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Optimizing the Peritumoral Region Size in Radiomics Analysis for Sentinel Lymph Node Status Prediction in Breast Cancer

  • Jie Ding
    Affiliations
    Department of Biomedical Engineering, Stony Brook University, 100 Nicolls Rd, Stony Brook, NY 11794, USA

    Department of Radiation Oncology, Medical College of Wisconsin, 8701 W Watertown Plank Rd, Wauwatosa, WI 53226, USA
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  • Shenglan Chen
    Affiliations
    Department of Biomedical Engineering, Stony Brook University, 100 Nicolls Rd, Stony Brook, NY 11794, USA
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  • Mario Serrano Sosa
    Affiliations
    Department of Biomedical Engineering, Stony Brook University, 100 Nicolls Rd, Stony Brook, NY 11794, USA
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  • Renee Cattell
    Affiliations
    Department of Biomedical Engineering, Stony Brook University, 100 Nicolls Rd, Stony Brook, NY 11794, USA
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  • Lan Lei
    Affiliations
    Pogram in Program in Public Health, Renaissance School of Medicine, Stony Brook University, 101 Nicolls Rd, Stony Brook, NY 11794, USA
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  • Junqi Sun
    Affiliations
    Department of Radiology, Renaissance School of Medicine, Stony Brook University, 101 Nicolls Rd, Stony Brook, NY 11794, USA

    Department of Radiology, Yuebei People's Hospital, 133 Huimin S Rd, Shaoguan, Guangdong 512025, China
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  • Prateek Prasanna
    Affiliations
    Department of Biomedical Informatics, Renaissance School of Medicine, Stony Brook University, 101 Nicolls Rd, Stony Brook, NY 11794, USA
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  • Chunling Liu
    Affiliations
    Department of Radiology, Renaissance School of Medicine, Stony Brook University, 101 Nicolls Rd, Stony Brook, NY 11794, USA

    Department of Radiology, Guangdong General Hospital/Guangdong Academy of Medical Sciences, 106 Zhongshan 2nd Rd, Guangzhou, Guangdong 510080, China
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  • Chuan Huang
    Correspondence
    Address correspondence to: C.H.
    Affiliations
    Department of Biomedical Engineering, Stony Brook University, 100 Nicolls Rd, Stony Brook, NY 11794, USA

    Department of Radiology, Renaissance School of Medicine, Stony Brook University, 101 Nicolls Rd, Stony Brook, NY 11794, USA

    Department of Psychiatry, Renaissance School of Medicine, Stony Brook University, 101 Nicolls Rd, Stony Brook, NY 11794, USA
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Published:November 04, 2020DOI:https://doi.org/10.1016/j.acra.2020.10.015

      Rationale and Objectives

      Peritumoral features have been suggested to be useful in improving the prediction performance of radiomic models. The aim of this study is to systematically investigate the prediction performance improvement for sentinel lymph node (SLN) status in breast cancer from peritumoral features in radiomic analysis by exploring the effect of peritumoral region sizes.

      Materials and Methods

      This retrospective study was performed using dynamic contrast-enhanced MRI scans of 162 breast cancer patients. The effect of peritumoral features was evaluated in a radiomics pipeline for predicting SLN metastasis in breast cancer. Peritumoral regions were generated by dilating the tumor regions-of-interest (ROIs) manually annotated by two expert radiologists, with thicknesses of 2 mm, 4 mm, 6 mm, and 8 mm. The prediction models were established in the training set (∼67% of cases) using the radiomics pipeline with and without peritumoral features derived from different peritumoral thicknesses. The prediction performance was tested in an independent validation set (the remaining ∼33%).

      Results

      For this specific application, the accuracy in the validation set when using the two radiologists’ ROIs could be both improved from 0.704 to 0.796 by incorporating peritumoral features. The choice of the peritumoral size could affect the level of improvement.

      Conclusion

      This study systematically investigates the effect of peritumoral region sizes in radiomic analysis for prediction performance improvement. The choice of the peritumoral size is dependent on the ROI drawing and would affect the final prediction performance of radiomic models, suggesting that peritumoral features should be optimized in future radiomics studies.

      Key Words

      Abbreviations:

      AUC (area under the receiver operating characteristic curve), DCE-MRI (dynamic contrast-enhanced MRI), LASSO (least absolute shrinkage selection operator), NPV (negative predictive value), ROC (receiver operating characteristic), ROI (region-of-interest), SER (signal enhancement ratio), SLN (sentinel lymph node), TE (echo time), TR (repetition time)
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