Prediction of solitary &le;3‑cm small adrenal metastasis based on <sup>18</sup>F‑FDG PET/CT using a support vector machine model in lung cancer

Zhao,Lin; Cai,Haifeng; Li,Jingwu; Zhao,Wenzhe; Liu,Yongliang; Cao,Lixiu

doi:10.3892/ol.2025.15395

January-2026 Volume 31 Issue 1

Full Size Image

Journals

International Journal of Molecular Medicine

International Journal of Molecular Medicine is an international journal devoted to molecular mechanisms of human disease.

International Journal of Oncology

International Journal of Oncology is an international journal devoted to oncology research and cancer treatment.

Molecular Medicine Reports

Covers molecular medicine topics such as pharmacology, pathology, genetics, neuroscience, infectious diseases, molecular cardiology, and molecular surgery.

Oncology Reports

Oncology Reports is an international journal devoted to fundamental and applied research in Oncology.

Experimental and Therapeutic Medicine

Experimental and Therapeutic Medicine is an international journal devoted to laboratory and clinical medicine.

Oncology Letters

Oncology Letters is an international journal devoted to Experimental and Clinical Oncology.

Biomedical Reports

Explores a wide range of biological and medical fields, including pharmacology, genetics, microbiology, neuroscience, and molecular cardiology.

Molecular and Clinical Oncology

International journal addressing all aspects of oncology research, from tumorigenesis and oncogenes to chemotherapy and metastasis.

World Academy of Sciences Journal

Multidisciplinary open-access journal spanning biochemistry, genetics, neuroscience, environmental health, and synthetic biology.

International Journal of Functional Nutrition

Open-access journal combining biochemistry, pharmacology, immunology, and genetics to advance health through functional nutrition.

International Journal of Epigenetics

Publishes open-access research on using epigenetics to advance understanding and treatment of human disease.

Medicine International

An International Open Access Journal Devoted to General Medicine.

January-2026 Volume 31 Issue 1

Full Size Image

Article Open Access

Prediction of solitary ≤3‑cm small adrenal metastasis based on ¹⁸F‑FDG PET/CT using a support vector machine model in lung cancer

Authors:
- Lin Zhao
- Haifeng Cai
- Jingwu Li
- Wenzhe Zhao
- Yongliang Liu
- Lixiu Cao
View Affiliations / Copyright

Affiliations: Department of Computed Tomography, Tangshan People's Hospital, Tangshan, Hebei 063000, P.R. China, Department of Breast Surgery, Tangshan People's Hospital, Tangshan, Hebei 063000, P.R. China, Hebei Key Laboratory of Molecular Oncology, Tangshan People's Hospital, Tangshan, Hebei 063000, P.R. China, Department of Neurosurgery, Tangshan People's Hospital, Tangshan, Hebei 063000, P.R. China

Copyright: © Zhao et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
Article Number: 42
|
Published online on: November 24, 2025

https://doi.org/10.3892/ol.2025.15395
Expand metrics +

Abstract

The purpose of the present study was to develop a simplified scoring system based on the support vector machine (SVM) classification method to improve the diagnostic performance of ¹⁸F‑fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) in differentiating solitary small (≤3 cm) adrenal metastases from benign lesions in patients with lung cancer. A total of 197 patients with histopathologically confirmed diagnoses of lung cancer were retrospectively included. All patients had solitary adrenal lesions (long diameter ≤3 cm) showing hyperattenuating features (unenhanced CT values ≥10 HU) on pre‑treatment ¹⁸F‑FDG PET/CT scans. The cohort included 128 cases of metastases and 69 cases of benign lesions. SVM models were developed using five adrenal lesion features and one primary lung cancer feature. Model performance was evaluated by comparing the area under the receiver operating characteristic curve (AUC) and accuracy across eight candidate models. The optimal model was further simplified based on feature weights and value distributions to enhance clinical applicability. The best‑performing SVM model demonstrated maximum, minimum and mean accuracies of 98.0% (95% CI, 93.9‑100%), 71.4% (95% CI, 57.1‑83.7%) and 84.3% (95% CI, 69.4‑91.8%), respectively, with corresponding AUC values of 1.000, 0.770 and 0.913, respectively. The simplified scoring system stratified adrenal lesions into three diagnostic categories: Scores <5, benign; scores >6.5, metastatic; and scores 5‑6.5, suspicious for metastasis. In conclusion, the present study developed an SVM‑based model using ¹⁸F‑FDG PET/CT imaging features to differentiate solitary small adrenal metastases in patients with lung cancer. The maximum standardized uptake value of the adrenal lesions was the predominant predictive feature. After simplification, the model was converted into a clinically practical scoring system, which may assist clinicians in streamlining clinical staging decisions for patients with lung cancer.

