Predictive value of 0.35 T magnetic resonance imaging radiomic features in stereotactic ablative body radiotherapy of pancreatic cancer: A pilot study

Abstract

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 33À50 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.

Delta radiomics analysis of Magnetic Resonance guided radiotherapy imaging data can enable treatment response prediction in pancreatic cancer

“…Previous reports showed that radiomics may potentially be used to quantify image textures in MRgRT data, and relate them to outcome in pancreatic [ 12 ] and rectal cancers [ 10 , 11 ] both in static and delta radiomic setting. Given the large number of features tested in small patient cohorts, as well as lack of image normalization, the results presented will require further validation.…”

“…In early proof-of-concept MRgRT radiomic studies, Boldrini et al [ 10 ] considered changes in extracted features using ratios pre- to post-treatment in 16 rectal cancer patients treated on a 0.35T MRgRT system showing promise for response correlation, later they followed up with another study in pancreatic cancer [ 11 ]. Simpson et al [ 12 ] looked at static, absolute values of radiomic features derived from 0.35T MRgRT scans for response prediction in 20 pancreatic cancer patients. With the increasing interest and promise in the field, there is an unmet need for establishing a robust MRgRT image quantification framework to extract the informational content of the imaging data and ensure reproducibility and reliability of extracted features.…”

Delta Radiomics Analysis for Local Control Prediction in Pancreatic Cancer Patients Treated Using Magnetic Resonance Guided Radiotherapy

“…As regards the Delta Radiomics MR-based models, the only experience to the best of our knowledge is represented by the study of Simpson et al, who showed how the delta approach on 0.35 T MR images may have a promising role in tumour response prediction of pancreatic cancer patients [47].…”

The role of radiomics in prostate cancer radiotherapy

“…Precise localization of the tumor and nearby organs at risk in real time has allowed for safe tumor dose escalation on hybrid MRI/RT machines [70]. In addition, on-board MRIgRT holds promise for improving [71] and pancreatic cancer [72]. Future studies will determine whether radiomics analysis of daily images from MRIgRT can similarly add prognostic value to prostate cancer treatment.…”