Dataset Information

Breast Cancer Subtype Prediction Model Employing Artificial Neural Network and ¹⁸F-Fluorodeoxyglucose Positron Emission Tomography/ Computed Tomography.

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 ¹⁸F-fluoro-D-glucose (¹⁸F-FDG) PET/CT to identify the various subtypes of the disease. ¹⁸F-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.

SUBMITTER: Hossain A

PROVIDER: S-EPMC11309150 | biostudies-literature | 2024 Apr-Jun

REPOSITORIES: biostudies-literature

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Publications

Breast Cancer Subtype Prediction Model Employing Artificial Neural Network and <sup>18</sup>F-Fluorodeoxyglucose Positron Emission Tomography/ Computed Tomography.

Hossain Alamgir A Chowdhury Shariful Islam SI

Journal of medical physics 20240401 2

<h4>Introduction</h4>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.<h4>Materials and methods</h4>In our nuclear medical facility, 122 BC patients (training and testing) had <sup>18</sup>F-fluoro-D-glucose (<sup>18</ ...[more]

PMID: 39131430

Similar Datasets

Project description:BackgroundTo investigate the current knowledge and attitudes towards [18F]fluorodeoxyglucose (18F-FDG) positron emission tomography (PET)/computed tomography (CT) among non-radiologist medical staff in China.MethodsThis cross-sectional study was conducted between November 18, 2022, and December 6, 2022, at the First Affiliated Hospital (Southwest Hospital) of the Army Medical University (Third Military Medical University) among non-radiologist staff (medical, nursing, and others). The questionnaire survey (Cronbach's α = 0.887) included 28 items: 10 on demographics, 11 on knowledge (maximum score of 22), and seven on attitudes (maximum score of 35). The factors influencing knowledge and attitudes were identified using multivariable logistic regression analysis.ResultsThis study analyzed 631 valid questionnaires. The mean knowledge score was 7.16 ± 6.48 (32.55%), indicating poor knowledge. The mean attitude score was 22.859 ± 2.36 (65.29%), indicating positive attitudes. Nursing (OR = 0.301, 95%CI: 0.159-0.571), other occupations (OR = 0.426, 95%CI: 0.200-0.905), departments with high volumes of PET/CT prescriptions (OR = 0.419, 95%CI: 0.269-0.652), radioprotection training (OR = 2.520, 95%CI: 1.576-4.030), underwent (or a relative) a PET/CT (OR = 1.713, 95%CI: 1.063-2.761), and contact with 1-10 (OR = 2.429, 95%CI: 1.627-3.627) or > 10 (OR = 3.575, 95%CI: 1.762-7.252) patients per month were independently associated with higher knowledge scores. Only the knowledge scores (OR = 1.063, 95%CI: 1.032-1.094) were independently associated with higher attitude scores.ConclusionNon-radiologist medical staff members in China have poor knowledge but positive attitudes toward PET/CT. This study identified knowledge areas worth improving in future training interventions. Radioprotection training appears particularly useful in improving the knowledge of PET/CT, which should translate into more favorable attitudes.Trial registrationNot applicable.

Dataset Information

Breast Cancer Subtype Prediction Model Employing Artificial Neural Network and ¹⁸F-Fluorodeoxyglucose Positron Emission Tomography/ Computed Tomography.

Introduction

Materials and methods

Results

Conclusion

Publications

Breast Cancer Subtype Prediction Model Employing Artificial Neural Network and <sup>18</sup>F-Fluorodeoxyglucose Positron Emission Tomography/ Computed Tomography.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets

Dataset Information

Breast Cancer Subtype Prediction Model Employing Artificial Neural Network and 18F-Fluorodeoxyglucose Positron Emission Tomography/ Computed Tomography.

Introduction

Materials and methods

Results

Conclusion

Publications

Breast Cancer Subtype Prediction Model Employing Artificial Neural Network and <sup>18</sup>F-Fluorodeoxyglucose Positron Emission Tomography/ Computed Tomography.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets

Breast Cancer Subtype Prediction Model Employing Artificial Neural Network and ¹⁸F-Fluorodeoxyglucose Positron Emission Tomography/ Computed Tomography.