Investigating multi-radiomic models for enhancing prediction power of cervical cancer treatment outcomes

Abstract

Quantitative image features, also known as radiomic features, have shown potential for predicting treatment outcomes in several body sites. We quantitatively analyzed Fluorine-fluorodeoxyglucose (F-FDG) Positron Emission Tomography (PET) uptake heterogeneity in the Metabolic Tumor Volume (MTV) of eighty cervical cancer patients to investigate the predictive performance of radiomic features for two treatment outcomes: the development of distant metastases (DM) and loco-regional recurrent disease (LRR). We aimed to fit the highest predictive features in multiple logistic regression models (MLRs). To generate such models, we applied backward feature selection method as part of Leave-One-Out Cross Validation (LOOCV) within a training set consisting of 70% of the original patient cohort. The trained MLRs were tested on an independent set consisted of 30% of the original cohort. We evaluated the performance of the final models using the Area under the Receiver Operator Characteristic Curve (AUC). Accordingly, six models demonstrated superior predictive performance for both outcomes (four for DM and two for LRR) when compared to both univariate-radiomic feature models and Standard Uptake Value (SUV) measurements. This demonstrated approach suggests that the ability of the pre-radiochemotherapy PET radiomics to stratify patient risk for DM and LRR could potentially guide management decisions such as adjuvant systemic therapy or radiation dose escalation.

[18F]FDG PET radiomics to predict disease-free survival in cervical cancer: a multi-scanner/center study with external validation

“…the characterisation of tumour phenotypes via the extraction of high-dimensional quantitative features from medical images, with the aim to support clinical decision-making [5][6][7]. Radiomic features have shown to predict treatment outcome in several cancer diseases including cervical cancer, and using various imaging modalities [8][9][10][11]. However, most of radiomic features show high sensitivity to multiple factors, including the scanner manufacturer and specific properties, acquisition protocols and the reconstruction algorithm and settings of each clinical center [12][13][14][15][16][17][18].…”

Radiomics Feature Selection in 18F-FDG PET Imaging: Investigation of the Most robust and Reproducible Candidates.

“…The former two belongs to the Discovery STE while the latter two spheres belong to the Siemens Biograph. (23) .…”

The Two-dimensional and Three-dimensional T2 Weighted Imaging-based Radiomic Signatures Selected for the Preoperative Discrimination of Ovarian Borderline Tumors and Epithelial Cancer.

“…As a research hotspot, radiomics is de ned as a new 'data-driven' approach for extracting large sets of quantitative signatures from radiological images and shows its potential application in medicine (14,15). MR-based radiomic signatures has been shown to help to categorize tumor subtypes and assess tumor presence, spread, recurrence or response to treatment in female cancer patients (16)(17)(18)(19)(20)(21). To date, there have been limited MRI radiomics studies concerning ovarian BOT and epithelial cancer categorization.…”