Development and Validation of Deep Learning-Based Automated Detection of Cervical Lymphadenopathy in Patients with Lymphoma for Treatment Response Assessment: A Bi-institutional Feasibility Study.
The purpose is to train and evaluate a deep learning (DL) model for the accurate detection and segmentation of abnormal cervical lymph nodes (LN) on head and neck contrast-enhanced CT scans in patients diagnosed with lymphoma and evaluate the clinical utility of the DL model in response assessment....
| Publicado en: | Journal of Digital Imaging Vol. 37; no. 2; pp. 734 - 744 |
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| Autores principales: | , , , , , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Apr2024
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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=177626015&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 177626015 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Apr2024 vid: 37 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 177626015 177626015 177626015 10.1007/s10278-024-00966-6 177626015 ppf: 734 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Development and Validation of Deep Learning-Based Automated Detection of Cervical Lymphadenopathy in Patients with Lymphoma for Treatment Response Assessment: A Bi-institutional Feasibility Study. aug: au: Nam, Yoonho Kim, Su-Youn Kim, Kyu-Ah Kwon, Euna Lee, Yoo Hyun Jang, Jinhee Lee, Min Kyoung Kim, Jiwoong Choi, Yangsean affil: https://ror.org/051q2m369 Division of Biomedical Engineering, Hankuk University of Foreign Studies, Yongin-Si, Gyeonggi‐do, Republic of Korea sug: subj: Lymphatic Diseases Diagnosis Lymphoma Diagnosis Cervical Vertebrae Deep Learning Models, Theoretical Automation Program Development Tomography, X-Ray Computed Education Treatment Outcomes Evaluation Radiographic Image Enhancement Human Retrospective Design Record Review Descriptive Statistics Prospective Studies Lymphatic Diseases Radiography Comparative Studies Reliability and Validity Prediction Models Disease Attributes Funding Source Contrast Media Diagnostic Use ab: The purpose is to train and evaluate a deep learning (DL) model for the accurate detection and segmentation of abnormal cervical lymph nodes (LN) on head and neck contrast-enhanced CT scans in patients diagnosed with lymphoma and evaluate the clinical utility of the DL model in response assessment. This retrospective study included patients who underwent CT for abnormal cervical LN and lymphoma assessment between January 2021 and July 2022. Patients were grouped into the development (n = 76), internal test 1 (n = 27), internal test 2 (n = 87), and external test (n = 26) cohorts. A 3D SegResNet model was used to train the CT images. The volume change rates of cervical LN across longitudinal CT scans were compared among patients with different treatment outcomes (stable, response, and progression). Dice similarity coefficient (DSC) and the Bland–Altman plot were used to assess the model's segmentation performance and reliability, respectively. No significant differences in baseline clinical characteristics were found across cohorts (age, P = 0.55; sex, P = 0.13; diagnoses, P = 0.06). The mean DSC was 0.39 ± 0.2 with a precision and recall of 60.9% and 57.0%, respectively. Most LN volumes were within the limits of agreement on the Bland–Altman plot. The volume change rates among the three groups differed significantly (progression (n = 74), 342.2%; response (n = 8), − 79.2%; stable (n = 5), − 8.1%; all P < 0.01). Our proposed DL segmentation model showed modest performance in quantifying the cervical LN burden on CT in patients with lymphoma. Longitudinal changes in cervical LN volume, as predicted by the DL model, were useful for treatment response assessment. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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