Survival Prediction in Pancreatic Ductal Adenocarcinoma by Quantitative Computed Tomography Image Analysis

Attiyeh, Marc A.; Chakraborty, Jayasree; Doussot, Alexandre; Langdon‐Embry, Liana; Mainarich, Shiana; Gönen, Mithat; Balachandran, Vinod P.; D’Angelica, Michael I.; DeMatteo, Ronald P.; Jarnagin, William R.; Kingham, T. Peter; Allen, Peter J.; Simpson, Amber L.; Richard, K. G.

doi:10.1245/s10434-017-6323-3

Cited by 103 publications

(77 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The application of radiomics to PDAC has generated optimism, but it is also challenging because of nonspecific clinical presentation and subtle imaging findings. Previous studies on the application of radiomics to PDAC have focused on prognostic assessments and differential diagnosis [40][41][42][43]. The current study used radiomics features of the entire 3D volume to assess preoperative LN status in patients with PDAC.…”

Section: Discussionmentioning

confidence: 99%

Contrast-enhanced CT radiomics for predicting lymph node metastasis in pancreatic ductal adenocarcinoma: a pilot study

Yao

Xiao

et al. 2020

Cancer Imaging

View full text Add to dashboard Cite

Background:We developed a computational model integrating clinical data and imaging features extracted from contrast-enhanced computed tomography (CECT) images, to predict lymph node (LN) metastasis in patients with pancreatic ductal adenocarcinoma (PDAC).Methods: This retrospective study included 159 patients with PDAC (118 in the primary cohort and 41 in the validation cohort) who underwent preoperative contrast-enhanced computed tomography examination between 2012 and 2015. All patients underwent surgery and lymph node status was determined. A total of 2041 radiomics features were extracted from venous phase images in the primary cohort, and optimal features were extracted to construct a radiomics signature. A combined prediction model was built by incorporating the radiomics signature and clinical characteristics selected by using multivariable logistic regression. Clinical prediction models were generated and used to evaluate both cohorts. Results: Fifteen features were selected for constructing the radiomics signature based on the primary cohort. The combined prediction model for identifying preoperative lymph node metastasis reached a better discrimination power than the clinical prediction model, with an area under the curve of 0.944 vs. 0.666 in the primary cohort, and 0.912 vs. 0.713 in the validation cohort.Conclusions: This pilot study demonstrated that a noninvasive radiomics signature extracted from contrastenhanced computed tomography imaging can be conveniently used for preoperative prediction of lymph node metastasis in patients with PDAC.

show abstract

Section: Discussionmentioning

confidence: 99%

Contrast-enhanced CT radiomics for predicting lymph node metastasis in pancreatic ductal adenocarcinoma: a pilot study

Yao

Xiao

et al. 2020

Cancer Imaging

View full text Add to dashboard Cite

show abstract

“…Most approaches to image-based survival analysis perform a large-scale image feature extraction and feature selection, followed by a linear combination of the selected features in a Cox model [2,5,6,7]. Recently, modern neural networks were employed for survival analysis based on non-image data in [8,9,10], significantly outperforming traditional methods such as Cox models.…”

Section: Related Work and Contributionsmentioning

confidence: 99%

Image-Based Survival Prediction for Lung Cancer Patients Using CNNS

Haarburger

Weitz

Rippel

et al. 2019

2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)

View full text Add to dashboard Cite

Traditional survival models such as the Cox proportional hazards model are typically based on scalar or categorical clinical features. With the advent of increasingly large image datasets, it has become feasible to incorporate quantitative image features into survival prediction. So far, this kind of analysis is mostly based on radiomics features, i.e. a fixed set of features that is mathematically defined a priori. To capture highly abstract information, it is desirable to learn the feature extraction using convolutional neural networks. However, for tomographic medical images, model training is difficult because on the one hand, only few samples of 3D image data fit into one batch at once and on the other hand, survival loss functions are essentially ordering measures that require large batch sizes. In this work, we show that by simplifying survival analysis to median survival classification, convolutional neural networks can be trained with small batch sizes and learn features that predict survival equally well as end-to-end hazard prediction networks. Our approach outperforms the previous state of the art in a publicly available lung cancer dataset.

show abstract

“…Currently, detection or segmentation at localized disease stage followed by complete resection can offer the best chance of survival, i.e., with a 5-year survival rate of 32%. The accurate segmentation of PDAC mass is also important for further quantitative analysis, e.g., survival prediction [1]. Computed tomography (CT) is the most commonly used imaging modality for the initial evaluation of PDAC.…”

Section: Introductionmentioning

confidence: 99%

Hyper-Pairing Network for Multi-phase Pancreatic Ductal Adenocarcinoma Segmentation

Zhou

Zhang

et al. 2019

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers with an overall five-year survival rate of 8%. Due to subtle texture changes of PDAC, pancreatic dual-phase imaging is recommended for better diagnosis of pancreatic disease. In this study, we aim at enhancing PDAC automatic segmentation by integrating multi-phase information (i.e., arterial phase and venous phase). To this end, we present Hyper-Pairing Network (HPN), a 3D fully convolution neural network which effectively integrates information from different phases. The proposed approach consists of a dual path network where the two parallel streams are interconnected with hyper-connections for intensive information exchange. Additionally, a pairing loss is added to encourage the commonality between high-level feature representations of different phases. Compared to prior arts which use single phase data, HPN reports a significant improvement up to 7.73% (from 56.21% to 63.94%) in terms of DSC.

show abstract

Survival Prediction in Pancreatic Ductal Adenocarcinoma by Quantitative Computed Tomography Image Analysis

Cited by 103 publications

References 35 publications

Contrast-enhanced CT radiomics for predicting lymph node metastasis in pancreatic ductal adenocarcinoma: a pilot study

Contrast-enhanced CT radiomics for predicting lymph node metastasis in pancreatic ductal adenocarcinoma: a pilot study

Image-Based Survival Prediction for Lung Cancer Patients Using CNNS

Hyper-Pairing Network for Multi-phase Pancreatic Ductal Adenocarcinoma Segmentation

Contact Info

Product

Resources

About