Access to this full-text is provided by Springer Nature.
Content available from BMC Gastroenterology
This content is subject to copyright. Terms and conditions apply.
R E S E A R C H A R T I C L E Open Access
A prospective study on endoscopic
ultrasound for the differential diagnosis
of serous cystic neoplasms and mucinous
cystic neoplasms
Lisen Zhong
†
, Ningli Chai
†
, Enqiang Linghu
*
, Huikai Li, Jing Yang and Ping Tang
Abstract
Background: To provide criteria for the differential diagnosis of serous cystic neoplasms (SCNs) and mucinous
cystic neoplasms (MCNs) by analyzing the imaging features of these two neoplasms by endoscopic ultrasound (EUS).
Methods: From April 2015 to December 2017, a total of 69 patients were enrolled in this study. All patients were
confirmed to have MCNs (31 patients) or SCNs (38 patients) by surgical pathology. All patients underwent EUS
examination. The observation and recorded items were size, location, shape, cystic wall thickness, number of septa, and
solid components.
Results: Head/neck location, lobulated shape, thin wall and > 2 septa were the specific imaging features for the diagnosis
of SCNs. When any two imaging features were combined, we achieved the highest area under the curve (Az) (0.824), as
well as the appropriate sensitivity (84.2%), specificity (80.6%), positive predictive value (PPV) (84.2%), and negative
predictive value (NPV) (80.6%). Body/tail location, round shape, thick wall and 0–2 septa were the specific imaging
features for the diagnosis of MCNs. When any three imaging features were combined, we obtained the highest Az value
(0.808), as well as the appropriate sensitivity (77.4%), specificity (84.2%), PPV (80.0%) and NPV (82.1%).
Conclusions: Pancreatic cystadenomas that meet any two of the four imaging features of head/neck location, lobulated
shape, thin wall and > 2 septa could be diagnosed as SCNs, and those that meet any three of the four imaging features
of body/tail location, round shape, thick wall and 0–2septacouldbeconsideredasMCNs.
Trial registration: The study was registered at the Chinese Clinical Trial Registry. The registration identification number is
ChiCTR-OOC-15006118. The date of registration is 2015-03-20.
Keywords: Endoscopic ultrasound, Serous cystic neoplasm, Mucinous cystic neoplasm, Pancreatic cystic neoplasm,
Diagnosis
Introduction
Pancreatic cystic neoplasms (PCNs) mainly include serous
cystic neoplasms (SCNs) and mucinous cystic neoplasms
(MCNs), accounting for 10 to 15% of pancreatic cystic
lesions (PCLs), and 1 to 2% of pancreatic tumors [1].
Because of its deep location, slow growth and no clinical
symptoms in the early stage, PCNs are easily
misdiagnosed [2]. SCNs and MCNs have different bio-
logical behaviors. Relevant studies have reported that only
1 to 3% of SCNs have been transformed into serous cysta-
denocarcinomas [3]. To date, only 25–30 serous
cystadenocarcinomas have been reported worldwide [4].
Therefore, SCNs are generally considered benign and can
be followed up [5]. MCNs have malignant potential and
are recommended for surgical resection once an adequate
diagnosis has been performed [6,7]. Therefore, it is crucial
to accurately differentiate between SCNs and MCNs for
the appropriate treatment.
© The Author(s). 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver
(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
* Correspondence: linghuenqiang@vip.sina.com;listen1005@163.com
†
Lisen Zhong and Ningli Chai contributed equally to this work.
†
Lisen Zhong and Ningli Chai are co-first authors.
Department of Gastroenterology and Hepatology, Chinese PLA General
Hospital, Fuxing Road 28, Haidian District, Beijing 100853, China
Zhong et al. BMC Gastroenterology (2019) 19:127
https://doi.org/10.1186/s12876-019-1035-8
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Computed tomography (CT) and magnetic resonance
imaging (MRI) are routine abdominal examinations in
China that can effectively screen pancreatic masses.
However, due to the limited resolution, these two
imaging modalities do not accurately and effectively
present the microstructure of PCLs, which increases the
difficulty of differential diagnosis of SCNs and MCNs.
However, due to its high spatial resolution, endoscopic
ultrasound (EUS) can effectively reveal the micro-
structure of PCLs (such as the septa and thickness of the
cystic wall), which greatly improves the diagnostic accu-
racy of PCLs [8]. At present, many studies have demon-
strated the imaging features of SCNs and MCNs [9,10],
but these studies do not generate effective diagnostic
criteria. The purpose of our study is to provide cri-
teria for the differential diagnosis of SCNs and MCNs
by analyzing the imaging features of these two neo-
plasms by EUS.
