Differentiation of pancreatobiliary-type from intestinal-type periampullary carcinomas using 3.0T MRI

Machine learning for differentiating between pancreatobiliary-type and intestinal-type periampullary carcinomas based on CT imaging and clinical findings

PURPOSE

To develop a diagnostic model for distinguishing pancreatobiliary-type and intestinal-type periampullary adenocarcinomas using preoperative contrast-enhanced computed tomography (CT) findings combined with clinical characteristics.

METHODS

This retrospective study included 140 patients with periampullary adenocarcinoma who underwent preoperative enhanced CT, including pancreaticobiliary (N = 100) and intestinal (N = 40) types. They were randomly assigned to the training or internal validation set in an 8:2 ratio. Additionally, an independent external cohort of 28 patients was enrolled. Various CT features of the periampullary region were evaluated and data from clinical and laboratory tests were collected. Five machine learning classifiers were developed to identify the histologic type of periampullary adenocarcinoma, including logistic regression, random forest, multi-layer perceptron, light gradient boosting, and eXtreme gradient boosting (XGBoost).

RESULTS

All machine learning classifiers except multi-layer perceptron used achieved good performance in distinguishing pancreatobiliary-type and intestinal-type adenocarcinomas, with the area under the curve (AUC) ranging from 0.75 to 0.98. The AUC values of the XGBoost classifier in the training set, internal validation set and external validation set are 0.98, 0.89 and 0.84 respectively. The enhancement degree of tumor, the growth pattern of tumor, and carbohydrate antigen 19-9 were the most important factors in the model.

CONCLUSION

Machine learning models combining CT with clinical features can serve as a noninvasive tool to differentiate the histological subtypes of periampullary adenocarcinoma, in particular using the XGBoost classifier.

Analysis of Pancreatobiliary and Intestinal Type Periampullary Carcinomas Using Volumetric Apparent Diffusion Coefficient Histograms

RATIONALE AND OBJECTIVES

Magnetic resonance imaging plays an important role in the evaluation of patients with known or suspected periampullary masses. The utilization of volumetric apparent diffusion coefficient (ADC) histogram evaluation for the entire lesion eradicates the potential for subjectivity in the region of interest placement, thus guaranteeing the accuracy of computation and repeatability.

PURPOSE

To investigate the value of volumetric ADC histogram analysis in the differentiation of intestinal-type (IPAC) and pancreatobiliary-type periampullary adenocarcinomas (PPAC).

MATERIALS AND METHODS

This retrospective study included 69 patients with histopathologically confirmed periampullary adenocarcinoma (54 PPAC and 15 IPAC). Diffusion-weighted imaging was obtained at b values of 1000 mm²/s. The histogram parameters of ADC values, comprising the mean, minimum, maximum, 5th, 10th, 25th, 50th, 75th, 90th, and 95th percentiles, as well as skewness, kurtosis, and variance, were calculated independently by two radiologists. Using the interclass correlation coefficient, the interobserver agreement was evaluated.

RESULTS

The ADC parameters for the PPAC group were all lower than those of the IPAC group. The PPAC group had higher variance, skewness, and kurtosis than the IPAC group. However, the difference between the kurtosis (P = .003), the 5th (P = .032), 10th (P = .043), and 25th (P = .037) percentiles of ADC values was statistically significant. The area under the curve (AUC) of the kurtosis was the highest (AUC=0.752; cut-off value=-0.235; sensitivity=61.1%; specificity=80.0%).

CONCLUSION

Volumetric ADC histogram analysis with b values of 1000 mm²/s can discriminate subtypes noninvasively before surgery.

Evaluation of Imaging Findings of Pancreatobiliary and Intestinal Type Periampullary Carcinomas with 3.0T MRI

RATIONALE AND OBJECTIVES

The aim of this study was to differentiate pancreatobiliary and intestinal type periampullary carcinomas using dynamic contrast MRI and MRCP (Magnetic Resonance Cholangiopancreatography) with diffusion-weighted imaging (DWI) MATERIALS AND METHODS: MRI and MRCP images of 70 patients with pathologically proven periampullary adenocarcinoma were included. MRCP image features, extra-ampullary features, enhancement patterns, and apparent diffusion coefficient (ADC) values derived from b-values of 1000 s/mm² were evaluated by two radiologists independently. The interclass correlation coefficient (ICC) or Cohen's kappa statistic was used to evaluate the interobserver agreement.

