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October 3, 2025BMC CancerOpen Access

A CT-based radiomics nomogram incorporating adipose tissue to differentiate invasive adenocarcinomas among part-solid pulmonary nodules

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Authors

LQLing QinZLZhao LinXLXuemeng Li

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Overview

This analysis integrates radiomic features and adipose tissue to enhance understanding of lung adenocarcinoma invasiveness.

Key Points

  • The nomogram classified invasive lung adenocarcinomas with a high accuracy of 0.927 in internal validation cohorts.
  • Utilizing multivariable logistic regression, the model incorporated radiomic features and clinical factors for assessment.
  • Adding intrathoracic adipose tissue features significantly improved the model's performance in all validation cohorts.
  • The study involved 608 patients and demonstrated the potential for individualizing lung cancer treatment approaches.

Cite This Study

Qin et al. (2025) studied this question.

synapsesocial.com/papers/68e02f2cf0e39f13e7fa1ff3https://doi.org/10.1186/s12885-025-14875-6
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Value of a radiomics model based on high-resolution large matrix target reconstruction images in predicting the invasiveness of lung adenocarcinoma with pure ground-glass nodules2026
  2. 2Computed tomography radiomics of intratumoral and peritumoral microenvironments for identifying the invasiveness of subcentimeter lung adenocarcinomas2025
  3. 3Incorporating adipose tissue into a CT-based deep learning nomogram to differentiate granulomas from lung adenocarcinomas2024
  4. 4Integrated CT Radiomics and Circulating Tumor Cell Analysis in Predicting Lung Adenocarcinoma Invasion: A Dual-Center Study with Implications for Personalized Treatment2026
  5. 5Nomogram for predicting invasive lung adenocarcinoma in small solitary pulmonary nodules2024 · 1 citations