ImmunoPET imaging of hematological malignancies: From preclinical promise to clinical reality

Immuno-positron emission tomography (immunoPET) imaging is a paradigm-shifting imaging technique for whole-body and all-lesion tumor detection, based on the combined specificity of tumor-targeting vectors [e.g., monoclonal antibodies (mAbs), nanobodies, and bispecific antibodies] and the sensitivity of PET imaging. By noninvasively, comprehensively, and serially revealing heterogeneous tumor antigen expression, immunoPET imaging is gradually improving the theranostic prospects for hematological malignancies. In this review, we summarize the available literature regarding immunoPET in imaging hematological malignancies. We also highlight the pros and cons of current conjugation strategies, and modular chemistry that can be leveraged to develop novel immunoPET probes for hematological malignancies. Lastly, we discuss the use of immunoPET imaging in guiding antibody drug development.     

Prevalence and characteristics of post-gastroscopy gastric cancer: A retrospective study from an academic medical center

Background and Study AimsGastric cancer is diagnosed by endoscopy but false negative rates of up to 10% in the west and 40% in Asia have been reported. In Lebanon, little is known about the rates of post-gastroscopy gastric cancer (PGGC), defined as the proportion of patients diagnosed with gastric cancer with a negative previous examination within 2 years of diagnosis. We aimed to examine the rate of PGGC and its risk factors, clinico-pathologic and endoscopic characteristics at a University medical Center.Patients and MethodsRetrospective analysis of patients with histologically proven gastric malignancy over the last 14 years. Patients with history of upper endoscopy preceding the index diagnostic endoscopy by 6 to 24 months were included.Results18,976 patients underwent upper endoscopy and gastric cancer was diagnosed in 323 (1.7%). Of those, only 4 (1.2%) had a preceding endoscopy within 6 to 24 months of diagnosis: 3 adenocarcinoma and one MALT lymphoma. Upon review of the initial endoscopy, a mucosal abnormality had been noted in all 4 patients and biopsies taken in 3 were negative for cancer. The mean time to cancer diagnosis was 8 months (range 6–13 months).ConclusionA small proportion of gastric carcinomas are missed on endoscopy in this study. Patients with endoscopic evidence of mucosal abnormalities and negative biopsies should undergo repeat examination with multiple biopsies. Proper endoscopic technique, lesion recognition and adoption of performance improvement measures are important to optimize endoscopic practice.     

Machine learning models for Parkinson’s disease detection and stage classification based on spatial-temporal gait parameters

BackgroundParkinson’s disease (PD) is a chronic and progressive neurodegenerative disease with no cure, presenting a challenging diagnosis and management. However, despite a significant number of criteria and guidelines have been proposed to improve the diagnosis of PD and to determine the PD stage, the gold standard for diagnosis and symptoms monitoring of PD is still mainly based on clinical evaluation, which includes several subjective factors. The use of machine learning (ML) algorithms in spatial-temporal gait parameters is an interesting advance with easy interpretation and objective factors that may assist in PD diagnostic and follow up.Research questionThis article studies ML algorithms for: i) distinguish people with PD vs. matched-healthy individuals; and ii) to discriminate PD stages, based on selected spatial-temporal parameters, including variability and asymmetry.MethodsGait data acquired from 63 people with PD with different levels of PD motor symptoms severity, and 63 matched-control group individuals, during self-selected walking speed, was study in the experiments.ResultsIn the PD diagnosis, a classification accuracy of 84.6 %, with a precision of 0.923 and a recall of 0.800, was achieved by the Naïve Bayes algorithm. We found four significant gait features in PD diagnosis: step length, velocity and width, and step width variability. As to the PD stage identification, the Random Forest outperformed the other studied ML algorithms, by reaching an Area Under the ROC curve of 0.786. We found two relevant gait features in identifying the PD stage: stride width variability and step double support time variability.SignificanceThe results showed that the studied ML algorithms have potential both to PD diagnosis and stage identification by analysing gait parameters.     

The effect of different statistical approaches on image quality data obtained from radiological examinations

IntroductionSelection of optimal image acquisition protocols in medical imaging remains a grey area, the superimposed use of the Likert scale in radiological image quality evaluations creates an additional challenge for the statistical analysis of image quality data.Using a simulation study, we have trialled a novel approach to analysing radiological image quality Likert scale data.MethodsA simulation study was undertaken where simulated datasets were generated based on the distribution of Likert scale values according to varying image acquisition protocols from a real dataset. Simulated Likert scale values were pooled in four different ways; the mean, median, mode and the summation of patient Likert scale values of which the total was assigned a categorical Likert scale value. Estimates of bias, MAPE and RMSPE were then calculated for all four pooling approaches to determine which method most accurately represented an expert's opinion.ResultsWhen compared to an expert's opinion, the method of summation and categorisation of Likert scale values was most accurate 49 times out of the 114 (43.0%) tests. The mean 28 times out of 114 (24.6%), the median 23 times out of 114 (20.2%) and the mode 17 times out of 114 (14.9%).ConclusionWe conclude that our method of summation and categorisation of Likert scale values is most often the best representation of the simulated data compared to the expert's opinion.Implications for practiceThere is scope to reproduce this simulation study with multiple observers to reflect clinical reality more accurately with the dynamic nature of multiple observers. This also prompts future investigation into other anatomical areas, to see if the same methods produce similar results.     

骨筋膜室综合征诊断研究进展

骨筋膜室综合征是各种原因导致筋膜室内容物增多、容积减小、压力升高,进而引起一系列肌肉神经症状。(Osteofascial Compartment Syndrome,OCS)重在早期诊断、早期切开,但目前临床常用的诊断方法,如根据临床症状“5P”征诊断骨筋膜室综合征有很多局限性,当患者出现“5P”征时往往病情已经进展到晚期,所以限制了临床的应用。OCS属中医学“筋伤”范畴,多属实证。机体受到创伤后必损气血,血不循经,溢于脉外,闭阻经脉不通,影响气机运行,而致肿胀疼痛,出现气滞血瘀,治宜活血消肿、行气止痛。中医药在创伤早期干预会对OCS产生积极影响。下面笔者将目前OCS的诊断方法及中医药研究进行综述。     

“中国科技期刊卓越行动计划”资助期刊特征分析

【目的】 对“中国科技期刊卓越行动计划”资助期刊的特征进行分析,可以了解这些具有较高国际影响力的国内科技期刊的特点,为其他有潜力的科技期刊发展提供参考。【方法】 利用文献调研、网站调研、统计分析等方法对入选科技期刊的主办及主管单位、学科类别、国内外学术影响力等方面进行系统剖析,同时通过SCI收录情况与JCR分区指标对顶层的领军期刊和重点期刊进行重点分析。【结果】 领军期刊和重点期刊项目重点支持英文期刊;以域选刊,重点关注优先建设领域;所有期刊均被SCI收录,主要引证指标表现优秀。梯队期刊兼顾中、英文期刊;资助数量最多,覆盖学科领域较广;整体国际学术影响力不高,但其中的中文期刊具有较高的国内影响力。高起点新刊项目主要支持英文期刊,关注优先建设领域。【结论】 “中国科技期刊卓越行动计划”资助期刊在国际影响力、主要引证指标、出版体系等方面呈现出尖兵引领、梯次跟进的态势,同层次期刊可以逐步探索良性的竞争与合作模式,冲击更优、更强的国际一流期刊;未受到资助的期刊可参照相应的指标不断追跑,从而促进我国科技期刊体系的持续健康发展。