MSCS-DeepLN: Evaluating lung nodule malignancy using multi-scale cost-sensitive neural networks

Xu, Xiang; Wang, Chengdi; Guo, Jixiang; Gan, Yuncui; Wang, Jianyong; Bai, Hongwei; Zhang, Lei; Li, Weimin

doi:10.1016/j.media.2020.101772

Cited by 87 publications

(49 citation statements)

References 26 publications

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“…It is further explained that this is due to the data imbalance of the LCID‐CPI data set we constructed, and has nothing to do with the constructed models themselves. There are no zero values in the results of Mr‐Mc and the model in Xu et al, 30 indicating that these two models are more reliable than the other three models in the multi‐classification of multiple pathological types of pulmonary nodules. Through further analysis, it can be seen that our proposed Mr‐Mc model and the model in Xu et al 30 have their own advantages in six evaluation metrics.…”

Section: Experiments and Resultsmentioning

confidence: 84%

3D multi‐resolution deep learning model for diagnosis of multiple pathological types on pulmonary nodules

Xue

Zhao

et al. 2021

Int J Imaging Syst Tech

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To accurately diagnose multiple pathological types of pulmonary nodules based on lung computed tomography (CT) images, a multi‐resolution three‐dimensional (3D) multi‐classification deep learning model (Mr‐Mc) was proposed. The Mr‐Mc model was constructed by using our own constructed lung CT image dataset of pulmonary nodules with clinical pathological information (LCID‐CPI), which can accurately diagnose inflammation, squamous cell carcinoma, adenocarcinoma, and other benign diseases. In order to process nodules with different sizes, a multi‐resolution extraction method was proposed to extract 3D volume data with different resolutions from lung CT images. The Mr‐Mc was composed of three different resolution networks, each of which has input volume data of a specific resolution. Experiments showed that the constructed Mr‐Mc model can achieve an average accuracy of 0.81 on LCID‐CPI. Besides, the Mr‐Mc model can also achieve a high accuracy of 0.87 on the Lung Image Database Consortium and Image Database Resource Initiative dataset.

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Section: Experiments and Resultsmentioning

confidence: 84%

3D multi‐resolution deep learning model for diagnosis of multiple pathological types on pulmonary nodules

Xue

Zhao

et al. 2021

Int J Imaging Syst Tech

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“…As the previous literature suggested, residual block could relieve the gradient disappearance problem caused by the depth of neural network, and three-dimensional residual network showed a good performance on not only natural images [20] but also medical images [21][22][23][24]. In the current study, considering the format of CT scans, we constructed a 3D convolutional neural network (CNN) model for classifying the EGFR and PD-L1 status.…”

Section: Development Of the Deep Learning Modelmentioning

confidence: 92%

Deep Learning to Predict EGFR Mutation and PD-L1 Expression Status in Non-Small-Cell Lung Cancer on Computed Tomography Images

Wang

Shao

et al. 2021

Journal of Oncology

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Objective. The detection of epidermal growth factor receptor (EGFR) mutation and programmed death ligand-1 (PD-L1) expression status is crucial to determine the treatment strategies for patients with non-small-cell lung cancer (NSCLC). Recently, the rapid development of radiomics including but not limited to deep learning techniques has indicated the potential role of medical images in the diagnosis and treatment of diseases. Methods. Eligible patients diagnosed/treated at the West China Hospital of Sichuan University from January 2013 to April 2019 were identified retrospectively. The preoperative CT images were obtained, as well as the gene status regarding EGFR mutation and PD-L1 expression. Tumor region of interest (ROI) was delineated manually by experienced respiratory specialists. We used 3D convolutional neural network (CNN) with ROI information as input to construct a classification model and established a prognostic model combining deep learning features and clinical features to stratify survival risk of lung cancer patients. Results. The whole cohort (N = 1262) was divided into a training set (N = 882, 70%), validation set (N = 125, 10%), and test set (N = 255, 20%). We used a 3D convolutional neural network (CNN) to construct a prediction model, with AUCs of 0.96 (95% CI: 0.94–0.98), 0.80 (95% CI: 0.72–0.88), and 0.73 (95% CI: 0.63–0.83) in the training, validation, and test cohorts, respectively. The combined prognostic model showed a good performance on survival prediction in NSCLC patients (C-index: 0.71). Conclusion. In this study, a noninvasive and effective model was proposed to predict EGFR mutation and PD-L1 expression status as a clinical decision support tool. Additionally, the combination of deep learning features with clinical features demonstrated great stratification capabilities in the prognostic model. Our team would continue to explore the application of imaging markers for treatment selection of lung cancer patients.

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“…Their work significantly improved the data efficacy of CNN and provided a potential solution to the problem of insufficient labeled data in training a CNN model with satisfying performance (17). To stress, other solutions to solve the problem of relatively small datasets include data argumentation (18), transfer learning (19), extracting patches on multiple planar view (20,21) and ensemble learning (22), etc.…”

Section: Radiomics and Deep Learningmentioning

confidence: 99%

Artificial intelligence-assisted decision making for prognosis and drug efficacy prediction in lung cancer patients: a narrative review

Li¹,

Wu²,

Zhao³

et al. 2021

J Thorac Dis

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In this review, we aim to present frontier studies in patients with lung cancer as it related to artificial intelligence (AI)-assisted decision-making and summarize the latest advances, challenges and future trend in this field.Background: Despite increasing survival rate in cancer patients over the last decades, lung cancer remains one of the leading causes of death worldwide. The early diagnosis, accurate evaluation and individualized treatment are vital approaches to improve the survival rate of patients with lung cancer. Thus, decision making based on these approaches requires accuracy and efficiency beyond manpower. Recent advances in AI and precision medicine have provided a fertile environment for the development of AI-based models. These models have the potential to assist radiologists and oncologists in detecting lung cancer, predicting prognosis and developing personalized treatment plans for better outcomes of the patients.Methods: We searched literature from 2000 through July 31 th , 2021 in Medline/PubMed, the Web of Science, the Cochrane Library, ACM Digital Library, INSPEC and EMBASE. Key words such as "artificial intelligence", "AI", "deep learning", "lung cancer", "NSCLC", "SCLC" were combined to identify related literatures. These literatures were then selected by two independent authors. Articles chosen by only one author will be examined by another author to determine whether this article was relative and valuable. The selected literatures were read by all authors and discussed to draw reliable conclusions. Conclusions: AI, especially for those based on deep learning and radiomics, is capable of assisting clinical decision making from many aspects, for its quantitatively interpretation of patients' information and its potential to deal with the dynamics, individual differences and heterogeneity of lung cancer. Hopefully, remaining problems such as insufficient data and poor interpretability may be solved to put AI-based models into clinical practice.

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MSCS-DeepLN: Evaluating lung nodule malignancy using multi-scale cost-sensitive neural networks

Cited by 87 publications

References 26 publications

3D multi‐resolution deep learning model for diagnosis of multiple pathological types on pulmonary nodules

3D multi‐resolution deep learning model for diagnosis of multiple pathological types on pulmonary nodules

Deep Learning to Predict EGFR Mutation and PD-L1 Expression Status in Non-Small-Cell Lung Cancer on Computed Tomography Images

Artificial intelligence-assisted decision making for prognosis and drug efficacy prediction in lung cancer patients: a narrative review

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