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24-08-2022 | Computed Tomography | Imaging Informatics and Artificial Intelligence

ITHscore: comprehensive quantification of intra-tumor heterogeneity in NSCLC by multi-scale radiomic features

Authors: Jiaqi Li, Zhenbin Qiu, Chao Zhang, Sijie Chen, Mengmin Wang, Qiuchen Meng, Haiming Lu, Lei Wei, Hairong Lv, Wenzhao Zhong, Xuegong Zhang

Published in: European Radiology | Issue 2/2023

Abstract

Objectives

To quantify intra-tumor heterogeneity (ITH) in non-small cell lung cancer (NSCLC) from computed tomography (CT) images.

Methods

We developed a quantitative ITH measurement—ITHscore—by integrating local radiomic features and global pixel distribution patterns. The associations of ITHscore with tumor phenotypes, genotypes, and patient’s prognosis were examined on six patient cohorts (n = 1399) to validate its effectiveness in characterizing ITH.

Results

For stage I NSCLC, ITHscore was consistent with tumor progression from stage IA1 to IA3 (p < 0.001) and captured key pathological change in terms of malignancy (p < 0.001). ITHscore distinguished the presence of lymphovascular invasion (p = 0.003) and pleural invasion (p = 0.001) in tumors. ITHscore also separated patient groups with different overall survival (p = 0.004) and disease-free survival conditions (p = 0.005). Radiogenomic analysis showed that the level of ITHscore in stage I and stage II NSCLC is correlated with heterogeneity-related pathways. In addition, ITHscore was proved to be a stable measurement and can be applied to ITH quantification in head-and-neck cancer (HNC).

Conclusions

ITH in NSCLC can be quantified from CT images by ITHscore, which is an indicator for tumor phenotypes and patient’s prognosis.

Key Points

• ITHscore provides a radiomic quantification of intra-tumor heterogeneity in NSCLC.

• ITHscore is an indicator for tumor phenotypes and patient’s prognosis.

• ITHscore has the potential to be generalized to other cancer types such as HNC.

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Title: ITHscore: comprehensive quantification of intra-tumor heterogeneity in NSCLC by multi-scale radiomic features
Authors: Jiaqi Li
Zhenbin Qiu
Chao Zhang
Sijie Chen
Mengmin Wang
Qiuchen Meng
Haiming Lu
Lei Wei
Hairong Lv
Wenzhao Zhong
Xuegong Zhang
Publication date: 24-08-2022
Publisher: Springer Berlin Heidelberg
Keywords: Computed Tomography
NSCLC
Computed Tomography
NSCLC
Published in: European Radiology / Issue 2/2023
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI: https://doi.org/10.1007/s00330-022-09055-0

Keynote webinar | Spotlight on medication adherence

Springer Medicine

ITHscore: comprehensive quantification of intra-tumor heterogeneity in NSCLC by multi-scale radiomic features

Abstract

Objectives

Methods

Results

Conclusions

Key Points

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Objectives

Methods

Results

Conclusions

Key Points

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