scholarly journals Dynamic perfusion CT – A promising tool to diagnose pancreatic ductal adenocarcinoma

Open Medicine ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. 284-292
Author(s):  
Inga Zaborienė ◽  
Giedrius Barauskas ◽  
Antanas Gulbinas ◽  
Povilas Ignatavičius ◽  
Saulius Lukoševičius ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Diagnostic Criteria ◽  
Ct Perfusion ◽  
Characteristic Curve ◽  
Perfusion Ct ◽  
Ductal Adenocarcinoma ◽  
Control Group ◽  
Predictive Values ◽  
Promising Tool

Abstract Background and objective This study deals with an important issue of setting the role and value of the dynamic computed tomography (CT) perfusion analysis in diagnosing pancreatic ductal adenocarcinoma (PDAC). The study aimed to assess the efficacy of perfusion CT in identifying PDAC, even isodense or hardly depicted in conventional multidetector computed tomography. Methods A total of 56 patients with PDAC and 56 control group patients were evaluated in this study. A local perfusion assessment, involving the main perfusion parameters, was evaluated for all the patients. Sensitivity, specificity, positive, and negative predictive values for each perfusion CT parameter were defined using cutoff values calculated using receiver operating characteristic curve analysis. We accomplished logistic regression to identify the probability of PDAC. Results Blood flow (BF) and blood volume (BV) values were significant independent diagnostic criteria for the presence of PDAC. If both values exceed the determined cutoff point, the estimated probability for the presence of PDAC was 97.69%. Conclusions Basic CT perfusion parameters are valuable in providing the radiological diagnosis of PDAC. The estimated BF and BV parameters may serve as independent diagnostic criteria predicting the probability of PDAC.

scholarly journals Dynamic Perfusion CT – A Promising Tool to Diagnose Pancreatic Ductal Adenocarcinoma

HPB ◽  
2021 ◽  
Vol 23 ◽  
pp. S883-S884
Author(s):  
I. Zaborienė ◽  
K. Žvinienė ◽  
S. Lukoševičius ◽  
P. Ignatavičius ◽  
A. Gulbinas ◽  
...  
Keyword(s):  
Pancreatic Ductal Adenocarcinoma ◽  
Perfusion Ct ◽  
Ductal Adenocarcinoma ◽  
Promising Tool

scholarly journals Diagnostic accuracy of perfusion-CT using Peak Enhancement Intensity (PEI) in detecting high grade pancreatic ductal adenocarcinoma keeping histopathology as gold standard.

2019 ◽  
Vol 26 (11) ◽  
pp. 1829-1834
Author(s):  
Abdul Raouf ◽  
Adeela Abid Bukhari ◽  
Natasha Arshad ◽  
Muhammad Ahsan
Keyword(s):  
Diagnostic Accuracy ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Predictive Value ◽  
Gold Standard ◽  
Ct Perfusion ◽  
Perfusion Ct ◽  
Ductal Adenocarcinoma ◽  
Correct Identification ◽  
High Grade ◽  
Ct Guided