Methods
This study was approved by the Ethics Committee of the
Chinese People’s Liberation Army General Hospital.
Patients
From April 2015 to December 2017, we prospectively
enrolled 88 patients with PCNs who underwent EUS
and ultimately received surgery at the Chinese PLA
General Hospital. Among these patients, 69 were proven
to have MCNs (31 patients) or SCNs (38 patients) by
surgical pathology. All patients had neither contraindi-
cations to EUS examination nor a history of acute
pancreatitis and pancreatic necrosis. All of patients
signed informed consent forms.
EUS examination
Before the examination, the patient fasted for at least 8 h.
In our study, we used an ultrasonic endoscope (GF-
UCT260; Olympus, Tokyo, Japan) in the procedures. To
ensure the imaging quality, an echoprobe was routinely
covered with a water-filled balloon. During the pro-
cedures, the patients were under general anesthesia. The
examination was performed by endoscopic physicians
with at least 5 years of experience. The EUS findings were
recorded in the form of video or picture.
Imaging analysis
Two endoscopic physicians with more than at least 5 years
of experience independently completed the analysis of
EUS images without knowing the clinical data, other im-
aging findings and pathological diagnosis of the patients.
The final conclusion was drawn after the endoscopic
physicians discussed the results and came to an agreement
if there was dissent. The observation and recorded items
were: size (the longest axis), location (head/neck or
body/tail), shape (round, lobulated, and irregular), cystic
wall thickness (0–2 mm and > 2 mm, the thickest part of
cystic wall was considered thick if it was > 2 mm and thin
if it was 2 mm or less), number of septa (0–2 and > 2), and
solid components (solid tissues, such as mural nodules,
except septa in cystic lesions).
Statistical analysis
SPSS 17 statistical software was used for statistical ana-
lysis. The measurement data were presented as the
mean ± SD and tested by the t-test. The count data was
tested by the chi-square test or continuity correction. In-
terobserver agreement was assessed by Kappa statistics.
The agreement was graded as follows: poor (0.01–0.20),
moderate (0.21–0.40), fair (0.41–0.60), good (0.61–0.80),
or excellent (0.81–1.00). The sensitivity, specificity,
positive predictive value (PPV), negative predictive value
(NPV) and area under the receiver operating charac-
teristic curves (Az) were used to analyze the efficacy of
different imaging features for the differential diagnosis
of SCNs and MCNs. The difference was considered
statistically significant at P< 0.05.
Results
Basic characteristics of the patients
As shown in Table 1, a total of 69 patients were enrolled
in this study. Thirty-eight patients (8 males, 30 females)
were confirmed to have SCNs by surgical pathology,
with an average age of 49.16 ± 14.52 (range, 18–77)
years. Thirty-one patients (4 males, 27 females) were
confirmed to have MCNs by surgical pathology, with an
average age of 46.39 ± 13.30 (range, 19–69) years. There
was no significant difference in age and sex between
SCNs and MCNs (P= 0.277).
Imaging features
The comparison of imaging features between SCNs and
MCNs is shown in Table 2.
SCNs (n=38) exhibited the widest range in size (11.4–
100 mm) with a mean size of 45.3 mm. Notably, 18/38
of SCNs were detected in a head/neck location and
20/38 were detected in a body/tail location. SCNs
were mainly lobulated (Fig. 1). A lobulated shape was
observed in 21/38 SCNs, a round shape in 10/38
cases and an irregular shape (Fig. 2) in 7/38 cases.
Thin walls (Fig. 3) were found in 29/38 of SCNs and
thick walls were detected in 9/38 cases. More than 2
septa (Fig. 1) were present in 27/38 SCNs, while 0–2
Table 1 Basic characteristics of the 69 patients enrolled
SCNs MCNs P
Sex(male/female) 8/30 4/27 0.374
Age, mean ± SD, yr 49.16 ± 13.30 46.39 ± 13.30 0.416
SCNs serous cystic neoplasms, MCNs mucinous cystic neoplasms
Zhong et al. BMC Gastroenterology (2019) 19:127 Page 2 of 8
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
septa were found in 11/38 cases. Solid components
were rare and found in only 3/38 SCNs. □
MCNs (n= 31) exhibited the widest range in size
(14.8–98.8 mm) with a mean size of 49.0 mm. Addition-
ally, 5/31 of MCNs were detected in a head/neck
location and 26/31 were detected in a body/tail location.
