A novel statistical approach for detection of suspicious regions in digital mammogram

In this paper, we propose a novel algorithm to detect the suspicious regions on digital mammograms that based on the Fisher information measure. The proposed algorithm is tested different types and categories of mammograms (fatty, fatty-glandular and dense glandular) within mini-MIAS database (Mammogram Image Analysis Society database (UK)). The proposed method is compared with a different segmentation based information theoretical methods to demonstrate their effectiveness. The experimental results on mammography images showed the effectiveness in the detection of suspicious regions. This study can be a part of developing a computer-aided decision (CAD) system for early detection of breast cancer.     

ACO classification of thermogram symmetry features for breast cancer diagnosis

Breast cancer is the most commonly diagnosed form of cancer in women. While mammography is the standard modality for detecting breast cancer, it has been shown that medical thermography, which uses cameras with sensitivities in the thermal infrared, is also well suited for the task, especially in dense tissue and for small tumors. In this paper, we present an approach of analysing breast thermograms that first extracts a series of image features describing bilateral (a)symmetry between left and right breast regions, and then uses these features in a subsequent classification step. For the classification, we employ an ant colony optimisation based pattern recognition algorithm that is shown to provide a concise rule base with good classification performance.     

The analysis of cancer chemoprevention experiments In which there is heterogeneous poisson sampling

In certain cancer chemoprevention experiments both the number of observed tumors per animal and their times to detection are used in subsequent statistical analyses. The mathematical models used to represent these experiments usually include the Poisson distribution to characterize the tumor multiplicity data. Very often however, there is excess variance due to interanimal heterogeneity of tumor response. Thus, the number of induced tumors is better characterized by the negative binomial distribution. In this paper we modify an existing statistical technique, which explicitly acknowledges the confounding inherent in these systems, in order to provide a more efficient procedure for utilizing the information in a sample and to more accurately assess treatment effects.     

A model of breast carcinogenesis and recurrence after radiotherapy

Breast conserving surgery and adjuvant radiotherapy are the standard treatment of early stage breast cancer. We present simulations of a mathematical model of breast carcinogenesis and identify fields of mutated cells in the breast (formed during puberty or pregnancy) as a potential source of local recurrence after treatment with radiotherapy. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

A Mathematical Programming Based Procedure for Breast Cancer Classification

In this paper we propose a new procedure for classification based on a hybrid approach. The classification problem is solved by minimizing the distance between the components of each clusters and the centers of the clusters. The determination of the cluster centers is therefore a critical step in our approach and was addressed used the k-means algorithm. Once the centers of each class are determined, the rule of center neighbourhood is applied to assign an element to a class using mathematical programming. The implementation of our hybrid approach was validated on benchmark datasets and applied to an original biological dataset on 84 breast cancer tumours. Each tumour was measured for five parameters corresponding to the expression of five biomarkers (proteins). The obtained classification was discussed using biological knowledge and classical clinical experts’ classification of breast tumors.     

Estimating Parameters of a Microsimulation Model for Breast Cancer Screening Using the Score Function Method

In developing decision-making models for the evaluation of medical procedures, the model parameters can be estimated by fitting the model to data observed in (randomized) trials. For complex models that are implemented by discrete event simulation (microsimulation) of individual life histories, the Score Function (SF) method can potentially be an appropriate approach for such estimation exercises. We test this approach for a microsimulation model for breast cancer screening that is fitted to data from the HIP randomized trial for early detection of breast cancer. Comparison of the parameter values estimated using the SF method and the analytical solution shows that method performs well on this simple model. The precision of the estimated parameter values depends (as expected) on the size of the sample of simulated life histories, and on the number of parameters estimated. Using analytical representations for parts of the microsimulation model can increase the precision of the estimated parameter values. Compared to the Nelder and Mead Simplex method which is often used in stochastic simulation because of its ease of implementation, the SF method is clearly more efficient (ratio computer time: precision of estimates). The additional analytical investment needed to implement the SF method in an (existing) simulation model may well be worth the effort.     

Scheduling the adjuvant endocrine therapy for early stage breast cancer

Based on the data available through published trial results, we build a mixed integer nonlinear programming (MINLP) model in order to find an optimal treatment plan for a given HR+ early stage breast cancer patient who is postmenopausal. The objective is to maximize the disease-free survival percentage at the end of the treatment period subject to the constraints on the risk of contralateral breast cancer and the risks of several side effects, including endometrial cancer, thromboembolic events, cardiovascular diseases, bone fractures, hot flushes, and vaginal bleeding. The results of numerical experiments suggest the effectiveness of some of the schedules currently used in practice, as well as suggest some attractive alternative treatment plans.     

