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Differential diagnosis of atypical and anaplastic meningiomas based on conventional MRI features and ADC histogram parameters using a logistic regression model nomogram

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Abstract

The purpose of the study was to determine the value of a logistic regression model nomogram based on conventional magnetic resonance imaging (MRI) features and apparent diffusion coefficient (ADC) histogram parameters in differentiating atypical meningioma (AtM) from anaplastic meningioma (AnM). Clinical and imaging data of 34 AtM and 21 AnM diagnosed by histopathology were retrospectively analyzed. The whole tumor delineation along the tumor edge on ADC images and ADC histogram parameters were automatically generated and comparisons between the two groups using the independent samples t test or Mann–Whitney U test. Univariate and multivariate logistic regression analyses were used to construct the nomogram of the AtM and AnM prediction model, and the model’s predictive efficacy was evaluated using calibration and decision curves. Significant differences in the mean, enhancement, perc.01%, and edema were noted between the AtM and AnM groups (P < 0.05). Age, sex, location, necrosis, shape, max-D, variance, skewness, kurtosis, perc.10%, perc.50%, perc.90%, and perc.99% exhibited no significant differences (P > 0.05). The mean and enhancement were independent risk factors for distinguishing AtM from AnM. The area under the curve, accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the nomogram were 0.871 (0.753–0.946), 80.0%, 81.0%, 79.4%, 70.8%, and 87.1%, respectively. The calibration curve demonstrated that the model’s probability to predict AtM and AnM was in favorable agreement with the actual probability, and the decision curve revealed that the prediction model possessed satisfactory clinical availability. A logistic regression model nomogram based on conventional MRI features and ADC histogram parameters is potentially useful as an auxiliary tool for the preoperative differential diagnosis of AtM and AnM.

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Funding

This study was supported by grants of the National Natural Science Foundation of China (No. 82071872, 82371914), Lanzhou University Second Hospital Second Hospital “Cuiying Technology Innovation Plan” Applied Basic Research Project (No. CY2018-QN09), Science and Technology Program of Gansu Province (21YF5FA123, 21JR11RA105), China International Medical Foundation (Z-2014–07-2101), and Medical Innovation and Development Project of Lanzhou University.

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Author notes

  1. Tao Han, Changyou Long, and Xianwang Liu contributed equally to this work.

Authors and Affiliations

  1. Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China

    Tao Han, Xianwang Liu, Mengyuan Jing, Yuting Zhang, Liangna Deng, Bin Zhang & Junlin Zhou

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

    Tao Han, Xianwang Liu, Mengyuan Jing, Yuting Zhang, Liangna Deng & Bin Zhang

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

    Tao Han, Xianwang Liu, Mengyuan Jing, Yuting Zhang, Liangna Deng, Bin Zhang & Junlin Zhou

  4. Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, 730030, China

    Tao Han, Xianwang Liu, Mengyuan Jing, Yuting Zhang, Liangna Deng, Bin Zhang & Junlin Zhou

  5. Image Center of Affiliated Hospital of Qinghai University, Xining, China

    Changyou Long

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  1. Tao Han
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Contributions

Guarantor of integrity of the entire study: Tao Han and Junlin Zhou, MD, PhD. Study concepts and design: Tao Han and Junlin Zhou, MD, PhD. Literature research: Changyou Long and Xianwang Liu. Clinical studies: Changyou Long and Yuting Zhang. Experimental studies/data analysis: Yuting Zhang and Xianwang Liu. Statistical analysis: Bin Zhang and Liangna Deng. Manuscript preparation: Tao Han and Mengyuan Jing. Manuscript editing: Tao Han and Junlin Zhou, MD, PhD.

Corresponding author

Correspondence to Junlin Zhou.

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Ethical approval

This study was approved by the Medical Ethics Committee of the Second Hospital of Lanzhou University (approval number: 2020A-109), and informed consent was waived.

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The authors declare no competing interests.

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Han, T., Long, C., Liu, X. et al. Differential diagnosis of atypical and anaplastic meningiomas based on conventional MRI features and ADC histogram parameters using a logistic regression model nomogram. Neurosurg Rev 46, 245 (2023). https://doi.org/10.1007/s10143-023-02155-5

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