University of Tunis El Manar, Laboratory of Biophysics and Medical Technology, Higher Institute of Medical Technologies, 1006 Tunis, Tunisia.
Radiophysics Unit, Radiotherapy Department, Salah Azaeiz Institute, Boulevard 9 Avril, 1006 Tunis, Tunisia.
Biomed Phys Eng Express. 2024 Oct 11;10(6). doi: 10.1088/2057-1976/ad81ff.
The integration of artificial intelligence (AI) into lung cancer management offers immense potential to revolutionize diagnostic and treatment strategies. The aim is to develop a resilient AI framework capable of two critical tasks: firstly, achieving accurate and automated segmentation of lung tumors and secondly, facilitating the T classification of lung cancer according to the ninth edition of TNM staging 2024 based on PET/CT imaging. This study presents a robust AI framework for the automated segmentation of lung tumors and T classification of lung cancer using PET/CT imaging. The database includes axial DICOM CT andFDG-PET/CT images. A modified ResNet-50 model was employed for segmentation, achieving high precision and specificity. Reconstructed 3D models of segmented slices enhance tumor boundary visualization, which is essential for treatment planning. The Pulmonary Toolkit facilitated lobe segmentation, providing critical diagnostic insights. Additionally, the segmented images were used as input for the T classification using a CNN ResNet-50 model. Our classification model demonstrated excellent performance, particularly for T1a, T2a, T2b, T3 and T4 tumors, with high precision, F1 scores, and specificity. The T stage is particularly relevant in lung cancer as it determines treatment approaches (surgery, chemotherapy and radiation therapy or supportive care) and prognosis assessment. In fact, for Tis-T2, each increase of one centimeter in tumor size results in a worse prognosis. For locally advanced tumors (T3-T4) and regardless of size, the prognosis is poorer. This AI framework marks a significant advancement in the automation of lung cancer diagnosis and staging, promising improved patient outcomes.
人工智能（AI）在肺癌管理中的融合具有彻底改变诊断和治疗策略的巨大潜力。目标是开发一个有弹性的 AI 框架，能够完成两项关键任务：首先，实现肺肿瘤的准确和自动分割；其次，根据 2024 年第九版 TNM 分期标准，基于 PET/CT 成像，促进肺癌 T 分类。本研究提出了一种用于使用 PET/CT 成像进行肺肿瘤自动分割和肺癌 T 分类的强大 AI 框架。该数据库包括轴向 DICOM CT 和 FDG-PET/CT 图像。使用改进的 ResNet-50 模型进行分割，实现了高精度和特异性。分割切片的重建 3D 模型增强了肿瘤边界的可视化，这对治疗计划至关重要。Pulmonary Toolkit 促进了叶段分割，提供了关键的诊断见解。此外，使用 CNN ResNet-50 模型将分割后的图像作为输入进行 T 分类。我们的分类模型表现出色，特别是对于 T1a、T2a、T2b、T3 和 T4 肿瘤，具有高精度、F1 分数和特异性。T 分期在肺癌中尤为重要，因为它决定了治疗方法（手术、化疗和放疗或支持性护理）和预后评估。事实上，对于Tis-T2，肿瘤大小每增加一厘米，预后就会更差。对于局部晚期肿瘤（T3-T4），无论大小如何，预后都更差。这个 AI 框架标志着肺癌诊断和分期自动化的重大进展，有望改善患者的预后。
