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Radiomics model–based algorithm for preoperative prediction of pancreatic ductal adenocarcinoma grade

  • Oncology
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Abstract

Objective

To develop diagnostic radiomic model–based algorithm for pancreatic ductal adenocarcinoma (PDAC) grade prediction.

Methods

Ninety-one patients with histologically confirmed PDAC and preoperative CT were divided into subgroups based on tumor grade. Two histology-blinded radiologists independently segmented lesions for quantitative texture analysis in all contrast enhancement phases. The ratio of densities of PDAC and unchanged pancreatic tissue, and relative tumor enhancement (RTE) in arterial, portal venous, and delayed phases of the examination were calculated. Principal component analysis was used for multivariate predictor analysis. The selection of predictors in the binary logistic model was carried out in 2 stages: (1) using one-factor logistic models (selection criterion was p < 0.1); (2) using regularization (LASSO regression after standardization of variables). Predictors were included in proportional odds models without interactions.

Results

There were significant differences in 4, 16, and 8 texture features out of 62 for the arterial, portal venous, and delayed phases of the study, respectively (p < 0.1). After selection, the final diagnostic model included such radiomics features as DISCRETIZED HU standard, DISCRETIZED HUQ3, GLCM Correlation, GLZLM LZLGE for the portal venous phase of the contrast enhancement, and CONVENTIONAL_HUQ3 for the delayed phase of CT study. On its basis, a diagnostic model was built, showing AUC for grade ≥ 2 of 0.75 and AUC for grade 3 of 0.66.

Conclusion

Radiomics features vary in PDAC of different grades and increase the accuracy of CT in preoperative diagnosis. We have developed a diagnostic model, including texture features, which can be used to predict the grade of PDAC.

Key Points

• A diagnostic algorithm based on CT texture features for preoperative PDAC grade prediction was developed.

• The assumption that the scanning protocol can influence the results of texture analysis was confirmed and assessed.

• Our results show that tumor differentiation grade can be assessed with sufficient diagnostic accuracy using CT texture analysis presented in this study.

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Abbreviations

3D ROI:

Three-dimensional region of interest

AUC:

Area under the curve

CAP:

College of American Pathologists

CE:

Contrast enhancement

CECT:

Contrast-enhanced computed tomography

CM:

Contrast media

CT:

Computed tomography

DRI:

DoseRight software

HU:

Hounsfield units

MRI:

Magnetic resonance imaging

PDAC:

Pancreatic ductal adenocarcinoma

ROC:

Receiver operating characteristic

ROI:

Region of interest

RTE:

Relative tumor enhancement

VIF:

Variance inflation factor

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The authors state that this work has not received any funding.

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Authors and Affiliations

  1. A.V. Vishnevsky National Medical Research Centre of Surgery, Moscow, Russia

    Valeriya S. Tikhonova, Grigory G. Karmazanovsky, Evvgeny V. Kondratyev, Ivan S. Gruzdev, Kseniya A. Mikhaylyuk & Amiran Sh. Revishvili

  2. Pirogov Russian National Research Medical University, Moscow, Russia

    Grigory G. Karmazanovsky

  3. Research Institute of Human Morphology, Moscow, Russia

    Mikhail Y. Sinelnikov

  4. Sechenov University, Moscow, Russia

    Mikhail Y. Sinelnikov

Authors
  1. Valeriya S. Tikhonova
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  2. Grigory G. Karmazanovsky
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  4. Ivan S. Gruzdev
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  5. Kseniya A. Mikhaylyuk
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  6. Mikhail Y. Sinelnikov
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  7. Amiran Sh. Revishvili
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Correspondence to Mikhail Y. Sinelnikov.

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The scientific guarantor of this publication is Amiran Sh. Revishvili.

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The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

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Tikhonova, V.S., Karmazanovsky, G.G., Kondratyev, E.V. et al. Radiomics model–based algorithm for preoperative prediction of pancreatic ductal adenocarcinoma grade. Eur Radiol 33, 1152–1161 (2023). https://doi.org/10.1007/s00330-022-09046-1

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