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

Factors associated with radiological misstaging of pancreatic ductal adenocarcinoma: A retrospective observational study

PURPOSE

Accurate staging of disease is vital in determining appropriate care for patients with pancreatic ductal adenocarcinoma (PDAC). It has been shown that the quality of scans and the experience of a radiologist can impact computed tomography (CT) based assessment of disease. The aim of the current study was to evaluate the impact of the rereading of outside hospital (OH) CT by an expert radiologist and a repeat pancreatic protocol CT (PPCT) on staging of disease.

METHODS

Patients evaluated at the our institute's pancreatic multidisciplinary clinic (2006 to 2014) with OH scan and repeat PPCT performed within 30 days were included. In-house radiologists staged disease using OH scans and repeat PPCT, and factors associated with misstaging were determined.

RESULTS

The study included 100 patients, with a median time between OH scan and PPCT of 19 days (IQR: 13-23 days.) Stage migration was mostly accounted for by upstaging of disease (58.8 % to 83.3 %) in all comparison groups. When OH scans were rereviewed, 21.5 % of the misstaging was due to missed metastases, however, when rereads were compared to the PPCT, occult metastases accounted for the majority of misstaged patients (62.5 %). Potential factors associated with misstaging were primarily related to imaging technique.

CONCLUSION

A repeat PPCT results in increased detection of metastatic disease that rereviews of OH scans may otherwise miss. Accessible insurance coverage for repeat PPCT imaging even within 30 days of an OH scan could help optimize delivery of care and alleviate burdens associated with misstaging.

Reinterpretation of Hepatopancreaticobiliary Imaging Exams: Assessment of Clinical Impact, Peer Learning, and Physician Satisfaction

OBJECTIVES

To assess the impact on clinical management, potential for peer learning, and referring physician satisfaction with subspecialist reinterpretations of hepatopancreaticobiliary (HPB) imaging examinations.

MATERIALS AND METHODS

HPB CTs and MRIs from outside hospitals were reinterpreted by two subspecialty radiologists between March 2021 and August 2022. Reinterpretation reports were mailed to radiologists that issued primary reports. The electronic record was reviewed to assess for changes in clinical management based on the reinterpretations (yes/no/unavailable). To assess the potential for peer learning, a survey using a 5-point Likert scale was sent to radiologists who issued primary reports. A separate survey was sent to referring physicians to assess satisfaction with reinterpretations.

RESULTS

Two hundred fifty imaging examinations (122 CT, 128 MRI) were reinterpreted at the request of 19 referring physicians. Ninety-six radiologists issued primary reports. RADPEER scores 1-3 were assigned to 131/250 (52%), 86/250 (34%), and 33/250 (13%) examinations, respectively. Of 213 reinterpretations with adequate records for assessment, 75/213 (35%) were associated with a change in management; of these, 71/75 (95%) were classified as RADPEER 2 or 3. Most radiologists agreed or strongly agreed with the following: prefer to receive reinterpretations (34/36, 94%); reinterpretations changed practice of reporting HPB imaging examinations (23/36, 64%); and reinterpretations offer opportunities for peer learning (34/36, 94%). Referring physicians agreed or strongly agreed (7/7, 100%) that reinterpretations are valuable and often change or clarify management of patients with complex HPB disease, and offer an opportunity for peer learning.

CONCLUSION

Radiologists and referring physicians strongly agree that HPB imaging reinterpretations help support peer learning and patient management, respectively.

