Can we skip invasive biopsy of sentinel lymph nodes? A preliminary investigation to predict sentinel lymph node status using PET/CT-based radiomics.
Humans
Female
Positron Emission Tomography Computed Tomography
/ methods
Middle Aged
Retrospective Studies
Sentinel Lymph Node Biopsy
/ methods
Breast Neoplasms
/ pathology
Fluorodeoxyglucose F18
Aged
Adult
Lymphatic Metastasis
/ diagnostic imaging
Sentinel Lymph Node
/ diagnostic imaging
Carcinoma, Ductal, Breast
/ diagnostic imaging
Radiopharmaceuticals
/ administration & dosage
ROC Curve
Radiomics
Breast neoplasms
Positron emission tomography- computed tomography
Radiomics
Sentinel lymph node
Journal
BMC cancer
ISSN: 1471-2407
Titre abrégé: BMC Cancer
Pays: England
ID NLM: 100967800
Informations de publication
Date de publication:
25 Oct 2024
25 Oct 2024
Historique:
received:
25
07
2024
accepted:
04
10
2024
medline:
26
10
2024
pubmed:
26
10
2024
entrez:
25
10
2024
Statut:
epublish
Résumé
Sentinel lymph node (SLN) biopsy (SLNB) is considered the gold standard for detecting SLN metastases in patients with invasive ductal breast cancer (IDC). However, SLNB is invasive and associated with several complications. Thus, this study aimed to evaluate the diagnostic performance of a non-invasive radiomics analysis utilizing 2-deoxy-2-[ This retrospective study included 132 patients with biopsy-confirmed IDC, who underwent The study included 91 cases (32 SLN-positive and 59 SLN-negative patients) in the training cohort and 41 cases (29 SLN-positive and 12 SLN-negative patients) in the validation cohort. Multivariate logistic regression analysis identified Ki 67 and TLG as independent predictors of SLN status. Five PET-derived features, three CT-derived features, and two clinical variables were selected for model development. The AUC values of the RF, KNN, and DT models for the training cohort were 0.887, 0.849, and 0.824, respectively, and for the validation cohort were 0.856, 0.830, and 0.819, respectively. The RF model demonstrated the highest accuracy for the preoperative prediction of SLN metastasis in IDC patients. The PET-CT radiomics approach may offer robust and non-invasive predictors for SLN status in IDC patients, potentially aiding in the planning of personalized treatment strategies for IDC patients.
Sections du résumé
BACKGROUND
BACKGROUND
Sentinel lymph node (SLN) biopsy (SLNB) is considered the gold standard for detecting SLN metastases in patients with invasive ductal breast cancer (IDC). However, SLNB is invasive and associated with several complications. Thus, this study aimed to evaluate the diagnostic performance of a non-invasive radiomics analysis utilizing 2-deoxy-2-[
METHODS
METHODS
This retrospective study included 132 patients with biopsy-confirmed IDC, who underwent
RESULTS
RESULTS
The study included 91 cases (32 SLN-positive and 59 SLN-negative patients) in the training cohort and 41 cases (29 SLN-positive and 12 SLN-negative patients) in the validation cohort. Multivariate logistic regression analysis identified Ki 67 and TLG as independent predictors of SLN status. Five PET-derived features, three CT-derived features, and two clinical variables were selected for model development. The AUC values of the RF, KNN, and DT models for the training cohort were 0.887, 0.849, and 0.824, respectively, and for the validation cohort were 0.856, 0.830, and 0.819, respectively. The RF model demonstrated the highest accuracy for the preoperative prediction of SLN metastasis in IDC patients.
CONCLUSION
CONCLUSIONS
The PET-CT radiomics approach may offer robust and non-invasive predictors for SLN status in IDC patients, potentially aiding in the planning of personalized treatment strategies for IDC patients.
Identifiants
pubmed: 39455907
doi: 10.1186/s12885-024-13031-w
pii: 10.1186/s12885-024-13031-w
doi:
Substances chimiques
Fluorodeoxyglucose F18
0Z5B2CJX4D
Radiopharmaceuticals
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
1316Subventions
Organisme : Haiyan Fund of Harbin Medical University Cancer Hospital
ID : JJMS-2023-05
Organisme : Provincial Key Research and Development Program of Heilongjiang Province
ID : GA21C001
Informations de copyright
© 2024. The Author(s).
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