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Predicting histopathology markers of endometrial carcinoma with a quantitative image analysis approach based on spherical harmonics in multiparametric MRI - 01/11/22

Doi : 10.1016/j.diii.2022.10.007 
Thierry L. Lefebvre a, b, Ozan Ciga c, d, Sahir Rai Bhatnagar e, f, g, Yoshiko Ueno e, h, Sameh Saif e, Eric Winter-Reinhold g, Anthony Dohan i, j, Philippe Soyer i, j, Reza Forghani e, g, Kaleem Siddiqi c, Jan Seuntjens a, Caroline Reinhold e, g, k, 1, Peter Savadjiev c, e, g, 1,
a Medical Physics Unit, McGill University, Montreal, QC H4A 3J1, Canada 
b Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge CB2 0RE, United Kingdom 
c School of Computer Science and Centre for Intelligent Machines, McGill University, Montreal, QC H3A 2A7, Canada 
d Department of Medical Biophysics, University of Toronto, Toronto ON M5G 1L7, Canada 
e Department of Diagnostic Radiology, McGill University, Montreal, QC H4A 3J1, Canada 
f Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC H3A 1G1, Canada 
g Augmented Intelligence & Precision Health Laboratory (AIPHL), Research Institute of McGill University Health Centre (RI-MUHC), Montreal, QC H4A 3J1, Canada 
h Department of Radiology, Kobe University Graduate School of Medicine, Kobe City, Hyogo, 650-0017, Japan 
i Department of Radiology, Hopital Cochin, AP-HP, 75014, Paris, France 
j Université Paris Cité, Faculté de Médecine, 75006, Paris, France 
k Montreal Imaging Experts Inc., Montreal, QC H9R 5K3, Canada 

Corresponding author.
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Highlights

Several algorithms for predicting histopathology factors of endometrial carcinoma have been reported.
Compared to radiomics, spherical harmonic signatures provide increased diagnostic performance for predicting myometrial invasion by endometrial carcinoma as well as high tumor grade.
The diagnostic performance of spherical harmonics for predicting myometrial invasion by endometrial carcinoma as well as high tumor grade in the absence of precise lesion segmentation shows promise.

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Abstract

Purpose

Identifying optimal machine learning pipelines for computer-aided diagnosis is key for the development of robust, reproducible, and clinically relevant imaging biomarkers for endometrial carcinoma. The purpose of this study was to introduce the mathematical development of image descriptors computed from spherical harmonics (SPHARM) decompositions as well as the associated machine learning pipeline, and to evaluate their performance in predicting deep myometrial invasion (MI) and histopathological high-grade in preoperative multiparametric magnetic resonance imaging (MRI).

Patients and methods

This retrospective study included 128 women with histopathology-confirmed endometrial carcinomas who underwent 1.5-T MRI before hysterectomy between January 2011 and July 2015. SPHARM descriptors of each tumor were computed on multiparametric MRI images (T2-weighted, diffusion-weighted, dynamic contrast-enhanced-MRI and apparent diffusion coefficient maps). Tensor-based logistic regression was used to classify two-dimensional SPHARM rotationally-invariant descriptors. Head-to-head comparisons with radiomics analyses were performed with DeLong tests with Bonferroni-Holm correction to compare diagnostic performances.

Results

With all MRI contrasts, SPHARM analysis resulted in area under the curve, sensitivity, specificity, and balanced accuracy values of 0.94 (95% confidence interval [CI]: 0.85, 1.00), 100% (95% CI: 100, 100), 74% (95% CI: 51, 92), 87% (95% CI: 78, 98), respectively, for predicting deep MI. For predicting high-grade tumor histology, the corresponding values for the same diagnostic metrics were 0.81 (95% CI: 0.64, 0.90), 93% (95% CI: 67, 100), 63% (95% CI: 45, 79) and 78% (95% CI: 64, 86). The corresponding values achieved via radiomics were 0.92 (95% CI: 0.82, 0.95), 82% (95% CI: 65, 93), 80% (95% CI: 51, 94), 81% (95% CI: 70, 91) for deep MI and 0.72 (95% CI: 0.58, 0.83), 93% (95% CI: 65, 100), 55% (95% CI: 41, 69), 74% (95% CI: 52, 88) for high-grade histology. The diagnostic performance of the SPHARM analysis was not significantly different (P = 0.62) from that of radiomics for predicting deep MI but was significantly higher (P = 0.044) for predicting high-grade histology.

Conclusion

The proposed SPHARM analysis yields similar or higher diagnostic performance than radiomics in identifying deep MI and high-grade status in histology-proven endometrial carcinoma.

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Key Words : Artificial intelligence, Endometrial cancer, Machine learning, Magnetic resonance imaging, Spherical harmonics (SPHARM), Three-dimensional imaging

Abbreviations : 2D, 3D, ADC, AUC, CI, CNN, DCE, DWI, FIGO, IBSI, MI, mpMRI, MRI, RF, ROC, SPHARM, T2WI, VOI


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