Confined growth of Ag nanogap shells emitting stable Raman label signals for SERS liquid biopsy of pancreatic cancer

To develop a reliable surface-enhanced Raman scattering (SERS) immunoassay as a new liquid biopsy modality, SERS nanoprobes emitting strong and stable signals are necessary. However, Ag nanoparticles used as SERS nanoprobes are prone to rapid fading of SERS signals by oxidation. This has driven the development of a new strategy for Ag-based SERS nanoprobes emitting stable and strong SERS signals over time. Herein, Ag nanogap shells entrapping Raman labels are created in the confined pores of mesoporous silica nanoparticles (AgNSM) through a rapid single-step reaction for SERS liquid biopsy. Each AgNSM nanoprobe possesses multiple nanogaps of 1.58 nm to entrap Raman labels, allowing superior long-term SERS signal stability and large enhancement of 1.5 × 106. AgNSM nanoprobes conjugated with an antibody specific for carbohydrate antigen (CA)19-9 are employed in the SERS sandwich immunoassay including antibody-conjugated magnetic nanoparticles for CA19-9 detection, showing a two orders of magnitude lower limit of detection (0.025 U mL-1) than an enzyme-linked immunosorbent assay (0.3 U mL-1). The AgNSM nanoprobe immunoassay accurately quantifies CA19-9 levels from clinical serum samples of early and advanced pancreatic cancer. AgNSM nanoprobes with stable SERS signals provide a new route to SERS liquid biopsy for effective detection of blood biomarkers.

Clinical value of endoscopic ultrasound sound speed in differential diagnosis of pancreatic solid lesion and prognosis of pancreatic cancer

BACKGROUND

Differential diagnosis of pancreatic solid lesion (PSL) and prognosis of pancreatic cancer (PC) is a clinical challenge. We aimed to explore the differential diagnostic value of sound speed (SS) obtained from endoscopic ultrasonography (EUS) in PSL and the prognostic value of SS in PC.

METHODS

Patients with PSL in The Third Xiangya Hospital of Central South University from March 2019 to October 2019 were prospectively enrolled, who obtained SS from PSL. Patients were divided into the PC group and the pancreatic benign lesion (PBL) group. SS1 is the SS of lesions and SS2 is the SS of normal tissues adjacent to lesions. Ratio1 is equal to SS1 divided by SS2 of PSL (ratio1 = SS1/SS2).

RESULTS

Eighty patients were enrolled (24 PBL patients, 56 PC patients). SS1 and ratio1 in PC group were higher compared with PBL group (SS1:1568.00 vs. 1550.00, Z = -2.066, p = 0.039; ratio1: 1.0110 vs. 1.0051, Z = -3.391, p = 0.001). The SS1 in PC (Z = -6.503, p < 0.001) was higher compared to SS2. In the nonsurgical group of PC, low ratio1 predicted high overall survival (OS) (7.000 months vs. 4.000 months; p = 0.039). In the surgical group of PC, low SS1 was associated with low median OS (4.000 months vs. 12.000 months; p = 0.033).

CONCLUSIONS

SS plays a vital role in distinguishing between PBL and PC. Higher SS1 and ratio1 obtained by EUS are more related to PC than PBL. In PC patients, high SS1 may predict pancreatic lesions. In the nonsurgical group of PC, low ratio1 may predict high OS. However, in the surgical group of PC, low SS1 may predict low OS.

Can we rely on contrast-enhanced CT to identify pancreatic ductal adenocarcinoma? A population-based study in sensitivity and factors associated with false negatives

OBJECTIVES

To determine the sensitivity of contrast-enhanced computed tomography (CECT) in detecting pancreatic ductal adenocarcinoma (PDAC) and identify factors associated with false negatives (FNs).

METHODS

Patients diagnosed with PDAC in 2014-2015 were retrospectively identified by a cancer registry. CECTs performed during the diagnostic interval were retrospectively classified as true positive (TP), indeterminate, or FN. Sensitivity TP/(TP+FN) was calculated for all CECTs and the following subgroups: protocol (uniphasic vs. biphasic); tumor size (≤ 2 cm vs. > 2 cm); and resectability (potentially resectable vs. unresectable). Multivariate logistic regression was performed to assess which of the following factors were associated with FN: clinical suspicion of PDAC; size >2 cm; presence of metastases; protocol; isoattenuating tumor; and potentially resectable disease on imaging.

