Objective To observe the value of hypoperfusion intensity ratio(HIR)of CT perfusion(CTP)for predicting infarct core progression and prognosis of acute ischemic stroke(AIS).Methods Totally 271 AIS patients were retrospectively enrolled and divided into rapid progression group(group A,n=92)and slow progression group(group B,n=179)according to infarction growth rate(IGR).Clinical data,CTP parameters,treatment strategies and patients' outcome were compared between groups.Receiver operating characteristic curve was drawn,the area under the curve(AUC)was calculated to evaluate the efficacy of HIR for predicting rapid progression in infarct core of AIS.The mediating relationships among HIR,IGR and modified Rankin scale(mRS)90 days after treatment were analyzed.Results Significant differences of National Institute of Health stroke scale(NIHSS)score,Alberta stroke program early CT score(ASPECTS),also of interval time between onset and CTP,infarct core volume,hypoperfusion volume,HIR,whether intravenous thrombolysis and mRS score 90 days after treatments were found between groups(all P＜0.05).The AUC of HIR for predicting infarct core progression of AIS was 0.856,with sensitivity and specificity was 73.91％and 81.56％,respectively,when the optimal cutoff value was 0.42.IGR was a complete mediating variable between HIR and mRS score 90 days after treatment.Conclusion HIR of CTP could be used to effectively predict infarct core progression of AIS,which completely affected prognosis through mediating variable IGR.

Objective:To assess common errors in head and neck CT angiography (CTA) image reports using four types of large language models (LLM), namely GPT-4, DeepSeek, ERNIE Bot and SparkDesk, and to assess the feasibility of using existing LLMs to support quality control of radiology reports in Chinese.Methods:The study was a cross-sectional study. Totally 1 000 head and neck CTA image reports were randomly selected using the simple random sampling method from Xuanwu Hospital, Capital Medical University in 2023, including 500 primary reports and 500 finalized reports. Two radiologists collaboratively identified six types of errors in the reports: description errors, writing errors, left-right confusion errors, diagnostic omissions, logical sequence errors, and other errors. The overall quality of the reports was assessed using a 5-point Likert scale. Subsequently, GPT-4, DeepSeek, ERNIE Bot and SparkDesk models were employed to detect the same six types of errors in the imaging reports and to provide overall scoring. The results from manual review were considered the gold standard for calculating F1 score to evaluate model performance. Intra-class correlation coefficients ( ICC) were used to assess the consistency between manual scores and the overall scores from the four LLMs. Results:In the primary imaging reports, the proportions of manually detected errors were as follows: descriptive errors 2.6% (13/500), writing errors 0.6% (3/500), left-right confusion errors 0, diagnostic omissions 6.4% (32/500), logical sequence errors 5.2% (26/500), and other errors 0. In the finalized imaging reports, the proportions of errors across the six categories were 0.2% (1/500), 0, 0, 0, 0, and 0.2% (1/500), respectively. For error detection in the primary imaging reports, the F1 scores of GPT-4 for the six error types were 0.992, 0.997, 0.997, 0.967, 0.980, and 0.992, respectively. DeepSeek achieved F1 scores of 0.980, 0.955, 0.981, 0.920, 0.995, and 0.960; ERNIE Bot scored 0.982, 0.990, 1.000, 0.956, 0.976, and 0.999; and SparkDesk achieved 0.985, 0.995, 1.000, 0.961, 0.982, and 1.000. In the detection of errors in finalized imaging reports, GPT-4′s F1 scores were 0.994, 0.995, 0.998, 0.973, 0.989, and 0.993; DeepSeek scored 0.968, 0.965, 0.985, 0.971, 0.991, and 0.983; ERNIE Bot achieved 0.996, 0.992, 1.000, 0.983, 0.999, and 0.997; and SparkDesk achieved 0.999, 0.999, 1.000, 1.000, 1.000, and 0.999. The consistency between GPT-4, DeepSeek, and SparkDesk models and human ratings was moderate, with ICC values of 0.514, 0.560, and 0.515 respectively (all P0.001); in contrast, the overall score of ERNIE Bot showed poor consistency with human ratings, with an ICC of 0.221 ( P0.001). Conclusion:LLMs demonstrate high accuracy in detecting errors in head and neck CTA imaging reports. The overall scoring of report quality shows moderate consistency with manual assessments, indicating a certain feasibility for automated quality control in reporting.

