Imaging Techniques and Biochemical Biomarkers: New Insights into Diagnosis of Pancreatic Cancer

Pancreatic cancer (PaC) incidence is increasing, but our current screening and diagnostic strategies are not very effective. However, screening could be helpful in the case of PaC, as recent evidence shows that the disease progresses gradually. Unfortunately, there is no ideal screening method or program for detecting PaC in its early stages. Conventional imaging techniques, such as abdominal ultrasound, CT, MRI, and EUS, have not been successful in detecting early-stage PaC. On the other hand, biomarkers may be a more effective screening tool for PaC and have greater potential for further evaluation compared to imaging. Recent studies on biomarkers and artificial intelligence (AI)-enhanced imaging have shown promising results in the early diagnosis of PaC. In addition to proteins, non-coding RNAs are also being studied as potential biomarkers for PaC. This review consolidates the current literature on PaC screening modalities to provide an organized framework for future studies. While conventional imaging techniques have not been effective in detecting early-stage PaC, biomarkers and AI-enhanced imaging are promising avenues of research. Further studies on the use of biomarkers, particularly non-coding RNAs, in combination with imaging modalities may improve the accuracy of PaC screening and lead to earlier detection of this deadly disease.

Quantitative Edge Analysis Can Differentiate Pancreatic Carcinoma from Normal Pancreatic Parenchyma

This study aimed to introduce specific image feature analysis, focusing on pancreatic margins, and to provide a quantitative measure of edge irregularity, evidencing correlations with the presence/absence of pancreatic adenocarcinoma. We selected 50 patients (36 men, 14 women; mean age 63.7 years) who underwent Multi-detector computed tomography (MDCT) for the staging of pancreatic adenocarcinoma of the tail of the pancreas. Computer-assisted quantitative edge analysis was performed on the border fragments in MDCT images of neoplastic and healthy glandular parenchyma, from which we obtained the root mean square deviation SD of the actual border from the average boundary line. The SD values relative to healthy and neoplastic borders were compared using a paired t-test. A significant SD difference was observed between healthy and neoplastic borders. A threshold SD value was also found, enabling the differentiation of adenocarcinoma with 96% specificity and sensitivity. We introduced a quantitative measure of boundary irregularity, which correlates with the presence/absence of pancreatic adenocarcinoma. Quantitative edge analysis can be promptly performed on select border fragments in MDCT images, providing a useful supporting tool for diagnostics and a possible starting point for machine learning recognition based on lower-dimensional feature space.

Artificial intelligence to predict T4 stage of pancreatic ductal adenocarcinoma using CT imaging

BACKGROUND

The accurate assessment of T4 stage of pancreatic ductal adenocarcinoma (PDAC) has consistently presented a considerable difficulty for radiologists. This study aimed to develop and validate an automated artificial intelligence (AI) pipeline for the prediction of T4 stage of PDAC using contrast-enhanced CT imaging.

METHODS

The data were obtained retrospectively from consecutive patients with surgically resected and pathologically proved PDAC at two institutions between July 2017 and June 2022. Initially, a deep learning (DL) model was developed to segment PDAC. Subsequently, radiomics features were extracted from the automatically segmented region of interest (ROI), which encompassed both the tumor region and a 3 mm surrounding area, to construct a predictive model for determining T4 stage of PDAC. The assessment of the models' performance involved the calculation of the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity.

RESULTS

The study encompassed a cohort of 509 PDAC patients, with a median age of 62 years (interquartile range: 55-67). The proportion of patients in T4 stage within the model was 16.9%. The model achieved an AUC of 0.849 (95% CI: 0.753-0.940), a sensitivity of 0.875, and a specificity of 0.728 in predicting T4 stage of PDAC. The performance of the model was determined to be comparable to that of two experienced abdominal radiologists (AUCs: 0.849 vs. 0.834 and 0.857).

CONCLUSION

The automated AI pipeline utilizing tumor and peritumor-related radiomics features demonstrated comparable performance to that of senior abdominal radiologists in predicting T4 stage of PDAC.

18F-FAPI-04 Outperforms 18F-FDG PET/CT in Clinical Assessments of Patients with Pancreatic Adenocarcinoma

Accurate diagnosis and staging are crucial for selecting treatment for patients with pancreatic ductal adenocarcinoma (PDAC). The desmoplastic responses associated with PDAC are often characterized by hypometabolism. Here, we investigated 18F-fibroblast activation protein inhibitor (FAPI)-04 PET/CT in evaluation of PDAC and compared the findings with those obtained using 18F-FDG. Methods: Sixty-two PDAC patients underwent 18F-FAPI-04 PET/CT and 18F-FDG PET/CT. Identification of primary lesions, lymph node (LN) metastasis, and distant metastasis (DM) by these methods was evaluated, and TNM staging was performed. Correlation between SUVmax of the primary lesion and treatment response was explored in patients who received systemic therapy. Results: 18F-FAPI-04 PET/CT identified all patients with PDAC; 18F-FDG PET/CT missed 1 patient. Tracer uptake was higher in 18F-FAPI-04 PET/CT than in 18F-FDG PET/CT in primary tumors (10.63 vs. 2.87, P < 0.0001), LN metastasis (2.90 vs. 1.43, P < 0.0001), and DM (liver, 6.11 vs. 3.10, P = 0.002; peritoneal, 4.70 vs. 2.08, P = 0.015). The methods showed no significant difference in the T staging category, but the N and M values were significantly higher for 18F-FAPI-04 PET/CT than for 18F-FDG PET/CT (P = 0.002 and 0.008, respectively). Thus, 14 patients were upgraded, and only 1 patient was downgraded, by 18F-FAPI-04 PET/CT compared with 18F-FDG PET/CT. A high SUVmax of the primary tumor did not correlate with treatment response for either 18F-FAPI-04 or 18F-FDG. Conclusion: 18F-FAPI-04 PET/CT performed better than 18F-FDG PET/CT in identification of primary tumors, LN metastasis, and DM and in TNM staging of PDAC.

