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18F-FDG PET/CT radiomic predictors of pathologic complete response (pCR) to neoadjuvant chemotherapy in breast cancer patients

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Abstract

Purpose

Pathologic complete response (pCR) to neoadjuvant chemotherapy (NAC) is commonly accepted as the gold standard to assess outcome after NAC in breast cancer patients. 18F-Fluorodeoxyglucose positron emission tomography/computed tomography (PET/CT) has unique value in tumor staging, predicting prognosis, and evaluating treatment response. Our aim was to determine if we could identify radiomic predictors from PET/CT in breast cancer patient therapeutic efficacy prior to NAC.

Methods

This retrospective study included 100 breast cancer patients who received NAC; there were 2210 PET/CT radiomic features extracted. Unsupervised and supervised machine learning models were used to identify the prognostic radiomic predictors through the following: (1) selection of the significant (p < 0.05) imaging features from consensus clustering and the Wilcoxon signed-rank test; (2) selection of the most discriminative features via univariate random forest (Uni-RF) and the Pearson correlation matrix (PCM); and (3) determination of the most predictive features from a traversal feature selection (TFS) based on a multivariate random forest (RF). The prediction model was constructed with RF and then validated with 10-fold cross-validation for 30 times and then independently validated. The performance of the radiomic predictors was measured in terms of area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).

Results

The PET/CT radiomic predictors achieved a prediction accuracy of 0.857 (AUC = 0.844) on the training split set and 0.767 (AUC = 0.722) on the independent validation set. When age was incorporated, the accuracy for the split set increased to 0.857 (AUC = 0.958) and 0.8 (AUC = 0.73) for the independent validation set and both outperformed the clinical prediction model. We also found a close association between the radiomic features, receptor expression, and tumor T stage.

Conclusion

Radiomic predictors from pre-treatment PET/CT scans when combined with patient age were able to predict pCR after NAC. We suggest that these data will be valuable for patient management.

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Funding

This work was supported by the National Natural Science Foundation of China (81771861, 81830052, 81530053) and Shanghai Scientific and Technological Innovation Program (No. 18410711200, 16DZ0503700).

Author information

Author notes

  1. Panli Li, Xiuying Wang and Chongrui Xu contributed equally to this work.

Authors and Affiliations

  1. Shanghai Key Laboratory for Molecular Imaging, Shanghai University of Medicine and Health Sciences, Shanghai, China

    Panli Li, Shaoli Song & Gang Huang

  2. Department of Nuclear Medicine, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China

    Panli Li & Gang Huang

  3. SJTU-USYD Joint Research Alliance for Translational Medicine, Shanghai Jiao Tong University, Shanghai, China

    Panli Li, Xiuying Wang, Michael J Fulham, Dagan Feng, Lisheng Wang, Shaoli Song & Gang Huang

  4. Biomedical and Multimedia Information Technology Research Group, School of Computer Science, The University of Sydney, Sydney, NSW, 2006, Australia

    Xiuying Wang, Chongrui Xu, Chaojie Zheng & Dagan Feng

  5. Department of Nuclear Medicine, Fudan University Shanghai Cancer Center, Shanghai, 200032, China

    Cheng Liu & Shaoli Song

  6. Department of Molecular Imaging, Royal Prince Alfred Hospital, Australia and Sydney Medical School, University of Sydney, Sydney, Australia

    Michael J Fulham

  7. Department of Automation, Shanghai Jiao Tong University, Shanghai, China

    Lisheng Wang

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  1. Panli Li
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Contributions

PL, XW, and CR X conceptualized the study and provided the overall design of the experiments and contributed equally to the experiments. PL L, XW, CX, and MJF wrote the manuscript. PL and CL carried out tumor VOI mapping. XW, CZ, and CX performed the radiomics analysis and model building. MJF, DF, and LW edited the manuscript. SL S and G H supervised the study. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Shaoli Song or Gang Huang.

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Ethics approval and consent to participate

This retrospective study was approved by the Ethics Committee of Fudan University Shanghai Cancer Center; the study was carried out in compliance with the International guidelines for human research protection of the Declaration of Helsinki and International Conference on Harmonization in Good Clinical Practical (ICH-GCP).

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Li, P., Wang, X., Xu, C. et al. 18F-FDG PET/CT radiomic predictors of pathologic complete response (pCR) to neoadjuvant chemotherapy in breast cancer patients. Eur J Nucl Med Mol Imaging 47, 1116–1126 (2020). https://doi.org/10.1007/s00259-020-04684-3

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