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As a new entrant in the Philippine Cancer Health Information Network, the Philippine Cancer Center (PCC) was pushed to establish the National Cancer Registry as mandated by the National Integrated Cancer Control Act (NICCA). The researchers used a rapid review and rapid realist review approaches which made use of evidence from literature and data from 43 key informant interviews and FGDs to create the National Cancer Registry Development Plan. The results were summarized using a cancer data process flow diagram, context‐view system architecture model, and logic models detailing the roadmap plans for the next 10 years. The study was able to show how the development of a national cancer registry plan that seeks to unite existing population‐based cancer registries (PBCRs) and hospital‐based cancer registries (HBCRs) is only possible through extensive collaboration and effective stakeholder engagement. Using an evidence‐based approach with expert knowledge and validation, the Philippine National Cancer Registry Development Plan was created, an example of an e‐health policy making process from which other developing countries can learn from. [ABSTRACT FROM AUTHOR]

Introduction: Recently, researchers have introduced new generated families of univariate lifetime distributions. These new generators are obtained by adding one or more extra shape parameters to the underlying distribution or compounding two distributions to get more flexibility in fitting data in different areas such as medical sciences, environmental sciences, and engineering. The addition of parameter(s) has been proven useful in exploring tail properties and for improving the goodness-of-fit of the family of the proposed distributions. Methods:A new Three-Parameter Weibull-Generalized Gamma distribution which provides more flexibility in modeling lifetime data is developed using a two-component mixture of Weibull distribution (with parameters θ and λ) and Generalised Gamma distribution (with parameters α=4,θ and λ). Some of its mathematical properties such as the density function, cumulative distribution function, survival function, hazard rate function, moment generating function, Renyi entropy and order statistics are obtained. The maximum likelihood estimation method was used in estimating the parameters of the proposed distribution and a simulation study is performed to examine the performance of the maximum likelihood estimators of the parameters. Results: Real life applications of the proposed distribution to two cancer datasets are presented and its fit was compared with the fit attained by some existing lifetime distributions to show how the Three-Parameter Weibull-Generalized Gamma distribution works in practice. Conclusion: The results suggest that the proposed model performed better than its competitors and it's a useful alternative to the existing models. [ABSTRACT FROM AUTHOR]

In this article a new flexible survival cure rate model is introduced by assuming that the number of competing causes of the event of interest follows the Flory–Schulz distribution and the competing causes follow the generalized truncated Nadarajah–Haghighi distribution. Parameter estimation for the proposed model is derived based on the maximum likelihood estimation method. A simulation study is performed to show the performance of the ML estimators. We discuss three real data applications related to real cancer data sets to assess the usefulness of the proposed model compared with some existing cure rate models for the sake of comparison. [ABSTRACT FROM AUTHOR]

In this article, the two-stage sequential sampling procedure is proposed to estimate the mean of an exponential distribution under the modified square error loss function. The main aim of the article is to consider the associated risk per unit cost function by bounding it from above with a fixed preassigned positive number, ω. We provide the exact distribution of the total sample size, explicit formulas for the mathematical expectation of the rth power of the total sample size, the expected value and mean squared error of the maximum likelihood estimate of the scale, and mean parameters of the exponential distribution under the two-stage sequential procedure. The performances of the proposed methodologies are investigated with the help of simulations. In the end, we supplement with extensive sets of data analysis via computer simulations, validating that the two-stage sequential method performs very well. Finally, using a real data set, the procedure is clearly illustrated. [ABSTRACT FROM AUTHOR]

