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Performance of threshold-based stone segmentation and radiomics for determining the composition of kidney stones from single-energy CT

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Japanese Journal of Radiology Aims and scope Submit manuscript

Abstract

Purpose

Knowledge of kidney stone composition can help in patient management; urine composition analysis and dual-energy CT are frequently used to assess stone type. We assessed if threshold-based stone segmentation and radiomics can determine the composition of kidney stones from single-energy, non-contrast abdomen–pelvis CT.

Methods

With IRB approval, we identified 218 consecutive patients (mean age 64 ± 13 years; male:female 138:80) with the presence of kidney stones on non-contrast, abdomen–pelvis CT and surgical or biochemical proof of their stone composition. CT examinations were performed on one of the seven multidetector-row scanners from four vendors (GE, Philips, Siemens, Toshiba). Deidentified CT images were processed with a radiomics prototype (Frontier, Siemens Healthineers) to segment the entire kidney volumes with an AI-based organ segmentation tool. We applied a threshold of 130 HU to isolate stones in the segmented kidneys and to estimate radiomics over the segmented stone volume. A coinvestigator verified kidney stone segmentation and adjusted the volume of interest to include the entire stone volume when necessary. We applied multiple logistic regression tests with precision recall plots to obtain area under the curve (AUC) using a built-in R statistical program.

Results

The threshold-based stone segmentation successfully isolated kidney stones (uric acid: n = 102 patients, calcium oxalate/phosphate: n = 116 patients) in all patients. Radiomics differentiated between calcium and uric acid stones with an AUC of 0.78 (p < 0.01, 95% CI 0.73–0.83), 0.79 sensitivity, and 0.90 specificity regardless of CT vendors (GE CT: AUC = 0.82, p < 0.01, 95% CI 0.740–0896; Siemens CT: AUC = 0.77, 95% CI 0.700–0.846, p < 0.01).

Conclusion

Automated threshold-based stone segmentation and radiomics can differentiate between calcium oxalate/phosphate and urate stones from non-contrast, single-energy abdomen CT.

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Funding

This work has not received any findings. A study coinvestigator received research grants for unrelated work (MKK—Siemens Healthineers, Riverain Tech, Coreline Inc.). Another study coauthor (AP) is an employee of Siemens Healthineers. No other co-authors have financial disclosures.

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Authors and Affiliations

  1. Department of Radiology, Massachusetts General Hospital and Harvard Medical School, 75 Blossom Court, Suite 248, Boston, MA, 02114, USA

    Parisa Kaviani, Bernardo Bizzo, Shadi Ebrahimian, Keith J. Dreyer & Mannudeep K. Kalra

  2. Siemens Medical Solutions USA Inc, Malvern, PA, 19355, USA

    Andrew Primak

  3. MGH and BWH Center for Clinical Data Science, Boston, USA

    Bernardo Bizzo & Keith J. Dreyer

  4. Department of Radiology, Massachusetts General Hospital, 55 Fruit St, Boston, MA, 02114, USA

    Sanjay Saini

  5. Department of Radiology, Massachusetts General Hospital, 25 New Chardon Street, Boston, MA, 02114, USA

    Keith J. Dreyer

Authors
  1. Parisa Kaviani
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  2. Andrew Primak
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  3. Bernardo Bizzo
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  4. Shadi Ebrahimian
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  5. Sanjay Saini
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  6. Keith J. Dreyer
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  7. Mannudeep K. Kalra
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Corresponding author

Correspondence to Mannudeep K. Kalra.

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Kaviani, P., Primak, A., Bizzo, B. et al. Performance of threshold-based stone segmentation and radiomics for determining the composition of kidney stones from single-energy CT. Jpn J Radiol 41, 194–200 (2023). https://doi.org/10.1007/s11604-022-01349-z

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  • DOI: https://doi.org/10.1007/s11604-022-01349-z

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