Response to repeat echoendoscopic celiac plexus neurolysis in pancreatic cancer patients: A machine learning approach

Artificial Intelligence and Machine Learning in Cancer Related Pain: A Systematic Review

Background/objective

Pain is a challenging multifaceted symptom reported by most cancer patients, resulting in a substantial burden on both patients and healthcare systems. This systematic review aims to explore applications of artificial intelligence/machine learning (AI/ML) in predicting pain-related outcomes and supporting decision-making processes in pain management in cancer.

Methods

A comprehensive search of Ovid MEDLINE, EMBASE and Web of Science databases was conducted using terms including "Cancer", "Pain", "Pain Management", "Analgesics", "Opioids", "Artificial Intelligence", "Machine Learning", "Deep Learning", and "Neural Networks" published up to September 7, 2023. The screening process was performed using the Covidence screening tool. Only original studies conducted in human cohorts were included. AI/ML models, their validation and performance and adherence to TRIPOD guidelines were summarized from the final included studies.

Results

This systematic review included 44 studies from 2006-2023. Most studies were prospective and uni-institutional. There was an increase in the trend of AI/ML studies in cancer pain in the last 4 years. Nineteen studies used AI/ML for classifying cancer patients' pain development after cancer therapy, with median AUC 0.80 (range 0.76-0.94). Eighteen studies focused on cancer pain research with median AUC 0.86 (range 0.50-0.99), and 7 focused on applying AI/ML for cancer pain management decisions with median AUC 0.71 (range 0.47-0.89). Multiple ML models were investigated with. median AUC across all models in all studies (0.77). Random forest models demonstrated the highest performance (median AUC 0.81), lasso models had the highest median sensitivity (1), while Support Vector Machine had the highest median specificity (0.74). Overall adherence of included studies to TRIPOD guidelines was 70.7%. Lack of external validation (14%) and clinical application (23%) of most included studies was detected. Reporting of model calibration was also missing in the majority of studies (5%).

Conclusion

Implementation of various novel AI/ML tools promises significant advances in the classification, risk stratification, and management decisions for cancer pain. These advanced tools will integrate big health-related data for personalized pain management in cancer patients. Further research focusing on model calibration and rigorous external clinical validation in real healthcare settings is imperative for ensuring its practical and reliable application in clinical practice.

Artificial intelligence in pancreatic cancer: diagnosis, limitations, and the future prospects-a narrative review

PURPOSE

This review aims to explore the role of AI in the application of pancreatic cancer management and make recommendations to minimize the impact of the limitations to provide further benefits from AI use in the future.

METHODS

A comprehensive review of the literature was conducted using a combination of MeSH keywords, including "Artificial intelligence", "Pancreatic cancer", "Diagnosis", and "Limitations".

RESULTS

The beneficial implications of AI in the detection of biomarkers, diagnosis, and prognosis of pancreatic cancer have been explored. In addition, current drawbacks of AI use have been divided into subcategories encompassing statistical, training, and knowledge limitations; data handling, ethical and medicolegal aspects; and clinical integration and implementation.

CONCLUSION

Artificial intelligence (AI) refers to computational machine systems that accomplish a set of given tasks by imitating human intelligence in an exponential learning pattern. AI in gastrointestinal oncology has continued to provide significant advancements in the clinical, molecular, and radiological diagnosis and intervention techniques required to improve the prognosis of many gastrointestinal cancer types, particularly pancreatic cancer.

Artificial intelligence and machine learning in precision medicine: A paradigm shift in big data analysis

The integration of artificial intelligence in precision medicine has revolutionized healthcare delivery. Precision medicine identifies the phenotype of particular patients with less-common responses to treatment. Recent studies have demonstrated that translational research exploring the convergence between artificial intelligence and precision medicine will help solve the most difficult challenges facing precision medicine. Here, we discuss different aspects of artificial intelligence in precision medicine that improve healthcare delivery. First, we discuss how artificial intelligence changes the landscape of precision medicine and the evolution of artificial intelligence in precision medicine. Second, we highlight the synergies between artificial intelligence and precision medicine and promises of artificial intelligence and precision medicine in healthcare delivery. Third, we briefly explain the promise of big data analytics and the integration of nanomaterials in precision medicine. Last, we highlight the challenges and opportunities of artificial intelligence in precision medicine.

Efficacy and Safety of Deep Sedation and Anaesthesia for Complex Endoscopic Procedures—A Narrative Review

Propofol sedation for advanced endoscopic procedures is a widespread technique at present, which generates controversy worldwide when anaesthetic or non-anaesthetic personnel administer this form of sedation. There is some evidence for safe administered propofol sedation by non-anaesthetic personnel in patients undergoing endoscopy procedures, but there are only few randomised trials addressing the safety and efficacy of propofol in patients undergoing advanced procedures. A serious possible consequence of propofol sedation is the rapid and unpredictable progression from deep sedation to general anaesthesia mostly when elderly and frail patients are involved in the diagnosis or treatment of various neoplasia. This situation requires rescue measures with skilled airway management. The aim of this paper is to review the safety and efficacy aspects of sedation techniques, with special reference to propofol administration covering the whole patient journey, including preassessment, sedation options and discharge when advanced endoscopic procedures are performed.

