scholarly journals Radiomics for intracerebral hemorrhage: are all small hematomas benign?

2020 ◽  
pp. 20201047
Author(s):  
Chenyi Zhan ◽  
Qian Chen ◽  
Mingyue Zhang ◽  
Yilan Xiang ◽  
Jie Chen ◽  
...  
Keyword(s):  
Intracerebral Hemorrhage ◽  
Clinical Decision Making ◽  
Characteristic Curve ◽  
Clinical Information ◽  
Clinical Decision ◽  
Hematoma Expansion ◽  
Clinical Factors ◽  
Combined Model ◽  
Clinical Model ◽  
Discrimination Ability

Objectives: We hypothesized that not all small hematomas are benign and that radiomics could predict hematoma expansion (HE) and short-term outcomes in small hematomas. Methods: We analyzed 313 patients with small (<10 ml) intracerebral hemorrhage (ICH) who underwent baseline non-contrast CT within 6 h of symptom onset between September 2013 and February 2019. Poor outcome was defined as a Glasgow Outcome Scale score ≤3. A radiomic model and a clinical model were built using least absolute shrinkageand selection operator algorithm or multivariate analysis. A combined model that incorporated the developed radiomic score and clinical factors was then constructed. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of these models. Results: The addition of radiomics to clinical factors significantly improved the prediction performance of HE compared with the clinical model alone in both the training {AUC, 0.762 [95% CI (0.665–0.859)] versus AUC, 0.651 [95% CI (0.556–0.745)], p = 0.007} and test {AUC, 0.776 [95% CI (0.655–0.897) versus AUC, 0.631 [95% CI (0.451–0.810)], p = 0.001} cohorts. Moreover, the radiomic-based model achieved good discrimination ability of poor outcomes in the 3–10 ml group (AUCs 0.720 and 0.701). Conclusion: Compared with clinical information alone, combined model had greater potential for discriminating between benign and malignant course in patients with small ICH, particularly 3–10 ml hematomas. Advances in knowledge: Radiomics can be used as a supplement to conventional medical imaging, improving clinical decision-making and facilitating personalized treatment in small ICH.

scholarly journals A CT-based radiomics nomogram for predicting prognosis of coronavirus disease 2019 (COVID-19) radiomics nomogram predicting COVID-19

2021 ◽  
Vol 94 (1117) ◽  
pp. 20200634
Author(s):  
Hang Chen ◽  
Ming Zeng ◽  
Xinglan Wang ◽  
Liping Su ◽  
Yuwei Xia ◽  
...  
Keyword(s):  
Clinical Decision Making ◽  
Predictive Performance ◽  
Clinical Decision ◽  
Ct Images ◽  
Prediction Performance ◽  
Clinical Factors ◽  
Training Set ◽  
Clinical Model ◽  
Potential Biomarker ◽  
Validation Set

Objectives: To identify the value of radiomics method derived from CT images to predict prognosis in patients with COVID-19. Methods: A total of 40 patients with COVID-19 were enrolled in the study. Baseline clinical data, CT images, and laboratory testing results were collected from all patients. We defined that ROIs in the absorption group decreased in the density and scope in GGO, and ROIs in the progress group progressed to consolidation. A total of 180 ROIs from absorption group (n = 118) and consolidation group (n = 62) were randomly divided into a training set (n = 145) and a validation set (n = 35) (8:2). Radiomics features were extracted from CT images, and the radiomics-based models were built with three classifiers. A radiomics score (Rad-score) was calculated by a linear combination of selected features. The Rad-score and clinical factors were incorporated into the radiomics nomogram construction. The prediction performance of the clinical factors model and the radiomics nomogram for prognosis was estimated. Results: A total of 15 radiomics features with respective coefficients were calculated. The AUC values of radiomics models (kNN, SVM, and LR) were 0.88, 0.88, and 0.84, respectively, showing a good performance. The C-index of the clinical factors model was 0.82 [95% CI (0.75–0.88)] in the training set and 0.77 [95% CI (0.59–0.90)] in the validation set. The radiomics nomogram showed optimal prediction performance. In the training set, the C-index was 0.91 [95% CI (0.85–0.95)], and in the validation set, the C-index was 0.85 [95% CI (0.69–0.95)]. For the training set, the C-index of the radiomics nomogram was significantly higher than the clinical factors model (p = 0.0021). Decision curve analysis showed that radiomics nomogram outperformed the clinical model in terms of clinical usefulness. Conclusions: The radiomics nomogram based on CT images showed favorable prediction performance in the prognosis of COVID-19. The radiomics nomogram could be used as a potential biomarker for more accurate categorization of patients into different stages for clinical decision-making process. Advances in knowledge: Radiomics features based on chest CT images help clinicians to categorize the patients of COVID-19 into different stages. Radiomics nomogram based on CT images has favorable predictive performance in the prognosis of COVID-19. Radiomics act as a potential modality to supplement conventional medical examinations.

