MERVIN BURNETT¹, VITO RODOLICO², FAN SHEN¹, ROGER LENG¹, MINGYONG ZHANG³, DAVID D. EISENSTAT^4,5, CONSOLATO SERGI^1,3,6,*

BIOCELL, Vol.45, No.1, pp. 17-26, 2021, DOI:10.32604/biocell.2021.013973

Abstract MicroRNAs (miRNAs) are small single-stranded, non-coding RNA molecules involved in the pathogenesis and progression of cancer, including osteosarcoma. We aimed to clarify the pathways involving miRNAs using new bioinformatics tools. We applied WikiPathways and PathVisio, two open-source platforms, to analyze miRNAs in osteosarcoma using miRTar and ONCO.IO as integration tools. We found 1298 records of osteosarcoma papers associated with the word “miRNA”. In osteosarcoma patients with good response to chemotherapy, miR-92a, miR- 99b, miR-193a-5p, and miR-422a expression is increased, while miR-132 is decreased. All identified miRNAs seem to be centered on the TP53 network. This is the first application of… More >

Muhammad Sohaib Aslam¹, Muhammad Younas¹, Muhammad Umar Sarwar¹, Muhammad Arif Shah^2,*, Atif Khan³, M. Irfan Uddin⁴, Shafiq Ahmad⁵, Muhammad Firdausi⁵, Mazen Zaindin⁶

CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 1899-1914, 2021, DOI:10.32604/cmc.2021.015151

Abstract Liver tumor is the fifth most occurring type of tumor in men and the ninth most occurring type of tumor in women according to recent reports of Global cancer statistics 2018. There are several imaging tests like Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and ultrasound that can diagnose the liver tumor after taking the sample from the tissue of the liver. These tests are costly and time-consuming. This paper proposed that image processing through deep learning Convolutional Neural Network (CNNs) ResUNet model that can be helpful for the early diagnose of tumor instead of conventional methods. The existing studies… More >

Prabakaran Rajamanickam¹, Shiloah Elizabeth Darmanayagam^1,*, Sunil Retmin Raj Cyril Raj²

CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 709-722, 2021, DOI:10.32604/cmc.2021.014347

Abstract Liver Segmentation is one of the challenging tasks in detecting and classifying liver tumors from Computed Tomography (CT) images. The segmentation of hepatic organ is more intricate task, owing to the fact that it possesses a sizeable quantum of vascularization. This paper proposes an algorithm for automatic seed point selection using energy feature for use in level set algorithm for segmentation of liver region in CT scans. The effectiveness of the method can be determined when used in a model to classify the liver CT images as tumorous or not. This involves segmentation of the region of interest (ROI) from… More >

Mavra Mehmood¹, Ember Ayub¹, Fahad Ahmad^1,6,*, Madallah Alruwaili², Ziyad A. Alrowaili³, Saad Alanazi², Mamoona Humayun², Muhammad Rizwan¹, Shahid Naseem⁴, Tahir Alyas⁵

CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 641-657, 2021, DOI:10.32604/cmc.2021.013774

Abstract Clinical image processing plays a significant role in healthcare systems and is currently a widely used methodology. In carcinogenic diseases, time is crucial; thus, an image’s accurate analysis can help treat disease at an early stage. Ductal carcinoma in situ (DCIS) and lobular carcinoma in situ (LCIS) are common types of malignancies that affect both women and men. The number of cases of DCIS and LCIS has increased every year since 2002, while it still takes a considerable amount of time to recommend a controlling technique. Image processing is a powerful technique to analyze preprocessed images to retrieve useful information… More >

Ihsan Jasim Hussein¹, M. A. Burhanuddin², Mazin Abed Mohammed^3,*, Mohamed Elhoseny⁴, Begonya Garcia-Zapirain⁵, Marwah Suliman Maashi⁶, Mashael S. Maashi⁷

CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 3161-3182, 2021, DOI:10.32604/cmc.2021.012691

Abstract One of the most complex tasks for computer-aided diagnosis (Intelligent decision support system) is the segmentation of lesions. Thus, this study proposes a new fully automated method for the segmentation of ovarian and breast ultrasound images. The main contributions of this research is the development of a novel Viola–James model capable of segmenting the ultrasound images of breast and ovarian cancer cases. In addition, proposed an approach that can efficiently generate region-of-interest (ROI) and new features that can be used in characterizing lesion boundaries. This study uses two databases in training and testing the proposed segmentation approach. The breast cancer… More >

