Predictive value of 0.35 T magnetic resonance imaging radiomic features in stereotactic ablative body radiotherapy of pancreatic cancer: A pilot study. Issue 8 (16th May 2020)

• Record Type: Journal Article
• Authors: Simpson, Garrett; Spieler, Benjamin; Dogan, Nesrin; Portelance, Lorraine; Mellon, Eric A.; Kwon, Deukwoo; Ford, John C.; Yang, Fei
• Is Part Of: Medical physics. Volume 47:Issue 8(2020)
• Page Start: 3682
• Page End: 3690
• Publication Date: 2020-05-16
• Subjects: imaging biomarkers -- MRI -- MR image guided radiotherapy -- pancreatic cancer -- radiomics -- stereotactic body radiotherapy -- texture analysis; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1002/mp.14200
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: The aim of this study was to evaluate the potential and feasibility of radiomic features extracted from low field strength (0.35 T) magnetic resonance images (MRIs) in predicting treatment response for patients with pancreatic cancer undergoing stereotactic body radiotherapy (SBRT). Methods: Twenty patients with unresected, non‐metastatic pancreatic ductal adenocarcinoma (PDAC) were enrolled, all of whom received neoadjuvant chemotherapy followed by five‐fraction MR‐guided SBRT with a radiation dose range of 33−50 Gy. For each patient, five daily setup scans were acquired from a hybrid 0.35 T MRI/radiotherapy unit. Tumor heterogeneity quantified with radiomic features extracted from the gross tumor volume (GTV) was averaged over the course of treatment. Random forest (RF) and adaptive least absolute shrinkage and selection operator (LASSO) classification models were constructed to identify radiomics features predictive of treatment response. Predictive capability of the top‐performing features was then evaluated using the receiver operating characteristic area under curve (AUC) obtained using leave‐one‐out cross‐validation. Results: Half of the 20 patients showed response to treatment, defined by tumor regression on histopathology or tumor response on follow‐up dynamic contrast‐enhanced computed tomography (CT). The most predictive features selected by the RF method were GLCM energy and GLSZM gray‐level variance. The RF‐based model achieved an AUC = 0.81 with a 95% confidence interval of [0.594 to 1] The LASSO algorithm selected GLCM energy as the only predictive feature, achieving an AUC = 0.81 with 95% confidence interval of [0.596 to 1]. Conclusion: The findings of this study suggest that radiomic features extracted during MR‐guided SBRT may contain predictive information about response of PDAC patients to treatment. Using the images acquired during treatment of PDAC patients supports continued expansion of radiomic analysis based on low field strength MR images and may hold the potential for providing timely indications of response to treatment.