A Novel Deep Learning-Based (3D U-Net Model) Automated Pulmonary Nodule Detection Tool for CT Imaging.
Background: Precise detection and characterization of pulmonary nodules on computed tomography (CT) is crucial for early diagnosis and management. Objectives: In this study, we propose the use of a deep learning-based algorithm to automatically detect pulmonary nodules in computed tomography (CT) sc...
| Publicado en: | Current Oncology Vol. 32; no. 2; pp. 95 - 109 |
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| Autores principales: | , , , , , , , , , , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
MDPI
Feb2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=183289072&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 183289072 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 11980052 5EKK jtl: Current Oncology issn: 11980052 maglogo: N pubinfo: dt: Feb2025 vid: 32 iid: 2 pid: 97109 pub: MDPI artinfo: ui: 183289072 10.3390/curroncol32020095 183289072 ppf: 95 ppct: 14 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A Novel Deep Learning-Based (3D U-Net Model) Automated Pulmonary Nodule Detection Tool for CT Imaging. aug: au: Mahajan, Abhishek Agarwal, Rajat Agarwal, Ujjwal Ashtekar, Renuka M. Komaravolu, Bharadwaj Madiraju, Apparao Vaish, Richa Pawar, Vivek Punia, Vivek Patil, Vijay Maruti Noronha, Vanita Joshi, Amit Menon, Nandini Prabhash, Kumar Chaturvedi, Pankaj Rane, Swapnil Banwar, Priya Gupta, Sudeep affil: Department of Imaging, The Clatterbridge Cancer Centre NHS Foundation Trust, Liverpool L7 8YA, UK sug: ab: Background: Precise detection and characterization of pulmonary nodules on computed tomography (CT) is crucial for early diagnosis and management. Objectives: In this study, we propose the use of a deep learning-based algorithm to automatically detect pulmonary nodules in computed tomography (CT) scans. We evaluated the performance of the algorithm against the interpretation of radiologists to analyze the effectiveness of the algorithm. Materials and Methods: The study was conducted in collaboration with a tertiary cancer center. We used a collection of public (LUNA) and private (tertiary cancer center) datasets to train our deep learning models. The sensitivity, the number of false positives per scan, and the FROC curve along with the CPM score were used to assess the performance of the deep learning algorithm by comparing the deep learning algorithm and the radiology predictions. Results: We evaluated 491 scans consisting of 5669 pulmonary nodules annotated by a radiologist from our hospital; our algorithm showed a sensitivity of 90% and with only 0.3 false positives per scan with a CPM score of 0.85. Apart from the nodule-wise performance, we also assessed the algorithm for the detection of patients containing true nodules where it achieved a sensitivity of 0.95 and specificity of 1.0 over 491 scans in the test cohort. Conclusions: Our multi-institutional validated deep learning-based algorithm can aid radiologists in confirming the detection of pulmonary nodules through computed tomography (CT) scans and identifying further abnormalities and can be used as an assistive tool. This will be helpful in national lung screening programs guiding early diagnosis and appropriate management. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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