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1 |
Student nurses knowledge about the management of chemotherapy-induced neutropenia: Multi-national survey
دانش پرستاران دانشجویی در مورد مدیریت نوتروپنی ناشی از شیمی درمانی: مرور چند ملیتی-2021 Background: Chemotherapy-induced neutropenia is a serious global health concern. It is essential that student
nurses who are the future of healthcare are equipped with the right knowledge to care for the unique needs of
patients with neutropenia.
Objective: The study assesses student nurses’ knowledge of neutropenia management and examines the difference in their knowledge with regard to their demographics. Design: A descriptive cross-sectional survey design was used. Settings: Participants for this survey were recruited from four nursing schools from three countries: Jordan, Oman, and Saudi Arabia. Participants: The study sample comprised 230 student nurses representing all three countries. Methods: Online data collection was implemented. A message including the link to the study questionnaire was sent to students through their university portal. Demographic data and the neutropenia knowledge questionnaire were collected. Results: The student nurses showed poor knowledge of neutropenia and its management (mean = 10.1 out of 30). The bridging students (M = 12.6, SD = 9.8) had significantly higher mean total knowledge scores than the regular students (M = 9.8, SD = 5.5) (t = 2.9, df = 38.9, p = 0.006). However, students who had received previous education about neutropenia management (M = 11.6, SD = 5.0) had significantly higher mean knowledge scores than those who had not (M = 9.5, SD = 5.6) (t = − 2.73, df = 134.8, p = 0.007). Conclusions: The study findings underscore the overarching necessity to improve students’ knowledge of neutropenia and its management. However, addressing this concern is multifaceted and requires deliberate effort from various agencies. Developing innovative strategies to increase the coverage of oncology nursing in the curriculum, improving faculty expertise, enhancing staff nurses’ knowledge and skills, provision of funding, and adoption of oncology-related competencies in the nursing program need to be explored as key solutions. keywords: دانش | نوتروپنی | نوتروپنی ناشی از شیمی درمانی | دانش آموزان: پرستاری | پرستاری انکولوژی | نئوپلاسم | Knowledge | Neutropenia | Chemotherapy-induced febrile neutropenia | Students: nursing | Oncology nursing | Neoplasm |
مقاله انگلیسی |
2 |
MRI-based traditional radiomics and computer-vision nomogram for predicting lymphovascular space invasion in endometrial carcinoma
رادیومیک سنتی مبتنی بر MRI و نام نگاری رایانه ای برای پیش بینی تهاجم فضایی لنفاوی در سرطان آندومتر-2021 Purpose: To determine the capabilities of MRI-based traditional radiomics and computer-vision (CV)
nomogram for predicting lymphovascular space invasion (LVSI) in patients with endometrial carcinoma
(EC).
Materials and methods: A total of 184 women (mean age, 52.9 ± 9.0 [SD] years; range, 28–82 years) with EC were retrospectively included. Traditional radiomics features and CV features were extracted from preoperative T2-weighted and dynamic contrast-enhanced MR images. Two models (Model 1, the radiomics model; Model 2, adding CV radiomics signature into the Model 1) were built. The performance of the models was evaluated by the area under the curve (AUC) of the receiver operator characteristic (ROC) in the training and test cohorts. A nomogram based on clinicopathological metrics and radiomics signatures was developed. The predictive performance of the nomogram was assessed by AUC of the ROC in the training and test cohorts. Results: For predicting LVSI, the AUC values of Model 1 in the training and test cohorts were 0.79 (95% confidence interval [CI]: 0.702–0.889; accuracy: 65.9%; sensitivity: 88.8%; specificity: 57.8%) and 0.75 (95% CI: 0.585–0.914; accuracy: 69.5%; sensitivity: 85.7%; specificity: 62.5%), respectively. The AUC values of Model 2 in the training and test cohorts were 0.93 (95% CI: 0.875–0.991; accuracy: 94.9%; sensitivity: 91.6%; specificity: 96.0%) and 0.81 (95% CI: 0.666–0.962; accuracy: 71.7%; sensitivity: 92.8%; specificity: 62.5%), respectively. The discriminative ability of Model 2 was significantly improved compared to Model 1 (Net Reclassification Improvement [NRI] = 0.21; P = 0.04). Based on histologic grade, FIGO stage, Radscore and CV-score, AUC values of the nomogram to predict LVSI in the training and test cohorts were 0.98 (95% CI: 0.955–1; accuracy: 91.6%; sensitivity: 91.6%; specificity: 96.0%) and 0.92 (95% CI: 0.823–1; accuracy: 91.3%; sensitivity: 78.5%; specificity: 96.8%), respectively. Conclusions: MRI-based traditional radiomics and computer-vision nomogram are useful for preoperative risk stratification in patients with EC and may facilitate better clinical decision-making. Keywords: Uterus | Endometrial neoplasm | Magnetic resonance imaging | Nomogram | Computer vision |
مقاله انگلیسی |
3 |
Integrative data mining and meta-analysis to investigate the prognostic role of microRNA-200 family in various human malignant neoplasms: A consideration on heterogeneity
استخراج داده های یکپارچه و متاآنالیز به منظور بررسی نقش پیش آگهی خانواده microRNA-200 در نئوپلاسم های مختلف بدخیم انسانی: توجه به ناهمگونی-2019 Background: Existing meta-analysis have shown that the miR-200 family can be taken as a prognostic biomarker
for many tumors. However, great heterogeneity was shown in predicting overall survival (OS) and progressionfree
survival (PFS). Emerging studies indicate that the expression levels of members of the miR-200 family are
tissue-specific among various tumor tissues, which may be the main reason of the heterogeneity in predicting
survival prognosis of tumor patients with the miR-200 family as biomarkers. By further analysis of heterogeneity
of the miR-200 family as a biomarker for predicting survival prognosis of patients with different tumors, we
expected to provide an accurate basis for the clinical application of the miR-200 family to predict the prognosis
of patients with different tumors.