In predictive modelling it is important to use any feature selection methods as irrelevant features when used with powerful classifiers can lead to over-fitting and thus create models which fail to perform as good as when these features are not used. Particularly it is important in case of disease datasets where various features or attributes are available through the patients' medical records and many features in these datasets may not be relevant to the diagnosis of some specific disease. Wrong models in this case can be disastrous and lead to wrong diagnosis, and maybe in extreme cases lead to loss of life. To this end, we have used a wrapper based feature selection model for the said purpose. In recent years, Grasshopper Optimization Algorithm (GOA) has proved its superiority over other optimization algorithms in different research areas. In this paper, we propose an improved version of GOA, called (LAGOA), which uses Learning Automata (LA) for adjusting the parameters of GOA in an adaptive way, and two-phase mutation for enhancing exploitation capability of the algorithm. LA is used for adjusting the parameter values of each grasshopper in the population individually. In two-phase mutation the first phase reduces the number of selected features while preserving high classification accuracy, while the second phase adds relevant features which increase the classification accuracy. Proposed method has been applied to Breast Cancer (Wisconsin), Breast Cancer (Diagnosis), Statlog (Heart), Lung Cancer, SpectF Heart and Hepatitis datasets taken from UCI Machine Learning Repository. Experimental results confirm its superiority over state-of-the-art methods considered here for comparison. [ABSTRACT FROM AUTHOR]

Abstract Background In recent years, various sequencing techniques have been used to collect biomedical omics datasets. It is usually possible to obtain multiple types of omics data from a single patient sample. Clustering of omics data plays an indispensable role in biological and medical research, and it is helpful to reveal data structures from multiple collections. Nevertheless, clustering of omics data consists of many challenges. The primary challenges in omics data analysis come from high dimension of data and small size of sample. Therefore, it is difficult to find a suitable integration method for structural analysis of multiple datasets. Results In this paper, a multi-view clustering based on Stiefel manifold method (MCSM) is proposed. The MCSM method comprises three core steps. Firstly, we established a binary optimization model for the simultaneous clustering problem. Secondly, we solved the optimization problem by linear search algorithm based on Stiefel manifold. Finally, we integrated the clustering results obtained from three omics by using k-nearest neighbor method. We applied this approach to four cancer datasets on TCGA. The result shows that our method is superior to several state-of-art methods, which depends on the hypothesis that the underlying omics cluster class is the same. Conclusion Particularly, our approach has better performance than compared approaches when the underlying clusters are inconsistent. For patients with different subtypes, both consistent and differential clusters can be identified at the same time.

Azadeh Yazdanian,1 Haleh Ayatollahi,2,3 Azin Nahvijou4 1Department of Medical Records and Health Information Technology, School of Allied Medical Sciences, Mazandaran University of Medical Sciences, Sari, Iran; 2Health Management and Economics Research Center, Iran University of Medical Sciences, Tehran, Iran; 3Department of Health Information Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran; 4Cancer Research Center of Cancer Institute, Tehran University of Medical Sciences, Tehran, IranCorrespondence: Haleh Ayatollahi Email ayatollahi.h@iums.ac.irIntroduction: Oncologists are usually faced with a huge amount of diagnostic and therapeutic data in the process of cancer care. However, they do not have access to the integrated data. This research aimed to present a conceptual model of an oncology information system based on the users’ requirements.Methods: This study was conducted in 2019 and composed of two phases. Initially, a questionnaire was designed, and clinical experts (n=34) were asked to identify the most important data elements and functional requirements in an oncology information system. In the second phase, conceptual, structural and behavioral diagrams of the system were drawn based on the results of the first phase. These diagrams were also reviewed and validated by five experts.Results: Most of the data elements and all functional requirements were found important by the experts. The data elements were related to different phases of cancer care including screening, prevention, diagnosis, treatment, mental care and pain relief, and end-of-life care. Then, conceptual, structural and behavioral diagrams of the system were designed and approved by the experts or revised based on their comments.Conclusion: The conceptual model and the diagrams presented in the current study can be used for developing an oncology information system. This system will be able to manage patients’ cancer data from screening to the end-of-life care. However, the system needs to be designed and implemented in a real healthcare setting to see how it can meet users’ requirements.Keywords: oncology information system, hospital, neoplasm, functional requirements, cancer data

