Endosonographers performing on-site evaluation of solid pancreatic specimens for EUS-guided biopsy: A formal training method and learning curves

Exploring KRAS-mutant pancreatic ductal adenocarcinoma: a model validation study

Introduction

Pancreatic ductal adenocarcinoma (PDAC) has the highest mortality rate among all solid tumors. Tumorigenesis is promoted by the oncogene KRAS, and KRAS mutations are prevalent in patients with PDAC. Therefore, a comprehensive understanding of the interactions between KRAS mutations and PDAC may expediate the development of therapeutic strategies for reversing the progression of malignant tumors. Our study aims at establishing and validating a prediction model of KRAS mutations in patients with PDAC based on survival analysis and mRNA expression.

Methods

A total of 184 and 412 patients with PDAC from The Cancer Genome Atlas (TCGA) database and the International Cancer Genome Consortium (ICGC), respectively, were included in the study.

Results

After tumor mutation profile and copy number variation (CNV) analyses, we established and validated a prediction model of KRAS mutations, based on survival analysis and mRNA expression, that contained seven genes: CSTF2, FAF2, KIF20B, AKR1A1, APOM, KRT6C, and CD70. We confirmed that the model has a good predictive ability for the prognosis of overall survival (OS) in patients with KRAS-mutated PDAC. Then, we analyzed differential biological pathways, especially the ferroptosis pathway, through principal component analysis, pathway enrichment analysis, Gene Ontology (GO) enrichment analysis, and gene set enrichment analysis (GSEA), with which patients were classified into low- or high-risk groups. Pathway enrichment results revealed enrichment in the cytokine-cytokine receptor interaction, metabolism of xenobiotics by cytochrome P450, and viral protein interaction with cytokine and cytokine receptor pathways. Most of the enriched pathways are metabolic pathways predominantly enriched by downregulated genes, suggesting numerous downregulated metabolic pathways in the high-risk group. Subsequent tumor immune infiltration analysis indicated that neutrophil infiltration, resting CD4 memory T cells, and resting natural killer (NK) cells correlated with the risk score. After verifying that the seven gene expression levels in different KRAS-mutated pancreatic cancer cell lines were similar to that in the model, we screened potential drugs related to the risk score.

Discussion

This study established, analyzed, and validated a model for predicting the prognosis of PDAC based on risk stratification according to KRAS mutations, and identified differential pathways and highly effective drugs.

Diagnostic value of endoscopic ultrasonography in pelvic masses with bowel involvement

Benign and malignant pelvic masses with or without intestinal invasion are common in women of childbearing age. Patients may have nonspecific symptoms and signs or experience no symptoms. Laparoscopic resection of pelvic masses is currently the mainstream treatment; therefore, accurate preoperative evaluation is not only essential for patients suspected of having intestinal invasion, but also extremely important for the selection of follow-up treatment. Procedures, including endoscopic ultrasonography (EUS), pelvic magnetic resonance imaging, abdominal computed tomography, vaginal ultrasonography, barium enema, and colonoscopy, aid in determining the presence, depth, and histology of the disease. In particular, the wide application and continuous developments in EUS techniques have improved the diagnostic accuracy for intestinal subepithelial and peripheral organ lesions. This article reviewed the clinical value of EUS in the diagnosis of benign and malignant pelvic masses with bowel involvement.

Deep learning-based diagnosis from endobronchial ultrasonography images of pulmonary lesions

Endobronchial ultrasonography with a guide sheath (EBUS-GS) improves the accuracy of bronchoscopy. The possibility of differentiating benign from malignant lesions based on EBUS findings may be useful in making the correct diagnosis. The convolutional neural network (CNN) model investigated whether benign or malignant (lung cancer) lesions could be predicted based on EBUS findings. This was an observational, single-center cohort study. Using medical records, patients were divided into benign and malignant groups. We acquired EBUS data for 213 participants. A total of 2,421,360 images were extracted from the learning dataset. We trained and externally validated a CNN algorithm to predict benign or malignant lung lesions. Test was performed using 26,674 images. The dataset was interpreted by four bronchoscopists. The accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of the CNN model for distinguishing benign and malignant lesions were 83.4%, 95.3%, 53.6%, 83.8%, and 82.0%, respectively. For the four bronchoscopists, the accuracy rate was 68.4%, sensitivity was 80%, specificity was 39.6%, PPV was 76.8%, and NPV was 44.2%. The developed EBUS-computer-aided diagnosis system is expected to read EBUS findings that are difficult for clinicians to judge with precision and help differentiate between benign lesions and lung cancers.