Methods: Eligible published studies were identified by searching the databases of PubMed, Embase and Web of
Science. The clinical data of patients in the studies were pooled, and pooled hazard ratios (HR) with 95%
confidence intervals (95% CI) were used to calculate the strength of this association. The expressions of miRNAs
were extracted from The Cancer Genome Atlas (TCGA). We presented the expressions of each member in miR-
200 family in 15 types of cancer by boxplot, and analyzed the correlation among the members of miR-200 family
by Spearman method. Different subgroup analyses were then performed based on the correlation among the
members of miR-200 family, and the publication bias was assessed using the funnel plot of the Egger bias
indicator test.
Results: Of 36 articles, including 15 tumor types and 4644 patients were included to perform meta-analysis. It
was found that miR-200 family members can be used as independent protective factors in patients with various
tumors but the miR-200 family has a higher heterogeneity in predicting prognosis: OS (HR=0.82, 95% CI:
0.66–1.03, I2=85%, P < 0.01) and PFS (HR=0.81, 95% CI: 0.57–1.16, I2=97%, P < 0.01). The data from
TCGA database were used to analyze the expression levels of the miR-200 family and the results showed that the
expression of miR-429 in different cancers is very different, and there are significant differences in expression
levels compared with other miR-200 family members; the expression levels of miR-200a and miR-200b in
various tumor tissues were similar to each other, respectively; miR-200c and miR-141 showed similar expression
levels in each of most types of cancer tissues except ovarian cancer (OC). The expression levels of members of the
miR-200 family in breast cancer (BRCA), cervical cancer (CESC), colon cancer (COAD), esophageal cancer
(ESCA), head and neck cancer (HNSC), lung adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC) are
relatively stable, but great variations can be found in the expression levels of miR-200 family members in
ovarian cancer (OC), liver cancer (LIHC), renal clear cell carcinoma (KIRC) and renal papillary cell carcinoma
(KIRP). Cluster analysis of expression of target genes of miR-200 family in different cancers yielded similar
results to the expression level of the miR-200 family. Subgroup analysis of OC, LIHC, GC and LUAD based on
expression levels and clustering results reduced or even eliminated the heterogeneity of miR-200 family
members in predicting patient outcomes.
Conclusions: Our results convincingly demonstrated that the miR-200 family could serve as a prognostic biomarker
for cancers mentioned above and has potential value in clinical practice. MiR-200 family as prognostic
biomarkers needs to be performed according to different tumor tissues and correlation between members in miR-
200 family. Keywords: miR-200 family | Meta-analysis | miRNAs | Cancer | Prognosis | Heterogeneity |
مقاله انگلیسی |
4 |
A predictive model for high/low risk group according to oncotype DX recurrence score using machine learning
یک مدل پیش بینی برای گروه پرخطر و کم خطر با توجه به نمره عود توموری DX با استفاده از یادگیری ماشین-2019 Background: Oncotype DX(ODX) is a 21-gene breast cancer recurrence score(RS) assay that aids in
decision-making for chemotherapy in early-stage hormone receptor-positive(HRþ)breast cancer. We
developed a prediction tool using machine learning for high- or low-risk ODX criteria (i.e., RS < 11 for
low-risk; RS > 25 for high-risk).
Methods: We performed a retrospective review of 301 breast cancer patients who underwent surgery
between April 2011 and July 2017 and then an ODX test at Samsung Medical Center in Seoul, Korea.
Among them, 208 cases were defined as the modeling group and 76 cases were defined as the validation
group. We built a supervised machine learning classification model using the Azure ML platform.
Results: For the high RS group, accuracywas 0.903 through Two-class Decision Junglemethod in test set. For
the lowRS group, the accuracywas 0.726when the Two-class NeuralNetwork methodwas applied. The AUC
of the ROC curve was 0.917 in the high RS group and 0.744 in the low RS group in test set. In addition, we
conducted an internal validation using 76 patients who underwent ODX testing between January 2017 and
July 2017. The accuracy of validationwas 0.880 in the high RS group and 0.790 in the low RS group.
