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Everything you need to know about Computed Tomography (CT) & CT Scanning

Pancreas: Artificial Intelligence Imaging Pearls - Educational Tools | CT Scanning | CT Imaging | CT Scan Protocols - CTisus
Imaging Pearls ❯ Pancreas ❯ Artificial Intelligence

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  • “Pancreatic cancer remains a major health problem, and only less than 20% of patients have resectable disease at the time of initial diagnosis. Systemic chemotherapy is often used in the patients with borderline resectable, locally advanced unresectable disease and metastatic disease. CT is often used to assess for therapeutic response; however, conventional imaging including CT may not correctly reflect treatment response after chemotherapy.”
    Assessment of iodine uptake by pancreatic cancer following chemotherapy using dual-energy CT.  
    Kawamoto S, Fuld MK, Laheru D, Huang P, Fishman EK.  
    Abdom Radiol (NY). 2018;43(2):445-456. 
  • "Dual-energy (DE) CT can acquire datasets at two different photon spectra in a single CT acquisition, and permits separating materials and extract iodine by applying a material decomposition algorithm. Quantitative iodine mapping may have an added value over conventional CT imaging for monitoring the treatment effects in patients with pancreatic cancer and potentially serve as a unique biomarker for treatment response. In this pictorial essay, we will review the technique for iodine quantification of pancreatic cancer by DECT and discuss our observations of iodine quantification at baseline and after systemic chemotherapy with conventional cytotoxic agents.”
    Assessment of iodine uptake by pancreatic cancer following chemotherapy using dual-energy CT.  
    Kawamoto S, Fuld MK, Laheru D, Huang P, Fishman EK.  
    Abdom Radiol (NY). 2018;43(2):445-456. 
  • “The parameters obtained using tumor segmentation software included (1) RECIST diameter (mm), (2) tumor volume (mL), (3) mean CT number of tumor (HU) at simulated weighted-average 120-kVp images, (4) iodine uptake by tumor per volume of tissue (mg/mL), and (5) normalized tumor iodine uptake (tumor iodine uptake normalized to the reference value acquired using region of interest place in the abdominal aorta at the level of the pancreatic tumor, calculated by tumor iodine uptake [mg/dL]/abdominal aortic uptake [mg/dL]).”
  • “In conclusion, iodine uptake by pancreatic adenocarcinoma using DECT may add supplemental information for assessment of treatment response, although tumor iodine uptake by pancreatic adenocarcinoma is small, and it may be difficult to apply to each case. Normalized tumor iodine uptake might be more sensitive than iodine concentration to measure treatment response. More data are necessary to confirm these observations.”
    Assessment of iodine uptake by pancreatic cancer following chemotherapy using dual-energy CT.  
    Kawamoto S, Fuld MK, Laheru D, Huang P, Fishman EK.  
    Abdom Radiol (NY). 2018;43(2):445-456.
  • Purpose: Evaluate utility of dual energy CT iodine material density images to identify preoperatively nodal positivity in pancreatic cancer patients who underwent neoadjuvant therapy.
    Conclusion: The dual energy based minimum normalized iodine value of all nodes in the surgical field on preoperative studies has modest utility in differentiating N0 from N1/2, and generally outperformed conventional features for identifying nodal metastases.
    CT features predictive of nodal positivity at surgery in pancreatic cancer patients following neoadjuvant therapy in the setting of dual energy CT.  
    Le O, Javadi S, Bhosale PR et al.  
    Abdom Radiol (NY). 2021 Jan 20. doi: 10.1007/s00261-020-02917-5. Epub ahead of print. PMID: 33471129.
  • “Radiomics analysis extracts a large number of features from conventional radiological cross-sectional images that were traditionally undetectable by the naked human eye. It identifies tumor heterogeneity in a comprehensive and noninvasive way, reflecting the biological behaviour of lesions, and thus assists in clinical diagnosis and treatment evaluation. This review describes the radiomics approach and its uses in the evaluation of pancreatic ductal adenocarcinoma (PDAC). This discipline holds the potential to characterize lesions more accurately, assesses the primary tumour and predicts the response to therapy and prognosis in PDAC. Existing studies have provided significant insights into the application of radiomics in managing the PDAC. However, a variety of challenges, including data quality and quantity, imaging segmentation, and the standardization of the radiomics process need to be solved before its widespread clinical implementation.”
