Differential diagnosis of pancreatic cystic neoplasms through a radiomics-assisted system

Enhancing noninvasive pancreatic cystic neoplasm diagnosis with multimodal machine learning

Pancreatic cystic neoplasms (PCNs) are a complex group of lesions with a spectrum of malignancy. Accurate differentiation of PCN types is crucial for patient management, as misdiagnosis can result in unnecessary surgeries or treatment delays, affecting the quality of life. The significance of developing a non-invasive, accurate diagnostic model is underscored by the need to improve patient outcomes and reduce the impact of these conditions. We developed a machine learning model capable of accurately identifying different types of PCNs in a non-invasive manner, by using a dataset comprising 449 MRI and 568 CT scans from adult patients, spanning from 2009 to 2022. The study's results indicate that our multimodal machine learning algorithm, which integrates both clinical and imaging data, significantly outperforms single-source data algorithms. Specifically, it demonstrated state-of-the-art performance in classifying PCN types, achieving an average accuracy of 91.2%, precision of 91.7%, sensitivity of 88.9%, and specificity of 96.5%. Remarkably, for patients with mucinous cystic neoplasms (MCNs), regardless of undergoing MRI or CT imaging, the model achieved a 100% prediction accuracy rate. It indicates that our non-invasive multimodal machine learning model offers strong support for the early screening of MCNs, and represents a significant advancement in PCN diagnosis for improving clinical practice and patient outcomes. We also achieved the best results on an additional pancreatic cancer dataset, which further proves the generality of our model.

Machine Learning-Based Radiomics in Malignancy Prediction of Pancreatic Cystic Lesions: Evidence from Cyst Fluid Multi-Omics

The malignant potential of pancreatic cystic lesions (PCLs) varies dramatically, leading to difficulties when making clinical decisions. This study aimed to develop noninvasive clinical-radiomic models using preoperative CT images to predict the malignant potential of PCLs. It also investigates the biological mechanisms underlying these models. Patients from two retrospective and one prospective cohort, all undergoing surgical resection for PCLs, are divided into four datasets: training, internal test, external test, and prospective application sets. Eleven machine learning classifiers are employed to construct radiomic models based on selected features. Cyst fluid from the prospective cohort is collected for proteomic and lipidomic analysis. The radiomic models demonstrated high accuracy, with area under the receiver operating characteristic curves (AUCs) > 0.93 across the training (n = 262), internal test (n = 50), and external test (n = 50) sets. AUCs ranged from 0.92 to 0.96 for the prospective cohort (n = 34). Meanwhile, differentially-expressed proteins and lipid molecules, along with their associated signaling pathways, are identified between high and low groups of clinical-radiomic scores. This models can effectively and accurately predict the malignant potential of PCLs, with multi-omics evidence suggesting the biological mechanisms involving secretion function and lipid metabolism underlying clinical-radiomic models.

Radiomics for Differentiating Pancreatic Mucinous Cystic Neoplasm from Serous Cystic Neoplasm: Systematic Review and Meta-Analysis

BACKGROUND

As pancreatic cystic neoplasms (PCN) differ in current standard of care, and these treatments can affect quality of life to varying degrees, a definitive preoperative diagnosis must be reliable. Current diagnostic approaches, specifically traditional cross-sectional imaging techniques, face certain limitations. But radiomics has been shown to have high diagnostic accuracy across a range of diseases. Objective to conduct a comprehensive review of the literature on the use of radiomics to differentiate Mucinous Cystic Neoplasm (MCN) from Serous Cystic Neoplasm (SCN).

METHODS

This study was comprehensively searched in Pubmed, Scopus and Web of Science databases for meta-analysis of studies that used radiomics to distinguish MCN from SCN. Risk of bias was assessed using the diagnostic accuracy study quality assessment method and combined with sensitivity, specificity, diagnostic odds ratio, and summary receiver operating characteristic (SROC)curve analysis.

RESULTS

A total of 884 patients from 8 studies were included in this analysis, including 365 MCN and 519 SCN. The Meta-analysis found that radiomics identified MCN and SCN with high sensitivity and specificity, with combined sensitivity and specificity of 0.84(0.82-0.87) and 0.82(0.79-0.84). The positive likelihood ratio (PLR) and the negative likelihood ratio (NLR) are 5.61(3.72, 8.47) and 0.14(0.09-0.26). In addition, the area under the SROC curve (AUC) was drawn at 0.93. No significant risk of publication bias was detected through the funnel plot analysis. The performances of feature extraction from the volume of interest (VOI) or Using AI classifier in the radiomics models were superior to those of protocols employing region of interest (ROI) or absence of AI classifier.

CONCLUSION

This meta-analysis demonstrates that radiomics exhibits high sensitivity and specificity in distinguishing between MCN and SCN, and has the potential to become a reliable diagnostic tool for their identification.

A case report of huge pancreas mucinous cystic neoplasm during pregnancy: How doctors think

RATIONALE

Pancreas mucinous cystic neoplasm (PMCN) is uncommon, and its occurrence during pregnancy is rare. The management of PMCN during pregnancy, including diagnosis and surgical timing, is a great challenge.

PATIENT CONCERNS

A nontender epigastric mass of the upper abdomen was detected by palpation in a 35-year-old woman, gravida 2, para 1, during the 36th week of gestation. She was referred to our institution for further evaluation.

DIAGNOSES

Magnetic resonance imaging (MRI) showed a multilocular cystic mass in the body and tail of the pancreas (16.7/12.1/17.6 cm), well-circumscribed with a hyper signal on T2-weighted MRI images. The diagnosis of a pancreatic cyst, probable mucinous, was established.

INTERVENTIONS

The patient was informed of the possibilities of malignancy, rapid growth, and rupture of the tumor. After a laparotomy and cesarean section, a large cystic tumor was discovered adherent to the pancreas, spleen, mesocolon, and retroperitoneum. The spleen was preserved since there was no evidence of invasion. According to macroscopic examinations, the tumor measured 18 cm was filled with a dark yellow-brownish mucinous fluid and did not appear to communicate with the pancreatic ducts.

OUTCOMES

After six months of follow-up, there were no signs of recurrence in the patient.

LESSONS

PMCN may need to be surgically resected in cases characterized by malignancy risk during pregnancy. As female sex hormones may influence the behavior of PMCN during pregnancy, surgical timing should be determined based on the stage of pregnancy, malignancy status, and condition of the mother and fetus.

Update on the Applications of Radiomics in Diagnosis, Staging, and Recurrence of Intrahepatic Cholangiocarcinoma

BACKGROUND

This paper offers an assessment of radiomics tools in the evaluation of intrahepatic cholangiocarcinoma.

METHODS

The PubMed database was searched for papers published in the English language no earlier than October 2022.

RESULTS

We found 236 studies, and 37 satisfied our research criteria. Several studies addressed multidisciplinary topics, especially diagnosis, prognosis, response to therapy, and prediction of staging (TNM) or pathomorphological patterns. In this review, we have covered diagnostic tools developed through machine learning, deep learning, and neural network for the recurrence and prediction of biological characteristics. The majority of the studies were retrospective.

CONCLUSIONS

It is possible to conclude that many performing models have been developed to make differential diagnosis easier for radiologists to predict recurrence and genomic patterns. However, all the studies were retrospective, lacking further external validation in prospective and multicentric cohorts. Furthermore, the radiomics models and the expression of results should be standardized and automatized to be applicable in clinical practice.