Evidence map›Paper›PMID 38378839›Full record

ArticleAbdominal radiology (New York)2024

Utilization of texture features of volumetric ADC maps in differentiating between serous cystadenoma and intraductal papillary neoplasms.

Golnoosh Ansari, Mohammad Mirza-Aghazadeh-Attari, Shadi Afyouni, Alireza Mohseni, Haneyeh Shahbazian, Ihab R Kamel

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Article in Abdominal radiology (New York), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.8field-weighted citation impact, top 25% 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

3 citing papers in PubMed, 3 citations in OpenAlex.

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4 · The record

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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

6 authors at 1 institution in 1 country.

Golnoosh Ansari *Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins University, 600 North Wolfe Street, MRI 143, Baltimore, MD, 21287, USA.
Mohammad Mirza-Aghazadeh-Attari *Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins University, 600 North Wolfe Street, MRI 143, Baltimore, MD, 21287, USA.
Shadi AfyouniRussell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins University, 600 North Wolfe Street, MRI 143, Baltimore, MD, 21287, USA.
Alireza MohseniRussell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins University, 600 North Wolfe Street, MRI 143, Baltimore, MD, 21287, USA.
Haneyeh ShahbazianRussell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins University, 600 North Wolfe Street, MRI 143, Baltimore, MD, 21287, USA.
Ihab R KamelRussell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins University, 600 North Wolfe Street, MRI 143, Baltimore, MD, 21287, USA. ikamel@jhmi.edu.ORCID 0000-0001-7927-6912
Johns Hopkins University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionThe rising incidence of incidental detection of pancreatic cystic neoplasms has compelled radiologists to determine new diagnostic methods for the differentiation of various kinds of lesions. We aim to demonstrate the utility of texture features extracted from ADC maps in differentiating intraductal papillary mucinous neoplasms (IPMN) from serous cystadenomas (SCA).

methodsThis retrospective study was performed on 136 patients (IPMN = 87, SCA = 49) split into testing and training datasets. A total of 851 radiomics features were extracted from volumetric contours drawn by an expert radiologist on ADC maps of the lesions. LASSO regression analysis was used to determine the most predictive set of features and a radiomics score was developed based on their respective coefficients. A hyper-optimized support vector machine was then utilized to classify the lesions based on their radiomics score.

resultsA total of four Wavelet features (LHL/GLCM/LCM2, HLL/GLCM/LCM2, /LLL/First Order/90percent, /LLL/GLCM/MCC) were selected from all of the features to be included in our classifier. The classifier was optimized by altering hyperparameters and the trained model was applied to the validation dataset. The model achieved a sensitivity of 92.8, specificity of 90%, and an AUC of 0.97 in the training data set, and a sensitivity of 83.3%, specificity of 66.7%, and AUC of 0.90 in the testing dataset.

conclusionA support vector machine model trained and validated on volumetric texture features extracted from ADC maps showed the possible beneficence of these features in differentiating IPMNs from SCAs. These results are in line with previous regarding the role of ADC maps in classifying cystic lesions and offers new evidence regarding the role of texture features in differentiation of potentially neoplastic and benign lesions.

Indexed as

Cystadenoma, SerousPancreatic Intraductal NeoplasmsPancreatic NeoplasmsHumansPancreasRetrospective StudiesAdenocarcinomaCancerDiffusion weighted imagingIntraductal papillary mucinous neoplasmMRIPancreas

Identifiers

PMID38378839
OpenAlexW4391953751

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Registered trials

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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.