The early detection of pancreatic ductal adenocarcinoma (PDAC) is essential for optimal treatment of pancreatic cancer patients. We propose a tumor detection framework to improve the detection of pancreatic head tumors on CT scans. In this retrospective research study, CT images of 99 patients with pancreatic head cancer and 98 control cases from the Catharina Hospital Eindhoven were collected. A multi-stage 3D U-Net-based approach was used for PDAC detection including clinically significant secondary features such as pancreatic duct and common bile duct dilation. The developed algorithm was evaluated using a local test set comprising 59 CT scans. The model was externally validated in 28 pancreatic cancer cases of a publicly available medical decathlon dataset. The tumor detection framework achieved a sensitivity of 0.97 and a specificity of 1.00, with an area under the receiver operating curve (AUROC) of 0.99, in detecting pancreatic head cancer in the local test set. In the external test set, we obtained similar results, with a sensitivity of 1.00. The model provided the tumor location with acceptable accuracy obtaining a DICE Similarity Coefficient (DSC) of 0.37. This study shows that a tumor detection framework utilizing CT scans and secondary signs of pancreatic cancer can detect pancreatic tumors with high accuracy.

Malignant ovarian tumors (OTs) are a leading cause of gynecological cancer deaths, and often remain asymptomatic until advanced stages, making early and accurate diagnosis crucial for effective treatment and good patient outcome. Current diagnostic methods often fall short due to the heterogeneous nature of OTs and the complexities in distinguishing benign from malignant forms. To overcome these limitations, this study proposes a novel framework leveraging transformer-based multiple-instance learning (MIL) and hierarchical self-supervised pre-training. To validate the model, a comprehensive multi-center dataset has been compiled, encompassing diverse patient demographics and imaging protocols. Benchmarking against conventional radiomics methods and other deep learning approaches, the hierarchical MIL model demonstrates superior performance with a median AUROC of 0.84 and high recall of 0.91. These results highlight significant improvements in sensitivity, essential for minimizing false negatives in clinical settings. The performed study emphasizes the importance of multi-center validation and external dataset testing to ensure generalization of the proposed model and obtain a higher robustness. The encountered complexity of multi-center data is found significant, since various clinical factors play an influential role. This makes baseline comparisons virtually impossible and the need for more multi-center research increasingly compelling and encouraging.

Lung cancer has both high incidence and mortality rates compared to other cancer types. One important factor for improved patient survival is early detection. Deep learning for lung nodule detection has been extensively studied, as a tool to facilitate clinicians with early nodule detection and classification. Many publications are reporting high detection accuracy and several models have been introduced to clinical practice. However, certain models may have reduced performance in real-world clinical practice. In this study, we introduce a method to assess the robustness of lung nodule detection models. Medically relevant image perturbations are used to assess the robustness of these models. The perturbations include noise and motion perturbations, which have been created in consultation with an expert radiologist to ensure the clinical relevance of the artifacts for thoracic computed tomography (CT) scans. The evaluated models demonstrate robustness to clinically relevant noise simulations, but it shows less resilience to motion artifacts in perturbed CT scans. This robustness evaluation method, incorporating simulated relevant artifacts, can be extended for use in other applications involving the analysis of CT scans.

The development of Artificial Intelligence (AI) for detection and characterization of Pancreatic Ductal Adenocarcinoma (PDAC) is a challenging task, since PDAC data is scarce compared to data of other types of cancer. However, due to the high mortality rate of the disease, early detection is crucial. For this reason, recent work has focused on exploiting indirect pathological features, e.g. dilated bile ducts due to tumor involvement, as an additional input for supportive algorithms. However, the presented methods require manual annotations of several structures in a CT volume, which is a cumbersome task and not feasible in clinical practice. Therefore, this work investigates the automated segmentation of bile ducts to facilitate improved tumor detection by such methods. Using a coarse-to-fine segmentation architecture, the pancreas, pancreatic duct and the common bile duct are segmented from 3D CT-scans. The resulting yet individual segmentations form a primary stage, of which the outputs are supplied as input to a secondary pre-trained U-Net-based PDAC detection algorithm, to ultimately detect tumors. We evaluate the performance of the proposed primary segmentation and secondary detection models on a publicly available test set in terms of mean Dice Similarity Coefficient (DSC). The pancreas, common bile duct and pancreatic duct are segmented with a mean DSC of 0.86, 0.69 and 0.57, respectively. With these segmentations as input, a tumor detection sensitivity of 100% is maintained for the tumor detection model. This continuously high detection sensitivity for tumor detection is comparable to the tumor detection score achieved by using manually annotated structures. This study highlights the benefit of primarily segmenting relevant structures, to use as input for a secondary model for final PDAC detection.