Pancreatic Cancer and Detection Methods

The pancreas is a vital organ with exocrine and endocrine functions. Pancreatitis is an inflammation of the pancreas caused by alcohol consumption and gallstones. This condition can heighten the risk of pancreatic cancer (PC), a challenging disease with a high mortality rate. Genetic and epigenetic factors contribute significantly to PC development, along with other risk factors. Early detection is crucial for improving PC outcomes. Diagnostic methods, including imagining modalities and tissue biopsy, aid in the detection and analysis of PC. In contrast, liquid biopsy (LB) shows promise in early tumor detection by assessing biomarkers in bodily fluids. Understanding the function of the pancreas, associated diseases, risk factors, and available diagnostic methods is essential for effective management and early PC detection. The current clinical examination of PC is challenging due to its asymptomatic early stages and limitations of highly precise diagnostics. Screening is recommended for high-risk populations and individuals with potential benign tumors. Among various PC screening methods, the N-NOSE plus pancreas test stands out with its high AUC of 0.865. Compared to other commercial products, the N-NOSE plus pancreas test offers a cost-effective solution for early detection. However, additional diagnostic tests are required for confirmation. Further research, validation, and the development of non-invasive screening methods and standardized scoring systems are crucial to enhance PC detection and improve patient outcomes. This review outlines the context of pancreatic cancer and the challenges for early detection.

A nomogram diagnostic prediction model of pancreatic metastases of small cell lung carcinoma based on clinical characteristics, radiological features and biomarkers

Objective

To investigate clinical characteristics, radiological features and biomarkers of pancreatic metastases of small cell lung carcinoma (PM-SCLC), and establish a convenient nomogram diagnostic predictive model to differentiate PM-SCLC from pancreatic ductal adenocarcinomas (PDAC) preoperatively.

Methods

A total of 299 patients with meeting the criteria (PM-SCLC n=93; PDAC n=206) from January 2016 to March 2022 were retrospectively analyzed, including 249 patients from hospital 1 (training/internal validation cohort) and 50 patients from hospital 2 (external validation cohort). We searched for meaningful clinical characteristics, radiological features and biomarkers and determined the predictors through multivariable logistic regression analysis. Three models: clinical model, CT imaging model, and combined model, were developed for the diagnosis and prediction of PM-SCLC. Nomogram was constructed based on independent predictors. The receiver operating curve was undertaken to estimate the discrimination.

Results

Six independent predictors for PM-SCLC diagnosis in multivariate logistic regression analysis, including clinical symptoms, CA199, tumor size, parenchymal atrophy, vascular involvement and enhancement type. The nomogram diagnostic predictive model based on these six independent predictors showed the best performance, achieved the AUCs of the training cohort (n = 174), internal validation cohort (n = 75) and external validation cohort (n = 50) were 0.950 (95%CI, 0.917-0.976), 0.928 (95%CI, 0.873-0.971) and 0.976 (95%CI, 0.944-1.00) respectively. The model achieved 94.50% sensitivity, 83.20% specificity, 86.80% accuracy in the training cohort and 100.00% sensitivity, 80.40% specificity, 86.70% accuracy in the internal validation cohort and 100.00% sensitivity, 88.90% specificity, 87.50% accuracy in the external validation cohort.

Conclusion

We proposed a noninvasive and convenient nomogram diagnostic predictive model based on clinical characteristics, radiological features and biomarkers to preoperatively differentiate PM-SCLC from PDAC.

Exploring the potential of exosomes in diagnosis and drug delivery for pancreatic ductal adenocarcinoma

Pancreatic cancer (PC) is a cancer of the digestive system, and pancreatic ductal adenocarcinoma (PDAC) accounts for approximately 90% of all PC cases. Exosomes derived from PDAC (PDAC-exosomes) promote PDAC development and metastasis. Exosomes are nanoscale vesicles secreted by most cells, which can carry biologically active molecules and mediate communication and cargo transportation among cells. Recent studies have focused on transforming exosomes into good drug delivery systems (DDSs) to improve the clinical treatment of PDAC. This review considers PDAC as the main research object to introduce the role of PDAC-exosomes in PDAC development and metastasis. This review focuses on the following two themes: (a) the great potential of PDAC-exosomes as new diagnostic markers for PDAC, and (b) the transformation of exosomes into potential DDSs.

Imaging features of intraductal tubulopapillary neoplasm of the pancreas and its differentiation from conventional pancreatic ductal adenocarcinoma

Intraductal tubulopapillary neoplasms (ITPN) are rare pancreatic tumors (< 1% of exocrine neoplasms) and are considered to have better prognosis than classical pancreatic ductal adenocarcinoma (PDAC). The present study aimed to evaluate imaging features of ITPN in computed tomography (CT) and magnetic resonance (MR) imaging. We performed monocentric retrospective analysis of 14 patients with histopathologically verified ITPN, operated in 2003–2018. Images were available for 12 patients and were analysed independently by two radiologists, blinded to reports. Imaging features were compared to a matched control group consisting of 43 patients with PDAC, matched for sex and age. Histopathologic analysis showed invasive carcinoma component in all ITPN patients. CT-attenuation values of ITPN were higher in arterial and venous phases (62.3 ± 14.6 HU and 68 ± 15.6 HU) than in unenhanced phase (39.2 ± 7.9 HU), compatible with solid lesion enhancement. Compared to PDAC, ITPN lesions had significantly higher HU-values in both arterial and venous phases (arterial and venous phases, p < 0.001). ITPN were significantly larger than PDAC (4.1 ± 2.0 cm versus 2.6 ± 0.84 cm, p = 0.021). ITPN lesions were more often well-circumscribed (p < 0.002). Employing a multiple logistic regression analysis with forward stepwise method, higher HU density in the arterial phase (p = 0.012) and well-circumscribed lesion margins (p = 0.047) were found to be significant predictors of ITPN versus PDAC. Our study identified key imaging features for differentiation of ITPN and PDAC. Isodensity or moderate hypodensity and well-circumscribed margins favor the diagnosis of ITPN over PDAC. Being familiar with CT-features of these rare pancreatic tumors is essential for radiologists to accelerate the diagnosis and narrow the differentials.

Impact of iterative reconstruction algorithms on image quality and radiation dose in computed tomography scan of patients with malignant pancreatic lesions

Background:

The objective of this study was to investigate the influence of iterative reconstruction (IR) algorithm on radiation dose and image quality of computed tomography (CT) scans of patients with malignant pancreatic lesions by designing a new protocol.

Methods:

The pancreas CT was performed on 40 patients (23 males and 17 females) with a 160-slice CT scan machine. The pancreatic parenchymal phase was performed in two stages: one with a usual dose of radiation and the other one after using a reduced dose of radiation. The images obtained with usual dose were reconstructed with Filtered Back Projection (FBP) method (Protocol A); and the images obtained with the reduced dose were reconstructed with both FBP (Protocol B) and IR method (Protocol C). The quality of images and radiation dose were compared among the three protocols.

