Evidence map›Paper›PMID 36673048›Full record

ArticleDiagnostics (Basel, Switzerland)2023

Prediction of Pathological Grades of Pancreatic Neuroendocrine Tumors Based on Dynamic Contrast-Enhanced Ultrasound Quantitative Analysis.

Dao-Hui Yang, Juan Cheng, Xiao-Fan Tian, Qi Zhang, Ling-Yun Yu, Yi-Jie Qiu, Xiu-Yun Lu, Wen-Hui Lou, Yi Dong, Wen-Ping Wang

Open access · goldAbstract read
In one paragraph

Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
1.9field-weighted citation impact, top 14% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed, 9 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors at 6 institutions in 1 country.

Dao-Hui YangDepartment of Ultrasound, Zhongshan Hospital, Fudan University (Xiamen Branch), Xiamen 361006, China.
Juan ChengDepartment of Ultrasound, Xinhua Hospital Affiliated to Shanghai Jiaotong University School of Medicine, Shanghai 200092, China.
Xiao-Fan TianDepartment of Ultrasound, Zhongshan Hospital, Fudan University, Shanghai 200092, China.
Qi ZhangDepartment of Ultrasound, Zhongshan Hospital, Fudan University, Shanghai 200092, China.
Ling-Yun YuDepartment of Ultrasound, Zhongshan Hospital, Fudan University (Xiamen Branch), Xiamen 361006, China.
Yi-Jie QiuDepartment of Ultrasound, Zhongshan Hospital, Fudan University, Shanghai 200092, China.
Xiu-Yun LuDepartment of Ultrasound, Zhongshan Hospital, Fudan University, Shanghai 200092, China.
Wen-Hui LouDepartment of Pancreatic Surgery, Zhongshan Hospital, Fudan University, Shanghai 200032, China.
Yi DongDepartment of Ultrasound, Xinhua Hospital Affiliated to Shanghai Jiaotong University School of Medicine, Shanghai 200092, China.
Wen-Ping WangDepartment of Ultrasound, Zhongshan Hospital, Fudan University, Shanghai 200092, China.
Fudan University · CNThe First Affiliated Hospital, Sun Yat-sen University · CNXinHua Hospital · CNZhongshan Hospital · CNSun Yat-sen University · CNZhongshan Hospital of Xiamen University · CN

Funding

Clinical Research Plan of SHDC SHDC2020CR1031BNational Natural Science Foundation of China 82071942Natural Science Foundation of Fujian Province, China 2022J011420Shanghai Pujiang Program 2020PJD008Xiamen Medical and Health Guidance Project, China 3502Z20224ZD1090
6 · The paper itself

Abstract

Objective: To investigate whether the dynamic contrast-enhanced ultrasound (DCE-US) analysis and quantitative parameters could be helpful for predicting histopathologic grades of pancreatic neuroendocrine tumors (pNETs). Methods: This retrospective study conducted a comprehensive review of the CEUS database between March 2017 and November 2021 in Zhongshan Hospital, Fudan University. Ultrasound examinations were performed by an ACUSON Sequioa unit equipped with a 3.5 MHz 6C−1 convex array transducer, and an ACUSON OXANA2 unit equipped with a 3.5 MHz 5C−1 convex array transducer. SonoVue® (Bracco Inc., Milan, Italy) was used for all CEUS examinations. Time intensity curves (TICs) and quantitative parameters of DCE-US were created by Vuebox® software (Bracco, Italy). Inclusion criteria were: patients with histopathologically proved pNETs, patients who underwent pancreatic B-mode ultrasounds (BMUS) and CEUS scans one week before surgery or biopsy and had DCE-US imaging documented for more than 2 min, patients with solid or predominantly solid lesions and patients with definite diagnosis of histopathological grades of pNETs. Based on their prognosis, patients were categorized into two groups: pNETs G1/G2 group and pNETs G3/pNECs group. Results: A total of 42 patients who underwent surgery (n = 38) or biopsy (n = 4) and had histopathologically confirmed pNETs were included. According to the WHO 2019 criteria, all pNETs were classified into grade 1 (G1, n = 10), grade 2 (G2, n = 21), or grade 3 (G3)/pancreatic neuroendocrine carcinomas (pNECs) (n = 11), based on the Ki−67 proliferation index and the mitotic activity. The majority of the TICs (27/31) of pNETs G1/G2 were above or equal to those of pancreatic parenchyma in the arterial phase, but most (7/11) pNETs G3/pNECs had TICs below those of pancreatic parenchyma from arterial phase to late phase (p < 0.05). Among all the CEUS quantitative parameters of DCE-US, values of relative rise time (rPE), relative mean transit time (rmTT) and relative area under the curve (rAUC) were significantly higher in pNETs G1/G2 group than those in pNETs G3/pNECs group (p < 0.05). Taking an rPE below 1.09 as the optimal cut-off value, the sensitivity, specificity and accuracy for prediction of pNETs G3/pNECs from G1/G2 were 90.91% [58.70% to 99.80%], 67.64% [48.61% to 83.32%] and 85.78% [74.14% to 97.42%], respectively. Taking rAUC below 0.855 as the optimal cut-off value, the sensitivity, specificity and accuracy for prediction of pNETs G3/pNECs from G1/G2 were 90.91% [66.26% to 99.53%], 83.87% [67.37% to 92.91%] and 94.72% [88.30% to 100.00%], respectively. Conclusions: Dynamic contrast-enhanced ultrasound analysis might be helpful for predicting the pathological grades of pNETs. Among all quantitative parameters, rPE, rmTT and rAUC are potentially useful parameters for predicting G3/pNECs with aggressive behavior.

Indexed as

dynamic contrast-enhanced ultrasound (DCE-US)pancreatic neuroendocrine tumors (pNETs)predictionquantitative parameterstumor histopathological grades

Identifiers

PMID36673048
PMCPMC9858178
OpenAlexW4313825590

What Socratic holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.