A CT based radiomics nomogram for differentiation between focal-type autoimmune pancreatitis and pancreatic ductal adenocarcinoma

Serum IgG4-Negative Focal Autoimmune Pancreatitis Type 1 That Was Difficult to Diagnose Preoperatively Even with Frequent Endoscopic Ultrasound-Guided Fine-Needle Aspiration and Fine-Needle Biopsy: A Surgical Case Report

Introduction: Focal autoimmune pancreatitis (AIP) without elevated serum IgG4 levels presents a diagnostic challenge compared to pancreatic tumors, often leading to surgical intervention. Case Presentation: We report a case of serum IgG4-negative focal AIP type 1 in a 52-year-old male. Despite repeated endoscopic ultrasound-guided fine-needle aspiration (EUS-FNA) and fine-needle biopsy (FNB), preoperative diagnosis was elusive. Initially, a 30-mm hypoechoic mass in the pancreatic head was detected by ultrasonography, with dynamic computed tomography revealing well-defined borders and homogeneous delayed enhancement. Serum IgG4 levels were within the normal range (115 mg/dL). Two EUS-FNAs and one EUS-FNB failed to provide a definitive diagnosis, leading to suspicion of a solid pseudopapillary neoplasm and subsequent pancreaticoduodenectomy. Postoperative histopathology confirmed focal AIP type 1 with IgG4-positive plasma cells. After 38 months, there is no recurrence, and serum IgG4 levels remain normal. Conclusion: Diagnosis of focal AIP, particularly when serum IgG4 is negative, warrants consideration despite its difficulty. Imaging findings, such as a well-defined mass with homogeneous delayed enhancement, should prompt evaluation for characteristic features like capsule-like rim, pancreatic duct penetration, and biliary tract wall thickening.

Noninvasive prediction of lymph node metastasis in pancreatic cancer using an ultrasound-based clinicoradiomics machine learning model

OBJECTIVES

This study was designed to explore and validate the value of different machine learning models based on ultrasound image-omics features in the preoperative diagnosis of lymph node metastasis in pancreatic cancer (PC).

METHODS

This research involved 189 individuals diagnosed with PC confirmed by surgical pathology (training cohort: n = 151; test cohort: n = 38), including 50 cases of lymph node metastasis. Image-omics features were extracted from ultrasound images. After dimensionality reduction and screening, eight machine learning algorithms, including logistic regression (LR), support vector machine (SVM), K-nearest neighbors (KNN), random forest (RF), extra trees (ET), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and multilayer perceptron (MLP), were used to establish image-omics models to predict lymph node metastasis in PC. The best omics prediction model was selected through ROC curve analysis. Machine learning models were used to analyze clinical features and determine variables to establish a clinical model. A combined model was constructed by combining ultrasound image-omics and clinical features. Decision curve analysis (DCA) and a nomogram were used to evaluate the clinical application value of the model.

RESULTS

A total of 1561 image-omics features were extracted from ultrasound images. 15 valuable image-omics features were determined by regularization, dimension reduction, and algorithm selection. In the image-omics model, the LR model showed higher prediction efficiency and robustness, with an area under the ROC curve (AUC) of 0.773 in the training set and an AUC of 0.850 in the test set. The clinical model constructed by the boundary of lesions in ultrasound images and the clinical feature CA199 (AUC = 0.875). The combined model had the best prediction performance, with an AUC of 0.872 in the training set and 0.918 in the test set. The combined model showed better clinical benefit according to DCA, and the nomogram score provided clinical prediction solutions.

CONCLUSION

The combined model established with clinical features has good diagnostic ability and can be used to predict lymph node metastasis in patients with PC. It is expected to provide an effective noninvasive method for clinical decision-making, thereby improving the diagnosis and treatment of PC.

Pancreatic Adenocarcinoma: Imaging Modalities and the Role of Artificial Intelligence in Analyzing CT and MRI Images

Pancreatic ductal adenocarcinoma (PDAC) stands out as the predominant malignant neoplasm affecting the pancreas, characterized by a poor prognosis, in most cases patients being diagnosed in a nonresectable stage. Image-based artificial intelligence (AI) models implemented in tumor detection, segmentation, and classification could improve diagnosis with better treatment options and increased survival. This review included papers published in the last five years and describes the current trends in AI algorithms used in PDAC. We analyzed the applications of AI in the detection of PDAC, segmentation of the lesion, and classification algorithms used in differential diagnosis, prognosis, and histopathological and genomic prediction. The results show a lack of multi-institutional collaboration and stresses the need for bigger datasets in order for AI models to be implemented in a clinically relevant manner.