Retrospective study of sputum cytology in primary adenocarcinoma of the lung detected by chest X-ray film in population-based mass screening.

INTRODUCTION: Various chest X-ray scoring systems have been discovered and are employed to correlate with clinical severity, outcome and progression of diseases. With, the coronavirus outbreak, few chest radiograph classication were formulated, like the BSTI classication and the Brixia chest X-ray score. Brixia CXR scoring is used for assessing the clinical severity and outcome of COVID-19. This study aims to compare the Brixia CXR score with clinical severity of COVID-19 patients. MATERIAL& METHODS:This was a retrospective study in which medical records of patients aged 18 years or above, who tested for RTPCR or st st Rapid Antigen Test (RAT) for COVID positive from 1 February 2021 to 31 July 2021 (6 months) were taken. These subjects were stratied into mild, moderate and severe patients according to the ICMR guidelines. Chest X Rays were obtained and lesions were classied according to Brixia scoring system. RESULTS: Out of these 375 patients, 123 (32.8%) were female and 252 (67.2%) were male subjects. The average brixia score was 11.12. Average Brixia CXR score for mild, moderate and severe diseased subjects were 5.23, 11.20, and 14.43 respectively. DISCUSSION:The extent of chest x-ray involvement is proportional to the clinical severity of the patient. Although, a perplexing nding was that the average Brixia score of the female subjects were slightly higher than their male counterparts in the same clinical groups. CONCLUSION: Brixia CXR score correlates well with the clinical severity of the COVID-19.

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Correlation between chest x-ray findings and outcomes of patients with mechanical ventilation

Paediatrica Indonesiana ◽

10.14238/pi53.1.2013.6-11 ◽

2013 ◽

Vol 53 (1) ◽

pp. 6

Author(s):

Indah Nurhayati ◽

Muhammad Supriatna ◽

Kamilah Budhi Raharjani ◽

Eddy Sudijanto

Keyword(s):

Mechanical Ventilation ◽

Retrospective Study ◽

Pleural Effusion ◽

Respiratory Distress ◽

Pulmonary Edema ◽

Patient Outcomes ◽

Morbidity And Mortality ◽

Chi Square ◽

X Ray ◽

Chest X Ray

Background Most infants and children admitted to the pediatricintensive care unit (PICU) have respiratory distress and pulmonarydisease as underlying conditions. Mechanical ventilation may beused to limit morbidity and mortality in children with respiratoryfailure.Objective To assess a correlation between chest x-ray findingsand outcomes of patients with mechanical ventilation.Methods This retrospective study was held in Dr. KariadiHospital, Semarang, Indonesia. Data was collected from themedical records of children admitted to the PICU from Januaryto December 2010, who suffered from respiratory distress andused mechanical ventilation. We compared chest x-ray findings tothe outcomes of patients. Radiological expertise was provided byradiologists on duty at the time. Chi-square and logistic regressiontests were used for statistical analysis.Results There were 63 subjects in our study, consisting of 28 malesand 35 females. Patient outcomes were defined as survived or died,43 subjects ( 68%) and 20 subjects (3 2%), respectively. Chest x-rayfindings revealed the following conditions: bronchopneumonia48% (P=0.298; 95%CI 0.22 to 1.88), pleural effusion 43%(P=0.280; 95%CI 0.539 to 4.837) , pulmonary edema 6%(P=0.622; 95%CI 0.14 to 14.62) and atelectasis 3% (P=0.538;95%CI 0.03 to 7 .62). None of the chest x-ray findings significantlycorrelated to patient outcomes.Conclusion Chest x-ray findings do not correlate to patientoutcomes in pediatric subjects with mechanical ventilation inthe PICU of Dr. Kariadi Hospital, Semarang, Indonesia.

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Chest x-ray findings and outcomes of children with suspected ventilator .. associated pneumonia

Paediatrica Indonesiana ◽

10.14238/pi52.4.2012.233-8 ◽

2012 ◽

Vol 52 (4) ◽

pp. 233

Author(s):

Neni Sumarni ◽

Muhammad Sholeh Kosim ◽

Mohammad Supriatna ◽

Eddy Sudijanto

Keyword(s):

