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Clinical Analysis of Early-Stage Pancreatic Cancer and Proposal for a New Diagnostic Algorithm: A Multicenter Observational Study

池本珠莉広島大学

2021.09.17

概要

Patients with pancreatic ductal adenocarcinoma (PDAC) are generally diagnosed at
the advanced stage and generally have a very poor prognosis. The American Cancer Society
estimated that in 2019, approximately 56,770 patients were diagnosed with PDAC in the
USA and 45,750 would be dead due to the disease [1]. The 5-year survival rate for patients
with PDAC is only 6 to 8% in the USA [2]. The national Cancer Center Japan reported
that 35,390 individuals died from PDAC in Japan in 2018 [3]. In contrast, according to an
analysis of the recent Japan Pancreatic Cancer Registry, which included more than 350 highvolume centers, the 5-year survival rate of PDAC patients with a tumor diameter <10 mm
was 80.4%, and that for stage 0 PDAC patients defined by the Union for International
Cancer Control (UICC) was 85.8% [4]. However, the cancer registry has reported that
UICC stage 0 and IA patients accounted for only 1.7% and 4.1%, respectively, of all PDAC
patients [4]. These findings indicate that although early diagnosis is essential for improving
the prognosis of patients with PDAC [5], it remains to be a great challenge.
Recently, several reports on imaging features of early-stage PDAC have been published [6–9]. Indirect imaging findings, such as dilatation or stenosis of main pancreatic
duct (MPD), pancreatic cysts, and local fatty changes were crucial signs of early-stage
PDAC [6–9]. However, no consensus on the diagnostic algorithm and pathological diagnosis has been established yet. Additionally, there are typically only few patients with
early-stage PDAC in each single institution, and it has proved difficult to study these.
These previous observations prompted us to establish this multicenter study to clarify
the clinicopathological features of early-stage PDAC and to propose an effective diagnostic
algorithm for detecting early-stage PDAC based on the data from this study.
2. Materials and Methods
2.1. Patients
This study was a retrospective, multicenter, observational study. From January 2000
to September 2020, patients with early-stage PDAC from Hiroshima University Hospital
and 12 affiliated high-volume centers were enrolled. Early-stage PDAC was defined as
patients with stage 0 (high-grade pancreatic intraepithelial neoplasia/pancreatic carcinoma
in situ (PCIS)) and stage IA (invasive carcinoma with tumor diameter of <20 mm localized
within the pancreas, along with the absence of regional lymph nodes metastasis and distant
metastasis) based on post-operative pathological classification according to the seventh
edition of the Japanese Classification of Pancreatic Carcinoma [10]. Patients with intraductal
papillary mucinous neoplasm (IPMN) concomitant PDAC were included, but patients with
high-grade IPMN or IPMN-derived invasive cancer showing a histologic transition between
IPMN and PDAC were excluded. In stage 0 cases, an experienced pancreatic pathologist
not affiliated with the participating institutions reevaluated each pathologic specimen for
tumor characteristics, including validity of the diagnosis, histological type, invasion of the
tumor, and exclusion of IPMN and IPMN-derived invasive cancer, regardless of previous
diagnosis that had been made at each of the institutions.
We retrospectively reviewed data on the following items: (1) patient characteristics,
(2) reasons for medical examination, (3) blood tests and imaging findings, and (4) preoperative pathological examination.
2.2. Imaging and Pathological Diagnosis
2.2.1. Imaging Diagnosis
Depending on the facilities available at each institution, CT (computed tomography)
was performed using a multidetector row (from 64 to 320 slices). CT included unenhanced
and contrast material-enhanced biphasic imaging, the latter of which comprised arterial
and portal phases. Magnetic resonance imaging (MRI) was performed using 1.5 or 3 T. ...

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Clinical Analysis of Early-Stage Pancreatic Cancer and Proposal for a New Diagnostic Algorithm: A Multicenter Observational Study

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