The evolution of drug resistance leads to treatment failure and tumor progression. Intermittent androgen deprivation therapy (IADT) helps responsive cancer cells compete with resistant cancer cells in intratumoral competition. However, conventional IADT is population-based, ignoring the heterogeneity of patients and cancer. Additionally, existing IADT relies on pre-determined thresholds of prostate-specific antigen to pause and resume treatment, which is not optimized for individual patients. To address these challenges, we framed a data-driven method in two steps. First, we developed a time-varied, mixed-effect and generative Lotka-Volterra (tM-GLV) model to account for the heterogeneity of the evolution mechanism and the pharmacokinetics of two ADT drugs Cyproterone acetate and Leuprolide acetate for individual patients. Then, we proposed a reinforcement-learning-enabled individualized IADT framework, namely, I$^{2}$ADT, to learn the patient-specific tumor dynamics and derive the optimal drug administration policy. Experiments with clinical trial data demonstrated that the proposed I$^{2}$ADT can significantly prolong the time to progression of prostate cancer patients with reduced cumulative drug dosage. We further validated the efficacy of the proposed methods with a recent pilot clinical trial data. Moreover, the adaptability of I$^{2}$ADT makes it a promising tool for other cancers with the availability of clinical data, where treatment regimens might need to be individualized based on patient characteristics and disease dynamics. Our research elucidates the application of deep reinforcement learning to identify personalized adaptive cancer therapy.

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.

Here we investigate the application of convolutional neural networks (CNNs) to predict the survival time of patients with Glioblastoma Multiforme (GBM) brain tumor. Our dataset consists of T1-weighted high-resolution MRI images of just 68 GBM patients. We compare two analytic methods for predicting survival time. The first consists of training a small convolutional neural network (CNN) and the second uses extracted deep features from a pre-trained CNN. Our method is completely automated, except for tumor region segmentation. In addition, we utilize a snapshot ensemble approach to boost test accuracy when dealing with limited availability of medical images for CNN training purposes. Our approach achieves an accuracy of 72.06% using a trained small network and 66.18% using a pre-trained deep CNN. Our results compare favorably with the accuracy of 54.41% using histogram of oriented gradients (HOG) features and a non-neural network classifier.

Intracellular thermometry has recently demonstrated temperatures in the nucleus, mitochondria, and centrosome to be significantly higher than those of the cytoplasm and cell membrane. This local thermogenesis and the resulting temperature gradient could facilitate the development of persistent, self-organizing convection currents in the cytoplasm of large eukaryotes. Using 3-dimensional computational simulations of intracellular fluid motion, we quantify the convective velocities that could result from the temperature differences observed experimentally. Based on these velocities, we identify the conditions necessary for this temperature-driven bulk flow to dominate over random thermal diffusive motion at the scale of a single eukaryotic cell. With temperature gradients of the order 1°C and diffusion coefficients comparable to those described in the literature, Péclet numbers ≥ 1 are feasible and permit comparable or greater effects of convection than diffusion in determining intracellular mass flux. In addition to the temperature gradient, the resulting flow patterns would also depend on the spatial localization of the heat source, the shape of the cell membrane, and the complex intracellular structure including the cytoskeleton. While this intracellular convection would be highly context-dependent, in certain settings, convective motion could provide a previously unrecognized mechanism for directed, bulk transport within eukaryotic cells.

Real-world datasets are often imbalanced, with an important class having many fewer examples than other classes. In medical data, normal examples typically greatly outnumber disease examples. A classifier learned from imbalanced data, will tend to be very good at the predicting examples in the larger (normal) class, yet the smaller (disease) class is typically of more interest. Imbalance is dealt with at the feature vector level (create synthetic feature vectors or discard some examples from the larger class) or by assigning differential costs to errors. Here, we introduce a novel method for over-sampling minority class examples at the image level, rather than the feature vector level. Our method was applied to the problem of Glioblastoma patient survival group prediction. Synthetic minority class examples were created by adding Gaussian noise to original medical images from the minority class. Uniform local binary patterns (LBP) histogram features were then extracted from the original and synthetic image examples with a random forests classifier. Experimental results show the new method (Image SMOTE) increased minority class predictive accuracy and also the AUC (area under the receiver operating characteristic curve), compared to using the imbalanced dataset directly or to creating synthetic feature vectors.

Finding appropriate feature representations from radiological images is a vital task for prediction and diagnosis. Deep convolutional neural networks have recently achieved state-of-the-art performance in classification problems from several different domains. Research has also shown the feasibility of using a pre-trained deep neural network as a feature extractor when only a small dataset is available. This paper proposes a novel image feature extraction method for predicting survival time from brain tumor magnetic resonance images using pretrained deep neural networks. Since all tumors are different sizes, we also explore different image resizing methods in the paper. We demonstrate that deep features can result in better survival time prediction with the highest accuracy of 95.45% versus conventional feature extraction methods from magnetic resonance images of the brain.

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.

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.

The increasing rate of antibiotic resistance and slowing discovery of novel antibiotic treatments presents a growing threat to public health. Here, we consider a simple model of evolution in asexually reproducing populations which considers adaptation as a biased random walk on a fitness landscape. This model associates the global properties of the fitness landscape with the algebraic properties of a Markov chain transition matrix and allows us to derive general results on the non-commutativity and irreversibility of natural selection as well as antibiotic cycling strategies. Using this formalism, we analyze 15 empirical fitness landscapes of E. coli under selection by different β-lactam antibiotics and demonstrate that the emergence of resistance to a given antibiotic can be either hindered or promoted by different sequences of drug application. Specifically, we demonstrate that the majority, approximately 70%, of sequential drug treatments with 2-4 drugs promote resistance to the final antibiotic. Further, we derive optimal drug application sequences with which we can probabilistically 'steer' the population through genotype space to avoid the emergence of resistance. This suggests a new strategy in the war against antibiotic-resistant organisms: drug sequencing to shepherd evolution through genotype space to states from which resistance cannot emerge and by which to maximize the chance of successful therapy.

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%.

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.