Pancreatic ductal carcinoma is the most common primary malignancy of the pancreas and is associated with a very poor prognosis, being worldwide one of the leading cause of cancer related death. The pre-operative correct identification of this group of patients is very important to minimize unnecessary resections but remains difficult owing to the post-operative assessment of some factors such as tumor resection margins and grading. Perfusion CT (P-CT) is a new imaging technique able to provide qualitative and quantitative information on perfusion parameters of tissues, which have been demonstrated to be correlated with histological markers of angiogenesis. Objectives: To estimate the diagnostic accuracy of CT perfusion using PEI in detecting high grade pancreatic ductal adenocarcinoma keeping histopathology as gold standard. Study Design: Cross sectional study. Setting: Radiology department of Allied Hospital Faisalabad. Period: 6 months after approval from June, 2016 to Nov, 2016. Material and Methods: Permission for research was sought from hospital ethical committee. Patients were collected from OPD & indoor of Radiology and surgical department of Allied Hospital Faisalabad. Confounding variables were controlled by restriction (by excluding the subjects with history of metastatic disease or chemotherapy). CT-Perfusion examination was performed with the patient in supine position on a 128 slice Optima Multi detector CT scanner. Image guided (CT guided) biopsy was done on all patients and specimen was sent to the hospital pathology lab and histopathology was done by senior pathologist, who kept blinded to perfusion-CT analysis. Results: In this study, out of 100 cases, the diagnostic accuracy of CT perfusion using PEI in detecting high grade pancreatic ductal adenocarcinoma keeping histopathology as gold standard was recorded as 90.59%, 91.49%, 92.31%, 89.58% and 91% for sensitivity, specificity, positive predictive value, negative predictive value and accuracy rate. Conclusion: We concluded that diagnostic accuracy of CT perfusion using PEI is higher in detection of high grade pancreatic ductal adenocarcinoma keeping histopathology as gold standard. 

scholarly journals XGBoost Classifier Based on Computed Tomography Radiomics for Prediction of Tumor-Infiltrating CD8+ T-Cells in Patients With Pancreatic Ductal Adenocarcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Jing Li ◽  
Zhang Shi ◽  
Fang Liu ◽  
Xu Fang ◽  
Kai Cao ◽  
...  
Keyword(s):  
Computed Tomography ◽  
T Cells ◽  
T Cell ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Ductal Adenocarcinoma ◽  
Validation Dataset ◽  
Training Set ◽  
Predictive Values ◽  
Extreme Gradient Boosting ◽  
Validation Set

ObjectivesThis study constructed and validated a machine learning model to predict CD8+ tumor-infiltrating lymphocyte expression levels in patients with pancreatic ductal adenocarcinoma (PDAC) using computed tomography (CT) radiomic features.Materials and MethodsIn this retrospective study, 184 PDAC patients were randomly assigned to a training dataset (n =137) and validation dataset (n =47). All patients were divided into CD8+ T-high and -low groups using X-tile plots. A total of 1409 radiomics features were extracted from the segmentation of regions of interest, based on preoperative CT images of each patient. The LASSO algorithm was applied to reduce the dimensionality of the data and select features. The extreme gradient boosting classifier (XGBoost) was developed using a training set consisting of 137 consecutive patients admitted between January 2017 and December 2017. The model was validated in 47 consecutive patients admitted between January 2018 and April 2018. The performance of the XGBoost classifier was determined by its discriminative ability, calibration, and clinical usefulness.ResultsThe cut-off value of the CD8+ T-cell level was 18.69%, as determined by the X-tile program. A Kaplan−Meier analysis indicated a correlation between higher CD8+ T-cell levels and better overall survival (p = 0.001). The XGBoost classifier showed good discrimination in the training set (area under curve [AUC], 0.75; 95% confidence interval [CI]: 0.67–0.83) and validation set (AUC, 0.67; 95% CI: 0.51–0.83). Moreover, it showed a good calibration. The sensitivity, specificity, accuracy, positive and negative predictive values were 80.65%, 60.00%, 0.69, 0.63, and 0.79, respectively, for the training set, and 80.95%, 57.69%, 0.68, 0.61, and 0.79, respectively, for the validation set.ConclusionsWe developed a CT-based XGBoost classifier to extrapolate the infiltration levels of CD8+ T-cells in patients with PDAC. This method could be useful in identifying potential patients who can benefit from immunotherapies.

scholarly journals Fully Automatic Deep Learning Framework for Pancreatic Ductal Adenocarcinoma Detection on Computed Tomography

2021 ◽  
Author(s):  
Natália Alves ◽  
Megan Schuurmans ◽  
Geke Litjens ◽  
Joeran S. Bosma ◽  
John Hermans ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Deep Learning ◽  
Pancreatic Ductal Adenocarcinoma ◽  
State Of The Art ◽  
Characteristic Curve ◽  
Lesion Detection ◽  
Ductal Adenocarcinoma ◽  
Test Set ◽  
Pancreas Tumor ◽  
The Impact

