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Whole-tumor histogram analysis of multi-parametric MRI for differentiating brain metastases histological subtypes in lung cancers: relationship with the Ki-67 proliferation index

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Abstract

This study aims to investigate the predictive value of preoperative whole-tumor histogram analysis of multi-parametric MRI for histological subtypes in patients with lung cancer brain metastases (BMs) and explore the correlation between histogram parameters and Ki-67 proliferation index. The preoperative MRI data of 95 lung cancer BM lesions obtained from 73 patients (42 men and 31 women) were retrospectively analyzed. Multi-parametric MRI histogram was used to distinguish small-cell lung cancer (SCLC) from non-small cell lung cancer (NSCLC), and adenocarcinoma (AC) from squamous cell carcinoma (SCC), respectively. The T1-weighted contrast-enhanced (T1C) and apparent diffusion coefficient (ADC) histogram parameters of the volumes of interest (VOIs) in all BMs lesions were extracted using FireVoxel software. The following histogram parameters were obtained: maximum, minimum, mean, standard deviation (SD), variance, coefficient of variation (CV), skewness, kurtosis, entropy, and 1st–99th percentiles. Then investigated their relationship with the Ki-67 proliferation index. The skewness-T1C, kurtosis-T1C, minimum-ADC, mean-ADC, CV-ADC and 1st – 90th ADC percentiles were significantly different between the SCLC and NSCLC groups (all p < 0.05). When the 10th-ADC percentile was 668, the sensitivity, specificity, and accuracy (90.80%, 76.70% and 86.32%, respectively) for distinguishing SCLC from NSCLC reached their maximum values, with an AUC of 0.895 (0.824 – 0.966). Mean-T1C, CV-T1C, skewness-T1C, 1st – 50th T1C percentiles, maximum-ADC, SD-ADC, variance-ADC and 75th – 99th ADC percentiles were significantly different between the AC and SCC groups (all p < 0.05). When the CV-T1C percentiles was 3.13, the sensitivity, specificity and accuracy (75.00%, 75.60% and 75.38%, respectively) for distinguishing AC and SCC reached their maximum values, with an AUC of 0.829 (0.728–0.929). The 5th-ADC and 10th-ADC percentiles were strongly correlated with the Ki-67 proliferation index in BMs. Multi-parametric MRI histogram parameters can be used to identify the histological subtypes of lung cancer BMs and predict the Ki-67 proliferation index.

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Funding

This work was supported by the National Natural Science Foundation of China (grant number 82071872).

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Author notes

  1. Bin Zhang and Fengyu Zhou contributed equally to this study.

Authors and Affiliations

  1. Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No. 82, Chengguan District, Lanzhou, Gansu, 730030, People’s Republic of China

    Bin Zhang, Fengyu Zhou, Qing Zhou, Caiqiang Xue, Xiaoai Ke, Tao Han, Liangna Deng, Mengyuan Jing & Junlin Zhou

  2. Second Clinical School, Lanzhou University, Lanzhou, Gansu, China

    Bin Zhang, Fengyu Zhou, Qing Zhou, Caiqiang Xue, Xiaoai Ke, Tao Han, Liangna Deng, Mengyuan Jing & Junlin Zhou

  3. Key Laboratory of Medical Imaging of Gansu Province, Lanzhou University Second Hospital, Lanzhou, Gansu, China

    Bin Zhang, Fengyu Zhou, Qing Zhou, Caiqiang Xue, Xiaoai Ke, Tao Han, Liangna Deng, Mengyuan Jing & Junlin Zhou

  4. Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, Gansu, China

    Bin Zhang, Fengyu Zhou, Qing Zhou, Caiqiang Xue, Xiaoai Ke, Tao Han, Liangna Deng, Mengyuan Jing & Junlin Zhou

  5. Department of Pathology, Lanzhou University Second Hospital, Lanzhou, Gansu, China

    Peng Zhang

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  1. Bin Zhang
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Contributions

Bin Zhang and Fengyu Zhou wrote the manuscript. Bin Zhang, Fengyu Zhou, Qing Zhou, and Caiqiang Xue collected multimodal MRI data. Bin Zhang, Tao Han, Liangna Deng, and Mengyuan Jing analyzed the MRI data. Peng Zhang analyzed the pathological data. Bin Zhang, Qing Zhou, and Junlin Zhou designed and coordinated the study. Junlin Zhou revised the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Junlin Zhou.

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This study was performed in line with the principles of the Declaration of Helsinki. Approved by the Clinical Ethics Committee of the Lanzhou University Second Hospital (Project Number: 2020A-070).

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Zhang, B., Zhou, F., Zhou, Q. et al. Whole-tumor histogram analysis of multi-parametric MRI for differentiating brain metastases histological subtypes in lung cancers: relationship with the Ki-67 proliferation index. Neurosurg Rev 46, 218 (2023). https://doi.org/10.1007/s10143-023-02129-7

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