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Is radiomics a useful addition to magnetic resonance imaging in the preoperative classification of PitNETs?

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Abstract

Background

The WHO 2021 introduced the term pituitary neuroendocrine tumours (PitNETs) for pituitary adenomas and incorporated transcription factors for subtyping, prompting the need for fresh diagnostic methods. Current biomarkers struggle to distinguish between high- and low-risk non-functioning PitNETs. We explored if radiomics can enhance preoperative decision-making.

Methods

Pre-treatment magnetic resonance (MR) images of patients who underwent surgery between 2015 and 2019 with available WHO 2021 classification were used. The tumours were manually segmented on the T1w, T1-contrast enhanced, and T2w images using 3D Slicer. One hundred Pyradiomic features were extracted from each MR sequence. Models were built to classify (1) somatotroph and gonadotroph PitNETs and (2) high- and low-risk subtypes of non-functioning PitNETs. Feature were selected independently from the MR sequences and multi-sequence (combining data from more than one MR sequence) using Boruta and Pearson correlation. Support vector machine (SVM), logistic regression (LR), random forest (RF), and multi-layer perceptron (MLP) were the classifiers used. Data imbalance was addressed using the Synthetic Minority Oversampling TEchnique (SMOTE). Performance of the models were evaluated using area under the receiver operating curve (AUC), accuracy, sensitivity, and specificity.

Results

A total of 222 PitNET patients (train, n = 149; test, n = 73) were enrolled in this retrospective study. Multi-sequence-based LR model discriminated best between somatotroph and gonadotroph PitNETs, with a test AUC of 0.84, accuracy of 0.74, specificity of 0.81, and sensitivity of 0.70. Multi-sequence-based MLP model perfomed best for the high- and low-risk non-functioning PitNETs, achieving a test AUC of 0.76, accuracy of 0.67, specificity of 0.72, and sensitivity of 0.66.

Conclusions

Utilizing pre-treatment MRI and radiomics holds promise for distinguishing high-risk from low-risk non-functioning PitNETs based on the latest WHO classification. This could assist neurosurgeons in making critical decisions regarding surgery or alternative management strategies for PitNETs after further clinical validation.

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Data availability

Available on reasonable request.

Code availability

Codes will be made available on GitHub shortly.

Abbreviations

WHO:

World Health Organization

MR:

Magnetic resonance

PitNETs:

Pituitary neuroendocrine tumours

SVM:

Support vector machine

LR:

Logistic regression

RF:

Random forest

MLP:

Multi-layer perceptron

SMOTE:

Synthetic Minority Oversampling TEchnique

NCA:

Null cell adenoma

GA:

Gonadotroph adenomas

SCA:

Silent corticotroph adenoma

SPA:

Silent PIT-1 positive adenomas

T1w:

T1-weighted MRI

T2w:

T2-weighted MRI

T1-CE:

T1-gadolinium contrast enhanced MRI

GLCM:

Gray-level co-occurrence matrix

GLRLM:

Gray level run length matrix

GLSZM:

Gray level size zone matrix

GLDM:

Gray level dependence matrix

ROC:

Receiver operating characteristic

AUC:

Area under the receiver operating characteristic (ROC) curve

DWI:

Diffusion-weighted imaging

ADC:

Apparent diffusion coefficient

ASL:

Arterial spin labelling

PIT1:

Pituitary-specific positive transcription factor 1

TPIT:

T-box transcription factor

SF1:

Steroidogenic factor

ER:

Estrogen receptor

SA:

Somatotroph adenoma

GATA3:

GATA binding protein 3

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Acknowledgements

The authors acknowledge the support provided by Mr. Reji towards data anonymization and transfer of images.

Author information

Authors and Affiliations

  1. Quantitative Imaging Research and Artificial Intelligence Lab, Department of Radiation Oncology Unit II, Ida B Scudder Cancer Centre, Christian Medical College, Vellore, India

    Sathya A, Balu Krishna Sasidharan, Simon P Pavamani & Hannah Mary T Thomas

  2. Department of Neurosurgery, Christian Medical College, Vellore, India

    Abhijit Goyal-Honavar & Ari G Chacko

  3. Department of Radiodiagnosis, Christian Medical College, Vellore, India

    Anitha Jasper & Joshua Anand Seelam

  4. Department of General Pathology, Christian Medical College, Vellore, India

    Geeta Chacko

  5. Department of Nuclear Medicine, Christian Medical College, Vellore, India

    Devadhas Devakumar

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Contributions

The authors confirm contribution to the paper as follows: study conception and design: AGC, AJ, GC, DD, BKS, SPP, HMTT; data collection: SA, AGH, JAS. Author; analysis, and interpretation of results: SA, AGH, AGC, AJ, GC, DD, JAS, BKS, SPP, HMTT; draft manuscript preparation: SA, AGH; revised manuscript critically for important intellectual content: AGC, AJ, GC, DD, BKS, SPP, HMTT. All authors reviewed the results and approved the final version of the manuscript.

Corresponding author

Correspondence to Hannah Mary T Thomas.

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Ethical approval

This study was conducted in accordance with the 1964 Declaration of Helsinki and was approved by the Institutional Review Board with the need for informed consent waived due to the retrospective nature of the study.

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Waiver granted due to retrospective nature of study.

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Sathya A and Abhijit Goyal-Honavar are equal contributing first authors.

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Sathya A, Goyal-Honavar, A., Chacko, A.G. et al. Is radiomics a useful addition to magnetic resonance imaging in the preoperative classification of PitNETs?. Acta Neurochir 166, 91 (2024). https://doi.org/10.1007/s00701-024-05977-4

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