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Localization is one of the essential problems in internet of things (IoT) and wireless sensor network (WSN) applications. However, most traditional range-free localization algorithms cannot fulfill the practical demand for high localization accuracy. Therefore, a localization algorithm based on an enhanced flower pollination algorithm (FPA) with Gaussian perturbation (EFPA-G) and the DV-Hop method is proposed.FPA is widely applied, but premature convergence still cannot be avoided. How to balance its global exploration and local exploitation capabilities still remains an outstanding problem. Therefore, the following improvement schemes are introduced. A search strategy based on Gaussian perturbation is proposed to solve the imbalance between the global exploration and local exploitation search capabilities. Meanwhile, to fully exploit the variability of population information, an enhanced strategy is proposed based on optimal individual and Lévy flight. Finally, in the experiments with 26 benchmark functions and WSN simulations, the former verifies that the proposed algorithm outperforms other state-of-the-art algorithms in terms of convergence and search capability. In the simulation experiment, the best value for the normalized mean squared error obtained by the most advanced algorithm, RACS, is 20.2650%, and the best value for the mean distance error is 5.07E+00. However, EFPA-G reached 19.5182% and 4.88E+00, respectively. It is superior to existing algorithms in terms of positioning, accuracy, and robustness.
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Deep stromal invasion is an important pathological factor associated with the treatments and prognosis of cervical cancer patients. Accurate determination of deep stromal invasion before radical hysterectomy (RH) is of great value for early clinical treatment decision-making and improving the prognosis of these patients. Machine learning is gradually applied in the construction of clinical models to improve the accuracy of clinical diagnosis or prediction, but whether machine learning can improve the preoperative diagnosis accuracy of deep stromal invasion in patients with cervical cancer was still unclear. This cross-sectional study was to construct three preoperative diagnostic models for deep stromal invasion in patients with early cervical cancer based on clinical, radiomics, and clinical combined radiomics data using the machine learning method. We enrolled 229 patients with early cervical cancer receiving RH combined with pelvic lymph node dissection (PLND). The least absolute shrinkage and selection operator (LASSO) and the fivefold cross-validation were applied to screen out radiomics features. Univariate and multivariate logistic regression analyses were applied to identify clinical predictors. All subjects were divided into the training set (n = 160) and testing set (n = 69) at a ratio of 7:3. Three light gradient boosting machine (LightGBM) models were constructed in the training set and verified in the testing set. The radiomics features were statistically different between deep stromal invasion < 1/3 group and deep stromal invasion ≥ 1/3 group. In the training set, the area under the curve (AUC) of the prediction model based on radiomics features was 0.951 (95% confidence interval (CI) 0.922-0.980), the AUC of the prediction model based on clinical predictors was 0.769 (95% CI 0.703-0.835), and the AUC of the prediction model based on radiomics features and clinical predictors was 0.969 (95% CI 0.947-0.990). The AUC of the prediction model based on radiomics features and clinical predictors was 0.914 (95% CI 0.848-0.980) in the testing set. The prediction model for deep stromal invasion in patients with early cervical cancer based on clinical and radiomics data exhibited good predictive performance with an AUC of 0.969, which might help the clinicians early identify patients with high risk of deep stromal invasion and provide timely interventions.
RESUMO
RATIONALE AND OBJECTIVES: The novel International Association for the Study of Lung Cancer (IASLC) grading system of invasive lung adenocarcinoma (ADC) demonstrated a remarkable prognostic effect and enabled numerous patients to benefit from adjuvant chemotherapy. We sought to build a CT-based nomogram for preoperative prediction of the IASLC grading. MATERIALS AND METHODS: This work retrospectively analyzed the CT images and clinical data of 303 patients with pathologically confirmed invasive ADC. The histological subtypes and radiological characteristics of the patients were re-evaluated. Radiomics features were extracted, and the optimal subset of features was established by ANOVA, spearman correlation analysis, and the least absolute shrinkage and selection operator (LASSO). Univariate and multivariate analyses identified the independent clinical and radiological variables. Finally, multivariate logistic regression analysis incorporated clinical, radiological, and optimal radiomics features into the nomogram. Receiver operating characteristic (ROC) curve, and accuracy were applied to assess the model's performance. Decision curve analysis (DCA), and calibration curve were applied to assess the clinical usefulness. RESULTS: Nine selected CT image features were used to develop the radiomics model. The accuracy, precision, sensitivity, and specificity of the radiomics model outperformed the clinic-radiological model in the training and testing sets. Integrating Radscore with independent radiological characteristics showed higher prediction performance than clinic-radiological characteristics alone in the training (AUC, 0.915 vs. 0.882; DeLong, p < 0.05) and testing (AUC, 0.838 vs. 0.782; DeLong, p < 0.05) sets. Good calibration and decision curve analysis demonstrated the clinical usefulness of the nomogram. CONCLUSION: Radiomics features effectively predict high-grade ADC. The combined nomogram may facilitate selecting patients who benefit from adjuvant treatment.