Machine learning principles applied to CT radiomics to predict mucinous pancreatic cysts

Awe, Adam; Heuvel, Michael M. Vanden; Yuan, Tianyuan; Rendell, Victoria R.; Shen, Mei‐Hua; Kampani, Agrima; Liang, Shanchao; Morgan, Dane; Winslow, Emily R.; Lubner, Meghan G.

doi:10.1007/s00261-021-03289-0

Cited by 10 publications

(9 citation statements)

References 27 publications

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“…In addition, pancreatic cystic lesions have been categorized using radiomics methods in multiple studies [332,333]. Although these findings confirmed the feasibility of radiomics for the assessment of pancreatic cystic lesions [334][335][336][337][338][339][340][341][342][343][344], the robustness of these radiomics diagnostic models may be limited due to the relatively small datasets included in most studies. Therefore, the accumulation of additional research data is required for the study of pancreatic cystic lesions.…”

Section: Discussionmentioning

confidence: 94%

Risk Assessment and Pancreatic Cancer: Diagnostic Management and Artificial Intelligence

Granata

Fusco

Setola

et al. 2023

Cancers

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Pancreatic cancer (PC) is one of the deadliest cancers, and it is responsible for a number of deaths almost equal to its incidence. The high mortality rate is correlated with several explanations; the main one is the late disease stage at which the majority of patients are diagnosed. Since surgical resection has been recognised as the only curative treatment, a PC diagnosis at the initial stage is believed the main tool to improve survival. Therefore, patient stratification according to familial and genetic risk and the creation of screening protocol by using minimally invasive diagnostic tools would be appropriate. Pancreatic cystic neoplasms (PCNs) are subsets of lesions which deserve special management to avoid overtreatment. The current PC screening programs are based on the annual employment of magnetic resonance imaging with cholangiopancreatography sequences (MR/MRCP) and/or endoscopic ultrasonography (EUS). For patients unfit for MRI, computed tomography (CT) could be proposed, although CT results in lower detection rates, compared to MRI, for small lesions. The actual major limit is the incapacity to detect and characterize the pancreatic intraepithelial neoplasia (PanIN) by EUS and MR/MRCP. The possibility of utilizing artificial intelligence models to evaluate higher-risk patients could favour the diagnosis of these entities, although more data are needed to support the real utility of these applications in the field of screening. For these motives, it would be appropriate to realize screening programs in research settings.

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Section: Discussionmentioning

confidence: 94%

Risk Assessment and Pancreatic Cancer: Diagnostic Management and Artificial Intelligence

Granata

Fusco

Setola

et al. 2023

Cancers

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“…Surgeons can use such machine algorithms to minimize morbidity and the extensive economic expenses of current management practices ( 14 ). From a synonymous perspective, Awe et al examined the integration of ML in pancreatic surgery to predict the occurrence of mucinous pancreatic cysts through the integration of computed tomography (CT) radiomics ( 15 ). The specific ML algorithm implemented was XGBoost, which facilitated the generation of mucinous classifiers via texture characteristics or radiological and clinical combined models.…”

Section: Discussionmentioning

confidence: 99%

“…Springer et al demonstrated the benefits of applying a ML algorithm in pancreas surgery by demonstrating its capability to improve the management of patients with pancreatic cysts (14). The researchers established the CompCyst classifier to suggest various approaches to managing cysts.…”

Section: Summary Of Evidencementioning

confidence: 99%

“…The study's outcome indicated that the ML algorithm prompted the sparing of surgery among half of the patients who had undergone unnecessary cyst resection. Surgeons can use such machine algorithms to minimize morbidity and the extensive economic expenses of current management practices (14). From a synonymous perspective, Awe et al examined the integration of ML in pancreatic surgery to predict the occurrence of mucinous pancreatic cysts through the integration of computed tomography (CT) radiomics (15).…”

Section: Summary Of Evidencementioning

confidence: 99%

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Machine learning in pancreas surgery, what is new? literature review

Taha

Taha‐Mehlitz²,

Ortlieb

et al. 2023

Front. Surg.

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BackgroundMachine learning (ML) is an inquiry domain that aims to establish methodologies that leverage information to enhance performance of various applications. In the healthcare domain, the ML concept has gained prominence over the years. As a result, the adoption of ML algorithms has become expansive. The aim of this scoping review is to evaluate the application of ML in pancreatic surgery.MethodsWe integrated the preferred reporting items for systematic reviews and meta-analyses for scoping reviews. Articles that contained relevant data specializing in ML in pancreas surgery were included.ResultsA search of the following four databases PubMed, Cochrane, EMBASE, and IEEE and files adopted from Google and Google Scholar was 21. The main features of included studies revolved around the year of publication, the country, and the type of article. Additionally, all the included articles were published within January 2019 to May 2022.ConclusionThe integration of ML in pancreas surgery has gained much attention in previous years. The outcomes derived from this study indicate an extensive literature gap on the topic despite efforts by various researchers. Hence, future studies exploring how pancreas surgeons can apply different learning algorithms to perform essential practices may ultimately improve patient outcomes.

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“…In addition, pancreatic cystic lesions have been categorized using radiomics methods in multiple studies [19,20]. Although these findings confirmed the feasibility of radiomics for the assessment of pancreatic cystic lesions [21][22][23][24][25][26][27][28], the robustness of these radiomics diagnostic models may be limited due to the relatively small datasets included in most studies. Therefore, the accumulation of additional research data is required for the study of pancreatic cystic lesions.…”

Section: Introductionmentioning

confidence: 93%

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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Machine learning principles applied to CT radiomics to predict mucinous pancreatic cysts

Cited by 10 publications

References 27 publications

Risk Assessment and Pancreatic Cancer: Diagnostic Management and Artificial Intelligence

Risk Assessment and Pancreatic Cancer: Diagnostic Management and Artificial Intelligence

Machine learning in pancreas surgery, what is new? literature review

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

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