Results:

Image noise was significantly lower with Protocol C (10.80) than with Protocol A (14.98) and Protocol B (20.60) (P < 0.001). Signal-to-noise ratio and contrast-to-noise ratio were significantly higher with Protocol C than with Protocol A and Protocol B (P < 0.001). Protocol A and Protocol C were not significantly different in terms of image quality scores. Effective dose was reduced by approximately 48% in Protocol C compared with Protocol A (1.20 ± 0.53 mSv vs. 2.33 ± 0.86 mSv, P < 0.001).

Conclusion:

Results of this study showed that applying the IR method compared to the FBP method can improve objective image quality, maintain subjective image quality, and reduce the radiation dose of the patients undergo pancreas CT.

Role of multidetector computed tomography in evaluation of resectability of pancreatic cancer

Background

Pancreatic cancer is one of the most significant causes and one of the most lethal malignant neoplasms in the world of cancer death in the developed nations. It was named the “silent killer” for its quiet course, late clinical presentation, and the trend of rapid growth. The aim of this study is to detect the reliability of multidetector CT (MDCT) as diagnostic tool in assessing the possibility of eradicating pancreatic cancer.

Results

Twenty-four patients (57%) were not suitable for surgery with non-resectable mass; the remaining eighteen patients (43%) were considered suitable according to MDCT criteria for surgical resection of the tumor. Fourteen out of the sixteen patients (87.5%) had a successful removal of the lump, while the remaining two cases (12.5%) during surgery, the mass was unresectable. The results of the pathology specimens showed that fourteen out of the fourteen patients (100%) had successful operation with no cancer cells in the margin, and a positive predictive value of 87.5% and accuracy of 89.47%.

Conclusions

The advancement of MDCT expertise improves the outcome of pancreatic cancer resectability.

Atypical and uncommon CT and MR imaging presentations of pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinomas (PDACs) occasionally have atypical and uncommon imaging presentations that can present a diagnostic dilemma and result in false interpretation. This article aimed to illustrate these CT and MR imaging findings, including isoattenuating PDAC, coexisting acute pancreatitis, PDAC with a cystic feature, groove PDAC, diffuse PDAC, hypointensity on diffusion-weighted imaging (DWI), multifocal PDAC, intratumoral calcification, and extrapancreatic invasion with a barely discernable mass. A subset of PDACs with atypical features are occasionally encountered during routine clinical practice. Knowledge of and attention to these atypical and uncommon variable imaging features may allow radiologists to avoid misinterpretation and a delayed diagnosis.

Aortoesophageal fistula involving the central aortic arch salvaged with emergent percutaneous TEVAR, great vessel coverage and in vivo graft fenestration

Immediate intervention is needed for aortoesophageal fistulas (AEF), a rare but highly lethal cause of massive gastrointestinal hemorrhage. Emergent thoracic endovascular aortic repair (TEVAR) is considered first-line treatment for massive bleeding from AEFs. We describe an unusual and challenging case of TEVAR coverage of an AEF involving the central aortic arch immediately followed by in vivo endograft fenestration to regain arch vessel perfusion. In vivo fenestration, currently a procedure for emergency or investigational purposes only, was shown to be life saving in our case. The main complications associated with the procedure included stroke and infection, requiring esophagectomy and cervical diversion as well as ongoing antibiotic treatment.

Computer-aided differentiates benign from malignant IPMN and MCN with a novel feature selection algorithm

In clinical practice, differentiating benign from malignant intraductal papillary mucinous neoplasm (IPMN) and mucinous cystic neoplasm (MCN) preoperatively is crucial for deciding future treating algorithm. However, it remains challenging as benign and malignant lesions usually show similarities in both imaging appearances and clinical indices. Therefore, a robust and accurate computer-aided diagnosis (CAD) system based on radiomics and clinical indices was proposed in this paper to solve this dilemma. In the proposed CAD system, 107 patients were enrolled, where 90 cases were randomly selected for the training set with 5-fold cross validation to build the diagnostic model, while 17 cases were remained for an independent testing set to validate the performance. 436 high-throughput radiomics features while 9 clinical indices were designed and extracted. A novel feature selection algorithm named BLR (Bootstrapping repeated LASSO with Random selections) was proposed to select the most effective features. Then the selected features were sent to Support Vector Machine (SVM) to differentiate the benign or malignant. In the cross-validation cohort and independent testing cohort, the area under receiver operating characteristic curve (AUC) of CAD scheme were 0.83 and 0.92, respectively. The results fully prove the proposed CAD system achieves significant effect in tumors diagnosis.

Pancreatic Ductal Adenocarcinoma at CT: A Combined Nomogram Model to Preoperatively Predict Cancer Stage and Survival Outcome

Objectives

To construct a nomogram model that combines clinical characteristics and radiomics signatures to preoperatively discriminate pancreatic ductal adenocarcinoma (PDAC) in stage I-II and III-IV and predict overall survival.

Methods

A total of 135 patients with histopathologically confirmed PDAC who underwent contrast-enhanced CT were included. A total of 384 radiomics features were extracted from arterial phase (AP) or portal venous phase (PVP) images. Four steps were used for feature selection, and multivariable logistic regression analysis were used to build radiomics signatures and combined nomogram model. Performance of the proposed model was assessed by using receiver operating characteristic (ROC) curves, calibration curves and decision curve analysis (DCA). Kaplan-Meier analysis was applied to analyze overall survival in the stage I-II and III-IV PDAC groups.

Results

The AP+PVP radiomics signature showed the best performance among the three radiomics signatures [training cohort: area under the curve (AUC) = 0.919; validation cohort: AUC = 0.831]. The combined nomogram model integrating AP+PVP radiomics signature with clinical characteristics (tumor location, carcinoembryonic antigen level, and tumor maximum diameter) demonstrated the best discrimination performance (training cohort: AUC = 0.940; validation cohort: AUC = 0.912). Calibration curves and DCA verified the clinical usefulness of the combined nomogram model. Kaplan-Meier analysis showed that overall survival of patients in the predicted stage I-II PDAC group was longer than patients in stage III-IV PDAC group (p<0.0001).

Conclusions

We propose a combined model with excellent performance for the preoperative, individualized, noninvasive discrimination of stage I-II and III-IV PDAC and prediction of overall survival.