View Figures

View References

1	Bray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I and Jemal A: Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 74:229–263. 2024.PubMed/NCBI
2	Besse B, Adjei A, Baas P, Meldgaard P, Nicolson M, Paz-Ares L, Reck M, Smit EF, Syrigos K, Stahel R, et al: 2nd ESMO consensus conference on lung cancer: Non-small-cell lung cancer first-line/second and further lines of treatment in advanced disease. Ann Oncol. 25:1475–1484. 2014. View Article : Google Scholar : PubMed/NCBI
3	Burt M, Heelan RT, Coit D, McCormack PM, Bains MS, Martini N, Rusch V and Ginsberg RJ: Prospective evaluation of unilateral adrenal masses in patients with operable non-small-cell lung cancer. Impact of magnetic resonance imaging. J Thorac Cardiovasc Surg. 107:584–599. 1994. View Article : Google Scholar : PubMed/NCBI
4	Kawai N, Tozawa K, Yasui T, Moritoki Y, Sasaki H, Yano M, Fujii Y and Kohri K: Laparoscopic adrenalectomy for solitary adrenal metastasis from lung cancer. JSLS. 18:e2014.00062. 2014. View Article : Google Scholar : PubMed/NCBI
5	Porte HL, Roumilhac D, Graziana JP, Eraldi L, Cordonier C, Puech P and Wurtz AJ: Adrenalectomy for a solitary adrenal metastasis from lung cancer. Ann Thorac Surg. 65:331–335. 1998. View Article : Google Scholar : PubMed/NCBI
6	Gao XL, Zhang KW, Tang MB, Zhang KJ, Fang LN and Liu W: Pooled analysis for surgical treatment for isolated adrenal metastasis and non-small cell lung cancer. Interact Cardiovasc Thorac Surg. 24:1–7. 2017. View Article : Google Scholar : PubMed/NCBI
7	Holy R, Piroth M, Pinkawa M and Eble MJ: Stereotactic body radiation therapy (SBRT) for treatment of adrenal gland metastases from non-small cell lung cancer. Strahlenther Onkol. 187:245–251. 2011. View Article : Google Scholar : PubMed/NCBI
8	Cao L, Yang H, Yao D, Cai H, Wu H, Yu Y, Zhu L, Xu W, Liu Y and Li J: Clinical-imaging-radiomic nomogram based on unenhanced CT effectively predicts adrenal metastases in patients with lung cancer with small hyperattenuating adrenal incidentalomas. Oncol Lett. 28:3402024. View Article : Google Scholar : PubMed/NCBI
9	Brady MJ, Thomas J, Wong TZ, Franklin KM, Ho LM and Paulson EK: Adrenal nodules at FDG PET/CT in patients known to have or suspected of having lung cancer: A proposal for an efficient diagnostic algorithm. Radiology. 250:523–530. 2009. View Article : Google Scholar : PubMed/NCBI
10	Zeiger MA, Siegelman SS and Hamrahian AH: Medical and surgical evaluation and treatment of adrenal incidentalomas. J Clin Endocrinol Metab. 96:2004–2015. 2011. View Article : Google Scholar : PubMed/NCBI
11	Cao L, Yang H, Wu H, Zhong H, Cai H, Yu Y, Zhu L, Liu Y and Li J: Adrenal indeterminate nodules: CT-based radiomics analysis of different machine learning models for predicting adrenal metastases in lung cancer patients. Front Oncol. 14:14112142024. View Article : Google Scholar : PubMed/NCBI
12	Caoili EM, Korobkin M, Francis IR, Cohan RH, Platt JF, Dunnick NR and Raghupathi KI: Adrenal masses: Characterization with combined unenhanced and delayed enhanced CT. Radiology. 222:629–633. 2002. View Article : Google Scholar : PubMed/NCBI
13	Fujiyoshi F, Nakajo M, Fukukura Y and Tsuchimochi S: Characterization of adrenal tumors by chemical shift fast low-angle shot MR imaging: Comparison of four methods of quantitative evaluation. AJR Am J Roentgenol. 180:1649–1657. 2003. View Article : Google Scholar : PubMed/NCBI
14	Mayo-Smith WW, Song JH, Boland GL, Francis IR, Israel GM, Mazzaglia PJ, Berland LL and Pandharipande PV: Management of incidental adrenal masses: A white paper of the ACR incidental findings committee. J Am Coll Radiol. 14:1038–1044. 2017. View Article : Google Scholar : PubMed/NCBI
