Computed tomography based radiomic signature as predictive of survival and local control after stereotactic body radiation therapy in pancreatic carcinoma

Abstract

PurposeTo appraise the ability of a radiomics signature to predict clinical outcome after stereotactic body radiation therapy (SBRT) for pancreas carcinoma.MethodsA cohort of 100 patients was included in this retrospective, single institution analysis. Radiomics texture features were extracted from computed tomography (CT) images obtained for the clinical target volume. The cohort of patients was randomly divided into two separate groups for the training (60 patients) and validation (40 patients). Cox regression models were built to predict overall survival and local control. The significant predictors at univariate analysis were included in a multivariate model. The quality of the models was appraised by means of area under the curve and concordance index.ResultsA clinical-radiomic signature associated with Overall Survival (OS) was found significant in both training and validation sets (p = 0.01 and 0.05 and concordance index 0.73 and 0.75 respectively). Similarly, a signature was found for Local Control (LC) with p = 0.007 and 0.004 and concordance index 0.69 and 0.75. In the low risk group, the median OS and LC in the validation group were 14.4 and 28.6 months while in the high-risk group were 9.0 and 17.5 months respectively.ConclusionA CT based radiomic signature was identified which correlate with OS and LC after SBRT and allowed to identify low and high-risk groups of patients.

Radiomics-Based Outcome Prediction for Pancreatic Cancer Following Stereotactic Body Radiotherapy

“…A later study published in 2019 also used radiomics to analyze pancreatic adenocarcinoma tumors [8]. This study focused on prognosis prediction.…”

“…Though in its infancy, radiomics has already proven to be helpful in better understanding the behavior of pancreatic cancer. For example, a 2019 study identified a specific radiomic signature of pancreatic cancer that correlated with overall survival and local control after treatment with stereotactic body radiation therapy [8]. Another study in 2018 analyzed texture features of tumors of the pancreatic head, finding that some features (such as certain filter values and contrast) served as independent prognostic factors in predicting decreased disease free survival [9].…”

Performance of Multiparametric Functional Imaging and Texture Analysis in Predicting Synchronous Metastatic Disease in Pancreatic Ductal Adenocarcinoma Patients by Hybrid PET/MR: Initial Experience

“…The rationale is that image texture features and radiomics characteristics may contain information of tumor phenotypes, which can reflect patient prognosis indirectly. Texture analysis and radiomics using CT images, which are widely available, has been used to predict aggressiveness, disease-free survival (DFS), and overall survival (OS) in patients with PDAC (13)(14)(15). DWI can reflect the tissue cellularity, and has been used in texture analysis in many other studies (16)(17)(18)(19).…”

Anatomically Informed 3D Printed CT phantoms: The First Step of a Pipeline To Identify Robust Quantitative Radiomic Features

“…From CT scans of the phantom with each setting described above, a subset of radiomic features that have been found to be repeatable and prognostic for NSCLC (energy, grey level nonuniformity) and PDAC [2,30] (entropy, energy, contrast and dissimilarity) were computed from each tumor. These features were then evaluated for their deviation from reference values, which were determined in the first part of this study.…”