Radiomic signatures derived from baseline 18F FDG PET/CT imaging can predict tumor-infiltrating lymphocyte values in patients with primary breast cancer

Özge Vural Topuz; Sidar Bağbudar; Ayşegül Aksu; Tuçe Söylemez Akkurt; Burcu Esen Akkaş

doi:10.1055/a-2512-8212

Nuklearmedizin - NuclearMedicine, Table of Contents

Nuklearmedizin
DOI: 10.1055/a-2512-8212

Original Article

Radiomic signatures derived from baseline 18F FDG PET/CT imaging can predict tumor-infiltrating lymphocyte values in patients with primary breast cancer

Radiomische Signaturen, die aus der Baseline-18F-FDG-PET/CT-Bildgebung abgeleitet werden, können tumorinfiltrierende Lymphozytenwerte bei Patienten mit primärem Brustkrebs vorhersagen

Özge Vural Topuz

¹Department of Nuclear Medicine, Başakşehir Cam and Sakura City Hospital, University of Health Sciences, Istanbul, Turkey (Ringgold ID: RIN622463)

,

Sidar Bağbudar

²Department of Pathology, Başakşehir Cam and Sakura City Hospital, University of Health Sciences, Istanbul, Turkey (Ringgold ID: RIN622463)

,

Ayşegül Aksu

³Department of Nuclear Medicine, İzmir Kâtip Çelebi University, Atatürk Training and Research Hospital, Izmir, Turkey (Ringgold ID: RIN226844)

,

Tuçe Söylemez Akkurt

²Department of Pathology, Başakşehir Cam and Sakura City Hospital, University of Health Sciences, Istanbul, Turkey (Ringgold ID: RIN622463)

,

Burcu Esen Akkaş

¹Department of Nuclear Medicine, Başakşehir Cam and Sakura City Hospital, University of Health Sciences, Istanbul, Turkey (Ringgold ID: RIN622463)

› Author Affiliations

Abstract

Objective

To determine the value of radiomics data extraction from baseline 18F FDG PET/CT in the prediction of tumor-infiltrating lymphocytes (TILs) among patients with primary breast cancer (BC).

Methods

We retrospectively evaluated 74 patients who underwent baseline 18F FDG PET/CT scans for BC evaluation between October 2020 and April 2022. Radiomics data extraction resulted in a total of 131 radiomic features from primary tumors. TILs status was defined based on histological analyses of surgical specimens and patients were categorized as having low TILs or moderate & high TILs. The relationships between TILs groups and tumor features, patient characteristics and molecular subtypes were examined. Features with a correlation coefficient of less than 0.6 were analyzed by logistic regression to create a predictive model. The diagnostic performance of the model was calculated via receiver operating characteristics (ROC) analysis.

Results

Menopausal status, histological grade, nuclear grade, and four radiomics features demonstrated significant differences between the two TILs groups. Multivariable logistic regression revealed that nuclear grade and three radiomics features (Morphological COMShift, GLCM Correlation, and GLSZM Small Zone Emphasis) were independently associated with TIL grouping. The diagnostic performance analysis of the model showed an AUC of 0.864 (95% CI: 0.776–0.953; p < 0.001). The sensitivity, specificity, PPV, NPV and accuracy values of the model were 69.6%, 82.4%, 64%, 85.7% and 78.4%, respectively

Conclusion

The pathological TIL scores of BC patients can be predicted by using radiomics feature extraction from baseline 18F FDG PET/CT scans.

Keywords

18F FDG PET/CT - breast cancer - tumor-infiltrating lymphocytes - radiomics

Full Text

References

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