    Radiomics in pancreatic ductal adenocarcinoma: a state of art review  
    Ming He et al.
    Journal of Pancreatology (2020) 3:4

  • Radiomics in pancreatic ductal adenocarcinoma: a state of art review  
    Ming He et al.
    Journal of Pancreatology (2020) 3:4
  • "The application of radiomics in PDAC mainly includes the following 3 aspects: lesion characterization, primary tumour assessment and response to therapy and prognosis. It has also been used in other nononcologic conditions associated with PDAC. The initial results of radiomics related to PDAC are promising. However, there are still many problems and challenges that need to be solved, including data quality and quantity, imaging segmentation and the standardization of the radiomics process. Radiologists need to work closely with researchers such as information scientists to establish the standardized process of radiomics analysis. Multi-centre data sharing and public database establishment would provide more high-quality data for radiomics analysis. With the development of the radiomics in PDAC, it has a considerable potential to be a useful assistant in the clinical workflow for PDAC’s personalized medicine.”
    Radiomics in pancreatic ductal adenocarcinoma: a state of art review  
    Ming He et al.
    Journal of Pancreatology (2020) 3:4
  • “The procedure of the radiomics analysis should be carefully evaluated and standardized in every step to eliminate the potential bias and confounding factors. Extensive disclosure of the imaging protocols, evaluation criteria, reproducibility and/ or clinical utility is of great significance. Multiple studies had a limitation of unclear description about the detailed process of radiomics performed pre-processing, reconstruction, variations in feature nomenclature, mathematical definition, methodology, and software implementation of the applied feature extraction algorithms. The process of feature reduction and/or exclusion should be described clearly in the future. designs and a head-to-head comparison of quantitative features against standard diagnostic radiologist assessment are needed in the future.”
    Radiomics in pancreatic ductal adenocarcinoma: a state of art review  
    Ming He et al.
    Journal of Pancreatology (2020) 3:4
  • Background: The diagnostic performance of CT for pancreatic cancer is interpreter-dependent, and approximately 40% of tumours smaller than 2 cm evade detection. Convolutional neural networks (CNNs) have shown promise in image analysis, but the networks’ potential for pancreatic cancer detection and diagnosis is unclear. We aimed to investigate whether CNN could distinguish individuals with and without pancreatic cancer on CT, compared with radiologist interpretation.
    Interpretation: CNN could accurately distinguish pancreatic cancer on CT, with acceptable generalisability to images of patients from various races and ethnicities. CNN could supplement radiologist interpretation.
    Deep learning to distinguish pancreatic cancer tissue from non-cancerous pancreatic tissue: a retrospective study with cross-racial external validation
    Kao-Lang Liu et al.
    Lancet Digital Health 2020; 2: e303–13
  • “Findings Between Jan 1, 2006, and Dec 31, 2018, we obtained CT images. In local test set 1, CNN-based analysis had a sensitivity of 0·973, specificity of 1·000, and accuracy of 0·986 (area under the curve [AUC] 0·997 (95% CI 0·992–1·000). In local test set 2, CNN-based analysis had a sensitivity of 0·990, specificity of 0·989, and accuracy of 0·989 (AUC 0·999 [0·998–1·000]). In the US test set, CNN-based analysis had a sensitivity of 0·790, specificity of 0·976, and accuracy of 0·832 (AUC 0·920 [0·891–0·948)]. CNN-based analysis achieved higher sensitivity than radiologists did (0·983 vs 0·929, difference 0·054 [95% CI 0·011–0·098]; p=0·014) in the two local test sets combined. CNN missed three (1·7%) of 176 pancreatic cancers (1·1–1·2 cm). Radiologists missed 12 (7%) of 168 pancreatic cancers (1·0–3·3 cm), of which 11 (92%) were correctly classified using CNN. The sensitivity of CNN for tumours smaller than 2 cm was 92·1% in the local test sets and 63·1% in the US test set.”
    Deep learning to distinguish pancreatic cancer tissue from non-cancerous pancreatic tissue: a retrospective study with cross-racial external validation
    Kao-Lang Liu et al.
