Kui Jiang

The clinical value of combination of immune checkpoint inhibitors in cancer patients: A meta‐analysis of efficacy and safety

The use of immune checkpoint inhibitors (ICIs) in combination therapy is an emerging trend in tumor immunology. However, the value of combination immunotherapy remains controversial, because of the toxic effects induced by combination. The added benefit of each additional drug has not been assessed against the added toxicity. We searched for clinical trials that evaluated ICI monotherapies and combination therapies in lung cancer and melanoma patients. The overall response rate (ORR), grade 3/4 treatment‐related adverse event rate, overall survival (OS), and progression‐free survival (PFS) were extracted from the most recently published studies to determine the relative risk (RR), hazard ratios (HRs), and 95% confidence intervals (CIs). Seven randomized controlled trials and one open‐label study were identified (n = 3,097). Treatments included combinations of several ICIs, a combination of an ICI and dacarbazine, two combinations of an ICI, paclitaxel and carboplatin, and a combination of an ICI and gp100 vaccine. Higher ORR (RR: 1.51, 95% CI: 1.03–2.20, p = 0.034), OS (HR: 0.86, 95% CI: 0.78–0.95, p = 0.000), and PFS (HR: 0.93, 95% CI: 0.72–1.14, p = 0.000) values were observed in combination therapy than in monotherapy. In addition, the toxicity of combination ICI immunotherapy was higher (RR: 1.50, 95% CI: 1.03–2.19, p = 0.036) than that of monotherapy. This meta‐analysis showed that the addition of nivolumab to ipilimumab better benefits PFS and ORR. Adding sargramostim was associated with better OS and safety. The efficacy and safety of a nivolumab‐ipilimumab‐sargramostim combination should be investigated further.

The Association of Haptoglobin Gene Variants and Retinopathy in Type 2 Diabetic Patients: A Meta-Analysis

Aims/Introduction To collectively evaluate the association between haptoglobin (Hp) gene variants and diabetic retinopathy (DR) in patients with type 2 diabetes mellitus (T2DM). Methods A comprehensive literature review was performed for eligible studies. After inclusion and exclusion selection as well as quality assessment, those studies meeting quality standards were included. In this study, diabetic patients with retinopathy were selected as the case group and those ones without DR were treated as the control group. The recessive model, allele model, additive model, heterozygote model, and homozygote model were utilized to investigate the association of three Hp gene variants and DR. Subgroup analysis on different severity of DR including nonproliferative diabetic retinopathy (NPDR) and proliferative diabetic retinopathy (PDR) was also conducted. Results Six trials from different regions were finally included. A total of 1145 subjects containing 564 T2DM patients with retinopathy were included. The recessive model, allele model, additive model, and homozygote model results showed that Hp gene variants were not associated with DR, NPDR, and PDR. However, the heterozygote model indicated the association of Hp gene variants with DR. Conclusions No association was found between the Hp gene variants and PDR and NPDR. More studies are required to verify these findings.

Negation Detection in Chinese Electronic Medical Record Based on Rules and Word Co-occurrence

In order to extract negative terminologies in Chinese Electronic Record. Many methods have been developed. One popular and simple method is based on rules. However, the negative predictive value drops significant if the sentence contains several kinds of punctuation. In our research, a new method is used to solve the problem. The new method combines rules with word co-occurrence. In the experiments, 200 medical texts including 150,865 Chinese characters are used to test the new method. The negative predictive value is 99.85 %, which is 7.85 % higher than the rule-based method. That is to say, this method can tolerate various kinds of punctuations existing in the sentences. Therefore, the value of false-positive probability drops obviously.