Methodological conduct of prognostic prediction models developed using machine learning in oncology: a systematic review

BACKGROUND

Describe and evaluate the methodological conduct of prognostic prediction models developed using machine learning methods in oncology.

METHODS

We conducted a systematic review in MEDLINE and Embase between 01/01/2019 and 05/09/2019, for studies developing a prognostic prediction model using machine learning methods in oncology. We used the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement, Prediction model Risk Of Bias ASsessment Tool (PROBAST) and CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS) to assess the methodological conduct of included publications. Results were summarised by modelling type: regression-, non-regression-based and ensemble machine learning models.

RESULTS

Sixty-two publications met inclusion criteria developing 152 models across all publications. Forty-two models were regression-based, 71 were non-regression-based and 39 were ensemble models. A median of 647 individuals (IQR: 203 to 4059) and 195 events (IQR: 38 to 1269) were used for model development, and 553 individuals (IQR: 69 to 3069) and 50 events (IQR: 17.5 to 326.5) for model validation. A higher number of events per predictor was used for developing regression-based models (median: 8, IQR: 7.1 to 23.5), compared to alternative machine learning (median: 3.4, IQR: 1.1 to 19.1) and ensemble models (median: 1.7, IQR: 1.1 to 6). Sample size was rarely justified (n = 5/62; 8%). Some or all continuous predictors were categorised before modelling in 24 studies (39%). 46% (n = 24/62) of models reporting predictor selection before modelling used univariable analyses, and common method across all modelling types. Ten out of 24 models for time-to-event outcomes accounted for censoring (42%). A split sample approach was the most popular method for internal validation (n = 25/62, 40%). Calibration was reported in 11 studies. Less than half of models were reported or made available.

CONCLUSIONS

The methodological conduct of machine learning based clinical prediction models is poor. Guidance is urgently needed, with increased awareness and education of minimum prediction modelling standards. Particular focus is needed on sample size estimation, development and validation analysis methods, and ensuring the model is available for independent validation, to improve quality of machine learning based clinical prediction models.

Application of artificial intelligence to pancreatic adenocarcinoma

BACKGROUND AND OBJECTIVES

Pancreatic cancer (PC) is one of the deadliest cancers worldwide although substantial advancement has been made in its comprehensive treatment. The development of artificial intelligence (AI) technology has allowed its clinical applications to expand remarkably in recent years. Diverse methods and algorithms are employed by AI to extrapolate new data from clinical records to aid in the treatment of PC. In this review, we will summarize AI's use in several aspects of PC diagnosis and therapy, as well as its limits and potential future research avenues.

METHODS

We examine the most recent research on the use of AI in PC. The articles are categorized and examined according to the medical task of their algorithm. Two search engines, PubMed and Google Scholar, were used to screen the articles.

RESULTS

Overall, 66 papers published in 2001 and after were selected. Of the four medical tasks (risk assessment, diagnosis, treatment, and prognosis prediction), diagnosis was the most frequently researched, and retrospective single-center studies were the most prevalent. We found that the different medical tasks and algorithms included in the reviewed studies caused the performance of their models to vary greatly. Deep learning algorithms, on the other hand, produced excellent results in all of the subdivisions studied.

CONCLUSIONS

AI is a promising tool for helping PC patients and may contribute to improved patient outcomes. The integration of humans and AI in clinical medicine is still in its infancy and requires the in-depth cooperation of multidisciplinary personnel.

Artificial intelligence in gastroenterology: A state-of-the-art review

The development of artificial intelligence (AI) has increased dramatically in the last 20 years, with clinical applications progressively being explored for most of the medical specialties. The field of gastroenterology and hepatology, substantially reliant on vast amounts of imaging studies, is not an exception. The clinical applications of AI systems in this field include the identification of premalignant or malignant lesions (e.g., identification of dysplasia or esophageal adenocarcinoma in Barrett’s esophagus, pancreatic malignancies), detection of lesions (e.g., polyp identification and classification, small-bowel bleeding lesion on capsule endoscopy, pancreatic cystic lesions), development of objective scoring systems for risk stratification, predicting disease prognosis or treatment response [e.g., determining survival in patients post-resection of hepatocellular carcinoma), determining which patients with inflammatory bowel disease (IBD) will benefit from biologic therapy], or evaluation of metrics such as bowel preparation score or quality of endoscopic examination. The objective of this comprehensive review is to analyze the available AI-related studies pertaining to the entirety of the gastrointestinal tract, including the upper, middle and lower tracts; IBD; the hepatobiliary system; and the pancreas, discussing the findings and clinical applications, as well as outlining the current limitations and future directions in this field.