    Lancet Digital Health 2020; 2: e303–13
  • Findings: CNN-based analysis achieved higher sensitivity than radiologists did (0·983 vs 0·929, difference 0·054 [95% CI 0·011–0·098]; p=0·014) in the two local test sets combined. CNN missed three (1·7%) of 176 pancreatic cancers (1·1–1·2 cm). Radiologists missed 12 (7%) of 168 pancreatic cancers (1·0–3·3 cm), of which 11 (92%) were correctly classified using CNN. The sensitivity of CNN for tumours smaller than 2 cm was 92·1% in the local test sets and 63·1% in the US test set.
    Deep learning to distinguish pancreatic cancer tissue from non-cancerous pancreatic tissue: a retrospective study with cross-racial external validation
    Kao-Lang Liu et al.
    Lancet Digital Health 2020; 2: e303–13
  • “CNN can accurately differentiate pancreatic cancer from non-cancerous pancreas, and with improvements might accommodate variations in patient race and ethnicity and imaging parameters that are inevitable in real-world clinical practice. CNN holds promise for developing computer-aided detection and diagnosis tools for pancreatic cancer to supplement radiologist interpretation.”
    Deep learning to distinguish pancreatic cancer tissue from non-cancerous pancreatic tissue: a retrospective study with cross-racial external validation
    Kao-Lang Liu et al.
    Lancet Digital Health 2020; 2: e303–13
  • “In conclusion, this study provided a proof of concept that CNN can accurately distinguish pancreatic cancer on portal venous CT images. The CNN model holds promise as a compute r­aided diagnostic tool to assist radiologists and clinicians in diagnosing pancreatic cancer.”
    Deep learning to distinguish pancreatic cancer tissue from non-cancerous pancreatic tissue: a retrospective study with cross-racial external validation
    Kao-Lang Liu et al.
    Lancet Digital Health 2020; 2: e303–13
  • “Interpretation CNN could accurately distinguish pancreatic cancer on CT, with acceptable generalisability to images of patients from various races and ethnicities. CNN could supplement radiologist interpretation.”
    Deep learning to distinguish pancreatic cancer tissue from non-cancerous pancreatic tissue: a retrospective study with cross-racial external validation
    Kao-Lang Liu et al.
    Lancet Digital Health 2020; 2: e303–13
  • “Deep learning and radiomics are two broad categories of artificial intelligence (AI) research that have the potential to facilitate automatic disease detection and to provide quantitative imaging biomarkers for individualized disease assessment. The large volumes of digital data inherent in radiology images make radiology a natural field for AI research. Cinematic Rendering is a recently described post-processing technique that uses sophisticated illumination modeling to achieve more photorealistic images, and these images, in turn, have the potential to aid treatment planning. Here we review these AI and advanced visualization techniques and highlight how they can be used to improve the detection and management of pancreatic cancers.”
    Pancreatic Cancer Imaging: A New Look at an Old Problem
    Linda C. Chu MD, Seyoun Park, Satomi Kawamoto, Alan L. Yuille , Ralph H. Hruban, Elliot K. Fishman
    Current Problems in Diagnostic Radiology (in press)
  • “Deep learning is a type of machine learning method in which algorithms are trained to perform tasks by learning patterns from data rather than by explicit programming. Deep neural networks are inspired by biological neural networks and use a matrix of interconnected nodes to mimic the function of a biologic neuron. The basic unit of an artificial neural network is a node. It takes a set of input features, multiplies these features by corresponding weights in the form of mathematical equations, and then passes the output to the next layer of nodes. The deep network architecture uses multiple layers of interconnected nodes to develop a mathematical model that best fits the data. The outputs are compared with the “ground truth,” and errors are used as feedback to adjust the weights in the network to minimize error in subsequent iterations.”
    Pancreatic Cancer Imaging: A New Look at an Old Problem
    Linda C. Chu MD, Seyoun Park, Satomi Kawamoto, Alan L. Yuille , Ralph H. Hruban, Elliot K. Fishman
    Current Problems in Diagnostic Radiology (in press)

  • Automatic detection of pancreatic ductal adenocarcinoma (PDAC) with deep learning. (Left panel) Axial IV contrast- enhanced CT image shows a hypoenhancing mass in the pancreatic body (arrow) with dilated pancreatic duct (arrowhead). (Middle panel) Manual segmentation of the tumor (red), pancreatic duct (green), and background pancreas (blue). (Right panel) Deep network prediction of tumor (red), pancreatic duct (green) and background pancreas (blue).

