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Abstract This study aimed to investigate whether lymphocyte-C-reactive protein ratio (LCR) upon admission can predict disease progression and intensive care unit (ICU) admission in adult patients with diabetic ketoacidosis (DKA). A single-center retrospective study was conducted, including adult DKA patients admitted to the First Affiliated Hospital of Harbin Medical University between March 2018 and March 2023. Multiple demographic and clinical data were collected from the medical records upon admission and during hospitalization. Subsequently, sequential organ failure assessment (SOFA) score and LCR were calculated based on relevant clinical parameters within 24 h of admission. These indicators were compared among different disease severity groups, and factors related to severe DKA, concurrent acute kidney injury (AKI), and ICU admission were further analyzed. Receiver operating characteristic (ROC) curve analysis was performed to determine the sensitivity, specificity, area under the ROC curve (AUC), and cut-off value of LCR. A total of 271 adult DKA patients were enrolled and categorized into three groups: mild group (n = 42), moderate group (n = 64), and severe group (n = 165). Significant differences in demographic and clinical data were observed among these groups. Glasgow coma scale (GCS) score, LCR, pH, and bicarbonate (HCO3 -) were identified as protective factors for severe DKA. Conversely, SOFA score, neutrophil count (NEUT), serum creatinine (SCr), and glucose (GLU) were risk factors for concurrent AKI. Concurrent AKI and SOFA score were risk factors for ICU admission, while pH was a protective factor. The areas under the ROC curve (AUC) of LCR to classify adult DKA patients into mild group, severe group, and ICU admission were 0.679, 0.718, and 0.621, respectively, with cut-off value of 212.80, 96.16, and 63.35, sensitivity of 54.8%, 76.4%, and 78.9%, and specificity of 76.0%, 62.4%, and 46.3%. LCR upon admission provides great potential to predict disease progression and ICU admission in adult patients with DKA.

Abstract The purpose of this study is to develop a nomogram model for early prediction of the severe mycoplasma pneumoniae pneumonia (SMPP) in Pediatric and Adult Patients. A retrospective analysis was conducted on patients with MPP, classifying them into SMPP and non-severe MPP (NSMPP) groups. A total of 550 patients (NSMPP 374 and SMPP 176) were enrolled in the study and allocated to training, validation cohorts. 278 patients (NSMPP 224 and SMPP 54) were retrospectively collected from two institutions and allocated to testing cohort. The risk factors for SMPP were identified using univariate analysis. For radiomic feature selection, Spearman’s correlation and the least absolute shrinkage and selection operator (LASSO) were utilized. Logistic regression was used to build different models, including clinical, imaging, radiomics, and integrated models (combining clinical, imaging, and radiomics features selected). The model’s discrimination was evaluated using a receiver operating characteristic curve, its calibration with a calibration curve, and the results were visualized using the Hosmer–Lemeshow goodness-of-fit test. Thirteen clinical features and fourteen imaging features were selected for constructing the clinical and imaging models. Simultaneously, a set of twenty-five radiomics features were utilized to build the radiomics model. The integrated model demonstrated good calibration and discrimination in the training cohorts (AUC, 0.922; 95% CI: 0.900, 0.942), validation cohorts (AUC, 0.879; 95% CI: 0.806, 0.920), and testing cohorts (AUC, 0.877; 95% CI: 0.836, 0.916). The discriminatory and predictive efficacy of the clinical model in testing cohorts increased further after clinical and radiological features were incorporated (AUC, 0.849 vs. 0.922, P = 0.002). The model demonstrated exemplary predictive efficacy for SMPP by leveraging a comprehensive set of inputs, encompassing clinical data, quantitative and qualitative radiological features, along with radiomics features. The integration of these three aspects in the predictive model further enhanced the performance of the clinical model, indicating the potential for extensive clinical applications.

Abstract To validate a radiomics model based on multi-sequence magnetic resonance imaging (MRI) in predicting the ki-67 expression levels in early-stage endometrial cancer, 131 patients with early endometrial cancer who had undergone pathological examination and preoperative MRI scan were retrospectively enrolled and divided into two groups based on the ki-67 expression levels. The radiomics features were extracted from the T2 weighted imaging (T2WI), dynamic contrast enhanced T1 weighted imaging (DCE-T1WI), and apparent diffusion coefficient (ADC) map and screened using the Pearson correlation coefficients (PCC). A multi-layer perceptual machine and fivefold cross-validation were used to construct the radiomics model. The receiver operating characteristic (ROC) curves analysis, calibration curves, and decision curve analysis (DCA) were used to assess the models. The combined multi-sequence radiomics model of T2WI, DCE-T1WI, and ADC map showed better discriminatory powers than those using only one sequence. The combined radiomics models with multi-sequence fusions achieved the highest area under the ROC curve (AUC). The AUC value of the validation set was 0.852, with an accuracy of 0.827, sensitivity of 0.844, specificity of 0.773, and precision of 0.799. In conclusion, the combined multi-sequence MRI based radiomics model enables preoperative noninvasive prediction of the ki-67 expression levels in early endometrial cancer. This provides an objective imaging basis for clinical diagnosis and treatment.