14 research outputs found

    Solid-pseudopapillary tumor of the pancreas: MR imaging findings in 21 patients.

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    PURPOSE: Solid-pseudopapillary tumor (SPT) of the pancreas is a rare, low-grade malignancy, which mostly occurs in adolescent and young adult females. The goal of this study was to retrospectively analyze the magnetic resonance (MR) imaging presentation of SPT of the pancreas. METHODS: We retrospectively reviewed the preoperative MR imaging examinations and the medical, surgical and histopathological records of 21 patients who underwent surgery for SPT of the pancreas. MR imaging included T1-weighted, T2-weighted, and gadolinium chelate-enhanced MR imaging. In addition, 10 patients had diffusion-weighted (DW) MR imaging. MR examinations were retrospectively reviewed for location, size, morphological features and signal intensity of the tumors. RESULTS: Nineteen women and 2 men (median age, 23 years; range, 14-59) were included. Seven patients (7/21; 33%) presented with abdominal symptoms. The median largest tumor diameter was 53mm (range, 32-141 mm). SPTs were located in the pancreatic head, body, and tail in 9 (9/21; 43%), 5 (5/21; 24%) and 7 (7/21, 33%) patients, respectively. All patients (21/21; 100%) had a single SPT. SPTs were more frequently oval (12/21; 57%), predominantly solid (12/21; 57%), fully encapsulated (16/21; 76%), larger than 30 mm (21/21; 100%), hypointense on T1-weighted MR images (21/21, 100%), hyperintense on T2-weighted MR images (21/21; 100%) and with an enhancing capsule after gadolinium-chelate administration (21/21; 100%). CONCLUSIONS: There is trend of appearance for SPT of the pancreas on MR imaging but that variations may be observed in a number of cases. SPT uniformly presents as a single, well-demarcated and encapsulated pancreatic mass

    Texture analysis of ultrasound liver images with contrast agent to characterize the fibrosis stage

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    Value of Contrast-Enhanced Ultrasound Quantification Criteria for Identifying Patients not Responding to Bevacizumab-Based Therapy for Colorectal Liver Metastases

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    IF 3.892International audiencePurpose To evaluate changes in tumor vascularization parameters based on contrast-enhanced ultrasound (CEUS) quantification criteria of at least one visible liver metastasis as an early predictor of non-response to chemotherapy, including bevacizumab for colorectal cancer (CRC) liver metastases. Materials and Methods This multicenter prospective study included patients who received first-line bevacizumab-based chemotherapy. Tumor enhancement measured using CEUS within one liver metastasis and in relation to the surrounding healthy liver was quantified within 8 days before the first infusion of bevacizumab (E0), 24 hours after the end of the first infusion of bevacizumab (E1), in the 24 hours before the 2nd and 3 rd infusion of bevacizumab on day 15 (E2) and day 30 (E3), respectively, and after 2 months of treatment (E4). Endpoints were tumor response using RECIST criteria at 2 months, progression-free survival (PFS) and overall survival (OS). Results Among the 137 patients included in this study, 109 were analyzed. Only CEUS parameters calculated in relation to healthy liver were significant. High wash-in and wash-out rates at baseline were significantly associated with a better tumor response. Increases over time E2-E0 and E3-E0 for peak enhancement were significantly associated with shorter progression-free survival. Increases over time E2-E0 and E3-E0 for peak enhancement and wash-in area under the curve were significantly associated with a shorter overall survival. Conclusion This large study demonstrated that early dynamic changes in the vascularity of liver metastases evaluated by quantified CEUS are associated with outcome in patients receiving first-line bevacizumab-based treatment for metastatic CRC

    Deep Treatment Response Assessment and Prediction of Colorectal Cancer Liver Metastases

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    Evaluating treatment response is essential in patients who develop colorectal liver metastases to decide the necessity for second-line treatment or the admissibility for surgery. Currently, RECIST1.1 is the most widely used criteria in this context. However, it involves time-consuming, precise manual delineation and size measurement of main liver metastases from Computed Tomography (CT) images. Moreover, an early prediction of the treatment response given a specific chemotherapy regimen and the initial CT scan would be of tremendous use to clinicians. To overcome these challenges, this paper proposes a deep learning-based treatment response assessment pipeline and its extension for prediction purposes. Based on a newly designed 3D Siamese classification network, our method assigns a response group to patients given CT scans from two consecutive follow-ups during the treatment period. Further, we extended the network to predict the treatment response given only the image acquired at first time point. The pipelines are trained on the PRODIGE20 dataset collected from a phase-II multi-center clinical trial in colorectal cancer with liver metastases and exploit an in-house dataset to integrate metastases delineations derived from a U-Net inspired network as additional information. Our approach achieves overall accuracies of 94.94% and 86.86% for treatment response assessment and early prediction respectively, suggesting that both treatment response assessment and prediction issues can be effectively solved with deep learning
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