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The purpose of this retrospective diagnostic study was to develop and validate a preoperative CT-based nomogram combined with radiomic and clinical-radiological signatures to distinguish preinvasive lesions from pulmonary invasive lesions. The authors were able to demonstrate that a nomogram constructed by identified clinical-radiological signatures and combined radiomic signatures has the potential to precisely predict pathology invasiveness.

Key points

  • The radiomic signature from the perinodular area has the potential to predict pathology invasiveness of the solitary pulmonary nodule.
  • The new radiomic nomogram was useful in clinical decision-making associated with personalized surgical intervention and therapeutic regimen selection in patients with early-stage non-small-cell lung cancer.

Article: Development and validation of a preoperative CT-based radiomic nomogram to predict pathology invasiveness in patients with a solitary pulmonary nodule: a machine learning approach, multicenter, diagnostic study

Authors: Luyu Huang, Weihuan Lin, Daipeng Xie, Yunfang Yu, Hanbo Cao, Guoqing Liao, Shaowei Wu, Lintong Yao, Zhaoyu Wang, Mei Wang, Siyun Wang, Guangyi Wang, Dongkun Zhang, Su Yao, Zifan He, William Chi-Shing Cho, Duo Chen, Zhengjie Zhang, Wanshan Li, Guibin Qiao, Lawrence Wing-Chi Chan & Haiyu Zhou

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