Objective To include patients with clinical data matched by propensity scores and to explore the value of 18F-fluorode-oxyglucose(18F-FDG)PET/CT metabolic parameters and radiomics in differentiating benign and malignant solitary pulmonary nod-ule(SPN).Methods A total of 54 patients with SPN(27 benign and 27 malignant)were retrospectively selected,all of them under-went 18F-FDG PET/CT scans.Then the metabolic parameters were analyzed,and the metabolic parameters model was established.After delineating the lesion,imaging features were selected through variance and correlation analysis.The logistic regression was used to build the model,and balance accuracy(bACC)was used to compare the performance of the models.The correlation between meta-bolic parameters and radiomics features was analyzed.Results The maximum standardized uptake value(SUVmax),total lesion uptake(TLU),and coefficient of variation(COV)of malignant were higher than those of benign(P＜0.05).SUVmax and COV had positive predictive value for malignant lesions[odds ratio(OR)＞1,P＜0.05].There was no statistical difference between the performance of the metabolic parameters model and the radiomics model(P＞0.05).There was a strong correlation between radiomics features and metabolic parameters.Conclusion After propensity score matching,metabolic parameters and radiomics show no statistical difference in differentiating benign from malignant SPN.

Objective To include patients with clinical data matched by propensity scores and to explore the value of 18F-fluorode-oxyglucose(18F-FDG)PET/CT metabolic parameters and radiomics in differentiating benign and malignant solitary pulmonary nod-ule(SPN).Methods A total of 54 patients with SPN(27 benign and 27 malignant)were retrospectively selected,all of them under-went 18F-FDG PET/CT scans.Then the metabolic parameters were analyzed,and the metabolic parameters model was established.After delineating the lesion,imaging features were selected through variance and correlation analysis.The logistic regression was used to build the model,and balance accuracy(bACC)was used to compare the performance of the models.The correlation between meta-bolic parameters and radiomics features was analyzed.Results The maximum standardized uptake value(SUVmax),total lesion uptake(TLU),and coefficient of variation(COV)of malignant were higher than those of benign(P＜0.05).SUVmax and COV had positive predictive value for malignant lesions[odds ratio(OR)＞1,P＜0.05].There was no statistical difference between the performance of the metabolic parameters model and the radiomics model(P＞0.05).There was a strong correlation between radiomics features and metabolic parameters.Conclusion After propensity score matching,metabolic parameters and radiomics show no statistical difference in differentiating benign from malignant SPN.

The number of patients with eye diseases in China is enormous,and the negative effects of these conditions,such as impaired visual function,psychological burdens,and restricted social participation,are becoming increasingly severe.Due to the limited and unevenly distributed ophthalmic resources,and the significant limitations of traditional diagnostic and therapeutic approaches in terms of accuracy and efficiency,there is an urgent need for more sensitive and efficient modalities.With the rapid advancement of artificial intelligence technology,ophthalmic diagnosis has entered a new stage of intelligent transformation.Facial photos,as a noninvasive and convenient medium,show unique advantages in eye disease diagnosis.Artificial intelligence systems based on facial photo analysis have been applied to the screening and diagnosis of conditions such as myopia,strabismus,ptosis,and thyroid eye disease,showing promising results.This review introduces the workflow of intelligent diagnosis for ocular diseases based on facial photographs,with a focus on recapitulating relevant research findings both domestically and internationally in recent years.It summarizes the innovative features and application advantages of intelligent diagnosis systems for eye diseases based on facial photos,analyzes the current technical bottlenecks and challenges in application,proposes corresponding countermeasures,and discusses future development directions,aiming to provide references and new insights for the intelligent screening and diagnosis of eye diseases.

Cochlear implantation is the most effective method to restore hearing in patients with severe and profound hearing impairment.There are individual differences in rehabilitation after cochlear implantation.In order to better understand the underlying factors of rehabilitation,machine learning has been gradually applied to the reha-bilitation prediction of cochlear implant patients.Since related literature is still in its infancy,many problems still remain unresolved,such as insufficient sample size and lack of diversification of data features.We suggest that fu-ture research may expand the sample size,optimize machine learning models,and fully explore the predictive factors affecting the rehabilitation of cochlear implantation,and make machine learning a better tool in the area of cochlear implantation.