Abstract Background The increasing global cancer incidence corresponds to serious health impact in countries worldwide. Knowledge-powered health system in different languages would enhance clinicians’ healthcare practice, patients’ health management and public health literacy. High-quality corpus containing cancer information is the necessary foundation of cancer education. Massive non-structural information resources exist in clinical narratives, electronic health records (EHR) etc. They can only be used for training AI models after being transformed into structured corpus. However, the scarcity of multilingual cancer corpus limits the intelligent processing, such as machine translation in medical scenarios. Thus, we created the cancer specific cross-lingual corpus and open it to the public for academic use. Methods Aiming to build an English-Chinese cancer parallel corpus, we developed a workflow of seven steps including data retrieval, data parsing, data processing, corpus implementation, assessment verification, corpus release, and application. We applied the workflow to a cross-lingual, comprehensive and authoritative cancer information resource, PDQ (Physician Data Query). We constructed, validated and released the parallel corpus named as ECCParaCorp, made it openly accessible online. Results The proposed English-Chinese Cancer Parallel Corpus (ECCParaCorp) consists of 6685 aligned text pairs in Xml, Excel, Csv format, containing 5190 sentence pairs, 1083 phrase pairs and 412 word pairs, which involved information of 6 cancers including breast cancer, liver cancer, lung cancer, esophageal cancer, colorectal cancer, and stomach cancer, and 3 cancer themes containing cancer prevention, screening, and treatment. All data in the parallel corpus are online, available for users to browse and download ( http://www.phoc.org.cn/ECCParaCorp/ ). Conclusions ECCParaCorp is a parallel corpus focused on cancer in a cross-lingual form, which is openly accessible. It would make up the imbalance of scarce multilingual corpus resources, bridge the gap between human readable information and machine understanding data resources, and would contribute to intelligent technology application as a preparatory data foundation e.g. cancer-related machine translation, cancer system development towards medical education, and disease-oriented knowledge extraction.

In this article a new flexible survival cure rate model is introduced by assuming that the number of competing causes of the event of interest follows the Flory–Schulz distribution and the competing causes follow the generalized truncated Nadarajah–Haghighi distribution. Parameter estimation for the proposed model is derived based on the maximum likelihood estimation method. A simulation study is performed to show the performance of the ML estimators. We discuss three real data applications related to real cancer data sets to assess the usefulness of the proposed model compared with some existing cure rate models for the sake of comparison.

Background: In recent years, various sequencing techniques have been used to collect biomedical omics datasets. It is usually possible to obtain multiple types of omics data from a single patient sample. Clustering of omics data plays an indispensable role in biological and medical research, and it is helpful to reveal data structures from multiple collections. Nevertheless, clustering of omics data consists of many challenges. The primary challenges in omics data analysis come from high dimension of data and small size of sample. Therefore, it is difficult to find a suitable integration method for structural analysis of multiple datasets. Results: In this paper, a multi-view clustering based on Stiefel manifold method (MCSM) is proposed. The MCSM method comprises three core steps. Firstly, we established a binary optimization model for the simultaneous clustering problem. Secondly, we solved the optimization problem by linear search algorithm based on Stiefel manifold. Finally, we integrated the clustering results obtained from three omics by using k-nearest neighbor method. We applied this approach to four cancer datasets on TCGA. The result shows that our method is superior to several state-of-art methods, which depends on the hypothesis that the underlying omics cluster class is the same. Conclusion: Particularly, our approach has better performance than compared approaches when the underlying clusters are inconsistent. For patients with different subtypes, both consistent and differential clusters can be identified at the same time. [ABSTRACT FROM AUTHOR]

