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Apparent Diffusion Coefficient Histogram Analysis for the Preoperative Evaluation of Ki-67 Expression in Pituitary Macroadenoma

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Abstract

Purpose

To explore the value of an apparent diffusion coefficient (ADC) histogram in predicting the Ki-67 proliferation index in pituitary macroadenomas.

Material and Methods

This retrospective study analyzed the pathological and imaging data of 102 patients with pathologically confirmed pituitary macroadenoma. Immunohistochemistry staining was used to assess Ki-67 expression in tumor tissue samples, and a high Ki-67 labeling index was defined as 3%. The ADC images of the maximum slice of tumors were selected and the region of interest (ROI) of each slice was delineated using the MaZda software (version 4.7, Technical University of Lodz, Institute of Electronics, Łódź, Poland) and analyzed by ADC histogram. Histogram characteristic parameters were compared between the high Ki-67 group (n = 42) and the low Ki-67 group (n = 60). The important parameters were further analyzed by receiver operating characteristic (ROC).

Results

The mean value, and the 1st, 10th, 50th, 90th, and 99th percentiles were found to be negatively correlated with Ki-67 expression (all P < 0.05), with correlation coefficients of −0.292, −0.352, −0.344, −0.289, −0.253 and −0.267, respectively. The mean ADC and the 1st, 10th, 50th, 90th, and 99th quantiles extracted from the histogram were significantly lower in the high Ki-67 group than in the low Ki-67 group (all P < 0.05). The area under the ROC curve was 0.699–0.720; however, there were no significant between-group differences in variance, skewness and kurtosis (all P > 0.05).

Conclusion

An ADC histogram can be a reliable tool to predict the Ki-67 proliferation status in patients with pituitary macroadenomas.

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Abbreviations

ADC:

Apparent diffusion coefficient

AUC:

Area under the ROC curve

CI:

Confidence interval

DWI:

Diffusion-weighted imaging

FOV:

Field of view

ICC:

Intra-group correlation coefficient

MRI:

Magnetic resonance imaging

PA:

Pituitary adenoma

PACS:

Picture archiving and communication system

ROC:

Receiver operating characteristic

ROI:

Region of interest

T1WI:

T1-weighted imaging

T2WI:

T2-weighted imaging

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Funding

This work was supported by the National Natural Science Foundation of China under project name Imaging Quantitative Evaluation of Radiotherapy Efficacy of Glioma Based on Spectral CT [grant number 81772006].

Author information

Authors and Affiliations

  1. Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No. 82, 730030, Chengguan District, Lanzhou, China

    Caiqiang Xue, Suwei Liu, Juan Deng, Xianwang Liu, Shenglin Li & Junlin Zhou

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

    Caiqiang Xue, Suwei Liu, Juan Deng, Xianwang Liu, Shenglin Li & Junlin Zhou

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

    Caiqiang Xue, Suwei Liu, Juan Deng, Xianwang Liu, Shenglin Li & Junlin Zhou

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

    Caiqiang Xue, Suwei Liu, Juan Deng, Xianwang Liu, Shenglin Li & Junlin Zhou

  5. Department of Pathology, Lanzhou University Second Hospital, Cuiyingmen No. 82, 730030, Chengguan District, Lanzhou, China

    Peng Zhang

Authors
  1. Caiqiang Xue
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  2. Suwei Liu
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  3. Juan Deng
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  4. Xianwang Liu
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  5. Shenglin Li
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  6. Peng Zhang
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  7. Junlin Zhou
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Contributions

Caiqiang Xue and Suwei Liu contributed equally to this work.

Corresponding author

Correspondence to Junlin Zhou.

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Conflict of interest

C. Xue, S. Liu, J. Deng, X. Liu, S. Li, P. Zhang and J. Zhou declare that they have no competing interests.

Ethical standards

For this article no studies with human participants or animals were performed by any of the authors. All studies performed were in accordance with the ethical standards indicated in each case.

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Xue, C., Liu, S., Deng, J. et al. Apparent Diffusion Coefficient Histogram Analysis for the Preoperative Evaluation of Ki-67 Expression in Pituitary Macroadenoma. Clin Neuroradiol 32, 269–276 (2022). https://doi.org/10.1007/s00062-021-01134-x

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  • DOI: https://doi.org/10.1007/s00062-021-01134-x

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