A modified forward-backward splitting methods for the sum of two monotone operators with applications to breast cancer prediction

This work proposes a modified forward-backward splitting algorithm combining an inertial technique for solving the monotone variational inclusion problem. The weak convergence theorem is established under some suitable conditions in Hilbert space, and a new step size is presented for our algorithm to speed up the convergence. We give an example and numerical results for supporting our main theorem in infinite dimensional spaces. We also provide an application to predict breast cancer by using our proposed algorithm for updating the optimal weight in machine learning. Moreover, we use the Wisconsin original breast cancer data set as a training set to show efficiency comparing with the other three algorithms in terms of three key parameters, namely, accuracy, recall, and precision.     

基于提升小波的基因芯片数据的分类预测

针对肿瘤的早期诊断,提出了一种基于提升小波变换的特征提取的方法,对肿瘤数据样本进行分析鉴别.该方法利用提升小波变换对190例肝癌（包括对照）和107例肺癌（包括对照）基因表达谱芯片数据进行处理后,提取信号的低频信息,经支持向量机训练学习,构造分类器模型,用于癌和非癌样本的区分甄别.实验结果表明,经提升小波变换提取的特征基因,送入分类器中能得到较高的分类率,且在支持向量机中选取线性核函数或径向基函数都能达到较好的分类效果.通过随机选取的20例基因表达谱芯片样本,对所建立的模型进行了测试,获得了很好的效果,因此,本文提出的方法对肿瘤的诊断有一定的应用意义.     

Color image segmentation for Bladder Cancer Diagnosis

In this paper, a new color image segmentation method is proposed to extract the region of bladder tumor from a color bladder image. In the past, the diagnosis of bladder tumors mainly relies upon cystoscopic examination with an in vivo staining technique. This manner heavily depends on the interpreter's experience and often results in errors in diagnosis. Instead of previous method, we use methylene blue in vivo staining combined with color segmentation techniques to improve the accuracy of the diagnosis of bladder tumors. The segmentation task can be decomposed into two stages. First, cluster detection combined with probabilistic relaxation is used to extract the clusters of specified colors from the HLS color space. Then, in order to refine the chromatic properties, the Bayesian algorithm is employed to reject the false region from the clusters obtained in the first stage. Experimental results show that the proposed method can segment the bladder tumor successfully. The technique could serve as an auxiliary tool for doctors or researchers in the diagnosis of bladder tumors.     

Early screening of ovarian cancer

The mortality of ovarian cancer is higher than any other female genital malignant tumors, while there exists a strong correlation between early-stage detection and cure for it. CA125 and HE4 are two most common and effective serum markers in recent screening research of ovarian cancer. This paper derives a sequential screening strategy for ovarian cancer by jointly modeling the longitudinal profiles of CA125 and HE4. We construct a Bayesian hierarchical mixture model with changepoint, and propose two approaches for diagnosis: the risk of cancer index and the hypothesis test on the true incidence time. We simulated a 7-year sequential screening research and compared with the standard approach based on a fixed cutoff level. Our approach achieves a 15% higher sensitivity for a fixed specificity, indicating that the sequential strategy combining multiple markers is more effective in the early-stage detection of ovarian cancer.     

The shape of the hazard function for cancer incidence

A population-based cohort consisting of 126,141 men and 122,208 women born between 1874 and 1931 and at risk for breast or colorectal cancer after 1965 was identified by linking the Utah Population Data Base and the Utah Cancer Registry. The hazard function for cancer incidence is estimated from left truncated and right censored data based on the conditional likelihood. Four estimation procedures based on the conditional likelihood are used to estimate the age-specific hazard function from the data; these were the life-table method, a kernel method based on the Nelson Aalen estimator, a spline estimate, and a proportional hazards estimate based on splines with birth year as sole covariate.The results are consistent with an increasing hazard for both breast and colorectal cancer through age 85 or 90. After age 85 or 90, the hazard function for female breast and colorectal cancer may reach a plateua or decrease, although the hazard function for male colorectal cancer appears to continue to rise through age 105. The hazard function for both breast and colorectal cancer appears to be higher for more recent birth cohorts, with a more pronounced birth-cohort effect for breast cancer than for colorectal cancer. The age specific for colorectal cancer appears to be higher for men than for women. The shape of the hazard function for both breast and colorectal cancer appear to be consistent with a two-stage model for spontaneous carcinogenesis in which the initiation rate is constant or increasing. Inheritance of initiated cells appears to play a minor role.     

A free boundary problem arising from DCIS mathematical model

Ductal carcinoma in situ – a special cancer – is confined within the breast ductal only. We derive the mathematical ductal carcinoma in situ model in a form of a nonlinear parabolic equation with initial, boundary, and free boundary conditions. Existence, uniqueness, and stability of problem are proved. Algorithm and illustrative examples are included to demonstrate the validity and applicability of the technique in this paper. Copyright © 2016 John Wiley & Sons, Ltd.     