European Cancer Organisation Essential Requirements for Quality Cancer Care (ERQCC): Pancreatic Cancer

European Cancer Organisation Essential Requirements for Quality Cancer Care (ERQCC) are written by experts representing all disciplines involved in cancer care in Europe. They give patients, health professionals, managers and policymakers a guide to essential care throughout the patient journey. Pancreatic cancer is an increasing cause of cancer mortality and has wide variation in treatment and care in Europe. It is a major healthcare burden and has complex diagnosis and treatment challenges. Care must be carried out only in pancreatic cancer units or centres that have a core multidisciplinary team (MDT) and an extended team of health professionals detailed here. Such units are far from universal in European countries. To meet European aspirations for comprehensive cancer control, healthcare organisations must consider the requirements in this paper, paying particular attention to multidisciplinarity and patient-centred pathways from diagnosis, to treatment, to survivorship.

Diagnosis of Pancreatic Ductal Adenocarcinoma by Immuno-Positron Emission Tomography

Diagnosis of pancreatic ductal adenocarcinoma (PDAC) by current imaging techniques is useful and widely used in the clinic but presents several limitations and challenges, especially in small lesions that frequently cause radiological tumors infra-staging, false-positive diagnosis of metastatic tumor recurrence, and common occult micro-metastatic disease. The revolution in cancer multi-"omics" and bioinformatics has uncovered clinically relevant alterations in PDAC that still need to be integrated into patients' clinical management, urging the development of non-invasive imaging techniques against principal biomarkers to assess and incorporate this information into the clinical practice. "Immuno-PET" merges the high target selectivity and specificity of antibodies and engineered fragments toward a given tumor cell surface marker with the high spatial resolution, sensitivity, and quantitative capabilities of positron emission tomography (PET) imaging techniques. In this review, we detail and provide examples of the clinical limitations of current imaging techniques for diagnosing PDAC. Furthermore, we define the different components of immuno-PET and summarize the existing applications of this technique in PDAC. The development of novel immuno-PET methods will make it possible to conduct the non-invasive diagnosis and monitoring of patients over time using in vivo, integrated, quantifiable, 3D, whole body immunohistochemistry working like a "virtual biopsy".

Preoperative CT-based detection of extrapancreatic perineural invasion in pancreatic cancer

Accuracy for computed tomography (CT) diagnosis of extrapancreatic perineural invasion (EPNI) in pancreatic ductal adenocarcinoma (PDAC), which is a significant cause of recurrence, has not been established. The aim of the study was to evaluate the diagnostic accuracy of CT in detecting EPNI preoperatively in resectable PDAC of the pancreatic head. Retrospective study design was approved by institutional review board. Preoperative CT-series of 46 patients with resectable PDAC were evaluated by two independent observers. Plexus Pancreaticus Capitalis-II (PPC-II) was assessed as this area is more susceptible for EPNI. All patients underwent surgery with dedicated histopathology, which served as the reference standard. Histologically EPNI was confirmed in 63.1%. Sensitivity of MDCT was 93.1% (95% confidence interval (CI) 77.23% to 99.15%), specificity 64.7% (95% CI 38.33% to 85.79%) with area under the curve (AUC) 0.789 for the first observer. Positive predictive value (PPV) was 81.82% (95% CI 70.12% to 89.62%), negative predictive value (NPV-84.62% (95% CI 57.98% to 95.64%) with diagnostic accuracy of 82.61% (95% CI 68.58% to 92.18%). Interobserver agreement showed k-value of 0.893 ([Formula: see text]), which represents very good agreement between observers. Median actual survival in patients without EPNI was 30 months (95% CI 18.284-41.716), in patients with EPNI-13 months (95% CI 12.115-13.885). CT provides sufficient diagnostic information to detect PPC-II invasion in patients with resectable PDAC of the pancreatic head. Preoperative detection of EPNI might be an additional argument to perform neoadjuvant chemotherapy in patients with resectable PDAC. It should be included in preoperative evaluation form of CT-findings.

CT-guided 125I brachytherapy combined with chemotherapy for the treatment of unresectable or locally advanced pancreatic carcinoma

PURPOSE

We aimed to explore the feasibility and clinical effectiveness of percutaneous CT-guided iodine-125 (¹²⁵I) brachytherapy combined with chemotherapy for the treatment of patients with unresectable or locally advanced pancreatic carcinoma (PC).

METHODS

We retrospectively reviewed 66 patients with Stage III and IV PC who had received chemotherapy. A total of 35 (53%) patients receiving 125I brachytherapy and chemotherapy (gemcitabine + cisplatin, GP) were classified as Group A, and 31 (47%) patients who received GP chemotherapy alone were categorized as Group B. The evaluated indications were local control rate (LCR), local progression-free survival (LPFS), overall survival (OS), treatment-related complications, and the degree of symptom relief. Kaplan-Meier curves, log-rank test and Cox regression models were generated and used for further analysis to identify predictors of outcomes.

RESULTS

The median follow-up time was 6.00±0.84 months. The 1-, 3-, 6-, 12- and 18-month LCRs for Group A were 100% (35/35), 89.3% (25/28), 71.4% (15/21), 37.5% (3/8) and 33.3% (1/3), respectively; and those for Group B were 87.1% (27/31), 69.6% (16/23), 41.2% (7/17), 14.3% (1/7) and 0% (0/3), respectively. The LCR differed at 1-, 3- and 6-months (P = 0.032; P = 0.009; P = 0.030; respectively). The median LPFS was 7.00±0.30 months and 5.00±0.75 months for Groups A and B (P = 0.023), respectively; however, the median OS of the groups were not significantly different (8.00±0.77 months vs. 6.00±1.04 months. P = 0.917). No life-threatening complications occurred during or after the procedures. Patients in Group A experienced better pain control and relief of abdominal distension than those in Group B.

CONCLUSION

CT-guided 125I brachytherapy is a feasible, safe, and valuable treatment for patients with unresectable PC.

Improved detectability of hypoattenuating focal pancreatic lesions by dual-layer computed tomography using virtual monoenergetic images

Background

Multidetector CT is the mainstay for radiologic evaluation of pancreatic pathology. Still, imaging of focal pancreatic lesions using MDCT is faced by a number of challenges that are related to the limited contrast between the lesion and surrounding parenchyma, such as detecting early-stage pancreatic cancer and subtle features of cystic lesions that point to malignancy. Dual-layer CT is the first dual-energy CT machine based on separation of high- and low-energy photons at the detector level. If improved contrast between the lesions and normal pancreatic parenchyma could be achieved on CT images, we may expect enhanced CT detection of pancreatic lesions. The purpose of this study was to evaluate whether virtual monoenergetic reconstructions generated using contrast-enhanced dual-layer CT could improve detectability of hypoattenuating focal pancreatic lesions compared to conventional polyenergetic reconstructions.

