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Purpose. Artificial intelligence (AI) techniques are used in precision medicine to explore novel genotypes and phenotypes data. The main aims of precision medicine include early diagnosis, screening, and personalized treatment regime for a patient based on genetic-oriented features and characteristics. The main objective of this study was to review AI techniques and their effectiveness in neoplasm precision medicine. Materials and Methods. A comprehensive search was performed in Medline (through PubMed), Scopus, ISI Web of Science, IEEE Xplore, Embase, and Cochrane databases from inception to December 29, 2021, in order to identify the studies that used AI methods for cancer precision medicine and evaluate outcomes of the models. Results. Sixty-three studies were included in this systematic review. The main AI approaches in 17 papers (26.9%) were linear and nonlinear categories (random forest or decision trees), and in 21 citations, rule-based systems and deep learning models were used. Notably, 62% of the articles were done in the United States and China. R package was the most frequent software, and breast and lung cancer were the most selected neoplasms in the papers. Out of 63 papers, in 34 articles, genomic data like gene expression, somatic mutation data, phenotype data, and proteomics with drug-response which is functional data was used as input in AI methods; in 16 papers' (25.3%) drug response, functional data was utilized in personalization of treatment. The maximum values of the assessment indicators such as accuracy, sensitivity, specificity, precision, recall, and area under the curve (AUC) in included studies were 0.99, 1.00, 0.96, 0.98, 0.99, and 0.9929, respectively. Conclusion. The findings showed that in many cases, the use of artificial intelligence methods had effective application in personalized medicine. [ABSTRACT FROM AUTHOR]

Purpose: eHealth could potentially support colorectal cancer survivors; however, little is known regarding the overall recent eHealth systems for colorectal cancer survivors. The present study was conducted to address which types of eHealth supports have been provided to colorectal cancer survivors in the past two decades.Methods: An electronic search was conducted in four databases including Scopus, PubMed, Embase, and Web of Science. The search query was based on two concepts: the first concept represented colorectal cancer and the second one comprised of information technology tools. The search was limited to 20 years (from 19 January 1999 to 19 January 2019). Obtained results were tabulated and represented as a framework.Results: Fifteen papers were included in this systematic review. Information including intervention type, eHealth tools, main features of the system, and outcomes were extracted from selected papers. Obtained results were characterized using a four-layer framework. This framework included layers of hardware, software, service (educating the patient, medication intake, physical activity, health status monitoring, hospital visit reminder, and discussion group), and outcome. Outcome layer was composed of the following domains: quality of life, psychological and cognitive, physical activity, physical functioning, symptoms, engagement, and the outcome of the process and IT tools.Conclusion: eHealth could provide useful services for supporting colorectal cancer survivors. Represented framework might be used for a better understanding of current technology and services provided to support these survivors. Also, this framework may be used as a basis for designing eHealth applications for colorectal cancer survivors after further validations. [ABSTRACT FROM AUTHOR]

Background: Detecting brain tumors in their early stages is crucial. Brain tumors are classified by biopsy, which can only be performed through definitive brain surgery. Computational intelligence-oriented techniques can help physicians identify and classify brain tumors. Herein, we proposed two deep learning methods and several machine learning approaches for diagnosing three types of tumor, i.e., glioma, meningioma, and pituitary gland tumors, as well as healthy brains without tumors, using magnetic resonance brain images to enable physicians to detect with high accuracy tumors in early stages. Materials and Methods: A dataset containing 3264 Magnetic Resonance Imaging (MRI) brain images comprising images of glioma, meningioma, pituitary gland tumors, and healthy brains were used in this study. First, preprocessing and augmentation algorithms were applied to MRI brain images. Next, we developed a new 2D Convolutional Neural Network (CNN) and a convolutional auto-encoder network, both of which were already trained by our assigned hyperparameters. Then 2D CNN includes several convolution layers; all layers in this hierarchical network have a 2*2 kernel function. This network consists of eight convolutional and four pooling layers, and after all convolution layers, batch-normalization layers were applied. The modified auto-encoder network includes a convolutional auto-encoder network and a convolutional network for classification that uses the last output encoder layer of the first part. Furthermore, six machine-learning techniques that were applied to classify brain tumors were also compared in this study. Results: The training accuracy of the proposed 2D CNN and that of the proposed auto-encoder network were found to be 96.47% and 95.63%, respectively. The average recall values for the 2D CNN and auto-encoder networks were 95% and 94%, respectively. The areas under the ROC curve for both networks were 0.99 or 1. Among applied machine learning methods, Multilayer Perceptron (MLP) (28%) and K-Nearest Neighbors (KNN) (86%) achieved the lowest and highest accuracy rates, respectively. Statistical tests showed a significant difference between the means of the two methods developed in this study and several machine learning methods (p-value < 0.05). Conclusion: The present study shows that the proposed 2D CNN has optimal accuracy in classifying brain tumors. Comparing the performance of various CNNs and machine learning methods in diagnosing three types of brain tumors revealed that the 2D CNN achieved exemplary performance and optimal execution time without latency. This proposed network is less complex than the auto-encoder network and can be employed by radiologists and physicians in clinical systems for brain tumor detection. [ABSTRACT FROM AUTHOR]

Purpose: Decision Aid systems (DAs) provide information on the pros and cons of mammography. This study aimed to review the research on mammography DAs, synthesize the findings related to their outcomes and characteristics, and address the existed research gap.Methods: Relevant studies were identified through a comprehensive search on some e-databases, including PubMed, EMBASE, Scopus, and Web of Science in August 2020; by searching the keywords of "Breast cancer", "Screening", and "Decision aid systems" as well as their synonyms in the titles and abstracts of the papers with no time limits. Among the selected English journal papers with the interventional study design, those measuring outcome values of using mammography DAs were recognized as eligible for being included in this review.Results: The systematic search results in 16 DAs regarding mammography that were designed and then evaluated from 18 selected studies. The results showed that DAs provide improvements in knowledge and informed choice, the decreased decisional conflicts and decisional confidence, almost without changing any attitude towards mammography, mammography participation rates, psychological issues, anticipated regret, and perceived risk of breast cancer. The DAs' effects on women's inclination to screening were divergent. In other words, the DAs affect individuals' inclination in rare cases; however, on occasion, they could affect women's decision to undergo screening.Conclusion: DAs could correct the bias attached to the existing knowledge on mammography and breast cancer in women so that they are more likely to make a precise decision. Additionally, it might be of central importance in shared decision-making and assisting health providers, in order to promote the quality of care. Accordingly, performing more studies is needed to develop more professional DAs in various countries with different facilities, cultures, and languages. [ABSTRACT FROM AUTHOR]