indicate biological variability, can vary substantially. For example, in the training set, there are nodules with a diameter of a couple millimeters up to a couple dozen millimeters. The premise is that benign and malignant nodules have different radiomic quantitative descriptors related to size. After splitting training and testing datasets into three subsets based on the longest nodule diameter (LD) parameter accuracy was improved from 74.68% to 81.01% and the AUC improved from 0.69 to 0.79. We show that if AUC is the main factor in choosing parameters then accuracy improved from 72.57% to 77.5% and AUC improved from 0.78 to 0.82. Additionally, we show the impact of an oversampling technique for the minority cancer class. In some particular cases from 0.82 to 0.87.

The goal of this paper is to show the use of data mining techniques to predict the Soft Tissue Sarcoma (STS) tumor progression. STS are cancers which occur in different parts of the body such as fat, muscle and nerves. The lack of effective treatments and the difficulty in predicting treatment response make them challenging for physicians, and has likely slowed the evolution of new therapeutic agents. To design a prediction model, we propose a novel quantitative histogram-based method to analyze the difference in histograms obtained from pre and post-treatment multi-modality magnetic resonance images. Here, we used Radiomics techniques as a non-invasive method for outcome prediction. This study could help physicians identify distinctive patterns within each tumor to find more patient-specific treatments. We demonstrated the new approach on two practical tasks: tumor recurrence prediction (metastasis) and rate of necrosis prediction. Our learned model shows 87.79% prediction accuracy for metastasis with a 0.73 AUC and 82.22% prediction accuracy for necrosis with a 0.65 AUC.

Lung cancer is caused by abnormal and uncontrolled growth of cells in the lungs and the mortality rate of lung cancer is the highest among all types of cancer. It can be identified and treated with the help of computed tomography (CT) images. For an automated classifier, identifying good features from an image is a key concern. Deep feature extraction using pre-trained convolutional neural networks has been successful for some image domains recently. In our study, we apply a pre-trained convolutional neural network (CNN) to extract deep features from lung cancer CT images and then train classifiers to predict short and long term survivors. The best accuracy of 77.5% was with a cropping approach using a decision tree classifier in a leave one out cross validation with ten features chosen using symmetric uncertainty feature ranking. We mixed extracted deep neural network features along with quantitative (traditional image) features and obtained the best accuracy of 82.5% with a nearest neighbor classifier in a leave one out cross validation using the symmetric uncertainty feature ranking algorithm.

An important problem in quantitative medical image analysis is a large number of features (often highly correlated) to instance ratio. To handle this, we developed a feature selector and an ensemble classifier based on a modified version of random subspace method. We propose using a fusion of feature selection concepts: ranking based, correlation based and random subspaces, to develop a concordance correlation coefficient based random subspace method (CCC RSM) feature selector. It forms random feature subsets with weakly correlated yet relevant features while the ensemble classification is achieved by training the base classifier with these feature subsets. Axillary lymph node (ALNs) metastases is one of the most important prognostic factors in breast cancer. We applied CCC RSM for four binary class classifications based on the number of metastatic ALNs: (i) = 1 vs 0 (ii) 1-3 vs 0 (iii) 1-3 vs = 4, and (iv) 4 vs 0. We extracted textural kinetics from habitats of fifty eight dynamic contrast enhanced magnetic resonance imaging breast tumors. We used three classifiers to compare the accuracies achieved by CCC RSM with random subspaces (RS), wrappers and correlation based feature selector (CFS). For each binary classification we achieved the best accuracy (= 78%) using CCC RSM.

Soft Tissue Sarcomas (STS) are malignant tumors which emanate from soft tissues of the body. They are challenging for physicians because of the infrequency of their occurrence and non-predictable outcomes. In this paper, we propose a novel framework to classify STS which focuses on radio logically defined sub-regions, so-called 'habitats'. The distinctive habitats are regions where tumor evolution may be observed. We assess T1 post- and pre-contrast gadolinium and T2 non-contrast Magnetic Resonance Images (MRIs) of 36 patients prior to treatment. This paper considers spatially distinct habitats, which may be helpful in clinical treatment, especially chemotherapy and radiation. Our approach contains three main steps: (1) intra-tumor segmentation into habitats based on pixel intensity, (2) texture analysis within each distinctive habitat to capture heterogeneity, and (3) prediction of metastatic and necrotic tumor. The experimental results show the individual cases were correctly classified as metastatic or non-metastatic disease with 86.11% accuracy based on 5 features and for necrosis =90% or necrosis <; 90% with 81.81% accuracy based on 4 features by using several meta-classifiers.