Objective To observe the value of hypoperfusion intensity ratio(HIR)of CT perfusion(CTP)for predicting infarct core progression and prognosis of acute ischemic stroke(AIS).Methods Totally 271 AIS patients were retrospectively enrolled and divided into rapid progression group(group A,n=92)and slow progression group(group B,n=179)according to infarction growth rate(IGR).Clinical data,CTP parameters,treatment strategies and patients' outcome were compared between groups.Receiver operating characteristic curve was drawn,the area under the curve(AUC)was calculated to evaluate the efficacy of HIR for predicting rapid progression in infarct core of AIS.The mediating relationships among HIR,IGR and modified Rankin scale(mRS)90 days after treatment were analyzed.Results Significant differences of National Institute of Health stroke scale(NIHSS)score,Alberta stroke program early CT score(ASPECTS),also of interval time between onset and CTP,infarct core volume,hypoperfusion volume,HIR,whether intravenous thrombolysis and mRS score 90 days after treatments were found between groups(all P＜0.05).The AUC of HIR for predicting infarct core progression of AIS was 0.856,with sensitivity and specificity was 73.91％and 81.56％,respectively,when the optimal cutoff value was 0.42.IGR was a complete mediating variable between HIR and mRS score 90 days after treatment.Conclusion HIR of CTP could be used to effectively predict infarct core progression of AIS,which completely affected prognosis through mediating variable IGR.

This paper focuses on the application of health big data in cancer screening. Firstly, the sources and characteristics of health big data are introduced, then the commonly used epidemiological designs and analytical techniques in hospital-based cancer screening studies are summarized and the application scenarios of such studies are described. Finally, the challenges and future development in the application of health big data are analyzed to provide reference for the future studies.

This paper focuses on the application of health big data in cancer screening. Firstly, the sources and characteristics of health big data are introduced, then the commonly used epidemiological designs and analytical techniques in hospital-based cancer screening studies are summarized and the application scenarios of such studies are described. Finally, the challenges and future development in the application of health big data are analyzed to provide reference for the future studies.

T-cell acute lymphoblastic leukemia (T-ALL) is a highly aggressive hematologic malignancy with a poor prognosis, despite advancements in treatment. Many patients struggle with relapse or refractory disease. Investigating the role of the super-enhancer (SE) regulated gene ubiquitin-specific protease 20 (USP20) in T-ALL could enhance targeted therapies and improve clinical outcomes. Analysis of histone H3 lysine 27 acetylation (H3K27ac) chromatin immunoprecipitation sequencing (ChIP-seq) data from six T-ALL cell lines and seven pediatric samples identified USP20 as an SE-regulated driver gene. Utilizing the Cancer Cell Line Encyclopedia (CCLE) and BloodSpot databases, it was found that USP20 is specifically highly expressed in T-ALL. Knocking down USP20 with short hairpin RNA (shRNA) increased apoptosis and inhibited proliferation in T-ALL cells. In vivo studies showed that USP20 knockdown reduced tumor growth and improved survival. The USP20 inhibitor GSK2643943A demonstrated similar anti-tumor effects. Mass spectrometry, RNA-Seq, and immunoprecipitation revealed that USP20 interacted with hypoxia-inducible factor 1 subunit alpha (HIF1A) and stabilized it by deubiquitination. Cleavage under targets and tagmentation (CUT&Tag) results indicated that USP20 co-localized with HIF1A, jointly modulating target genes in T-ALL. This study identifies USP20 as a therapeutic target in T-ALL and suggests GSK2643943A as a potential treatment strategy.

