Breast Cancer Subtype Prediction Model Employing Artificial Neural Network and F-Fluorodeoxyglucose Positron Emission Tomography/ Computed Tomography

Overview

Journal J Med Phys

Date 2024 Aug 12

PMID 39131430

Authors

Alamgir Hossain

Shariful Islam Chowdhury

Affiliations

Soon will be listed here.

Abstract

Introduction: Although positron emission tomography/computed tomography (PET/CT) is a common tool for measuring breast cancer (BC), subtypes are not automatically classified by it. Therefore, the purpose of this research is to use an artificial neural network (ANN) to evaluate the clinical subtypes of BC based on the value of the tumor marker.

Materials And Methods: In our nuclear medical facility, 122 BC patients (training and testing) had F-fluoro-D-glucose (F-FDG) PET/CT to identify the various subtypes of the disease. F-FDG-18 injections were administered to the patients before the scanning process. We carried out the scan according to protocol. Based on the tumor marker value, the ANN's output layer uses the Softmax function with cross-entropy loss to detect different subtypes of BC.

Results: With an accuracy of 95.77%, the result illustrates the ANN model for K-fold cross-validation. The mean values of specificity and sensitivity were 0.955 and 0.958, respectively. The area under the curve on average was 0.985.

Conclusion: Subtypes of BC may be categorized using the suggested approach. The PET/CT may be updated to diagnose BC subtypes using the appropriate tumor maker value when the suggested model is clinically implemented.

References

Peare R, Staff R, Heys S . The use of FDG-PET in assessing axillary lymph node status in breast cancer: a systematic review and meta-analysis of the literature. Breast Cancer Res Treat. 2010; 123(1):281-90. DOI: 10.1007/s10549-010-0771-9. View

Arpino G, Bardou V, Clark G, Elledge R . Infiltrating lobular carcinoma of the breast: tumor characteristics and clinical outcome. Breast Cancer Res. 2004; 6(3):R149-56. PMC: 400666. DOI: 10.1186/bcr767. View

Shamsi M, Islamian J . Breast cancer: early diagnosis and effective treatment by drug delivery tracing. Nucl Med Rev Cent East Eur. 2017; 20(1):45-48. DOI: 10.5603/NMR.2017.0002. View

Luo Y, Wong Y, Kankanhalli M, Zhao Q . G -Softmax: Improving Intraclass Compactness and Interclass Separability of Features. IEEE Trans Neural Netw Learn Syst. 2019; 31(2):685-699. DOI: 10.1109/TNNLS.2019.2909737. View

Moons K, Altman D, Reitsma J, Ioannidis J, Macaskill P, Steyerberg E . Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): explanation and elaboration. Ann Intern Med. 2015; 162(1):W1-73. DOI: 10.7326/M14-0698. View

Sastre-Garau X, Jouve M, Asselain B, Vincent-Salomon A, Beuzeboc P, Dorval T . Infiltrating lobular carcinoma of the breast. Clinicopathologic analysis of 975 cases with reference to data on conservative therapy and metastatic patterns. Cancer. 1996; 77(1):113-20. DOI: 10.1002/(SICI)1097-0142(19960101)77:1<113::AID-CNCR19>3.0.CO;2-8. View

Groheux D, Cochet A, Humbert O, Alberini J, Hindie E, Mankoff D . ¹⁸F-FDG PET/CT for Staging and Restaging of Breast Cancer. J Nucl Med. 2016; 57 Suppl 1:17S-26S. DOI: 10.2967/jnumed.115.157859. View

Jia D, Chen C, Chen C, Chen F, Zhang N, Yan Z . Breast Cancer Case Identification Based on Deep Learning and Bioinformatics Analysis. Front Genet. 2021; 12:628136. PMC: 8165442. DOI: 10.3389/fgene.2021.628136. View

Hirata K, Kobayashi K, Wong K, Manabe O, Surmak A, Tamaki N . A semi-automated technique determining the liver standardized uptake value reference for tumor delineation in FDG PET-CT. PLoS One. 2014; 9(8):e105682. PMC: 4146536. DOI: 10.1371/journal.pone.0105682. View

10.

Usmani S, Albader F, Marafi F, Elhaggracy R . Primary Breast Lymphoma Mimicking Metastatic Breast Cancer on 18F-FDG PET-CT. J Pak Med Assoc. 2022; 72(6):1241-1242. DOI: 10.47391/JPMA.22-75. View

11.

Manohar K, Mittal B, Senthil R, Kashyap R, Bhattacharya A, Singh G . Clinical utility of F-18 FDG PET/CT in recurrent breast carcinoma. Nucl Med Commun. 2012; 33(6):591-6. DOI: 10.1097/MNM.0b013e3283516716. View

12.

Li C, Anderson B, Daling J, Moe R . Trends in incidence rates of invasive lobular and ductal breast carcinoma. JAMA. 2003; 289(11):1421-4. DOI: 10.1001/jama.289.11.1421. View

13.

Song B, Kim H, Won K . Predictive Value of F-FDG PET/CT for Axillary Lymph Node Metastasis in Invasive Ductal Breast Cancer. Ann Surg Oncol. 2017; 24(8):2174-2181. DOI: 10.1245/s10434-017-5860-0. View

14.

Chai Q, Mei L, Zou Z, Peng H . Value of Artificial Neural Network Ultrasound in Improving Breast Cancer Diagnosis. Comput Intell Neurosci. 2022; 2022:1779337. PMC: 9357767. DOI: 10.1155/2022/1779337. View

15.

Maughan K, Lutterbie M, Ham P . Treatment of breast cancer. Am Fam Physician. 2010; 81(11):1339-46. View

16.

Groheux D, Mankoff D, Espie M, Hindie E . ¹⁸F-FDG PET/CT in the early prediction of pathological response in aggressive subtypes of breast cancer: review of the literature and recommendations for use in clinical trials. Eur J Nucl Med Mol Imaging. 2016; 43(5):983-993. DOI: 10.1007/s00259-015-3295-z. View

17.

Luo Y, Li J, Huang L, Han Y, Tian X, Ma W . Value of dynamic metabolic curves and artificial neural network prediction models based on 18F-FDG PET/CT multiphase imaging in differentiating nonspecific solitary pulmonary lesions: a pilot study. Nucl Med Commun. 2022; 43(12):1204-1216. DOI: 10.1097/MNM.0000000000001627. View

18.

Azim Jr H, Partridge A . Biology of breast cancer in young women. Breast Cancer Res. 2014; 16(4):427. PMC: 4303229. DOI: 10.1186/s13058-014-0427-5. View

19.

Gucalp A, Traina T, Eisner J, Parker J, Selitsky S, Park B . Male breast cancer: a disease distinct from female breast cancer. Breast Cancer Res Treat. 2018; 173(1):37-48. PMC: 7513797. DOI: 10.1007/s10549-018-4921-9. View

20.

Mathew A, Rajagopal P, Villgran V, Sandhu G, Jankowitz R, Jacob M . Distinct Pattern of Metastases in Patients with Invasive Lobular Carcinoma of the Breast. Geburtshilfe Frauenheilkd. 2017; 77(6):660-666. PMC: 5489406. DOI: 10.1055/s-0043-109374. View