Algorithm For Cancer Detection Research Articles

Compressive sensing unmixing algorithm for breast cancer detection

Traditional breast cancer imaging methods using microwave imaging (MWI) seek to recover the complex permittivity of the tissues at each voxel in the imaging region. This approach is suboptimal in that it does not directly consider the permittivity values that healthy and cancerous breast tissues typically have. The authors describe a novel unmixing algorithm for detecting breast cancer. In this approach, the breast tissue is separated into three components, low water content, high water content, and cancerous tissues, and the goal of the optimisation procedure is to recover the mixture proportions of each component. By utilising this approach in a hybrid digital breast tomosynthesis/MWI system, the unmixing reconstruction process can be posed as a sparse recovery problem such that compressive sensing techniques can be employed. A numerical analysis is performed, which demonstrates that cancerous lesions can be detected from their mixture proportion under the appropriate conditions.

An Intraoperative Visualization System Using Hyperspectral Imaging to Aid in Brain Tumor Delineation.

Hyperspectral imaging (HSI) allows for the acquisition of large numbers of spectral bands throughout the electromagnetic spectrum (within and beyond the visual range) with respect to the surface of scenes captured by sensors. Using this information and a set of complex classification algorithms, it is possible to determine which material or substance is located in each pixel. The work presented in this paper aims to exploit the characteristics of HSI to develop a demonstrator capable of delineating tumor tissue from brain tissue during neurosurgical operations. Improved delineation of tumor boundaries is expected to improve the results of surgery. The developed demonstrator is composed of two hyperspectral cameras covering a spectral range of 400–1700 nm. Furthermore, a hardware accelerator connected to a control unit is used to speed up the hyperspectral brain cancer detection algorithm to achieve processing during the time of surgery. A labeled dataset comprised of more than 300,000 spectral signatures is used as the training dataset for the supervised stage of the classification algorithm. In this preliminary study, thematic maps obtained from a validation database of seven hyperspectral images of in vivo brain tissue captured and processed during neurosurgical operations demonstrate that the system is able to discriminate between normal and tumor tissue in the brain. The results can be provided during the surgical procedure (~1 min), making it a practical system for neurosurgeons to use in the near future to improve excision and potentially improve patient outcomes.

Improving medical diagnosis performance using hybrid feature selection via relieff and entropy based genetic search (RF-EGA) approach: application to breast cancer prediction

In this research a new hybrid prediction algorithm for breast cancer has been made from a breast cancer data set. Many approaches are available in diagnosing the medical diseases like genetic algorithm, ant colony optimization, particle swarm optimization, cuckoo search algorithm, etc., The proposed algorithm uses a ReliefF attribute reduction with entropy based genetic algorithm for breast cancer detection. The hybrid combination of these techniques is used to handle the dataset with high dimension and uncertainties. The data are obtained from the Wisconsin breast cancer dataset; these data have been categorized based on different properties. The performance of the proposed method is evaluated and the results are compared with other well known feature selection methods. The obtained result shows that the proposed method has a remarkable ability to generate reduced-size subset of salient features while yielding significant classification accuracy for large datasets.

A novel lung cancer detection algorithm for CADs based on SSP and Level Set.

The fuzzy degree of lung nodule boundary is the most important cue to judge the lung cancer in CT images. Based on this feature, the paper proposes a novel lung cancer detection method for CT images based on the super-pixels and the level set segmentation methods. In the proposed methods, the super-pixels method is used to segment the lung region and the suspected lung cancer lesion region in the CT image. The super-pixels method and a level set method are used to segment the suspected lung cancer lesion region simultaneously. Finally, the cancer is determined by the difference between results of the two segmentation methods. Experimental results show that the proposed algorithm has a high accuracy for lung cancer detection in CT images. For gross glass nodule, pleural nodule, the vascular nodules and solitary nodules, the sensitivity of the detection algorithm are respectively 91.3%, 96.3%, 80.9% and 82.3%.

