Browsing by Subject "Pancreatic cysts"

Now showing 1 - 6 of 6
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    Artificial Intelligence in Pancreatic Intraductal Papillary Mucinous Neoplasm Imaging: A Systematic Review
    (medRxiv, 2025-01-09) Qadir, Muhammad Ibtsaam; Baril, Jackson A.; Yip-Schneider, Michele T.; Schonlau, Duane; Tran, Thi Thanh Thoa; Schmidt, C. Max; Kolbinger, Fiona R.; Surgery, School of Medicine
    Background: Based on the Fukuoka and Kyoto international consensus guidelines, the current clinical management of intraductal papillary mucinous neoplasm (IPMN) largely depends on imaging features. While these criteria are highly sensitive in detecting high-risk IPMN, they lack specificity, resulting in surgical overtreatment. Artificial Intelligence (AI)-based medical image analysis has the potential to augment the clinical management of IPMNs by improving diagnostic accuracy. Methods: Based on a systematic review of the academic literature on AI in IPMN imaging, 1041 publications were identified of which 25 published studies were included in the analysis. The studies were stratified based on prediction target, underlying data type and imaging modality, patient cohort size, and stage of clinical translation and were subsequently analyzed to identify trends and gaps in the field. Results: Research on AI in IPMN imaging has been increasing in recent years. The majority of studies utilized CT imaging to train computational models. Most studies presented computational models developed on single-center datasets (n=11,44%) and included less than 250 patients (n=18,72%). Methodologically, convolutional neural network (CNN)-based algorithms were most commonly used. Thematically, most studies reported models augmenting differential diagnosis (n=9,36%) or risk stratification (n=10,40%) rather than IPMN detection (n=5,20%) or IPMN segmentation (n=2,8%). Conclusion: This systematic review provides a comprehensive overview of the research landscape of AI in IPMN imaging. Computational models have potential to enhance the accurate and precise stratification of patients with IPMN. Multicenter collaboration and datasets comprising various modalities are necessary to fully utilize this potential, alongside concerted efforts towards clinical translation.
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    Pancreatic Cysts Identification Using Unstructured Information Management Architecture
    (Office of the Vice Chancellor for Research, 2013-04-05) Mehrabi, Saeed; Schmidt, C. Max; Waters, Joshua A.; Beesley, Chris; Krishnan, Anand; Kesterson, Joe; Dexter, Paul; Al-Haddad, Mohammed A.; Palakal, Mathew
    Pancreatic cancer is one of the deadliest cancers, mostly diagnosed at late stages. Patients with pancreatic cysts are at higher risk of developing cancer and surveillance of these patients can help with early diagnosis. Much information about pancreatic cysts can be found in free text format in various medical narratives. In this retrospective study, a corpus of 1064 records from 44 patients at Indiana University Hospital from 1990 to 2012 was collected. A natural language processing system was developed and used to identify patients with pancreatic cysts. The input goes through series of tasks within the Unstructured Information Management Architecture (UIMA) framework consisting of report separation, metadata detection, sentence detection, concept annotation and writing into the database. Metadata such as medical record number (MRN), report id, report name, report date, report body were extracted from each report. Sentences were detected and concepts within each sentence were extracted using regular expression. Regular expression is a pattern of characters matching specific string of text. Our medical team assembled concepts that are used to identify pancreatic cysts in medical reports and additional keywords were added by searching through literature and Unified Medical Language System (UMLS) knowledge base. The Negex Algorithm was used to find out negation status of concepts. The 1064 reports were divided into sets of train and test sets. Two pancreatic-cyst surgeons created the gold standard data (Inter annotator agreement K=88%). The training set was analyzed to modify the regular expression. The concept identification using the NegEx algorithm resulted in precision and recall of 98.9% and 89% respectively. In order to improve the performance of negation detection, Stanford Dependency parser (SDP) was used. SDP finds out how words are related to each other in a sentence. SDP based negation algorithm improved the recall to 95.7%.
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    Pancreatic hydatid cyst diagnosed on EUS-guided FNA
    (Elsevier, 2018-11-30) Mohamadnejad, Mehdi; Kheyri, Zahedin; Zamani, Farhad; Sotoudeh, Masoud; Al-Haddad, Mohammad; Medicine, School of Medicine
    A 22-year-old woman presented with abdominal pain. Abdominal CT scan demonstrated a mass lesion in the pancreatic body (Fig. 1A). There was no lesion in the liver on CT scan (Fig. 1B). EUS showed a 50- × 35-mm cystic mass lesion containing numerous floating serpentine-like linear structures (Fig. 2; Video 1, available online at www.VideoGIE.org). EUS-guided FNA was performed with a 22-gauge needle.