RESULTS

51 patients were diagnosed with pancreatobiliary type carcinomas, and 19 with intestinal type. In the pancreatobiliary subtype, the distal wall of the common bile duct was usually irregular (p = 0.047). Although the progressive enhancement pattern was evident in the pancreatobiliary type, an oval filling defect in the distal common bile duct was found to be more common in the intestinal type (p<0.001). The pancreatic duct cut-off sign (p<0.001), gastroduodenal artery involvement (p <0,001), and lymphadenopathy (p<0.05) were mostly observed in pancreatobiliary carcinomas. The ADCmin, ADCmean, and ADCmax values of the pancreatobiliary type carcinomas were all lower compared to the intestinal type carcinomas (p <0.05).

CONCLUSION

The oval filling defect seen in MRI and MRCP examinations suggests intestinal type, whereas the progressive contrasting pattern of the masses with irregular narrowing in the distal margin of the common bile duct, the pancreatic duct cut-off sign, gastroduodenal artery involvement, lymphadenopathy, and low ADC values indicate pancreatobiliary type carcinomas.

Dynamic contract-enhanced CT-based radiomics for differentiation of pancreatobiliary-type and intestinal-type periampullary carcinomas

AIM

To investigate whether computed tomography (CT) radiomics can differentiate pancreatobiliary-type from intestinal-type periampullary carcinomas.

MATERIALS AND METHODS

CT radiomics of 96 patients (54 pancreatobiliary type and 42 intestinal type) with surgically confirmed periampullary carcinoma were assessed retrospectively. Volumes of interest (VOIs) were delineated manually. Radiomic features were extracted from preoperative CT images. A single-phase model and combined-phase model were constructed. Five-fold cross-validation and five machine-learning algorithms were utilised for model construction. The diagnostic performance of the models was evaluated by receiver operating characteristic (ROC) curves, and indicators included area under the curve (AUC), accuracy, sensitivity, specificity, and precision. ROC curves were compared using DeLong's test.

RESULTS

A total of 788 features were extracted on each phase. After feature selection using least absolute shrinkage and selection operator (LASSO) algorithm, the number of selected optimal feature was 18 (plain scan), nine (arterial phase), two (venous phase), 23 (delayed phase), 15 (three enhanced phases), and 29 (all phases), respectively. For the single-phase model, the delayed-phase model using the logistic regression (LR) algorithm showed the best prediction performance with AUC, accuracy, sensitivity, specificity, and precision of 0.89, 0.83, 0.80, 0.88, and 0.93, respectively. Two combined-phase models showed better results than the single-phase models. The model of all phases using the LR algorithm showed the best prediction performance with AUC, accuracy, sensitivity, specificity, and precision of 0.96, 0.88, 0.90, 0.93, and 0.92, respectively.

CONCLUSION

Radiomic models based on preoperative CT images can differentiate pancreatobiliary-type from intestinal-type periampullary carcinomas, in particular, the model of all phases using the LR algorithm.

Apparent diffusion coefficient-based histogram analysis differentiates histological subtypes of periampullary adenocarcinoma

BACKGROUND

For periampullary adenocarcinoma, the histological subtype is a better prognostic predictor than the site of tumor origin. Intestinal-type periampullary adenocarcinoma (IPAC) is reported to have a better prognosis than the pan-creatobiliary-type periampullary adenocarcinoma (PPAC). However, the classification of histological subtypes is difficult to determine before surgery. Apparent diffusion coefficient (ADC) histogram analysis is a noninvasive, non-enhanced method with high reproducibility that could help differentiate the two subtypes.

AIM

To investigate whether volumetric ADC histogram analysis is helpful for distinguishing IPAC from PPAC.

METHODS

Between January 2015 and October 2018, 476 consecutive patients who were suspected of having a periampullary tumor and underwent magnetic resonance imaging (MRI) were reviewed in this retrospective study. Only patients who underwent MRI at 3.0 T with different diffusion-weighted images (b-values = 800 and 1000 s/mm²) and who were confirmed with a periampullary adenocarcinoma were further analyzed. Then, the mean, 5th, 10th, 25th, 50th, 75th, 90th, and 95th percentiles of ADC values and ADC_min, ADC_max, kurtosis, skewness, and entropy were obtained from the volumetric histogram analysis. Comparisons were made by an independent Student's t-test or Mann-Whitney U test. Multiple-class receiver operating characteristic curve analysis was performed to determine and compare the diagnostic value of each significant parameter.

