Identification of the optimal threshold for predicting the infiltration degree of T1-stage lung adenocarcinoma using solid component volume and three-dimensional consolidation-to-tumor ratio in threshold segmentation

Regardless of the CT threshold setting, the solid component volume and 3D CTR calculated based on the threshold segmentation method were demonstrated to be stable predictive factors that significantly contributed to the assessment of the invasiveness of T1-stage lung adenocarcinoma. The optimal predictive performance was achieved when the CT threshold was set to -350 HU. A solid component volume exceeding 45.5 mm<sup>3</sup> or a 3D CTR greater than 10.85% indicated a higher likelihood of MIA or IAC.

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Publication Details

Journal
Quantitative Imaging in Medicine and Surgery
Published
2025-07-01
DOI
https://doi.org/10.21037/qims-24-1160
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
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article

Identification of the optimal threshold for predicting the infiltration degree of T1-stage lung adenocarcinoma using solid component volume and three-dimensional consolidation-to-tumor ratio in threshold segmentation

Guotao Chang, Zhengpan Wei, Huiyu Zheng, Wensong Shi et al.
Quantitative Imaging in Medicine and Surgery
Radiomics and Machine Learning in Medical Imaging
article

Identification of the optimal threshold for predicting the infiltration degree of T1-stage lung adenocarcinoma using solid component volume and three-dimensional consolidation-to-tumor ratio in threshold segmentation

Guotao Chang, Zhengpan Wei, Huiyu Zheng, Wensong Shi, Yingli Sun, Yulun Yang, Yuzhui Hu, Xiangnan Li, Qian He, Ming Li, Liang Zhao
article en

Abstract

Regardless of the CT threshold setting, the solid component volume and 3D CTR calculated based on the threshold segmentation method were demonstrated to be stable predictive factors that significantly contributed to the assessment of the invasiveness of T1-stage lung adenocarcinoma. The optimal predictive performance was achieved when the CT threshold was set to -350 HU. A solid component volume exceeding 45.5 mm<sup>3</sup> or a 3D CTR greater than 10.85% indicated a higher likelihood of MIA or IAC.

Quantitative Imaging in Medicine and SurgeryVol. 15(7)
Fudan University (CN), Huadong Hospital (CN), Fifth Affiliated Hospital of Zhengzhou University (CN), Zhengzhou People's Hospital (CN), First Affiliated Hospital of Zhengzhou University (CN)
Zero hunger
Openalex Percentile: Top 77%
Radiomics and Machine Learning in Medical Imaging
DOIOpen Access PDFOpenAlex
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Identification of the optimal threshold for predicting the infiltration degree of T1-stage lung adenocarcinoma using solid component volume and three-dimensional consolidation-to-tumor ratio in threshold segmentation — Guotao Chang, Zhengpan Wei, et al. · Quantitative Imaging in Medicine and Surgery (2025) | TGRS Research Map | TGRS