A Lung Cancer Detection Algorithm Based on CV Model and Improved ME Model

A Lung Cancer Detection Algorithm Based on CV Model and Improved ME Model

Porting a PCA-based hyperspectral image dimensionality reduction algorithm for brain cancer detection on a manycore architecture

This paper presents a study of the parallelism of a Principal Component Analysis (PCA) algorithm and its adaptation to a manycore MPPA (Massively Parallel Processor Array) architecture, which gathers 256 cores distributed among 16 clusters. This study focuses on porting hyperspectral image processing into manycore platforms by optimizing their processing to fulfill real-time constraints, fixed by the image capture rate of the hyperspectral sensor. Real-time is a challenging objective for hyperspectral image processing, as hyperspectral images consist of extremely large volumes of data and this problem is often solved by reducing image size before starting the processing itself. To tackle the challenge, this paper proposes an analysis of the intrinsic parallelism of the different stages of the PCA algorithm with the objective of exploiting the parallelization possibilities offered by an MPPA manycore architecture. Furthermore, the impact on internal communication when increasing the level of parallelism is also analyzed. Experimenting with medical images obtained from two different surgical use cases, an average speedup of 20 is achieved. Internal communications are shown to rapidly become the bottleneck that reduces the achievable speedup offered by the PCA parallelization. As a result of this study, PCA processing time is reduced to less than 6 s, a time compatible with the targeted brain surgery application requiring 1 frame-per-minute.

PD11-08 COMPUTER-AIDED DIAGNOSIS OF PROSTATE CANCER USING A DEEP NEURAL NETWORKS ALGORITHM IN PRE-BIOPSY MULTIPARAMETRIC MAGNETIC RESONANCE IMAGING

You have accessJournal of UrologyImaging/Radiology: Uroradiology II1 Apr 2017PD11-08 COMPUTER-AIDED DIAGNOSIS OF PROSTATE CANCER USING A DEEP NEURAL NETWORKS ALGORITHM IN PRE-BIOPSY MULTIPARAMETRIC MAGNETIC RESONANCE IMAGING Junichiro Ishioka, Yoh Matsuoka, Masaya Itoh, Masaharu Inoue, Toshiki Kijima, Soichiro Yoshida, Minato Yokoyama, Kazutaka Saito, Kazunori Kihara, Yasuhisa Fujii, Hiroshi Tanaka, and Tomo Kimura Junichiro IshiokaJunichiro Ishioka More articles by this author , Yoh MatsuokaYoh Matsuoka More articles by this author , Masaya ItohMasaya Itoh More articles by this author , Masaharu InoueMasaharu Inoue More articles by this author , Toshiki KijimaToshiki Kijima More articles by this author , Soichiro YoshidaSoichiro Yoshida More articles by this author , Minato YokoyamaMinato Yokoyama More articles by this author , Kazutaka SaitoKazutaka Saito More articles by this author , Kazunori KiharaKazunori Kihara More articles by this author , Yasuhisa FujiiYasuhisa Fujii More articles by this author , Hiroshi TanakaHiroshi Tanaka More articles by this author , and Tomo KimuraTomo Kimura More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2017.02.584AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES Magnetic resonance imaging (MRI) provides a noninvasive assessment of the prostate that improves the detection of prostate cancer and can reduce unnecessary biopsies. The excessive variation in the performance and interpretation of MRI is, however, a major barrier to its widespread acceptance and use. In this study, we employed computer-aided diagnosis with a deep neural networks algorithm for prostate cancer detection using multiparametric MRI (mpMRI). METHODS Between 2010 and 2015, 354 patients underwent extended systematic prostate biopsy with MRI-targeted biopsy (MTB), and 209 patients with negative mpMRI underwent systematic prostate biopsy (SB). All patients with PSA levels of less than 20 ng/ml and negative findings of digital rectal examination were included as subjects. mpMRI was interpreted by an experienced radiologist. For the supervised training using deep neural network architecture, we selected 163 mpMRI-positive patients who were diagnosed with prostate cancer by MTB and 135 mpMRI-negative patients who were not diagnosed with prostate cancer by SB. We chose representative T2-weighted and diffusion-weighted (DW) MRI images from both mpMRI-positive and mpMRI-negative patients. The 298 pairs of T2-weighted and DW images labeled as ″cancer″ or ″no cancer″ were randomly divided into 248 training and 50 test datasets, and the measure of diagnostic accuracy was calculated. The structure of the deep neural network model we used contains an input layer, three fully connected hidden layers, and an output layer (figure 1). The layers in the networks have ReLu non-linear activation units, and their learning rate was 0.01 for 10 epochs with a dropout ratio of 0.5. RESULTS In the 50 hold-out validation test datasets, the mean area under the curve and the accuracy were 0.84 (0.72-0.99) and 0.81 (0.72-0.96). The mean positive predictive value, negative predictive value, sensitivity, and specificity of the algorithm were 0.89 (0.74-1.00), 0.75 (0.50-1.00), 0.76 (0.50-1.00), and 0.88 (0.18-1.00), respectively. CONCLUSIONS Computer-aided diagnosis with a deep neural networks algorithm for prostate cancer with mpMRI can provide reproducible interpretation and a greater level of standardization and consistency. © 2017FiguresReferencesRelatedDetails Volume 197Issue 4SApril 2017Page: e209 Advertisement Copyright & Permissions© 2017MetricsAuthor Information Junichiro Ishioka More articles by this author Yoh Matsuoka More articles by this author Masaya Itoh More articles by this author Masaharu Inoue More articles by this author Toshiki Kijima More articles by this author Soichiro Yoshida More articles by this author Minato Yokoyama More articles by this author Kazutaka Saito More articles by this author Kazunori Kihara More articles by this author Yasuhisa Fujii More articles by this author Hiroshi Tanaka More articles by this author Tomo Kimura More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...

