Ryan Larry, Agaian Sos
Department of Computer Science, Graduate Center, CUNY, City University of New York, New York, NY 10016, USA.
Bioengineering (Basel). 2025 Jun 11;12(6):639. doi: 10.3390/bioengineering12060639.
Breast cancer remains a leading cause of cancer-related deaths among women worldwide, highlighting the urgent need for early detection. While mammography is the gold standard, it faces cost and accessibility barriers in resource-limited areas. Infrared thermography is a promising cost-effective, non-invasive, painless, and radiation-free alternative that detects tumors by measuring their thermal signatures through thermal infrared radiation. However, challenges persist, including limited clinical validation, lack of Food and Drug Administration (FDA) approval as a primary screening tool, physiological variations among individuals, differing interpretation standards, and a shortage of specialized radiologists. This survey uniquely focuses on integrating texture analysis and machine learning within infrared thermography for breast cancer detection, addressing the existing literature gaps, and noting that this approach achieves high-ranking results. It comprehensively reviews the entire processing pipeline, from image preprocessing and feature extraction to classification and performance assessment. The survey critically analyzes the current limitations, including over-reliance on limited datasets like DMR-IR. By exploring recent advancements, this work aims to reduce radiologists' workload, enhance diagnostic accuracy, and identify key future research directions in this evolving field.
乳腺癌仍然是全球女性癌症相关死亡的主要原因，这凸显了早期检测的迫切需求。虽然乳房X光检查是金标准，但在资源有限的地区，它面临成本和可及性障碍。红外热成像技术是一种很有前景的具有成本效益、非侵入性、无痛且无辐射的替代方法，它通过热红外辐射测量肿瘤的热信号来检测肿瘤。然而，挑战依然存在，包括临床验证有限、缺乏作为主要筛查工具的美国食品药品监督管理局（FDA）批准、个体间的生理差异、不同的解读标准以及专业放射科医生短缺。这项调查独特地聚焦于将纹理分析和机器学习整合到红外热成像技术中用于乳腺癌检测，解决现有文献中的空白，并指出这种方法取得了很高的排名结果。它全面回顾了从图像预处理、特征提取到分类和性能评估的整个处理流程。该调查批判性地分析了当前的局限性，包括过度依赖像DMR - IR这样的有限数据集。通过探索近期进展，这项工作旨在减轻放射科医生的工作量，提高诊断准确性，并确定这个不断发展领域未来的关键研究方向。
