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

IF 9.7 1区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Radiologia Medica Pub Date : 2025-05-22 DOI:10.1007/s11547-025-02022-x

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

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引用次数: 0

Abstract

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.

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CT成像虚拟活检：放射组学能否区分非小细胞肺癌的亚型？

目的：本研究评估CT放射组学在基线成像中区分肺腺癌（ADC）和鳞状细胞癌（SCC）的性能，探讨其作为无创虚拟活检的潜力。材料与方法：2015年9月至2023年1月，对330例患者进行回顾性分析。纳入标准为组织学证实的ADC或SCC和基线胸部CT增强。结果：最终队列包括200例ADC和100例SCC患者（平均年龄68±10岁，男性184例）。结论：放射学-放射组学联合模型在鉴别ADC和SCC方面表现最佳。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Radiologia Medica 医学-核医学

CiteScore

14.10

自引率

7.90%

发文量

133

审稿时长

4-8 weeks

期刊介绍： Felice Perussia founded La radiologia medica in 1914. It is a peer-reviewed journal and serves as the official journal of the Italian Society of Medical and Interventional Radiology (SIRM). The primary purpose of the journal is to disseminate information related to Radiology, especially advancements in diagnostic imaging and related disciplines. La radiologia medica welcomes original research on both fundamental and clinical aspects of modern radiology, with a particular focus on diagnostic and interventional imaging techniques. It also covers topics such as radiotherapy, nuclear medicine, radiobiology, health physics, and artificial intelligence in the context of clinical implications. The journal includes various types of contributions such as original articles, review articles, editorials, short reports, and letters to the editor. With an esteemed Editorial Board and a selection of insightful reports, the journal is an indispensable resource for radiologists and professionals in related fields. Ultimately, La radiologia medica aims to serve as a platform for international collaboration and knowledge sharing within the radiological community.