Robust and fast characterization of OCT-based optical attenuation using a novel frequency-domain algorithm for brain cancer detection

Cancer is known to alter the local optical properties of tissues. The detection of OCT-based optical attenuation provides a quantitative method to efficiently differentiate cancer from non-cancer tissues. In particular, the intraoperative use of quantitative OCT is able to provide a direct visual guidance in real time for accurate identification of cancer tissues, especially these without any obvious structural layers, such as brain cancer. However, current methods are suboptimal in providing high-speed and accurate OCT attenuation mapping for intraoperative brain cancer detection. In this paper, we report a novel frequency-domain (FD) algorithm to enable robust and fast characterization of optical attenuation as derived from OCT intensity images. The performance of this FD algorithm was compared with traditional fitting methods by analyzing datasets containing images from freshly resected human brain cancer and from a silica phantom acquired by a 1310 nm swept-source OCT (SS-OCT) system. With graphics processing unit (GPU)-based CUDA C/C++ implementation, this new attenuation mapping algorithm can offer robust and accurate quantitative interpretation of OCT images in real time during brain surgery.

Highly Sensitive Marker Panel for Guidance in Lung Cancer Rapid Diagnostic Units

While evidence for lung cancer screening implementation in Europe is awaited, Rapid Diagnostic Units have been established in many hospitals to accelerate the early diagnosis of lung cancer. We seek to develop an algorithm to detect lung cancer in a symptomatic population attending such unit, based on a sensitive serum marker panel. Serum concentrations of Epidermal Growth Factor, sCD26, Calprotectin, Matrix Metalloproteinases −1, −7, −9, CEA and CYFRA 21.1 were determined in 140 patients with respiratory symptoms (lung cancer and controls with/without benign pathology). Logistic Lasso regression was performed to derive a lung cancer prediction model, and the resulting algorithm was tested in a validation set. A classification rule based on EGF, sCD26, Calprotectin and CEA was established, able to reasonably discriminate lung cancer with 97% sensitivity and 43% specificity in the training set, and 91.7% sensitivity and 45.4% specificity in the validation set. Overall, the panel identified with high sensitivity stage I non-small cell lung cancer (94.7%) and 100% small-cell lung cancers. Our study provides a sensitive 4-marker classification algorithm for lung cancer detection to aid in the management of suspicious lung cancer patients in the context of Rapid Diagnostic Units.

