Testicular cancer is often overshadowed by other cancers despite being the most common cancer in men aged 15 to 34 years. This systematic review focuses on the potential of machine learning and deep learning techniques in the areas of testicular cancer imaging and histopathology, where artificial intelligence (AI) could assist in diagnosis, evaluation, and prognostication. Various studies have highlighted AI’s ability to accurately distinguish between benign and malignant lesions and characterisation within malignant lesions using magnetic resonance imaging (MRI) radiomics. Models have also been used in predicting histopathological findings to allow for greater accuracy and reproducibility. Further work is required to explore AI implementation in ultrasound imaging, which is the cheapest and most used modality.

Pancreatic cancer, namely pancreatic ductal adenocarcinoma (PDAC), is an intractable cancer with a 5-year survival of around 7%-10%. Surgery with adjuvant chemotherapy remains the mainstay of curative treatment. The pancreas is a retroperitoneal organ that lies close to major arterial and venous structures, and it is the involvement of these structures that currently technically limits surgical resection with curative intent for pancreatic cancer. It is possible to resect venous and arterial structures involved in cancer to expand options for patients for whom surgery was previously deemed infeasible, but this is best performed in high-volume pancreatic surgery centres. Here, we explore the role that 3D visualisation and navigation surgery have in improving preoperative planning and operative execution, the role they may play in training and education and in enabling the development of novel surgical techniques in pancreatic surgery.

Rising in incidence, hepatobiliary and pancreatic (HPB) cancers continue to exhibit dismal long-term survival. The overall poor prognosis of HPB cancers is reflective of the advanced stage at which most patients are diagnosed. Late diagnosis is driven by the often-asymptomatic nature of these diseases, as well as a dearth of screening modalities. Additionally, standard imaging modalities fall short of providing accurate and detailed information regarding specific tumor characteristics, which can better inform surgical planning and sequencing of systemic therapy. Therefore, precise therapeutic planning must be delayed until histopathological examination is performed at the time of resection. Given the current shortcomings in the management of HPB cancers, investigations of numerous noninvasive biomarkers, including circulating tumor cells and DNA, proteomics, immunolomics, and radiomics, are underway. Radiomics encompasses the extraction and analysis of quantitative imaging features. Along with summarizing the general framework of radiomics, this review synthesizes the state of radiomics in HPB cancers, outlining its role in various aspects of management, present limitations, and future applications for clinical integration. Current literature underscores the utility of radiomics in early detection, tumor characterization, therapeutic selection, and prognostication for HPB cancers. Seeing as single-center, small studies constitute the majority of radiomics literature, there is considerable heterogeneity with respect to steps of the radiomics workflow such as segmentation, or delineation of the region of interest on a scan. Nonetheless, the introduction of the radiomics quality score (RQS) demonstrates a step towards greater standardization and reproducibility in the young field of radiomics. Altogether, in the setting of continually improving artificial intelligence algorithms, radiomics represents a promising biomarker avenue for promoting enhanced and tailored management of HPB cancers, with the potential to improve long-term outcomes for patients.

This is a scoping review of artificial intelligence (AI) in flexible endoscopy (FE), encompassing both computer vision (CV) and autonomous actions (AA). While significant progress has been made in AI and FE, particularly in polyp detection and malignancy prediction, resulting in several available market products, these achievements only scratch the surface potential of AI in flexible endoscopy. Many doctors still do not fully grasp that contemporary robotic FE systems, which operate the endoscope through telemanipulation, represent the most basic autonomy level, specifically categorized as level 1. Although these console systems allow remote control, they lack the more sophisticated forms of autonomy. This manuscript aims to review the current examples of AI applications in FE and hopefully act as a stimulus for more advanced AA in FE.