scholarly journals A Nomogram-Based Model to Predict Neoplastic Risk For Patients With Gallbladder Polyps

2021 ◽  
Author(s):  
Xudong Zhang ◽  
Jin-Cheng Wang ◽  
Baoqiang Wu ◽  
Tao Li ◽  
Lei Jin ◽  
...  
Keyword(s):  
Clinical Decision Making ◽  
Early Stage ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Training Group ◽  
Clinical Decision ◽  
Polyp Size ◽  
Training Cohort ◽  
Discrimination Ability ◽  
Gallbladder Polyps

Abstract Background: Gallbladder polyps (GBPs) assessment seeks to identify early-stage gallbladder carcinoma (GBC). Many studies have analyzed the risk factors for malignant GBPs, and we try to establish a more accurate predictive model for potential neoplastic polyps in patients with GBPs.Methods: This retrospective study developed a nomogram-based model in a training cohort of 233 GBP patients. Clinical information, ultrasonographic findings, and blood tests were retrospectively analyzed. Spearman correlation and logistic regression analysis were used to identify independent predictors and establish a nomogram model. An internal validation was conducted in 225 consecutive patients. Performance of models was evaluated through the receiver operating characteristic curve (ROC) and decision curve analysis (DCA). Results: Age, cholelithiasis, CEA, polyp size and sessile were confirmed as independent predictors for neoplastic potential of GBPs in the training group. Compared with other proposed prediction methods, the established nomogram model presented good discrimination ability in the training cohort (area under the curve [AUC]: 0.845) and the validation cohort (AUC: 0.836). DCA demonstrated the most clinical benefits can be provided by the nomogram. Conclusions: Our developed preoperative nomogram model can successfully evaluate the neoplastic potential of GBPs based on simple clinical variables, that maybe useful for clinical decision-making.

scholarly journals Defining the biological basis of radiomic phenotypes in lung cancer

eLife ◽  
2017 ◽  
Vol 6 ◽  
Author(s):  
Patrick Grossmann ◽  
Olya Stringfield ◽  
Nehme El-Hachem ◽  
Marilyn M Bui ◽  
Emmanuel Rios Velazquez ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Clinical Decision Making ◽  
Medical Images ◽  
Human Cancer ◽  
Tumor Biology ◽  
Clinical Information ◽  
Clinical Decision ◽  
Imaging Features ◽  
Clinical Factors ◽  
Complementary Value

Medical imaging can visualize characteristics of human cancer noninvasively. Radiomics is an emerging field that translates these medical images into quantitative data to enable phenotypic profiling of tumors. While radiomics has been associated with several clinical endpoints, the complex relationships of radiomics, clinical factors, and tumor biology are largely unknown. To this end, we analyzed two independent cohorts of respectively 262 North American and 89 European patients with lung cancer, and consistently identified previously undescribed associations between radiomic imaging features, molecular pathways, and clinical factors. In particular, we found a relationship between imaging features, immune response, inflammation, and survival, which was further validated by immunohistochemical staining. Moreover, a number of imaging features showed predictive value for specific pathways; for example, intra-tumor heterogeneity features predicted activity of RNA polymerase transcription (AUC = 0.62, p=0.03) and intensity dispersion was predictive of the autodegration pathway of a ubiquitin ligase (AUC = 0.69, p<10-4). Finally, we observed that prognostic biomarkers performed highest when combining radiomic, genetic, and clinical information (CI = 0.73, p<10-9) indicating complementary value of these data. In conclusion, we demonstrate that radiomic approaches permit noninvasive assessment of both molecular and clinical characteristics of tumors, and therefore have the potential to advance clinical decision-making by systematically analyzing standard-of-care medical images.

