Radiomics Based on F-FDG PET/CT Could Differentiate Breast Carcinoma from Breast Lymphoma Using Machine-learning Approach: A Preliminary Study

Overview

Journal Cancer Med

Specialty Oncology

Date 2019 Nov 27

PMID 31769230

Citations 26

Authors

Xuejin Ou

Jing Zhang

Jian Wang

Fuwen Pang

Yongsheng Wang

Xiawei Wei

Xuelei Ma

Affiliations

Soon will be listed here.

Abstract

Purpose: Our study assessed the ability F-fluorodeoxyglucose (FDG) positron emission tomography (PET)/computed tomography (CT) radiomics to differentiate breast carcinoma from breast lymphoma using machine-learning approach.

Methods: Sixty-five breast nodules from 44 patients diagnosed as breast carcinoma or breast lymphoma were included. Standardized uptake value (SUV) and radiomic features from CT and PET images were extracted using local image features extraction software. Six discriminative models including PETa (based on clinical, SUV and radiomic features from PET images), PETb (SUV and radiomic features from PET images), PETc (radiomic features only from PET images), CTa (clinical and radiomic features from CT images), CTb (radiomic features only from CT images), and SUV model were generated using least absolute shrinkage and selection operator method and linear discriminant analysis. The areas under the receiver operating characteristic curve (AUCs), accuracy, sensitivity, and specificity were calculated to evaluate the discriminative ability of these models.

Results: PETa and CTa models showed the best ability to differentiation in training and validation group (AUCs of 0.867 and 0.806 for PETa model, AUCs of 0.891 and 0.759 for CTa model, respectively).

Conclusion: Models based on clinical, SUV, and radiomic features of F-FDG PET/CT images could accurately discriminate breast carcinoma from breast lymphoma.

Citing Articles

Transparency and Representation in Clinical Research Utilizing Artificial Intelligence in Oncology: A Scoping Review.

DAmiano A, Cheunkarndee T, Azoba C, Chen K, Mak R, Perni S Cancer Med. 2025; 14(5):e70728.

PMID: 40059400 PMC: 11891267. DOI: 10.1002/cam4.70728.

Semiquantitative 2-[F]FDG PET/CT-based parameters role in lymphoma.

Albano D, Ravanelli M, Durmo R, Versari A, Filice A, Rizzo A Front Med (Lausanne). 2025; 11:1515040.

PMID: 39744526 PMC: 11688351. DOI: 10.3389/fmed.2024.1515040.

Enhancing Lymphoma Diagnosis, Treatment, and Follow-Up Using F-FDG PET/CT Imaging: Contribution of Artificial Intelligence and Radiomics Analysis.

Hasanabadi S, Aghamiri S, Abin A, Abdollahi H, Arabi H, Zaidi H Cancers (Basel). 2024; 16(20).

PMID: 39456604 PMC: 11505665. DOI: 10.3390/cancers16203511.

Prognostic F-FDG Radiomic Features in Advanced High-Grade Serous Ovarian Cancer.

Travaglio Morales D, Huerga Cabrerizo C, Losantos Garcia I, Coronado Poggio M, Cordero Garcia J, Lopez Llobet E Diagnostics (Basel). 2023; 13(22).

PMID: 37998530 PMC: 10670627. DOI: 10.3390/diagnostics13223394.

End-to-end deep learning radiomics: development and validation of a novel attention-based aggregate convolutional neural network to distinguish breast diffuse large B-cell lymphoma from breast invasive ductal carcinoma.

Chen W, Liu F, Wang R, Qi M, Zhang J, Liu X Quant Imaging Med Surg. 2023; 13(10):6598-6614.

PMID: 37869296 PMC: 10585556. DOI: 10.21037/qims-22-1333.

References

Davnall F, Yip C, Ljungqvist G, Selmi M, Ng F, Sanghera B . Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice?. Insights Imaging. 2012; 3(6):573-89. PMC: 3505569. DOI: 10.1007/s13244-012-0196-6. View

Kassner A, Thornhill R . Texture analysis: a review of neurologic MR imaging applications. AJNR Am J Neuroradiol. 2010; 31(5):809-16. PMC: 7964174. DOI: 10.3174/ajnr.A2061. View

Nicholson B, Bhatti R, Glassman L . Extranodal Lymphoma of the Breast. Radiol Clin North Am. 2016; 54(4):711-26. DOI: 10.1016/j.rcl.2016.03.005. View

Bicchierai G, Rigacci L, Miele V, Meattini I, de Benedetto D, Selvi V . Role of core needle biopsy in primary breast lymphoma. Radiol Med. 2017; 122(9):651-655. DOI: 10.1007/s11547-017-0773-3. View

Sun Y, Joks M, Xu L, Chen X, Qian D, You J . Diffuse large B-cell lymphoma of the breast: prognostic factors and treatment outcomes. Onco Targets Ther. 2016; 9:2069-80. PMC: 4827925. DOI: 10.2147/OTT.S98566. View

