Comparing Radiomics features of tumour and healthy liver tissue in a limited CT dataset: A machine learning study

IntroductionLiver cancer lesions on Computed Tomography (CT) withholds a great amount of data, which is not visible to the radiologists and radiographer. Radiomics features can be extracted from the lesions and used to train Machine Learning (ML) algorithms to predict between tumour and liver tissue. The purpose of this study was to investigate and classify Radiomics features extracted from liver tumours and normal liver tissue in a limited CT dataset.MethodsThe Liver Tumour Segmentation Benchmark (LiTS) dataset consisting of 131 CT scans of the liver with segmentations of tumour tissue and healthy liver was used to extract Radiomic features. Extracted Radiomic features included size, shape, and location extracted with morphological and statistical techniques according to the International Symposium on Biomedical Imaging manual. Relevant features was selected with chi2 correlation and principal component analysis (PCA) with tumour and healthy liver tissue as outcome according to a consensus between three experienced radiologists. Logistic regression, random forest and support vector machine was used to train and validate the dataset with a 10-fold cross-validation method and the Grid Search as hyper-parameter tuning. Performance was evaluated with sensitivity, specificity and accuracy.ResultsThe performance of the ML algorithms achieved sensitivities, specificities and accuracy ranging from 96.30% (95% CI: 81.03%–99.91%) to 100.00% (95% CI: 86.77%–100.00%), 91.30% (95% CI: 71.96%–98.93%) to 100.00% (95% CI: 83.89%–100.00%)and 94.00% (95% CI: 83.45%–98.75%) to 100.00% (95% CI: 92.45%–100.00%), respectively.ConclusionML algorithms classifies Radiomics features extracted from healthy liver and tumour tissue with perfect accuracy. The Radiomics signature allows for a prognostic biomarker for hepatic tumour screening on liver CT.Implications for practiceDifferentiation between tumour and liver tissue with Radiomics ML algorithms have the potential to increase the diagnostic accuracy, assist in the decision-making of supplementary multiphasic enhanced medical imaging, as well as for developing novel prognostic biomarkers for liver cancer patients.     

保乳术后放疗中钛夹动度与乳腺大小及钛夹位置的相关性

【摘要】目的:分析保乳术后放疗患者钛夹动度，并探讨其与乳腺大小、钛夹位置的相关性。方法:随机选取保乳术后放疗患者15例，每周两次锥形束CT。测量乳腺基底面直径（D）和乳高轴（H），以瘤床质心为原点将临床靶区分4个象限。记录各象限内钛夹在左右、腹背、头脚方向动度（MLR、MAP、MSI）以及其与临床靶区最内、最前、最上的距离（DSLR、DSAP、DSSI）。结果:MLR、MAP、MSI分别为（2.2±3.0）、（-1.1±3.6）、（0.8±4.7） mm；系统误差Σ在左、右、腹、背、头、脚分别为1.7、2.2、2.0、2.4、2.9、3.0 mm，随机误差σ分别为2.4、4.0、3.2、4.0、4.7、4.7 mm；靶区对应外放5.9、8.3、7.2、8.8、10.5、10.9 mm。一象限内，当D×H<99.89 cm2，MAP和D×H强相关（r=0.805），MLR、MAP均和DSLR、DSAP、DSSI强正相关（r=0.94, 0.94, 0.91；0.87, 0.91, 0.92），MSI和DSLR、DSAP强正相关（r=0.91, 0.94）；四象限内，当D×H<90.71 cm2，MAP和DSLR，MSI和DSAP均强负相关（r=-0.96；-0.95），MLR和DSLR强正相关（r=0.91）。结论:钛夹动度有各向异性，以SI方向外扩最大，并易受乳腺大小、钛夹位置影响。     

China special issue on gastrointestinal tumors-Radiological features of pathological complete response in mismatch repair deficient colorectal cancer after neoadjuvant PD-1 blockade: A post hoc analysis of the PICC phase II trial

Neoadjuvant programmed cell death protein 1 (PD-1) blockade exhibits promising efficacy in patients with mismatch repair deficient (dMMR) colorectal cancer (CRC). However, discrepancies between radiological and histological findings have been reported in the PICC phase II trial (NCT 03926338). Therefore, we strived to discern radiological features associated with pathological complete response (pCR) based on computed tomography (CT) images. Data were obtained from the PICC trial that included 36 tumors from 34 locally advanced dMMR CRC patients, who received neoadjuvant PD-1 blockade for 3 months. Among the 36 tumors, 28 (77.8%) tumors achieved pCR. There were no statistically significant differences in tumor longitudinal diameter, the percentage change in tumor longitudinal diameter from baseline, primary tumor sidedness, clinical stage, extramural venous invasion status, intratumoral calcification, peritumoral fat infiltration, intestinal fistula and tumor necrosis between the pCR and non-pCR tumors. Otherwise, tumors with pCR had smaller posttreatment tumor maximum thickness (median: 10 mm vs 13 mm, P = .004) and higher percentage decrease in tumor maximum thickness from baseline (52.9% vs 21.6%, P = .005) compared to non-pCR tumors. Additionally, a higher proportion of the absence of vascular sign (P = .003, odds ratio [OR] = 25.870 [95% CI, 1.357-493.110]), nodular sign (P < .001, OR = 189.000 [95% CI, 10.464-3413.803]) and extramural enhancement sign (P = .003, OR = 21.667 [2.848-164.830]) was observed in tumors with pCR. In conclusion, these CT-defined radiological features may have the potential to serve as valuable tools for clinicians in identifying patients who have achieved pCR after neoadjuvant PD-1 blockade, particularly in individuals who are willing to adopt a watch-and-wait strategy.     