Early detection improves prognosis in pancreatic ductal adenocarcinoma (PDAC) but is challenging as lesions are often small and poorly defined on contrast-enhanced computed tomography scans (CE-CT). Deep learning can facilitate PDAC diagnosis, however current models still fail to identify small (<2cm) lesions. In this study, state-of-the-art deep learning models were used to develop an automatic framework for PDAC detection, focusing on small lesions. Additionally, the impact of integrating surrounding anatomy was investigated. CE-CT scans from a cohort of 119 pathology-proven PDAC patients and a cohort of 123 patients without PDAC were used to train a nnUnet for automatic lesion detection and segmentation (nnUnet_T). Two additional nnUnets were trained to investigate the impact of anatomy integration: (1) segmenting the pancreas and tumor (nnUnet_TP), (2) segmenting the pancreas, tumor, and multiple surrounding anatomical structures (nnUnet_MS). An external, publicly available test set was used to compare the performance of the three networks. The nnUnet_MS achieved the best performance, with an area under the receiver operating characteristic curve of 0.91 for the whole test set and 0.88 for tumors <2cm, showing that state-of-the-art deep learning can detect small PDAC and benefits from anatomy information.

scholarly journals Fully Automatic Deep Learning Framework for Pancreatic Ductal Adenocarcinoma Detection on Computed Tomography

Cancers ◽  
2022 ◽  
Vol 14 (2) ◽  
pp. 376
Author(s):  
Natália Alves ◽  
Megan Schuurmans ◽  
Geke Litjens ◽  
Joeran S. Bosma ◽  
John Hermans ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Deep Learning ◽  
Pancreatic Ductal Adenocarcinoma ◽  
State Of The Art ◽  
Characteristic Curve ◽  
Lesion Detection ◽  
Ductal Adenocarcinoma ◽  
Test Set ◽  
Pancreas Tumor ◽  
The Impact

Early detection improves prognosis in pancreatic ductal adenocarcinoma (PDAC), but is challenging as lesions are often small and poorly defined on contrast-enhanced computed tomography scans (CE-CT). Deep learning can facilitate PDAC diagnosis; however, current models still fail to identify small (<2 cm) lesions. In this study, state-of-the-art deep learning models were used to develop an automatic framework for PDAC detection, focusing on small lesions. Additionally, the impact of integrating the surrounding anatomy was investigated. CE-CT scans from a cohort of 119 pathology-proven PDAC patients and a cohort of 123 patients without PDAC were used to train a nnUnet for automatic lesion detection and segmentation (nnUnet_T). Two additional nnUnets were trained to investigate the impact of anatomy integration: (1) segmenting the pancreas and tumor (nnUnet_TP), and (2) segmenting the pancreas, tumor, and multiple surrounding anatomical structures (nnUnet_MS). An external, publicly available test set was used to compare the performance of the three networks. The nnUnet_MS achieved the best performance, with an area under the receiver operating characteristic curve of 0.91 for the whole test set and 0.88 for tumors <2 cm, showing that state-of-the-art deep learning can detect small PDAC and benefits from anatomy information.

scholarly journals Age-specific cut-off levels of anti-Müllerian hormone can be used as diagnostic markers for polycystic ovary syndrome

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Fahimeh Ramezani Tehrani ◽  
Maryam Rahmati ◽  
Fatemeh Mahboobifard ◽  
Faezeh Firouzi ◽  
Nazanin Hashemi ◽  
...  
Keyword(s):  
Bayesian Method ◽  
Polycystic Ovary ◽  
Characteristic Curve ◽  
Generalized Additive Models ◽  
Additive Models ◽  
Control Group ◽  
Age Group ◽  
Predictive Values ◽  
Pcos Group