Clinical Diagnosis and the Order of Information

1998 ◽  
Vol 18 (4) ◽  
pp. 412-417 ◽  
Author(s):  
George R. Bergus ◽  
Gretchen B. Chapman ◽  
Barcey T. Levy ◽  
John W. Ely ◽  
Robert A. Oppliger
Keyword(s):  
Urinary Tract ◽  
Physical Examination ◽  
Clinical Decision Making ◽  
Laboratory Data ◽  
Clinical Information ◽  
Clinical History ◽  
Clinical Decision ◽  
Background Information ◽  
Likelihood Ratios ◽  
Patient’S History

Background. Information order can influence judgment. However, it remains unclear whether the order of clinical data affects physicians' interpretations of these data when they are engaged in familiar diagnostic tasks. Methods. Of 400 randomly selected family physicians who were given a questionnaire involving a brief written scenario about a young woman with acute dysuria, 315 (79%) returned usable responses. The physicians had been randomized into two groups, and both groups had received the same clinical information but in different orders. After learning the patient's chief com plaint, physicians received either the patient's history and physical examination results followed by the laboratory data (the H&P-first group) or the laboratory data followed by the history and physical examination results (the H&P-last group). The results of the history and physical examination were supportive of the diagnosis of UTI, while the laboratory data were not. All physicians judged the probability of a urinary tract infection (UTI) after each piece of information. Results. The two groups had similar mean estimates of the probability of a UTI after learning the chief complaint (67.4% vs 67.8%, p = 0.85). At the end of the scenario, the H&P-first group judged UTI to be less likely than did the H&P-last group (50.9% vs 59.1 %, p = 0.03) despite having identical information. Comparison of the mean likelihood ratios attributed to the clinical information showed that the H&P-first group gave less weight to the history and phys ical than did the H&P-last group (p = 0.04). Conclusions. The order in which clinical information was presented influenced physicians' estimates of the probability of dis ease. The clinical history and physical examination were given more weight by phy sicians who received this information last. Key words: diagnosis; urinary tract infec tions ; judgment; primary care; clinical decision making. (Med Decis Making 1998;18: 412-417)

scholarly journals NEWS2 score in the practice of infectious diseases hospital in COVID-19 patients. Implementation and results

2021 ◽  
Vol 18 (1) ◽  
pp. 7-16
Author(s):  
К. N. Popova ◽  
A. A. Zhukov ◽  
I. L. Zykina ◽  
D. V. Troschanskiy ◽  
I. N. Tyurin ◽  
...  
Keyword(s):  
Mobile Application ◽  
Clinical Decision Making ◽  
Medical Information ◽  
Prognostic Significance ◽  
Clinical Information ◽  
Clinical Decision ◽  
Clinical State ◽  
Medical Assistance ◽  
Therapy Effectiveness ◽  
Analytical System

Amidst the new COVID-19 pandemic, there is a need for a reliable medical tool to monitor patients’ vital conditions with clinical information continuity. This tool is essential for timely detection of the risk of the patient’s clinical state deterioration throughout all the stages of medical assistance.  The objective is to assess results of the NEWS2 score implementation at the in-patient stage of medical care.Methods. 183,732 scores of the NEWS2 score in 10,290 hospitalized patients were analyzed.  All the assessed results of the NEWS2 score were retrospectively analyzed. The NEWS2 score results were added to the United Medical Information and Analytical System of Moscow (EMIAS) database through the NEWS2 mobile application. The researchers analyzed the descriptive statistics of the score; the prognostic significance of NEWS2 in the prediction of the disease outcome was assessed as well as the accuracy of the used methods. Results. As the result of the research, deviations from standard methods in the application of the NEWS2 score were outlined, which allowed the researchers to develop the corrective measures.  The received data confirmed that interval assessment by the NEWS2 score and the trend analysis were important when making clinical and organizational decisions. Specific parameters of the score use during the COVID-19 pandemic were outlined, which helped to adjust the in-hospital procedures for clinical decision-making process, routing, and the continuity of all stages of medical assistance was established. Conclusion. The use of the NEWS2 score in medical practice makes it possible to predict the risks of clinical deterioration in the patient's condition, conduct bedside monitoring of therapy effectiveness, and optimize in-hospital routing. However, to ensure the validity of the score, it is necessary to plan activities for the personnel training and motivation, as well as to monitor careful adherence to the protocol. 

scholarly journals Case Report: Detection of Symptomatic Treatment-Related Changes in a Patient With Anaplastic Oligodendroglioma Using FET PET

