Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, 200032, China.
Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention, Shanghai, 200032, China.
Radiol Med. 2023 May;128(5):537-543. doi: 10.1007/s11547-023-01621-w. Epub 2023 Mar 28.
In clinical applications, accurate histologic subtype classification of lung cancer is important for determining appropriate treatment plans. The purpose of this paper is to evaluate the role of multi-task learning in the classification of adenocarcinoma and squamous cell carcinoma.
In this paper, we propose a novel multi-task learning model for histologic subtype classification of non-small cell lung cancer based on computed tomography (CT) images. The model consists of a histologic subtype classification branch and a staging branch, which share a part of the feature extraction layers and are simultaneously trained. By optimizing on the two tasks simultaneously, our model could achieve high accuracy in histologic subtype classification of non-small cell lung cancer without relying on physician's precise labeling of tumor areas. In this study, 402 cases from The Cancer Imaging Archive (TCIA) were used in total, and they were split into training set (n = 258), internal test set (n = 66) and external test set (n = 78).
Compared with the radiomics method and single-task networks, our multi-task model could reach an AUC of 0.843 and 0.732 on internal and external test set, respectively. In addition, multi-task network can achieve higher accuracy and specificity than single-task network.
Compared with the radiomics methods and single-task networks, our multi-task learning model could improve the accuracy of histologic subtype classification of non-small cell lung cancer by sharing network layers, which no longer relies on the physician's precise labeling of lesion regions and could further reduce the manual workload of physicians.
在临床应用中，准确的肺癌组织学亚型分类对于确定合适的治疗计划非常重要。本文旨在评估多任务学习在腺癌和鳞状细胞癌分类中的作用。
在本文中，我们提出了一种基于计算机断层扫描（CT）图像的非小细胞肺癌组织学亚型分类的新型多任务学习模型。该模型由组织学亚型分类分支和分期分支组成，它们共享部分特征提取层，并同时进行训练。通过同时优化两个任务，我们的模型可以在不依赖于医生对肿瘤区域进行精确标记的情况下，实现非小细胞肺癌组织学亚型分类的高精度。在这项研究中，总共使用了来自癌症成像档案（TCIA）的 402 例病例，将其分为训练集（n=258）、内部测试集（n=66）和外部测试集（n=78）。
与放射组学方法和单任务网络相比，我们的多任务模型在内部和外部测试集上的 AUC 分别为 0.843 和 0.732。此外，多任务网络可以比单任务网络达到更高的准确性和特异性。
与放射组学方法和单任务网络相比，我们的多任务学习模型可以通过共享网络层来提高非小细胞肺癌组织学亚型分类的准确性，不再依赖医生对病变区域的精确标记，并进一步减少医生的手动工作量。