Objective The application of 18F-PSMA-1007 PET/CT in the precise diagnosis and treatment of prostate cancer(PCa)is gradually increasing.This study aimed to explore the predictive value of 18F-PSMA-1007 PET/CT for positive surgical margins after radical prostatectomy(RP).Methods A total of 173 PCa patients who were pathologically confirmed and underwent RP in our hospital from July 2020 to August 2023 were retrospectively recruited.All patients underwent 18F-PSMA-1007 PET/CT whole-body imaging before surgery.According to the postoperative pathological results,they were divided into a negative surgical margin group with 83 cases and a positive surgical margin group with 90 cases.The differences in age,SUVmax,SUVmean,preoperative total prostate specific antigen(tPSA),prostate specific antigen density(PSAD),prostate volume,Gleason score,and clinical T staging between the groups were compared,and the risk factors were further analyzed by Logistic regression.Results The preoperative tPSA(Z=-3.252,P=0.001),SUVmax(Z=2.531,P=0.011),and clinical T staging(P=0.018)were all lower in the negative surgical margin group than in the positive surgical margin group.PSAD(OR=3.492,95％CI:1.095-11.133,P=0.035),SUVmax(OR=1.036,95％CI:1.003-1.070,P=0.034),and clinical T staging(OR=3.364,95％CI:1.117-10.133,P=0.045)were independent risk factors for positive surgical margins.Conclusion 18F-PSMA-1007 PET/CT has certain predictive value for positive surgical margins after RP in PCa patients.High PSAD,high SUVmax and high clinical T stage were independent risk factors for predicting positive margin after PR surgery,thus providing some reference for the clinical treatment strategy of PCa patients.

Objective:To explore the value of ultrasound imaging characteristics combined with clinical indicators in assessing the prognosis of patients with pancreatic ductal adenocarcinoma (PDAC).Methods:A retrospective analysis was conducted for patients who underwent pancreatic contrast-enhanced ultrasound (CEUS) from September 2017 to October 2023 at Peking Union Medical College Hospital and were diagnosed with PDAC based on pathological findings. Various parameters were recorded, including CA19-9 levels, tumor size, location, morphologic features, echogenicity, presence of internal cystic components, dilatation of the main pancreatic duct, peripheral vascular invasion, CEUS characteristics, presence or absence of liver metastasis, and treatment methods. In April 2024, patient survival information was obtained through telephone follow-up or review of medical records. Based on the results of the cox regression model analysis, a nomogram model of the risk of death was developed. The receiver operating characteristic (ROC) curves were applied to evaluate the predictive efficacy of the model. The calibration curves were plotted to evaluate the accuracy of the model, and clinical decision curves were used to evaluate the clinical benefit of the model.Results:This study included a total of 207 patients with PDAC. As of April 2024, 71 patients were alive and 136 died, with a median survival time of 14 months (95% CI: 12 -17). Multivariate analysis confirmed that the elevated CA19-9 ( HR=1.689, 95% CI: 1.102-2.588), tumor size >4 cm ( HR=1.641, 95% CI: 1.159-2.322), taller-than-wide shapes ( HR=1.450, 95% CI: 1.019-2.065), incomplete hypo-enhancement ( HR=1.618, 95% CI: 1.100-2.380), and liver metastasis ( HR=1.687, 95% CI: 1.175-2.423) were independent risk factors for survival in patients with PDAC. A nomogram model was further constructed for 6-month, 12-month and 3-year survival of patients with PDAC. The areas under the ROC curve were 0.679, 0.705 and 0.815, respectively. The calibration curves suggested that the model was more accurate, and the clinical decision curves showed that the model had a better clinical benefit. Conclusion:The combined use of ultrasound imaging characteristics and clinical indicators could effectively predict the prognosis of PDAC patients. Specifically, tumor size >4 cm, taller-than-wide shapes, incomplete hypo-enhancement, elevated CA19-9, and the presence of liver metastasis are correlated with poorer survival outcomes. The nomogram model constructed on the basis of these factors can be used to assess the survival of patients with PDAC.

Objective:To explore the differences in sociodemographic and clinical characteristics between pa-tients with unipolar depression bipolar depression and to establish a nomogram for identifying bipolar depression.Methods:Using data from the China Mental Disorders Cohort Study,the sociodemographic and clinical characteristics of 2 643 patients with unipolar depression and 250 patients with bipolar depression diagnosed accord-ing to the criteria of the Diagnostic and Statistical Manual of Mental Disorders,Fifth Edition(DSM-5)were includ-ed to compare their sociodemographic and clinical characteristics.These characteristics included general demograph-ic information,disease-related information,clinical examination results,and the severity of the disease assessed with the Global Assessment of Functioning(GAF)and Hamilton Depression Rating Scale.Logistic regression analysis was employed to identify factors influencing bipolar depression,and a nomogram was constructed for its identifica-tion.Results:The risk factors for bipolar depression included being male(OR=1.48),being employed(OR=1.38),having non-melancholic features during episodes(OR=2.33),a Body Mass Index ranging from normal to obese(OR=2.48,2.49,4.65),psychotic features(OR=2.14),mixed episode(OR=9.36),comorbid physical diseases(OR=2.47),four or more depressive episodes(OR=1.67),earlier age of onset(OR=0.95),longer ill-ness duration(OR=1.03),and higher GAF scores(OR=1.02).The nomogram model achieved an AUC of 0.81(95％CI:0.78-0.84).The Hosmer-Lemeshow test result was x2=6.96(P＞0.05),indicating good model fit.The calibration curve showed good performance.The decision curve analysis revealed that the nomogram pro-vides significant clinical benefit when the risk of bipolar depression was within the range of 0 to 0.9.Conclusion:The nomogram established based on the identified sociodemographic and clinical factors can accurately assess the risk of bipolar depression,providing a useful tool for early identification and intervention.

