Department of Computer Engineering, Qom Branch, Islamic Azad University, Qom, Iran.
Department of Molecular Genetics, Ahar Branch, Islamic Azad University, Ahar, Iran.
Comput Biol Med. 2024 Dec;183:109326. doi: 10.1016/j.compbiomed.2024.109326. Epub 2024 Oct 25.
Lung cancer (LuC) is one of the leading causes of death in the world, and due to the complex mechanisms and widespread metastasis, diagnosis and treatment are challenging. In recent years, the application of reinforcement learning (RL) techniques as a new tool to improve LuC diagnosis and treatment has been dramatically expanded. These techniques can potentially increase the accuracy of diagnosis and optimize treatment processes by learning from limited data and improving clinical decisions. However, RL in LuC diagnosis and treatment faces challenges such as limited access to clinical data, the complexity of algorithms, and the need for technical expertise for proper implementation. Our systematic review article aims to evaluate the latest developments in applications and challenges of using RL techniques in LuC diagnosis and treatment. The findings showed that RL has increased the accuracy of identifying disease trends by 37 % and enhancing treatment decisions by 23 %. Also, using this approach reduces data processing time by 17 % and streamlining treatment processes by 12 %. Ultimately, analyzing the current challenges and offering recommendations to researchers could help develop new strategies for improving the diagnosis and treatment of LuC.
肺癌（LuC）是全球主要死因之一，由于其复杂的机制和广泛的转移，诊断和治疗具有挑战性。近年来，强化学习（RL）技术作为一种新工具，在提高 LuC 诊断和治疗方面的应用得到了极大的扩展。这些技术可以通过从有限的数据中学习并改进临床决策，潜在地提高诊断的准确性并优化治疗过程。然而，RL 在 LuC 诊断和治疗方面面临着一些挑战，如获取临床数据的限制、算法的复杂性以及正确实施所需的技术专业知识。我们的系统综述文章旨在评估在 LuC 诊断和治疗中应用 RL 技术的最新进展和挑战。研究结果表明，RL 提高了识别疾病趋势的准确性，增加了 37%；增强了治疗决策，增加了 23%。此外，使用这种方法减少了 17%的数据处理时间，并简化了 12%的治疗过程。最终，分析当前的挑战并为研究人员提供建议，有助于制定新的策略，以改善 LuC 的诊断和治疗。
