Risk prediction of pancreatic cancer using AI analysis of pancreatic subregions in computed tomography images.

Early detection of Pancreatic Ductal Adenocarcinoma (PDAC) is complicated as PDAC remains asymptomatic until cancer advances to late stages when treatment is mostly ineffective. Stratifying the risk of developing PDAC can improve early detection as subsequent screening of high-risk individuals through specialized surveillance systems reduces the chance of misdiagnosis at the initial stage of cancer. Risk stratification is however challenging as PDAC lacks specific predictive biomarkers. Studies reported that the pancreas undergoes local morphological changes in response to underlying biological evolution associated with PDAC development. Accurate identification of these changes can help stratify the risk of PDAC. In this retrospective study, an extensive radiomic analysis of the precancerous pancreatic subregions was performed using abdominal Computed Tomography (CT) scans. The analysis was performed using 324 pancreatic subregions identified in 108 contrast-enhanced abdominal CT scans with equal proportion from healthy control, pre-diagnostic, and diagnostic groups. In a pairwise feature analysis, several textural features were found potentially predictive of PDAC. A machine learning classifier was then trained to perform risk prediction of PDAC by automatically classifying the CT scans into healthy control (low-risk) and pre-diagnostic (high-risk) classes and specifying the subregion(s) likely to develop a tumor. The proposed model was trained on CT scans from multiple phases. Whereas using 42 CT scans from the venous phase, model validation was performed which resulted in ~89.3% classification accuracy on average, with sensitivity and specificity reaching 86% and 93%, respectively, for predicting the development of PDAC (i.e., high-risk). To our knowledge, this is the first model that unveiled microlevel precancerous changes across pancreatic subregions and quantified the risk of developing PDAC. The model demonstrated improved prediction by 3.3% in comparison to the state-of-the-art method that considers the global (whole pancreas) features for PDAC prediction.

Segmentation of Pancreatic Subregions in Computed Tomography Images.

The accurate segmentation of pancreatic subregions (head, body, and tail) in CT images provides an opportunity to examine the local morphological and textural changes in the pancreas. Quantifying such changes aids in understanding the spatial heterogeneity of the pancreas and assists in the diagnosis and treatment planning of pancreatic cancer. Manual outlining of pancreatic subregions is tedious, time-consuming, and prone to subjective inconsistency. This paper presents a multistage anatomy-guided framework for accurate and automatic 3D segmentation of pancreatic subregions in CT images. Using the delineated pancreas, two soft-label maps were estimated for subregional segmentation-one by training a fully supervised naïve Bayes model that considers the length and volumetric proportions of each subregional structure based on their anatomical arrangement, and the other by using the conventional deep learning U-Net architecture for 3D segmentation. The U-Net model then estimates the joint probability of the two maps and performs optimal segmentation of subregions. Model performance was assessed using three datasets of contrast-enhanced abdominal CT scans: one public NIH dataset of the healthy pancreas, and two datasets D1 and D2 (one for each of pre-cancerous and cancerous pancreas). The model demonstrated excellent performance during the multifold cross-validation using the NIH dataset, and external validation using D1 and D2. To the best of our knowledge, this is the first automated model for the segmentation of pancreatic subregions in CT images. A dataset consisting of reference anatomical labels for subregions in all images of the NIH dataset is also established.

Predicting pancreatic ductal adenocarcinoma using artificial intelligence analysis of pre-diagnostic computed tomography images.

BACKGROUND: Early stage diagnosis of Pancreatic Ductal Adenocarcinoma (PDAC) is challenging due to the lack of specific diagnostic biomarkers. However, stratifying individuals at high risk of PDAC, followed by monitoring their health conditions on regular basis, has the potential to allow diagnosis at early stages. OBJECTIVE: To stratify high risk individuals for PDAC by identifying predictive features in pre-diagnostic abdominal Computed Tomography (CT) scans. METHODS: A set of CT features, potentially predictive of PDAC, was identified in the analysis of 4000 raw radiomic parameters extracted from pancreases in pre-diagnostic scans. The naïve Bayes classifier was then developed for automatic classification of CT scans of the pancreas with high risk for PDAC. A set of 108 retrospective CT scans (36 scans from each healthy control, pre-diagnostic, and diagnostic group) from 72 subjects was used for the study. Model development was performed on 66 multiphase CT scans, whereas external validation was performed on 42 venous-phase CT scans. RESULTS: The system achieved an average classification accuracy of 86% on the external dataset. CONCLUSIONS: Radiomic analysis of abdominal CT scans can unveil, quantify, and interpret micro-level changes in the pre-diagnostic pancreas and can efficiently assist in the stratification of high risk individuals for PDAC.