In India, population‐based cancer registries (PBCRs) cover less than 15% of the urban and 1% of the rural population. Our study examines practices of registration in PBCRs in India to understand efforts to include rural populations in registries and efforts to measure social inequalities in cancer incidence. We selected a purposive sample of six PBCRs in Maharashtra, Kerala, Punjab and Mizoram and conducted semistructured interviews with staff to understand approaches and challenges to cancer registration, and the sociodemographic information collected by PBCRs. We also conducted a review of peer‐reviewed literature utilizing data from PBCRs in India. Findings show that in a context of poor access to cancer diagnosis and treatment and weak death registration, PBCRs have developed additional approaches to cancer registration, including conducting village and home visits to interview cancer patients in rural areas. Challenges included PBCR funding and staff retention, abstraction of data in medical records, address verification and responding to cancer stigma and patient migration. Most PBCRs published estimates of cancer outcomes disaggregated by age, sex and geography. Data on education, marital status, mother tongue and religion were collected, but rarely reported. Two PBCRs collected information on income and occupation and none collected information on caste. Most peer‐reviewed studies using PBCR data did not publish estimates of social inequalities in cancer outcomes. Results indicate that collecting and reporting sociodemographic data collected by PBCRs is feasible. Improved PBCR coverage and data will enable India's cancer prevention and control programs to be guided by data on cancer inequities. What's new? In India, cancer registries cover less than 15% of the urban and 1% of the rural population. Given this, can such registries reveal how poverty and social inequalities contribute to unequal cancer incidence? In this study, the authors found that, in many cases, the answer is yes. Equity analysis of cancer‐incidence data in India is feasible, as many registries have collected extensive sociodemographic information. These results indicate that improved coverage and information collection will enable India's cancer prevention and control programs to be guided by data on cancer inequities. [ABSTRACT FROM AUTHOR]

Background: The increasing global cancer incidence corresponds to serious health impact in countries worldwide. Knowledge-powered health system in different languages would enhance clinicians' healthcare practice, patients' health management and public health literacy. High-quality corpus containing cancer information is the necessary foundation of cancer education. Massive non-structural information resources exist in clinical narratives, electronic health records (EHR) etc. They can only be used for training AI models after being transformed into structured corpus. However, the scarcity of multilingual cancer corpus limits the intelligent processing, such as machine translation in medical scenarios. Thus, we created the cancer specific cross-lingual corpus and open it to the public for academic use.Methods: Aiming to build an English-Chinese cancer parallel corpus, we developed a workflow of seven steps including data retrieval, data parsing, data processing, corpus implementation, assessment verification, corpus release, and application. We applied the workflow to a cross-lingual, comprehensive and authoritative cancer information resource, PDQ (Physician Data Query). We constructed, validated and released the parallel corpus named as ECCParaCorp, made it openly accessible online.Results: The proposed English-Chinese Cancer Parallel Corpus (ECCParaCorp) consists of 6685 aligned text pairs in Xml, Excel, Csv format, containing 5190 sentence pairs, 1083 phrase pairs and 412 word pairs, which involved information of 6 cancers including breast cancer, liver cancer, lung cancer, esophageal cancer, colorectal cancer, and stomach cancer, and 3 cancer themes containing cancer prevention, screening, and treatment. All data in the parallel corpus are online, available for users to browse and download ( http://www.phoc.org.cn/ECCParaCorp/ ).Conclusions: ECCParaCorp is a parallel corpus focused on cancer in a cross-lingual form, which is openly accessible. It would make up the imbalance of scarce multilingual corpus resources, bridge the gap between human readable information and machine understanding data resources, and would contribute to intelligent technology application as a preparatory data foundation e.g. cancer-related machine translation, cancer system development towards medical education, and disease-oriented knowledge extraction. [ABSTRACT FROM AUTHOR]

We begin with a review of asymptotic properties of a purely sequential minimum risk point estimation (MRPE) methodology for an unknown mean in a one-parameter exponential distribution under a class of generalized loss functions. This class of powered absolute error loss (PAEL) includes both squared error loss (SEL) and absolute error loss (AEL) plus cost of sampling. We prove the asymptotic second-order efficiency property and asymptotic first-order risk efficiency property associated with the purely sequential MRPE problem. For operational convenience, we then move to implement an accelerated sequential MRPE methodology and prove the analogous asymptotic second-order efficiency property and asymptotic first-order risk efficiency property. We follow up with extensive data analysis from simulations and provide illustrations using cancer data. [ABSTRACT FROM AUTHOR]

