Can we skip invasive biopsy of sentinel lymph nodes? A preliminary investigation to predict sentinel lymph node status using PET/CT-based radiomics
- Liping Yang 1, Hongchao Ding 2, Xing Gao 2, Yuchao Xu 3, Shichuan Xu 4, Kezheng Wang 5
- Liping Yang 1, Hongchao Ding 2, Xing Gao 2
- 1Department of PET/CT, Harbin Medical University Cancer Hospital, Harbin, 150001, China.
- 2Department of Physical Diagnostics, Heilongjiang Provincial Hospital, Harbin, China.
- 3School of Nuclear Science and Technology, University of South China, Hengyang, China.
- 4Department of Medical Instruments, Second Hospital of Harbin, Harbin, 150001, China. 69744792@qq.com.
- 5Department of PET/CT, Harbin Medical University Cancer Hospital, Harbin, 150001, China. wangkezheng9954001@163.com.
- 0Department of PET/CT, Harbin Medical University Cancer Hospital, Harbin, 150001, China.
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View abstract on PubMed
Summary
This summary is machine-generated.This study shows that 2-deoxy-2-[<sup>18</sup>F]fluoro-d-glucose positron emission tomography/computed tomography (<sup>18</sup>F-FDG-PET/CT) radiomics can accurately predict sentinel lymph node (SLN) metastasis in invasive ductal breast cancer (IDC) patients non-invasively.
Area Of Science
- Oncology
- Radiology
- Medical Imaging
Background
- Sentinel lymph node biopsy (SLNB) is standard for detecting breast cancer metastasis but is invasive.
- Complications associated with SLNB necessitate the development of less invasive diagnostic methods.
- Evaluating non-invasive techniques for SLN metastasis assessment in invasive ductal cancer (IDC) is crucial.
Purpose Of The Study
- To assess the diagnostic performance of non-invasive radiomics analysis using <sup>18</sup>F-FDG-PET/CT for SLN metastasis in IDC patients.
- To identify predictors of SLN metastasis through clinical and metabolic parameters.
- To develop and validate predictive models for SLN status.
Main Methods
- Retrospective analysis of 132 IDC patients who underwent <sup>18</sup>F-FDG-PET/CT before surgery.
- Extraction of radiomic features from PET and CT scans.
- Development of prediction models using Random Forest (RF), Decision Tree (DT), and k-Nearest Neighbors (KNN) classifiers.
Main Results
- Multivariate analysis identified Ki 67 and tumor-to-liver ratio (TLG) as independent predictors.
- The RF model achieved the highest AUC of 0.887 in the training cohort and 0.856 in the validation cohort.
- Radiomic features combined with clinical variables showed high accuracy in predicting SLN metastasis.
Conclusions
- <sup>18</sup>F-FDG-PET/CT radiomics offers a robust, non-invasive method for predicting SLN status in IDC.
- This approach can aid in personalized treatment planning for breast cancer patients.
- Non-invasive radiomics may reduce the need for invasive SLNB procedures.
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