Nan Geng¹, Wenxia Hu¹, Zhikun Liu², Jingwei Su², Wenyu Sun³, Shaonan Xie⁴, Cuimin Ding^1,*

Oncologie, Vol.22, No.4, pp. 245-254, 2020, DOI:10.32604/oncologie.2020.014125

Abstract Objective: This study was set out to inquire into the expression and clinical significance of lncRNA activated by transforming growth factor β (LncRNA-ATB) and in cancer tissues of patients with non-small cell lung cancer (NSCLC). Methods: LncRNA-ATB in cancer tissues and adjacent tissues of 89 NSCLC patients was detected by quantitative real-time polymerase chain reaction (qRT-PCR), and its clinical diagnostic value in NSCLC was determined by receiver operating characteristic (ROC) curves. Based on the median expression of LncRNAATB in NSCLC tissues, 89 patients were allocated into high- and low-expression groups. The 3-year survival rate was calculated using Kaplan-Meier method and… More >

Sophie Gardrat^*, Vincent Cockenpot

Oncologie, Vol.22, No.4, pp. 195-202, 2020, DOI:10.32604/oncologie.2020.013698

Abstract The pathological examination of ocular tumors has specificities in terms of macroscopic management, microscopic analysis, and molecular examinations requiring special attention. We discuss here the difficulties encountered in the reception in the pathological anatomy laboratory of conjunctival samples, enucleation and orbital exenteration pieces, then detail the diagnostic and theranostic, microscopic and molecular characteristics of ocular tumor pathologies. Conjunctival tumors (epithelial, melanocytic and lymphoid), choroidal tumors (including uveal melanoma) and retinoblastoma are treated. Because of their low frequency and their features, these tumors should be the subject of anatomo-clinical discussions.

Résumé:
L’examen anatomopathologique des tumeurs oculaires comporte des spécificités en termes… More >

Laurence Desjardins^*

Oncologie, Vol.22, No.4, pp. 189-194, 2020, DOI:10.32604/oncologie.2020.012377

Abstract We describe the most frequent malignant intraocular tumors, conjunctival tumors and some lids and orbital tumors. Primary intraocular malignant tumors are retinoblastoma in children and uveal melanoma in adults. For uveal melanoma, the liver is the most frequent site of metastasis and this is why it is justified to prescribe liver ultrasonography every 6 months to these patients. Metastatic tumors can occur in the uvea and more frequently in the posterior part called the choroid. They are more frequent after breast cancer and lung cancer. Conjunctival tumors can be epithelial (benign papillomas and epidermoid carcinomas) or melanocytic (benign naevi and… More >

Panjie Yang^1,*,#, Gang Liu^2,#, Xiaoyu Li^1,*, Liyuan Qin¹, Xiaoxia Liu³

Journal of New Media, Vol.2, No.3, pp. 111-119, 2020, DOI:10.32604/jnm.2020.010135

Abstract In order to solve the problems of low efficiency and heavy workload of tumor coding in hospitals, we proposed a Drools-based intelligent tumors coding method. At present, most tumor hospitals use manual coding, the trained coders follow the main diagnosis selection rules to select the main diagnosis from the discharge diagnosis of the tumor patients, and then code all the discharge diagnoses according to the coding rules. Owing to different coders have different familiarity with the main diagnosis selection rules and ICD-10 disease coding, it will reduce the efficiency of the artificial coding results and affect the quality of the… More >

Li-Hong Juang^a, Ming-Ni Wu^b

Intelligent Automation & Soft Computing, Vol.24, No.2, pp. 257-266, 2018, DOI:10.1080/10798587.2016.1272778

Abstract In this paper we explore these math approaches for medical image applications. The application of the proposed method for detection tumor will be able to distinguish exactly tumor size and region. In this research, some major design and experimental results of tumor objects detection method for medical brain images is developed to utilize an automatic multi-thresholding method to handle this problem by combining the histogram analysis and the Otsu clustering. The histogram evaluations can decide the superior number of clusters firstly. The Otsu classification algorithm solves the given medical image by continuously separating the input gray-level image by multi-thresholding until… More >