Introduction

Pancreatic ductal adenocarcinoma (PDAC) is a malignancy with poor prognosis, frequently presenting at an advanced stage, with a 5-year survival rate of only 11% [1, 2]. Despite advancements in diagnostic and staging technologies such as CT and MR, gains in detection and outcomes have been minimal over the last decades, highlighting the need for more effective and optimised PDAC management.

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].

The extraction of RFs from regions of interest (ROIs) in two (2D) or three dimensions (3D) is common practice in image analysis. 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].

Despite this vast scope, compared to other solid organ cancers, such as the liver and lungs, PDAC radiomics implementation into clinical practice has been limited [11], the reproducibility of radiomic models is highly impacted by variations in scanning parameters, such as scanner model used, pixel spacing, and the contrast administration rate, as well as the manual segmentation of ROIs.

Additionally, there are shortcomings in research methodologies, such as data harmonisation and the use of software that adheres to the Image Biomarker Standardisation Initiative (IBSI) guidelines, within an already-complex clinical environment [7, 12, 13].

This review aims to provide an overview of the current state of primary research in PDAC diagnosis, treatment and prognosis, with a particular focus on radiomics and applied methodology. It highlights areas of strength and weakness in the field, with an emphasis on reproducibility and offers guidance to radiomic researchers to generate more robust results.

Material and methods

Database search (MEDLINE, PubMed, and Scopus)

A literature search of the online databases MEDLINE, PubMed, and Scopus was conducted between June and August 2022. The search formula contained [pancreas OR pancreatic] AND [radiomic OR [quantitative AND imaging] OR [texture AND analysis]].

Titles and abstracts of articles were initially screened by two raters (H.S.K (radiology subspecialist with 16 years abdominal specialty experience), J.A.M (3rd year medical graduate student)). Inclusion criteria included primary human research articles on CT radiomics in PDAC diagnosis, treatment, and/or prognosis published in English between 2017 and August 2022, and studies using non IBSI-compliant software were excluded. Final selection of articles were then analysed by three raters (H.S.K, J.A.M, M.T. (biostatistician)) (Table 1).

Table 1 Inclusion and exclusion assessment criteria performed after initial MEDLINE, PubMed, and Scopus literature search (June–August 2022)
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Data extraction

Articles were evaluated and categorised according to CT slice thickness (≤ 1 mm, > 1 to 3 mm, > 3 to 5 mm) so to align with prevalent standards and enable comparison of future radiomic investigations. Studies were further subcategorised into various RF clinical applications to address the following research questions: (1) Are there commonly identified RFs across PDAC studies that suggest trends in development of a validated imaging biomarker? (2) What is the reported cohort size, CT technical factors, and described radiomics methodology steps? Are there factors in methods that could impede reproducibility?

Our analysis did not to apply the radiomics quality score (RQS) by Lambin et al [14] given its complex structure (16 components, six key domains, score of 0–36 points) and also based on a recent extensive systematic review comprising 77 articles in high ranking medical journals that demonstrated an overall basic low RQS adherence rate at 38.7%, with many RQS components receiving a score of 0 points [15].

Results

Figure 1 illustrates the PRISMA flow diagram outlining the literature search. A total of 1112 articles were found (MEDLINE n = 584, PubMed n = 144, Scopus n = 384), with duplicates removed resulting in a total of 650 articles. The initial screening of titles and abstracts identified 49 articles that met the eligibility criteria for full-text assessment. After further exclusions, a total of 12 articles were included in this review (Table 2).

Fig. 1
figure 1

PRISMA flow diagram of MEDLINE, PubMed, and Scopus literature search. Abbreviations: IBSI, Image Biomarker Standardisation Initiative; PDAC, pancreatic ductal adenocarcinoma

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Table 2 Included full-text articles on radiomics in PDAC in this review (in alphabetical order)
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Full-text analysis revealed a lack of common RFs, highly variable methodologies, and a lack of sufficient information to ensure reproducibility (Table 3).

Table 3 Missing detailed methodology steps impeding reproducibility
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Cohort size and CT technique

Patient cohort sizes ranged from 37 to 352 (median = 106, mean = 155.8). Three of the 12 selected studies for review had a small cohort size ranging from 37 to 54 [12]. The 160 + pages framework places a significant emphasis on mathematical and technical aspects, while giving less attention to the clinical implementation. This is likely due to the fact that the majority of authors are of non-clinical backgrounds. As a result, clinicians have to trust and rely on IBSI-compliant radiomics software for quality assurance and reproducibility purposes. This is also underpinned in a study by Fornacon-Wood et al, which found a higher number of RFs exhibiting excellent statistical reliability when extracted using IBSI-compliant software, as opposed to non-compliant software [13]. In our initial eligibility assessment, we identified 9 of 49 studies that used non-IBSI compliant software, and as a result, these studies were excluded from our final analysis.

Statistical extrapolations and radiomic models were noted to be highly variable. For instance, Hang et al [17] sought to correlate RFs of primary PDAC tumours and liver metastases to overall survival by incorporating four texture features into a radiomics score based on a statistical significance of p < 0.1, as opposed to the commonly employed threshold of p < 0.05. Furthermore, the authors failed to provide information on whether selected RFs were considered for or actually included in the multivariable model. Similarly, Attiyeh et al [22] developed two models to predict overall survival in resectable PDAC patients, incorporating additional characteristics such as serum CA19-9 and Brennan pathology scoring. However, the chosen CT contrast phase for RF extraction as well as RF selection was not disclosed for either model or univariate analysis. 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 (< 1mm3).

The study by Chen et al [

Conclusion

There is a limited number of primary research publications of PDAC CT radiomics using IBSI compliant software. However, as advancements in methodology and standardisation of practice continue to develop, radiomics has the potential to serve as a valuable non-invasive biomarker in the management of pancreatic cancer.