Objective:To assess common errors in head and neck CT angiography (CTA) image reports using four types of large language models (LLM), namely GPT-4, DeepSeek, ERNIE Bot and SparkDesk, and to assess the feasibility of using existing LLMs to support quality control of radiology reports in Chinese.Methods:The study was a cross-sectional study. Totally 1 000 head and neck CTA image reports were randomly selected using the simple random sampling method from Xuanwu Hospital, Capital Medical University in 2023, including 500 primary reports and 500 finalized reports. Two radiologists collaboratively identified six types of errors in the reports: description errors, writing errors, left-right confusion errors, diagnostic omissions, logical sequence errors, and other errors. The overall quality of the reports was assessed using a 5-point Likert scale. Subsequently, GPT-4, DeepSeek, ERNIE Bot and SparkDesk models were employed to detect the same six types of errors in the imaging reports and to provide overall scoring. The results from manual review were considered the gold standard for calculating F1 score to evaluate model performance. Intra-class correlation coefficients ( ICC) were used to assess the consistency between manual scores and the overall scores from the four LLMs. Results:In the primary imaging reports, the proportions of manually detected errors were as follows: descriptive errors 2.6% (13/500), writing errors 0.6% (3/500), left-right confusion errors 0, diagnostic omissions 6.4% (32/500), logical sequence errors 5.2% (26/500), and other errors 0. In the finalized imaging reports, the proportions of errors across the six categories were 0.2% (1/500), 0, 0, 0, 0, and 0.2% (1/500), respectively. For error detection in the primary imaging reports, the F1 scores of GPT-4 for the six error types were 0.992, 0.997, 0.997, 0.967, 0.980, and 0.992, respectively. DeepSeek achieved F1 scores of 0.980, 0.955, 0.981, 0.920, 0.995, and 0.960; ERNIE Bot scored 0.982, 0.990, 1.000, 0.956, 0.976, and 0.999; and SparkDesk achieved 0.985, 0.995, 1.000, 0.961, 0.982, and 1.000. In the detection of errors in finalized imaging reports, GPT-4′s F1 scores were 0.994, 0.995, 0.998, 0.973, 0.989, and 0.993; DeepSeek scored 0.968, 0.965, 0.985, 0.971, 0.991, and 0.983; ERNIE Bot achieved 0.996, 0.992, 1.000, 0.983, 0.999, and 0.997; and SparkDesk achieved 0.999, 0.999, 1.000, 1.000, 1.000, and 0.999. The consistency between GPT-4, DeepSeek, and SparkDesk models and human ratings was moderate, with ICC values of 0.514, 0.560, and 0.515 respectively (all P0.001); in contrast, the overall score of ERNIE Bot showed poor consistency with human ratings, with an ICC of 0.221 ( P0.001). Conclusion:LLMs demonstrate high accuracy in detecting errors in head and neck CTA imaging reports. The overall scoring of report quality shows moderate consistency with manual assessments, indicating a certain feasibility for automated quality control in reporting.

Objective:To explore the application value of semantic information retrieval (semantic retrieval) based on case reports dataset of Adverse Drug Reactions Journal. Methods:The dataset used in this study consists of 2 597 PDF files of case reports published on Adverse Drug Reactions Journal from 1999 to 2022. The semantic retrieval system is built by Baidu PaddlePaddle′s deep learning framework, the code was written in Python, and the text encoding model was Baidu RocketQA model. The precision at position k (P@k), recall at position k (R@k), mean reciprocal rank (MRR), mean average precision (MAP) and precision-recall (P-R) curve were used to evaluate the performance of semantic retrieval. The performance of semantic retrieval and keyword matching retrieval were compared by calculating the recall. Results:The set of preprocessed theme fields as items to be retrieved contained 2 597 documents, the set of search terms (queries) after removing deplicates and reorganizing included 1 388 drug name queries and 1 118 adverse reactions/events queries. The precision of drug name queries and adverse reactions/events queries by semantic retrieval were 0.667-1 and 0.566-1, and their recall were 0.667-0.871 and 0.566-0.863, respectively. The P-R curves of the top 1, 3, 5 and 10 documents in the semantic retrieval results using drug names queries and adverse reactions/events search terms showed that the precision decreased slowly in top 1 and 3 documents but significantly in top 5 and 10 documents with the increase of recall. The MRR of the 2 types of search terms were 0.854 and 0.871, and the MAP were 0.778 and 0.773, respectively. Using adverse reactions/events as search terms, semantic retrieval has a higher recall rate than keyword matching retrieval; using drug names as search terms, the recall rate of keyword matching retrieval is generally higher than that of semantic retrieval.Conclusions:The semantic retrieval system based on Baidu PaddlePaddle deep learning framework has good retrieval performance on the case reports dataset of Adverse Drug Reactions Journal. The semantic retrieval performs better with adverse reactions/events queries, while the keyword matching retrieval performs better with drug name queries.

