Objective To observe the value of transvaginal two-dimensional ultrasonography(2D-TVS)combined with three-dimensional ultrasonography OmniView for diagnosing intrauterine adhesion(IUA).Methods Totally 200 women with suspected IUA were retrospectively enrolled,including 172 cases who were ultimately diagnosed IUA and the rest 28 without IUA.The efficacy of 2D-TVS combined with OmniView for diagnosing IUA was analyzed.Results Among 200 cases,150 cases were diagnosed IUA with 2D-TVS(135 cases were diagnosed correctly),167 cases were diagnosed IUA with OmniView(159 cases were diagnosed correctly),and 170 cases were diagnosed with combination of the above two(165 cases were diagnosed correctly).The area under the curve(AUC)of 2D-TVS,OmniView and 2D-TVS combined with OmniView for diagnosing IUA was 0.625,0.819 and 0.890,respectively,and 2D-TVS combined with OmniView had the highest AUC(both P＜0.05),with sensitivity of 95.93％,specificity of 82.14％,positive predictive value of 97.06％and negative predictive value of 76.67％.Conclusion 2D-TVS combined with OmniView could be helpful for diagnosing IUA.

Objective To explore the value of ultrasound intelligent diagnostic system combined with mammography for differentiating benign and malignant non-mass breast lesions(NMBL).Methods Totally 107 patients with NMBL were retrospectively enrolled,including 64 cases of malignant(malignant group)and 43 cases of benign lesions(benign group).Clinical,routine ultrasound,ultrasound intelligent diagnostic system(artificial intelligence[AI]system)and mammography data were compared between groups.Logistic regression analysis was performed,receiver operating characteristic(ROC)curves were drawn,the areas under the curves(AUC)were calculated,and the efficacy of AI system combined with mammography for differentiating benign and malignant NMBL was analyzed.Results Significant differences of the maximum diameter of lesion,ratio of axillary lymph node enlargement and suspected malignant calcification on mammography,as well as of AI system malignancy risk and AI system breast imaging reporting and data system(BI-RADS)classification were found between groups(all P＜0.05).AI system binary classification was obtained based on AI system malignancy risk.The AUC of suspected malignant calcification on mammography,AI system BI-RADS classification and AI system binary classification for differential diagnosis of benign and malignant NMBL was 0.840,0.810 and 0.817,respectively,while of suspected malignant calcification on mammography combined with AI system BI-RADS classification or AI system binary classification were both 0.856,higher than that of AI system BI-RADS classification/AI system binary classification alone(both P＜0.05)but not significantly different with that of suspected malignant calcification on mammography alone(both P＞0.05).Logistic regression analysis of age,the maximum diameter of lesion,axillary lymph node enlargement and suspected malignant calcification on mammography combined with AI system malignancy risk(model 1),AI system BI-RADS classification(model 2)or AI system binary classification(model 3)showed that suspected malignant calcification on mammography,AI system malignancy risk,AI system BI-RADS classification and AI system binary classification were all independent risk factors of malignant NMBL(all P＜0.05),and AUC of model 1,2 and 3 for differentiating benign and malignant NMBL was 0.966,0.964 and 0.957,respectively.Conclusion Ultrasound intelligent diagnostic system combined with mammography was helpful for differentiating benign and malignant NMBL.Combining with clinical indicators might improve diagnostic efficacy.

Objective:To explore the optimization potential of clinical target volume (CTV) delineation proposed in the guidelines of the Oncology Group (RTOG), the Francophone Group of Urological Radiotherapy (GFRU), and the European Society for Radiotherapy and Oncology (ESTRO) based on prostate bed local occurrence patterns after radical prostatectomy identified using prostate-specific membrane antigen positron emission tomography (PSMA PET).Methods:A retrospective analysis was conducted on patients with local prostate bed recurrence after radical prostatectomy who underwent PSMA PET at the Department of Nuclear Medicine, Peking University First Hospital from September 2021 to February 2024. The central point of each recurrence was marked. A six-zone method was established based on prostate bed anatomy and the characteristics of cross-sectional imaging. Then, the positional relationships (within or outside) were recorded with respect to recurrences and CTV defined by the RTOG, GFRU, and ESTRO (CTV RTOG, CTV GFRU, and CTV ESTRO), followed the analysis of the recurrence rates and distribution characteristics of various zones. Results:A total of 63 patients with prostate bed recurrence after radical prostatectomy were enrolled in this study, including 97 recurrences. The recurrence rates in the six zones were as follows: 10% of zone 1, 22% of zone 2, 29% of zone 3, 2% of zone 4, 12% of zone 5a, 18% of zone 5b, and 7% of zone 6. Among these zones, zones 2 and 3 showed the highest and second-highest recurrence rates, respectively. CTV GFRU and CTV ESTRO completely covered zones 2 and 3, while CTV RTOG covered zone 2 completely and zone 3 partially. Zone 4, characterized by a low recurrence rate, was not covered by CTV GFRU and CTV ESTRO but was entirely covered by CTV RTOG. Zone 5a, with a recurrence rate of 12%, was completely covered by CTV RTOG but was partially covered by CTV GFRU and CTV ESTRO. The range of 1.3 cm in front of the posterior wall of the bladder covered all recurrences in zone 5a. Conclusions:For CTV delineation of the prostate cancer surgical bed, zone 4, the anterior half of the bladder above the pubic symphysis midpoint, should be contracted due to the low recurrence rate in this zone. In contrast, the anterior boundary above the pubic symphysis midpoint should extend to 1.3 cm in front of the posterior wall of the bladder to completely cover the recurrence zones.

