CT Morphological Features Integrated With Whole-Lesion Histogram Parameters to Predict Lung Metastasis for Colorectal Cancer Patients With Pulmonary Nodules

TingDan Hu; ShengPing Wang; Xiangyu E; Ye Yuan; Lv Huang; JiaZhou Wang; DeBing Shi; Yuan Li; WeiJun Peng; Tong Tong

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

Front Oncol. 2019 Nov 19:9:1241. doi: 10.3389/fonc.2019.01241. eCollection 2019.

Authors

TingDan Hu^{1

2}, ShengPing Wang^{1

2}, Xiangyu E³, Ye Yuan³, Lv Huang³, JiaZhou Wang³, DeBing Shi⁴, Yuan Li⁵, WeiJun Peng^{1

2}, Tong Tong^{1

2}

Affiliations

¹ Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China.
² Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
³ Department of Radiotherapy, Fudan University Shanghai Cancer Center, Fudan University, Shanghai, China.
⁴ Department of Colorectal Surgery, Fudan University Shanghai Cancer Center, Fudan University, Shanghai, China.
⁵ Department of Pathology, Fudan University Shanghai Cancer Center, Fudan University, Shanghai, China.

Abstract

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 196 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; histogram; morphological; morphological features; nomogram; pulmonary metastases.