Differential diagnostic value of benign and malignant solid lung nodules based on deep learning

[1] Shao, G., Pang, D., Zhang, J., Kong, F., Liuru, T., & Wang, H., et al. (2021). P05.06 clinical application of anatomical landmarkers based 3d precise pulmonary nodule localization during thoracoscopic surgery. Journal of Thoracic Oncology. Search in Google Scholar

[2] Horst, C., Dickson, J., Tisi, S., Hall, H., & Janes, S. (2021). P41.04 the summit study: pulmonary nodule and incidental findings in the first 10,000 participants of a population-based low-dose ct screening study. Journal of Thoracic Oncology, 16(3), S473-S474. Search in Google Scholar

[3] Su, J., & Liu, D. (2021). Blau syndrome with pulmonary nodule in a child. Australasian Journal of Dermatology. Search in Google Scholar

[4] Jett, J. (2021). Es08.04 biomarkers in the evaluation of pulmonary nodule. Journal of Thoracic Oncology, 16(3), S73. Search in Google Scholar

[5] Zheng, SunyiCornelissen, Ludo J.Cui, XiaonanJing, XuepingVeldhuis, Raymond N. J.Oudkerk, Matthijsvan Ooijen, Peter M. A. (2021). Deep convolutional neural networks for multiplanar lung nodule detection: improvement in small nodule identification. Medical Physics, 48(2). Search in Google Scholar

[6] Zhang, J., Xia, K., He, Z., Yin, Z., & Wang, S. (2021). Semi-supervised ensemble classifier with improved sparrow search algorithm and its application in pulmonary nodule detection. Mathematical Problems in Engineering, 2021. Search in Google Scholar

[7] Zheng, F., Lavin, J., & Sprafka, J. (2021). Pcn91 patient responsible out-of-pocket costs for solitary pulmonary nodule biopsy in lung cancer patients. Value in Health, 24, S36. Search in Google Scholar

[8] Ferrer, V., Morales, S., & Rodriguez-Cintron, W. (2022). Wary nodule: a rare case of pulmonary maltoma. Chest. Search in Google Scholar

[9] Ma, L., Xiu, G., Muscat, J., Sinha, R., Sun, D., & Xiu, G. (2021). Comparative proteomic analysis of exhaled breath condensate between lung adenocarcinoma and ct-detected benign pulmonary nodule patients. Cancer biomarkers : section A of Disease markers. Search in Google Scholar

[10] Zhong, M., Zhang, Y., Pan, Z., Wang, W., Zhang, Y., & Weng, Y., et al. (2021). Clinical utility of circulating tumor cells in the early detection of lung cancer in patients with a solitary pulmonary nodule:. Technology in Cancer Research & Treatment, 20(6), 1710-1717. Search in Google Scholar

[11] A, C. P., B, G. R. L., C, I. M. A., A, L. D., A, D. C., & A, C. R. R. (2021). Lung ultrasound nodule sign for detection of pulmonary nodule lesions in dogs: comparison to thoracic radiography using computed tomography as the criterion standard. The Veterinary Journal. Search in Google Scholar

[12] Giles, A. E., Teferi, Y., Kidane, B., Bayaraa, B., & Srinathan, S. (2021). Lung resection without tissue diagnosis: a pragmatic perspective on the indeterminate pulmonary nodule. Clinical Lung Cancer(3). Search in Google Scholar

[13] Gijs A., B., Amsterdam, W. A. C. V., Harder, A. M. D., Martin J., W., Robbert W., V. H., & Pim A., D. J., et al. (2023). Robustness of pulmonary nodule radiomic features on computed tomography as a function of varying radiation dose levels-a multi-dose in vivo patient study. European radiology.. Search in Google Scholar

[14] Zheng, C., Huang, B. Z., Agazaryan, A. A., Creekmur, B., & Gould, M. K. (2021). Natural language processing to identify pulmonary nodules and extract nodule characteristics from radiology reports. Chest(22). Search in Google Scholar

[15] Sweta, J., Pruthviraj, C., & Mahesh, G. (2021). Pulmonary lung nodule detection from computed tomography images using two-stage convolutional neural network. The Computer Journal(4), 4. Search in Google Scholar

[16] Lan, Z., Zhang, X., Ma, X., Hu, Y., & Yang, F. (2021). Utility of liquid‐based cytology on residual needle rinses collected from core needle biopsy for lung nodule diagnosis. Cancer Medicine, 10(5). Search in Google Scholar

[17] Jiang, H., Shen, F., Gao, F., & Han, W. (2021). Learning efficient, explainable and discriminative representations for pulmonary nodules classification. Pattern Recognition, 113(5), 107825. Search in Google Scholar

[18] Duan, Xue-QinWang, Xiao-LiZhang, Li-FenLiu, Xi-ZhiZhang, Wen-WenLiu, Yi-HuiDong, Chun-HuiZhao, Xin-HanChen, Ling. (2021). Establishment and validation of a prediction model for the probability of malignancy in solid solitary pulmonary nodules in northwest china. Journal of Surgical Oncology, 123(4). Search in Google Scholar

[19] Zhu, B., Zheng, S., Jiang, T., & Hu, B. (2021). Evaluation of dual-energy and perfusion ct parameters for diagnosing solitary pulmonary nodules. Thoracic Cancer. Search in Google Scholar

[20] Shi, L., Ma, H., & Zhang, J. (2021). Automatic detection of pulmonary nodules in ct images based on 3d res-i network. The Visual Computer, 37(6), -. Search in Google Scholar

[21] Langan, R., & Goodbred, A. J. (2023). Pulmonary nodules: common questions and answers. American family physician, 107 3, 282-291. Search in Google Scholar

[22] Fu, X., Bi, L., Kumar, A., Fulham, M., & Kim, J. (2022). An attention-enhanced cross-task network to analyse lung nodule attributes in ct images. Pattern Recognition, 126, 108576-. Search in Google Scholar

[23] A, M. T. T., B, M. M., C, L. P., A, H. P., C, T. K., & A, F. G. (2021). The utility of a convolutional neural network (cnn) model score for cancer risk in indeterminate small solid pulmonary nodules, compared to clinical practice according to british thoracic society guidelines. European Journal of Radiology, 137. Search in Google Scholar