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

  • Imaging Informatics and Artificial Intelligence
  • Published:
European Radiology Aims and scope Submit manuscript

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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Abbreviations

AAH:

Atypical adenomatous hyperplasia

AIS:

Adenocarcinoma in situ

CPH:

Cox proportional hazard

DE:

Differentially expressed

DFS:

Disease-free survival

DMFS:

Distant metastasis-free survival

GDPH:

Guangdong Provincial Peoples’ Hospital

GSEA:

Gene set enrichment analysis

HNC:

Head-and-neck cancer

HR:

Hazard ratio

IAC:

Invasive adenocarcinoma

ITH:

Intra-tumor heterogeneity

K-M:

Kaplan-Meier

LRFS:

Local recurrence-free survival

LUAD:

Lung adenocarcinoma

LVI:

Lymphovascular invasion

MIA:

Minimally invasive adenocarcinoma

NSCLC:

Non-small cell lung cancer

OS:

Overall survival

PI:

Pleural invasion

TCIA:

The Cancer Imaging Archive

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Funding

This work was supported by the National Science Foundation of China (Grant Nos. 61721003 and 81872510), National Key R&D Program of China (2021YFF1200901), High-level Hospital Construction Project (DFJH201801), Guangdong Basic and Applied Basic Research Foundation (No. 2019B1515130002), and Tsinghua-Fuzhou Institute of Data Technology Project (TFIDT2021005).

Author information

Author notes

  1. Jiaqi Li and Zhenbin Qiu contributed equally to this work.

Authors and Affiliations

  1. Bioinformatics Division, BNRIST and MOE Key Lab of Bioinformatics, Department of Automation, Tsinghua University, Beijing, China

    Jiaqi Li, Sijie Chen, Qiuchen Meng, Haiming Lu, Lei Wei, Hairong Lv & Xuegong Zhang

  2. Guangdong Lung Cancer Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China

    Zhenbin Qiu, Chao Zhang, Mengmin Wang & Wenzhao Zhong

  3. Guangdong Provincial Key Laboratory of Translational Medicine in Lung Cancer, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China

    Zhenbin Qiu, Chao Zhang, Mengmin Wang & Wenzhao Zhong

  4. Fuzhou Institute of Data Technology, Fuzhou, China

    Hairong Lv

  5. School of Medicine, Tsinghua University, Beijing, China

    Xuegong Zhang

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  1. Jiaqi Li
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Corresponding authors

Correspondence to Wenzhao Zhong or Xuegong Zhang.

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Guarantor

The scientific guarantor of this publication is Xuegong Zhang.

Conflict of interest

The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained. This study was approved by the ethics committee of Guangdong Provincial Peoples’ Hospital (No. GDRHEC2018115H) and was conducted in accordance with ethical standards of the 1964 Helsinki Declaration and its later amendments.

Study subjects or cohorts overlap

This study includes some public patient cohorts available on The Cancer Imaging Archive (TCIA):

• LUNG1 (https://wiki.cancerimagingarchive.net/display/Public/NSCLC-Radiomics)

• R01 (https://wiki.cancerimagingarchive.net/display/Public/NSCLC+Radiogenomics)

• HN1 (https://wiki.cancerimagingarchive.net/display/Public/Head-Neck-Radiomics-HN1)

• RIDER (https://wiki.cancerimagingarchive.net/display/Public/RIDER+Lung+CT).

This study also includes a patient cohort collected from Guangdong Provincial Peoples’ Hospital which was not included in other studies.

Methodology

• retrospective

• diagnostic or prognostic study

• performed at one institution

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Li, J., Qiu, Z., Zhang, C. et al. ITHscore: comprehensive quantification of intra-tumor heterogeneity in NSCLC by multi-scale radiomic features. Eur Radiol 33, 893–903 (2023). https://doi.org/10.1007/s00330-022-09055-0

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