  • Automatic detection of pancreatic neuroendocrine tumor (PanNET) with deep learning. (Left panel) Axial IV contrast-enhanced CT image shows a subtle hyperenhancing mass within the head of the pancreas (arrow). (Middle panel) Manual segmentation of tumor (pink) and background pancreas (blue). (Right panel) Deep network prediction of tumor (pink) and background pancreas (blue).

  • Automatic detection of intraductal papillary mucinous neoplasm (IPMN) with deep learning. (Left panel) Axial IV contrast-enhanced CT image shows multiple well-circumscribed cystic lesions in the pancreas (arrow). (Middle panel) Manual segmentation of cystic tumors (yellow) and background pancreas (blue). (Right panel) Deep network prediction of cystic tumors (yellow) and background pancreas (blue).

  • A schematic illustrating the radiomics feature extraction and analysis process. Radiomics features can be classified into signal intensity, shape, texture, and filtered features (e.g., wavelets and Laplacian of Gaussian [LoG]). (Left panel) Input of imaging datasets (normal vs. abnormal) with annotation of regions of interest. (Middle panel) Extraction of radiomics features, including histogram of voxel signal intensities, shape features based on surface rendering of region of interest, and filtered features. (Right panel) The raw data are processed through feature selection to identify the most relevant features. These features can be correlated with clinical outcomes in classification tasks.
  • “Radiomics features have also been used to predict PanNET grade, one of the most important prognostic factors in predicting patient survival. Qualitative features such as ill-defined margins, heterogeneous enhancement, low- level enhancement, vascular involvement, and main pancreatic duct dilatation have been reported to be helpful features in predicting higher tumor grade. Radiomics features achieved equivalent or superior performance compared to traditional clinical and imaging features. in most, but not all studies, with higher tumor grades in the majority of these studies, and with worse progression free survival. The addition of radiomics features to traditional CT features may improve the accuracy of PanNET grade prediction.”
    Pancreatic Cancer Imaging: A New Look at an Old Problem
    Linda C. Chu MD, Seyoun Park, Satomi Kawamoto, Alan L. Yuille , Ralph H. Hruban, Elliot K. Fishman
    Current Problems in Diagnostic Radiology (in press)
  • “Radiomics features have also been reported to be predictive of overall survival in patients with unresectable or locally advanced PDAC. Not surprisingly, the presence of metastatic disease at presentation was the most predictive of poor overall survival. factors. Radiomics features associated with tumor heterogeneity were also found to be poor prognostic factors. There is speculation that tumor hypoattenuation may reflect areas of hypoxic necrosis, which may suggest more aggressive underlying tumor biology as well as impaired response to chemotherapy and radiation therapy. Low attenuation may also be evidence of extensive venous invasion by the cancer.”
    Pancreatic Cancer Imaging: A New Look at an Old Problem
    Linda C. Chu MD, Seyoun Park, Satomi Kawamoto, Alan L. Yuille , Ralph H. Hruban, Elliot K. Fishman
    Current Problems in Diagnostic Radiology (in press)
  • "While VR uses a simple ray cast method to generate 3D images, CR uses Monte Carlo path tracing that takes direct and indirect illumination into account. With CR, each pixel is formed by thousands of rays passing through the volumetric dataset and includes effects of light rays from scatter and from voxels adjacent to the paths of the rays. CR has the potential to more accurately depict complex anatomy. When applied to pancreatic imaging, CR can be used to accentuate focal textural change and enhance appreciation of internal architecture (e.g., septations, mural nodules) to improve their visualization and assist in tumor classification.”
    Pancreatic Cancer Imaging: A New Look at an Old Problem
    Linda C. Chu MD, Seyoun Park, Satomi Kawamoto, Alan L. Yuille , Ralph H. Hruban, Elliot K. Fishman
    Current Problems in Diagnostic Radiology (in press)
  • “Pancreatic ductal adenocarcinoma (PDAC) segmentation is one of the most challenging tumor segmentation tasks, yet critically important for clinical needs. Previous work on PDAC segmentation is limited to the moderate amounts of annotated patient images (n<300) from venous or venous+arterial phase CT scans. Based on a new self-learning framework, we propose to train the PDAC segmentation model using a much larger quantity of patients (n≈1,000), with a mix of annotated and un- annotated venous or multi-phase CT images. Pseudo annotations are generated by combining two teacher models with different PDAC segmentation specialties on unannotated images, and can be further refined by a teaching assistant model that identifies associated vessels around the pancreas.”
