The objective of our study was to determine the utility of radiomics features in differentiating CT cases of pancreatic ductal adenocarcinoma (PDAC) from normal pancreas.
In this retrospective ...case-control study, 190 patients with PDAC (97 men, 93 women; mean age ± SD, 66 ± 9 years) from 2012 to 2017 and 190 healthy potential renal donors (96 men, 94 women; mean age ± SD, 52 ± 8 years) without known pancreatic disease from 2005 to 2009 were identified from radiology and pathology databases. The 3D volume of the pancreas was manually segmented from the preoperative CT scans by four trained researchers and verified by three abdominal radiologists. Four hundred seventy-eight radiomics features were extracted to express the phenotype of the pancreas. Forty features were selected for analysis because of redundancy of computed features. The dataset was divided into 255 training cases (125 normal control cases and 130 PDAC cases) and 125 validation cases (65 normal control cases and 60 PDAC cases). A random forest classifier was used for binary classification of PDAC versus normal pancreas of control cases. Accuracy, sensitivity, and specificity were calculated.
Mean tumor size was 4.1 ± 1.7 (SD) cm. The overall accuracy of the random forest binary classification was 99.2% (124/125), and AUC was 99.9%. All PDAC cases (60/60) were correctly classified. One case from a renal donor was misclassified as PDAC (1/65). The sensitivity was 100%, and specificity was 98.5%.
Radiomics features extracted from whole pancreas can be used to differentiate between CT cases from patients with PDAC and healthy control subjects with normal pancreas.
Purpose
The purpose of this study is to evaluate diagnostic performance of a commercially available radiomics research prototype vs. an in-house radiomics software in the binary classification of CT ...images from patients with pancreatic ductal adenocarcinoma (PDAC) vs. healthy controls.
Materials and methods
In this retrospective case–control study, 190 patients with PDAC (97 men, 93 women; 66 ± 9 years) from 2012 to 2017 and 190 healthy potential renal donors (96 men, 94 women; 52 ± 8 years) without known pancreatic disease from 2005 to 2009 were identified from radiology and pathology databases. 3D volume of the pancreas was manually segmented from preoperative CT scans. Four hundred and seventy-eight radiomics features were extracted using in-house radiomics software. Eight hundred and fifty-four radiomics features were extracted using a commercially available research prototype. Random forest classifier was used for binary classification of PDAC vs. normal pancreas. Accuracy, sensitivity, and specificity of commercially available radiomics software were compared to in-house software.
Results
When 40 radiomics features were used in the random forest classification, in-house software achieved superior sensitivity (1.00) and accuracy (0.992) compared to the commercially available research prototype (sensitivity = 0.950, accuracy = 0.968). When the number of features was reduced to five features, diagnostic performance of the in-house software decreased to sensitivity (0.950), specificity (0.923), and accuracy (0.936). Diagnostic performance of the commercially available research prototype was unchanged.
Conclusion
Commercially available and in-house radiomics software achieve similar diagnostic performance, which may lower the barrier of entry for radiomics research and allow more clinician-scientists to perform radiomics research.
Hypoxia, or low oxygen tension, is a hallmark of the tumor microenvironment. The hypoxia-inducible factor-1α (HIF-1α) subunit plays a critical role in the adaptive cellular response of hypoxic tumor ...cells to low oxygen tension by activating gene-expression programs that control cancer cell metabolism, angiogenesis, and therapy resistance. Phosphorylation is involved in the stabilization and regulation of HIF-1α transcriptional activity. HIF-1α is activated by several factors, including the mitogen-activated protein kinase (MAPK) superfamily. MAPK phosphatase 3 (MKP-3) is a cytoplasmic dual-specificity phosphatase specific for extracellular signal-regulated kinase 1/2 (Erk1/2). Recent evidence indicates that hypoxia increases the endogenous levels of both MKP-3 mRNA and protein. However, its role in the response of cells to hypoxia is poorly understood. Herein, we demonstrated that small-interfering RNA (siRNA)-mediated knockdown of MKP-3 enhanced HIF-1α (not HIF-2α) levels. Conversely, MKP-3 overexpression suppressed HIF-1α (not HIF-2α) levels, as well as the expression levels of hypoxia-responsive genes (
,
,
, and
), in hypoxic colon cancer cells. These findings indicated that MKP-3, induced by HIF-1α in hypoxia, negatively regulates HIF-1α protein levels and hypoxia-responsive genes. However, we also found that long-term hypoxia (
12 h) induced proteasomal degradation of MKP-3 in a lactic acid-dependent manner. Taken together, MKP-3 expression is modulated by the hypoxic conditions prevailing in colon cancer, and plays a role in cellular adaptation to tumor hypoxia and tumor progression. Thus, MKP-3 may serve as a potential therapeutic target for colon cancer treatment.
Pancreatic Cancer Imaging: A New Look at an Old Problem Chu, Linda C.; Park, Seyoun; Kawamoto, Satomi ...
Current problems in diagnostic radiology,
July-August 2021, 2021 Jul-Aug, 2021-07-00, 20210701, Letnik:
50, Številka:
4
Journal Article
Recenzirano
Computed tomography is the most commonly used imaging modality to detect and stage pancreatic cancer. Previous advances in pancreatic cancer imaging have focused on optimizing image acquisition ...parameters and reporting standards. However, current state-of-the-art imaging approaches still misdiagnose some potentially curable pancreatic cancers and do not provide prognostic information or inform optimal management strategies beyond stage. Several recent developments in pancreatic cancer imaging, including artificial intelligence and advanced visualization techniques, are rapidly changing the field. The purpose of this article is to review how these recent advances have the potential to revolutionize pancreatic cancer imaging.
