Pragati Patharia, Prabira Kumar Sethy, Aziz Nanthaamornphong
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引用次数: 0
Abstract
Image-based diagnosis has become a crucial tool in the identification and management of various cancers, particularly lung and colon cancer. This review delves into the latest advancements and ongoing challenges in the field, with a focus on deep learning, machine learning, and image processing techniques applied to X-rays, CT scans, and histopathological images. Significant progress has been made in imaging technologies like computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET), which, when combined with machine learning and artificial intelligence (AI) methodologies, have greatly enhanced the accuracy of cancer detection and characterization. These advances have enabled early detection, more precise tumor localization, personalized treatment plans, and overall improved patient outcomes. However, despite these improvements, challenges persist. Variability in image interpretation, the lack of standardized diagnostic protocols, unequal access to advanced imaging technologies, and concerns over data privacy and security within AI-based systems remain major obstacles. Furthermore, integrating imaging data with broader clinical information is crucial to achieving a more comprehensive approach to cancer diagnosis and treatment. This review provides valuable insights into the recent developments and challenges in image-based diagnosis for lung and colon cancers, underscoring both the remarkable progress and the hurdles that still need to be overcome to optimize cancer care.
基于图像的诊断已成为识别和管理各种癌症,尤其是肺癌和结肠癌的重要工具。本综述深入探讨了该领域的最新进展和持续挑战,重点关注应用于 X 射线、CT 扫描和组织病理学图像的深度学习、机器学习和图像处理技术。计算机断层扫描(CT)、磁共振成像(MRI)和正电子发射断层扫描(PET)等成像技术取得了重大进展,这些技术与机器学习和人工智能(AI)方法相结合,大大提高了癌症检测和定性的准确性。这些进步使得早期检测、更精确的肿瘤定位、个性化治疗方案以及患者预后的全面改善成为可能。然而,尽管取得了这些进步,挑战依然存在。图像解读的不一致性、缺乏标准化的诊断方案、先进成像技术的使用机会不平等,以及对基于人工智能系统的数据隐私和安全性的担忧,仍然是主要障碍。此外,将成像数据与更广泛的临床信息相结合对于实现更全面的癌症诊断和治疗方法至关重要。本综述就基于图像的肺癌和结肠癌诊断的最新进展和挑战提供了宝贵的见解,强调了在优化癌症治疗方面取得的显著进展和仍需克服的障碍。
期刊介绍:
The field of cancer research relies on advances in many other disciplines, including omics technology, mass spectrometry, radio imaging, computer science, and biostatistics. Cancer Informatics provides open access to peer-reviewed high-quality manuscripts reporting bioinformatics analysis of molecular genetics and/or clinical data pertaining to cancer, emphasizing the use of machine learning, artificial intelligence, statistical algorithms, advanced imaging techniques, data visualization, and high-throughput technologies. As the leading journal dedicated exclusively to the report of the use of computational methods in cancer research and practice, Cancer Informatics leverages methodological improvements in systems biology, genomics, proteomics, metabolomics, and molecular biochemistry into the fields of cancer detection, treatment, classification, risk-prediction, prevention, outcome, and modeling.