Diagnosis and Detection of Pancreatic Cancer Chu, Linda C; Goggins, Michael G; Fishman, Elliot K
The cancer journal (Sudbury, Mass.),
2017 Nov/Dec, 2017-11-00, 20171101, Letnik:
23, Številka:
6
Journal Article
Recenzirano
Computed tomography is the first-line imaging modality for suspected pancreatic cancer. Magnetic resonance cholangiopancreatography is a second-line modality for suspected pancreatic cancer and is ...usually reserved for equivocal cases. Both computed tomography and MR are highly sensitive in the detection of pancreatic cancer, with up to 96% and 93.5% sensitivity, respectively. Computed tomography is superior to MR in the assessment of tumor resectability, with accuracy rates of up to 86.8% and 78.9%, respectively. Close attention to secondary signs of pancreatic cancer, such as pancreatic duct dilatation, abrupt pancreatic duct caliber change, and parenchymal atrophy, are critical in the diagnosis of pancreatic cancer. Emerging techniques such as radiomics and molecular imaging have the potential of identifying malignant precursors and lead to earlier disease diagnosis. The results of these promising techniques need to be validated in larger clinical studies.
Study objective The study objective was to determine whether intravenous contrast administration for computed tomography (CT) is independently associated with increased risk for acute kidney injury ...and adverse clinical outcomes. Methods This single-center retrospective cohort analysis was performed in a large, urban, academic emergency department with an average census of 62,179 visits per year; 17,934 ED visits for patients who underwent contrast-enhanced, unenhanced, or no CT during a 5-year period (2009 to 2014) were included. The intervention was CT scan with or without intravenous contrast administration. The primary outcome was incidence of acute kidney injury. Secondary outcomes included new chronic kidney disease, dialysis, and renal transplantation at 6 months. Logistic regression modeling and between-groups odds ratios with and without propensity-score matching were used to test for an independent association between contrast administration and primary and secondary outcomes. Treatment decisions, including administration of contrast and intravenous fluids, were examined. Results Rates of acute kidney injury were similar among all groups. Contrast administration was not associated with increased incidence of acute kidney injury (contrast-induced nephropathy criteria odds ratio=0.96, 95% confidence interval 0.85 to 1.08; and Acute Kidney Injury Network/Kidney Disease Improving Global Outcomes criteria odds ratio=1.00, 95% confidence interval 0.87 to 1.16). This was true in all subgroup analyses regardless of baseline renal function and whether comparisons were made directly or after propensity matching. Contrast administration was not associated with increased incidence of chronic kidney disease, dialysis, or renal transplant at 6 months. Clinicians were less likely to prescribe contrast to patients with decreased renal function and more likely to prescribe intravenous fluids if contrast was administered. Conclusion In the largest well-controlled study of acute kidney injury following contrast administration in the ED to date, intravenous contrast was not associated with an increased frequency of acute kidney injury.
OBJECTIVE:To describe accurately the pattern, timing, and predictors of disease recurrence after a potentially curative resection for pancreatic ductal adenocarcinoma (PDAC).
SUMMARY BACKGROUND ...DATA:After surgery for PDAC, most patients will develop disease recurrence. Understanding the patterns and timing of disease failure can help guide improvements in therapy.
METHODS:Patients who underwent pancreatectomy for PDAC at the Johns Hopkins Hospital between 2000 and 2010 were included. Exclusion criteria were incomplete follow-up records, follow-up <24 months, and neoadjuvant therapy. The first recurrence site was recorded and recurrence-free survival (RFS) was estimated using Kaplan–Meier curves. Predictive factors for specific recurrence patterns were assessed by univariate and multivariate analyses using Cox-proportional hazard regression models.
RESULTS:From the identified cohort of 1103 patients, 692 patients had comprehensive and detailed follow-up data available. At a median follow-up of 25.3 months, 531 (76.7%) of the 692 had recurred after a median RFS of 11.7 months. Most patients recurred at isolated distant sites (n = 307, 57.8%), while isolated local recurrence was seen in 126 patients (23.7%). Liver-only recurrence (n = 134, 25.2%) tended to occur early (median 6.9 mo), while lung-only recurrence (n = 78, 14.7%) occurred later (median 18.6 mo). A positive lymph node ratio >0.2 was a strong predictor for all distant disease recurrence. Patients receiving adjuvant chemotherapy or chemoradiotherapy had fewer recurrences and a longer RFS of 18.0 and 17.2 months, respectively.
CONCLUSIONS:Specific recurrence locations have different predictive factors and possess distinct RFS curves, supporting the hypothesis that unique biological differences exist among tumors leading to distinct patterns of recurrence.
OBJECTIVE:The aim of the study was to identify the survival of patients with locally advanced pancreatic cancer (LAPC) and assess the effect of surgical resection after neoadjuvant therapy on patient ...outcomes.
BACKGROUND:An increasing number of LAPC patients who respond favorably to neoadjuvant therapy undergo surgical resection. The impact of surgery on patient survival is largely unknown.