Conclusion: We developed a predictive model using machine learning that could represent a useful and
easy-to-access tool for the selection of high ODX RS patients. After additional evaluation with large data
and external validation, worldwide use of our model could be expected. Keywords: Breast neoplasm | Prediction | Machine learning |
مقاله انگلیسی |
5 |
Machine Learning to Differentiate T2-Weighted Hyperintense Uterine Leiomyomas from Uterine Sarcomas by Utilizing Multiparametric Magnetic Resonance Quantitative Imaging Features
یادگیری ماشین برای تمایز لیپوماتیک رحمی T2 با وزنی T2 با استفاده از ویژگیهای تصویربرداری رزونانس مغناطیسی چند پارامتری مغناطیسی از سارکوم رحمی-2019 Rationale and Objective: Uterine leiomyomas with high signal intensity on T2-weighted imaging (T2WI)
can be difficult to distinguish from sarcomas. This study assessed the feasibility of using machine learning
to differentiate uterine sarcomas from leiomyomas with high signal intensity on T2WI on multiparametric
magnetic resonance imaging.
Materials and Methods: This retrospective study included 80 patients (50 with benign leiomyoma and 30
with uterine sarcoma) who underwent pelvic 3 T magnetic resonance imaging examination for the evaluation
of uterine myometrial smooth muscle masses with high signal intensity on T2WI. We used six machine
learning techniques to develop prediction models based on 12 texture parameters on T1WI and T2WI,
apparent diffusion coefficient maps, and contrast-enhanced T1WI, as well as tumor size and age. We calculated
the areas under the curve (AUCs) using receiver-operating characteristic analysis for each model
by 10-fold cross-validation and compared these to those for two board-certified radiologists.
Results: The eXtreme Gradient Boosting model gave the highest AUC (0.93), followed by the random
forest, support vector machine, multilayer perceptron, k-nearest neighbors, and logistic regression
models. Age was the most important factor for differentiation (leiomyoma 44.9 § 11.1 years; sarcoma
58.9 § 14.7 years; p < 0.001). The AUC for the eXtreme Gradient Boosting was significantly higher than
those for both radiologists (0.93 vs 0.80 and 0.68, p = 0.03 and p < 0.001, respectively).
Conclusion: Machine learning outperformed experienced radiologists in the differentiation of uterine
sarcomas from leiomyomas with high signal intensity on T2WI. Key Words: Magnetic resonance imaging | Uterine neoplasm | Leiomyoma | Machine learning | Sarcoma |
مقاله انگلیسی |
6 |
The Rise of Big Data in Oncology
ظهور داده های بزرگ در انکولوژی-2018 OBJECTIVES: To describe big data and data science in the context of oncology
nursing care.
DATA SOURCES: Peer-reviewed and lay publications.
CONCLUSION: The rapid expansion of real-world evidence from sources such
as the electronic health record, genomic sequencing, administrative claims and
other data sources has outstripped the ability of clinicians and researchers to
manually review and analyze it. To promote high-quality, high-value cancer
care, big data platforms must be constructed from standardized data sources
to support extraction of meaningful, comparable insights.
IMPLICATIONS FOR NURSING PRACTICE: Nurses must advocate for the use of stan
dardized vocabularies and common data elements that represent terms and
concepts that are meaningful to patient care.
he term “big data” first appeared in the literature in 1997 by researchers at NASA as they described the challenges to store the volume of information generated as a result of a new, data-intensive type of
computational work.1 In 2008, a white paper entitled “Big-Data Computing: Creating revolutionary
breakthroughs in commerce, science and society,”
highlighted the rapid integration of data-driven strategies across settings ranging from Wal-Mart’s (then)
4 petabyte (4000 trillion bytes) data warehouse to
the 15 petabytes of data projected to be generated annually by the Large Hadron Collider particle
accelerator project,2 and is credited with widespread adoption of the term.3
KEY WORDS: electronic health records, meaningful use, artificial intelligence, neoplasms |
مقاله انگلیسی |
7 |
Retro-peritoneal paraganglioma, diagnosis and management
paraganglioma یکپارچه صفاقی، تشخیص و مدیریت-2018 Introduction. — Paragangliomas, defined as extra-adrenal chromaffin-cells tumors, are rarely
located in the retro-peritoneum. Clinical presentation is similar to pheochromocytoma, and
mainly depends on the producing character of the tumor. Positive diagnosis requires plasmatic
and urinary hormonal assays. Radiological and isotopic explorations are essential before surgery.
The only curative therapeutic strategy is surgical, associated to peri-operative prevention and
monitoring of the frequently reported hemodynamic and cardiovascular disorders. Outcome
depends of the metastatic character of the tumor, the presence of tumor remnant after surgi
cal resection. Genetic study is recommended; the risk of recurrence and association to other
neoplasm is more described in genetic forms.
Material and methods. — Authors report 5 cases of retro-peritoneal paraganglioma, operated
in the department of urology of Hospital, between 2013 and 2017. Observations are about
2 men and 3 women. Clinical presentation is not always specific and paraganglioma may be
discovered fortuitously. Two patients have been operated by coelioscopic approach, midline
incision was performed in two other cases, and dorsal lumbotomy associated to a Rutherford
Morrison incision in a patient.
KEYWORDS : Paraganglioma ; Retroperitoneal neoplasms ; Anesthesia ; Surgery |
مقاله انگلیسی |