Results

Fifty-four lesions were identified and verified by histopathology or follow-up CT, MRCP, and/or EUS along with clinical data. Across the virtual monoenergetic spectrum, 40 KeV images had the highest contrast-to-noise and signal-to-noise ratios (p < 0.001, p < 0.001) and were significantly higher than conventional images (p < 0.001). Subjective scores for lesion visibility at low kiloelectron volt monoenergetic (40 and 50 KeV) images greatly exceeded conventional images (p < 0.001).

Conclusion

Low kiloelectron volt monoenergetic reconstructions of contrast-enhanced dual-layer CT significantly improve detectability of hypoattenuating focal pancreatic lesions compared to conventional polyenergetic reconstructions.

Advances and challenges of neoadjuvant therapy in pancreatic cancer

Pancreatic cancer has been becoming the second cause of cancer death in the western world, and its disease burden has increased. Neoadjuvant therapy is one of the current research hotspots in the field of pancreatic cancer, aiming to improve the surgical rate and prognosis of pancreatic cancer. Based on the latest evidence, this review discussed neoadjuvant therapy in pancreatic cancer from the following three aspects: patient selection, protocols selection of neoadjuvant therapy, and treatment response evaluation and resectability prediction. A big controversy existed on the indications of neoadjuvant treatment, but it was agreed that any patient who is likely to achieve R0 resection due to neoadjuvant therapy should be the targeted population. A variety of chemotherapy regimens were tried for neoadjuvant therapy in pancreatic cancer, and FOLFIRINOX and Nab-Paclitaxel plus Gemcitabine are two preferred regimens at present. It was challenging to evaluate treatment response and predict resectability after neoadjuvant therapy, although imaging by CT is widely used. Based on new findings of the remarkable performance of several chemotherapy regimens with or without radiotherapy, the neoadjuvant indications of pancreatic cancer have extended in recent years. However, it is still a challenge to assess the neoadjuvant treatment response and determine the timing of surgery.

Imaging of acute abdomen in cancer patients

The pattern of disease causing acute abdominal pain has changed over last few decades, some of this has been attributed to intraabdominal cancers. The most common acute abdominal complaints in cancer patients are related to the gastrointestinal system. Abdominal emergencies in cancer patients can result from the underlying malignancy itself, cancer therapy and/or result from the standard pathologies causing acute abdomen in otherwise healthy population. Therapy-related or disease-related immunosuppression or high dose analgesics often blunt many of the findings which are usually expected in non-cancer general population. This complicates the clinical picture rendering the clinical exam less reliable in many cancer patients, and resulting in different pathologies which clinicians and the radiologists should remain aware of. This article focuses on imaging illustrations with differential diagnosis for various emergency scenarios related to acute abdomen specifically in oncologic settings.

Comparative analysis and assessment of diagnostic accuracy of 256 slice CT and endoscopic ultrasound in evaluation of pancreatic masses

CONTEXT

Pancreatic masses are routinely encountered on imaging and often present as a diagnostic dilemma. These masses range from benign inflammatory masses, requiring no intervention to malignant masses, which carry grave prognosis and hence require aggressive management.

AIMS

Compare the diagnostic accuracy of 256 multislice CT and endoscopic ultrasound (EUS) in characterization and assessment of resectability of pancreatic masses and compare the multidetector computed tomography (MDCT) and EUS findings with histopathological findings.

SETTINGS AND DESIGN

Prospective study.

SUBJECTS AND METHODS

36 patients with pancreatic masses were included who underwent dual phase CT using pancreatic protocol and EUS using 5-13 MHz transducer. Fine needle aspiration cytology (FNAC) was done wherever feasible. Parameters regarding tumor size, location, imaging morphology, and vessel involvement were recorded. Findings were compared with histopathological/operative diagnosis/clinical follow-up.

STATISTICAL ANALYSIS USED

Descriptive statistics with percentages and proportions and Chi-square test.

RESULTS

Multidetector computed tomography (MDCT) and EUS established diagnosis consistent with tissue diagnosis in 30 (83%) and 22 (61%) patients, respectively. However, the best results were obtained with the combined use of MDCT and EUS. The number of patients categorized as inconclusive by MDCT were lower compared to EUS. Assessing resectability for pancreatic adenocarcinoma, MDCT showed specificity and positive predictive value (PPV) of 100% compared to EUS, which had specificity and PPV of 75% and 92.3%, respectively. MDCT is the first-line imaging modality in detection, characterization of pancreatic masses, and assessment of resectability in malignant neoplasms. EUS is beneficial in the detection of masses <2 cm in size causing pancreatic contour deformity on CT, for guiding FNAC. MDCT and EUS with EUS-guided FNA are complementary not competitive tools in preoperative imaging of pancreatic masses.

Magnetic resonance imaging in the assessment of pancreatic cancer with quantitative parameter extraction by means of dynamic contrast-enhanced magnetic resonance imaging, diffusion kurtosis imaging and intravoxel incoherent motion diffusion-weighted imaging

BACKGROUND

Despite great technical advances in imaging, such as multidetector computed tomography and magnetic resonance imaging (MRI), diagnosing pancreatic solid lesions correctly remains challenging, due to overlapping imaging features with benign lesions. We wanted to evaluate functional MRI to differentiate pancreatic tumors, peritumoral inflammatory tissue, and normal pancreatic parenchyma by means of dynamic contrast-enhanced MRI (DCE-MRI)-, diffusion kurtosis imaging (DKI)-, and intravoxel incoherent motion model (IVIM) diffusion-weighted imaging (DWI)-derived parameters.

METHODS

We retrospectively analyzed 24 patients, each with histopathological diagnosis of pancreatic tumor, and 24 patients without pancreatic lesions. Functional MRI was acquired using a 1.5 MR scanner. Peritumoral inflammatory tissue was assessed by drawing regions of interest on the tumor contours. DCE-MRI, IVIM and DKI parameters were extracted. Nonparametric tests and receiver operating characteristic (ROC) curves were calculated.