A self-adaptive distance regularized level set evolution method for optical disk segmentation

The optic disc (OD) is one of the important anatomic structures on the retina, the changes of which shape and area may indicate disease processes, thus needs computerized quantification assistance. In this study, we proposed a self-adaptive distance regularized level set evolution method for OD segmentation without the periodically re-initializing steps in the level set function execution to a signed distance function during the evolution. In that framework, preprocessing of an image was performed using Fourier correlation coefficient filtering to obtain initial boundary as the beginning contour, then, an accurate boundary of the optic disc was obtained using the self-adaptive distance regularized level set evolution method. One hundred eye fundus color numerical images from public database were selected to validate our algorithm. Therefore, we believe that such automatic OD segmentation method could assist the ophthalmologist to segment OD more efficiently, which is of significance for future computer-aided early detection of glaucoma and retinopathy diseases.

The Development of a Smart Personalized Evidence Based Medicine Diabetes Risk Factor Calculator

Type 2 diabetes mellitus (T2DM) is a chronic disease affected with complex risk factors and has been regarded as one of the major social burdens due to its high occurrence. In this study, we aim to incorporate the idea of evidence based medicine (EBM) into our diabetes risk factor App development. We acquired and extracted the relative risk of different risk factors from relevant literature by searching academic databases. A total of 19 items of risk factors in our daily lives has been finally selected. To design App graphic interface, a total of three pages were designed to let user answer the questions and show the results of their level or risk to have T2DM. We validated the feasibility of our App in 100 users and the results were promising. Therefore, the personalized EBM diabetes risk factor calculator might be a feasible approach to remind those T2DM risky populations by revealing their potential risk factors, thus making implementation of personalized and prevention medicine achievable at hand.

iT2DMS: a Standard-Based Diabetic Disease Data Repository and its Pilot Experiment on Diabetic Retinopathy Phenotyping and Examination Results Integration

Type 2 diabetes mellitus (T2DM) is a common chronic disease, and the fragment data collected through separated vendors makes continuous management of DM patients difficult. The lack of standard of fragment data from those diabetic patients also makes the further potential phenotyping based on the diabetic data difficult. Traditional T2DM data repository only supports data collection from T2DM patients, lack of phenotyping ability and relied on standalone database design, limiting the secondary usage of these valuable data. To solve these issues, we proposed a novel T2DM data repository framework, which was based on standards. This repository can integrate data from various sources. It would be used as a standardized record for further data transfer as well as integration. Phenotyping was conducted based on clinical guidelines with KNIME workflow. To evaluate the phenotyping performance of the proposed system, data was collected from local community by healthcare providers and was then tested using algorithms. The results indicated that the proposed system could detect DR cases with an average accuracy of about 82.8%. Furthermore, these results had the promising potential of addressing fragmented data. The proposed system has integrating and phenotyping abilities, which could be used for diabetes research in future studies.

The design and integration of retinal CAD-SR to diabetes patient ePR system

Diabetic retinopathy (DR) is one of the serious complications of diabetes that could lead to blindness. Digital fundus camera is often used to detect retinal changes but the diagnosis relies too much on ophthalmologist’s experience. Based on our previously developed algorithms for quantifying retinal vessels and lesions, we developed a computer aided detection-structured report (CAD-SR) template and implemented it into picture archiving and communication system (PACS). Furthermore, we mapped our CAD-SR into HL7 CDA to integrate CAD findings into diabetes patient electronic patient record (ePR) system. Such integration could provide more quantitative features from fundus image into ePR system, which is valuable for further data mining researches.

Biological fiducial point based registration for multiple brain tissues reconstructed from different imaging modalities

With the development of computer aided navigation system, more and more tissues shall be reconstructed to provide more useful information for surgical pathway planning. In this study, we aimed to propose a registration framework for different reconstructed tissues from multi-modalities based on some fiducial points on lateral ventricles. A male patient with brain lesion was admitted and his brain scans were performed by different modalities. Then, the different brain tissues were segmented in different modality with relevant suitable algorithms. Marching cubes were calculated for three dimensional reconstructions, and then the rendered tissues were imported to a common coordinate system for registration. Four pairs of fiducial markers were selected to calculate the rotation and translation matrix using least-square measure method. The registration results were satisfied in a glioblastoma surgery planning as it provides the spatial relationship between tumors and surrounding fibers as well as vessels. Hence, our framework is of potential value for clinicians to plan surgery.