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Prediction of tumor size in patients with invasive ductal carcinoma using FT-IR spectroscopy combined with chemometrics: a preliminary study

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Abstract

Precise detection of tumor size is essential for early diagnosis, treatment, and evaluation of the prognosis of breast cancer. However, there are some errors between the tumor size of breast cancer measured by conventional imaging methods and the pathological tumor size. Invasive ductal carcinoma (IDC) is a common pathological type of breast cancer. In this study, serum Fourier transform infrared spectroscopy (FT-IR) combined with chemometric methods was used to predict the maximum diameter and maximum vertical diameter of tumors in IDC patients. Three models were evaluated based on the pathological tumor size measured after surgery and included grid search support vector machine regression (GS-SVR), back propagation neural network optimized by genetic algorithm (GA-BP-ANN), and back propagation neural network optimized by particle swarm optimization (PSO-BP-ANN). The results show that three models can accurately predict tumor size. The GA-BP-ANN model provided the best fitting quality of the largest tumor diameter with the determination coefficients of 0.984 in test set. And the GS-SVR model provided the best fitting quality of the largest vertical tumor diameter with the determination coefficients of 0.982 in test set. The GS-SVR model had the highest prediction efficiency and the lowest time complexity of the models. The results indicate that serum FT-IR spectroscopy combined with chemometric methods can predict tumor size in IDC patients. In addition, compared with traditional imaging methods, we found that the experimental results of the three models are better than traditional imaging methods in terms of correlation and fitting degree. And the average fitting error of PSO-BP-ANN and GA-BP-ANN models was less than 0.3 mm. The minimally invasive detection method is expected to be developed into a new clinical diagnostic method for tumor size estimation to reduce the diagnostic trauma of patients and provide new diagnostic experience for patients.

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Acknowledgements

We acknowledge the support received from the Graduate Student Innovation Project of Xinjiang Uygur Autonomous Region (XJ2020G061). We sincerely thank the two anonymous referees for their constructive comments on this paper and all these comments have played a good role in enriching our paper. In addition, we gratefully acknowledge the assistance of Jong Uk Lee in the spectrum acquisition.

Funding

This work was supported by Graduate the Student Innovation Project of Xinjiang Uygur Autonomous Region (XJ2020G061) and the Science and Technology Project on aid to Xinjiang Uygur Autonomous Region under Grant 2019E0215.

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  1. Zhimin Zhu and Chen Chen contributed equally to this work.

Authors and Affiliations

  1. College of Information Science and Engineering, Xinjiang University, Urumqi, 830046, China

    Zhimin Zhu, Cheng Chen, Chen Chen, Ziwei Yan, Fangfang Chen, Bo Yang & Huiting Zhang

  2. Key Laboratory of Signal Detection and Processing, Xinjiang University, Urumqi, 830046, China

    Cheng Chen & Xiaoyi Lv

  3. College of Software, Xinjiang University, Urumqi, 830046, China

    Xiaoyi Lv

  4. School of Pharmacy, Shanghai Jiao Tong University, Minghang Area, Shanghai, 200240, China

    Huijie Han

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Zhu, Z., Chen, C., Chen, C. et al. Prediction of tumor size in patients with invasive ductal carcinoma using FT-IR spectroscopy combined with chemometrics: a preliminary study. Anal Bioanal Chem 413, 3209–3222 (2021). https://doi.org/10.1007/s00216-021-03258-y

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