Purpose:
This paper introduces a novel approach to classify pulmonary arteries and veins from volumetric chest computed tomography (CT) images. Although there is known to be a relationship between ...the alteration of vessel distributions and the progress of various pulmonary diseases, there has been relatively little research on the quantification of pulmonary vesselsin vivo due to morphological difficulties. In particular, there have been few efforts to quantify the morphology and distribution of only arteries or veins through automated algorithms despite the clinical importance of such work. In this study, the authors classify different types of vessels by constructing a tree structure from vascular points while minimizing the construction cost using the vascular geometries and features of CT images.
Methods:
First, a vascular point set is extracted from an input volume and the weights of the points are calculated using the intensity, distance from the boundaries, and the Laplacian of the distance field. The tree construction cost is then defined as the summation of edge connection costs depending on the vertex weights. As a solution, the authors can obtain a minimum spanning tree whose branches correspond to different vessels. By cutting the edges in the mediastinal region, branches can be separated. From the root points of each branch, the cut region is regrouped toward the entries of pulmonary vessels in the same framework of the initial tree construction. After merging branches with the same orientation as much as possible, it can be determined manually whether a given vessel is an artery or vein. Our approach can handle with noncontrast CT images as well as vascular contrast enhanced images.
Results:
For the validation, mathematical virtual phantoms and ten chronic obstructive pulmonary disease (COPD) noncontrast volumetric chest CT scans with submillimeter thickness were used. Based on experimental findings, the suggested approach shows 9.18 ± 0.33 (mean ± SD) visual scores for ten datasets, 91% and 98% quantitative accuracies for two cases, a result which is clinically acceptable in terms of classification capability.
Conclusions:
This automatic classification approach with minimal user interactions may be useful in assessing many pulmonary disease, such as pulmonary hypertension, interstitial lung disease and COPD.
Hybrid grid generation for viscous flow analysis Park, Seyoun; Jeong, Byungduk; Lee, Jin Gyu ...
International journal for numerical methods in fluids,
10 March 2013, Letnik:
71, Številka:
7
Journal Article
•A novel deep network framework for accurate multi-organ segmentation on abdominal CT.•Organ-attention network to enhance discriminative information from complex background.•Reverse connection to ...handle multi-scale localization.•Statistical fusion based on local structural similarity.•State-of-the-art performance on automatic segmentation of 13 abdominal structures.
Display omitted
Accurate and robust segmentation of abdominal organs on CT is essential for many clinical applications such as computer-aided diagnosis and computer-aided surgery. But this task is challenging due to the weak boundaries of organs, the complexity of the background, and the variable sizes of different organs. To address these challenges, we introduce a novel framework for multi-organ segmentation of abdominal regions by using organ-attention networks with reverse connections (OAN-RCs) which are applied to 2D views, of the 3D CT volume, and output estimates which are combined by statistical fusion exploiting structural similarity. More specifically, OAN is a two-stage deep convolutional network, where deep network features from the first stage are combined with the original image, in a second stage, to reduce the complex background and enhance the discriminative information for the target organs. Intuitively, OAN reduces the effect of the complex background by focusing attention so that each organ only needs to be discriminated from its local background. RCs are added to the first stage to give the lower layers more semantic information thereby enabling them to adapt to the sizes of different organs. Our networks are trained on 2D views (slices) enabling us to use holistic information and allowing efficient computation (compared to using 3D patches). To compensate for the limited cross-sectional information of the original 3D volumetric CT, e.g., the connectivity between neighbor slices, multi-sectional images are reconstructed from the three different 2D view directions. Then we combine the segmentation results from the different views using statistical fusion, with a novel term relating the structural similarity of the 2D views to the original 3D structure. To train the network and evaluate results, 13 structures were manually annotated by four human raters and confirmed by a senior expert on 236 normal cases. We tested our algorithm by 4-fold cross-validation and computed Dice–Sørensen similarity coefficients (DSC) and surface distances for evaluating our estimates of the 13 structures. Our experiments show that the proposed approach gives strong results and outperforms 2D- and 3D-patch based state-of-the-art methods in terms of DSC and mean surface distances.
Purpose To test whether computer-aided diagnosis (CAD) approaches can increase the positive predictive value (PPV) and reduce the false-positive rate in lung cancer screening for small nodules ...compared with human reading by thoracic radiologists. Materials and Methods A matched case-control sample of low-dose computed tomography (CT) studies in 186 participants with 4-20-mm noncalcified lung nodules who underwent biopsy in the National Lung Screening Trial (NLST) was selected. Variables used for matching were age, sex, smoking status, chronic obstructive pulmonary disease status, body mass index, study year of the positive screening test, and screening results. Studies before lung biopsy were randomly split into a training set (70 cancers plus 70 benign controls) and a validation set (20 cancers plus 26 benign controls). Image features from within and outside dominant nodules were extracted. A CAD algorithm developed from the training set and a random forest classifier were applied to the validation set to predict biopsy outcomes. Receiver operating characteristic analysis was used to compare the prediction accuracy of CAD with the NLST investigator's diagnosis and readings from three experienced and board-certified thoracic radiologists who used contemporary clinical practice guidelines. Results In the validation cohort, the area under the receiver operating characteristic curve for CAD was 0.9154. By default, the sensitivity, specificity, and PPV of the NLST investigators were 1.00, 0.00, and 0.43, respectively. The sensitivity, specificity, PPV, and negative predictive value of CAD and the three radiologists' combined reading were 0.95, 0.88, 0.86, and 0.96 and 0.70, 0.69, 0.64, and 0.75, respectively. Conclusion CAD could increase PPV and reduce the false-positive rate in the early diagnosis of lung cancer.
RSNA, 2017 Online supplemental material is available for this article.
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