Diagnostic accuracy and added value of blood-based protein biomarkers for pancreatic cancer: a meta-analysis of aggregate and individual participant data

Boyd, Lenka N.C.; Ali, Mahsoem; Leeflang, Mariska; Treglia, Giorgio; Vries, Ralph de; Large, Tessa Y.S. Le; Besselink, Marc G.; Giovannetti, Elisa; Laarhoven, Hanneke W.M. van; Kazemier, Geert

doi:10.1016/j.eclinm.2022.101747

Cited by 22 publications

(15 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The primary strength of our model is that it relies exclusively on readily available laboratory tests, whereas previous predictive models for PDAC have traditionally focused on novel blood-based markers that are not routinely measured in the clinic, such as microRNAs, inflammation-associated proteins, and circulating tumor DNA . In addition, our model showed high discriminative performance in a population of early-stage PDAC patients and benign periampullary diseases.…”

Section: Discussionmentioning

confidence: 99%

“…This is important because distinguishing PDAC from benign diseases is the primary diagnostic challenge . Most analyses are based on differentiating PDAC from healthy controls, which does not correspond to the clinical target population . In addition, diagnostic accuracy is considerably overestimated in studies using healthy controls compared with studies using a clinically representative sample …”

Section: Discussionmentioning

confidence: 99%

“…19 Ideally, prediction models for detecting PDAC should aim to distinguish early-stage PDAC from clinically similar benign diseases, and should demonstrate adequate discriminative performance, calibration, and clinical utility at external validation. 4 Evaluating prediction models for their calibration and clinical utility, as determined through decision curve analysis, is of particular importance because the AUC of a prediction model is not directly related to a model's potential clinical usefulness. 8 A model with high diagnostic accuracy but poor calibration can negatively influence clinical decision-making, and can produce overtreatment and overdiagnosis if risk predictions are consistently too high, or undertreatment and underdiagnosis if risk predictions are consistently too low.…”

Section: Measure Estimate (95% Ci)mentioning

confidence: 99%

“…With this goal in mind, numerous research groups have recently tried to identify PDAC-specific blood-based biomarkers, including circulating tumor cells, cell-free tumor DNA, exosomes, and serum proteins . However, none of these models have entered clinical practice yet.…”

Section: Introductionmentioning

confidence: 99%

“…Our recent meta-analysis demonstrated that the most researched novel blood-based proteins for PDAC had a lower diagnostic power than CA19-9 and did not have added diagnostic value in combination with CA19-9. In addition, the implemented study designs were not relevant to a realistic clinical setting, despite the widespread use of these designs, such as diagnostic case-control designs with patients with PDAC and healthy controls.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Prediction Model for Early-Stage Pancreatic Cancer Using Routinely Measured Blood Biomarkers

Boyd,

Ali,

Comandatore

et al. 2023

JAMA Netw Open

Self Cite

View full text Add to dashboard Cite

ImportanceAccurate risk prediction models using routinely measured biomarkers—eg, carbohydrate antigen 19-9 (CA19-9) and bilirubin serum levels—for pancreatic cancer could facilitate early detection of pancreatic cancer and prevent potentially unnecessary diagnostic tests for patients at low risk. An externally validated model using CA19-9 and bilirubin serum levels in a larger cohort of patients with pancreatic cancer or benign periampullary diseases is needed.ObjectiveTo assess the discrimination, calibration, and clinical utility of a prediction model using readily available blood biomarkers (carbohydrate antigen 19-9 [CA19-9] and bilirubin) to distinguish early-stage pancreatic cancer from benign periampullary diseases.Design, Setting, and ParticipantsThis diagnostic study used data from 4 academic hospitals in Italy, the Netherlands, and the UK on adult patients with pancreatic cancer or benign periampullary disease treated from 2014 to 2022. Analyses were conducted from September 2022 to February 2023.ExposuresSerum levels of CA19-9 and bilirubin from samples collected at diagnosis and before start of any medical intervention.Main Outcomes and MeasuresDiscrimination (measured by the area under the curve [AUC]), calibration, and clinical utility of the prediction model and the biomarkers, separately.ResultsThe study sample comprised 249 patients in the development cohort (mean [SD] age at diagnosis, 67 [11] years; 112 [45%] female individuals), and 296 patients in the validation cohort (mean [SD] age at diagnosis, 68 [12] years; 157 [53%] female individuals). At external validation, the prediction model showed an AUC of 0.89 (95% CI, 0.84-0.93) for early-stage pancreatic cancer vs benign periampullary diseases, and outperformed CA19-9 (difference in AUC [ΔAUC], 0.10; 95% CI, 0.06-0.14; P &lt; .001) and bilirubin (∆AUC, 0.07; 95% CI, 0.02-0.12; P = .004). In the subset of patients without elevated tumor marker levels (CA19-9 &lt;37 U/mL), the model showed an AUC of 0.84 (95% CI, 0.77-0.92). At a risk threshold of 30%, decision curve analysis indicated that performing biopsies based on the prediction model was equivalent to reducing the biopsy procedure rate by 6% (95% CI, 1%-11%), without missing early-stage pancreatic cancer in patients.Conclusions and RelevanceIn this diagnostic study of patients with pancreatic cancer or benign periampullary diseases, an easily applicable risk score showed high accuracy for distinguishing early-stage pancreatic cancer from benign periampullary diseases. This model could be used to assess the added diagnostic and clinical value of novel biomarkers and prevent potentially unnecessary invasive diagnostic procedures for patients at low risk.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%