MCNs were mainly round-like (Fig. 4). A round shape
was detected in 24/31 MCNs, a lobulated shape in 4/31
cases and an irregular shape in 3/31 cases. Thin walls
(Fig. 5) were found in 10/31 of MCNs, and thick walls
were detected in 21/31 cases. Zero to two septa (Fig. 4)
were present in 20/31 MCNs, while > 2 septa were found
in 11/31 cases. Solid components (Fig. 6) were found in
7/38 SCNs.
Compared with the imaging features of SCNs and
MCNs, there were significant differences in the loca-
tion (P=0.006), shape (P< 0.001), cystic wall thickness
(P < 0.001), and number of septa (P= 0.003).
In the determination of location, shape, cystic wall
thickness and number of septa, there were 1 case, 3
cases, 4 cases, 2 cases with inconsistent results, respec-
tively. The interobserver agreement was excellent. The
Table 2 Comparison of imaging features between SCNs and
MCNs
Imaging features SCNs MCNs P
Size (cm), mean ± SD 4.53 ± 2.34 4.90 ± 2.38 0.520
Location
Head/neck 18 (47.4%) 5 (16.1%) 0.006
Body/tail 20 (52.6%) 26 (83.9%)
Shape 0.000
Round 10 (26.3%) 24 (77.4%)
Lobulated 21 (55.3%) 4 (12.9%)
Irregular 7 (18.4%) 3 (9.7%)
Wall thickness 0.000
Thin (0–2 mm) 29 (76.3%) 10 (32.3%)
Thick (> 2 mm) 9 (23.7%) 21 (67.7%)
Number of septa 0.003
0–2 septa 11 (28.9%) 20 (64.5%)
>2 septa 27 (71.1%) 11 (35.5%)
Solid components 0.168
Positive 3 (7.9%) 7 (22.6%)
Negative 35 (92.1%) 24 (77.4%)
SCNs serous cystic neoplasms, MCNs mucinous cystic neoplasms
Fig. 1 Serous cystic neoplasm with lobulated shape and
multiple septa
Fig. 2 Serous cystic neoplasm with irregular shape
Fig. 3 Serous cystic neoplasm with thin wall (1.8 mm)
Zhong et al. BMC Gastroenterology (2019) 19:127 Page 3 of 8
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Kappa values for identifying the four features were 0.97,
0.93, 0.88 and 0.94, respectively.
Combination of imaging features
Table 3presents the sensitivity, specificity, PPV, NPV
and Az value of the imaging features with significant
differences in Table 2in the diagnosis of SCNs.
Head/neck location, lobulated shape, thin wall (0–2 mm)
and 0–2 septa were the specific imaging features for the
diagnosis of SCNs. Head/neck location and lobulated shape
had high specificity (83.9, 87.1%, respectively) but low sen-
sitivity (47.4, 55.3%, respectively). Thin wall (0–2 mm)
and > 2 septa had limited sensitivity (76.3, 71.1%, res-
pectively), specificity (67.7, 64.5%, respectively), PPV (74.4,
70.0%, respectively), and NPV (71.1, 64.5%, respectively).
Among these features, only the Az value of lobulated
shape and thin wall (0–2 mm) in diagnosing SCNs
were greater than 0.700 (0.712, 0.720, respectively).
However, when any two imaging features were com-
bined, the sensitivity, specificity, PPV, NPV and Az
values for the diagnosis of SCNs were 84.2, 80.6, 84.2,
80.6% and 0.824, respectively. When any three im-
aging features were combined, the sensitivity, specifi-
city, PPV, NPV and Az values were 55.3, 93.5, 91.3,
63.0%, and 0.744, respectively. When the four imaging
features were combined, the specificity was as high as
100%, the sensitivity was only 15.8%, and the Az value
was also reduced to 0.579 (Fig. 7). Through com-
parative analysis, we determine a criterion, that is, pan-
creatic cystadenomas that meet any two imaging features
could be diagnosed as SCNs.
Table 4presents the sensitivity, specificity, PPV, NPV
and Az value of the imaging features with significant
differences in Table 2in the diagnosis of MCNs.
Body/tail location, round shape, thick wall (> 2 mm)
and 0–2 septa were the specific imaging features for the
diagnosis of MCNs. Body/tail location had a relatively
high sensitivity (83.9%) but a low specificity (47.4%).
