Background: Screening indexes of tumor serum markers for benign and malignant solitary pulmonary nodules (SPNs) were analyzed to find the optimum method for diagnosis. Methods: Enzyme-linked immunosorbent assays, an automatic immune analyzer and radioimmunoassay methods were used to examine the levels of 8 serum markers in 164 SPN patients, and the sensitivity for differential diagnosis of malignant or benign SPN was compared for detection using a single plasma marker or a combination of markers. The results for serological indicators that closely relate to benign and malignant SPNs were screened using the Fisher discriminant analysis and a non-conditional logistic regression analysis method, respectively. The results were then verified by the k-means clustering analysis method. Results: The sensitivity when using a combination of serum markers to detect SPN was higher than that using a single marker. By Fisher discriminant analysis, cytokeratin 19 fragments (CYFRA21-1), carbohydrate antigen 125 (CA125), squamous cell carcinoma antigen (SCC) and breast cancer antigen (CA153), which relate to the benign and malignant SPNs, were screened. Through non-conditional logistic regression analysis, CYFRA21-1, SCC and CA153 were obtained. Using the k-means clustering analysis, the cophenetic correlation coefficient (0.940) obtained by the Fisher discriminant analysis was higher than that obtained with logistic regression analysis (0.875). Conclusion: This study indicated that the Fisher discriminant analysis functioned better in screening out serum markers to recognize the benign and malignant SPN. The combined detection of CYFRA21-1, CA125, SCC and CA153 is an effective way to distinguish benign and malignant SPN, and will find an important clinical application in the early diagnosis of SPN.

Spontaneous intracerebral hemorrhage (SICH) has been common in China with high morbidity and mortality rates. This study aims to develop a machine learning (ML)-based predictive model for the 90-day evaluation after SICH. We retrospectively reviewed 751 patients with SICH diagnosis and analyzed clinical, radiographic, and laboratory data. A modified Rankin scale (mRS) of 0–2 was defined as a favorable functional outcome, while an mRS of 3–6 was defined as an unfavorable functional outcome. We evaluated 90-day functional outcome and mortality to develop six ML-based predictive models and compared their efficacy with a traditional risk stratification scale, the intracerebral hemorrhage (ICH) score. The predictive performance was evaluated by the areas under the receiver operating characteristic curves (AUC). A total of 553 patients (73.6%) reached the functional outcome at the 3rd month, with the 90-day mortality rate of 10.2%. Logistic regression (LR) and logistic regression CV (LRCV) showed the best predictive performance for functional outcome (AUC = 0.890 and 0.887, respectively), and category boosting presented the best predictive performance for the mortality (AUC = 0.841). Therefore, ML might be of potential assistance in the prediction of the prognosis of SICH.

Aims: To compare serum protein expression profiles between lung cancer patients and healthy individuals, and to examine whether there are differences in serum protein expression profiles among patients with lung cancers of different histological types and whether the characteristic expression of serum proteins may assist in differential diagnosis of various subtypes of lung cancers. Methods: Blood samples were collected from 123 lung cancer patients before commencement of treatment who attended Shanxi Cancer Hospital, China, between 2008 and 2013. Blood samples from 60 healthy individuals were also collected in the same period. Serum protein expression profiles were analyzed using surface-enhanced laser desorption/ionization time-of-flight mass spectrometry. The differences in the serum protein spectrums of lung cancer patients with different histological subtypes were analyzed by one-way Analysis of Variance and receiver operating characteristic curves. Results: A cluster of 48 protein mass-to-change ratio (M/Z) peaks was differentially expressed between sera of lung cancer patients and healthy individuals. The M/Z 1205, 4673, 1429 and 4279 peaks were differentially expressed among patients with lung squamous cell carcinomas, adenocarcinomas and small-cell lung carcinomas. Conclusion: These results reinforce the notion that profiling of serum proteins may be of diagnostic value in lung cancer, and suggest that the differences in serum protein profiles may be useful in differential diagnosis of lung cancers of varying histological subtypes.