Objective:To explore the alterations in functional connectivity density (FCD) resulted from mild cognitive impairment (MCI) and their correlations with cognitive scores in various cognitive domains in patients with Parkinson's disease (PD).Methods:Forty-three PD patients admitted to Department of Neurology, Sixth Affiliated Hospital of Nantong University from January 2022 to April 2024 were selected and divided into PD-MCI group (MoCA scores<26) and PD with normal cognition (PD-NC) group (MoCA scores≥26) according to Montreal Cognitive Assessment (MoCA). Another 23 middle-aged and elderly healthy volunteers (HC group) matched with PD patients in age, gender and education level were recruited at the same period. Resting-state functional MRI (rs-fMRI) data were collected and whole brain FCD was calculated. Differences of clinical data, whole brain FCD, and FCD in brain regions with significantly different FCD among the 3 groups were compared. Efficiency of FCD in brain regions with significantly different FCD between PD-MCI group and PD-NC group in differentially diagnosing PD-MCI and PD-NC was analyzed by receiver operating characteristic (ROC) curve. Pearson correlation was used to the analyze the correlations of FCD in brain regions with significantly different FCD with MoCA score and cognitive scores in various cognitive domains.Results:Among the 43 patients, 23 were into the PD-MCI group and 20 into the PD-NC group. PD-MCI group had significantly lower scores in the visuospatial and executive function, abstraction, and delayed memory cognitive domains than PD-NC group ( P<0.05). Brain regions with significantly different FCD among the 3 groups were the right parahippocampal gyrus, left gyrus rectus, right rolandic operculum, left middle occipital gyrus, right precentral gyrus, left middle frontal gyrus, and left medial superior frontal gyrus. Compared with the HC group, the PD-MCI group and PD-NC group had significantly increased FCD at the right parahippocampal gyrus, left gyrus rectus and right rolandic operculum, statistically decreased FCD at the right precentral gyrus, left middle frontal gyrus, and left medial superior frontal gyrus ( P<0.05). Compared with the HC group, the PD-MCI group had significantly increased FCD at the left middle occipital gyrus ( P<0.05). Compared with the PD-NC group, the PD-MCI group had significantly decreased FCD at the right parahippocampal gyrus, and statistically increased FCD at the left middle occipital gyrus and left middle frontal gyrus ( P<0.05). Area under ROC curve (AUC) of FCD in brain regions with significantly different FCD in discriminating PD-MCI and PD-NC was 0.878, with sensitivity of 90.0% and specificity of 91.3%. FCD at right parahippocampal gyrus, left middle occipital gyrus and left middle frontal gyrus was negatively correlated with MoCA score ( P<0.05); FCD at right parahippocampal gyrus was positively correlated with cognitive scores in the visuospatial and executive function, and delayed memory domains ( P<0.05); FCD at left middle occipital gyrus was negatively correlated with cognitive scores in the executive function and visual-spatial skills, and abstraction domains ( P<0.05); FCD at the left medial frontal gyrus was negatively correlated with cognitive scores in the visuospatial and executive function, abstraction and delayed memory domains ( P<0.05). Conclusions:Abnormal FCD can be noted in some brain regions of PD patients, enjoying differences between PD-MCI patients and PD-NC patients. Combined FCD in brain regions with significantly different FCD has high value in differentially diagnosing PD-MCI and PD-NC, and FCD in brain regions with significantly different FCD is correlated with cognitive function changes in PD patients.

Objective To explore the clinical effect and nursing cooperation of the new robot-assisted puncture system RC120 in CT-guided lung puncture biopsy,so as to provide practical basis for the promotion and utilization of the new robot-assisted puncture system.Methods Using random sampling method,a total of 29 patients,who received robot-assisted lung puncture at the Shanghai Municipal Pulmonary Hospital of China between January and June of 2022,were collected as the study group,while other 29 patients,who received traditional manual lung puncture under CT guidance in the same period,were collected as the control group.For the patients of the control group traditional nursing was implemented,while for the patients of the study group on the basis of traditional nursing the additional targeted nursing care was carried out.The accuracy of puncture localization,satisfaction with the nursing care,and incidence of complications were compared between the two groups.Results The accuracy of puncture localization in the study group was 5.0 mm(3.6-6.2 mum),which was better than 6.4 mm(4.0-8.4 mm)in the control group,and the difference was statistically significant(Z=-2.179,P=0.034).The patient satisfaction score in the study group was(91.1±1.6)points,which was significantly higher than(82.2±1.7)points in control group,and the difference was statistically significant(t=21.047,P＜0.001).In the study group 2 patients developed complications with an incidence of 6.9％,while in the control group 8 patients developed complications with an incidence of 27.6％,and the difference was statistically significant(x2=4.350,P=0.037).Conclusion The implementation of targeted nursing care can reduce the risk of complications caused by CT-guided lung puncture with RC120 auxiliary puncture system,improve the accuracy of puncture localization,and improve patient satisfaction with nursing care,therefore,the targeted nursing care deserves promotion in clinical practice.(J Intervent Radiol,2024,33:549-553)