Can we skip invasive biopsy of sentinel lymph nodes? A preliminary investigation to predict sentinel lymph node status using PET/CT-based radiomics.

Autor: Yang L; Department of PET/CT, Harbin Medical University Cancer Hospital, Harbin, 150001, China., Ding H; Department of Physical Diagnostics, Heilongjiang Provincial Hospital, Harbin, China., Gao X; Department of Physical Diagnostics, Heilongjiang Provincial Hospital, Harbin, China., Xu Y; School of Nuclear Science and Technology, University of South China, Hengyang, China., Xu S; Department of Medical Instruments, Second Hospital of Harbin, Harbin, 150001, China. 69744792@qq.com., Wang K; Department of PET/CT, Harbin Medical University Cancer Hospital, Harbin, 150001, China. wangkezheng9954001@163.com.
Jazyk: angličtina
Zdroj: BMC cancer [BMC Cancer] 2024 Oct 25; Vol. 24 (1), pp. 1316. Date of Electronic Publication: 2024 Oct 25.
DOI: 10.1186/s12885-024-13031-w
Abstrakt: 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-[ 18 F]fluoro-d-glucose positron emission tomography/computed tomography ( 18 F-FDG-PET/CT) for assessing SLN metastasis in IDC patients.
Methods: This retrospective study included 132 patients with biopsy-confirmed IDC, who underwent 18 F-FDG PET/CT scans prior to mastectomy or breast-conserving surgery with SLNB. Tumor resection or SLNB was conducted within one-week post-scan. Clinical data and metabolic parameters were analyzed to identify independent SLN metastasis predictors. Radiomic features were extracted from each PET volume of interest (VOI) and CT-VOI. Feature selection involved univariate and multivariate logistic regression analysis, and the least absolute shrinkage and selection operator (LASSO) method. Three models were developed to predict SLN status using the random forest (RF), decision tree (DT), and k-Nearest Neighbors (KNN) classifiers. Model performance was assessed using the area under the receiver operating characteristic curve (AUC).
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: 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.
(© 2024. The Author(s).)
Databáze: MEDLINE
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