The combination of cyclin D1 and MRI radiomics parameters improves breast cancer diagnosis and NACT efficacy assessment

Background: The purpose of this study is to evaluate the combined diagnostic performance of Cyclin D1 and magnetic resonance imaging (MRI) parameters in breast cancer (BC), while also assessing their ability to forecast treatment response to neoadjuvant chemotherapy (NACT), so as to provide evidence for individualized treatment strategies. Methods: A total of 154 BC patients and 148 healthy women matched with BC patients in age and BMI were enrolled in this study. Peripheral blood Cyclin D1 quantification utilized enzyme-linked immunosorbent assay (ELISA), and CA15-3 and CA27.29 (both tumor markers) measurements were made using automated electrochemiluminescence immunoassay. Magnetic resonance imaging (MRI) was conducted to obtain dynamic contrast-enhanced scans, from which radiomics parameters (PSIER, TSICS, ADC) were derived. Diagnostic accuracy for BC and predictive value for chemotherapy response were evaluated through correlation analysis (Pearson), receiver operating characteristic (ROC) curves, and multivariate regression modeling. Results: Cyclin D1 levels were elevated in BC patients compared to healthy controls and showed a positive connection with CA15-3 and CA27.29 (P< 0.05). The diagnostic performance of Cyclin D1 alone yielded an AUC of 0.830, whereas a combined model incorporating MRI parameters (PSIER, TSICS, and ADC) significantly improved discrimination (AUC = 0.935, sensitivity 86.36%, specificity 87.84%). During NACT, Cyclin D1 levels declined dynamically. A predictive model integrating Cyclin D1 and imaging biomarkers achieved an AUC of 0.775 for identifying poor NACT responders (sensitivity 88.37%, specificity 56.76%). Conclusions: The combination of Cyclin D1 and MRI parameters enhances both BC diagnostic accuracy and NACT efficacy prediction.

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Publication Details

Journal
Journal of Medical Biochemistry
Published
2025-08-21
DOI
https://doi.org/10.5937/jomb0-60127
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
0.00

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article

The combination of cyclin D1 and MRI radiomics parameters improves breast cancer diagnosis and NACT efficacy assessment

Yue Shi, Xin Zhou, Zhen Wang, Yu-Fei Fu
Journal of Medical Biochemistry
Radiomics and Machine Learning in Medical Imaging
article

The combination of cyclin D1 and MRI radiomics parameters improves breast cancer diagnosis and NACT efficacy assessment

Yue Shi, Xin Zhou, Zhen Wang, Yu-Fei Fu
article en

Abstract

Background: The purpose of this study is to evaluate the combined diagnostic performance of Cyclin D1 and magnetic resonance imaging (MRI) parameters in breast cancer (BC), while also assessing their ability to forecast treatment response to neoadjuvant chemotherapy (NACT), so as to provide evidence for individualized treatment strategies. Methods: A total of 154 BC patients and 148 healthy women matched with BC patients in age and BMI were enrolled in this study. Peripheral blood Cyclin D1 quantification utilized enzyme-linked immunosorbent assay (ELISA), and CA15-3 and CA27.29 (both tumor markers) measurements were made using automated electrochemiluminescence immunoassay. Magnetic resonance imaging (MRI) was conducted to obtain dynamic contrast-enhanced scans, from which radiomics parameters (PSIER, TSICS, ADC) were derived. Diagnostic accuracy for BC and predictive value for chemotherapy response were evaluated through correlation analysis (Pearson), receiver operating characteristic (ROC) curves, and multivariate regression modeling. Results: Cyclin D1 levels were elevated in BC patients compared to healthy controls and showed a positive connection with CA15-3 and CA27.29 (P< 0.05). The diagnostic performance of Cyclin D1 alone yielded an AUC of 0.830, whereas a combined model incorporating MRI parameters (PSIER, TSICS, and ADC) significantly improved discrimination (AUC = 0.935, sensitivity 86.36%, specificity 87.84%). During NACT, Cyclin D1 levels declined dynamically. A predictive model integrating Cyclin D1 and imaging biomarkers achieved an AUC of 0.775 for identifying poor NACT responders (sensitivity 88.37%, specificity 56.76%). Conclusions: The combination of Cyclin D1 and MRI parameters enhances both BC diagnostic accuracy and NACT efficacy prediction.

Journal of Medical BiochemistryVol. 45(2)
Hubei Polytechnic University (CN), Huangshi Central Hospital (CN)
Sir Run Run Shaw Hospital
Openalex Percentile: Top 70%
Radiomics and Machine Learning in Medical Imaging
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