RESULTS

There were statistically significant differences in median values among the three groups observed by Kruskal-Wallis test for the DKI mean diffusivity (MD), IVIM perfusion fraction (fp) and IVIM tissue pure diffusivity (Dt). MD had the best results to discriminate normal pancreas plus peritumoral inflammatory tissue versus pancreatic tumor, to separate normal pancreatic parenchyma versus pancreatic tumor and to differentiate peritumoral inflammatory tissue versus pancreatic tumor, respectively, with an accuracy of 84%, 78%, 83% and area under ROC curve (AUC) of 0.85, 0.82, 0.89. The findings were statistically significant compared with those of other parameters (p value < 0.05 using McNemar's test). Instead, to discriminate normal pancreas versus peritumoral inflammatory tissue or pancreatic tumor and to differentiate normal pancreatic parenchyma versus peritumoral inflammatory tissue, there were no statistically significant differences between parameters' accuracy (p > 0.05 at McNemar's test).

CONCLUSIONS

Diffusion parameters, mainly MD by DKI, could be helpful for the differentiation of normal pancreatic parenchyma, perilesional inflammation, and pancreatic tumor.

Technical and Clinical Factors Affecting Success Rate of a Deep Learning Method for Pancreas Segmentation on CT

PURPOSE

Accurate pancreas segmentation has application in surgical planning, assessment of diabetes, and detection and analysis of pancreatic tumors. Factors that affect pancreas segmentation accuracy have not been previously reported. The purpose of this study is to identify technical and clinical factors that adversely affect the accuracy of pancreas segmentation on CT.

METHOD AND MATERIALS

In this IRB and HIPAA compliant study, a deep convolutional neural network was used for pancreas segmentation in a publicly available archive of 82 portal-venous phase abdominal CT scans of 53 men and 29 women. The accuracies of the segmentations were evaluated by the Dice similarity coefficient (DSC). The DSC was then correlated with demographic and clinical data (age, gender, height, weight, body mass index), CT technical factors (image pixel size, slice thickness, presence or absence of oral contrast), and CT imaging findings (volume and attenuation of pancreas, visceral abdominal fat, and CT attenuation of the structures within a 5 mm neighborhood of the pancreas).

RESULTS

The average DSC was 78% ± 8%. Factors that were statistically significantly correlated with DSC included body mass index (r = 0.34, p < 0.01), visceral abdominal fat (r = 0.51, p < 0.0001), volume of the pancreas (r = 0.41, p = 0.001), standard deviation of CT attenuation within the pancreas (r = 0.30, p = 0.01), and median and average CT attenuation in the immediate neighborhood of the pancreas (r = -0.53, p < 0.0001 and r = -0.52, p < 0.0001). There were no significant correlations between the DSC and the height, gender, or mean CT attenuation of the pancreas.

CONCLUSION

Increased visceral abdominal fat and accumulation of fat within or around the pancreas are major factors associated with more accurate segmentation of the pancreas. Potential applications of our findings include assessment of pancreas segmentation difficulty of a particular scan or dataset and identification of methods that work better for more challenging pancreas segmentations.

Pancreas Solid Tumors

Solid tumors of the pancreas encompass a variety of diagnoses with treatments ranging from observation to major abdominal surgery. Pancreatic ductal adenocarcinoma remains one of the most common and most lethal of these differential of diagnoses and requires a multimodality approach through a multidisciplinary team of specialists. This article reviews the classification, clinical presentation, and workup in differentiating solid tumors of the pancreas and serves as an additional tool for general surgeons faced with such a clinical finding, from a surgical oncology perspective.

Pitfalls in the MDCT of pancreatic cancer: strategies for minimizing errors

Multidetector computed tomography (MDCT) is a widely used cross-sectional imaging modality for initial evaluation of patients with suspected pancreatic ductal adenocarcinoma (PDAC). However, diagnosis of PDAC can be challenging due to numerous pitfalls associated with image acquisition and interpretation, including technical factors, imaging features, and cognitive errors. Accurate diagnosis requires familiarity with these pitfalls, as these can be minimized using systematic strategies. Suboptimal acquisition protocols and other technical errors such as motion artifacts and incomplete anatomical coverage increase the risk of misdiagnosis. Interpretation of images can be challenging due to intrinsic tumor features (including small and isoenhancing masses, exophytic masses, subtle pancreatic duct irregularities, and diffuse tumor infiltration), presence of coexisting pathology (including chronic pancreatitis and intraductal papillary mucinous neoplasm), mimickers of PDAC (including focal fatty infiltration and focal pancreatitis), distracting findings, and satisfaction of search. Awareness of pitfalls associated with the diagnosis of PDAC along with the strategies to avoid them will help radiologists to minimize technical and interpretation errors. Cognizance and mitigation of these errors can lead to earlier PDAC diagnosis and ultimately improve patient prognosis.

Differential diagnosis of solid pancreatic masses

Solid pancreatic lesions include mainly adenocarcinoma, neuroendocrine tumors pancreatic cystic neoplasms with solid component, solid pseudopapillary tumor, pancreatoblastoma, pancreatic lymphoma, and pancreatic metastasis. The most frequent pancreatic lesion is the adenocarcinoma, representing between 70% and 95% of all solid pancreatic neoplasm. The diagnosis of these lesions can be a challenge and currently, there are different imaging techniques such as CT scan, EUS and MRI with high sensitivity and specificity. The most widely used technique for the initial evaluation is the CT scan with a sensitivity between 76% and 92% for the diagnosis of pancreatic cancer. The EUS has a sensitivity for the detection of pancreatic lesions of around 98% and is accepted to be the most sensitive technique for the detection of small pancreatic tumors (<2 cm). The MRI, with a very high soft-tissue contrast resolution, provides an accuracy in the detection and staging of adenocarcinoma of 90-100%. A multimodality approach is usually necessary in patients with clinical suspicion of pancreatic lesion. The EUS is required for the local evaluation of the relation of the lesion with vessels and for tissue acquisition and the CT scan and/or MRI is usually required for the local and distance staging in case of pancreatic cancer. The purpose of this review is to provide an overview of solid pancreatic lesions and the role of the different imaging techniques in their evaluation.