Xu R, Kido S, Suga K, Hirano Y, Tachibana R, Muramatsu K . Texture analysis on (18)F-FDG PET/CT images to differentiate malignant and benign bone and soft-tissue lesions. Ann Nucl Med. 2014; 28(9):926-35. DOI: 10.1007/s12149-014-0895-9. View

Nioche C, Orlhac F, Boughdad S, Reuze S, Goya-Outi J, Robert C . LIFEx: A Freeware for Radiomic Feature Calculation in Multimodality Imaging to Accelerate Advances in the Characterization of Tumor Heterogeneity. Cancer Res. 2018; 78(16):4786-4789. DOI: 10.1158/0008-5472.CAN-18-0125. View

Ha S, Choi H, Cheon G, Kang K, Chung J, Kim E . Autoclustering of Non-small Cell Lung Carcinoma Subtypes on (18)F-FDG PET Using Texture Analysis: A Preliminary Result. Nucl Med Mol Imaging. 2015; 48(4):278-86. PMC: 4571663. DOI: 10.1007/s13139-014-0283-3. View

Harbeck N, Gnant M . Breast cancer. Lancet. 2016; 389(10074):1134-1150. DOI: 10.1016/S0140-6736(16)31891-8. View

10.

Torre L, Bray F, Siegel R, Ferlay J, Lortet-Tieulent J, Jemal A . Global cancer statistics, 2012. CA Cancer J Clin. 2015; 65(2):87-108. DOI: 10.3322/caac.21262. View

11.

Nicolau C, Sala E, Kumar A, Goldman D, Schoder H, Hricak H . Renal Masses Detected on FDG PET/CT in Patients With Lymphoma: Imaging Features Differentiating Primary Renal Cell Carcinomas From Renal Lymphomatous Involvement. AJR Am J Roentgenol. 2017; 208(4):849-853. PMC: 5548005. DOI: 10.2214/AJR.16.17133. View

12.

Kirienko M, Cozzi L, Antunovic L, Lozza L, Fogliata A, Voulaz E . Prediction of disease-free survival by the PET/CT radiomic signature in non-small cell lung cancer patients undergoing surgery. Eur J Nucl Med Mol Imaging. 2017; 45(2):207-217. DOI: 10.1007/s00259-017-3837-7. View

13.

Kirienko M, Cozzi L, Rossi A, Voulaz E, Antunovic L, Fogliata A . Ability of FDG PET and CT radiomics features to differentiate between primary and metastatic lung lesions. Eur J Nucl Med Mol Imaging. 2018; 45(10):1649-1660. DOI: 10.1007/s00259-018-3987-2. View

14.

Chicklore S, Goh V, Siddique M, Roy A, Marsden P, Cook G . Quantifying tumour heterogeneity in 18F-FDG PET/CT imaging by texture analysis. Eur J Nucl Med Mol Imaging. 2012; 40(1):133-40. DOI: 10.1007/s00259-012-2247-0. View

15.

Amador-Ortiz C, Chen L, Hassan A, Frater J, Burack R, Nguyen T . Combined core needle biopsy and fine-needle aspiration with ancillary studies correlate highly with traditional techniques in the diagnosis of nodal-based lymphoma. Am J Clin Pathol. 2011; 135(4):516-24. DOI: 10.1309/AJCP3WZ8ZDRJQDOU. View

16.

Chen S, Harmon S, Perk T, Li X, Chen M, Li Y . Diagnostic classification of solitary pulmonary nodules using dual time F-FDG PET/CT image texture features in granuloma-endemic regions. Sci Rep. 2017; 7(1):9370. PMC: 5571049. DOI: 10.1038/s41598-017-08764-7. View

17.

Soussan M, Orlhac F, Boubaya M, Zelek L, Ziol M, Eder V . Relationship between tumor heterogeneity measured on FDG-PET/CT and pathological prognostic factors in invasive breast cancer. PLoS One. 2014; 9(4):e94017. PMC: 3983104. DOI: 10.1371/journal.pone.0094017. View

18.

Loo E, Siddiqi I . Processing the lymph node biopsy. Methods Mol Biol. 2014; 1180:271-82. DOI: 10.1007/978-1-4939-1050-2_15. View

19.

Feng Z, Rong P, Cao P, Zhou Q, Zhu W, Yan Z . Machine learning-based quantitative texture analysis of CT images of small renal masses: Differentiation of angiomyolipoma without visible fat from renal cell carcinoma. Eur Radiol. 2017; 28(4):1625-1633. DOI: 10.1007/s00330-017-5118-z. View

20.

Dennie C, Thornhill R, Sethi-Virmani V, Souza C, Bayanati H, Gupta A . Role of quantitative computed tomography texture analysis in the differentiation of primary lung cancer and granulomatous nodules. Quant Imaging Med Surg. 2016; 6(1):6-15. PMC: 4775240. DOI: 10.3978/j.issn.2223-4292.2016.02.01. View