Relationship between multi-slice computed tomography features and pathological risk stratification assessment in gastric gastrointestinal stromal tumors.

BACKGROUND: Computed tomography (CT) imaging features are associated with risk stratification of gastric gastrointestinal stromal tumors (GISTs). AIM: To determine the multi-slice CT imaging features for predicting risk stratification in patients with primary gastric GISTs. METHODS: The clinicopathological and CT imaging data for 147 patients with histologically confirmed primary gastric GISTs were retrospectively analyzed. All patients had received dynamic contrast-enhanced CT (CECT) followed by surgical resection. According to the modified National Institutes of Health criteria, 147 lesions were classified into the low malignant potential group (very low and low risk; 101 lesions) and high malignant potential group (medium and high-risk; 46 lesions). The association between malignant potential and CT characteristic features (including tumor location, size, growth pattern, contour, ulceration, cystic degeneration or necrosis, calcification within the tumor, lymphadenopathy, enhancement patterns, unenhanced CT and CECT attenuation value, and enhancement degree) was analyzed using univariate analysis. Multivariate logistic regression analysis was performed to identify significant predictors of high malignant potential. The receiver operating curve (ROC) was used to evaluate the predictive value of tumor size and the multinomial logistic regression model for risk classification. RESULTS: There were 46 patients with high malignant potential and 101 with low-malignant potential gastric GISTs. Univariate analysis showed no significant differences in age, gender, tumor location, calcification, unenhanced CT and CECT attenuation values, and enhancement degree between the two groups (P > 0.05). However, a significant difference was observed in tumor size (3.14 ± 0.94 vs 6.63 ± 3.26 cm, P < 0.001) between the low-grade and high-grade groups. The univariate analysis further revealed that CT imaging features, including tumor contours, lesion growth patterns, ulceration, cystic degeneration or necrosis, lymphadenopathy, and contrast enhancement patterns, were associated with risk stratification (P < 0.05). According to binary logistic regression analysis, tumor size [P < 0.001; odds ratio (OR) = 26.448; 95% confidence interval (CI): 4.854-144.099)], contours (P = 0.028; OR = 7.750; 95%CI: 1.253-47.955), and mixed growth pattern (P = 0.046; OR = 4.740; 95%CI: 1.029-21.828) were independent predictors for risk stratification of gastric GISTs. ROC curve analysis for the multinomial logistic regression model and tumor size to differentiate high-malignant potential from low-malignant potential GISTs achieved a maximum area under the curve of 0.919 (95%CI: 0.863-0.975) and 0.940 (95%CI: 0.893-0.986), respectively. The tumor size cutoff value between the low and high malignant potential groups was 4.05 cm, and the sensitivity and specificity were 93.5% and 84.2%, respectively. CONCLUSION: CT features, including tumor size, growth patterns, and lesion contours, were predictors of malignant potential for primary gastric GISTs.

Is there a gap between artificial intelligence applications and priorities in health care and nursing management?

AIM: The article aims to outline a contrast between three priorities for nursing management proposed a decade ago and key features of the following 10 years of developments on artificial intelligence for health care and nursing management. This analysis intends to contribute to update the international debate on bridging the essence of health care and nursing management priorities and the focus of artificial intelligence developers. BACKGROUND: Artificial intelligence research promises innovative approaches to supporting nurses' clinical decision-making and to conduct tasks not related to patient interaction, including administrative activities and patient records. Yet, even though there has been an increase in international research and development of artificial intelligence applications for nursing care during the past 10 years, it is unclear to what extent the priorities of nursing management have been embedded in the devised artificial intelligence solutions. EVALUATION: Starting from three priorities for nursing management identified in 2011 in a special issue of the Journal Nursing Management, we went on to identify recent evidence concerning 10 years of artificial intelligence applications developed to support health care management and nursing activities since then. KEY ISSUE: The article discusses to what extent priorities in health care and nursing management may have to be revised while adopting artificial intelligence applications or, alternatively, to what extent the direction of artificial intelligence developments may need to be revised to contribute to long acknowledged priorities of nursing management. CONCLUSION: We have identified a conceptual gap between both sets of ideas and provide a discussion on the need to bridge that gap, while admitting that there may have been recent field developments still unreported in scientific literature. IMPLICATIONS FOR NURSING MANAGEMENT: Artificial intelligence developers and health care nursing managers need to be more engaged in coordinating the future development of artificial intelligence applications with a renewed set of nursing management priorities.