全身性遗传疾病对角膜移植排斥的影响

角膜移植为治疗角膜盲的主要手段，而角膜移植排斥则是决定角膜植片存活时间和病人术后视力的关键。角膜得益于其特殊的眼前节“免疫赦免”状态，使得角膜移植能够在众多器官移植中享有极低的排斥率，然而排斥反应发生的风险依然存在。当机体处于遗传物质异常的特殊状态时，宿主将通过宏观调控“免疫赦免”状态对植片的保护作用或受体对移植物排异产生的有害作用，延迟或促进角膜移植排斥反应的发生，进而影响移植物的存活时间和透明度。该文综述与角膜移植排斥相关的多种全身性遗传疾病，总结全身性遗传疾病对角膜移植排斥的影响，浅析其发生的病理生理学机制以及诊疗的特殊性。     

血清Hcy、IGFBP-1联合预测多囊卵巢综合征患者孕早期自然流产的价值

目的探讨血清同型半胱氨酸(Hcy)、胰岛素样生长因子结合蛋白-1(IGFBP-1)联合检测对多囊卵巢综合征患者孕早期自然流产的预测价值。方法选取2018年3月—2020年5月行促排卵治疗并成功临床妊娠的多囊卵巢综合征165例,检测血清Hcy、IGFBP-1水平。根据孕早期自然流产发生情况分为孕早期自然流产组与未自然流产组,比较两组妊娠前一次促排卵后血清Hcy、IGFBP-1水平;多囊卵巢综合征患者孕早期自然流产的危险因素采用多因素Logistic回归分析,并采用受试者工作特征(ROC)曲线分析血清Hcy、IGFBP-1水平单项及联合检测对多囊卵巢综合征患者孕早期自然流产的预测价值。结果患者妊娠前一次促排卵后血清Hcy水平低于入院时,血清IGFBP-1水平高于入院时(P<0.01);自然流产组妊娠前一次促排卵后血清Hcy水平高于未自然流产组,血清IGFBP-1水平低于未自然流产组(P<0.01)。孕早期自然流产的发生率为28.48%。年龄≥35岁、多囊卵巢综合征分型、空腹胰岛素水平偏高、空腹血糖水平偏高、血清睾酮水平偏高、血清叶酸水平偏低、妊娠前一次促排卵后血清Hcy水平偏高及血清IGFBP-1水平偏低均是多囊卵巢综合征患者孕早期自然流产的危险因素(P<0.01)。妊娠前一次促排卵后血清Hcy水平联合血清IGFBP-1水平预测多囊卵巢综合征患者孕早期自然流产的敏感度、ROC曲线下面积均高于单独预测(P<0.01)。结论多囊卵巢综合征孕早期自然流产患者血清Hcy水平偏高,IGFBP-1水平偏低,二者均与多囊卵巢综合征患者孕早期自然流产密切相关,并对多囊卵巢综合征患者孕早期自然流产的发生具有较好的预测价值,联合检测时的预测效能更高。     

Survival analysis of crown margin repair: A retrospective study in a dental school setting

BackgroundRepairing crowns with defective margins is minimally invasive and cost-effective compared with replacement. The authors’ objectives were to examine the survival trajectory of crown margin repairs and to determine the factors associated with survival.MethodsRecords of adult patients from January 2008 through August 2019 were reviewed for crown margin repairs completed at University of Iowa College of Dentistry. A total of 1,002 crown margin repairs were found. Each repair was followed through the end of study in 2019 or until an event (for example, additional repair, endodontic treatment, crown replacement, or extraction). A Cox proportional hazards model was used to study the relationship between selected covariates and time to event.ResultsDuring the follow-up period, 32.8% of the repairs needed reintervention. In the final model, repair material was the only significant covariate. No difference was found between the survival of repairs done with resin-modified glass ionomer and amalgam. However, the repairs done with resin-based composite and conventional glass ionomer were more likely (1.5 times: 95% CI, 1.02 to 2.10 times; and 2 times: 95% CI, 1.40 to 2.73 times, respectively) to need reintervention than were those done with amalgam.ConclusionsMedian survival time of crown margin repairs was 5.1 years (95% CI, 4.48 to 5.72 years). Median survival times for amalgam, resin-modified glass ionomer, resin-based composite, and glass ionomer repair materials were 5.7 years (95% CI, 4.80 to 6.25 years), 5.3 years (95% CI, 4.73 to 6.34 years), 3.2 years (95% CI, 2.51 to 6.19 years), and 3.0 years (95% CI, 2.53 to 3.62 years), respectively.Practical ImplicationsWhen considering crown margin repairs, resin-modified glass ionomer or amalgam is preferable to resin-based composite or glass ionomer.     

Electrical impedance tomography as a tool for monitoring mechanical ventilation. An introduction to the technique

Electrical impedance tomography (EIT) is a non-invasive, radiation-free method of diagnostics imaging, allowing for a bedside, real-time dynamic assessment of lung function. It stands as an alternative for other imagining methods, such as computed tomography (CT) or ultrasound. Even though the technique is rather novel, it has a wide variety of possible applications. In the era of modern mechanical ventilation, a dynamic assessment of patient's respiratory condition appears to fulfil the idea of personalized treatment. Additionally, an increasing frequency of respiratory failure among intensive care populations raises demand for improved monitoring tools. This review aims to raise awareness and presents possible implications for the use of EIT in the intensive care setting.