2021 ◽  
Vol 11 ◽  
Author(s):  
Elena Katharina Bauer ◽  
Jan-Michael Werner ◽  
Gereon R. Fink ◽  
Karl-Josef Langen ◽  
Norbert Galldiks
Keyword(s):  
Clinical Decision Making ◽  
Systemic Treatment ◽  
Clinical Information ◽  
Symptomatic Treatment ◽  
Clinical Decision ◽  
Anaplastic Oligodendroglioma ◽  
Mri Findings ◽  
Fet Pet ◽  
Conventional Mri ◽  
Amino Acid Pet

Following local and systemic treatment of gliomas, the differentiation between glioma relapse and treatment-related changes such as pseudoprogression or radiation necrosis using conventional MRI is limited. To overcome this limitation, various amino acid PET tracers such as O-[2-(18F)-fluoroethyl]-L-tyrosine (FET) are increasingly used and provide valuable additional clinical information. We here report neuroimaging findings in a clincally symptomatic 53-year-old woman with a recurrent anaplastic oligodendroglioma with MRI findings highly suspicious for tumor progression. In contrast, FET PET imaging suggested treatment-related changes considerably earlier than the regression of contrast enhancement on MRI. In patients with oligodendroglioma, the phenomenon of symptomatic treatment-related changes is not well described, making these imaging findings unique and important for clinical decision-making.

scholarly journals Comparison of Comprehensive Morphological and Radiomics Features of Subsolid Pulmonary Nodules to Distinguish Minimally Invasive Adenocarcinomas and Invasive Adenocarcinomas in CT Scan

2022 ◽  
Vol 11 ◽  
Author(s):  
Lu Qiu ◽  
Xiuping Zhang ◽  
Haixia Mao ◽  
Xiangming Fang ◽  
Wei Ding ◽  
...  
Keyword(s):  
Minimally Invasive ◽  
Clinical Decision Making ◽  
High Efficiency ◽  
Pulmonary Nodules ◽  
Roc Curves ◽  
Clinical Decision ◽  
Gray Level ◽  
Combined Model ◽  
Significant Difference ◽  
Morphological Model

ObjectiveTo investigative the diagnostic performance of the morphological model, radiomics model, and combined model in differentiating invasive adenocarcinomas (IACs) from minimally invasive adenocarcinomas (MIAs).MethodsThis study retrospectively involved 307 patients who underwent chest computed tomography (CT) examination and presented as subsolid pulmonary nodules whose pathological findings were MIAs or IACs from January 2010 to May 2018. These patients were randomly assigned to training and validation groups in a ratio of 4:1 for 10 times. Eighteen categories of morphological features of pulmonary nodules including internal and surrounding structure were labeled. The following radiomics features are extracted: first-order features, shape-based features, gray-level co-occurrence matrix (GLCM) features, gray-level size zone matrix (GLSZM) features, gray-level run length matrix (GLRLM) features, and gray-level dependence matrix (GLDM) features. The chi-square test and F1 test selected morphology features, and LASSO selected radiomics features. Logistic regression was used to establish models. Receiver operating characteristic (ROC) curves evaluated the effectiveness, and Delong analysis compared ROC statistic difference among three models.ResultsIn validation cohorts, areas under the curve (AUC) of the morphological model, radiomics model, and combined model of distinguishing MIAs from IACs were 0.88, 0.87, and 0.89; the sensitivity (SE) was 0.68, 0.81, and 0.83; and the specificity (SP) was 0.93, 0.79, and 0.87. There was no statistically significant difference in AUC between three models (p &gt; 0.05).ConclusionThe morphological model, radiomics model, and combined model all have a high efficiency in the differentiation between MIAs and IACs and have potential to provide non-invasive assistant information for clinical decision-making.

scholarly journals Radiogenomics and Radiomics in Liver Cancers

Diagnostics ◽  
2018 ◽  
Vol 9 (1) ◽  
pp. 4 ◽  
Author(s):  
Aman Saini ◽  
Ilana Breen ◽  
Yash Pershad ◽  
Sailendra Naidu ◽  
M. Knuttinen ◽  
...  
Keyword(s):  
Clinical Decision Making ◽  
Clinical Information ◽  
Clinical Decision ◽  
Molecular Profile ◽  
Imaging Features ◽  
Imaging Data ◽  
Prognostic Information ◽  
Cross Sectional ◽  
Related Field ◽  
Liver Cancers