Round shape, thick wall (> 2 mm) and 0–2 septa had
similar specificity (73.7, 76.3, 70.6%, respectively) in the
diagnosis of MCNs, while round shape had a relatively
high Az value (0.756). The sensitivity, PPV and NPV of
round shape in the diagnosis of MCNs were 77.4, 70.6,
80%. When any two imaging features were combined,
the sensitivity, specificity, PPV, NPV and Az values for
the diagnosis of MCNs were 90.3, 60.5, 65.1, 88.5%, and
0.754, respectively. When any three imaging features
were combined, we achieved the highest Az value
(0.808), with an appropriate specificity, sensitivity, PPV
and NPV (77.4, 84.2, 80, 82.1%, respectively). When the
Fig. 4 Mucinous cystic neoplasm with round shape and
unilocular cyst
Fig. 5 Mucinous cystic neoplasm with thick wall (3.2 mm)
Fig. 6 Mucinous cystic neoplasm with solid component
Zhong et al. BMC Gastroenterology (2019) 19:127 Page 4 of 8
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
four imaging features were combined, the specificity was
as high as 92.1%, the sensitivity was only 25.8%, and the
Az value was also reduced to 0.590 (Fig. 8). Through
comparative analysis, we determine a criterion, that is,
pancreatic cystadenomas that meet any three imaging
features could be considered as MCNs.
Discussion
PCLs can be divided into neoplastic cystic lesions and
nonneoplastic cystic lesions. Nonneoplastic cystic lesions
mainly refer to pseudocysts. Neoplastic cystic lesions
mainly include SCNs, MCNs, intraductal papillary mu-
cinous neoplasms (IPMNs) and cystic degeneration of
solid tumors. The pancreatic solid-cystic tumors are rare
and relatively easy to diagnose. Therefore, it is crucial to
differentiate SCNs and MCNs from pseudocysts and
IPMNs. Pseudocysts mostly form after inflammation,
necrosis or hemorrhage related to pancreatitis or trauma,
and are enclosed by a wall with fibrous tissue. Pseudocysts
mainly manifest as round cysts, generally without septa
[11]. IPMNs can be classified as main duct IPMNs
(MD-IPMNs) and branch duct IPMNs (BD-IPMNs).
The MD-IMPNs are characterized by segmental or
diffuse dilatation of the main pancreatic duct, which
may resemble chronic pancreatitis [12]. The BD-
IPMNs are composed of cysts that communicate with
the main pancreatic duct [12]. Both IPMN and pseu-
docysts are predominant in men and are prone to
pancreatitis [13,14]. Our study revealed that SCNs
and MCNs occurred more frequently in women,
which was consistent with previous studies. It was
relatively simple to differentiate pseudocysts and
IPMNsfromSCNsandMCNs.
SCNs have different biological characteristics than
MCNs. SCNs are generally benign, with only 1 to 3%
malignant potential [3], and can be followed up [5].
Approximately 10% of SCNs are manifested as unilocu-
lar without septa, which are easily misdiagnosed as
MCNs [13]. MCNs have malignant potential and are
recommended for surgical resection once an adequate
diagnosis has been performed [6,7]. Therefore, it is of
great significance to correctly differentiate between
SCNs and MCNs for the appropriate treatment. In
2005, Sahani et al. [15] proposed a simple imaging-
based classification system for guiding the management
of PCLs. PCLs were classified into unilocular cysts,
microcystic lesions, macrocystic lesions and cysts with
a solid component. However, this classification system
did not effectively identify SCNs and MCNs. In 2017,
Zhang WG et al. [16] first proposed a new criterion to
differentiate between SCNs and MCNs by EUS. This
study enrolled only 41 patients diagnosed with SCNs
and MCNs. The sample size included was limited. At
present, there is no uniform standard for the dif-
ferential diagnosis of SCNs and MCNs, and it is still
difficult to accurately differentiate between SCNs and
MCNs.
In our study, SCNs and MCNs were predisposed to
occur in middle-aged women (49.16 years vs 46.39 years),
which was slightly different from previous studies.
