Scholarlink

Objective <p>This study evaluated the performance of CT radiomics in distinguishing between lung adenocarcinoma (ADC) and squamous cell carcinoma (SCC) at baseline imaging, exploring its potential as a noninvasive virtual biopsy.</p> Materials and methods <p>A retrospective analysis was conducted, enrolling 330 patients between September 2015 and January 2023. Inclusion criteria were histologically proven ADC or SCC and baseline contrast-enhanced chest CT. Exclusion criteria included significant motion artifacts and nodules &lt; 6&#xa0;mm. Radiological features, including lung lobe affected, peripheral/central location, presence of emphysema, and T/N radiological stage, were assessed for each patient. Volumetric segmentation of lung cancers was performed on baseline CT scans at the portal-venous phase using 3DSlicer software (v5.2.2). A total of 107 radiomic features were extracted and selected using the least absolute shrinkage and selection operator (LASSO) and tenfold cross-validation. Multivariable logistic regression analysis was employed to develop three predictive models: radiological features-only, radiomics-only, and a combined model, with statistical significance set at <i>p</i> &lt; 0.05. Additionally, an independent external validation cohort of 16 patients, meeting the same inclusion and exclusion criteria, was identified.</p> Results <p>The final cohort comprised 200 ADC and 100 SCC patients (mean age 68 ± 10 years, 184 men). Two radiological and 21 radiomic features were selected (<i>p</i> &lt; 0.001). The Radiological model achieved AUC 0.73 (95% CI 0.68–0.78, <i>p</i> &lt; 0.001), 72.3% accuracy. The radiomics model achieved AUC 0.80 (95% CI 0.75–0.85, <i>p</i> &lt; 0.001), 75.6% accuracy. The combined model achieved AUC 0.84 (95% CI 0.80–0.88, <i>p</i> &lt; 0.001), 75.3% accuracy. External validation (<i>n</i> = 15) yielded AUC 0.78 (<i>p</i> = 0.05).</p> Conclusion <p>The combined radiologic-radiomics model showed the best performance in differentiating ADC from SCC.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Virtual biopsy through CT imaging: can radiomics differentiate between subtypes of non-small cell lung cancer?

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年5月22日

查看全文
  • Federica Palmeri,
  • Marta Zerunian,
  • Michela Polici,
  • Stefano Nardacci,
  • Chiara De Dominicis,
  • Bianca Allegra,
  • Andrea Monterubbiano,
  • Massimiliano Mancini,
  • Riccardo Ferrari,
  • Pasquale Paolantonio,
  • Domenico De Santis,
  • Andrea Laghi,
  • Damiano Caruso

摘要

Objective

This study evaluated the performance of CT radiomics in distinguishing between lung adenocarcinoma (ADC) and squamous cell carcinoma (SCC) at baseline imaging, exploring its potential as a noninvasive virtual biopsy.

Materials and methods

A retrospective analysis was conducted, enrolling 330 patients between September 2015 and January 2023. Inclusion criteria were histologically proven ADC or SCC and baseline contrast-enhanced chest CT. Exclusion criteria included significant motion artifacts and nodules < 6 mm. Radiological features, including lung lobe affected, peripheral/central location, presence of emphysema, and T/N radiological stage, were assessed for each patient. Volumetric segmentation of lung cancers was performed on baseline CT scans at the portal-venous phase using 3DSlicer software (v5.2.2). A total of 107 radiomic features were extracted and selected using the least absolute shrinkage and selection operator (LASSO) and tenfold cross-validation. Multivariable logistic regression analysis was employed to develop three predictive models: radiological features-only, radiomics-only, and a combined model, with statistical significance set at p < 0.05. Additionally, an independent external validation cohort of 16 patients, meeting the same inclusion and exclusion criteria, was identified.

Results

The final cohort comprised 200 ADC and 100 SCC patients (mean age 68 ± 10 years, 184 men). Two radiological and 21 radiomic features were selected (p < 0.001). The Radiological model achieved AUC 0.73 (95% CI 0.68–0.78, p < 0.001), 72.3% accuracy. The radiomics model achieved AUC 0.80 (95% CI 0.75–0.85, p < 0.001), 75.6% accuracy. The combined model achieved AUC 0.84 (95% CI 0.80–0.88, p < 0.001), 75.3% accuracy. External validation (n = 15) yielded AUC 0.78 (p = 0.05).

Conclusion

The combined radiologic-radiomics model showed the best performance in differentiating ADC from SCC.