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Radiómica para la Detección de Cáncer de Páncreas: Una Revisión Sistemática y Metaanálisis
Zayan Alidina1, Agha Ahmed Muhammad Hussain1, Illiyun Banani1
1Medical College, Aga Khan University, Karachi, Pakistan.
Background:
Pancreatic Ductal Adenocarcinoma (PDAC) is one of the lethal malignancies, where accurate and faster detection is required in high-risk population to improve prognosis and decrease cancer-associated mortality. Currently, radiomics has emerged as a promising computational approach to address this challenge, reporting increased accuracy in differentiating PDAC from benign lesions. Our study aims to evaluate radiomics-based models derived from CT, MRI, PET, or ultrasound for the detection of PDAC in patients under surveillance.
Methods:
A systematic literature search on PubMed, Embase, Scopus, and Cochrane was followed by a meta-analysis comparing diagnostic performance metrics, including AUC, sensitivity, and specificity. The DerSimonian-Laird method was employed to estimate the pooled sensitivity, specificity, positive likelihood ratios (PLR) and negative likelihood ratios (NLR), with subgroup analysis performed using Cochrane RevMan 5.4.1 software and OpenMeta analyst.
Results:
A total of 15 studies involving 14,688 patients were analyzed, with most studies published between 2019 and 2025. Among these patients, the number of patients with PDAC was 6,153 (41.8%) and the healthy cases were 7,145 (48.6%) and the rest of the patients were unspecified (9.6%). Artificial intelligence (AI)/Machine Learning (ML) reported a pooled sensitivity of 0.88 (95% confidence interval CI: 0.84-0.91; I2:87.8%) and specificity of 0.93 (95% CI: 0.87-0.96; I2:95.0%) in detecting PDAC. The pooled positive likelihood ratio (PLR) was 12.1 (95% CI, 8.4-21.4; I2: 95.5%), however, the negative likelihood ratio (NLR) was 0.12 (95% CI, 0.09-0.16; I2:83.1%).
Conclusion:
The use of AI and ML along with diagnostic modality presents a promising alternative to conventional diagnostic modality due to displaying of convincing diagnostic metric for detection of PDAC. Further prospective studies are needed to study the efficacy of this new approach, along with its incorporation with genomic, proteomic, and metabolomic data to develop multi-omic predictive frameworks to further improve PDAC detection.