Relevant studies have demonstrated that MCNs occurred
almost exclusively in women (> 98%) and were generally
Table 3 Specificity, sensitivity, PPV, NPV and Az value of the imaging features in the diagnosis of SCNs
Imaging features Sensitivity (95% CI) Specificity (95% CI) PPV (95% CI) NPV (95% CI) Az value (95% CI)
Head/neck location 47.4% (31.3–64.0%) 83.9% (65.5–93.9%) 78.3% (55.8–91.7%) 56.5% (41.2–70.8%) 0.656 (52.7–78.6%)
Lobulated shape 55.3% (38.5–71.0%) 87.1% (69.2–95.8%) 84.0% (63.1–94.7%) 61.3% (45.5–75.3%) 0.712 (58.9–83.5%)
Thin wall 76.3% (59.3–88.0%) 67.7% (48.5–82.7%) 74.4% (57.6–96.4%) 70.0% (50.4–84.6%) 0.720 (59.6–84.5%)
> 2 septa 71.1% (53.9–84.0%) 64.5% (45.4–80.2%) 71.1% (53.9–84.0%) 64.5% (45.3–80.2%) 0.678 (54.9–80.7%)
Two features 84.2% (68.1–93.4%) 80.6% (61.9–91.9%) 84.2% (68.1–93.4%) 80.6% (61.9–91.9%) 0.824 (71.9–93.0%)
Three features 55.3% (38.5–71.0%) 93.5% (77.2–98.9%) 91.3% (70.5–98.5%) 63.0% (47.5–76.4%) 0.744 (62.7–86.1%)
Four features 15.8% (6.6–31.9%) 100% (86.2–100%) 100% (51.7–100%) 49.2% (36.5–62.0%) 0.579 (44.5–71.3%)
SCNs serous cystic neoplasms, PPV positive predictive value, NPV negative predictive value, CI confidence interval
Fig. 7 Graph shows the ROC curves of the imaging features in the
diagnosis of SCNs
Zhong et al. BMC Gastroenterology (2019) 19:127 Page 5 of 8
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
diagnosed in patients in their 40s and 50s [17,18], while
SCNs occurred more commonly in women, who typically
presented in their 60s [19]. The difference may be related
to the insufficient sample size in our study.
SCNs and MCNs have different histological characte-
ristics. The location distribution of SCNs was different
from that of MCNs. MCNs were prone to occur in the
body/tail location, and SCNs were more likely to occur in
the head/neck location, which was in accordance with
previous literature [20,21]. SCNs are mainly lobulated,
while MCNs are mainly round-like [22]. Multiple thin
septa can be detected in SCNs. According to the number
and size of daughter cysts, SCNs can be classified as
microcystic, mixed macrocystic and microcystic, macro-
cystic, and solid types. Microcystic SCNs are composed of
multiple cysts of varying sizes, from a few millimeters up
to two centimeters. Macrocystic SCNs are characterized
by a predominantly or exclusively unilocular pattern [23],
which is also called an “oligo-cystic pattern”.Regarding
septa, the “oligo-cystic pattern”refers to the 0–2septa
pattern. Previous studies have demonstrated that micro-
cystic SCNs accounted for approximately 45%, while
macrocystic SCNs accounted for approximately 32% [24],
which was similar to our results. MCNs usually present as
round macrocystic lesions with no or few septa [12]. The
cystic wall of SCNs is thinner than that of MCNs. Khurana
Betal.[25] revealed that the cystic wall was less than 2
mm thick in four (80%) of the five SCNs.
Head/neck location, lobulated shape, thin wall (0–2 mm)
and > 2 septa were the specific imaging features in the
diagnosis of SCNs. The Az value of a single imaging
feature in the diagnosis of SCNs is not ideal. When
any two imaging features were combined, we obtained
the highest Az value (0.824) in diagnosing SCNs, as
well as the appropriate sensitivity (84.2%), specificity
(80.6%), PPV (84.2%), NPV(80.6%) which was almost
consistent with study by Sun Y [14]. The four specific
imaging features used in the study by Sun Y [14]for
the diagnosis of SCNs were head/neck location,
lobulated shape, thin wall (0–2 mm) and honeycomb
pattern, which were slightly different from the imaging
features used in our study. We believed that the honey-
comb pattern was more specific for the diagnosis of
SCNs than the number of septa > 2 but excluded a large
portion of atypical SCNs. In our study, > 2 septa were
eventually used as one of the four EUS image features,
and we achieved good diagnostic efficacy.
Body/tail location, round shape, thick wall and 0–2
septa were the specific imaging features for the diag-
nosis of MCNs. Compared with any other single im-
aging signs in the diagnosis of MCNs, round shape
had the highest Az value (0.756), with a relatively
appropriate sensitivity (77.4%), specificity (73.7%),
PPV (70.6%) and NPV (80.0%). When the imaging
features were combined, the combined diagnosis of
any three features could obtain the highest Az value
(0.808), as well as the appropriate sensitivity (77.4%),
specificity (84.2%), PPV (80.0%) and NPV (82.1%),
which was almost consistent with the results reported
by Sun Y [14]. The combined diagnosis of any three
features was more advantageous than the round shape
feature in the diagnosis of MCNs.