Lung segmentation in thoracic computed tomography (CT) scans is an important preprocessing step for computer-aided diagnosis (CAD) of lung diseases. This paper focuses on the segmentation of the lung field in thoracic CT images. Traditional lung segmentation is based on Gray level thresholding techniques, which often requires setting a threshold and is sensitive to image contrasts. In this paper, we present a fully automated method for robust and accurate lung segmentation, which includes a enhanced thresholding algorithm and a refinement scheme based on a texture-aware active contour model. In our thresholding algorithm, a histogram based image stretch technique is performed in advance to uniformly increase contrasts between areas with low Hounsfield unit (HU) values and areas with high HU in all CT images. This stretch step enables the following threshold-free segmentation, which is the Otsu algorithm with contour analysis. However, as a threshold based segmentation, it has common issues such as holes, noises and inaccurate segmentation boundaries that will cause problems in future CAD for lung disease detection. To solve these problems, a refinement technique is proposed that captures vessel structures and lung boundaries and then smooths variations via texture-aware active contour model. Experiments on 2,342 diagnosis CT images demonstrate the effectiveness of the proposed method. Performance comparison with existing methods shows the advantages of our method.

In breast cancer, tumor heterogeneity is a reflection of differing tumor subtypes, which may display markedly different genotypes and clinical phenotypes. Although pathological and qualitative (based on contrast enhancement patterns) studies suggest the presence of clinical and molecular predictive tumor subregions, this has not been fully investigated. Our goal is to develop a novel algorithm to utilize the potential information available in different tumor subregions (periphery and core) by extracting textural kinetic features, for the purpose of estrogen receptor (ER) classification. We show that features from different tumor subregions, at appropriate scales and quantization levels, can be used to better classify ER subtypes than features averaged from the whole tumor. We analyzed representative two dimensional (2D) slices from twenty breast tumors with volumetric dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) available by extracting multi-parametric textural kinetic features from the periphery, core and whole tumor. The utility of the features from different subregions are evaluated using six meta-classifiers (feature selector and classifier pairs), formed from two feature selectors and three classifiers. Classification accuracy approached 94%.

Low-dose helical computed tomography (LDCT) has facilitated the early detection of lung cancer through pulmonary screening of patients. There have been a few attempts to develop a computer-aided diagnosis system for classifying pulmonary nodules using size and shape, with little attention to texture features. In this work, texture and shape features were extracted from pulmonary nodules selected from the LIDC data set. Several classifiers including Decision Trees, Nearest Neighbor, and Support Vector Machines (SVM) were used for classifying malignant and benign pulmonary nodules. An accuracy of 90.91% was achieved using a 5-nearest-neighbors algorithm and a data set containing texture features only. Laws and Wavelet features received the highest rank when using feature selection implying a larger contribution in the classification process. Considering the improvement in classification accuracy, the use of texture features appears to be a promising direction in computer-aided diagnosis of pulmonary nodules in LDCT.

Automated prediction of patient-specific disease progression can significantly contribute to clinical treatment. This paper presents a computer-assisted framework to tackle the survival time prediction problem. Inspired by the assumption that niche tumor regions may play a significant role in cancer diagnosis, we explore local visual variations from multiple MRI sequences. The research consists of three parts: 1) the extraction of multi-scale Local Binary Patterns (LBP) to describe the visual variations, 2) a supervised forward feature selection approach, called the Feature Ranking Model (FRM) which captures single feature predictive ability efficiently, and combines the top features to form a feature subset, 3) We cast the clinical survival time prediction task as a binary category classification problem. We tested the framework using a dataset of 32 cases collected from The Cancer Genome Atlas (TCGA). We obtained a 93.75% accuracy rate for the prediction of survival time.

A CT-scan is a vital tool for the diagnosis of lung cancer via tumor detection. Developing a classifier to make use of the information in CT-scan images could provide a non-invasive alternative to histopathological techniques such as needle biopsy to identify tumor types. Image features extracted from 74 lung tumor objects of CT-scan images are used in classifying tumor types. Classification is done into two major classes of non-small cell lung tumors, Adenocarcinoma and Squamous-cell Carcinoma, each constituting 30% of all lung tumors. In this first of its kind investigation, a large group of 2D and 3D image features which were hypothesized to be useful are evaluated for effectiveness in classifying the tumors. Classifiers including decision trees and support vector machines are used along with feature selection techniques (Wrappers and Relief-F) to build models for tumor classification. Results show that over the large feature space for both 2D and 3D features it is possible to recognize tumor classes with about 68% accuracy, showing new features may be of help. The accuracy achieved using 2D and 3D features is similar with 3D easier to use.

This paper proposes a new automated system for segmentation of breast cancer tissue. The segmentation algorithm involves a principal component region growing scheme for high-resolution images. The number of candidate seed pixels is extremely large due to the high resolution. The main focus of this paper is to present a multi-resolution scheme for accurate selection of seed pixels to be presented as inputs to the region growing segmentation algorithm. The system is tested for accuracy, and the efficiency is measured in terms of percentage reduction in number of seed pixels, as well as accuracy of the segmentation results.