A combined parallel genetic algorithm and support vector machine model for breast cancer detection

A combined parallel genetic algorithm and support vector machine model for breast cancer detection

Validation of a Biomarker Panel and Longitudinal Biomarker Performance for Early Detection of Ovarian Cancer.

Longitudinal multimarker combinations have the potential to improve sensitivity while maintaining the high specificity required for the early detection of ovarian cancer. The use of multiple markers to improve sensitivity over cancer antigen 125 (CA125) in longitudinal algorithms for early ovarian cancer detection requires the selection of markers with optimal discriminatory power and low longitudinal variance relative to disease-initiated changes. Our objective was to identify a multimarker panel suitable for ovarian cancer, where each individual marker has its own baseline, permitting longitudinal algorithm development. In this retrospective study, we measured CA125, human epididymis protein 4 (HE4), matrix metalloproteinase-7 (MMP-7), CA72-4, CA19-9, CA15-3, carcinoembryonic antigen, and soluble vascular cell adhesion molecule (sVCAM) concentrations using immunoassays in pretreatment sera from 142 stage I ovarian cancer cases and 5 annual samples each from 217 healthy controls. After random division into training and validation sets, all possible biomarker combinations were explored exhaustively using linear classifiers to identify the panel with the greatest sensitivity for stage I disease at a high specificity of 98%. To evaluate longitudinal performance of the individual markers, the within-person over time and the between-person coefficient of variation (CV) were estimated. Hierarchical modeling across women of log-concentrations enabled the borrowing of information across subjects to moderate variance estimates given the small number of observations per subject. The 4-marker panel comprising CA125, HE4, MMP-7, and CA72-4 performed with the highest sensitivity (83.2%) at 98% specificity. The within-person CVs were lower for CA125, HE4, MMP-7, and CA72-4 (15%, 25%, 25%, and 21%, respectively) compared with their corresponding between-person CV (49%, 20%, 35%, and 84%, respectively) indicating baselines in healthy volunteers. After simple log-transformations, the within-volunteer variation across volunteers was modeled with a normal distribution permitting parsimonious hierarchical modeling. The multiplex panel chosen is suitable for the early detection of ovarian cancer and the individual markers have their own baseline permitting longitudinal algorithm development.

CS based confocal microwave imaging algorithm for breast cancer detection.

Based on compressive sensing (CS) technology, a high resolution confocal microwave imaging algorithm is proposed for breast cancer detection. With the exploitation of the spatial sparsity of the target space, the proposed image reconstruction problem is cast within the framework of CS and solved by the sparse constraint optimization. The effectiveness and validity of the proposed CS imaging method is verified by the full wave synthetic data from numerical breast phantom using finite-difference time-domain (FDTD) method. The imaging results have shown that the proposed imaging scheme can improve the imaging quality while significantly reducing the amount of data measurements and collection time when compared to the traditional delay-and-sum imaging algorithm.

Hyperspectral Imaging of Neoplastic Progression in a Mouse Model of Oral Carcinogenesis.

Hyperspectral imaging (HSI) is an emerging modality for medical applications and holds great potential for noninvasive early detection of cancer. It has been reported that early cancer detection can improve the survival and quality of life of head and neck cancer patients. In this paper, we explored the possibility of differentiating between premalignant lesions and healthy tongue tissue using hyperspectral imaging in a chemical induced oral cancer animal model. We proposed a novel classification algorithm for cancer detection using hyperspectral images. The method detected the dysplastic tissue with an average area under the curve (AUC) of 0.89. The hyperspectral imaging and classification technique may provide a new tool for oral cancer detection.

Effect of Image Enhancement on MRI Brain Images with Neural Networks

Since human body's standard control metabolism stops, old cells do not die and these abnormal cells forms a mass of tissue, known as tumor. In order to diagnose tumors, huge biomedical machines such as MRI, PET-CT, and MG machines are used. Last decade, there are many studies in brain tumor detection in magnetic resonance imaging (MRI). Location of the brain tumor and precise size are detected with brain tumor detection. In this paper, image processing techniques are applied on MRI images to preserve information details. These techniques are erosion, contrast enhancement and median filtering. The purpose of observe is develop an image processing algorithm for brain cancer detection on MRI images. Comparison of back propagation neural networks will be done using original images and reconstructed images on the effect of categorization.

