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Deconvolution-Based Pharmacokinetic Analysis to Improve the Prediction of Pathological Information of Breast Cancer

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Abstract

Pharmacokinetic (PK) parameters, revealing changes in the tumor microenvironment, are related to the pathological information of breast cancer. Tracer kinetic models (e.g., Tofts-Kety model) with a nonlinear least square solver are commonly used to estimate PK parameters. However, the method is sensitive to noise in images. To relieve the effects of noise, a deconvolution (DEC) method, which was validated on synthetic concentration–time series, was proposed to accurately calculate PK parameters from breast dynamic contrast-enhanced magnetic resonance imaging. A time-to-peak-based tumor partitioning method was used to divide the whole tumor into three tumor subregions with different kinetic patterns. Radiomic features were calculated from the tumor subregion and whole tumor-based PK parameter maps. The optimal features determined by the fivefold cross-validation method were used to build random forest classifiers to predict molecular subtypes, Ki-67, and tumor grade. The diagnostic performance evaluated by the area under the receiver operating characteristic curve (AUC) was compared between the subregion and whole tumor-based PK parameters. The results showed that the DEC method obtained more accurate PK parameters than the Tofts method. Moreover, the results showed that the subregion-based Ktrans (best AUCs = 0.8319, 0.7032, 0.7132, 0.7490, 0.8074, and 0.6950) achieved a better diagnostic performance than the whole tumor-based Ktrans (AUCs = 0.8222, 0.6970, 0.6511, 0.7109, 0.7620, and 0.5894) for molecular subtypes, Ki-67, and tumor grade. These findings indicate that DEC-based Ktrans in the subregion has the potential to accurately predict molecular subtypes, Ki-67, and tumor grade.

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Data Availability

The data that support the findings of this study are available upon request from the authors.

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Funding

This study was supported in part by grants from the National Key R&D Program of China (2018YFA0701700, 2021YFE0203700), the National Natural Science Foundation of China under Grant (62271178, U21A20521, U1809205, and 62302014), and the Natural Science Foundation of Zhejiang Province of China under Grant (LR23F010002).

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Authors and Affiliations

  1. School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, 310018, China

    Liangliang Zhang & Lihua Li

  2. Institute of Intelligent Biomedicine, School of Automation, Hangzhou Dianzi University, Hangzhou, 310018, China

    Ming Fan & Lihua Li

  3. School of Computer and Information, Anqing Normal University, Anqing, 246133, China

    Liangliang Zhang

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  1. Liangliang Zhang
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Correspondence to Ming Fan or Lihua Li.

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This retrospective study was approved by the Institutional Review Board of the First Affiliated Hospital of Zhejiang Chinese Medical University.

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Zhang, L., Fan, M. & Li, L. Deconvolution-Based Pharmacokinetic Analysis to Improve the Prediction of Pathological Information of Breast Cancer. J Digit Imaging. Inform. med. 37, 13–24 (2024). https://doi.org/10.1007/s10278-023-00915-9

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