Pub Date : 2023-01-20DOI: 10.1007/s42058-023-00115-y
F. Calderazzi, Piergiulio Valenti, A. Visigalli, Matteo Migliore, P. Bastia, M. De Filippo
{"title":"Insidious fractures of the articulating portion of the radial head: how to detect them using computed tomography","authors":"F. Calderazzi, Piergiulio Valenti, A. Visigalli, Matteo Migliore, P. Bastia, M. De Filippo","doi":"10.1007/s42058-023-00115-y","DOIUrl":"https://doi.org/10.1007/s42058-023-00115-y","url":null,"abstract":"","PeriodicalId":10059,"journal":{"name":"Chinese Journal of Academic Radiology","volume":"49 1","pages":"57-64"},"PeriodicalIF":0.0,"publicationDate":"2023-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73305533","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-07DOI: 10.1007/s42058-022-00112-7
Yan Zhou, Yong-Kang Xu, Di Geng, Guo-Yi Su, Xingbiao Chen, Y. Si, Meiyan Shen, Xiao-Quan Xu, F. Wu
{"title":"Added value of quantitative parameters derived from dual-layer spectral detector computed tomography for diagnosing metastatic cervical lymph nodes in patients with papillary thyroid cancer","authors":"Yan Zhou, Yong-Kang Xu, Di Geng, Guo-Yi Su, Xingbiao Chen, Y. Si, Meiyan Shen, Xiao-Quan Xu, F. Wu","doi":"10.1007/s42058-022-00112-7","DOIUrl":"https://doi.org/10.1007/s42058-022-00112-7","url":null,"abstract":"","PeriodicalId":10059,"journal":{"name":"Chinese Journal of Academic Radiology","volume":"59 1","pages":"32-40"},"PeriodicalIF":0.0,"publicationDate":"2023-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81925838","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1007/s42058-023-00116-x
Yuan-Cheng Wang, Shu-Hang Zhang, Wen-Hui Lv, Wei-Lang Wang, Shan Huang, Yue Qiu, Jian-Feng Xie, Yi Yang, Shenghong Ju
Background: Acute respiratory distress syndrome (ARDS) is a critical disease in the intensive care unit (ICU) with high morbidity and mortality. The accuracy for predicting ARDS patients' outcome with mechanical ventilation is limited, and most based on clinical information.
Methods: The patients diagnosed with ARDS between January 2014 and June 2019 were retrospectively recruited. Radiomics features were extracted from the upper, middle, and lower levels of the lung, and were further analyzed with the primary outcome (28-day mortality after ARDS onset). The univariate and multivariate logistic regression analyses were applied to figure out risk factors. Various predictive models were constructed and compared.
Results: Of 366 ARDS patients recruited in this study, 276 (median age, 64 years [interquartile range, 54-75 years]; 208 male) survive on the Day 28. Among all factors, the APACHE II Score (OR 2.607, 95% CI 1.896-3.584, P < 0.001), the Radiomics_Score of the middle lung (OR 2.230, 95% CI 1.387-3.583, P = 0.01), the Radiomics_Score of the lower lung (OR 1.633, 95% CI 1.143-2.333, P = 0.01) were associated with the 28-day mortality. The clinical_radiomics predictive model (AUC 0.813, 95% CI 0.767-0.850) show the best performance compared with the clinical model (AUC 0.758, 95% CI 0.710-0.802), the radiomics model (AUC 0.692, 95% CI 0.641-0.739) and the various ventilator parameter-based models (highest AUC 0.773, 95% CI 0.726-0.815).
Conclusions: The radiomics features of chest CT images have incremental values in predicting the 28-day mortality in ARDS patients with mechanical ventilation.
Supplementary information: The online version contains supplementary material available at 10.1007/s42058-023-00116-x.