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    Potential Health Disparities in the Early Detection and Prevention of Pancreatic Cancer
    (Springer Nature, 2024-05-13) Yip-Schneider, Michele T.; Muraru, Rodica; Rao, Nikita; Kim, Rachel C.; Rempala-Kurucz, Jennifer; Baril, Jackson A.; Roch, Alexandra M.; Schmidt, C. Max; Surgery, School of Medicine
    Introduction: Pancreatic cancer remains one of the deadliest cancers in the United States. Some types of pancreatic cysts, which are being detected more frequently and often incidentally on imaging, have the potential to develop into pancreatic cancer and thus provide a valuable window of opportunity for cancer interception. Although racial disparity in pancreatic cancer has been described, little is known regarding health disparities in pancreatic cancer prevention. In the present study, we investigate potential health disparities along the continuum of care for pancreatic cancer. Methods: The racial and ethnic composition of pancreatic patients at high-volume centers in Indiana were evaluated, representing patients undergoing surgery for pancreatic cancer (n=390), participating in biobanking (972 pancreatic cancer patients and 1984 patients with pancreatic disease), or being monitored for pancreatic cysts at an early detection center (n=1514). To assess racial disparities and potential differences in decision-making related to pancreatic cancer prevention and early detection, an exploratory online survey was administered through a volunteer registry (n=708). Results: We show that despite comprising close to 10% or 30% of the Indiana or Indianapolis population, respectively, African Americans make up only about 4-5% of our study cohorts consisting of patients undergoing pancreatic surgery or participating in biobanking and early detection. Analysis of online survey results revealed that given the hypothetical situation of being diagnosed with a pancreatic cyst or pancreatic cancer, the vast majority of respondents (>90%) would agree to undergo surveillance or surgery, respectively, regardless of race. Only a minority (3-12%) acknowledged any significant transportation, financial, or emotional barriers that would impact a decision to undergo surveillance or surgery. This suggests that the observed racial disparities may be due in part to the existence of other barriers that lie upstream of this decision point. Conclusion: Racial disparities exist not only for pancreatic cancer but also at earlier points along the continuum of care such as prevention and early detection. To our knowledge, this is the first study to document racial disparity in the management of patients with pancreatic cysts who are at risk of developing pancreatic cancer. Our results suggest that improving access to information and care for such at-risk individuals may lead to more equitable outcomes.
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    Radiomics Boosts Deep Learning Model for IPMN Classification
    (Springer, 2023) Yao, Lanhong; Zhang, Zheyuan; Demir, Ugur; Keles, Elif; Vendrami, Camila; Agarunov, Emil; Bolan, Candice; Schoots, Ivo; Bruno, Marc; Keswani, Rajesh; Miller, Frank; Gonda, Tamas; Yazici, Cemal; Tirkes, Temel; Wallace, Michael; Spampinato, Concetto; Bagci, Ulas; Radiology and Imaging Sciences, School of Medicine
    Intraductal Papillary Mucinous Neoplasm (IPMN) cysts are pre-malignant pancreas lesions, and they can progress into pancreatic cancer. Therefore, detecting and stratifying their risk level is of ultimate importance for effective treatment planning and disease control. However, this is a highly challenging task because of the diverse and irregular shape, texture, and size of the IPMN cysts as well as the pancreas. In this study, we propose a novel computer-aided diagnosis pipeline for IPMN risk classification from multi-contrast MRI scans. Our proposed analysis framework includes an efficient volumetric self-adapting segmentation strategy for pancreas delineation, followed by a newly designed deep learning-based classification scheme with a radiomics-based predictive approach. We test our proposed decision-fusion model in multi-center data sets of 246 multi-contrast MRI scans and obtain superior performance to the state of the art (SOTA) in this field. Our ablation studies demonstrate the significance of both radiomics and deep learning modules for achieving the new SOTA performance compared to international guidelines and published studies (81.9% vs 61.3% in accuracy). Our findings have important implications for clinical decision-making. In a series of rigorous experiments on multi-center data sets (246 MRI scans from five centers), we achieved unprecedented performance (81.9% accuracy). The code is available upon publication.
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    Short-Term Outcomes of Endoscopic Ultrasound-Guided Pancreatic Cyst Ablation: A Systematic Review and Meta-Analysis
    (Elsevier, 2024-11-29) Al Qady, Ahmed; Nayar, Kapil Dev; Elmustafa, Fatima; Salih, Mohamed; Emran, Joseph; Beirat, Amir; Menakuru, Sasmith; Harris, Dana; Echols, Dan J.; Ji, Baoan; DeWitt, John M.; Wang, Zhen; Stancampiano, Fernando F.; Bi, Yan; Medicine, School of Medicine
    Background and aims: Pancreatic cysts (PCs) are increasingly detected through abdominal imaging, prompting exploration of alternatives such as endoscopic ultrasound-guided PC ablation due to the risks and costs associated with surgery. This study conducts a systematic review and meta-analysis of endoscopic ultrasound-guided PC ablation's short-term efficacy and complications for PC management. Methods: A systematic review and meta-analysis were carried out on PubMed, Ovid, Cochrane, and TRIP electronic databases. The primary outcome was cyst resolution (partial and complete) and persistence on imaging 12 months after ablation. The secondary outcome was procedure-related adverse events. Results: Eight studies were eligible for analysis. Complete cyst resolution on imaging 12 months after endoscopic ultrasound ablation was 50% [95% CI 36‒63, I2 = 85.31%]. Partial cyst resolution was 27% [95% CI 15‒41, I2 = 87.07%], and cyst persistence was 17% [95% CI 11‒24, I2 = 62.11%]. The rate of complete resolution varied depending on the treatment agent (for ethanol 29% [95% CI 10‒53]; lauromacrogol 51% [95% Cl 36‒67]; ethanol and paclitaxel 63% [95% CI 48‒76]; paclitaxel and gemcitabine 67% [95% CI 45‒83]; and ethanol, paclitaxel, and gemcitabine 61% [95% CI 39‒80]). Postprocedure adverse events included abdominal pain in 4% [95% CI 0‒11], pancreatitis in 3% [95% CI 1‒5], and fever in 1% [95% CI 0‒3] of all patients. Conclusion: The treatment of pancreatic cysts with endoscopic ultrasound ablation results in acceptable levels of complete resolution, and low incidence of severe adverse events. The effectiveness of this treatment is further enhanced when chemoablative agents are employed.