Marathwada Mitra Mandal's Institute of Technology, Pune, India.
Department of Computer Engineering, Pimpri Chinchwad College of Engineering, Pune, India.
Biomed Res Int. 2022 Jul 4;2022:3947434. doi: 10.1155/2022/3947434. eCollection 2022.
At present, early lung cancer screening is mainly based on radiologists' experience in diagnosing benign and malignant pulmonary nodules by lung CT images. On the other hand, intraoperative rapid freezing pathology needs to analyse the invasive adenocarcinoma nodules with the worst recovery in adenocarcinoma. Moreover, rapid freezing pathology has a low diagnostic accuracy for small-diameter nodules. Because of the above problems, an algorithm for diagnosing invasive adenocarcinoma nodules in ground-glass pulmonary nodules is based on CT images. According to the nodule space information and plane features, sample data of different dimensions are designed, namely, 3D space and 2D plane feature samples. The network structure is designed based on the attention mechanism and residual learning unit; 2D and 3D neural networks are along built. By fusing the feature vectors extracted from networks of different dimensions, the diagnosis results of invasive adenocarcinoma nodules are finally obtained. The algorithm was studied on 1760 ground-glass nodules with 5-20 mm diameter collected from a city chest hospital with surgical and pathological results. There were 340 nodules with invasive adenocarcinoma and 340 with noninvasive adenocarcinoma. A total of 1420 invasive nodule samples were cross-validated on this example dataset. The classification accuracy of the algorithm was 82.7%, the sensitivity was 82.9%, and the specificity was 82.6%.
目前，早期肺癌筛查主要基于放射科医生通过肺部 CT 图像诊断良性和恶性肺结节的经验。另一方面，术中快速冷冻病理需要分析腺癌中恢复最差的侵袭性腺癌结节。此外，快速冷冻病理对小直径结节的诊断准确性较低。由于上述问题，基于 CT 图像开发了一种用于诊断磨玻璃肺结节中侵袭性腺癌结节的算法。根据结节空间信息和平面特征，设计了不同尺寸的样本数据，即 3D 空间和 2D 平面特征样本。该网络结构基于注意力机制和残差学习单元进行设计；同时构建 2D 和 3D 神经网络。通过融合来自不同维度网络提取的特征向量，最终获得侵袭性腺癌结节的诊断结果。该算法在一家市级胸科医院收集的 1760 个直径为 5-20mm 的磨玻璃结节上进行了研究，其中 340 个结节为侵袭性腺癌，340 个为非侵袭性腺癌。在这个示例数据集上共对 1420 个侵袭性结节样本进行了交叉验证。该算法的分类准确率为 82.7%，灵敏度为 82.9%，特异性为 82.6%。