Significance of adding progesterone to the Risk of Ovarian Malignancy Algorithm for early stage ovarian cancer detection in patients with a pelvic mass: A single-center case–control study

ObjectiveTo evaluate the clinical significance of the combination of cancer antigen-125 (CA-125), human epididymis protein 4 (HE4), and progesterone for the identification of ovarian masses in patients with suspected early stage ovarian cancer (OC). Materials and methodsThis was a case–control, single-center study of 225 women with a pelvic mass of suspected ovarian origin, including 75 patients with Stage I/II OC and 150 controls. Diagnostic procedures included pelvic and rectal examinations, transvaginal ultrasound, evaluation of CA-125 and HE4 levels alone and in the Risk of Ovarian Malignancy Algorithm (ROMA), and a new algorithm combining ROMA and progesterone. ResultsMedian CA-125 and HE4 levels were significantly higher in patients with OC compared with women with benign ovarian tumors, irrespective of menopausal status. The highest median progesterone levels occurred in premenopausal women with benign ovarian tumors, compared with premenopausal women with OC with or without benign ovarian disease. The combination of ROMA and progesterone was significantly more accurate at detecting OC compared with ROMA or CA-125 or HE4 alone, but only in premenopausal patients. ConclusionDifferent algorithms should be used for diagnosing OC, and the addition of progesterone might improve the performance of ROMA for the diagnosis of pelvic masses in premenopausal women.

Computer Aided Lung Cancer Detection and Tumor Staging in CT image using Image Processing

Lung cancer is one of the death threatening diseases among human beings. Early and accurate detection of lung cancer can increase the survival rate from lung cancer. Computed Tomography (CT) images are commonly used for detecting the lung cancer. Nowadays the lung cancer is staged according to the TNM staging method where T means Tumor, N means Nodule and M means Metastates. The existing lung cancer detection algorithms cannot stage cancer according to the TNM staging method. The proposed system can identify the T stage of the cancer accurately. The proposed system includes different stages such as preprocessing, segmentation, feature extraction, tumor detection and tumor stage identification. The proposed system promises better result than the existing systems, which would be beneficial for the radiologist for the accurate and early detection of cancer. The method has been tested on 200 slices of CT images of various stages of cancer obtained from Regional Cancer Centre Trivandrum and is found to give good results. The accuracy of the proposed method in this dataset is 94.4% General Terms Algorithms, Classification, Staging

Breast cancer detection using MRF-based probable texture feature and decision-level fusion-based classification using HMM on thermography images

Breast cancer is one of the major causes of death for women in the last decade. Thermography is a breast imaging technique that can detect cancerous masses much faster than the conventional mammography technology. In this paper, a breast cancer detection algorithm based on asymmetric analysis as primitive decision and decision-level fusion by using Hidden Markov Model (HMM) is proposed. In this decision structure, by using primitive decisions obtained from extracted features from left and right breasts and also asymmetric analysis, final decision is determined by a new application of HMM. For this purpose, a novel texture feature based on Markov Random Field (MRF) model that is named MRF-based probable texture feature and another texture feature based on a new scheme in Local Binary Pattern (LBP) of the images are extracted. In the MRF-based probable texture feature, we try to capture breast texture information by using proper definition of neighborhood system and clique and also determination of new potential functions. Ultimately, our proposed breast cancer detection algorithm is evaluated on a variety dataset of thermography images and false negative rate of 8.3% and false positive rate of 5% are obtained on test image dataset.