This article presents a case study of a patient who visited the Geriatric Department of Peking Union Medical College Hospital due to "palpitations, shortness of breath for more than 2 years, limb weakness for 6 months, edema, and nocturnal dyspnea for 2 months". The patient exhibited decreased muscle strength in the limbs and involvement of swallowing and respiratory muscles, alongside complications of heart failure and various arrhythmias which were predominantly atrial. Laboratory tests revealed the presence of multiple autoantibodies and notably anti-mitochondrial antibodies. Following a comprehensive multidisciplinary evaluation, the patient was diagnosed with anti-mitochondrial antibody-associated inflammatory myopathy. Treatment involved a combination of glucocorticoids and immunosuppressants, along with resistance exercises for muscle strength and rehabilitation training for lung function, resulting in significant improvement of clinical symptoms. The case underscores the importance of collaborative multidisciplinary approaches in diagnosing and treating rare diseases in elderly patients, where careful consideration of clinical manifestations and subtle abnormal clinical data can lead to effective interventions.

Objective To construct a radiomics model to improve the discriminatory ability of 18 F-PSMA-1007 PET/CT for focal prostate cancer.Methods We retrospectively collected data from 74 patients diagnosed with prostate cancer by biopsy at The First Affiliated Hospital of Xi'an Jiaotong University between July 2020 and April 2024.These patients had focal radionuclide accumulation observed on 18F-PSMA-1007 PET/CT,with the median age of 71 years.Among them,42 patients had a Gleason score＜8 and 32 patients had a Gleason score ≥8.An external validation set was randomly selected based on the timing of examination,while the remaining patients were randomly divided into training and test sets at a 7∶3 ratio.Region of interest(ROI)were semi-automatically drawn on registered images,manually adjusted,and symmetrically shifted to contralateral non-tumor tissue.We made variance and correlation analyses to choose features,and built models with Logistic regression and compared the results with those of visual evaluation.Receiver operating characteristics(ROC)curves were drawn to compare model performance,and subgroup analysis was performed to identify optimal features for distinguishing tumor tissue,based on Gleason score,serum total prostate specific antigen(tPSA)levels,and lesion location.Results A total of eight features were selected.The area under the curve(AUC)for visual evaluation,testing set,and external validation set were 0.858,0.933,and 0.891,respectively.The sensitivity was 0.757,0.800 and 0.917;the specificity was 0.960,0.800 and 0.792,respectively.Subgroup analysis showed that the radiomic features 10percentile and skewness had a high value in tumor differentiation.In tumor tissues,the 10percentile values were higher than in non-tumor tissues across all groups(P-values were 0.012,0.002,＜0.001,＜0.001,＜0.001,and＜0.001).When tPSA≤10 ng/mL and Gleason score ≥8,there was no statistically significant difference in skewness between tumor and non-tumor tissues(P=0.08).When tPSA ≥20 ng/mL,the skewness of non-tumor tissue was slightly higher than that of tumor tissue,but the difference was not statistically significant(P-values were 0.285 and 0.791).When the tumor was located in the posterior part of the prostate(left posterior and right posterior),the skewness was significantly higher in tumor tissue than in non-tumor tissue(P-values＜0.001 for both).Conclusion The radiomics model had better sensitivity and accuracy than visual evaluation in distinguishing focal prostate cancer tumors from non-tumor tissues,but visual evaluation had higher specificity.Skewness and 10percentile had a high value in differential diagnosis.

The number of patients with eye diseases in China is enormous,and the negative effects of these conditions,such as impaired visual function,psychological burdens,and restricted social participation,are becoming increasingly severe.Due to the limited and unevenly distributed ophthalmic resources,and the significant limitations of traditional diagnostic and therapeutic approaches in terms of accuracy and efficiency,there is an urgent need for more sensitive and efficient modalities.With the rapid advancement of artificial intelligence technology,ophthalmic diagnosis has entered a new stage of intelligent transformation.Facial photos,as a noninvasive and convenient medium,show unique advantages in eye disease diagnosis.Artificial intelligence systems based on facial photo analysis have been applied to the screening and diagnosis of conditions such as myopia,strabismus,ptosis,and thyroid eye disease,showing promising results.This review introduces the workflow of intelligent diagnosis for ocular diseases based on facial photographs,with a focus on recapitulating relevant research findings both domestically and internationally in recent years.It summarizes the innovative features and application advantages of intelligent diagnosis systems for eye diseases based on facial photos,analyzes the current technical bottlenecks and challenges in application,proposes corresponding countermeasures,and discusses future development directions,aiming to provide references and new insights for the intelligent screening and diagnosis of eye diseases.