Radiogenomics is a computational discipline that identifies correlations between cross-sectional imaging features and tissue-based molecular data. These imaging phenotypic correlations can then potentially be used to longitudinally and non-invasively predict a tumor’s molecular profile. A different, but related field termed radiomics examines the extraction of quantitative data from imaging data and the subsequent combination of these data with clinical information in an attempt to provide prognostic information and guide clinical decision making. Together, these fields represent the evolution of biomedical imaging from a descriptive, qualitative specialty to a predictive, quantitative discipline. It is anticipated that radiomics and radiogenomics will not only identify pathologic processes, but also unveil their underlying pathophysiological mechanisms through clinical imaging alone. Here, we review recent studies on radiogenomics and radiomics in liver cancers, including hepatocellular carcinoma, intrahepatic cholangiocarcinoma, and metastases to the liver.

scholarly journals Information needs for the OR and PACU electronic medical record

2014 ◽  
Vol 05 (03) ◽  
pp. 630-641 ◽  
Author(s):  
V. Herasevich ◽  
J.R. Hebl ◽  
M.J. Brown ◽  
B.W. Pickering ◽  
M.A. Ellsworth
Keyword(s):  
Medical Record ◽  
Electronic Medical Record ◽  
Medical Error ◽  
Information Needs ◽  
Clinical Decision Making ◽  
Information Overload ◽  
Clinical Information ◽  
Clinical Decision ◽  
Anesthesia Providers ◽  
Survey Responses

Summary Objective: The amount of clinical information that anesthesia providers encounter creates an environment for information overload and medical error. In an effort to create more efficient OR and PACU EMR viewer platforms, we aimed to better understand the intraoperative and post-anesthesia clinical information needs among anesthesia providers. Materials and Methods: A web-based survey to evaluate 75 clinical data items was created and distributed to all anesthesia providers at our institution. Participants were asked to rate the importance of each data item in helping them make routine clinical decisions in the OR and PACU settings. Results: There were 107 survey responses with distribution throughout all clinical roles. 84% of the data items fell within the top 2 proportional quarters in the OR setting compared to only 65% in the PACU. Thirty of the 75 items (40%) received an absolutely necessary rating by more than half of the respondents for the OR setting as opposed to only 19 of the 75 items (25%) in the PACU. Only 1 item was rated by more than 20% of respondents as not needed in the OR compared to 20 data items (27%) in the PACU. Conclusion: Anesthesia providers demonstrate a larger need for EMR data to help guide clinical decision making in the OR as compared to the PACU. When creating EMR platforms for these settings it is important to understand and include data items providers deem the most clinically useful. Minimizing the less relevant data items helps prevent information overload and reduces the risk for medical error. Citation: Herasevich V, Ellsworth MA, Hebl JR, Brown MJ, Pickering BW. Information needs for the OR and PACU electronic medical record. Appl Clin Inf 2014; 5: 630–641http://dx.doi.org/10.4338/ACI-2014-02-RA-0015

scholarly journals Health record systems that meet clinical needs

2012 ◽  
Author(s):  
Gabriella Negrini
Keyword(s):  
Health Care ◽  
Decision Making ◽  
Patient Safety ◽  
Health Care Professionals ◽  
Clinical Decision Making ◽  
Clinical Information ◽  
Clinical Decision ◽  
Health Record ◽  
Record System ◽  
Health Records

Introduction Increased attention has recently been focused on health record systems as a result of accreditation programs, a growing emphasis on patient safety, and the increase in lawsuits involving allegations of malpractice. Health-care professionals frequently express dissatisfaction with the health record systems and complain that the data included are neither informative nor useful for clinical decision making. This article reviews the main objectives of a hospital health record system, with emphasis on its roles in communication and exchange among clinicians, patient safety, and continuity of care, and asks whether current systems have responded to the recent changes in the Italian health-care system.Discussion If health records are to meet the expectations of all health professionals, the overall information need must be carefully analyzed, a common data set must be created, and essential specialist contributions must be defined. Working with health-care professionals, the hospital management should define how clinical information is to be displayed and organized, identify a functionally optimal layout, define the characteristics of ongoing patient assessment in terms of who will be responsible for these activities and how often they will be performed. Internet technology can facilitate data retrieval and meet the general requirements of a paper-based health record system, but it must also ensure focus on clinical information, business continuity, integrity, security, and privacy.Conclusions The current health records system needs to be thoroughly revised to increase its accessibility, streamline the work of health-care professionals who consult it, and render it more useful for clinical decision making—a challenging task that will require the active involvement of the many professional classes involved.

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