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Predicting pleural invasion of invasive lung adenocarcinoma in the adjacent pleura by imaging histology

  • Authors:
    • Lingxin Kong
    • Wenfei Xue
    • Huanfen Zhao
    • Xiaopeng Zhang
    • Shuangqing Chen
    • Dahu Ren
    • Guochen Duan
  • View Affiliations / Copyright

    Affiliations: Department of Thoracic Surgery, Hebei General Hospital, Shijiazhuang, Hebei 050000, P.R. China, Department of Thoracic Surgery, Hebei General Hospital, Shijiazhuang, Hebei 050000, P.R. China, Department of Pathology, Hebei General Hospital, Shijiazhuang, Hebei 050000, P.R. China, Department of Thoracic Surgery, Hebei General Hospital, Shijiazhuang, Hebei 050000, P.R. China
    Copyright: © Kong et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
  • Article Number: 438
    |
    Published online on: August 23, 2023
       https://doi.org/10.3892/ol.2023.14025
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Abstract

The aim of the present study was to develop a non‑invasive method based on histological imaging and clinical features for predicting the preoperative status of visceral pleural invasion (VPI) in patients with lung adenocarcinoma (LUAD) located near the pleura. VPI is associated with a worse prognosis of LUAD; therefore, early and accurate detection is critical for effective treatment planning. A total of 112 patients with preoperative computed tomography presentation of adjacent pleura and postoperative pathological findings confirmed as invasive LUAD were retrospectively enrolled. Clinical and histological imaging features were combined to develop a preoperative VPI prediction model and validate the model's efficacy. Finally, a nomogram for predicting LUAD was established and validated using a logistic regression algorithm. Both the clinical signature and radiomics signature (Rad signature) exhibited a perfect fit in the training cohort. The clinical signature was overfitted in the testing cohort, whereas the Rad signature showed a good fit. To combine clinical and radiomics signatures for optimal performance, a nomogram was created using the logistic regression algorithm. The results indicated that this approach had the highest predictive performance, with an area under the curve of 0.957 for the clinical signature and 0.900 for the Rad signature. In conclusion, histological imaging and clinical features can be combined in columnar maps to predict the preoperative VPI status of patients with adjacent pleural infiltrative lung carcinoma.
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Copy and paste a formatted citation
Spandidos Publications style
Kong L, Xue W, Zhao H, Zhang X, Chen S, Ren D and Duan G: Predicting pleural invasion of invasive lung adenocarcinoma in the adjacent pleura by imaging histology. Oncol Lett 26: 438, 2023.
APA
Kong, L., Xue, W., Zhao, H., Zhang, X., Chen, S., Ren, D., & Duan, G. (2023). Predicting pleural invasion of invasive lung adenocarcinoma in the adjacent pleura by imaging histology. Oncology Letters, 26, 438. https://doi.org/10.3892/ol.2023.14025
MLA
Kong, L., Xue, W., Zhao, H., Zhang, X., Chen, S., Ren, D., Duan, G."Predicting pleural invasion of invasive lung adenocarcinoma in the adjacent pleura by imaging histology". Oncology Letters 26.4 (2023): 438.
Chicago
Kong, L., Xue, W., Zhao, H., Zhang, X., Chen, S., Ren, D., Duan, G."Predicting pleural invasion of invasive lung adenocarcinoma in the adjacent pleura by imaging histology". Oncology Letters 26, no. 4 (2023): 438. https://doi.org/10.3892/ol.2023.14025
Copy and paste a formatted citation
x
Spandidos Publications style
Kong L, Xue W, Zhao H, Zhang X, Chen S, Ren D and Duan G: Predicting pleural invasion of invasive lung adenocarcinoma in the adjacent pleura by imaging histology. Oncol Lett 26: 438, 2023.
APA
Kong, L., Xue, W., Zhao, H., Zhang, X., Chen, S., Ren, D., & Duan, G. (2023). Predicting pleural invasion of invasive lung adenocarcinoma in the adjacent pleura by imaging histology. Oncology Letters, 26, 438. https://doi.org/10.3892/ol.2023.14025
MLA
Kong, L., Xue, W., Zhao, H., Zhang, X., Chen, S., Ren, D., Duan, G."Predicting pleural invasion of invasive lung adenocarcinoma in the adjacent pleura by imaging histology". Oncology Letters 26.4 (2023): 438.
Chicago
Kong, L., Xue, W., Zhao, H., Zhang, X., Chen, S., Ren, D., Duan, G."Predicting pleural invasion of invasive lung adenocarcinoma in the adjacent pleura by imaging histology". Oncology Letters 26, no. 4 (2023): 438. https://doi.org/10.3892/ol.2023.14025
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