RESULTS

In total, 176 CECTs (127 uniphasic; 49 biphasic) in 154 patients (90 men, mean age 72 ± 11 years) were included. Sensitivity was 125/149 (83.9%) overall and 87/106 (82.1%) and 38/43 (88.4%) for uniphasic and biphasic protocols, respectively. Sensitivity was decreased for tumors ≤ 2 cm (45.4% vs. 90.6%), no liver metastases (78.0% vs. 95.9%), and potentially resectable disease (65.3% vs. 93.0%). Factors significantly associated with FN were clinical suspicion (OR, 0.24, 95% CI: 0.07-0.75), size>2 cm (OR, 0.10, 95% CI: 0.02-0.44), absence of liver metastases (OR, 4.94, 95% CI: 1.29-22.99), and potentially resectable disease (OR, 4.13, 95% CI: 1.07-16.65).

CONCLUSIONS

In our population, the overall sensitivity of CECT to detect PDAC is 83.9%; however, this is substantially lower in several scenarios, including patients with potentially resectable disease. This finding has important implications for patient outcomes and efforts to maximize CECT sensitivity should be sought.

CLINICAL RELEVANCE STATEMENT

The sensitivity of CECT to detect PDAC is significantly decreased in the setting of sub-2 cm tumors and potentially resectable disease. A dedicated biphasic pancreatic CECT protocol has higher sensitivity and should be applied in patients with suspected pancreatic disease.

KEY POINTS

• The sensitivities of contrast-enhanced CT for the detection of PDAC were 87/106 (82.1%) and 38/43 (88.4%) for uniphasic and biphasic protocols, respectively. • The sensitivity of contrast-enhanced CT was decreased for small tumors ≤ 2 cm (45.4% vs. 90.6%), if there were no liver metastases (78.0% vs. 95.9%), and with potentially resectable disease (65.3% vs. 93.0%). • Absence of liver metastases (OR, 4.94, 95% CI: 1.29-22.99) and potentially resectable disease (OR, 4.13, 95% CI: 1.07-16.65) were associated with a false--negative (FN) CT result; suspicion of malignancy on the imaging requisition (OR, 0.24, 95% CI: 0.07-0.75) and size > 2 cm (OR, 0.10, 95% CI: 0.02-0.44) were negatively associated with FN.

Pancreatic Cancer and Its Mimics

Pancreatic ductal adenocarcinoma (PDAC) is the most common primary pancreatic malignancy, ranking fourth in cancer-related mortality in the United States. Typically, PDAC appears on images as a hypovascular mass with upstream pancreatic duct dilatation and abrupt duct cutoff, distal pancreatic atrophy, and vascular encasement, with metastatic involvement including lymphadenopathy. However, atypical manifestations that may limit detection of the underlying PDAC may also occur. Atypical PDAC features include findings related to associated conditions such as acute or chronic pancreatitis, a mass that is isointense to the parenchyma, multiplicity, diffuse tumor infiltration, associated calcifications, and cystic components. Several neoplastic and inflammatory conditions can mimic PDAC, such as paraduodenal "groove" pancreatitis, autoimmune pancreatitis, focal acute and chronic pancreatitis, neuroendocrine tumors, solid pseudopapillary neoplasms, metastases, and lymphoma. Differentiation of these conditions from PDAC can be challenging due to overlapping CT and MRI features; however, certain findings can help in differentiation. Diffusion-weighted MRI can be helpful but also can be nonspecific. Accurate diagnosis is pivotal for guiding therapeutic planning and potential outcomes in PDAC and avoiding biopsy or surgical treatment of some of these mimics. Biopsy may still be required for diagnosis in some cases. The authors describe the typical and atypical imaging findings of PDAC and features that may help to differentiate PDAC from its mimics. ©RSNA, 2023 Online supplemental material is available for this article. Quiz questions for this article are available through the Online Learning Center. See the invited commentary by Zins in this issue.

