MRI of pancreatic ductal adenocarcinoma: texture analysis of T2-weighted images for predicting long-term outcome

Choi, Moon Hyung; Lee, Young Joon; Yoon, Seung Bae; Choi, Joonsung; Jung, Seung Eun; Rha, Sung Eun

doi:10.1007/s00261-018-1681-2

Cited by 33 publications

(19 citation statements)

References 29 publications

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“…This leads to mutual advantages of high and low level SIFT features during the decoding. The mean dice value for PDAC mass segmentation using (1) Attention U-Net + LBP, (2) Attention U-Net + SIFT_1 + LBP, (3) Attention U-Net + SIFT_2 + LBP, (4) Attention U-Net + SIFT_4 + LBP and (5) Attention U-Net + SIFT_8 + LBP were 0.54, 0.55, 0.53, 0.58 and 0.55 respectively. SIFT_ n , means that the SIFT map with step size of n is added to the corresponding layer in the decoder path.…”

Section: Discussionmentioning

confidence: 99%

Segmentation of Pancreatic Ductal Adenocarcinoma (PDAC) and surrounding vessels in CT images using deep convolutional neural networks and texture descriptors

Mahmoudi

Kouzehkanan

Radmard

et al. 2021

Preprint

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Fully automated and volumetric segmentation of critical tumors may play a crucial role in diagnosis and surgical planning. One of the most challenging tumor segmentation tasks is localization of Pancreatic Ductal Adenocarcinoma (PDAC). Exclusive application of conventional methods does not appear promising. Deep learning approaches has achieved great success in the computer aided diagnosis, especially in biomedical image segmentation. This paper introduces a framework based on convolutional neural network (CNN) for segmentation of PDAC mass and surrounding vessels in CT images by incorporating powerful classic features, as well. First, a 3D-CNN architecture is used to localize the pancreas region from the whole CT volume using 3D Local Binary Pattern (LBP) map of the original image. Segmentation of PDAC mass is subsequently performed using 2D attention U-Net and Texture Attention U-Net (TAU-Net). TAU-Net is introduced by fusion of dense Scale-Invariant Feature Transform (SIFT) and LBP descriptors into the attention U-Net. An ensemble model is then used to cumulate the advantages of both networks using a 3D-CNN. In addition, to reduce the effects of imbalanced data, a new loss function is proposed as a weighted combination of three classic losses including Generalized Dice Loss (GDL), Weighted Pixel-Wise Cross Entropy loss (WPCE) and boundary loss. Due to insufficient sample size for vessel segmentation, we used the above-mentioned pre-trained networks and fin-tuned them. Experimental results show that the proposed method improves the Dice score for PDAC mass segmentation in portal-venous phase by 7.52% compared to state-of-the-art methods (from 53.08% to 60.6%) in term of DSC. Besides, three dimensional visualization of the tumor and surrounding vessels can facilitate the evaluation of PDAC treatment response.

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Section: Discussionmentioning

confidence: 99%

Segmentation of Pancreatic Ductal Adenocarcinoma (PDAC) and surrounding vessels in CT images using deep convolutional neural networks and texture descriptors

Mahmoudi

Kouzehkanan

Radmard

et al. 2021

Preprint

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“…The prognostic potential of quantitative imaging features for PDAC in the analysis was reported by several studies [29][30][31][32]. In an earlier study, twenty-six PDAC subjects were studied to identify clinical or textural features associated with survival by comparing the change between pre-and post-treatment 18 F-FDG-PET/CT images [32].…”

Section: Discussionmentioning

confidence: 99%

“…The identified features were provided as a feasible approach for evaluation and prediction of clinical outcomes. A recent study focused on T2-weighted MRI images to assess the association between radiomics features and pathological aggressiveness or clinical outcomes of 18.5 months after pancreatectomy procedure [30]. As tumor size was observed as a significant element to predict recurrence-free and overall survival, entropy with medium texture analysis was associated with the survival of PDAC patients.…”

Section: Discussionmentioning

confidence: 99%

MRI radiomics for early prediction of response to vaccine therapy in a transgenic mouse model of pancreatic ductal adenocarcinoma

et al. 2020

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Background: There is a lack of well-established clinical tools for predicting dendritic cell (DC) vaccination response of pancreatic ductal adenocarcinoma (PDAC). DC vaccine treatment efficiency was demonstrated using histological analysis in pre-clinical studies; however, its usage was limited due to invasiveness. In this study, we aimed to investigate the potential of MRI texture features for detection of early immunotherapeutic response as well as overall survival (OS) of PDAC subjects following dendritic cell (DC) vaccine treatment in LSL-Kras G12D ;LSL-Trp53 R172H ;Pdx-1-Cre (KPC) transgenic mouse model of pancreatic ductal adenocarcinoma (PDAC). Materials and methods:KPC mice were treated with DC vaccines, and tumor growth was dynamically monitored. A total of a hundred and fifty-two image features of T2-weighted MRI images were analyzed using a kernel-based support vector machine model to detect treatment effects following the first and third weeks of the treatment. Moreover, univariate analysis was performed to describe the association between MRI texture and survival of KPC mice as well as histological tumor biomarkers.Results: OS for mice in the treatment group was 54.8 ± 22.54 days while the control group had 35.39 ± 17.17 days. A subset of three MRI features distinguished treatment effects starting from the first week with increasing accuracy throughout the treatment (75% to 94%). Besides, we observed that short-run emphasis of approximate wavelet coefficients had a positive correlation with the survival of the KPC mice (r = 0.78, p < 0.001). Additionally, tissue-specific MRI texture features showed positive association with fibrosis percentage (r = 0.84, p < 0.002), CK19 positive percentage (r = − 0.97, p < 0.001), and Ki67 positive cells (r = 0.81, p < 0.02) as histological disease biomarkers. Conclusion:Our results demonstrate that MRI texture features can be used as imaging biomarkers for early detection of therapeutic response following DC vaccination in the KPC mouse model of PDAC. Besides, MRI texture can be utilized to characterize tumor microenvironment reflected with histology analysis.

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“…CTTA has been correlated with pathological results in a few studies (85,86) and shown to correlate with the outcomes of pancreatic adenocarcinoma patients. Choi et al (87) have retrospectively evaluated 66 pancreatic adenocarcinoma patients who had pre-operative 3T MRI scans and showed that tumor size was a significant predictive factor for both recurrence free and overall survival. However, other TA parameters which showed statistical significance to predict recurrence free and overall survival according to univariate analysis did not remain significant after multivariate analysis.…”

Section: Mrta For Other Abdominal and Pelvic Conditionsmentioning

confidence: 99%

MR Imaging Texture Analysis in the Abdomen and Pelvis

Thomas

Elkassem

Ganeshan

et al. 2020

Magnetic Resonance Imaging Clinics of North America

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MRI of pancreatic ductal adenocarcinoma: texture analysis of T2-weighted images for predicting long-term outcome

Abstract: Tumor size was a significant predictive factor for RFS and OS in PDAC patients. Although entropy with medium texture analysis was significantly associated with OS, there were also limitations in the texture analysis; thus, further study is necessary.

Cited by 33 publications

References 29 publications

Segmentation of Pancreatic Ductal Adenocarcinoma (PDAC) and surrounding vessels in CT images using deep convolutional neural networks and texture descriptors

Segmentation of Pancreatic Ductal Adenocarcinoma (PDAC) and surrounding vessels in CT images using deep convolutional neural networks and texture descriptors

MRI radiomics for early prediction of response to vaccine therapy in a transgenic mouse model of pancreatic ductal adenocarcinoma

MR Imaging Texture Analysis in the Abdomen and Pelvis

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