Clinical decision making in the management of pancreatic cystic neoplasms

Computer-Aided Diagnosis of Pancreas Serous Cystic Neoplasms: A Radiomics Method on Preoperative MDCT Images

OBJECTIVE

Our aim was to propose a preoperative computer-aided diagnosis scheme to differentiate pancreatic serous cystic neoplasms from other pancreatic cystic neoplasms, providing supportive opinions for clinicians and avoiding overtreatment.

MATERIALS AND METHODS

In this retrospective study, 260 patients with pancreatic cystic neoplasm were included. Each patient underwent a multidetector row computed tomography scan and pancreatic resection. In all, 200 patients constituted a cross-validation cohort, and 60 patients formed an independent validation cohort. Demographic information, clinical information, and multidetector row computed tomography images were obtained from Picture Archiving and Communication Systems. The peripheral margin of each neoplasm was manually outlined by experienced radiologists. A radiomics system containing 24 guideline-based features and 385 radiomics high-throughput features was designed. After the feature extraction, least absolute shrinkage selection operator regression was used to select the most important features. A support vector machine classifier with 5-fold cross-validation was applied to build the diagnostic model. The independent validation cohort was used to validate the performance.

RESULTS

Only 31 of 102 serous cystic neoplasm cases in this study were recognized correctly by clinicians before the surgery. Twenty-two features were selected from the radiomics system after 100 bootstrapping repetitions of the least absolute shrinkage selection operator regression. The diagnostic scheme performed accurately and robustly, showing the area under the receiver operating characteristic curve = 0.767, sensitivity = 0.686, and specificity = 0.709. In the independent validation cohort, we acquired similar results with receiver operating characteristic curve = 0.837, sensitivity = 0.667, and specificity = 0.818.

CONCLUSION

The proposed radiomics-based computer-aided diagnosis scheme could increase preoperative diagnostic accuracy and assist clinicians in making accurate management decisions.

Pancreatic cystic neoplasms: Review of current knowledge, diagnostic challenges, and management options

Pancreatic cystic lesions are being detected with increasing frequency, largely due to advances in cross-sectional imaging. The most common neoplasms include serous cystadenomas, mucinous cystic neoplasms, intraductal papillary mucinous neoplasms, solid pseudopapillary neoplasms, and cystic pancreatic endocrine neoplasms. Computed tomography (CT), magnetic resonance imaging (MRI), and endoscopic ultrasound (EUS) are currently used as imaging modalities. EUS-guided fine needle aspiration has proved to be a useful diagnostic tool, and enables an assessment of tumor markers, cytology, chemistries, and DNA analysis. Here, we review the current literature on pancreatic cystic neoplasms, including classification, diagnosis, treatment, and recommendations for surveillance. Data for this manuscript was acquired via searching the literature from inception to December 2014 on PubMed and Ovid MEDLINE.

Computer-Aided Diagnosis for Distinguishing Pancreatic Mucinous Cystic Neoplasms From Serous Oligocystic Adenomas in Spectral CT Images

OBJECTIVE

This preliminary study aims to verify the effectiveness of the additional information provided by spectral computed tomography (CT) with the proposed computer-aided diagnosis (CAD) scheme to differentiate pancreatic serous oligocystic adenomas (SOAs) from mucinous cystic neoplasms of pancreas cystic lesions.

MATERIALS AND METHODS

This study was conducted from January 2010 to October 2013. Twenty-three patients (5 men and 18 women; mean age, 43.96 years old) with SOA and 19 patients (3 men and 16 women; mean age, 41.74 years old) with MCN were included in this retrospective study. Two types of features were collected by dual-energy spectral CT imaging as follows: conventional and additional quantitative spectral CT features. Classification results of the CAD scheme were compared using the conventional features and full feature data set. Important features were selected using support vector machine classification method combined with feature-selection technique. The optimal cutoff values of selected features were determined through receiver-operating characteristic curve analyses.

RESULTS

Combining conventional features with additional spectral CT features improved the overall accuracy from 88.37% to 93.02%. The selected features of the proposed CAD scheme were tumor size, contour, location, and low-energy CT values (43 keV). Iodine-water basis material pair densities in both arterial phase (AP) and portal venous phase (PP) were important factors for differential diagnosis of SOA and MCN. The optimal cutoff values of long axis, short axis, 40 keV monochromatic CT value in AP, iodine (water) density in AP, 43 keV monochromatic CT value in PP, and iodine (water) density in PP were 3.4 mm, 3.1 mm, 35.7 Hu, 0.32533 mg/mL, 39.4 Hu, and 0.348 mg/mL, respectively.

CONCLUSION

The combination of conventional features and additional information provided by dual-energy spectral CT shows a high accuracy in the CAD scheme. The quantitative information of spectral CT may prove useful in the diagnosis and classification of SOAs and MCNs with machine learning algorithms.

A rare case of mixed mucinous cystadenoma with serous cystadenoma of the pancreas treated by laparoscopic central pancreatectomy

Mixed mucinous cystadenoma with serous cystadenoma of the pancreas is rare. There have been only two previous case reports in the English-language literature. We present a case of a 46-year-old woman who was diagnosed with mixed mucinous cystadenoma with serous cystadenoma of the pancreas. Computed tomography and magnetic resonance imaging showed a cystic neoplasm in the dorsal/proximal body of the pancreas with a clear-margin multilocular cavity and enhanced internal septum. The patient underwent laparoscopic central pancreatectomy. The diagnosis of mixed mucinous cystadenoma with serous cystadenoma of the pancreas was confirmed by pathological examination. The patient was followed up for 3 months and there were no signs of recurrence, or pancreatic exocrine or endocrine insufficiency. To the best of our knowledge, this is the first reported case treated by laparoscopic central pancreatectomy.