An Automated Prognostic Model for Pancreatic Ductal Adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) constitutes a leading cause of cancer-related mortality despite advances in detection and treatment methods. While computed tomography (CT) serves as the current gold standard for initial evaluation of PDAC, its prognostic value remains limited, as it relies on diagnostic stage parameters encompassing tumor size, lymph node involvement, and metastasis. Radiomics have recently shown promise in predicting postoperative survival of PDAC patients; however, they rely on manual pancreas and tumor delineation by clinicians. In this study, we collected a dataset of pre-operative CT scans from a cohort of 40 PDAC patients to evaluate a fully automated pipeline for survival prediction. Employing nnU-Net trained on an external dataset, we generated automated pancreas and tumor segmentations. Subsequently, we extracted 854 radiomic features from each segmentation, which we narrowed down to 29 via feature selection. We then combined these features with the Tumor, Node, Metastasis (TNM) system staging parameters, as well as the patient's age. We trained a random survival forest model to perform an overall survival prediction over time, as well as a random forest classifier for the binary classification of two-year survival, using repeated cross-validation for evaluation. Our results exhibited promise, with a mean C-index of 0.731 for survival modeling and a mean accuracy of 0.76 in two-year survival prediction, providing evidence of the feasibility and potential efficacy of a fully automated pipeline for PDAC prognostication. By eliminating the labor-intensive manual segmentation process, our streamlined pipeline demonstrates an efficient and accurate prognostication process, laying the foundation for future research endeavors.

Imaging Assessment of Pancreatic Cancer Resectability After Neoadjuvant Therapy: AJR Expert Panel Narrative Review

Despite important innovations in the treatment of pancreatic ductal adenocarcinoma (PDAC), PDAC remains a disease with poor prognosis and high mortality. A key area for potential improvement in the management of PDAC, aside from earlier detection in patients with treatable disease, is the improved ability of imaging techniques to differentiate treatment response after neoadjuvant therapy (NAT) from worsening disease. It is well established that current imaging techniques cannot reliably make this distinction. This narrative review provides an update on the imaging assessment of pancreatic cancer resectability after NAT. Current definitions of borderline resectable PDAC, as well as implications for determining likely patient benefit from NAT, are described. Challenges associated with PDAC pathologic evaluation and surgical decision making that are of relevance to radiologists are discussed. Also explored are the specific limitations of imaging in differentiating the response after NAT from stable or worsening disease, including issues relating to protocol optimization, tumor size assessment, vascular assessment, and liver metastasis detection. The roles of MRI as well as PET and/or hybrid imaging are considered. Finally, a short PDAC reporting template is provided for use after NAT. The highlighted methods seek to improve radiologists' assessment of PDAC treatment response after NAT.

CT and MRI of pancreatic tumors: an update in the era of radiomics

Radiomics is a relatively new approach for image analysis. As a part of radiomics, texture analysis, which consists in extracting a great amount of quantitative data from original images, can be used to identify specific features that can help determining the actual nature of a pancreatic lesion and providing other information such as resectability, tumor grade, tumor response to neoadjuvant therapy or survival after surgery. In this review, the basic of radiomics, recent developments and the results of texture analysis using computed tomography and magnetic resonance imaging in the field of pancreatic tumors are presented. Future applications of radiomics, such as artificial intelligence, are discussed.

Pancreatic Cancer Imaging: A New Look at an Old Problem

Computed tomography is the most commonly used imaging modality to detect and stage pancreatic cancer. Previous advances in pancreatic cancer imaging have focused on optimizing image acquisition parameters and reporting standards. However, current state-of-the-art imaging approaches still misdiagnose some potentially curable pancreatic cancers and do not provide prognostic information or inform optimal management strategies beyond stage. Several recent developments in pancreatic cancer imaging, including artificial intelligence and advanced visualization techniques, are rapidly changing the field. The purpose of this article is to review how these recent advances have the potential to revolutionize pancreatic cancer imaging.