An [18F]FDG-PET/CT deep learning method for fully automated detection of pathological mediastinal lymph nodes in lung cancer patients.
Purpose: The identification of pathological mediastinal lymph nodes is an important step in the staging of lung cancer, with the presence of metastases significantly affecting survival rates. Nodes are currently identified by a physician, but this process is time-consuming and prone to errors. In th...
| Publicado en: | European Journal of Nuclear Medicine & Molecular Imaging Vol. 49; no. 3; pp. 881 - 889 |
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| Autores principales: | , , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Feb2022
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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=154982419&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 154982419 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 16197070 NPC jtl: European Journal of Nuclear Medicine & Molecular Imaging issn: 16197070 maglogo: N pubinfo: dt: Feb2022 vid: 49 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 154982419 152441575 154982419 154982419 10.1007/s00259-021-05513-x 154982419 ppf: 881 ppct: 8 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: An [18F]FDG-PET/CT deep learning method for fully automated detection of pathological mediastinal lymph nodes in lung cancer patients. aug: au: Wallis, David Soussan, Michaël Lacroix, Maxime Akl, Pia Duboucher, Clément Buvat, Irène affil: Laboratoire D'Imagerie Translationnelle en Oncologie, U1288 Inserm, Institut Curie, PSL, Université Paris Saclay, Paris, France sug: subj: Lung Neoplasms Lymphatic Diseases Diagnosis Radiographic Image Interpretation, Computer-Assisted Deep Learning Human Retrospective Design Fludeoxyglucose F 18 Positron-Emission Tomography Tomography, X-Ray Computed Sensitivity and Specificity Confidence Intervals ab: Purpose: The identification of pathological mediastinal lymph nodes is an important step in the staging of lung cancer, with the presence of metastases significantly affecting survival rates. Nodes are currently identified by a physician, but this process is time-consuming and prone to errors. In this paper, we investigate the use of artificial intelligence–based methods to increase the accuracy and consistency of this process. Methods: Whole-body 18F-labelled fluoro-2-deoxyglucose ([18F]FDG) positron emission tomography/computed tomography ([18F]FDG-PET/CT) scans (Philips Gemini TF) from 134 patients were retrospectively analysed. The thorax was automatically located, and then slices were fed into a U-Net to identify candidate regions. These regions were split into overlapping 3D cubes, which were individually predicted as positive or negative using a 3D CNN. From these predictions, pathological mediastinal nodes could be identified. A second cohort of 71 patients was then acquired from a different, newer scanner (GE Discovery MI), and the performance of the model on this dataset was tested with and without transfer learning. Results: On the test set from the first scanner, our model achieved a sensitivity of 0.87 (95% confidence intervals [0.74, 0.94]) with 0.41 [0.22, 0.71] false positives/patient. This was comparable to the performance of an expert. Without transfer learning, on the test set from the second scanner, the corresponding results were 0.53 [0.35, 0.70] and 0.24 [0.10, 0.49], respectively. With transfer learning, these metrics were 0.88 [0.73, 0.97] and 0.69 [0.43, 1.04], respectively. Conclusion: Model performance was comparable to that of an expert on data from the same scanner. With transfer learning, the model can be applied to data from a different scanner. To our knowledge it is the first study of its kind to go directly from whole-body [18F]FDG-PET/CT scans to pathological mediastinal lymph node localisation. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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