Digital analysis of EUS images: "promising" method, but is it ready for "prime time"?

Digital image analysis is a useful adjunct to endoscopic ultrasonographic diagnosis of subepithelial lesions of the gastrointestinal tract

OBJECTIVE

The purpose of this study was to explore the role of digital image analysis in differentiating endoscopic ultrasonographic (EUS) features of potentially malignant gastrointestinal subepithelial lesions (SELs) from those of benign lesions.

METHODS

Forty-six patients with histopathologically confirmed gastrointestinal stromal tumors (GISTs), carcinoids, and lipomas who had undergone EUS evaluation were identified from our database. Representative regions of interest (ROIs) were selected from the EUS images, and features were extracted by texture analysis. On the basis of these features, an artificial neural network (ANN) was built, trained, and internally validated by unsupervised learning followed by supervised learning. Outcomes were the performance characteristics of the ANN.

RESULTS

A total of 106, 111, and 124 ROIs were selected from EUS images of 8, 10, and 28 patients with lipomas, carcinoids, and GISTs, respectively. For each ROI, 228 statistical parameters were extracted and later reduced to the 11 most informative features by principal component analysis. After training with 50% of the data, the remainder of the data were used to validate the ANN. The model was "good" in differentiating carcinoids and GISTs, with area under the receiver operating characteristic curve (AUC) values of 0.86 and 0.89, respectively. The model was "excellent" in identifying lipomas correctly, with an AUC of 0.92.

CONCLUSIONS

Digital image analysis of EUS images is a useful noninvasive adjunct to EUS evaluation of SELs.

Incidence of benign disease in patients that underwent resection for presumed pancreatic cancer diagnosed by endoscopic ultrasonography (EUS) and fine-needle aspiration (FNA)

INTRODUCTION

The lack of accurate markers makes preoperative differentiation between pancreatic cancer and non-malignant head lesions clinically challenging. In this study, we investigated the incidence of benign disease in patients that underwent resection for presumed pancreatic cancer diagnosed by EUS and EUS-guided FNA.

METHODS

Medical records of consecutive patients who underwent pancreaticoduodenectomy at Duke University were reviewed. Demographics, clinicopathologic characteristics, preoperative imaging, EUS, EUS-guided FNA, and postoperative outcomes were analyzed.

RESULTS

Seven percent of the total 494 patients studied were found to have benign disease on postoperative pathology. Fifty-nine percent of these patients with benign disease underwent preoperative EUS. EUS was positive for a head mass in 70%, demonstrated enlarged lymph nodes in 27%, and showed signs concerning for vascular invasion in 13%. FNA was suspicious or indeterminate for cancer in 63% of patients. Postoperative complications occurred in 47% and one patient died after surgery. The overall pancreatic leak rate was 15%.

CONCLUSIONS

Even with aggressive use of preoperative evaluation, there is still a small subset of patients where malignancy cannot be excluded without pancreaticoduodenectomy.

In vivo characterization of pancreatic and lymph node tissue by using EUS spectrum analysis: a validation study

BACKGROUND

Quantitative spectral analysis of the radiofrequency (RF) signals that underlie grayscale EUS images can be used to provide additional, objective information about tissue state.

OBJECTIVE

Our purpose was to validate RF spectral analysis as a method to distinguish between (1) benign and malignant lymph nodes and (2) normal pancreas, chronic pancreatitis, and pancreatic cancer.

DESIGN AND SETTING

A prospective validation study of eligible patients was conducted to compare with pilot study RF data.

PATIENTS

Forty-three patients underwent EUS of the esophagus, stomach, pancreas, and surrounding intra-abdominal and mediastinal lymph nodes (19 from a previous pilot study and 24 additional patients).

MAIN OUTCOME MEASUREMENTS

Midband fit, slope, intercept, and correlation coefficient from a linear regression of the calibrated RF power spectra were determined.

RESULTS

Discriminant analysis of mean pilot-study parameters was then performed to classify validation-study parameters. For benign versus malignant lymph nodes, midband fit and intercept (both with t test P < .058) provided classification with 67% accuracy and area under the receiver operating curve (AUC) of 0.86. For diseased versus normal pancreas, midband fit and correlation coefficient (both with analysis of variance P < .001) provided 93% accuracy and an AUC of 0.98. For pancreatic cancer versus chronic pancreatitis, the same parameters provided 77% accuracy and an AUC of 0.89. Results improved further when classification was performed with all data.

LIMITATIONS

Moderate sample size and spatial averaging inherent to the technique.

CONCLUSIONS

This study confirms that mean spectral parameters provide a noninvasive method to quantitatively discriminate benign and malignant lymph nodes as well as normal and diseased pancreas.