Radiological imaging plays a crucial role in the detection and treatment of pancreatic ductal adenocarcinoma (PDAC). However, there are several challenges associated with the use of these techniques in daily clinical practice. Determination of the presence or absence of cancer using radiological imaging is difficult and requires specific expertise, especially after neoadjuvant therapy. Early detection and characterization of tumors would potentially increase the number of patients who are eligible for curative treatment. Over the last decades, artificial intelligence (AI)-based computer-aided detection (CAD) has rapidly evolved as a means for improving the radiological detection of cancer and the assessment of the extent of disease. Although the results of AI applications seem promising, widespread adoption in clinical practice has not taken place. This narrative review provides an overview of current radiological CAD systems in pancreatic cancer, highlights challenges that are pertinent to clinical practice, and discusses potential solutions for these challenges.

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive cancer with limited treatment options. This research proposes a workflow and deep learning-based segmentation models to automatically assess tumor-vessel involvement, a key factor in determining tumor resectability. Correct assessment of resectability is vital to determine treatment options. The proposed workflow involves processing CT scans to segment the tumor and vascular structures, analyzing spatial relationships and the extent of vascular involvement, which follows a similar way of working as expert radiologists in PDAC assessment. Three segmentation architectures (nnU-Net, 3D U-Net, and Probabilistic 3D U-Net) achieve a high accuracy in segmenting veins, arteries, and the tumor. The segmentations enable automated detection of tumor involvement with high accuracy (0.88 sensitivity and 0.86 specificity) and automated computation of the degree of tumor-vessel contact. Additionally, due to significant inter-observer variability in these important structures, we present the uncertainty captured by each of the models to further increase insights into the predicted involvement. This result provides clinicians with a clear indication of tumor-vessel involvement and may be used to facilitate more informed decision-making for surgical interventions. The proposed method offers a valuable tool for improving patient outcomes, personalized treatment strategies and survival rates in pancreatic cancer.

Deep learning-based segmentation algorithms have the potential to expedite the cumbersome clinical task of creating detailed target delineations for disease diagnosis and prognosis. However, these algorithms have yet to be widely adopted in clinical practice, partly because the resulting model segmentations often fall short of the necessary accuracy and robustness that clinical practice demands. This research aims to make AI work in the real world, where domain shift is anticipated and inter-observer variability is inherent to medical practice. While current research aims to design models that can address these challenges, we propose an alternative approach that involves minimal user (clinician) interaction in the segmentation process. By combining the pattern recognition abilities of neural networks with the domain knowledge of clinicians, segmentation predictions can deliver the desired clinical result with little effort on the part of clinicians. To test this approach, we implemented, fine-tuned and compared three state-of-the-art (SOTA) interactive AI (IAI) methods for segmenting six different abdominal organs and pancreatic ductal adenocarcinoma (PDAC), an extremely challenging structure to segment, in CT images. We demonstrate that the fine-tuned RITM (Reviving Iterative Training with Mask Guidance for Interactive Segmentation) method can achieve higher segmentation accuracy than non-interactive SOTA models with as few as three clicks, potentially reducing the time required for treatment planning. Overall, IAI may be an effective method for bridging the gap between what deep learning-based segmentation algorithms have to offer and the high standard that is required for patient care.