High dimensional data have brobdingnagian number of features, but not all features are useful. Irrelevant and redundant features may even reduce the classification accuracy. Feature selection is a process of selecting a subset of relevant features to decrease the dimensionality of data. When applied on high dimensional datasets (Big Data) the feature selection methods perceives many challenges and it is pertinent to come up with the new methods or revamp the existing methods. In this study, a new method 'Newtonian particle swarm optimization (NPSO)' has been proposed. In the proposed method Newton's second law of motion has been used to update the learning mechanism of PSO. In NPSO, particle not only learn from the position but also from the mass and acceleration of neighboring particles. The proposed method is mathematically validated at equilibrium using eigen values. Further, the proposed method has been applied on high dimensional microarray gene expression dataset. The NPSO is also compared with other state of art feature selection methods. Selected features, classification accuracy and dimension reduction are used to appraise the goodness of the proposed method. Mathematical validation and experimental results clearly validates the merits of the proposed method in field of feature selection. This paper show the classwise analysis of SRBCT, Brain1, 11-Tumor and 14-Tumor datasets. When number of classes increased dimension reduction is increased but classification accuracy of dataset is decreased. [ABSTRACT FROM AUTHOR]

The new era of Deep learning has found many widespread uses in diverse fields such as Science, Fashion, Game, Medical, Health etc. which has gained a huge attention for the researchers. Recently, Generative Adversarial Network (GAN) has crucial contribution in the field of medical image analysis, along with different variants of GAN it enhances the capability to resolve the challenging problems in medical field which leads to the betterment of healthcare technologies. Furthermore, GAN has proven to be useful, to synthesize images that can resolve the scarcity of real training data especially in medical and healthcare field of research. In this paper, we present an attempt to improve dataset for CT scan images of stomach cancer using GAN approaches to various fields of medical image analysis. The paper also provides a statistical comparison of generated images which are found to be of high quality. [ABSTRACT FROM AUTHOR]

[Display omitted] • A novel feature selection approach for breast cancer prediction is proposed. • Feature selection was based on the three soft-computing algorithms. • The Bacterial foraging algorithm and the Emperor penguin algorithm are implemented. • Hybrid of Bacterial foraging algorithm and Emperor penguin algorithm is proposed. • Nine performance metrics are computed for six machine learning classifiers. Feature selection is one of the crucial data preprocessing techniques for improving the performance of machine learning (ML) models. Recently, metaheuristic feature selection algorithms have become popular because they select optimal features for ML problems. This paper presents three feature selection strategies based on metaheuristic algorithms: Bacterial Foraging (BFOA), Emperor Penguin (EPO), and a hybrid (hBFEPO) combining BFOA and EPO. The baseline algorithms have been investigated for feature selection in other ML tasks, but not for breast cancer classification. A hybrid of these two has been used for the first time. These strategies were initially tested on the COVID-19 dataset. After achieving satisfactory results, these strategies are evaluated on the WDBC Breast Cancer dataset. The performance of our models on WDBC is compared with recent eighteen state-of-the-art studies. The results indicate that the hBFEPO model outperforms other models, achieving 100% precision and specificity, 98.49% accuracy, 95.43% sensitivity, a 95.99% F1-score, and a 99.60% AUC. [ABSTRACT FROM AUTHOR]

This Letter proposes a customised approach for attribute selection applied to the fuzzy rough quick reduct algorithm. The unbalanced data is balanced using synthetic minority oversampling technique. The huge dimensionality of the cancer data is reduced using a correlation-based filter. The dimensionality reduced balanced attribute gene subset is used to compute the final minimal reduct set using a customised fuzzy triangular norm operator on the fuzzy rough quick reduct algorithm. The customised fuzzy triangular norm operator is used with a Lukasiewicz fuzzy implicator to compute the fuzzy approximation. The customised operator selects the least number of informative feature genes from the dimensionality reduced datasets. Classification accuracy using leave-one-out cross validation of 94.85, 76.54, 98.11, and 99.13% is obtained using a customised function for Lukasiewicz triangular norm operator on leukemia, central nervous system, lung, and ovarian datasets, respectively. Performance analysis of the conventional fuzzy rough quick reduct and the proposed method are performed using parameters such as classification accuracy, precision, recall, F-measure, scatter plots, receiver operating characteristic area, McNemar test, chi-squared test, Matthew's correlation coefficient and false discovery rate that are used to prove that the proposed approach performs better than available methods in the literature.