Comparison of two CYP2D6 genotyping methods and assessment of genotype-phenotype relationships.

BACKGROUND: There have been no published reports comparing the CYP450 GeneChip microarray assay with more standard methods of genetic testing. METHODS: We collected 20-mL blood samples from 236 volunteers for DNA isolation and testing before each individual ingested 60 mg of dextromethorphan, and collected their urine. CYP2D6 alleles *3 to *7, *9, *17, and *41, and multiple CYP2D6 gene copies were tested by allele-specific PCR (AS-PCR), whereas alleles *2 to *4 and *6 to *11 were tested by the Affymetrix CYP450 GeneChip assay. Five of the CYP2D6 alleles (*3, *4, *6, *7, and *9) were tested by both AS-PCR and the CYP450 GeneChip assay in an independent and blinded fashion in 232 of the 236 healthy volunteers. The combined CYP2D6 genotype from both methods was used to divide the population into four subgroups, poor metabolizers (PMs), intermediate metabolizers (IMs), extensive metabolizers (EMs), and ultrarapid metabolizers (UMs), based on their relative function and ability to express the CYP2D6 gene. The urinary elimination of dextromethorphan was assessed in each of these CYP2D6 subgroups. RESULTS: The CYP2D6*3, *4, *6, *7, and *9 alleles showed a high degree of concordance between the CYP450 GeneChip and AS-PCR methods (>99% concordance). The mean (SD) of the log[dextromethorphan metabolic ratio (MR)] in the four CYP2D6 subgroups was PM = 0.49 (0.38); IM = -1.24 (0.53); EM = -2.35 (0.61); and UM = -2.43 (0.38). CONCLUSIONS: Oligonucleotide microarray technology is an efficient and reliable way to test for CYP2D6 gene variation based on five alleles compared by separate methods. The methodology is influenced by the quality and amount of DNA present. The log(dextromethorphan MR) is a highly variable index that appears to reflect the crude nature of the dextromethorphan MR as an indicator of CYP2D6 in vivo enzyme activity.

Clinical evaluation of a new model of self-obtained method for the assessment of genital human papilloma virus infection in an underserved population.

BACKGROUND: We designed a self-sampling method to collect exfoliated genital cells for human papilloma virus (HPV) detection. The aim was to assess whether it was suitable as an assistant tool for the early detection of cervical pre-cancer and cancer in a special category of the women who are not frequently screened for cervical cancer. METHODS: We compared the results of HPV detection that were self-obtained and physician-obtained cervical swabs from the same patient that were analyzed using hybrid capture II assay. The diagnostic rate of cervical pre-cancer and cancer between self-obtained method and physician-obtained method were analyzed. RESULTS: A total of 1194 women were prospectively registered from September 1997 through September 1999. Among them, 144 (12.1%) of self-test samples and 155 (13%) of physician-obtained samples were oncogenetic associated-HPV positive. Statistically, no significant differences existed in the screening rate for cervical cancer using either the self-collected samples or the physician-obtained samples (p > .05). The sensitivity of cervical precancer or cancer detection using self-obtained HPV testing was higher (96.3%) as compared with the Pap smear (79.2%) (p < .02). CONCLUSION: The detection correlation of the HPV test between the self-obtained method and physician-obtained method was 93%. Our results indicated that self-sampling was a reliable method for testing for HPV. The identification of HPV infection through the self-obtained method can be used in early identification of high-risk women with cervical precancer and cancer especially in underserved populations.