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 personalised 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 harmonisation, 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.

Figure 1.

Flowchart of the image processing pipeline for pancreatic cancer, including image reconstruction and preparation (denoising, harmonisation, segmentation), feature extraction, data integration and modelling to predict clinical outcomes and diagnostic/prognostic factors.

Figure 2.

A 65-year-old female with locally advanced unresectable pancreatic adenocarcinoma (Stage III, T4N0M0). CECT showed a large mass within the pancreatic body with arterial vascular invasion. The patient was treated with gemcitabine plus Nab-paclitaxel and with radiotherapy after local progression. Overall survival was 1799 days. (A) Portal venous phase (35 s delay after pancreatic phase); (B) Manual tumour segmentation; (C) Kurtosis parametric map (tumour value of 13.87); (D) Grey Level Run Length Matrix—Non-uniformity parametric map (tumour value of 1098.54). Our in-house dedicated image processing pipeline (image denoising, tumour segmentation, radiomic feature extraction, feature selection with factor analysis and data clustering: unpublished results) successfully identified this patient as high survival, with the most predictive features being tumour Elongation and Range, Grey Level Non-uniformity, Grey Level, Grey Level Small Area Emphasis, and tumour Kurtosis. CECT, contrast-enhanced CT.

Figure 3.

A 70-year-old male with metastatic pancreatic adenocarcinoma (Stage IV, T1N0M1). CECT showed a small pancreatic mass located in the pancreatic tail. The patient was treated with gemcitabine plus Nab-paclitaxel and his overall survival was 150 days. (A) Portal venous phase (35 s delay after pancreatic phase); (B) Manual tumour segmentation; (C) Kurtosis parametric map (tumour value of 4.56); (D) Grey Level Run Length Matrix—Non-uniformity parametric map (tumour value of 457.15). Our dedicated image processing clustering analysis was successful in differentiating the patient as low survival with tumour Elongation and Range, Grey Level Non-uniformity, Grey Level, Grey Level Small Area Emphasis, and tumour Kurtosis features. CECT, contrast-enhanced CT.