Evidence map›Paper›PMID 36098760›Full record

ArticleAbdominal radiology (New York)2022

Classification of pancreatic cystic neoplasms using radiomic feature analysis is equivalent to an experienced academic radiologist: a step toward computer-augmented diagnostics for radiologists.

Linda C Chu, Seyoun Park, Sahar Soleimani, Daniel F Fouladi, Shahab Shayesteh, Jin He, Ammar A Javed, Christopher L Wolfgang, Bert Vogelstein, Kenneth W Kinzler and 5 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Abdominal radiology (New York), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed
–field-weighted citation impact
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

19 citing papers in PubMed.

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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

15 authors.

Linda C ChuRussell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA. lchu1@jhmi.edu.ORCID 0000-0001-9729-2756
Seyoun ParkRussell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Sahar SoleimaniRussell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Daniel F FouladiRussell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Shahab ShayestehRussell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Jin HeDepartment of Surgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Ammar A JavedDepartment of Surgery, New York University Grossman School of Medicine, New York, NY, USA.
Christopher L WolfgangDepartment of Surgery, New York University Grossman School of Medicine, New York, NY, USA.
Bert VogelsteinSidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Kenneth W KinzlerSidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Ralph H HrubanDepartment of Pathology, Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Elham AfghaniDivision of Gastroenterology and Hepatology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Anne Marie LennonDivision of Gastroenterology and Hepatology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Elliot K FishmanRussell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Satomi KawamotoRussell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA.

Funding

Tumor Antigens for Individual Signatures and TherapyP50CA062924 · NCI · JOHNS HOPKINS UNIVERSITY · PI JAFFEE, ELIZABETH M. · 1993 to 2022
$54.6M
NCI NIH HHS P50 CA062924
6 · The paper itself

Abstract

purposeA wide array of benign and malignant lesions of the pancreas can be cystic and these cystic lesions can have overlapping imaging appearances. The purpose of this study is to compare the diagnostic accuracy of a radiomics-based pancreatic cyst classifier to an experienced academic radiologist.

methodsIn this IRB-approved retrospective single-institution study, patients with surgically resected pancreatic cysts who underwent preoperative abdominal CT from 2003 to 2016 were identified. Pancreatic cyst(s) and background pancreas were manually segmented, and 488 radiomics features were extracted. Random forest classification based on radiomics features, age, and gender was evaluated with fourfold cross-validation. An academic radiologist blinded to the final pathologic diagnosis reviewed each case and provided the most likely diagnosis.

results214 patients were included (64 intraductal papillary mucinous neoplasms, 33 mucinous cystic neoplasms, 60 serous cystadenomas, 24 solid pseudopapillary neoplasms, and 33 cystic neuroendocrine tumors). The radiomics-based machine learning approach showed AUC of 0.940 in pancreatic cyst classification, compared with AUC of 0.895 for the radiologist.

conclusionRadiomics-based machine learning achieved equivalent performance as an experienced academic radiologist in the classification of pancreatic cysts. The high diagnostic accuracy can potentially maximize the efficiency of healthcare utilization by maximizing detection of high-risk lesions.

Indexed as

Pancreatic CystPancreatic NeoplasmsComputersHumansRadiologistsRetrospective StudiesCTIntraductal pancreatic mucinous neoplasm (IPMN)Machine learningPancreatic cystsPancreatic neoplasmsRadiomics

Identifiers

PMID36098760
PMCPMC10548448

What OpenQuestion holds

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Read underepoch 390

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 OpenQuestion graph.