Deep Learning Application in Leukemia Diagnosis

Cancer is an aggressive disease with a low median survival rate. Technically, the cost of the treatment is high due to its high recurrence and mortality rates. Accurate and early diagnosis is needed to cure cancer. Even though, there is a lot of applications in the field of medical by using Artificial Intelligence. Artificial Intelligence (AI), especially machine learning and deep learning, has found as popular application in clinical cancer researches in recent years. The prediction of cancer cells has been reached new heights, as the technology is improved day-by-day and lots of devices are invented to detect and to cure cancer cells. Artificial Intelligence (AI)assist cancer diagnosis and prognosis, specifically with regards with unprecedented accuracy, which is even higher than that of general statistical applications in Oncology. There are different types of cancer cells and to destroy these cells, humans required certain technologies to locate and identify the type of cancer. It is very complicated to cure the cancer if it is not found in the early days. This article is about the LEUKEMIA (Blood cancer) and the technologies used for curing Leukemia. The opportunities and the challenges faced in the clinical implementation of Artificial Intelligence (AI).Machine Learningis used to save a life in advance by the early cancer diagnosis and prognosis in the present and in future too.

Deep Learning in Cancer Diagnosis and Prognosis Prediction: A Minireview on Challenges, Recent Trends, and Future Directions

Deep learning (DL) is a branch of machine learning and artificial intelligence that has been applied to many areas in different domains such as health care and drug design. Cancer prognosis estimates the ultimate fate of a cancer subject and provides survival estimation of the subjects. An accurate and timely diagnostic and prognostic decision will greatly benefit cancer subjects. DL has emerged as a technology of choice due to the availability of high computational resources. The main components in a standard computer-aided design (CAD) system are preprocessing, feature recognition, extraction and selection, categorization, and performance assessment. Reduction of costs associated with sequencing systems offers a myriad of opportunities for building precise models for cancer diagnosis and prognosis prediction. In this survey, we provided a summary of current works where DL has helped to determine the best models for the cancer diagnosis and prognosis prediction tasks. DL is a generic model requiring minimal data manipulations and achieves better results while working with enormous volumes of data. Aims are to scrutinize the influence of DL systems using histopathology images, present a summary of state-of-the-art DL methods, and give directions to future researchers to refine the existing methods.

MiR-873-5p: A Potential Molecular Marker for Cancer Diagnosis and Prognosis

miR-873 is a microRNA located on chromosome 9p21.1. miR-873-5p and miR-873-3p are the two main members of the miR-873 family. Most studies focus on miR-873-5p, and there are a few studies on miR-873-3p. The expression level of miR-873-5p was down-regulated in 14 cancers and up-regulated in 4 cancers. miR-873-5p has many targeted genes, which have unique molecular functions such as catalytic activity, transcription regulation, and binding. miR-873-5p affects cancer development through the PIK3/AKT/mTOR, Wnt/β-Catenin, NF-κβ, and MEK/ERK signaling pathways. In addition, the target genes of miR-873-5p are closely related to the proliferation, apoptosis, migration, invasion, cell cycle, cell stemness, and glycolysis of cancer cells. The target genes of miR-873-5p are also related to the efficacy of several anti-cancer drugs. Currently, in cancer, the expression of miR-873-5p is regulated by a variety of epigenetic factors. This review summarizes the role and mechanism of miR-873-5p in human tumors shows the potential value of miR-873-5p as a molecular marker for cancer diagnosis and prognosis.

High-resolution metabolomic biomarkers for lung cancer diagnosis and prognosis

Lung cancer is the leading cause of human cancer mortality due to the lack of early diagnosis technology. The low-dose computed tomography scan (LDCT) is one of the main techniques to screen cancers. However, LDCT still has a risk of radiation exposure and it is not suitable for the general public. In this study, plasma metabolic profiles of lung cancer were performed using a comprehensive metabolomic method with different liquid chromatography methods coupled with a Q-Exactive high-resolution mass spectrometer. Metabolites with different polarities (amino acids, fatty acids, and acylcarnitines) can be detected and identified as differential metabolites of lung cancer in small volumes of plasma. Logistic regression models were further developed to identify cancer stages and types using those significant biomarkers. Using the Variable Importance in Projection (VIP) and the area under the curve (AUC) scores, we have successfully identified the top 5, 10, and 20 metabolites that can be used to differentiate lung cancer stages and types. The discrimination accuracy and AUC score can be as high as 0.829 and 0.869 using the five most significant metabolites. This study demonstrated that using 5 + metabolites (Palmitic acid, Heptadecanoic acid, 4-Oxoproline, Tridecanoic acid, Ornithine, and etc.) has the potential for early lung cancer screening. This finding is useful for transferring the diagnostic technology onto a point-of-care device for lung cancer diagnosis and prognosis.