背景:急性呼吸窘迫综合征(Acute respiratory distress syndrome, ARDS)是重症监护病房(ICU)中一种发病率和死亡率高的危重疾病。预测ARDS患者机械通气预后的准确性是有限的,而且大多基于临床信息。方法:回顾性招募2014年1月至2019年6月诊断为ARDS的患者。从肺的上、中、下水平提取放射组学特征,并进一步分析主要结局(ARDS发病后28天死亡率)。采用单因素和多因素logistic回归分析找出危险因素。建立了各种预测模型并进行了比较。结果:在本研究招募的366例ARDS患者中,276例(中位年龄64岁[四分位数间距54-75岁];208只(雄性)存活到第28天。在所有因素中,APACHE II评分(OR 2.607, 95% CI 1.896 ~ 3.584, P P = 0.01)、下肺Radiomics_Score (OR 1.633, 95% CI 1.143 ~ 2.333, P = 0.01)与28天死亡率相关。与临床模型(AUC 0.758, 95% CI 0.710-0.802)、放射组学模型(AUC 0.692, 95% CI 0.641-0.739)和各种基于呼吸机参数的模型(最高AUC 0.773, 95% CI 0.726-0.815)相比,clinical_radiomics预测模型(AUC 0.813, 95% CI 0.767-0.850)表现最佳。结论:胸部CT影像放射组学特征在预测机械通气ARDS患者28天死亡率方面具有递增价值。补充信息:在线版本包含补充资料,下载地址为10.1007/s42058-023-00116-x。
{"title":"Added value of chest CT images to a personalized prognostic model in acute respiratory distress syndrome: a retrospective study.","authors":"Yuan-Cheng Wang, Shu-Hang Zhang, Wen-Hui Lv, Wei-Lang Wang, Shan Huang, Yue Qiu, Jian-Feng Xie, Yi Yang, Shenghong Ju","doi":"10.1007/s42058-023-00116-x","DOIUrl":"https://doi.org/10.1007/s42058-023-00116-x","url":null,"abstract":"<p><strong>Background: </strong>Acute respiratory distress syndrome (ARDS) is a critical disease in the intensive care unit (ICU) with high morbidity and mortality. The accuracy for predicting ARDS patients' outcome with mechanical ventilation is limited, and most based on clinical information.</p><p><strong>Methods: </strong>The patients diagnosed with ARDS between January 2014 and June 2019 were retrospectively recruited. Radiomics features were extracted from the upper, middle, and lower levels of the lung, and were further analyzed with the primary outcome (28-day mortality after ARDS onset). The univariate and multivariate logistic regression analyses were applied to figure out risk factors. Various predictive models were constructed and compared.</p><p><strong>Results: </strong>Of 366 ARDS patients recruited in this study, 276 (median age, 64 years [interquartile range, 54-75 years]; 208 male) survive on the Day 28. Among all factors, the APACHE II Score (OR 2.607, 95% CI 1.896-3.584, <i>P</i> < 0.001), the Radiomics_Score of the middle lung (OR 2.230, 95% CI 1.387-3.583, <i>P</i> = 0.01), the Radiomics_Score of the lower lung (OR 1.633, 95% CI 1.143-2.333, <i>P</i> = 0.01) were associated with the 28-day mortality. The clinical_radiomics predictive model (AUC 0.813, 95% CI 0.767-0.850) show the best performance compared with the clinical model (AUC 0.758, 95% CI 0.710-0.802), the radiomics model (AUC 0.692, 95% CI 0.641-0.739) and the various ventilator parameter-based models (highest AUC 0.773, 95% CI 0.726-0.815).</p><p><strong>Conclusions: </strong>The radiomics features of chest CT images have incremental values in predicting the 28-day mortality in ARDS patients with mechanical ventilation.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s42058-023-00116-x.</p>","PeriodicalId":10059,"journal":{"name":"Chinese Journal of Academic Radiology","volume":"6 1","pages":"47-56"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9884509/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9254833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1007/s42058-022-00109-2
Bambang Satoto, Thomas Handoyo, Maya Nuriya Widya Sari, Antonius Gunawan Santoso, Nicolaus Erlangga Prasetyo
Introduction: The chest X-ray examination is an imaging modality that is widely used in screening for COVID-19 pneumonia. The problems with treating COVID-19 pneumonia patients are the high incidence and severity of the disease and the limitations of treatment room facilities. The chest X-ray Radiologic Severity Index is expected to assist clinicians in obtaining the relationship between the extent of lesions on X-ray and the duration of treatment and hospitalization for COVID-19 pneumonia patients.
Results: This study used an observational method using a retrospective approach. The research subjects were COVID-19 pneumonia patients from March 2020 to April 2021 who were hospitalized at Doctor Kariadi General Hospital Semarang. A total of 105 subjects confirmed positive RT-PCR and received serial X-ray examination services during treatment. The calculation of the RSI value was carried out on all X-ray chest X-rays and then statistically analyzed using the paired T test and Mann-Whitney methods. There was no significant relationship between the value of RSI1 and the duration of hospitalization with p = 0.566, as well as the value of RSI2 with the duration of hospitalization with p = 0.715. There is a significant relationship between the values of RSI1 and RSI2 with the use of the intensive care unit with p < 0.000, respectively. There was a significant relationship between the values of RSI1 and RSI2 with the use of ventilators in treatment, with p < 0.000. Furthermore RSI1 and RSI2 have a good result as predictor of intensive care and ventilator usage.