Retrospective Study ◽

Patient Outcomes ◽

Pulmonary Infection ◽

Ventilator Associated Pneumonia ◽

Chi Square ◽

X Ray ◽

Chest X Ray ◽

Hospital Acquired ◽

Pediatric Icu ◽

The Relationship

Background Ventilator􀁖associated pneumonia (VAP) is anosocomial infection in patients who have received mechanicalventilation (MV), either by endotracheal intubation ortracheostomy, for more than 48 hours. YAP represents 80% ofall hospital􀁖acquired pneumonias. VAP incidence varies from5.1 %􀁖33.3%. The modified clinical pulmonary infection scoreis a criteria for diagnosing suspected YAP and typically includesradiographic evidence. YAP is associated with significantmorbidity and mortality.Objective To determine the relationship between chest x􀁖rayfindings and outcomes in children Mth suspected VAP.Methods This retrospective study was held in Dr. Kariadi Hospitalfrom January - December 2010. Data was collected from medicalrecords of pediatric ICU (PICU) patients with suspected VAP.Chest x􀁖ray findings and patient outcomes were recorded. X􀁖rayfindings were assessed by the on􀁖duty radiologist. Chi square testwas used for statistical analysis.Results Subjects were 30 children consisting of 14 males and 16females. Patient outcomes were 23 patients survived and 7 patientsdied. Chest x􀁖ray findings were categorized into the followinggroups and compared to patient survivability: diffuse infiltrates76.7% (OR􀁗0.694; P􀁗0.532; 95% CI 0.102 to 4.717), localhedinfiltrates 13.3% (OR􀁗4.200; P􀁗 0.225; 95% CI 0.470 t037.49),and no infiltrates 10% (OR􀁗 1.222; P􀁗 0.436; 95% CI 0.593 to0.926). None of the x􀁖ray findings had a significant correlationto patient outcomes.Conclusion There was no significant relationship between chestx􀁖ray findings and outcomes in children with suspected VAP.[Paediatr rndones. 2012;52:233-8].

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Is Routine Chest X-ray After Ultrasound and Intracavitary Electrocardiogram Guided Peripherally Inserted Central Catheter Insertion Choosing Wisely? A Population-based Retrospective Study of 2857 Patients With Cancer

10.21203/rs.3.rs-711193/v1 ◽

2021 ◽

Author(s):

Yan-Fen Shen ◽

Jing Dong ◽

Xin-Peng Wang ◽

Xiao-Zheng Wang ◽

Yuan-Yuan Zheng ◽

...

Keyword(s):

Venous Access ◽

Population Based ◽

X Ray ◽

Patients With Cancer ◽

Potential Risk Factors ◽

Catheter Tip ◽

Chest X Ray ◽

Clinical Records ◽

The Cost ◽

Routine Chest

Abstract Background: In China, routine chest X-ray (CXR) is generally required for peripherally inserted central venous catheters (PICC) to determine the position of the catheter tip. The aim of this study is to assess the value of a routine post-procedural CXR in the era of ultrasound and intracavitary electrocardiography(IC-ECG) -guided PICC insertion. Methods: A retrospective population-based cohort study was conducted to review the clinical records of all patients who had PICCs in the Venous Access Center of Beijing Cancer Hospital between January 1, 2019 and June 30, 2020. The incidence of catheter misplacement after insertion was measured. A logistic regression analysis was performed to examine potential risk factors associated with PICC-related complications and a cost analysis to assess the economic impact of the use of CXR.Results: There were 2,857 samples from 2,647 patients included. The overall incidence of intraoperative and postoperative catheter misplacement was 7.4% (n=210) and 0.67% (n=19), respectively. There was a high risk of postoperative catheter misplacement when the left-arm was chosen for placement (OR: 10.478; 95% CI: 3.467-31.670; p<0.001). The cost of performing CXR for screening of PICC-related complications was $23,808 per year, and that of using CXR to diagnose one case of catheter misplacement was $1253.Conclusion: This study confirms that misplacement of PICCs guided by ultrasound and IC-ECG is rare and that postoperative CXR is very costly. In our setting, routine postoperative CXR is unnecessary and not a wise option.

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Clinical Relevance of Sputum Cytology and Chest X-Ray in Patients with Suspected Lung Tumors

Internal Medicine ◽

10.2169/internalmedicine.47.0777 ◽

2008 ◽

Vol 47 (13) ◽

pp. 1199-1205 ◽

Cited By ~ 6

Author(s):

Mitsuhiro Sumitani ◽

Nobuhide Takifuji ◽

Shigeki Nanjyo ◽

Yumiko Imahashi ◽

Hidemi Kiyota ◽

...