Table 4 Specificity, sensitivity, PPV, NPV and Az value of the imaging features in the diagnosis of MCNs
Imaging features Sensitivity (95% CI) Specificity (95% CI) PPV (95% CI) NPV (95% CI) Az value (95% CI)
Body/tail location 83.8% (65.5–93.9%) 47.4% (31.3–64.0%) 56.5% (41.2–70.8%) 78.3% (55.8–91.7%) 0.656 (52.7–78.6%)
Round shape 77.4% (58.5–89.7%) 73.7% (56.6–86.0%) 70.6% (52.3–84.3%) 80.0% (62.5–90.9%) 0.756 (63.7–87.4%)
Thick wall 67.7% (48.5–82.7%) 76.3% (59.4–88.0%) 70.0% (50.4–84.6%) 74.4% (57.6–86.4%) 0.720 (59.6–84.5%)
0–2 septa 64.5% (45.4–80.2%) 71.1% (53.9–84.0%) 64.5% (45.4–80.2%) 71.1% (53.9–84.0%) 0.678 (54.9–80.7%)
Two features 90.3% (73.1–97.4%) 60.5% (43.5–75.5%) 65.1% (49.0–78.5%) 88.5% (68.7–97.0%) 0.754 (63.8–87.1%)
Three features 77.4% (58.5–89.7%) 84.2% (68.1–93.4%) 80.0% (60.8–91.6%) 82.1% (65.9–91.9%) 0.808 (69.6–91.7%)
Four features 25.8% (12.5–44.9%) 92.1% (77.5–97.9%) 72.7% (39.3–92.7%) 60.3% (46.6–72.7%) 0.590 (45.2–72.7%)
MCNs mucinous cystic neoplasms, PPV positive predictive value, NPV negative predictive value, CI confidence interval
Fig. 8 Graph shows the ROC curves of the imaging features in the
diagnosis of MCNs
Zhong et al. BMC Gastroenterology (2019) 19:127 Page 6 of 8
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
There are three limitations in our research. First, the
sample size is not large enough; second, the criteria are
only applicable to the differential diagnosis of SCNs and
MCNs, but not to the diagnosis of other PCLs; third,
due to the limited sample size, we did not conduct a
comparative study of macrocystic SCNs and MCNs.
Conclusion
Head/neck location, lobulated shape, thin wall (0–2
mm) and > 2 septa were the specific imaging features
for the diagnosis of SCNs. Pancreatic cystadenomas
that meet any two imaging features could be diag-
nosed as SCNs.
Body/tail location, round shape, thick wall (> 2 mm) and
0–2 septa were the specific imaging features for the diagno-
sis of MCNs. Pancreatic cystadenomas that meet any three
imaging features could be considered as MCNs.
Abbreviations
BD-IPMNs: branch duct IPMNs; CI: confidence interval; CT: computed
tomography; EUS: endoscopic ultrasound; IPMNs: intraductal papillary
mucinous neoplasms; MCNs: mucinous cystic neoplasms; MD-IPMNs: main
duct IPMNs; MRI: magnetic resonance imaging; NPV: negative predictive
value; PCLs: pancreatic cystic lesions; PCNs: pancreatic cystic neoplasms;
PPV: positive predictive value; SCNs: serous cystic neoplasms
Acknowledgments
Not applicable.
Authors’contributions
Conception and design: EL, NC. Performed the experiments: LZ, NC, EL, HL,
JY, PT. Analysis and interpretation of the data: LZ. Drafting of the manuscript:
LZ. Critical revision of the article for important intellectual content: EL, NC.
Final approval of the manuscript: EL. All authors have read and approved the
final version of this manuscript.
Funding
This study was supported by the Scientific Research Fund of Army of China
(No. 14BJZ01). The funding body has no role in the design of the study and
collection, analysis and interpretation of data and in writing the manuscript.
Availability of data and materials
All datasets used and analyzed during the current study are available from
the corresponding author upon reasonable request.
Ethics approval and consent to participate
This study was approved by the Ethics Committee of the Chinese People’s
Liberation Army General Hospital. All patients signed informed consent form.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Received: 10 March 2019 Accepted: 24 June 2019
References
1. Adsay NV. Cystic neoplasia of the pancreas: pathology and biology. Journal
of gastrointestinal surgery: official journal of the Society for Surgery of the
Alimentary Tract. 2008;12(3):401–4.
2. Demos TC, Posniak HV, Harmath C, Olson MC, Aranha G. Cystic lesions of
the pancreas. AJR Am J Roentgenol. 2002;179(6):1375–88.
3. Van Dyke TJ, Johlin FC, Bellizzi AM, Howe JR. Serous cystadenocarcinoma of
the pancreas: clinical features and Management of a Rare Tumor. Dig Surg.
2016;33(3):240–8.
4. Prasad HL, Ballal R, Upadhyaya V, Makannavar JH, Shetty KJ, Prakash HM.
Serous microcystic adenoma to adenocarcinoma of pancreas-a case report.
Indian journal of surgical oncology. 2015;6(1):82–5.