15	Haider MA, Ghai S, Jhaveri K and Lockwood G: Chemical shift MR imaging of hyperattenuating (>10 HU) adrenal masses: Does it still have a role? Radiology. 231:711–716. 2004. View Article : Google Scholar : PubMed/NCBI
16	Koo HJ, Choi HJ, Kim HJ, Kim SO and Cho KS: The value of 15-minute delayed contrast-enhanced CT to differentiate hyperattenuating adrenal masses compared with chemical shift MR imaging. Eur Radiol. 24:1410–1420. 2014. View Article : Google Scholar : PubMed/NCBI
17	Hsiao R, Chow A, Kluijfhout WP, Bongers PJ, Verzijl R, Metser U, Veit-Haibach P and Pasternak JD: The clinical consequences of functional adrenal uptake in the absence of cross-sectional mass on FDG-PET/CT in oncology patients. Langenbecks Arch Surg. 407:1677–1684. 2022. View Article : Google Scholar : PubMed/NCBI
18	Arikan AE, Makay O, Teksoz S, Vatansever S, Alptekin H, Albeniz G, Demir A, Ozpek A and Tunca F: Efficacy of PET-CT in the prediction of metastatic adrenal masses that are detected on follow-up of the patients with prior nonadrenal malignancy: A nationwide multicenter case-control study. Medicine (Baltimore). 101:e302142022. View Article : Google Scholar : PubMed/NCBI
19	Lang BH, Cowling BJ, Li JY, Wong KP and Wan KY: High false positivity in positron emission tomography is a potential diagnostic pitfall in patients with suspected adrenal metastasis. World J Surg. 39:1902–1908. 2015. View Article : Google Scholar : PubMed/NCBI
20	Detterbeck FC, Woodard GA, Bader AS, Dacic S, Grant MJ, Park HS and Tanoue LT: The proposed ninth edition TNM classification of lung cancer. Chest. 166:882–895. 2024. View Article : Google Scholar : PubMed/NCBI
21	Vos EL, Grewal RK, Russo AE, Reidy-Lagunes D, Untch BR, Gavane SC, Boucai L, Geer E, Gopalan A, Chou JF, et al: Predicting malignancy in patients with adrenal tumors using ¹⁸F-FDG-PET/CT SUVmax. J Surg Oncol. 122:1821–1826. 2020. View Article : Google Scholar : PubMed/NCBI
22	Xu B, Gao J, Cui L, Wang H, Guan Z, Yao S, Shen Z and Tian J: Characterization of adrenal metastatic cancer using FDG PET/CT. Neoplasma. 59:92–99. 2012. View Article : Google Scholar : PubMed/NCBI
23	Orzechowski S, Gnass M, Czyżewski D, Wojtacha J, Sudoł B, Pankowski J, Zajęcki W, Ćmiel A, Zieliński M and Szlubowski A: Ultrasound predictors of left adrenal metastasis in patients with lung cancer: A comparison of computed tomography, positron emission tomography-computed tomography, and endoscopic ultrasound using ultrasound bronchoscope. Pol Arch Intern Med. 132:161272022.PubMed/NCBI
24	Noble WS: What is a support vector machine? Nat Biotechnol. 24:1565–1567. 2006. View Article : Google Scholar : PubMed/NCBI
25	Yin G, Song Y, Li X, Zhu L, Su Q, Dai D and Xu W: Prediction of mediastinal lymph node metastasis based on ¹⁸F-FDG PET/CT imaging using support vector machine in non-small cell lung cancer. Eur Radiol. 31:3983–3992. 2021. View Article : Google Scholar : PubMed/NCBI
26	Zhang R, Zhu L, Cai Z, Jiang W, Li J, Yang C, Yu C, Jiang B, Wang W, Xu W, et al: Potential feature exploration and model development based on 18F-FDG PET/CT images for differentiating benign and malignant lung lesions. Eur J Radiol. 121:1087352019. View Article : Google Scholar : PubMed/NCBI
27	Nishino M, Jagannathan JP, Ramaiya NH and Van den Abbeele AD: Revised RECIST guideline version 1.1: What oncologists want to know and what radiologists need to know. AJR Am J Roentgenol. 195:281–289. 2010. View Article : Google Scholar : PubMed/NCBI
28	Liang M, Su Q, Mouraux A and Iannetti GD: Spatial patterns of brain activity preferentially reflecting transient pain and stimulus intensity. Cereb Cortex. 29:2211–2227. 2019. View Article : Google Scholar : PubMed/NCBI
29	Mendez AM, Petre EN, Ziv E, Ridouani F, Solomon SB, Sotirchos V, Zhao K and Alexander ES: Safety and efficacy of thermal ablation of adrenal metastases secondary to lung cancer. Surg Oncol. 55:1021022024. View Article : Google Scholar : PubMed/NCBI