Keyword(s):

Clinical Relevance ◽

Lung Tumors ◽

Sputum Cytology ◽

X Ray ◽

Chest X Ray

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Automated segmentation and diagnosis of pneumothorax on chest X-rays with fully convolutional multi-scale ScSE-DenseNet: a retrospective study

BMC Medical Informatics and Decision Making ◽

10.1186/s12911-020-01325-5 ◽

2020 ◽

Vol 20 (S14) ◽

Author(s):

Qingfeng Wang ◽

Qiyu Liu ◽

Guoting Luo ◽

Zhiqin Liu ◽

Jun Huang ◽

...

Keyword(s):

Retrospective Study ◽

Automatic Segmentation ◽

Medical Emergency ◽

Dice Similarity Coefficient ◽

X Rays ◽

X Ray ◽

Learning Framework ◽

Multi Scale ◽

Life Threatening ◽

Chest X Ray

Abstract Background Pneumothorax (PTX) may cause a life-threatening medical emergency with cardio-respiratory collapse that requires immediate intervention and rapid treatment. The screening and diagnosis of pneumothorax usually rely on chest radiographs. However, the pneumothoraces in chest X-rays may be very subtle with highly variable in shape and overlapped with the ribs or clavicles, which are often difficult to identify. Our objective was to create a large chest X-ray dataset for pneumothorax with pixel-level annotation and to train an automatic segmentation and diagnosis framework to assist radiologists to identify pneumothorax accurately and timely. Methods In this study, an end-to-end deep learning framework is proposed for the segmentation and diagnosis of pneumothorax on chest X-rays, which incorporates a fully convolutional DenseNet (FC-DenseNet) with multi-scale module and spatial and channel squeezes and excitation (scSE) modules. To further improve the precision of boundary segmentation, we propose a spatial weighted cross-entropy loss function to penalize the target, background and contour pixels with different weights. Results This retrospective study are conducted on a total of eligible 11,051 front-view chest X-ray images (5566 cases of PTX and 5485 cases of Non-PTX). The experimental results show that the proposed algorithm outperforms the five state-of-the-art segmentation algorithms in terms of mean pixel-wise accuracy (MPA) with $$0.93\pm 0.13$$ 0.93 ± 0.13 and dice similarity coefficient (DSC) with $$0.92\pm 0.14$$ 0.92 ± 0.14 , and achieves competitive performance on diagnostic accuracy with 93.45% and $$F_1$$ F 1 -score with 92.97%. Conclusion This framework provides substantial improvements for the automatic segmentation and diagnosis of pneumothorax and is expected to become a clinical application tool to help radiologists to identify pneumothorax on chest X-rays.

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Network for subclinical prognostication of COVID 19 Patients from data of thoracic roentgenogram: A feasible alternative screening technology

10.1101/2020.09.07.20189852 ◽

2020 ◽

Author(s):

Akash Bararia ◽

Abhirup Ghosh ◽

Chiranjit Bose ◽

Debarati Bhar

Keyword(s):

Neural Network ◽

Prediction Model ◽

Mass Screening ◽

Rt Pcr ◽

Case Record ◽

Case Record Form ◽

X Ray ◽

Diagnostic Facility ◽

Cheap Alternative ◽

Chest X Ray

Background and Study Aim: COVID 19 is the terminology driving peoples life in the year 2020 without a supportive globally high mortality rate. Coronavirus lead pandemic is a new found disease with no gold standard diagnostic and therapeutic guideline across the globe. Amidst this scenario our aim is to develop a prediction model that makes mass screening easy on par with reducing strain on hospitals diagnostic facility and doctors alike. For this prediction model, a neural network based on Chest X-ray images has been developed. Alongside the aim is also to generate a case record form that would include prediction model result along with few other subclinical factors for generating disease identification. Once found positive then only it will proceed to RT-PCR for final validation. The objective was to provide a cheap alternative to RT-PCR for mass screening and to reduced burden on diagnostic facility by keeping RT-PCR only for final confirmation. Methods: Datasets of chest X-ray images gathered from across the globe has been used to test and train the network after proper dataset curing and augmentation. Results: The final neural network-based prediction model showed an accuracy of 81% with sensitivity of 82% and specificity of 90%. The AUC score obtained is 93.7%. Discussion and Conclusion: The above results based on the existing datasets showcase our model capability to successfully distinguish patients based on Chest X-ray (a non-invasive tool) and along with the designed case record form it can significantly contribute in increasing hospitals monitoring and health care capability.

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