5. Khashab MA, Shin EJ, Amateau S, Canto MI, Hruban RH, Fishman EK,
Cameron JL, Edil BH, Wolfgang CL, Schulick RD, et al. Tumor size and
location correlate with behavior of pancreatic serous cystic neoplasms. Am
J Gastroenterol. 2011;106(8):1521–6.
6. Kloppel G, Kosmahl M. Cystic lesions and neoplasms of the pancreas. The
features are becoming clearer. Pancreatology: official journal of the
International Association of Pancreatology. 2001;1(6):648–55.
7. Tanaka M, Fernandez-del Castillo C, Adsay V, Chari S, Falconi M, Jang JY,
Kimura W, Levy P, Pitman MB, Schmidt CM, et al. International consensus
guidelines 2012 for the management of IPMN and MCN of the pancreas.
Pancreatology: official journal of the International Association of
Pancreatology. 2012;12(3):183–97.
8. Du C, Chai NL, Linghu EQ, Li HK, Sun LH, Jiang L, Wang XD, Tang P, Yang J.
Comparison of endoscopic ultrasound, computed tomography and
magnetic resonance imaging in assessment of detailed structures of
pancreatic cystic neoplasms. World J Gastroenterol. 2017;23(17):3184–92.
9. Kim YH, Saini S, Sahani D, Hahn PF, Mueller PR, Auh YH. Imaging diagnosis
of cystic pancreatic lesions: pseudocyst versus nonpseudocyst.
Radiographics: a review publication of the Radiological Society of North
America, Inc. 2005;25(3):671–85.
10. Dewhurst CE, Mortele KJ. Cystic tumors of the pancreas: imaging and
management. Radiol Clin N Am. 2012;50(3):467–86.
11. Rickes S, Wermke W. Differentiation of cystic pancreatic neoplasms and
pseudocysts by conventional and echo-enhanced ultrasound. J
Gastroenterol Hepatol. 2004;19(7):761–6.
12. Acar M, Tatli S. Cystic tumors of the pancreas: a radiological perspective.
Diagn Interv Radiol. 2011;17(2):143–9.
13. Kubo H, Nakamura K, Itaba S, Yoshinaga S, Kinukawa N, Sadamoto Y, Ito T,
Yonemasu H, Takayanagi R. Differential diagnosis of cystic tumors of the
pancreas by endoscopic ultrasonography. Endoscopy. 2009;41(8):684–9.
14. Sun Y, Zhou F, Liu F, Hu Y, Tan S, Liang P, Linghu E, Yu X. Discrimination of
serous cystadenoma from mucinous cystadenoma in the pancreas with
contrast-enhanced ultrasonography: a prospective study in 61 patients.
OncoTargets and therapy. 2017;10:1285–94.
15. Sahani DV, Kadavigere R, Saokar A, Fernandez-del Castillo C, Brugge WR,
Hahn PF. Cystic pancreatic lesions: a simple imaging-based classification
system for guiding management. Radiographics: a review publication of the
Radiological Society of North America, Inc. 2005;25(6):1471–84.
16. Zhang W, Linghu E, Chai N, Li H. New criteria to differentiate between
mucinous cystic neoplasm and serous cystic neoplasm in pancreas by
endoscopic ultrasound: a preliminarily confirmed outcome of 41 patients.
Endoscopic ultrasound. 2017;6(2):116–22.
17. Goh BK, Tan YM, Chung YF, Chow PK, Cheow PC, Wong WK, Ooi LL. A
review of mucinous cystic neoplasms of the pancreas defined by ovarian-
type stroma: clinicopathological features of 344 patients. World J Surg. 2006;
30(12):2236–45.
18. Park JW, Jang JY, Kang MJ, Kwon W, Chang YR, Kim SW. Mucinous cystic
neoplasm of the pancreas: is surgical resection recommended for all
surgically fit patients? Pancreatology: official journal of the International
Association of Pancreatology. 2014;14(2):131–6.
19. Scheiman JM, Hwang JH, Moayyedi P. American gastroenterological association
technical review on the diagnosis and management of asymptomatic neoplastic
pancreatic cysts. Gastroenterology. 2015;148(4):824–848 e822.
20. Farrell JJ, Fernandez-del Castillo C. Pancreatic cystic neoplasms: management
and unanswered questions. Gastroenterology. 2013;144(6):1303–15.
21. Sakorafas GH, Smyrniotis V, Reid-Lombardo KM, Sarr MG. Primary
pancreatic cystic neoplasms revisited. Part I: serous cystic neoplasms.
Surg Oncol. 2011;20(2):e84–92.