Motivat ion: Uncovering the relationship between micro-RNAs (miRNAs) and their target messenger RNAs (mRNAs) can provide critical information regarding the mechanisms underlying certain types of cancers. In this context, we have proposed a computational method, referred to as prediction analysis by optimization method (PAOM), to predict miRNA-mRNA relations using data from normal and cancer tissues, and then applying the relevant algorithms to colon and breast cancers. Specifically, we used 26 miRNAs and 26 mRNAs with 676 (= 26 × 26) relationships to be recovered as unknown parameters. Results : Optimization methods were used to detect 61 relationships in breast cancer and 32 relationships in colon cancer. Using sequence filtering, we detected 18 relationships in breast cancer and 15 relationships in colon cancer. Among the 18 relationships, CD24 is the target gene of let-7a and miR-98, and E2F1 is the target gene of miR-20. In addition, the frequencies of the target genes of miR-223, miR-23a, and miR-20 were significant in breast cancer, and the frequencies of the target genes of miR-17, miR-124, and miR-30a were found to be significant in colon cancer. Availab ility: The numerical code is available from the authors on request. [ABSTRACT FROM AUTHOR]

Aim The aim of this study was to identify the most crucial interval to encourage earlier diagnosis in with gastroesophageal cancer and to identify potential factors effecting this interval. Background Gastroesophageal malignancy is the eighth most commonly presenting cancer with one of the worst survival rates. Identifying the most crucial period for intervention to inform earlier diagnosis is an important step towards improving survival. Design Mixed methods literature review. Data Sources CINAHL, MEDLINE and Academic search primer online databases were searched using keywords and inclusion/exclusion criteria. Empirical evidence published between 2000-2016 with a focus on gastroesophageal cancer presentation and survival was reviewed to inform this study. Review methods Twelve studies were extracted for further review. Selected studies were appraised and presented through Olensen's 'delay interval' framework to inform the most crucial interval to survival in gastroesophageal cancer. Results The findings identify the patient interval as the most critical period for encouraging earlier presentation and reducing advanced stage presentation in gastroesophageal cancer. The article also highlighted some methodological limitations to cancer research, such as a lack of consensus in definitions which prevent statistical meta-analysis of cancer data, survivor bias in gastroesophageal cancer studies and a significant lack of qualitative evidence to reveal patient experience in presenting with this cancer. Conclusion Further research into the patient interval is required to elicit information on how and why patients present with their cancer symptoms. [ABSTRACT FROM AUTHOR]

Background and Objective Micro or macro-level mapping of cancer statistics is a challenging task that requires long-term planning, prospective studies and continuous monitoring of all cancer cases. The objective of the current study is to present how cancer registry data could be processed using data mining techniques in order to improve the statistical analysis outcomes. Methods Data were collected from the Cancer Registry of Crete in Greece (counties of Rethymno and Lasithi) for the period 1998–2004. Data collection was performed on paper forms and manually transcribed to a single data file, thus introducing errors and noise (e.g. missing and erroneous values, duplicate entries etc.). Data were pre-processed and prepared for analysis using data mining tools and algorithms. Feature selection was applied to evaluate the contribution of each collected feature in predicting patients’ survival. Several classifiers were trained and evaluated for their ability to predict survival of patients. Finally, statistical analysis of cancer morbidity and mortality rates in the two regions was performed in order to validate the initial findings. Results Several critical points in the process of data collection, preprocessing and analysis of cancer data were derived from the results, while a road-map for future population data studies was developed. In addition, increased morbidity rates were observed in the counties of Crete (Age Standardized Morbidity/Incidence Rates ASIR = 396.45 ± 2.89 and 274.77 ± 2.48 for men and women, respectively) compared to European and world averages (ASIR= 281.6 and 207.3 for men and women in Europe and 203.8 and 165.1 in world level). Significant variation in cancer types between sexes and age groups (the ratio between deaths and reported cases for young patients, less than 34 years old, is at 0.055 when the respective ratio for patients over 75 years old is 0.366) was also observed. Conclusions This study introduced a methodology for preprocessing and analyzing cancer data, using a combination of data mining techniques that could be a useful tool for other researchers and further enhancement of the cancer registries. [ABSTRACT FROM AUTHOR]