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Go to Editorial ManagerTechnically, medical imaging modalities are quantitative, qualitative, and semi-quantitative. Such modalities can generate meaningful and valuable quantitative and qualitative data. Correlating predictive outcomes with quantitative and qualitative data is a difficult process. Thanks to modern computational hardware and advanced machine learning algorithms, it is not a demanding job to perform predictive analysis by cultivating quantitative and qualitative data. Radiomics is a popular topic that studies quantitative data from medical images in order to obtain biologically meaningful information for diagnosis, prognosis, theragnosis, and decision support. Handcrafted radiomics is a process including features based on shape, pixel, and texture-related knowledge from medical scans. In the pursuit of advancing the field of radiomics, we have developed a cutting-edge radiomics training simulator, powered by MATLAB. This tool has been designed for those familiar with MATLAB, making it easy for them to transition into the fascinating world of radiomics. MATLAB's user-friendly interface and strong support in the engineering community provide an ideal platform for this simulator, ensuring aspiring radiomics learners have access to the resources they need for success. Throughout the paper, purpose, design details and methodology of the simulator are described.
Artificial intelligence (AI) is rapidly advancing as a valuable tool in oncology for enhancing detection and management of cancer. The integration of AI with PET/CT imaging presents significant scenarios for improving efficiency and accuracy of cancer diagnosis. This study examines the current applications of AI with PET/CT imaging, highlighting its role in diagnosing, differentiating, delineating, staging, assessing therapy response, determining prognosis, and enhancing image quality. A comprehensive literature search was conducted in six data-bases to get the most recent works, use Springer, Scopus, PubMed, Web of Science, IEEE, and Google Scholar in the last five years (2019-2024), identifying 80 studies that met the criteria for inclusion that focused on AI-driven models applied to PET/CT data in various cancers, with lung cancer being the most studied. Other cancers examined include head and neck, breast, lymph nodes, whole body, and others. All studies involved human subjects. The findings indicate that AI holds promise in improving cancer detection, identifying benign from malignant tumors, aiding in segmentation, response evaluation, staging, and determining the prognosis. However, the application of AI-powered models and PET/CT-derived radiomics in clinical practice is limited because of issues of data normalization, reproducibility, and the requirement of large multi-center data sets for improving model generalizability. All these limitations have to be solved to guarantee the dependable and ethical use of AI in day-to-day clinical activities.
The standardization and preprocessing decisions determine the performance, reproducibility and generalizability of Artificial Intelligence (AI) models in cervical spine Magnetic Resonance Imaging (MRI). In this research, the author suggests a six-step standardized preprocessing pipeline that can help with the reliable analysis of heterogeneous MRI data using AI. It is obtained by the pipeline based on recurrent methodological patterns found in the literature, focusing on the operations reported to stabilize image intensity distribution and reduce unwanted sources of variability. A systematic literature review was screened on 2,882 records, and 43 articles were included per the inclusion criteria. Due to the large variability of imaging procedures, preprocessing plans, and reported outcomes metrics, no quantitative meta-analysis was performed and a qualitative synthesis was done. Basic methods such as segmentation, denoising, intensity normalization, augmentation, bias-field correction were consistently reported to improve AI performance. Studies evaluating tasks such as normalization and automated segmentation reported accuracies of 94% to 99.95%. Moreover, less-specific harmonization methods like Combatting Batch Effects (ComBat), z-score normalization and histogram matching, and Generative Adversarial Network (GAN)-based ones were also often attributed with lower scanner- and site-based variations. Regardless of these improvements, the AI workflow remains unstable because of the irregularity of predefined parameters, unequal standards of acquisition, and lack of methodological reporting. In efforts to deal with such challenges, the current work proposes a methodical six-stage preprocessing model that consists of quality control, noise removal, intensity normalization, anatomical segmentation, harmonization and validation. The suggested workflow model offers a practical and transparent basis of clinically translatable AI usage in cervical spine MRI based on conventional mathematical equations and clear documentation.