Abstract:BackgroundIn pancreatic cancer, methods to predict early recurrence (ER) and identify patients at increased risk of relapse are urgently required.PurposeTo develop a radiomic nomogram based on MR radiomics to stratify patients preoperatively and potentially improve clinical practice.Study TypeRetrospective.PopulationWe enrolled 303 patients from two medical centers. Patients with a disease‐free survival ≤12 months were assigned as the ER group (n = 130). Patients from the first medical center were divided into… Show more
“…As regards the use of MR imaging for treatment response prediction, the main experiences were focussed on the radiomic analysis of MR images acquired at a single time point: Kaissis et al proposed a model able to predict the overall survival by analysing the pre-operative diffusion weighted images, while Tang et al elaborated a model for early recurrence prediction from the analysis of pre-operative T2 and T1-weighted MR images [ 44 , 45 , 46 ].…”
The aim of this study is to investigate the role of Delta Radiomics analysis in the prediction of one-year local control (1yLC) in patients affected by locally advanced pancreatic cancer (LAPC) and treated using Magnetic Resonance guided Radiotherapy (MRgRT). A total of 35 patients from two institutions were enrolled: A 0.35 Tesla T2*/T1 MR image was acquired for each case during simulation and on each treatment fraction. Physical dose was converted in biologically effective dose (BED) to compensate for different radiotherapy schemes. Delta Radiomics analysis was performed considering the gross tumour volume (GTV) delineated on MR images acquired at BED of 20, 40, and 60 Gy. The performance of the delta features in predicting 1yLC was investigated in terms of Wilcoxon Mann–Whitney test and area under receiver operating characteristic (ROC) curve (AUC). The most significant feature in predicting 1yLC was the variation of cluster shade calculated at BED = 40 Gy, with a p-value of 0.005 and an AUC of 0.78 (0.61–0.94). Delta Radiomics analysis on low-field MR images might play a promising role in 1yLC prediction for LAPC patients: further studies including an external validation dataset and a larger cohort of patients are recommended to confirm the validity of this preliminary experience.
“…As regards the use of MR imaging for treatment response prediction, the main experiences were focussed on the radiomic analysis of MR images acquired at a single time point: Kaissis et al proposed a model able to predict the overall survival by analysing the pre-operative diffusion weighted images, while Tang et al elaborated a model for early recurrence prediction from the analysis of pre-operative T2 and T1-weighted MR images [ 44 , 45 , 46 ].…”
The aim of this study is to investigate the role of Delta Radiomics analysis in the prediction of one-year local control (1yLC) in patients affected by locally advanced pancreatic cancer (LAPC) and treated using Magnetic Resonance guided Radiotherapy (MRgRT). A total of 35 patients from two institutions were enrolled: A 0.35 Tesla T2*/T1 MR image was acquired for each case during simulation and on each treatment fraction. Physical dose was converted in biologically effective dose (BED) to compensate for different radiotherapy schemes. Delta Radiomics analysis was performed considering the gross tumour volume (GTV) delineated on MR images acquired at BED of 20, 40, and 60 Gy. The performance of the delta features in predicting 1yLC was investigated in terms of Wilcoxon Mann–Whitney test and area under receiver operating characteristic (ROC) curve (AUC). The most significant feature in predicting 1yLC was the variation of cluster shade calculated at BED = 40 Gy, with a p-value of 0.005 and an AUC of 0.78 (0.61–0.94). Delta Radiomics analysis on low-field MR images might play a promising role in 1yLC prediction for LAPC patients: further studies including an external validation dataset and a larger cohort of patients are recommended to confirm the validity of this preliminary experience.
“…Participants received 12 weekly cycles of paclitaxel alone (standard of care) or in combination with one of nine experimental agents, followed by four cycles of anthracycline-cyclophosphamide (AC) every 2–3 weeks, prior to definitive surgery (Fig. 4 ) 10 . Patients with HER2-positive cancer also received trastuzumab for the first 12 cycles.…”
Section: Methodsmentioning
confidence: 99%
“…All patients provided written informed consent to participate in the study. Subsets of the patient cohort were included in previous studies 10 , 26 , 27 . Fig.…”
Section: Methodsmentioning
confidence: 99%
“…FTV represents the active portion of tumor volume, as defined by pharmacokinetic thresholds applied to dynamic contrast-enhanced MRI (DCE-MRI) 9 . While FTV has shown effectiveness for the prediction of pCR, there is still potential for improvement, especially in the setting of hormone-positive tumors 10 . Additional features can be derived from the same DCE-MRI data, including longest diameter, sphericity, and contralateral background parenchymal enhancement (BPE).…”
Dynamic contrast-enhanced (DCE) MRI provides both morphological and functional information regarding breast tumor response to neoadjuvant chemotherapy (NAC). The purpose of this retrospective study is to test if prediction models combining multiple MRI features outperform models with single features. Four features were quantitatively calculated in each MRI exam: functional tumor volume, longest diameter, sphericity, and contralateral background parenchymal enhancement. Logistic regression analysis was used to study the relationship between MRI variables and pathologic complete response (pCR). Predictive performance was estimated using the area under the receiver operating characteristic curve (AUC). The full cohort was stratified by hormone receptor (HR) and human epidermal growth factor receptor 2 (HER2) status (positive or negative). A total of 384 patients (median age: 49 y/o) were included. Results showed analysis with combined features achieved higher AUCs than analysis with any feature alone. AUCs estimated for the combined versus highest AUCs among single features were 0.81 (95% confidence interval [CI]: 0.76, 0.86) versus 0.79 (95% CI: 0.73, 0.85) in the full cohort, 0.83 (95% CI: 0.77, 0.92) versus 0.73 (95% CI: 0.61, 0.84) in HR-positive/HER2-negative, 0.88 (95% CI: 0.79, 0.97) versus 0.78 (95% CI: 0.63, 0.89) in HR-positive/HER2-positive, 0.83 (95% CI not available) versus 0.75 (95% CI: 0.46, 0.81) in HR-negative/HER2-positive, and 0.82 (95% CI: 0.74, 0.91) versus 0.75 (95% CI: 0.64, 0.83) in triple negatives. Multi-feature MRI analysis improved pCR prediction over analysis of any individual feature that we examined. Additionally, the improvements in prediction were more notable when analysis was conducted according to cancer subtype.
“…To date, radiomics‐related studies on MRI of pancreatic tumors are limited. However, there are still a few ambitious reports, such as the study on the differentiation of pNETs from adenocarcinoma, or preoperative evaluation of early recurrence in resectable pancreatic cancer 9,10 . Bian et al successfully reported on the classification of G1 vs. G2/3 of pNETs by MRI radiomics; however, the distinction between pNET Grade 3 and NEC is also clinically relevant, because the prognosis differs significantly between them.…”
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