Abstract Background The majority of available studies on the AMH thresholds were not age-specific and performed the receiver operating characteristic curve (ROC) analysis, based on variations in sensitivity and specificity rather than positive and negative predictive values (PPV and NPV, respectively), which are more clinically applicable. Moreover, all of these studies used a pre-specified age categorization to report the age-specific cut-off values of AMH. Methods A total of 803 women, including 303 PCOS patients and 500 eumenorrheic non-hirsute control women, were enrolled in the present study. The PCOS group included PCOS women, aged 20–40 years, who were referred to the Reproductive Endocrinology Research Center, Tehran, Iran. The Rotterdam consensus criteria were used for diagnosis of PCOS. The control group was selected among women, aged 20–40 years, who participated in Tehran Lipid and Glucose cohort Study (TLGS). Generalized additive models (GAMs) were used to identify the optimal cut-off points for various age categories. The cut-off levels of AMH in different age categories were estimated, using the Bayesian method. Main results and the role of chance Two optimal cut-off levels of AMH (ng/ml) were identified at the age of 27 and 35 years, based on GAMs. The cut-off levels for the prediction of PCOS in the age categories of 20–27, 27–35, and 35–40 years were 5.7 (95 % CI: 5.48–6.19), 4.55 (95 % CI: 4.52–4.64), and 3.72 (95 % CI: 3.55–3.80), respectively. Based on the Bayesian method, the PPV and NPV of these cut-off levels were as follows: PPV = 0.98 (95 % CI: 0.96–0.99) and NPV = 0.40 (95 % CI: 0.30–0.51) for the age group of 20–27 years; PPV = 0.96 (95 % CI: 0.91–0.99) and NPV = 0.82 (95 % CI: 0.78–0.86) for the age group of 27–35 years; and PPV = 0.86 (95 % CI: 0.80–0.94) and NPV = 0.96 (95 % CI: 0.93–0.98) for the age group of 35–40 years. Conclusions Application of age-specific cut-off levels of AMH, according to the GAMs and Bayesian method, could elegantly assess the value of AMH in discriminating PCOS patients in all age categories.

scholarly journals Clinical Impact of 18F-FDG PET/CT in the Diagnostic Workup of Pancreatic Ductal Adenocarcinoma: A Systematic Review

Diagnostics ◽  
2020 ◽  
Vol 10 (12) ◽  
pp. 1042
Author(s):  
Annachiara Arnone ◽  
Riccardo Laudicella ◽  
Federico Caobelli ◽  
Priscilla Guglielmo ◽  
Marianna Spallino ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Positron Emission Tomography ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Fdg Pet ◽  
Emission Tomography ◽  
Ductal Adenocarcinoma ◽  
Positron Emission ◽  
Pet Ct ◽  
18F Fdg ◽  
Fdg Pet Ct

In this review, the performance of fluorodeoxyglucose (FDG)-positron emission tomography (PET)/computed tomography (CT) in the diagnostic workup of pancreatic ductal adenocarcinoma (PDAC) is evaluated. A comprehensive literature search up to September 2020 was performed, selecting studies with the presence of: sample size ≥10 patients and index test (i.e., “FDG” or “18F-FDG” AND “pancreatic adenocarcinoma” or “pancreas cancer” AND “PET” or “positron emission tomography”). The methodological quality was evaluated using the revised quality assessment of diagnostic accuracy studies (QUADAS-2) tool and presented according to the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. Basic data (authors, year of publication, country and study design), patients’ characteristics (number of enrolled subjects and age), disease phase, type of treatment and grading were retrieved. Forty-six articles met the adopted research criteria. The articles were divided according to the considered clinical context. Namely, besides conventional anatomical imaging, such as computed tomography (CT) and magnetic resonance imaging (MRI), molecular imaging with FDG PET/CT is an important tool in PDAC, for all disease stages. Further prospective studies will be necessary to confirm the cost-effectiveness of such imaging techniques by testing its real potential improvement in the clinical management of PDAC.

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