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Original Research ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Oncol. | doi: 10.3389/fonc.2019.01241

CT morphological features integrated with whole-lesion histogram parameters to predict lung metastasis for colorectal cancer patients with pulmonary nodules

 Tong Tong1, 2, 3*,  TingDan Hu4, ShengPing Wang4, XiangYu E4, Ye Yuan4, Huang Lv4, JiaZhou Wang4, DeBing Shi5, Yuan Li6 and WeiJun Peng4
  • 1Fudan University Shanghai Cancer Center, China
  • 2Department of Radiology, Shanghai Cancer Center, China
  • 3Department of Medical Oncology, Fudan University Shanghai Cancer Center, China
  • 4Department of Radiology, Fudan University Shanghai Cancer Center, China
  • 5Department of Colorectal Surgery, Fudan University Shanghai Cancer Center, China
  • 6Department of Pathology, Fudan University Shanghai Cancer Center, China

Purpose: To retrospectively identify the relationships between both CT morphological features and histogram parameters with pulmonary metastasis in patients with colorectal cancer(CRC) and compare the efficacy of single-slice and whole-lesion histogram analysis.
Methods: Our study enrolled one hundred ninety-six CRC patients with pulmonary nodules (136 in the training dataset and 60 in the validation dataset). Twenty morphological features of contrast-enhanced chest CT were evaluated. The regions of interests were delineated in single-slice and whole-tumor lesions, and 22 histogram parameters were extracted. Stepwise logistic regression analyses were applied to choose the independent factors of lung metastasis in the morphological features model, the single-slice histogram model and whole-lesion histogram model. The areas under the curve (AUC) was applied to quantify the predictive accuracy of each model. Finally, we built a morphological-histogram nomogram for pulmonary metastasis prediction.
Results: The whole-lesion histogram analysis (AUC of 0.888 and 0.865 in the training and validation datasets, respectively) outperformed the single-slice histogram analysis (AUC of 0.872 and 0.819 in the training and validation datasets, respectively) and the CT morphological features model (AUC of 0.869 and 0.845 in the training and validation datasets, respectively). The morphological-histogram model, developed with significant morphological features and whole-lesion histogram parameters, achieved favorable discrimination in both the training dataset (AUC=0.919) and validation dataset (AUC=0.895), and good calibration.
Conclusions: CT morphological features in combination with whole-lesion histogram parameters can be used to prognosticate pulmonary metastasis for patients with colorectal cancer.

Keywords: colorectal cancer, Pulmonary metastases, Histogram, morphological, Morphological features, nomogram

Received: 07 Jul 2019; Accepted: 29 Oct 2019.

Copyright: © 2019 Tong, Hu, Wang, E, Yuan, Lv, Wang, Shi, Li and Peng. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Mx. Tong Tong, Fudan University Shanghai Cancer Center, Shanghai, China,