Feasibility of thin-slice abdominal CT in overweight patients using a vendor neutral image-based denoising algorithm: Assessment of image noise, contrast, and quality

The purpose of this study was to investigate whether the novel image-based noise reduction software (NRS) improves image quality, and to assess the feasibility of using this software in combination with hybrid iterative reconstruction (IR) in image quality on thin-slice abdominal CT. In this retrospective study, 54 patients who underwent dynamic liver CT between April and July 2017 and had a body mass index higher than 25 kg/m² were included. Three image sets of each patient were reconstructed as follows: hybrid IR images with 1-mm slice thickness (group A), hybrid IR images with 5-mm slice thickness (group B), and hybrid IR images with 1-mm slice thickness denoised using NRS (group C). The mean image noise and contrast-to-noise ratio relative to the muscle of the aorta and liver were assessed. Subjective image quality was evaluated by two radiologists for sharpness, noise, contrast, and overall quality using 5-point scales. The mean image noise was significantly lower in group C than in group A (p < 0.01), but no significant difference was observed between groups B and C. The contrast-to-noise ratio was significantly higher in group C than in group A (p < 0.01 and p = 0.01, respectively). Subjective image quality was also significantly higher in group C than in group A (p < 0.01), in terms of noise and overall quality, but not in terms of sharpness and contrast (p = 0.65 and 0.07, respectively). The contrast of images in group C was greater than that in group A, but this difference was not significant. Compared with hybrid IR alone, the novel NRS combined with a hybrid IR could result in significant noise reduction without sacrificing image quality on CT. This combined approach will likely be particularly useful for thin-slice abdominal CT examinations of overweight patients.

Development and multicentre validation of a prognostic model to predict resectability of pancreatic head malignancy

Background

At the time of planned pancreatoduodenectomy patients frequently undergo exploratory laparotomy without resection, leading to delayed systemic therapy. This study aimed to develop and validate a prognostic model for the preoperative prediction of resectability of pancreatic head tumours.

Methods

This was a retrospective study of patients undergoing attempted resection for confirmed malignant tumours of the pancreatic head in a university hospital in Hannover, Germany. The prognostic value of patient and tumour characteristics was investigated in a multivariable logistic regression model. External validation was performed using data from two other centres.

Results

Some 109 patients were included in the development cohort, with 51 and 175 patients in the two validation cohorts. Eighty patients (73·4 per cent) in the development cohort underwent resection, and 37 (73 per cent) and 141 (80·6 per cent) in the validation cohorts. The main reasons for performing no resection in the development cohort were: local invasion of vasculature or arterial abutment (15 patients, 52 per cent), and liver (12, 41 per cent), peritoneal (8, 28 per cent) and aortocaval lymph node (6, 21 per cent) metastases. The final model contained the following variables: time to surgery (odds ratio (OR) 0·99, 95 per cent c.i. 0·98 to 0·99), carbohydrate antigen 19-9 concentration (OR 0·99, 0·99 to 0·99), jaundice (OR 4·45, 1·21 to 16·36) and back pain (OR 0·02, 0·00 to 0·22), with an area under the receiver operating characteristic (ROC) curve (AUROC) of 0·918 in the development cohort. AUROC values were 0·813 and 0·761 in the validation cohorts. The positive predictive value of the final model for prediction of resectability was 98·0 per cent in the development cohort, and 91·7 and 94·7 per cent in the two external validation cohorts. [Corrections added on 18 July 2018, after first online publication: The figures for OR of the variables time to surgery and CA19-9 in the abstract and in Table 3 and Table 4 were amended from 1·00 to 0·99].

Conclusion

For preoperative prediction of the likelihood of resectability of pancreatic head tumours, this validated model is a valuable addition to CT findings.

Challenges in diagnosis of pancreatic cancer

Pancreatic cancer is a growing source of cancer related death, yet has poor survival rates which have not improved in the last few decades. Its high mortality rate is attributed to pancreatic cancer biology, difficulty in early diagnosis and the lack of standardised international guidelines in assessing suspicious pancreatic masses. This review aims to provide an update in the current state of play in pancreatic cancer diagnosis and to evaluate the benefits and limitations of available diagnostic technology. The main modalities discussed are imaging with computed tomography, magnetic resonance imaging, endoscopic ultrasound and positron emission tomography and tissue acquisition with fine needle aspiration. We also review the improvements in the techniques used for tissue acquisition and the opportunity for personalised cancer medicine. Screening of high risk individuals, promising biomarkers and common mimickers of pancreatic cancer are also explored, as well as suggestions for future research directions to allow for earlier detection of pancreatic cancer. Timely and accurate diagnosis of pancreatic cancer can lead to improvements in the current poor outcome of this disease.

Pancreatic adenocarcinoma: cross-sectional imaging techniques

Pancreatic adenocarcinoma is a common malignancy that has a poor prognosis. Imaging is vital in its detection, staging, and management. Although a variety of imaging techniques are available, MDCT is the preferred imaging modality for staging and assessing the resectability of pancreatic adenocarcinoma. MR also has an important adjunct role, and may be used in addition to CT or as a problem-solving tool. A dedicated pancreatic protocol should be acquired as a biphasic technique optimized for the detection of pancreatic adenocarcinoma and to allow accurate local and distant disease staging. Emerging techniques like dual-energy CT and texture analysis of CT and MR images have a great potential in improving lesion detection, characterization, and treatment monitoring.

Surgery for pancreatic cancer: critical radiologic findings for clinical decision making

Pancreatic cancer is the fourth leading cause of cancer-related deaths in the United States, with an estimated 53,670 new cases diagnosed and an estimated 43,090 deaths in 2017. This high mortality rate is in part due to the small percentage of patients diagnosed with local disease, as well as the biologically aggressive nature of the disease. While only 10-20% of patients will present with surgically resectable disease, this is the only possible curative therapy. Five-year survival of resected pancreatic cancer ranges from 12 to 27%. The National Comprehensive Cancer Network (NCCN) guidelines recommend specific guidelines for imaging modalities used in the diagnosis and staging of pancreatic adenocarcinoma. Indeed, high-quality imaging is not only necessary to accurately stage the disease, but is critical for the determination of key clinical decision branch points such as the determination of surgical resectability. Identification of the lesion within the pancreas, the degree of extra-pancreatic extension, and potential involvement of surrounding vascular structures with the tumor are all findings necessary to classify patients as having resectable, borderline resectable, or with unresectable primary tumors. This article reviews imaging modalities used to evaluate the pancreatic cancer patient from the surgeon's perspective, with particular emphasis on determination of resectability and preoperative planning, as well as imaging in the postoperative period.

Assessment of iodine uptake by pancreatic cancer following chemotherapy using dual-energy CT

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. 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, and illustrate example cases.