Modification of cut-off values for HE4, CA125 and the ROMA algorithm for early-stage epithelial ovarian cancer detection: Results from 1021 cases in South China

ObjectivesThe purpose of this study was to evaluate the performance of human epididymis protein 4 (HE4) and the Risk of Ovarian Malignancy Algorithm (ROMA) in early stage epithelial ovarian cancer (EOC) detection in patients in southern China. Additionally, this study proposes a possible ideal cut-off value for each marker to its own population in South China. Design and methodsSerum HE4 and CA125 were measured in 756 patients with pelvic masses (275 malignancies, 53 borderline tumors and 428 benign diseases), and their ROMA values were calculated. Areas under the receiver operator characteristic (ROC) curves (AUC) were assessed for HE4, CA125, ROMA and combinations of these biomarkers. ResultsBoth HE4 and ROMA performed better diagnostically than CA125 alone for early stage EOC, with AUCs ranging from 0.714 for HE4, 0.699 for ROMA, and 0.463 for CA125 in premenopausal subjects, and 0.902 for ROMA, 0.880 for HE4, and 0.256 for CA125 in postmenopausal subjects. ConclusionsHE4 and ROMA alone were found to be better than CA125 for detecting borderline tumors and early-stage EOC. The optimal cut-off values (HE4: 70pmol/l for all; CA125: 60U/ml for pre- and 35U/ml for postmenopausal women) could notably improve diagnostic performance in EOC detection in patients in southern China.

Improving Sensitivity and Specificity in Breast Cancer Detection Using Raman Spectroscopy and Bayesian Classification

Automated real-time tissue assessment using Raman spectroscopy for breast cancer detection is feasible; however, long-term specificity and sensitivity as reported so far in the literature can still be improved by a more reliable algorithm for breast cancer detection. Applying automatic reduction of background fluorescence to the Raman spectra and Bayesian classification on 18 discriminant Raman bands in the range of 1200–1800 cm−1, we achieved 100% accuracy in classifying breast biopsies of healthy and cancerous tissues, making it suitable for automated breast cancer diagnosis and appropriate for long-term use in real time in a surgery room or research scenarios. The long-term reliability of this approach was cross-validated using three methods: resubstitution, leave-one-out, and holdout. The holdout method has the potential of estimating the upper bound of classification error probability of the Bayesian classifier; the holdout method allowed us to perform 50,000 classification trials with only three misclassifications, which demonstrates that the high performance in sensitivity and specificity will be retained in future applications of this approach for breast cancer detection.

Modification of Cutoff Values for HE4, CA125, the Risk of Malignancy Index, and the Risk of Malignancy Algorithm for Ovarian Cancer Detection in Jakarta, Indonesia

CA125 and HE4 are used in calculating Risk of Malignancy Algorithm (ROMA); and Risk of Malignancy Index (RMI). However, studies showed that normal levels of CA125, and HE4 differ among ethnicities such as between Asians and Caucasians, thus affecting the accuracy of the RMI score and ROMA in predicting ovarian malignancy. This study aimed to determine whether new or modified cutoff values for Ca- 125, HE4, the RMI score, and ROMA resulted in a better prediction of malignancy compared with the previous or standard ones. Serum level of CA125 and HE4 from 128 patients with diagnosis of ovarian tumor that had been collected before surgery at Cipto Mangunkusumo General Hospital (CMH) in Jakarta from November 2010 until May 2011 were reviewed and analysed. The standard cutoff values of these biomarkers, RMI, and ROMA were modified by using logistic regression model. The modified cutoff values were compared to the standard cutoff values in terms of sensitivity, specificity, and accuracy. The modified cutoff value of CA125, HE4, RMI score and ROMA were 165.2 U/mL, 103.4 pM, 368.7, 28/54. The sensitivity and specificity of the modified cutoff values CA125, HE 4, RMI score and ROMA in differentiating benign from malignant and borderline were 67% and 75,4%; 73.1% and 85.2%; 73.1% and 80.3%; and 77.6% and 86.9%. While the sensitivity and specificity of the standard cutoff value of CA125; HE4; RMI score; and ROMA were 91% and 24.6%; 83.6% and 65%; 80.6% and 65.6%; and 91.0% and 42.6%. The accuracy of modified cutoff values compared with standard cutoff values were: 71.2% vs 59.3%, 78.9% vs 75% vs, 76.5% vs 73.4%, and 82% vs 67.9%. The new or modified cutoff values of Ca125, HE4, RMI score and ROMA resulted in higher accuracy compared to the previous or standard ones, at the cost of reduced sensitivity.