MATERIALS AND METHODS:All LAPC patients who presented to the institutional pancreatic multidisciplinary clinic (PMDC) from January 2013 to September 2017 were included in the study. Demographics and clinical data on neoadjuvant treatment and surgical resection were documented. Primary tumor resection rates after neoadjuvant therapy and overall survival (OS) were the primary study endpoints.
RESULTS:A total of 415 LAPC patients were included in the study. Stratification of neoadjuvant therapy in FOLFIRINOX-based, gemcitabine-based, and combination of the two, and subsequent outcome comparison did not demonstrate significant differences in OS of 331 non-resected LAPC patients (P = 0.134). Eighty-four patients underwent resection of the primary tumor (20%), after a median duration of 5 months of neoadjuvant therapy. FOLFIRINOX-based therapy and stereotactic body radiation therapy correlated with increased probability of resection (P = 0.006). Resected patients had better performance status, smaller median tumor size (P = 0.029), and lower median CA19-9 values (P < 0.001) at PMDC. Patients who underwent surgical resection had significant higher median OS compared with those who did not (35.3 vs 16.3 mo, P < 0.001). The difference remained significant when non-resected patients were matched for time of neoadjuvant therapy (19.9 mo, P < 0.001).
CONCLUSIONS:Surgical resection of LAPC after neoadjuvant therapy is feasible in a highly selected cohort of patients (20%) and is associated with significantly longer median overall survival.
•A novel deep network framework for accurate multi-organ segmentation on abdominal CT.•Organ-attention network to enhance discriminative information from complex background.•Reverse connection to ...handle multi-scale localization.•Statistical fusion based on local structural similarity.•State-of-the-art performance on automatic segmentation of 13 abdominal structures.
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Accurate and robust segmentation of abdominal organs on CT is essential for many clinical applications such as computer-aided diagnosis and computer-aided surgery. But this task is challenging due to the weak boundaries of organs, the complexity of the background, and the variable sizes of different organs. To address these challenges, we introduce a novel framework for multi-organ segmentation of abdominal regions by using organ-attention networks with reverse connections (OAN-RCs) which are applied to 2D views, of the 3D CT volume, and output estimates which are combined by statistical fusion exploiting structural similarity. More specifically, OAN is a two-stage deep convolutional network, where deep network features from the first stage are combined with the original image, in a second stage, to reduce the complex background and enhance the discriminative information for the target organs. Intuitively, OAN reduces the effect of the complex background by focusing attention so that each organ only needs to be discriminated from its local background. RCs are added to the first stage to give the lower layers more semantic information thereby enabling them to adapt to the sizes of different organs. Our networks are trained on 2D views (slices) enabling us to use holistic information and allowing efficient computation (compared to using 3D patches). To compensate for the limited cross-sectional information of the original 3D volumetric CT, e.g., the connectivity between neighbor slices, multi-sectional images are reconstructed from the three different 2D view directions. Then we combine the segmentation results from the different views using statistical fusion, with a novel term relating the structural similarity of the 2D views to the original 3D structure. To train the network and evaluate results, 13 structures were manually annotated by four human raters and confirmed by a senior expert on 236 normal cases. We tested our algorithm by 4-fold cross-validation and computed Dice–Sørensen similarity coefficients (DSC) and surface distances for evaluating our estimates of the 13 structures. Our experiments show that the proposed approach gives strong results and outperforms 2D- and 3D-patch based state-of-the-art methods in terms of DSC and mean surface distances.
Recent progress in pancreatic cancer Wolfgang, Christopher L.; Herman, Joseph M.; Laheru, Daniel A. ...
CA: a cancer journal for clinicians,
September/October 2013, Letnik:
63, Številka:
5
Journal Article
Screening of individuals who have a high risk of pancreatic ductal adenocarcinoma (PDAC), because of genetic factors, frequently leads to identification of pancreatic lesions. We investigated the ...incidence of PDAC and risk factors for neoplastic progression in individuals at high risk for PDAC enrolled in a long-term screening study.
We analyzed data from 354 individuals at high risk for PDAC (based on genetic factors of family history), enrolled in Cancer of the Pancreas Screening cohort studies at tertiary care academic centers from 1998 through 2014 (median follow-up time, 5.6 years). All subjects were evaluated at study entry (baseline) by endoscopic ultrasonography and underwent surveillance with endoscopic ultrasonography, magnetic resonance imaging, and/or computed tomography. The primary endpoint was the cumulative incidence of PDAC, pancreatic intraepithelial neoplasia grade 3, or intraductal papillary mucinous neoplasm with high-grade dysplasia (HGD) after baseline. We performed multivariate Cox regression and Kaplan-Meier analyses.
During the follow-up period, pancreatic lesions with worrisome features (solid mass, multiple cysts, cyst size > 3 cm, thickened/enhancing walls, mural nodule, dilated main pancreatic duct > 5 mm, or abrupt change in duct caliber) or rapid cyst growth (>4 mm/year) were detected in 68 patients (19%). Overall, 24 of 354 patients (7%) had neoplastic progression (14 PDACs and 10 HGDs) over a 16-year period; the rate of progression was 1.6%/year, and 93% had detectable lesions with worrisome features before diagnosis of the PDAC or HGD. Nine of the 10 PDACs detected during routine surveillance were resectable; a significantly higher proportion of patients with resectable PDACs survived 3 years (85%) compared with the 4 subjects with symptomatic, unresectable PDACs (25%), which developed outside surveillance (log rank P < .0001). Neoplastic progression occurred at a median age of 67 years; the median time from baseline screening until PDAC diagnosis was 4.8 years (interquartile range, 1.6–6.9 years).