Extrapancreatic perineural invasion in pancreatic adenocarcinoma

Pancreatic ductal adenocarcinoma continues to be a highly lethal disease, despite advances in modern medicine. Curative surgical options continue to carry significant morbidity and offer little improvement in overall 5-year survival. Currently, imaging plays an essential role in the pre-operative evaluation of patients who are undergoing evaluation for resection. However, some pancreatic cancers have particularly aggressive biology, despite appearing resectable by conventional imaging criteria. Imaging biomarkers that serve as surrogates for tumors with such aggressive phenotype have been recently described, namely duodenal invasion and extrapancreatic perineural invasion. In this pictorial review, we will summarize key concepts of extrapancreatic perineural invasion, describe its association with a poor prognosis, and highlight the role of imaging in its detection.

Staging pancreatic carcinoma by computed tomography

Pancreatic carcinoma is becoming more common in our environment; the mortality rate for this tumor has barely changed over the last 20 years. Early diagnosis and accurate staging are crucial to ensure an appropriate therapeutic approach, which should aim to improve survival in patients in whom complete resection is possible and to minimize surgical morbidity and mortality in those with a high risk of residual disease after the intervention. Various imaging techniques are used for tumor staging: multidetector computed tomography (CT), magnetic resonance imaging, positron emission tomography (PET)-CT, endoscopic ultrasound, and diagnostic laparoscopy. Currently, multidetector CT is the technique of choice for the study of pancreatic tumors; thus, this article aims to review the state of the art in staging adenocarcinoma of the pancreas, focusing mainly on the applications and limitations of this technique.

Novel contrast-injection protocol for high-resolution abdominal CT-angiography: vascular visualization improvement with vasodilator

PURPOSE

To evaluate the usefulness of a novel contrast-injection protocol for high-resolution abdominal computed tomography angiography (CTA) using nitroglycerin (NTG).

METHODS

Abdominal CTA was performed in 80 patients using two 64-detector-row CT scanners. Forty patients were examined after administration of sublingual NTG (NTG group), while 40 were examined without NTG administration (non-NTG group). Arterial phase images were acquired with maximum intensity projection and volume rendering. Reduction rates: vessel cross-sectional areas ratio of 10 cm distal to origin at the superior mesenteric artery, contrast enhancements, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were assessed. Three reviewers evaluated degree of depiction of the peripancreatic vasculature using a four-point scale (1 = poor, 4 = excellent).

RESULTS

Reduction rates were significantly lower in the NTG group (P < 0.001), while there were no significant differences in contrast enhancements, SNR, or CNR between groups. Visual evaluation results of the NTG group were significantly better than those of the non-NTG group (P < 0.01).

CONCLUSION

Abdominal CTA using NTG improved visualization of the abdominal peripheral vessels. This improved arterial view may be beneficial for preoperative evaluation of the arterial anatomy.

Can diffusion-weighted imaging serve as a biomarker of fibrosis in pancreatic adenocarcinoma?

PURPOSE

To assess the relationship between diffusion-weighted imaging (DWI) and intravoxel incoherent motion (IVIM)-derived quantitative parameters (apparent diffusion coefficient [ADC], perfusion fraction [f], Dslow , diffusion coefficient [D], and Dfast , pseudodiffusion coefficient [D*]) and histopathology in pancreatic adenocarcinoma (PAC).

MATERIALS AND METHODS

Subjects with suspected surgically resectable PAC were prospectively enrolled in this Health Insurance Portability and Accountability Act (HIPAA)-compliant, Institutional Review Board-approved study. Imaging was performed at 1.5T with a respiratory-triggered echo planar DWI sequence using 10 b values. Two readers drew regions of interest (ROIs) over the tumor and adjacent nontumoral tissue. Monoexponential and biexponential fits were used to derive ADC2b , ADCall , f, D, and D*, which were compared to quantitative histopathology of fibrosis, mean vascular density, and cellularity. Two biexponential IVIM models were investigated and compared: 1) nonlinear least-square fitting based on the Levenberg-Marquardt algorithm, and 2) linear fit using a fixed D* (20 mm2 /s). Statistical analysis included Student's t-test, Pearson correlation (P < 0.05 was considered significant), intraclass correlation, and coefficients of variance.

RESULTS

Twenty subjects with PAC were included in the final cohort. Negative correlation between D and fibrosis (Reader 2: r = -0.57 P = 0.01; pooled P = -0.46, P = 0.04) was observed with a trend toward positive correlation between f and fibrosis (r = 0.44, P = 0.05). ADC2b was significantly lower in PAC with dense fibrosis than with loose fibrosis ADC2b (P = 0.03). Inter- and intrareader agreement was excellent for ADC, D, and f.

CONCLUSION

In PAC, D negatively correlates with fibrosis, with a trend toward positive correlation with f suggesting both perfusion and diffusion effects contribute to stromal desmoplasia. ADC2b is significantly lower in tumors with dense fibrosis and may serve as a biomarker of fibrosis architecture.

LEVEL OF EVIDENCE

1 Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2017;46:393-402.

Which is the Best Imaging Method in Pancreatic Adenocarcinoma Diagnosis and Staging - CT, MRI or EUS?

INTRODUCTION

Imaging has a decisive role in the diagnosis and staging of pancreatic cancer, the most used imaging methods being computed tomography, magnetic resonance imaging and endoscopic ultrasonography.

MATERIAL AND METHOD

They were studied retrospectively over a 3 years period, 140 patients with pancreatic cancer.

AIM OF THE STUDY

This study aims to determine the effectiveness of CT, MRI and EUS in diagnosis and staging of panceatic cancer.

RESULTS

CT showed a diagnostic accuracy of 83.3%, with sensitivity and specificity of 81.4% and 43% respectively. MRI showed superior diagnostic accuracy compared to CT (89,1%). However, EUS demonstrated the best diagnostic value in PC (accuracy of 92,7%). Concerning the locoregional staging, the 3 diagnostic methods showed similar result. There were no significant differences concerning the diagnosis of intra-abdominal metastases. Differences have appeared in the case of extra-abdominal. Thus, there were 4 cases of lung metastases which have been identified only on CT and MRI.

CONCLUSION

EUS is the most effective technique used in the diagnosis of pancreatic cancer, the present study demonstrating an accuracy of 92.7%. Moreover, EUS offers the possibility to collect samples for cytological examination by EUS guided fine needle aspiration. However, there are some limitations of EUS in identifying extra-abdominal metastases. Thus, the assessment of tumor extension must be completing by performing CT or MRI.