Search

BACKGROUND: Accurate diagnosis and subsequent delineated treatment planning require the experience of clinicians in the handling of their case numbers. However, applying deep learning in image processing is useful in creating tools that promise faster high-quality diagnoses, but the accuracy and precision of 3-D image processing from 2-D data may be limited by factors such as superposition of organs, distortion and magnification, and detection of new pathologies. The purpose of this research is to use radiomics and deep learning to develop a tool for lung cancer diagnosis. METHODS: This study applies radiomics and deep learning in the diagnosis of lung cancer to help clinicians accurately analyze the images and thereby provide the appropriate treatment planning. 86 patients were recruited from Bach Mai Hospital, and 1012 patients were collected from an open-source database. First, deep learning has been applied in the process of segmentation by U-NET and cancer classification via the use of the DenseNet model. Second, the radiomics were applied for measuring and calculating diameter, surface area, and volume. Finally, the hardware also was designed by connecting between Arduino Nano and MFRC522 module for reading data from the tag. In addition, the displayed interface was created on a web platform using Python through Streamlit. RESULTS: The applied segmentation model yielded a validation loss of 0.498, a train loss of 0.27, a cancer classification validation loss of 0.78, and a training accuracy of 0.98. The outcomes of the diagnostic capabilities of lung cancer (recognition and classification of lung cancer from chest CT scans) were quite successful. CONCLUSIONS: The model provided means for storing and updating patients' data directly on the interface which allowed the results to be readily available for the health care providers. The developed system will improve clinical communication and information exchange. Moreover, it can manage efforts by generating correlated and coherent summaries of cancer diagnoses. [ABSTRACT FROM AUTHOR]

The sensitivity to ionizing radiation is increasing by age of development for some malignant tumors. Therefore, children have higher risk to radiation induced tumors due the high cellular rate of proliferation and long lifespan probability. The risk is also increase with increase the effective and organ doses. Computed tomography (CT) exposed pediatric patients to higher radiation dose during multiphase image acquisition, repeated exams, for follow-up procedures. This research intended to estimate the radiogenic risks and effective radiation doses resulted from CT enhanced contrast for abdomen and pelvis. 126 (66 (62.3%) males, 60 (47.7%) females) pediatric patients underwent CT enhanced abdominal examination at Medical Imaging Department at King Khalid Hospital and Prince Sultan Center for Health Services, Alkharj, Saudi Arabia. The average and range of pediatric age (years) is 11.6 ± 5.0 (0.1-17). The mean, standard deviation, and range of the volume CT air kerma index (C VOL (mGy) and the air kerma length product (P KL , mGy.cm) were 9.8 ± 9.4 (2.1-45.8) and 1795 (221-3150) per abdominopelvic procedure, respectively. The mean and range of the effective dose (mSv) per procedure are 26.9 (2.4-59.1). The effective dose is higher compared to the most previously published studies. The effective dose per pediatric abdomen and pelvis with contrast procedure suggest that the patient dose is not optimized yet. Because the chest and pelvis region contain sensitive organs that are irradiated repeatedly, dose optimization is crucial., Competing Interests: Declaration of competing interest All authors declare that there is no conflict of Interest., (Copyright © 2024 Elsevier Ltd. All rights reserved.)