Pancreatic cancer, radiomics and artificial intelligence

Abstract

Patients with pancreatic ductal adenocarcinoma (PDAC) are generally classified into four categories based on contrast-enhanced CT at diagnosis: resectable, borderline resectable, unresectable, and metastatic disease. In the initial grading and staging of PDAC, structured radiological templates are useful but limited, as there is a need to define the aggressiveness and microscopic disease stage of these tumours to ensure adequate treatment allocation. Quantitative imaging analysis allows radiomics and dynamic imaging features to provide information of clinical outcomes, and to construct clinical models based on radiomics signatures or imaging phenotypes. These quantitative features may be used as prognostic and predictive biomarkers in clinical decision making, enabling personalized management of advanced PDAC. Deep Learning and Convolutional Neural Networks also provide high level bioinformatics tools that can help define features associated with a given aspect of PDAC biology and aggressiveness, paving the way to define outcomes based on these features. Thus, the prediction of tumour phenotype, treatment response and patient prognosis may be feasible by using such comprehensive and integrated radiomics models. Despite these promising results, quantitative imaging is not ready for clinical implementation in PDAC. Limitations include the instability of metrics and lack of external validation. Large properly annotated datasets, including relevant semantic features (demographics, blood markers, genomics), image harmonization, robust radiomics analysis, clinically significant tasks as outputs, comparisons with gold standards (such as TNM or pretreatment classifications) and fully independent validation cohorts, will be required for the development of trustworthy radiomics and Artificial Intelligence solutions to predict PDAC aggressiveness in a clinical setting.

Current state of radiomic research in pancreatic cancer: focusing on study design and reproducibility of findings

“…Two studies that aimed to use radiomics to assess tumour grading are limited by their methodologies and reporting [23,24]. Chang et al [24] did not disclose details regarding the significant RF selection while Tikhonova et al [23] used a p-value of < 0.1 for statistical significance and assessed contrast enhancement changes in a very small volume of interest (< 1mm 3 ).…”

“…Radiomics, a novel and promising higher computational method, involves extracting so-called radiomic features (RFs) from medical images that are not discernible by the human eye. The discovery of non-invasive RF-based imaging biomarkers could potentially enable better staging, and lead to improved response to treatment and overall survival as part of precision/personalised medicine [3][4][5].…”

“…RFs can be broadly classified into first, second, and higher order features: First-order features include shape/sphericity, voxel grey intensity, and coarse voxel distribution which can be represented in a histogram that demonstrates skewness, kurtosis, uniformity, and entropy. Second-order features describe the intensity relationships between neighbouring voxels and include characteristics such as grey-level co-occurrence matrix (GLCM) and grey-level run length matrix (GLRLM) [3,6]. Lastly, higher order features are extracted through mathematical modulation, or filtering techniques, with the goal of supressing noise or highlighting details and patterns [7].…”

CT Radiomics and Whole Genome Sequencing in Patients with Pancreatic Ductal Adenocarcinoma: Predictive Radiogenomics Modeling

“…In general, radiomics features can be classified into three main categories [ 42 ]. Shape features describe semantic and/or geometric properties (e.g., volume, maximum diameter).…”

A review of deep learning and radiomics approaches for pancreatic cancer diagnosis from medical imaging

“…By leveraging high-dimensional feature spaces, radiomics enables the discovery of complex patterns and correlations that may not be readily apparent through traditional visual inspection by physicians (or radiologists). Consequently, radiomics has shown promise [6][7][8]44] in improving diagnostic accuracy with an AUC of 0.7-0. ] both utilized the least absolute shrinkage and selection operator (LASSO) method to select features that were then integrated into their respective models.…”