Impact of Primary Tumor Diameter and SUVmax on Pathological Lymph Node Involvement in Non-small Cell Lung Cancer
Aim:The aim of our study was to determine the role of preoperative primary tumor diameter (PTD) and maximum standardized uptake (SUVmax) values on preoperative 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) in predicting regional lymph node (LN) involvement, lymphatic invasion (LI), vascular invasion (VI), and pleural invasion (PI) in patients with non-small cell lung cancer (NSCLC) who were operated without receiving neoadjuvant therapy.Methods: A total of 70 patients diagnosed with NSCLC who underwent surgery after 18 F-FDG PET/CT but did not receive neoadjuvant therapy were retrospectively examined.The effects of PTD and SUV max on postoperative LN involvement, LI, VI, and PI in patient groups below and above the determined threshold value on preoperative 18 F-FDG PET/CT were compared.Since an optimal cut-off value for specificity and sensitivity was not obtained with the receiver operating characteristic curve for both the PTD and SUVmax of the primary tumor, patients were grouped based on the median values for the two parameters.Results: The median PTD was 32 mm.The median SUVmax of 12.55 was obtained, and patients were grouped according to these median values.No significant difference was found in the primary tumor diameters 32 mm and <32 mm in terms of pathological LN involvement (p=0.322),VI (p=0.122),LI (p=0.122) and PI (p=1.000).Again, no significant difference was found in the patient groups with SUVmax values of the primary tumor 12.55 and <12.55 regarding pathological LN involvement (p=0.621),VI (p=0.122),LI (p=0.122), and PI (p=1.000).A low positive correlation (p=0.000,r=0.447) was found between the PTD and SUVmax values.Conclusion: 18 F-FDG PET/CT alone is not a reliable noninvasive method for predicting LN metastasis in patients with early-stage NSCLC for whom curative treatment is planned.
Authors
- Bilgin Demir (ORCID: https://orcid.org/0000-0003-4380-9419)
- Yakup Yürekli (ORCID: https://orcid.org/0000-0001-5823-0659)
- Ali Aytaç
- Sabri Barutça (ORCID: https://orcid.org/0000-0002-1941-0531)
Publication Details
- Journal
- Acta haematologica oncologica Turcica.
- Published
- 2024-10-31
- DOI
- https://doi.org/10.4274/ahot.galenos.2024.88785
- Citations
- 1
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.40