Artificial intelligence in pancreaticobiliary endoscopy

Artificial intelligence (AI) applications in health care have exponentially increased in recent years, and a few of these are related to pancreatobiliary disorders. AI-based methods were applied to extract information, in prognostication, to guide clinical treatment decisions and in pancreatobiliary endoscopy to characterize lesions. AI applications in endoscopy are expected to reduce inter-operator variability, improve the accuracy of diagnosis, and assist in therapeutic decision-making in real time. AI-based literature must however be interpreted with caution given the limited external validation. A multidisciplinary approach combining clinical and imaging or endoscopy data will better utilize AI-based technologies to further improve patient care.

Efficacy and Safety of Non-Anesthesiologist Administration of Propofol Sedation in Endoscopic Ultrasound: A Propensity Score Analysis

In spite of promising preliminary results, evidence supporting the use of non-anesthesiologist-administered propofol sedation (NAAP) in endoscopic ultrasound (EUS) procedures is still limited. The aim of this manuscript was to examine the safety and efficacy of NAAP as compared to anesthesiologist-administered propofol sedation in EUS procedures performed in a referral center. Out of 832 patients referred to our center between 2016 and 2019, after propensity score matching two groups were compared: 305 treated with NAAP and 305 controls who underwent anesthesiologist-administered propofol sedation. The primary outcome was the rate of major complications. The median age was 67 years and the proportion of patients with comorbidities was 31.8% in both groups. One patient in each group (0.3%) experienced a major complication, whereas minor complications were observed in 13 patients in the NAAP group (4.2%) and 10 patients in the control group (3.2%; p = 0.52). Overall pain during the procedure was 2.3 ± 1 in group 1 and 1.8 ± 1 in group 2 (p = 0.67), whereas pain/discomfort upon awakening was rated as 1 ± 0.5 in both groups (p = 0.72). NAAP is safe and effective even in advanced EUS procedures. Further randomized-controlled trials (RCTs) are warranted to confirm these findings.

Contrast-Enhanced Harmonic Endoscopic Ultrasound-Guided Fine-Needle Aspiration versus Standard Fine-Needle Aspiration in Pancreatic Masses: A Propensity Score Analysis

Background: Whether endoscopic ultrasound (EUS) contrast-enhanced fine-needle aspiration (CH-EUS-FNA) determines superior results in comparison to standard EUS-FNA in tissue acquisition of pancreatic masses remains unclear. The aim of this study was to compare these two techniques on a series of patients with solid pancreatic lesions. Methods: 362 patients underwent EUS-FNA (2008–2019), after the propensity score matching of two groups were compared; 103 treated with CH-EUS-FNA (group 1) and 103 with standard EUS-FNA (group 2). The primary outcome was the diagnostic accuracy. Secondary outcomes were sensitivity, specificity, and sample adequacy. Results: Diagnostic sensitivity was 87.6% in group 1 and 80% in group 2 (p = 0.18). The negative predictive value was 56% in group 1 and 41.5% in group 2 (p = 0.06). The specificity and positive predictive values were 100% for both groups. Diagnostic accuracy was 89.3% and 82.5%, respectively (p = 0.40). Sample adequacy was 94.1% in group 1 and 91.2% in group 2 (p = 0.42). The rate of adequate core histologic samples was 33% and 28.1%, respectively (p = 0.44), and the number of needle passes to obtain adequate samples were 2.4 ± 0.6 and 2.7 ± 0.8, respectively (p = 0.76). These findings were confirmed in subgroup analyses, conducted according to lesion size and contrast enhancement pattern. Conclusions: CH-EUS-FNA does not appear to be superior to standard EUS-FNA in patients with pancreatic masses.

Sarcopenia represents a negative prognostic factor in pancreatic cancer patients undergoing EUS celiac plexus neurolysis

Background and Objectives:

Increasing evidence suggests a prognostic role of sarcopenia in pancreatic cancer patients. The aim of this study was to assess the influence of sarcopenia on treatment outcomes after EUS-guided celiac plexus neurolysis (CPN).

Materials and Methods:

Data regarding 215 patients treated with EUS CPN between 2004 and 2019 were reviewed. Determination of body composition was conducted on contrast-enhanced CT scan, and pain response was considered as the primary outcome. Univariate and multivariate logistic regression was performed to identify the independent predictors of pain response.

Results:

Treatment was successful in 187 patients (86.9%). The median age was 62 (range 39–84) years, and most patients were male (61.8%). Of the whole study population, 139 patients (64.6%) were defined as sarcopenic, of which 116 (83.4%) responded to the treatment and 5 (3.5%) experienced a complete response. Among 76 nonsarcopenic participants, 71 (93.4%) responded to the treatment and 22 (28.9%) obtained a complete response (P = 0.03 and <0.001, respectively). The median duration of pain relief was 8 (2–10) and 15 (8–16) weeks in sarcopenic and nonsarcopenic patients, respectively (P = 0.01). The median overall survival after neurolysis was 4 months (3–5) in sarcopenic participants and 7 months (6–8) in nonsarcopenic participants (P = 0.05). Tumoral stage, interval from the diagnosis to treatment, and sarcopenia resulted as significant prognostic factors for treatment response both in univariate and multivariate regression analyses. No severe treatment-related adverse events were reported in the whole study population, with no difference between the two groups.

Conclusions:

Sarcopenia represents a predictor of poorer response to EUS CPN.