    Robust Pancreatic Ductal Adenocarcinoma Segmentation with Multi-Institutional Multi-Phase Partially-Annotated CT Scans
    Ling Zhang et al.
    arXiv: August 2020 (in press)
  • “Fully automated and accurate segmentation of pancreatic ductal adenocarcinoma (PDAC) is one of the most challenging tumor segmentation tasks, in the aspects of complex abdominal structures, large variations in morphology and appearance, low image contrast and fuzzy/uncertain boundary, etc. Previous studies introduce the cascade UNet for segmenting venous phase CT and hyperpairing network for segmenting venous+arterial phases CT and achieving mean Dice scores of 0.52 and 0.64, respectively. By incorporating nnUNet into a new self-learning framework with two teachers and one teaching assistant to segment three-phases of CT scans, our method reaches a Dice coefficient of 0.71, similar to the inter-observer variability between radiologists. This provides promise that a radiologist-level performance for accurate PDAC tumor segmentation in multi-phase CT imaging can be achieved through our computerized method.”
    Robust Pancreatic Ductal Adenocarcinoma Segmentation with Multi-Institutional Multi-Phase Partially-Annotated CT Scans
    Ling Zhang et al.
    arXiv: August 2020 (in press)

  • Robust Pancreatic Ductal Adenocarcinoma Segmentation with Multi-Institutional Multi-Phase Partially-Annotated CT Scans
    Ling Zhang et al.
    arXiv: August 2020 (in press)
  • Background: To identify preoperative computed tomography radiomics texture features which correlate with resection margin status and prognosis in resected pancreatic head adenocarcinoma.
    Methods: Improved prognostication methods utilizing novel non-invasive radiomic techniques may accurately predict resection margin status preoperatively. In an ongoing concerning pancreatic head adenocarcinoma, the venous enhanced CT images of 86 patients who underwent pancreaticoduodenectomy were selected, and the resection margin (>1 mm or ≤1 mm) was identified by pathological examination. Three regions of interests (ROIs) were then taken from superior to inferior facing the superior mesenteric vein and artery. Subsequent Laplacian-Dirichlet based texture analysis methods extracting algorithm of texture features within ROIs were analyzed and assessed in relation to patient prognosis.
    Results: Patients with >1 mm resection margin had an overall improved survival compared to ≤1 mm (P < 0.05). Distance 1 and 2 of Gray level co-occurrence matrix, high Gray-level run emphasis of run-length matrix and average filter of wavelet transform (all P < 0.05) were correlated with resection margin status (Area under the curve was 0.784, sensitivity was 75% and specicity was 79%). The energy of wavelet transform, the measure of smoothness of histogram and the variance in 2 direction of Gabor transform are independent predictors of overall survival prognosis, independent of resection margin.
    Conclusions: Resection margin status (>1 mm vs ≤1 mm) is a key prognostic factor in pancreatic adenocarcinoma and CT radiomic analysis have the potential to predict resection margin status preoperatively, and the radiomic labels may improve selection neoadjucant therapy.
    Radiomics Signatures of Computed Tomography Imaging for Predicting Resection Margin Status in Pancreatic Head Adenocarcinoma
    Jinheng Liu et al.
    BMC Surgery (in press)
  • Results: Patients with >1 mm resection margin had an overall improved survival compared to ≤1 mm (P < 0.05). Distance 1 and 2 of Gray level co-occurrence matrix, high Gray-level run emphasis of run-length matrix and average filter of wavelet transform (all P < 0.05) were correlated with resection margin status (Area under the curve was 0.784, sensitivity was 75% and specificity was 79%). The energy of wavelet transform, the measure of smoothness of histogram and the variance in 2 direction of Gabor transform are independent predictors of overall survival prognosis, independent of resection margin.
    Conclusions: Resection margin status (>1 mm vs ≤1 mm) is a key prognostic factor in pancreatic adenocarcinoma and CT radiomic analysis have the potential to predict resection margin status preoperatively, and the radiomic labels may improve selection neoadjucant therapy.
    Radiomics Signatures of Computed Tomography Imaging for Predicting Resection Margin Status in Pancreatic Head Adenocarcinoma
    Jinheng Liu et al.
    BMC Surgery (in press)
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