Update on quantitative radiomics of pancreatic tumors

Virarkar, Mayur; Wong, Vincenzo K.; Morani, Ajaykumar C.; Tamm, Eric P.; Bhosale, Priya

doi:10.1007/s00261-021-03216-3

Cited by 13 publications

(11 citation statements)

References 89 publications

(111 reference statements)

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“…The inadequate quality of radiomics studies has been addressed repeatedly [15][16][17][18][19][20][21][22][23][24][27][28][29]. In accordance with previous reviews, several items were always lacking including test-retest analysis, phantom study, cutoff analysis, and cost-effectiveness analysis in RQS, the blinded method during outcome assessment, sample size calculation, and handling of missing data in TRIPOD, and details of image preprocessing in selected IBSI items.…”

Section: Discussionmentioning

confidence: 90%

“…Our results implied that the level of evidence supporting clinical application and the overall study quality were suboptimal in pancreatitis radiomics research, requiring significant improvement. Several reviews have summarized the use of radiomics in multiple pancreatic diseases from pancreatic cystic lesions to pancreatic tumors [15][16][17][18][19][20][21][22]. A comprehensive review reported that most of the pancreatic radiomics studies investigated focal pancreatic lesions, but only four studies discussed the pancreatitis [12].…”

Section: Discussionmentioning

confidence: 99%

“…However, only 5.6% of pancreatic radiomics studies investigated the role of radiomics in acute pancreatitis [18]. Most radiomics studies on chronic, mass-forming, or autoimmune pancreatitis were aimed to differentiate these inflammatory conditions from malignancy lesions [19][20][21][22]. Implanting radiomics in acute pancreatitis could provide predictive information to identify patients with worse prognosis and therefore promote personalized medical treatment.…”

Section: Introductionmentioning

confidence: 99%

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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: 90%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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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“…The use of CT and more specifically CT radiomics as a prognostic indicator has been used in other cancers such as non-small cell lung cancer, renal cell carcinoma and colorectal cancer (28,32). In a retrospective study, Khalvati et al (28) assess the prognostic value of CT-derived radiomic features for resectable PDAC and identified several radiomic features which were statistically significant for prognostication of overall survival.…”

Section: Ctmentioning

confidence: 99%

Measuring response to neoadjuvant therapy using biomarkers in pancreatic cancer: a narrative review

Valukas¹,

Chawla²

2022

Chin Clin Oncol

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Background and Objective: Pancreatic cancer is the 4th leading cause of cancer death in the US, with incidence increasing over the last 20 years. Recently neoadjuvant therapy (NAT) has emerged as an important tool in improving resectability and overall survival. The objective is to describe and discuss the current literature on the use of biomarkers in measuring response to NAT in pancreatic adenocarcinoma.Methods: An electronic review of PubMed, Google Scholar and Cochrane was performed to obtain key literature on serum, imaging, clinical, and histologic biomarkers utilized to measure response to NAT in pancreatic cancer. This literature review included publications in English written between January 1, 2011 to March 31, 2022.

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“…In recent years, novel radiomics and radiogenomics studies have provided new ideas for the evaluation of tumors through deep learning and machine learning (ML) algorithms [8][9][10][11][12][13]. Recent studies have found that radiomics models contribute greatly to the individualized evaluation of pancreatic lesions, such as tumor detection, classification, differentiation, and antitumor drug effect prediction [14][15][16][17][18]. In addition, pancreatic cystic lesions have been categorized using radiomics methods in multiple studies [19,20].…”

Section: Introductionmentioning

confidence: 99%

Classification prediction of pancreatic cystic neoplasms based on radiomics deep learning models

et al. 2022

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Background Preoperative prediction of pancreatic cystic neoplasm (PCN) differentiation has significant value for the implementation of personalized diagnosis and treatment plans. This study aimed to build radiomics deep learning (DL) models using computed tomography (CT) data for the preoperative differential diagnosis of common cystic tumors of the pancreas. Methods Clinical and CT data of 193 patients with PCN were collected for this study. Among these patients, 99 were pathologically diagnosed with pancreatic serous cystadenoma (SCA), 55 were diagnosed with mucinous cystadenoma (MCA) and 39 were diagnosed with intraductal papillary mucinous neoplasm (IPMN). The regions of interest (ROIs) were obtained based on manual image segmentation of CT slices. The radiomics and radiomics-DL models were constructed using support vector machines (SVMs). Moreover, based on the fusion of clinical and radiological features, the best combined feature set was obtained according to the Akaike information criterion (AIC) analysis. Then the fused model was constructed using logistic regression. Results For the SCA differential diagnosis, the fused model performed the best and obtained an average area under the curve (AUC) of 0.916. It had a best feature set including position, polycystic features (≥6), cystic wall calcification, pancreatic duct dilatation and radiomics-DL score. For the MCA and IPMN differential diagnosis, the fused model with AUC of 0.973 had a best feature set including age, communication with the pancreatic duct and radiomics score. Conclusions The radiomics, radiomics-DL and fused models based on CT images have a favorable differential diagnostic performance for SCA, MCA and IPMN. These findings may be beneficial for the exploration of individualized management strategies.

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Update on quantitative radiomics of pancreatic tumors

Cited by 13 publications

References 89 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

Measuring response to neoadjuvant therapy using biomarkers in pancreatic cancer: a narrative review

Classification prediction of pancreatic cystic neoplasms based on radiomics deep learning models

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