Conclusion: The chest X-ray RSI has no significant relationship with the duration of hospitalization. The value of the chest X-ray RSI has a significant relationship with the use of intensive care rooms and the use of ventilators in treatment. The increase in the RSI value can describe the severity of the disease so that it plays a role in planning the treatment room.
{"title":"The chest X-ray radiologic severity index as a determinant of the severity of COVID-19 pneumonia: study based on the duration of treatment and inpatient rooms.","authors":"Bambang Satoto, Thomas Handoyo, Maya Nuriya Widya Sari, Antonius Gunawan Santoso, Nicolaus Erlangga Prasetyo","doi":"10.1007/s42058-022-00109-2","DOIUrl":"https://doi.org/10.1007/s42058-022-00109-2","url":null,"abstract":"<p><strong>Introduction: </strong>The chest X-ray examination is an imaging modality that is widely used in screening for COVID-19 pneumonia. The problems with treating COVID-19 pneumonia patients are the high incidence and severity of the disease and the limitations of treatment room facilities. The chest X-ray Radiologic Severity Index is expected to assist clinicians in obtaining the relationship between the extent of lesions on X-ray and the duration of treatment and hospitalization for COVID-19 pneumonia patients.</p><p><strong>Results: </strong>This study used an observational method using a retrospective approach. The research subjects were COVID-19 pneumonia patients from March 2020 to April 2021 who were hospitalized at Doctor Kariadi General Hospital Semarang. A total of 105 subjects confirmed positive RT-PCR and received serial X-ray examination services during treatment. The calculation of the RSI value was carried out on all X-ray chest X-rays and then statistically analyzed using the paired <i>T</i> test and Mann-Whitney methods. There was no significant relationship between the value of RSI1 and the duration of hospitalization with <i>p</i> = 0.566, as well as the value of RSI2 with the duration of hospitalization with <i>p</i> = 0.715. There is a significant relationship between the values of RSI1 and RSI2 with the use of the intensive care unit with <i>p</i> < 0.000, respectively. There was a significant relationship between the values of RSI1 and RSI2 with the use of ventilators in treatment, with <i>p</i> < 0.000. Furthermore RSI1 and RSI2 have a good result as predictor of intensive care and ventilator usage.</p><p><strong>Conclusion: </strong>The chest X-ray RSI has no significant relationship with the duration of hospitalization. The value of the chest X-ray RSI has a significant relationship with the use of intensive care rooms and the use of ventilators in treatment. The increase in the RSI value can describe the severity of the disease so that it plays a role in planning the treatment room.</p>","PeriodicalId":10059,"journal":{"name":"Chinese Journal of Academic Radiology","volume":"6 1","pages":"10-17"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9716507/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10758475","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-28DOI: 10.1007/s42058-022-00113-6
S. Biswas, S. Biswas, S. Awal, Hitesh Goyal
{"title":"Artificial intelligence & deep learning for the radiologist: a simple updated guide without the maths","authors":"S. Biswas, S. Biswas, S. Awal, Hitesh Goyal","doi":"10.1007/s42058-022-00113-6","DOIUrl":"https://doi.org/10.1007/s42058-022-00113-6","url":null,"abstract":"","PeriodicalId":10059,"journal":{"name":"Chinese Journal of Academic Radiology","volume":"72 1","pages":"7-9"},"PeriodicalIF":0.0,"publicationDate":"2022-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86331469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-19DOI: 10.1007/s42058-022-00105-6
Huadan Xue, Ming He, Zaiyi Liu, Xinming Zhao, Min Chen, Zhengyu Jin
{"title":"Chinese expert recommendation of scanning protocol and clinical application of magnetic resonance cholangiopancreatography","authors":"Huadan Xue, Ming He, Zaiyi Liu, Xinming Zhao, Min Chen, Zhengyu Jin","doi":"10.1007/s42058-022-00105-6","DOIUrl":"https://doi.org/10.1007/s42058-022-00105-6","url":null,"abstract":"","PeriodicalId":10059,"journal":{"name":"Chinese Journal of Academic Radiology","volume":"4 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2022-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78363647","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-02DOI: 10.1007/s42058-022-00110-9
Bita Soltanpour, A. Akhgar, M. Jalili
{"title":"Chest computed tomography scan alters the management plan in multiple trauma patients with a prior chest X-ray","authors":"Bita Soltanpour, A. Akhgar, M. Jalili","doi":"10.1007/s42058-022-00110-9","DOIUrl":"https://doi.org/10.1007/s42058-022-00110-9","url":null,"abstract":"","PeriodicalId":10059,"journal":{"name":"Chinese Journal of Academic Radiology","volume":"10 1","pages":"82 - 88"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82359389","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}