These authors contributed equally to this workPurpose: The purpose of this retrospective study was to identify preoperative inflammatory biomarkers and clinical parameters and evaluate their prognostic significance in patients with spinal metastasis from clear cell renal cell carcinoma (CCRCC). Patients and methods: Correlations of overall survival (OS) with traditional clinical parameters and inflammatory indicators including the neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), lymphocyte-monocyte ratio (LMR), albumin-globulin ratio (AGR), and C-reactive protein to albumin ratio (CRP/Alb ratio) were analyzed in 95 patients with spinal metastasis from CCRCA using the Kaplan-Meier method to identify potential prognostic factors. Factors with P values ≤ 0.1 were subjected to multivariate analysis by Cox regression analysis. P values ≤ 0.05 were considered statistically significant. Results: The 95 patients included in this study were followed up by a mean of 48.8 months (median 51 months; range 6-132 months), during which 21 patients died, with a death rate of 22.1%. The statistical results indicated that patients with total piecemeal spondylectomy (TPS), targeted therapy, NLR < 3.8 and PLR < 206.9 had a significantly longer OS rate. Conclusion: TPS and targeted therapy could significantly prolong the OS of patients with spinal metastasis from CCRCC. In addition, NLR and PLR are robust and convenient prognostic indicators that have a discriminatory ability superior to other inflammatory biomarkers.
The most significant technical challenges of current aerial image object-detection tasks are the extremely low accuracy for detecting small objects that are densely distributed within a scene and the lack of semantic information. Moreover, existing detectors with large parameter scales are unsuitable for aerial image object-detection scenarios oriented toward low-end GPUs. To address this technical challenge, we propose efficient-lightweight You Only Look Once (EL-YOLO), an innovative model that overcomes the limitations of existing detectors and low-end GPU orientation. EL-YOLO surpasses the baseline models in three key areas. Firstly, we design and scrutinize three model architectures to intensify the model’s focus on small objects and identify the most effective network structure. Secondly, we design efficient spatial pyramid pooling (ESPP) to augment the representation of small-object features in aerial images. Lastly, we introduce the alpha-complete intersection over union (α-CIoU) loss function to tackle the imbalance between positive and negative samples in aerial images. Our proposed EL-YOLO method demonstrates a strong generalization and robustness for the small-object detection problem in aerial images. The experimental results show that, with the model parameters maintained below 10 M while the input image size was unified at 640 × 640 pixels, the APS of the EL-YOLOv5 reached 10.8% and 10.7% and enhanced the APs by 1.9% and 2.2% compared to YOLOv5 on two challenging aerial image datasets, DIOR and VisDrone, respectively.
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