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Purpose: To evaluate the diagnostic accuracy of computed tomography (CT)-based radiomic algorithms and deep learning models to preoperatively identify lymph node metastasis (LNM) in patients with pancreatic ductal adenocarcinoma (PDAC)., Methods: PubMed, CENTRAL, Scopus, Web of Science and IEEE databases were searched to identify relevant studies published up until February 11, 2024. Two reviewers screened all papers independently for eligibility. Studies reporting the accuracy of CT-based radiomics or deep learning models for detecting LNM in PDAC, using histopathology as the reference standard, were included. Quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2, the Radiomics Quality Score (RQS) and the the METhodological RadiomICs Score (METRICS). Overall sensitivity (SE), specificity (SP), diagnostic odds ratio (DOR), and the area under the curve (AUC) were calculated., Results: Four radiomics studies comprising 213 patients and four deep learning studies with 272 patients were included. The average RQS total score was 12.00 ± 3.89, corresponding to an RQS percentage of 33.33 ± 10.80, while the average METRICS score was 63.60 ± 10.88. A significant and strong positive correlation was found between RQS and METRICS (p = 0.016; r = 0.810). The pooled SE, SP, DOR, and AUC of all the studies were 0.83 (95 %CI = 0.77-0.88), 0.76 (95 %CI = 0.62-0.86), 15.70 (95 %CI = 8.12-27.50) and 0.85 (95 %CI = 0.77-0.88). Meta-regression analysis results indicated that neither the study type (radiomics vs deep learning) nor the dataset size of the studies had a significant effect on the DOR (p = 0.09 and p = 0.26, respectively)., Conclusion: Based on our meta-analysis findings, preoperative CT-based radiomics algorithms and deep learning models demonstrate favorable performance in predicting LNM in patients with PDAC, with a strong correlation between RQS and METRICS of the included studies., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Purpose: Thermal ablation procedures represent an alternative treatment option for patients with T1a renal tumors. Radiofrequency ablation (RFA) and cryoablation (CA) are the most used and studied techniques, while microwave ablation (MWA) has progressively emerged in recent years. Our aim was to assess the effectiveness and safety of MWA in comparison to RFA and CA for the treatment of primary renal tumors., Method: Pubmed, CENTRAL, Web of Science and Scopus were searched until March 2023 to identify studies aimed at comparing the efficacy and safety of MWA with those of RFA and CA for the treatment of patients with primary renal tumors. We compared MWA and RFA/CA primary technique efficacy, local recurrences, overall and cancer-specific survival, major and overall complications, and eGFR changes. Moreover, subgroup analyses were conducted (MWA vs RFA; MWA vs CA; MWA vs RFA/CA in T1a renal tumors)., Results: Ten retrospective studies with 2258 thermal ablations were included (508 MWA and 1750 RFA /CA). MWA had fewer local recurrences (OR = 0.31; 95% CI, 0.16, 0.62; p = 0.0008) than RFA/CA; the other outcomes were not significantly different. In subgroup analyses, MWA resulted to have fewer overall complications than RFA (OR = 0.60; 95% CI, 0.38, 0.97; p = 0.04) and CA (OR = 0.49; 95% CI, 0.28, 0.85; p = 0.01); moreover, MWA was associated with fewer recurrences than CA (OR = 0.30; 95% CI, 0.11, 0.84; p = 0.02). In T1a renal tumors subgroup analysis, the outcomes were not significantly different., Conclusions: MWA is an ablative procedure as effective and safe as RFA or CA for the treatment of renal tumors., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)