Deep Learning Applications in Computed Tomography Images for Pulmonary Nodule Detection and Diagnosis: A Review

Li, Rui; Xiao, Chuda; Huang, Yongzhi; Hassan, Haseeb; Huang, Bingding

doi:10.3390/diagnostics12020298

Cited by 74 publications

(43 citation statements)

References 101 publications

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“…DL models emerged as particularly well-suited for screening applications, but they must be trained and tested on high-quality datasets in order to realize their full potential. Some open-source image repositories for lung cancer are available and partially meet this need [ 44 ]. The most used databases are The Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI), which comprises 1018 CT scans and 36,378 lung nodules [ 45 ], the extensive Lung Nodule Analysis 16 (LUNA 16) dataset, derived from LIDC-IDRI, which includes 888 selected CT scans and 13,799 lung nodules [ 46 ], and the Ali Tianchi dataset, which includes information on 1000 CTs and 1000 nodules.…”

Section: Lung Cancer Screening and Detectionmentioning

confidence: 99%

Artificial Intelligence in Lung Cancer Imaging: Unfolding the Future

et al. 2022

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Lung cancer is one of the malignancies with higher morbidity and mortality. Imaging plays an essential role in each phase of lung cancer management, from detection to assessment of response to treatment. The development of imaging-based artificial intelligence (AI) models has the potential to play a key role in early detection and customized treatment planning. Computer-aided detection of lung nodules in screening programs has revolutionized the early detection of the disease. Moreover, the possibility to use AI approaches to identify patients at risk of developing lung cancer during their life can help a more targeted screening program. The combination of imaging features and clinical and laboratory data through AI models is giving promising results in the prediction of patients’ outcomes, response to specific therapies, and risk for toxic reaction development. In this review, we provide an overview of the main imaging AI-based tools in lung cancer imaging, including automated lesion detection, characterization, segmentation, prediction of outcome, and treatment response to provide radiologists and clinicians with the foundation for these applications in a clinical scenario.

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Section: Lung Cancer Screening and Detectionmentioning

confidence: 99%

Artificial Intelligence in Lung Cancer Imaging: Unfolding the Future

et al. 2022

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“…Consequently, artificial intelligence (AI)-assisted lung nodule detection systems may be used to provide a second opinion for radiologists and make final decisions faster and more accurately. Deep-learning models are highly dependent on datasets; thus, they can effectively achieve the performance of advanced learning algorithms when high-quality datasets are used for training [14]. Public databases such as LIDC/IDRI [9], LUNA16 [10], NSCLC [11], ELCAP [12], and ANODE09 [13] have been used for lung nodule diagnosis research.…”

Section: Introductionmentioning

confidence: 99%

How Many Private Data Are Needed for Deep Learning in Lung Nodule Detection on CT Scans? A Retrospective Multicenter Study

Son¹,

Hong

Kim

et al. 2022

Cancers

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Early detection of lung nodules is essential for preventing lung cancer. However, the number of radiologists who can diagnose lung nodules is limited, and considerable effort and time are required. To address this problem, researchers are investigating the automation of deep-learning-based lung nodule detection. However, deep learning requires large amounts of data, which can be difficult to collect. Therefore, data collection should be optimized to facilitate experiments at the beginning of lung nodule detection studies. We collected chest computed tomography scans from 515 patients with lung nodules from three hospitals and high-quality lung nodule annotations reviewed by radiologists. We conducted several experiments using the collected datasets and publicly available data from LUNA16. The object detection model, YOLOX was used in the lung nodule detection experiment. Similar or better performance was obtained when training the model with the collected data rather than LUNA16 with large amounts of data. We also show that weight transfer learning from pre-trained open data is very useful when it is difficult to collect large amounts of data. Good performance can otherwise be expected when reaching more than 100 patients. This study offers valuable insights for guiding data collection in lung nodules studies in the future.

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“…Artificial intelligence (AI) has provided great improvements in cancer imaging (7). For example, many studies have used radiomics and deep learning to progress their fields (8)(9)(10)(11)(12). However, in the field of SSNs, little progress has been made with the use of AI or other automatic methods.…”

Section: Introductionmentioning

confidence: 99%

Deep learning-based growth prediction for sub-solid pulmonary nodules on CT images

Liao

Yan

et al. 2022

Front. Oncol.

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BackgroundEstimating the growth of pulmonary sub-solid nodules (SSNs) is crucial to the successful management of them during follow-up periods. The purpose of this study is to (1) investigate the measurement sensitivity of diameter, volume, and mass of SSNs for identifying growth and (2) seek to establish a deep learning-based model to predict the growth of SSNs.MethodsA total of 2,523 patients underwent at least 2-year examination records retrospectively collected with sub-solid nodules. A total of 2,358 patients with 3,120 SSNs from the NLST dataset were randomly divided into training and validation sets. Patients from the Yibicom Health Management Center and Guangdong Provincial People’s Hospital were collected as an external test set (165 patients with 213 SSN). Trained models based on LUNA16 and Lndb19 datasets were employed to automatically obtain the diameter, volume, and mass of SSNs. Then, the increase rate in measurements between cancer and non-cancer groups was studied to evaluate the most appropriate way to identify growth-associated lung cancer. Further, according to the selected measurement, all SSNs were classified into two groups: growth and non-growth. Based on the data, the deep learning-based model (SiamModel) and radiomics model were developed and verified.ResultsThe double time of diameter, volume, and mass were 711 vs. 963 days (P = 0.20), 552 vs. 621 days (P = 0.04) and 488 vs. 623 days (P< 0.001) in the cancer and non-cancer groups, respectively. Our proposed SiamModel performed better than the radiomics model in both the NLST validation set and external test set, with an AUC of 0.858 (95% CI 0.786–0.921) and 0.760 (95% CI 0.646–0.857) in the validation set and 0.862 (95% CI 0.789–0.927) and 0.681 (95% CI 0.506–0.841) in the external test set, respectively. Furthermore, our SiamModel could use the data from first-time CT to predict the growth of SSNs, with an AUC of 0.855 (95% CI 0.793–0.908) in the NLST validation set and 0.821 (95% CI 0.725–0.904) in the external test set.ConclusionMass increase rate can reflect more sensitively the growth of SSNs associated with lung cancer than diameter and volume increase rates. A deep learning-based model has a great potential to predict the growth of SSNs.

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Deep Learning Applications in Computed Tomography Images for Pulmonary Nodule Detection and Diagnosis: A Review

Cited by 74 publications

References 101 publications

Artificial Intelligence in Lung Cancer Imaging: Unfolding the Future

Artificial Intelligence in Lung Cancer Imaging: Unfolding the Future

How Many Private Data Are Needed for Deep Learning in Lung Nodule Detection on CT Scans? A Retrospective Multicenter Study

Deep learning-based growth prediction for sub-solid pulmonary nodules on CT images

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