Artificial intelligence and imaging for risk prediction of pancreatic cancer: a narrative review.

OBJECTIVE: To emphasize the importance of pancreatic imaging and the application of artificial intelligence (AI) for enhanced risk prediction of pancreatic ductal adenocarcinoma (PDAC). BACKGROUND: Detecting PDAC at the early stage is challenging as the disease either remains asymptomatic or presents nonspecific symptoms. Risk prediction of PDAC is an efficient strategy as subsequent targeted screening can assist in diagnosing cancer at the early stage even before the symptoms appear. However, the lack of specific clinical and epidemiological predictors of PDAC makes prediction a highly challenging task. Detecting precursor changes in the pancreas can potentially assist in the risk prediction of PDAC as the precancerous pancreas evolves through biological adaptations-presented as morphological and textural changes on abdominal imaging. However, such microlevel "clues" usually remain unnoticed or unappreciated, partly due to the unavailability of tools to detect and interpret such complex measurements, making the risk prediction of PDAC an unresolved problem. METHODS: This review study highlights the limitations of the current risk prediction models of PDAC and the importance of abdominal imaging for predicting PDAC. A suggestive narrative is made as to how recent AI tools can assist in extracting precise measurements of biomarkers, detecting early signs and precancerous abnormalities, quantifying tissue characteristics, and revealing complex features potentially indicative of future incidence of pancreatic cancer (PC) using abdominal imaging. With the help of peer examples of other cancers, a case is built about the application of AI in utilizing image features of the pancreas to enhance risk prediction of PDAC. Furthermore, the challenges of AI applications including insufficient data for model training, risk of data privacy violation, inconsistent data labeling, and limited computational resources, and their potential solutions are also discussed. CONCLUSIONS: The recent advancement in the domain of AI is a potential opportunity to utilize automated tools for the identification of imaging-based indicators of PDAC and perform enhanced risk prediction of cancer. With this awareness and motivation, better management of PDAC has expected.

Novel DZ-SIM Conjugate Targets Cancer Mitochondria and Prolongs Survival in Pancreatic Ductal Adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is a disease with no effective therapeutics. We have developed a novel targeted therapy drug consisting of a tumor-targeting ligand, near-infrared (NIR) organic heptamethine carbocyanine dye (HMCD), and HMG-CoA inhibitor simvastatin (SIM), and assessed its efficacy in PDAC. PDAC cell specific targeting of DZ-SIM was measured by determining the fluorescence in cells and animals. Mitochondrial bioenergetics and functions were measured by Seahorse and flow cytometry, respectively. Apoptosis was assessed by DNA fragmentation, AnnexinV/Propidium Iodide staining, and TUNEL. Markers of cell invasion, epithelial-to-mesenchymal transition, and cancer stemness were measured. The effect of DZ-SIM on survival, tumor growth and metastasis was measured in the Krasþ/LSLG12D;Trp53þ/LSLR172H;Pdx-1-Cre (KPC) transgenic mice and in syngeneic and subcutaneous PDAC models. NIR fluorescence imaging showed specific localization of DZ-SIM to cancer, but not to normal cells and tissues. DZ-SIM significantly inhibited tumor growth and re-sensitized therapeutically resistant PDAC cells to conventional therapies. DZ-SIM killed cancer cells through unique pathways involving decreasing mitochondrial bioenergetics, including oxygen consumption and ATP production, and increasing ROS production. Mitochondrial depletion prevented the effect of DZ-SIM. Administration of DZ-SIM in 3 PDAC animal models resulted in a marked increase in survival and a decrease in tumor growth and metastasis.