Publications

This systematic review examined the applications of deep learning for the interpretation of lymphoma positron emission tomography (PET) images. From 71 papers initially retrieved, 21 studies with a total of 9402 participants were ultimately included. The proposed deep learning models achieved promising performance in various medical tasks, including detection, histological subtyping, differential diagnosis, and prognostication. AI-based analysis of lymphoma whole-body FDG-PET/CT can inform all phases of clinical management including staging, prognostication, treatment planning, and treatment response evaluation. AI methods demonstrated promising predictive performance (AUC range = 0.68–0.85) on PET-based images, with higher values for deep learning methods. AI techniques for lymphoma PET evaluation are designed to assist physicians in handling large volumes of scans through rapid and accurate calculations.

While chemotherapy enhances survival rates for pancreatic cancer patients after surgery, less than 60% complete adjuvant therapy, with a smaller fraction undergoing neoadjuvant treatment. This study aimed to predict which patients would complete pre- or postoperative chemotherapy through machine learning, grouping patients with resectable pancreatic cancer into those who completed all intended treatments and those who did not. Researchers applied logistic regression with lasso penalization and an extreme gradient boosting model for prediction. Among 208 patients with median age of 69 (49.5% female, 62% white), neoadjuvant and adjuvant chemotherapies were received by 26% and 47.1%, respectively, but only 49% completed all treatments. Negative prognostic factors included worsening diabetes, age, congestive heart failure, high body mass index, family history of pancreatic cancer, initial bilirubin levels, and tumor location in the pancreatic head. Predictive accuracy (AUROC) was 0.67 for both models, with performance expected to improve with larger datasets.

Pancreatic cancer is one of the most lethal gastrointestinal malignancies. Despite advances in cross-sectional imaging, chemotherapy, radiation therapy, and surgical techniques, the 5-year overall survival is only 12%. With the advent and rapid adoption of artificial intelligence (AI), specifically deep learning (DL), into healthcare systems, there is potential for utilizing AI applications across the entire pancreatic cancer patient journey. This review examines the current applications of DL and other AI modalities in the diagnosis, management, monitoring, and prognostic assessment of patients with pancreatic cancer. The scope covers diagnostic imaging, surgical planning, therapeutic monitoring, and development of novel biomarkers. We conducted a comprehensive review of English language publications from January 2019 to November 2023 in the PubMed database using keywords including pancreatic cancer, deep learning, radiomics, large language models, and generative adversarial networks.