RESULTS

In total, 40 patients with histopathologically confirmed IPAC (n = 17) or PPAC (n = 23) were enrolled. The mean, 5th, 25th, 50th, 75th, 90th, and 95th percentiles and ADC_max derived from ADC₁₀₀₀ were significantly lower in the PPAC group than in the IPAC group (P < 0.05). However, values derived from ADC₈₀₀ showed no significant difference between the two groups. The 75th percentile of ADC₁₀₀₀ values achieved the highest area under the curve (AUC) for differentiating IPAC from PPAC (AUC = 0.781; sensitivity, 91%; specificity, 59%; cut-off value, 1.50 × 10^-3 mm²/s).

CONCLUSION

Volumetric ADC histogram analysis at a b-value of 1000 s/mm² might be helpful for differentiating the histological subtypes of periampullary adenocarcinoma before surgery.

Assessment of tumor heterogeneity: Differentiation of periampullary neoplasms based on CT whole-lesion histogram analysis

PURPOSE

To investigate the utility of whole-lesion histogram analysis from multidetector computed tomography (MDCT) for discrimination of duodenal adenocarcinoma (DAC), pancreatic ductal adenocarcinoma (PDAC) and gastrointestinal stromal tumor (GIST) around the periampullary area.

MATERIALS AND METHODS

171 patients suspicious of periampullary tumors were examined by MDCT (arterial and venous phases) and treated with surgery. A total of 74 patients were finally included in this retrospective study (26 DACs, 20 PDACs, and 28 GISTs). The interobserver agreement was evaluated by intra-class correlation coefficient (ICC) test between two radiologists. Volumetric histogram analysis based on CT Kinetics software was performed on enhanced MDCT images that recorded different histogram parameters of arterial and venous phases, including mean, median, 10th, 25th, 75th, and 90th percentiles, as well as skewness, kurtosis and entropy. The extracted histogram parameters were compared between DAC, PDAC and GIST respectively by Mann-Whitney U tests with Bonferroni corrections. Receiver operating characteristic (ROC) curve analysis was used to determine the diagnostic ability of each significant parameter and the area under the curve (AUC) was calculated.

RESULTS

The whole-lesion CT histogram analysis demonstrated significant differences between DAC, PDAC, and GIST with different histogram features on both arterial and venous phase scans (all P < 0.05). In the ROC analysis, the 90th percentile of venous phase demonstrated the highest AUC of 0.854 (P < 0.001) for discriminating DAC from PDAC. Excellent discriminators of periampullary tumors were noted among the histogram features, namely the 90th percentile of arterial phase, which demonstrated AUCs of 0.809 and 0.936 (P < 0.001) respectively for distinguishing DAC and PDAC from GIST.

CONCLUSION

The whole-lesion CT histogram analysis could be useful for differential diagnosis of DAC, PDAC and GIST arising from the periampullary area. Further assessment is warranted to investigate the clinical role of histogram analysis based on MDCT.

Evaluation of upper abdominal organs with DWI in patients with familial Mediterranean fever

PURPOSE

To investigate the diagnostic efficiency of diffusion-weighted magnetic resonance imaging (DWI) for the evaluation of functional changes that can occur in upper abdominal organs in patients with familial Mediterranean fever (FMF).

METHODS

The study included 50 controls, 45 patients with FMF, and 14 patients with FMF who had accompanying proteinuria. Measurement of apparent diffusion coefficient (ADC) was performed using DWI sections obtained from liver, spleen, kidney, and pancreas parenchyma with 1.5T MRI using b = 500 and b = 1000 s/mm² values both in patients and control groups. Mean ADC values were compared between patient and control groups.

RESULTS

Renal ADC values were lower in the patient groups compared to the control group. Additionally, renal ADC values showed further decrease in the patient group in the presence of accompanying proteinuria, when compared to the FMF group without proteinuria (p < 0.001). Based on the ROC analysis, calculated cutoff values for the determination of FMF and FMF accompanied by proteinuria were 2.26 × 10^-3 and 2.04 × 10^-3 mm²/s, respectively. Liver, spleen, and pancreas ADC values did not show remarkable change between patient and control groups.

CONCLUSION

Present findings indicate that the presence of FMF and its clinical progression expressed by proteinuria can be differentially determined with renal DWI.