CT Radiomics Features in Differentiation of Focal-Type Autoimmune Pancreatitis from Pancreatic Ductal Adenocarcinoma: A Propensity Score Analysis

Li, Jing; Liu, Fang; Fang, Xu; Cao, Kai; Meng, Yinghao; Zhang, Hao; Yu, Jieyu; Feng, Xinxin; Li, Qi; Liu, Yanfang; Wang, Li; Jiang, Hui; Shao, Chengwei; Lu, Jianping; Bian, Yun

doi:10.1016/j.acra.2021.04.014

Cited by 15 publications

(15 citation statements)

References 33 publications

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“…The accurate diagnosis of these lesions is hindered by mimicking imaging features [ 6 ]. Radiomics showed comparable and even better performance than radiologists’ assessment [ 38 , 42 , 46 , 52 , 56 , 58 ], but their level of evidence supporting clinical translation is still weak. Therefore, more validation for the establishment of a sound evidence basis is the main issue for diagnostic.…”

Section: Discussionmentioning

confidence: 99%

A systematic review of radiomics in pancreatitis: applying the evidence level rating tool for promoting clinical transferability

Zhong

Xing

et al. 2022

Insights Imaging

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Background Multiple tools have been applied to radiomics evaluation, while evidence rating tools for this field are still lacking. This study aims to assess the quality of pancreatitis radiomics research and test the feasibility of the evidence level rating tool. Results Thirty studies were included after a systematic search of pancreatitis radiomics studies until February 28, 2022, via five databases. Twenty-four studies employed radiomics for diagnostic purposes. The mean ± standard deviation of the adherence rate was 38.3 ± 13.3%, 61.3 ± 11.9%, and 37.1 ± 27.2% for the Radiomics Quality Score (RQS), the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) checklist, and the Image Biomarker Standardization Initiative (IBSI) guideline for preprocessing steps, respectively. The median (range) of RQS was 7.0 (− 3.0 to 18.0). The risk of bias and application concerns were mainly related to the index test according to the modified Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. The meta-analysis on differential diagnosis of autoimmune pancreatitis versus pancreatic cancer by CT and mass-forming pancreatitis versus pancreatic cancer by MRI showed diagnostic odds ratios (95% confidence intervals) of, respectively, 189.63 (79.65–451.48) and 135.70 (36.17–509.13), both rated as weak evidence mainly due to the insufficient sample size. Conclusions More research on prognosis of acute pancreatitis is encouraged. The current pancreatitis radiomics studies have insufficient quality and share common scientific disadvantages. The evidence level rating is feasible and necessary for bringing the field of radiomics from preclinical research area to clinical stage.

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Section: Discussionmentioning

confidence: 99%

A systematic review of radiomics in pancreatitis: applying the evidence level rating tool for promoting clinical transferability

Zhong

Xing

et al. 2022

Insights Imaging

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“…After removing irrelevant features and Bonferroni correction, four features by LASSO regression were still significantly associated with focal-type autoimmune pancreatitis (AIP) and PDAC. The LASSO logistic regression formula was used to obtain the rad-score for discriminating focal-type AIP from PDAC (AUC 0.97) 127 . In addition to using traditional algorithms (PyRadiomics), Ziegelmayer et al used deep CNN for radiomics feature extraction.…”

Section: Ai In Tumor Diagnosis Processmentioning

confidence: 99%

Artificial intelligence in pancreatic cancer

Huang¹,

Huang²,

Zhang³

et al. 2022

Theranostics

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Pancreatic cancer is the deadliest disease, with a five-year overall survival rate of just 11%. The pancreatic cancer patients diagnosed with early screening have a median overall survival of nearly ten years, compared with 1.5 years for those not diagnosed with early screening. Therefore, early diagnosis and early treatment of pancreatic cancer are particularly critical. However, as a rare disease, the general screening cost of pancreatic cancer is high, the accuracy of existing tumor markers is not enough, and the efficacy of treatment methods is not exact. In terms of early diagnosis, artificial intelligence technology can quickly locate high-risk groups through medical images, pathological examination, biomarkers, and other aspects, then screening pancreatic cancer lesions early. At the same time, the artificial intelligence algorithm can also be used to predict the survival time, recurrence risk, metastasis, and therapy response which could affect the prognosis. In addition, artificial intelligence is widely used in pancreatic cancer health records, estimating medical imaging parameters, developing computer-aided diagnosis systems, etc. Advances in AI applications for pancreatic cancer will require a concerted effort among clinicians, basic scientists, statisticians, and engineers. Although it has some limitations, it will play an essential role in overcoming pancreatic cancer in the foreseeable future due to its mighty computing power.

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“…Radiomics may play a positive role in the differential diagnosis of focal AIP and PDAC (28)(29)(30)(31)(32)(33). Among the studies, Linning et al (30) Differentiating Functional Abdominal Pain, RAP, and CP Abdominal pain is a common clinical symptom and one of the most important reasons for patients to see a doctor.…”

Section: Differentiating Focal Autoimmune Pancreatitis From Pdacmentioning

confidence: 99%

“…Radiomics may play a positive role in the differential diagnosis of focal AIP and PDAC ( 28 – 33 ). Among the studies, Linning et al ( 30 ) studied 45 patients with focal AIP and 51 patients with PDAC to evaluate the value of radiomics model based on multi-phase CECT for the differential diagnosis of focal AIP from PDAC.…”

Section: Clinical Applicationsmentioning

confidence: 99%

“…The diagnostic performances were higher than those of the two radiologists ( P < 0.05). In another study, Li et al ( 28 ) used propensity score matching (PSM) in 45 patients with focal AIP and 51 patients with PDAC who were matched in gender, age, body mass index (BMI), and CT characteristics. They evaluated the diagnostic performance of radiomics model based on portal phase CECT images in the differential diagnosis of focal AIP and PDAC.…”

Section: Clinical Applicationsmentioning

confidence: 99%

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Radiomics and Its Applications and Progress in Pancreatitis: A Current State of the Art Review

Yan

Gaowen²,

et al. 2022

Front. Med.

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Radiomics involves high-throughput extraction and analysis of quantitative information from medical images. Since it was proposed in 2012, there are some publications on the application of radiomics for (1) predicting recurrent acute pancreatitis (RAP), clinical severity of acute pancreatitis (AP), and extrapancreatic necrosis in AP; (2) differentiating mass-forming chronic pancreatitis (MFCP) from pancreatic ductal adenocarcinoma (PDAC), focal autoimmune pancreatitis (AIP) from PDAC, and functional abdominal pain (functional gastrointestinal diseases) from RAP and chronic pancreatitis (CP); and (3) identifying CP and normal pancreas, and CP risk factors and complications. In this review, we aim to systematically summarize the applications and progress of radiomics in pancreatitis and it associated situations, so as to provide reference for related research.

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CT Radiomics Features in Differentiation of Focal-Type Autoimmune Pancreatitis from Pancreatic Ductal Adenocarcinoma: A Propensity Score Analysis

Cited by 15 publications

References 33 publications

A systematic review of radiomics in pancreatitis: applying the evidence level rating tool for promoting clinical transferability

A systematic review of radiomics in pancreatitis: applying the evidence level rating tool for promoting clinical transferability

Artificial intelligence in pancreatic cancer

Radiomics and Its Applications and Progress in Pancreatitis: A Current State of the Art Review

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