30	Andersen MB, Bodtger U, Andersen IR, Thorup KS, Ganeshan B and Rasmussen F: Metastases or benign adrenal lesions in patients with histopathological verification of lung cancer: Can CT texture analysis distinguish? Eur J Radiol. 138:1096642021. View Article : Google Scholar : PubMed/NCBI
31	Al Fares HK, Abdullaj S, Gokden N and Menon LP: Incidental, solitary, and unilateral adrenal metastasis as the initial manifestation of lung adenocarcinoma. Cureus. 14:e326282022.PubMed/NCBI
32	Hashimoto K, Nishimura S and Akagi M: Lung adenocarcinoma presenting as a soft tissue metastasis to the shoulder: A case report. Medicina (Kaunas). 57:1812021. View Article : Google Scholar : PubMed/NCBI
33	Lococo F, Patricelli G, Galeone C and Rapicetta C: eComment. Surgery for isolated adrenal metastasis from non-small-cell lung cancer: The role of preoperative PET/CT scan'. Interact Cardiovasc Thorac Surg. 24:72017. View Article : Google Scholar : PubMed/NCBI
34	Evans PD, Miller CM, Marin D, Stinnett SS, Wong TZ, Paulson EK and Ho LM: FDG-PET/CT characterization of adrenal nodules: Diagnostic accuracy and interreader agreement using quantitative and qualitative methods. Acad Radiol. 20:923–929. 2013. View Article : Google Scholar : PubMed/NCBI
35	Kim BS, Lee JD and Kang WJ: Differentiation of an adrenal mass in patients with non-small cell lung cancer by means of a normal range of adrenal standardized uptake values on FDG PET/CT. Ann Nucl Med. 29:276–283. 2015. View Article : Google Scholar : PubMed/NCBI
36	Cho AR, Lim I, Na II, Choe du H, Park JY, Kim BI, Cheon GJ, Choi CW and Lim SM: Evaluation of adrenal masses in lung cancer patients using F-18 FDG PET/CT. Nucl Med Mol Imaging. 45:52–58. 2011. View Article : Google Scholar : PubMed/NCBI
37	Lu Y, Xie D, Huang W, Gong H and Yu J: 18F-FDG PET/CT in the evaluation of adrenal masses in lung cancer patients. Neoplasma. 57:129–134. 2010. View Article : Google Scholar : PubMed/NCBI
38	Koopman D, van Dalen JA, Stigt JA, Slump CH, Knollema S and Jager PL: Current generation time-of-flight (18)F-FDG PET/CT provides higher SUVs for normal adrenal glands, while maintaining an accurate characterization of benign and malignant glands. Ann Nucl Med. 30:145–152. 2016. View Article : Google Scholar : PubMed/NCBI
39	Smith TA: Facilitative glucose transporter expression in human cancer tissue. Br J Biomed Sci. 56:285–292. 1999.PubMed/NCBI
40	Zhang S, Li S, Pei Y, Huang M, Lu F, Zheng Q, Li N and Yang Y: Impact of maximum standardized uptake value of non-small cell lung cancer on detecting lymph node involvement in potential stereotactic body radiotherapy candidates. J Thorac Dis. 9:1023–1031. 2017. View Article : Google Scholar : PubMed/NCBI
41	Zhu SH, Zhang Y, Yu YH, Fu Z, Kong L, Han DL, Fu L, Yu JM and Li J: FDG PET-CT in non-small cell lung cancer: Relationship between primary tumor FDG uptake and extensional or metastatic potential. Asian Pac J Cancer Prev. 14:2925–2929. 2013. View Article : Google Scholar : PubMed/NCBI
42	Sherlock M, Scarsbrook A, Abbas A, Fraser S, Limumpornpetch P, Dineen R and Stewart PM: Adrenal incidentaloma. Endocr Rev. 41:775–820. 2020. View Article : Google Scholar : PubMed/NCBI
43	Chen J, He Y, Zeng X, Zhu S and Li F: Distinguishing between metastatic and benign adrenal masses in patients with extra-adrenal malignancies. Front Endocrinol (Lausanne). 13:9787302022. View Article : Google Scholar : PubMed/NCBI
44	Ali MA, El-Abd E, Morsi M, El Safwany MM and El-Sayed MZ: The effect of hepatic steatosis on 18F-FDG uptake in PET-CT examinations of cancer Egyptian patients. Eur J Hybrid Imaging. 7:192023. View Article : Google Scholar : PubMed/NCBI

Journals