Patharia Pragati, Sethy Prabira Kumar, Nanthaamornphong Aziz
Department of Electronics and Communication Engineering, Guru Ghasidas Vishwavidyalaya, Bilaspur, Chhattisgarh, India.
Department of Electronics, Sambalpur University, Burla, Odisha, India.
Cancer Inform. 2024 Oct 16;23:11769351241290608. doi: 10.1177/11769351241290608. eCollection 2024.
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）的关键工具。本综述深入探讨了该领域的最新进展和持续存在的挑战，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。在计算机断层扫描（CT）、磁共振成像（MRI）和正电子发射断层扫描（PET）等成像技术方面取得了重大进展，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特征描述的准确性。这些进展实现了早期检测、更精确的肿瘤定位、个性化治疗方案，并总体上改善了患者的治疗效果。然而，尽管有这些改进，挑战依然存在。图像解读的可变性、缺乏标准化诊断方案、先进成像技术获取机会不平等，以及对基于人工智能的系统中的数据隐私和安全问题的担忧，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。本综述为肺癌和结肠癌基于图像的诊断的最新进展和挑战提供了有价值的见解，强调了在优化癌症护理方面取得的显著进展以及仍需克服的障碍。
