Ren Ge, Li Bing, Lam Sai-Kit, Xiao Haonan, Huang Yu-Hua, Cheung Andy Lai-Yin, Lu Yufei, Mao Ronghu, Ge Hong, Kong Feng-Ming Spring, Ho Wai-Yin, Cai Jing
Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China.
Department of Radiotherapy, Affiliated Cancer Hospital of Zhengzhou University/Henan Cancer Hospital, Zhengzhou, China.
Front Oncol. 2022 Jul 1;12:883516. doi: 10.3389/fonc.2022.883516. eCollection 2022.
Deep learning model has shown the feasibility of providing spatial lung perfusion information based on CT images. However, the performance of this method on lung cancer patients is yet to be investigated. This study aims to develop a transfer learning framework to evaluate the deep learning based CT-to-perfusion mapping method specifically on lung cancer patients.
SPECT/CT perfusion scans of 33 lung cancer patients and 137 non-cancer patients were retrospectively collected from two hospitals. To adapt the deep learning model on lung cancer patients, a transfer learning framework was developed to utilize the features learned from the non-cancer patients. These images were processed to extract features from three-dimensional CT images and synthesize the corresponding CT-based perfusion images. A pre-trained model was first developed using a dataset of patients with lung diseases other than lung cancer, and subsequently fine-tuned specifically on lung cancer patients under three-fold cross-validation. A multi-level evaluation was performed between the CT-based perfusion images and ground-truth SPECT perfusion images in aspects of voxel-wise correlation using Spearman's correlation coefficient (R), function-wise similarity using Dice Similarity Coefficient (DSC), and lobe-wise agreement using mean perfusion value for each lobe of the lungs.
The fine-tuned model yielded a high voxel-wise correlation (0.8142 ± 0.0669) and outperformed the pre-trained model by approximately 8%. Evaluation of function-wise similarity indicated an average DSC value of 0.8112 ± 0.0484 (range: 0.6460-0.8984) for high-functional lungs and 0.8137 ± 0.0414 (range: 0.6743-0.8902) for low-functional lungs. Among the 33 lung cancer patients, high DSC values of greater than 0.7 were achieved for high functional volumes in 32 patients and low functional volumes in all patients. The correlations of the mean perfusion value on the left upper lobe, left lower lobe, right upper lobe, right middle lobe, and right lower lobe were 0.7314, 0.7134, 0.5108, 0.4765, and 0.7618, respectively.
For lung cancer patients, the CT-based perfusion images synthesized by the transfer learning framework indicated a strong voxel-wise correlation and function-wise similarity with the SPECT perfusion images. This suggests the great potential of the deep learning method in providing regional-based functional information for functional lung avoidance radiation therapy.
深度学习模型已显示出基于CT图像提供肺部空间灌注信息的可行性。然而，该方法在肺癌患者中的性能仍有待研究。本研究旨在开发一种迁移学习框架，以专门评估基于深度学习的CT到灌注映射方法在肺癌患者中的应用。
回顾性收集了两家医院33例肺癌患者和137例非癌症患者的SPECT/CT灌注扫描数据。为了使深度学习模型适用于肺癌患者，开发了一种迁移学习框架，以利用从非癌症患者中学习到的特征。对这些图像进行处理，以从三维CT图像中提取特征，并合成相应的基于CT的灌注图像。首先使用非肺癌肺部疾病患者的数据集开发一个预训练模型，随后在三倍交叉验证下对肺癌患者进行专门的微调。在基于CT的灌注图像和地面真值SPECT灌注图像之间进行了多层面评估，评估内容包括使用斯皮尔曼相关系数（R）的体素级相关性、使用骰子相似系数（DSC）的功能级相似性以及使用肺部各叶平均灌注值的叶级一致性。
微调后的模型产生了较高的体素级相关性（0.8142±0.0669），并且比预训练模型性能高出约8%。功能级相似性评估表明，高功能肺的平均DSC值为0.8112±0.0484（范围：0.6460 - 0.8984），低功能肺的平均DSC值为0.8137±0.0414（范围：0.6743 - 0.8902）。在33例肺癌患者中，32例高功能体积患者和所有患者的低功能体积患者的DSC值均大于0.7。左上叶、左下叶、右上叶、右中叶和右下叶的平均灌注值相关性分别为0.7314、0.7134、0.5108、0.4765和0.7618。
对于肺癌患者，迁移学习框架合成的基于CT的灌注图像与SPECT灌注图像显示出很强的体素级相关性和功能级相似性。这表明深度学习方法在为功能性肺避让放射治疗提供基于区域的功能信息方面具有巨大潜力。
