FDG PET/CT to Predict Recurrence of Early Breast Invasive Ductal Carcinoma.

Joon-Hyung Jo,Hyun Woo Chung,Young So,Sang Eun Nam,Kyoung Sik Park,Young Bum Yoo,Eun Jeong Lee,Woo Chul Noh

doi:10.3390/diagnostics12030694

Joon-Hyung Jo, Hyun Woo Chung + Show 6 more

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https://doi.org/10.3390/diagnostics12030694

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Abstract

This study investigated the prognostic value of FDG PET/CT radiomic features for predicting recurrence in patients with early breast invasive ductal carcinoma (IDC). The medical records of consecutive patients who were newly diagnosed with primary breast IDC after curative surgery were reviewed. Patients who received any neoadjuvant treatment before surgery were not included. FDG PET/CT radiomic features, such as a maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV), total lesion glycolysis (TLG), skewness, kurtosis, entropy, and uniformity, were measured for the primary breast tumor using LIFEx software to evaluate recurrence-free survival (RFS). A total of 124 patients with early breast IDC were evaluated. Eleven patients had a recurrence (8.9%). Univariate survival analysis identified large tumor size (>2 cm, p = 0.045), high Ki-67 expression (≥30%, p = 0.017), high AJCC prognostic stage (≥II, p = 0.044), high SUVmax (≥5.0, p = 0.002), high MTV (≥3.25 mL, p = 0.044), high TLG (≥10.5, p = 0.004), and high entropy (≥3.15, p = 0.003) as significant predictors of poor RFS. After multivariate survival analysis, only high MTV (p = 0.045) was an independent prognostic predictor. Evaluation of the MTV of the primary tumor by FDG PET/CT in patients with early breast IDC provides useful prognostic information regarding recurrence.

Highlights

Accepted: 9 March 2022Breast invasive ductal carcinoma (IDC) is the most common subtype of breast cancer worldwide
A total of 124 female patients with early breast IDC were evaluated for this study
83 of the 124 patients had less than 64 voxels of Volumetric regions of interest (VOIs) for the primary tumor on FDG PET/CT, texture-based radiomic features could not be calculated using Local image features extraction (LIFEx)

Summary

Introduction

Accepted: 9 March 2022Breast invasive ductal carcinoma (IDC) is the most common subtype of breast cancer worldwide. The incidence of breast cancer has increased over the past decade, mortality has declined progressively because of earlier detection and improved treatment [1]. Accurate assessment of the risk of recurrence after the initial treatment is important to the quality and quantity of a patient’s normal life expectancy. Radiomics is an intensely discussed topic in the medical imaging field. It is an approach for analyzing complex imaging patterns to extract quantitative and reproducible information that cannot be recognized by human vision [2]. Given that tumor tissues are a complex collection of cells with diverse molecular properties, radiomic features are thought to serve as a useful tool for evaluating intratumoral heterogeneity and identifying potentially important information for cancer characterization [3,4]. Local image features extraction (LIFEx) software measures the radiomic features of the entire tumor burden using images from various medical imaging modalities, and has been shown to provide convenient radiomics analysis [5]

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