22. HijiokaS,HaraK,MizunoN,ImaokaH,BhatiaV,YamaoK.
Morphological differentiation and follow-up of pancreatic cystic
neoplasms using endoscopic ultrasound. Endoscopic ultrasound. 2015;
4(4):312–8.
23. Lewandrowski K, Warshaw A, Compton C. Macrocystic serous cystadenoma
of the pancreas: a morphologic variant differing from microcystic adenoma.
Hum Pathol. 1992;23(8):871–5.
Zhong et al. BMC Gastroenterology (2019) 19:127 Page 7 of 8
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
24. Jais B, Rebours V, Malleo G, Salvia R, Fontana M, Maggino L, Bassi C, Manfredi R,
Moran R, Lennon AM, et al. Serous cystic neoplasm of the pancreas: a
multinational study of 2622 patients under the auspices of the International
Association of Pancreatology and European Pancreatic Club (European study
group on cystic tumors of the pancreas). Gut. 2016;65(2):305–12.
25. Khurana B, Mortele KJ, Glickman J, Silverman SG, Ros PR. Macrocystic serous
adenoma of the pancreas: radiologic-pathologic correlation. AJR Am J
Roentgenol. 2003;181(1):119–23.
Publisher’sNote
Springer Nature remains neutral with regard to jurisdictional claims in published
maps and institutional affiliations.
Zhong et al. BMC Gastroenterology (2019) 19:127 Page 8 of 8
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
1.
2.
3.
4.
5.
6.
Terms and Conditions
Springer Nature journal content, brought to you courtesy of Springer Nature Customer Service Center GmbH (“Springer Nature”).
Springer Nature supports a reasonable amount of sharing of research papers by authors, subscribers and authorised users (“Users”), for small-
scale personal, non-commercial use provided that all copyright, trade and service marks and other proprietary notices are maintained. By
accessing, sharing, receiving or otherwise using the Springer Nature journal content you agree to these terms of use (“Terms”). For these
purposes, Springer Nature considers academic use (by researchers and students) to be non-commercial.
These Terms are supplementary and will apply in addition to any applicable website terms and conditions, a relevant site licence or a personal
subscription. These Terms will prevail over any conflict or ambiguity with regards to the relevant terms, a site licence or a personal subscription
(to the extent of the conflict or ambiguity only). For Creative Commons-licensed articles, the terms of the Creative Commons license used will
apply.
We collect and use personal data to provide access to the Springer Nature journal content. We may also use these personal data internally within
ResearchGate and Springer Nature and as agreed share it, in an anonymised way, for purposes of tracking, analysis and reporting. We will not
otherwise disclose your personal data outside the ResearchGate or the Springer Nature group of companies unless we have your permission as
detailed in the Privacy Policy.
While Users may use the Springer Nature journal content for small scale, personal non-commercial use, it is important to note that Users may
not:
use such content for the purpose of providing other users with access on a regular or large scale basis or as a means to circumvent access
control;
use such content where to do so would be considered a criminal or statutory offence in any jurisdiction, or gives rise to civil liability, or is
otherwise unlawful;
falsely or misleadingly imply or suggest endorsement, approval , sponsorship, or association unless explicitly agreed to by Springer Nature in
writing;
use bots or other automated methods to access the content or redirect messages
override any security feature or exclusionary protocol; or
share the content in order to create substitute for Springer Nature products or services or a systematic database of Springer Nature journal
content.
In line with the restriction against commercial use, Springer Nature does not permit the creation of a product or service that creates revenue,
royalties, rent or income from our content or its inclusion as part of a paid for service or for other commercial gain. Springer Nature journal
content cannot be used for inter-library loans and librarians may not upload Springer Nature journal content on a large scale into their, or any
other, institutional repository.
These terms of use are reviewed regularly and may be amended at any time. Springer Nature is not obligated to publish any information or
content on this website and may remove it or features or functionality at our sole discretion, at any time with or without notice. Springer Nature
may revoke this licence to you at any time and remove access to any copies of the Springer Nature journal content which have been saved.
To the fullest extent permitted by law, Springer Nature makes no warranties, representations or guarantees to Users, either express or implied
with respect to the Springer nature journal content and all parties disclaim and waive any implied warranties or warranties imposed by law,
including merchantability or fitness for any particular purpose.
Please note that these rights do not automatically extend to content, data or other material published by Springer Nature that may be licensed
from third parties.
If you would like to use or distribute our Springer Nature journal content to a wider audience or on a regular basis or in any other manner not
expressly permitted by these Terms, please contact Springer Nature at
onlineservice@springernature.com
Available via license: CC BY 4.0
Content may be subject to copyright.
