Neutrophil-to-lymphocyte ratio and lactate dehydrogenase for early diagnosis of AIDS patients with Talaromyces marneffei infection.

BACKGROUND: This study aimed to explore the value of neutrophil-to-lymphocyte ratio (NLR) in combination with routine blood tests, lactate dehydrogenase (LDH), and T-lymphocyte subsets for the early diagnosis of acquired immunodeficiency syndrome (AIDS) combined with Talaromyces marneffei (TM) infection. METHODS: A total of 166 confirmed AIDS patients were enrolled in this study. The observation group included 80 AIDS patients with TM infection, and the control group consisted of 86 AIDS patients with other complications. Regression analysis was performed to evaluate the predictive value of each index and the combination of these indexes for AIDS combined with TM infection using receiver operating characteristic (ROC) curve analysis. RESULTS: NLR and LDH were significantly higher in patients in the observation group compared with those in the control group, and the differences were statistically significant (P<0.05). There was no statistical difference in platelets, infantile granulocytes (IGM), and nucleated red blood cells (NRBC) between the 2 groups (P>0.05). The area under the operating characteristic curve (AUC) of the observed indicators were: NLR, 0.628; hemoglobin (HGB), 0.704; LDH, 0.607; lymphocyte (LYM) count, 0.744; CD4+ T lymphocyte count, 0.789; and CD8+ T lymphocyte count, 0.701. The combined AUC of multiple indicators was 0.815, with a sensitivity and specificity of 76.2% and 76.1%, respectively. CONCLUSIONS: NLR, HGB, LYM, LDH, and T lymphocyte subsets were diagnostic for early AIDS combined with TM infection , and CD4+ T lymphocytes had the best diagnostic efficacy alone.

Comparison of malignancy-prediction efficiency between contrast and non-contract CT-based radiomics features in gastrointestinal stromal tumors: A multicenter study.

This work seeks the development and validation of radiomics signatures from nonenhanced computed tomography (CT, NE-RS) to preoperatively predict the malignancy degree of gastrointestinal stromal tumors (GISTs) and the comparison of these signatures with those from contrast-enhanced CT. A dataset for 370 GIST patients was collected from four centers. This dataset was divided into cohorts for training, as well as internal and external validation. The minimum-redundancy maximum-relevance algorithm and the least absolute shrinkage and selection operator (LASSO) algorithm were used to filter unstable features. (a) NE-RS and radiomics signature from contrast-enhanced CT (CE-RS) were built and compared for the prediction of malignancy potential of GIST based on the area under the receiver operating characteristic curve (AUC). (b) The radiomics model was also developed with both the tumor size and NE-RS. The AUC values were comparable between NE-RS and CE-RS in the training (.965 vs .936; P = .251), internal validation (.967 vs .960; P = .801), and external validation (.941 vs .899; P = .173) cohorts in diagnosis of high malignancy potential of GISTs. We next focused on the NE-RS. With 0.185 selected as the cutoff of NE-RS for diagnosis of the malignancy potential of GISTs, accuracy, sensitivity, and specificity for diagnosis high-malignancy potential GIST was 90.0%, 88.2%, and 92.3%, respectively, in the training cohort. For the internal validation set, the corresponding metrics are 89.1%, 94.9%, and 80.0%, respectively. The corresponding metrics for the external cohort are 84.6%, 76.1%, and 91.0%, respectively. Compared with only NE-RS, the radiomics model increased the sensitivity in the diagnosis of GIST with high-malignancy potential by 5.9% (P = .025), 2.5% (P = .317), 10.5% (P = .008) for the training set, internal validation set, and external validation set, respectively. The NE-RS had comparable prediction efficiency in the diagnosis of high-risk GISTs to CE-RS. The NE-RS and radiomics model both had excellent accuracy in predicting malignancy potential of GISTs.

Personalized CT-based radiomics nomogram preoperative predicting Ki-67 expression in gastrointestinal stromal tumors: a multicenter development and validation cohort.

BACKGROUND AND AIM: To develop and validate radiomic prediction models using contrast-enhanced computed tomography (CE-CT) to preoperatively predict Ki-67 expression in gastrointestinal stromal tumors (GISTs). METHOD: A total of 339 GIST patients from four centers were categorized into the training, internal validation, and external validation cohort. By filtering unstable features, minimum redundancy, maximum relevance, Least Absolute Shrinkage and Selection Operator (LASSO) algorithm, a radiomic signature was built to predict the malignant potential of GISTs. Individual nomograms of Ki-67 expression incorporating the radiomic signature or clinical factors were developed using the multivariate logistic model and evaluated regarding its calibration, discrimination, and clinical usefulness. RESULTS: The radiomic signature, consisting of 6 radiomic features had AUC of 0.787 [95% confidence interval (CI) 0.632-0.801], 0.765 (95% CI 0.683-0.847), and 0.754 (95% CI 0.666-0.842) in the prediction of high Ki-67 expression in the training, internal validation and external validation cohort, respectively. The radiomic nomogram including the radiomic signature and tumor size demonstrated significant calibration, and discrimination with AUC of 0.801 (95% CI 0.726-0.876), 0.828 (95% CI 0.681-0.974), and 0.784 (95% CI 0.701-0.868) in the training, internal validation and external validation cohort respectively. Based on the Decision curve analysis, the radiomics nomogram was found to be clinically significant and useful. CONCLUSIONS: The radiomic signature from CE-CT was significantly associated with Ki-67 expression in GISTs. A nomogram consisted of radiomic signature, and tumor size had maximum accuracy in the prediction of Ki-67 expression in GISTs. Results from our study provide vital insight to make important preoperative clinical decisions.