In a long-term (16-year) follow-up study of individuals at high-risk for PDAC, we found most PDACs detected during surveillance (9/10) to be resectable, and 85% of these patients survived for 3 years. We identified radiologic features associated with neoplastic progression.
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Pancreatic ductal adenocarcinoma is an aggressive malignancy with a high mortality rate. Proper determination of the extent of disease on imaging studies at the time of staging is one of the most ...important steps in optimal patient management. Given the variability in expertise and definition of disease extent among different practitioners as well as frequent lack of complete reporting of pertinent imaging findings at radiologic examinations, adoption of a standardized template for radiology reporting, using universally accepted and agreed on terminology for solid pancreatic neoplasms, is needed. A consensus statement describing a standardized reporting template authored by a multi-institutional group of experts in pancreatic ductal adenocarcinoma that included radiologists, gastroenterologists, and hepatopancreatobiliary surgeons was developed under the joint sponsorship of the Society of Abdominal Radiologists and the American Pancreatic Association. Adoption of this standardized imaging reporting template should improve the decision-making process for the management of patients with pancreatic ductal adenocarcinoma by providing a complete, pertinent, and accurate reporting of disease staging to optimize treatment recommendations that can be offered to the patient. Standardization can also help to facilitate research and clinical trial design by using appropriate and consistent staging by means of resectability status, thus allowing for comparison of results among different institutions.
The objective of our study was to determine the utility of radiomics features in differentiating CT cases of pancreatic ductal adenocarcinoma (PDAC) from normal pancreas.
In this retrospective ...case-control study, 190 patients with PDAC (97 men, 93 women; mean age ± SD, 66 ± 9 years) from 2012 to 2017 and 190 healthy potential renal donors (96 men, 94 women; mean age ± SD, 52 ± 8 years) without known pancreatic disease from 2005 to 2009 were identified from radiology and pathology databases. The 3D volume of the pancreas was manually segmented from the preoperative CT scans by four trained researchers and verified by three abdominal radiologists. Four hundred seventy-eight radiomics features were extracted to express the phenotype of the pancreas. Forty features were selected for analysis because of redundancy of computed features. The dataset was divided into 255 training cases (125 normal control cases and 130 PDAC cases) and 125 validation cases (65 normal control cases and 60 PDAC cases). A random forest classifier was used for binary classification of PDAC versus normal pancreas of control cases. Accuracy, sensitivity, and specificity were calculated.
Mean tumor size was 4.1 ± 1.7 (SD) cm. The overall accuracy of the random forest binary classification was 99.2% (124/125), and AUC was 99.9%. All PDAC cases (60/60) were correctly classified. One case from a renal donor was misclassified as PDAC (1/65). The sensitivity was 100%, and specificity was 98.5%.
Radiomics features extracted from whole pancreas can be used to differentiate between CT cases from patients with PDAC and healthy control subjects with normal pancreas.
We aim at segmenting a wide variety of organs, including tiny targets (e.g., adrenal gland), and neoplasms (e.g., pancreatic cyst), from abdominal CT scans. This is a challenging task in two aspects. ...First, some organs (e.g., the pancreas), are highly variable in both anatomy and geometry, and thus very difficult to depict. Second, the neoplasms often vary a lot in its size, shape, as well as its location within the organ. Third, the targets (organs and neoplasms) can be considerably small compared to the human body, and so standard deep networks for segmentation are often less sensitive to these targets and thus predict less accurately especially around their boundaries. In this paper, we present an end-to-end framework named recurrent saliency transformation network (RSTN) for segmenting tiny and/or variable targets. The RSTN is a coarse-to-fine approach that uses prediction from the first (coarse) stage to shrink the input region for the second (fine) stage. A saliency transformation module is inserted between these two stages so that 1) the coarse-scaled segmentation mask can be transferred as spatial weights and applied to the fine stage and 2) the gradients can be back-propagated from the loss layer to the entire network so that the two stages are optimized in a joint manner. In the testing stage, we perform segmentation iteratively to improve accuracy. In this extended journal paper, we allow a gradual optimization to improve the stability of the RSTN, and introduce a hierarchical version named H-RSTN to segment tiny and variable neoplasms such as pancreatic cysts. Experiments are performed on several CT datasets including a public pancreas segmentation dataset, our own multi-organ dataset, and a cystic pancreas dataset. In all these cases, the RSTN outperforms the baseline (a stage-wise coarse-to-fine approach) significantly. Confirmed by the radiologists in our team, these promising segmentation results can help early diagnosis of pancreatic cancer. The code and pre-trained models of our project were made available at https://github.com/198808xc/OrganSegRSTN.
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