Statistical Analysis of Retinal Tomographic Pseudo Images for Diagnostic Purpose

The majority of visual impairments are related to retinal disorders. Therefore, techniques useful for early detection of retinal anomalies are of great interest for diagnosis and treatment of related pathologies. Optical Coherence Tomography (OCT) is a non-invasive technique that is becoming increasingly important in the analysis of human retina. This technique produces sections of a three dimensional reflectance map, which are pseudo-images of the retinal structure. In these pseudo-images, early pathologies may produce local modifications of reflectance, which visually, appear as blots. Consequently, automatic procedures for detection of such blots may be of help in the diagnosis of many retinal diseases. Due to the complexity of the measurement technique and of the retinal structure, the pseudo-images produced by OCT can be modeled as 2D random signals: the above pathological blots correspond to sudden changes in the distribution of such OCT signals. In this work, we performed a statistical analysis of retinal OCT data. We found that the so-called stretched-exponential distribution, a two-parameter probability density function, provides a model that describes the data in a satisfactory way. In addition, we analyzed spatial-reflectance interdependence by studying the autocorrelation function; this analysis showed a significant correlation between contiguous pixels about 5 μm apart. We then developed a procedure to obtain the maximum-likelihood estimate of distribution parameters along the retinal pseudo-image. With this procedure, we analyzed their spatial dependence both along and across retinal layers. We observed that along suitable thin layers, the two parameters were statistically constant. By exploiting this result, a statistical procedure was developed for automatic detection of parameter variations due to retinal pathological blots. Some first results are reported for detection of diabetic retinopathy.     

A Monte Carlo simulation approach to the characterization of uncertainties in cancer staging and radiation treatment decisions

Radiation treatment (RT) for cancer is a critical medical procedure that occurs in a complex environment that is subject to uncertainties and errors. We employed a simulation (a variant of Monte Carlo) model that followed a cohort of hypothetical breast cancer patients to estimate the probability of incorrect staging and treatment decisions. As inputs, we used a combination of literature information and expert judgement. Input variables were defined as probability distributions within the model. Uncertainties were propagated via simulation. Sensitivity and value-of-information analyses were then conducted to quantify the effect of variable uncertainty on the model outputs. We found a small but non-trivial probability that patients would be incorrectly staged and thus be subjected to inappropriate treatment. Some routinely used tests for staging and metastasis detection have very limited informational value. This work has implications for the methods used in cancer staging and subsequent risk assessment of treatment errors.     

Effects of GSM microwaves, pulsed magnetic field, and temperature on fractal dimension of brain tumors

Fractal dimension of a two-dimensional C-6 rat glioma tumors growing in the microwave field generated by signal simulation of the Global System for Mobile communications (GSM) with frequency 960 MHz was found significantly enhanced as compared with field free tumors growing at different temperatures and on the other hand a strong pulsed magnetic field lowered fractal dimension of tumors.     

Optimal Biopsy Protocols for Prostate Cancer

Prostate cancer is the second leading cause of cancer-related death among American men. Biopsy for prostate cancer is a procedure known as transrectal ultrasound-guided needle biopsy. Because of the low resolution of ultrasound, the urologist cannot usually distinguish between cancerous and healthy tissue. For this reason, most biopsies follow standard protocols based on long-term experience of physicians. Recent studies indicate that these protocols have a significant rate of false negative diagnoses. In this research we use real prostate specimens removed by prostatectomy to develop a 3-D distribution map of cancer in the prostate, and use this to develop optimized biopsy procedures. The new procedures have detection rates that are significantly higher than those of current procedures, and thus have the potential to increase the rate of early detection of prostate cancer.     

Joint modeling of longitudinal proportional measurements and survival time with a cure fraction

In cancer clinical trials and other medical studies, both longitudinal measurements and data on a time to an event (survival time) are often collected from the same patients. Joint analyses of these data would improve the efficiency of the statistical inferences. We propose a new joint model for the longitudinal proportional measurements which are restricted in a finite interval and survival times with a potential cure fraction. A penalized joint likelihood is derived based on the Laplace approximation and a semiparametric procedure based on this likelihood is developed to estimate the parameters in the joint model. A simulation study is performed to evaluate the statistical properties of the proposed procedures. The proposed model is applied to data from a clinical trial on early breast cancer.     

复杂网络整合分析方法在乳腺癌疾病机理研究中的应用

将乳腺癌相关基因抽象为节点,利用基因表达谱分别构建正常组织样本(C组)与乳腺癌样本(E组)的pearson、spearman、kendall、mutual information和mic等5种相关性网络,进而分析网络节点度在C组和E组中的差异性,筛选出17个结构性关键基因,其中12个基因已有文献证实与乳腺癌显著相关.它...