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Multiparametric MRI-based radiomics nomogram for early prediction of pathological response to neoadjuvant chemotherapy in locally advanced gastric cancer

  • Gastrointestinal
  • Published:
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Abstract

Objectives

To build and validate a multi-parametric MRI (mpMRI)-based radiomics nomogram for early prediction of treatment response to neoadjuvant chemotherapy (NAC) in locally advanced gastric cancer.

Methods

Baseline MRI were retrospectively enrolled from 141 patients with gastric adenocarcinoma who received NAC followed by radical gastrectomy. According to pathologic response of tumor regression grading (TRG), patients were labeled as responders (TRG = 0 + 1) and non-responders (TRG = 2 + 3) and further divided into a training (n = 85) and validation dataset (n = 56). Radiomics score (Radscore) were built from T2WI, ADC, and venous phase of dynamic-contrasted-enhanced MR imaging. Clinical information, laboratory indicators, MRI parameters, and follow-up data were also recorded. According to multivariable regression analysis, an mpMRI radiomics nomogram was built and its predictive ability was evaluated by ROC analysis. Decision curve analysis was applied to evaluate the clinical usefulness. Kaplan-Meier survival curves based on the nomogram were used to estimate the progression-free survival (PFS) and overall survival (OS) in the validation dataset.

Results

Both single sequence–based Radscores and mpMRI radiomics nomogram were associated with pathologic response (p < 0.001). The nomogram achieved the highest diagnostic ability with area under ROC curve of 0.844 (95% CI, 0.749–0.914) and 0.820 (95% CI, 0.695–0.910) in the training and validation datasets. The hazard ratio of the nomogram for PFS and OS prediction was 2.597 (95% CI: 1.046–6.451, log-rank p = 0.023) and 2.570 (95% CI: 1.166–5.666, log-rank p = 0.011).

Conclusions

The mpMRI-based radiomics nomogram showed preferable performance in predicting pathologic response to NAC in LAGC.

Key Points

• This study investigated the value of multi-parametric MRI-based radiomics in predicting pathologic response to neoadjuvant chemotherapy in locally advanced gastric cancer.

• The nomogram incorporating T2WI Radscore, ADC Radscore, and DCE Radscore as well as Borrmann classification outperformed the single sequence–based Radscore.

• The nomogram also exhibited a promising prognostic ability for patient survival and enriched radiomics studies in gastric cancer.

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Abbreviations

ADC:

Apparent diffusion coefficients

AJCC:

American Joint Committee on Cancer

AUC:

Area under the ROC curve

CA199:

Carbohydrate antigen 199

CA724:

Carbohydrate antigen 724

CEA:

Carcinoembryonic antigen

CI:

Confidence interval

CT:

Computed tomography, X-ray

DCA:

Decision curve analysis

DCE-MRI:

Dynamic-contrasted-enhanced magnetic resonance imaging

DKI:

Diffusion kurtosis imaging

DL:

Deep learning

DWI:

Diffusion-weighted imaging

GC:

Gastric cancer

HR:

Hazard ratio

LAGC:

Locally advanced gastric cancer

mpMRI:

Multi-parametric magnetic resonance imaging

MRI:

Magnetic resonance imaging

NAC:

Neoadjuvant chemotherapy

NCCN:

National Comprehensive Cancer Network

NPV:

Negative predictive value

OS:

Overall survival

PFS:

Progression-free survival

PPV:

Positive predictive value

Radscore:

Radiomics score

ROC:

Receiver operating characteristic

Star-VIBE:

Stack-of-stars volume interpolated breath-hold examination

TRG:

Tumor regression grading

VP:

Venous phase

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Funding

This study has received funding by the Science and Technology Development Foundation of Henan Province (202102310736), the Henan Provincial Medical Science and Technology Project (SBGJ202003011), the Projects of the General Programs of the National Natural Science Foundation of China (No.81972802), and the Special funding of the Henan Health Science and Technology Innovation Talent Project (No.YXKC2021054, YXKC2020011).

Author information

Authors and Affiliations

  1. Department of Radiology, the Affiliated Cancer Hospital of Zhengzhou University (Henan Cancer Hospital), No. 127, Dongming Road, Zhengzhou, 450008, Henan, China

    Jing Li, Hongkai Zhang, Hailiang Li & Jinrong Qu

  2. Institute of Advanced Research, Infervision Medical Technology Co., Ltd, Beijing, China

    Hongkun Yin

  3. Department of Pathology, the Affiliated Cancer Hospital of Zhengzhou University (Henan Cancer Hospital), Zhengzhou, Henan, China

    Yi Wang

  4. Department of Gastrointestinal surgery, the Affiliated Cancer Hospital of Zhengzhou University (Henan Cancer Hospital), Zhengzhou, Henan, China

    Fei Ma

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  1. Jing Li
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Correspondence to Hailiang Li or Jinrong Qu.

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The scientific guarantor of this publication is Jinrong Qu.

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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.

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Written informed consent was not required for this study because this is a retrospective diagnostic study, and was waived by the Institutional Review Board of Zhengzhou University.

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• retrospective

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• performed at one institution

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Li, J., Yin, H., Wang, Y. et al. Multiparametric MRI-based radiomics nomogram for early prediction of pathological response to neoadjuvant chemotherapy in locally advanced gastric cancer. Eur Radiol 33, 2746–2756 (2023). https://doi.org/10.1007/s00330-022-09219-y

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