Objective To explore the value of ultrasound intelligent diagnostic system combined with mammography for differentiating benign and malignant non-mass breast lesions(NMBL).Methods Totally 107 patients with NMBL were retrospectively enrolled,including 64 cases of malignant(malignant group)and 43 cases of benign lesions(benign group).Clinical,routine ultrasound,ultrasound intelligent diagnostic system(artificial intelligence[AI]system)and mammography data were compared between groups.Logistic regression analysis was performed,receiver operating characteristic(ROC)curves were drawn,the areas under the curves(AUC)were calculated,and the efficacy of AI system combined with mammography for differentiating benign and malignant NMBL was analyzed.Results Significant differences of the maximum diameter of lesion,ratio of axillary lymph node enlargement and suspected malignant calcification on mammography,as well as of AI system malignancy risk and AI system breast imaging reporting and data system(BI-RADS)classification were found between groups(all P＜0.05).AI system binary classification was obtained based on AI system malignancy risk.The AUC of suspected malignant calcification on mammography,AI system BI-RADS classification and AI system binary classification for differential diagnosis of benign and malignant NMBL was 0.840,0.810 and 0.817,respectively,while of suspected malignant calcification on mammography combined with AI system BI-RADS classification or AI system binary classification were both 0.856,higher than that of AI system BI-RADS classification/AI system binary classification alone(both P＜0.05)but not significantly different with that of suspected malignant calcification on mammography alone(both P＞0.05).Logistic regression analysis of age,the maximum diameter of lesion,axillary lymph node enlargement and suspected malignant calcification on mammography combined with AI system malignancy risk(model 1),AI system BI-RADS classification(model 2)or AI system binary classification(model 3)showed that suspected malignant calcification on mammography,AI system malignancy risk,AI system BI-RADS classification and AI system binary classification were all independent risk factors of malignant NMBL(all P＜0.05),and AUC of model 1,2 and 3 for differentiating benign and malignant NMBL was 0.966,0.964 and 0.957,respectively.Conclusion Ultrasound intelligent diagnostic system combined with mammography was helpful for differentiating benign and malignant NMBL.Combining with clinical indicators might improve diagnostic efficacy.

Objective:To explore the optimization potential of clinical target volume (CTV) delineation proposed in the guidelines of the Oncology Group (RTOG), the Francophone Group of Urological Radiotherapy (GFRU), and the European Society for Radiotherapy and Oncology (ESTRO) based on prostate bed local occurrence patterns after radical prostatectomy identified using prostate-specific membrane antigen positron emission tomography (PSMA PET).Methods:A retrospective analysis was conducted on patients with local prostate bed recurrence after radical prostatectomy who underwent PSMA PET at the Department of Nuclear Medicine, Peking University First Hospital from September 2021 to February 2024. The central point of each recurrence was marked. A six-zone method was established based on prostate bed anatomy and the characteristics of cross-sectional imaging. Then, the positional relationships (within or outside) were recorded with respect to recurrences and CTV defined by the RTOG, GFRU, and ESTRO (CTV RTOG, CTV GFRU, and CTV ESTRO), followed the analysis of the recurrence rates and distribution characteristics of various zones. Results:A total of 63 patients with prostate bed recurrence after radical prostatectomy were enrolled in this study, including 97 recurrences. The recurrence rates in the six zones were as follows: 10% of zone 1, 22% of zone 2, 29% of zone 3, 2% of zone 4, 12% of zone 5a, 18% of zone 5b, and 7% of zone 6. Among these zones, zones 2 and 3 showed the highest and second-highest recurrence rates, respectively. CTV GFRU and CTV ESTRO completely covered zones 2 and 3, while CTV RTOG covered zone 2 completely and zone 3 partially. Zone 4, characterized by a low recurrence rate, was not covered by CTV GFRU and CTV ESTRO but was entirely covered by CTV RTOG. Zone 5a, with a recurrence rate of 12%, was completely covered by CTV RTOG but was partially covered by CTV GFRU and CTV ESTRO. The range of 1.3 cm in front of the posterior wall of the bladder covered all recurrences in zone 5a. Conclusions:For CTV delineation of the prostate cancer surgical bed, zone 4, the anterior half of the bladder above the pubic symphysis midpoint, should be contracted due to the low recurrence rate in this zone. In contrast, the anterior boundary above the pubic symphysis midpoint should extend to 1.3 cm in front of the posterior wall of the bladder to completely cover the recurrence zones.

Through analyzing the every textual research literature on the origin of the location method of ashi points, it is pointed out that the location method of ashi points has been generalized. It is known through systematic research and theoretical identification that this method refers to the simple way to locate the points, originated from the folk, and it is widely used to find the sites for pain relief when compared to the official education of meridians and acupoints, as well as treatment. At the time with the shortage of medical services and supplies, the poor people had no alternative, which results in a certain limitation. Modern acupuncture practitioners must associate ashi points with the acupoints of fourteen meridians and the extraordinary points in clinical practice, and deepen the exploration with modern medical knowledge and technology adopted.
Humans ; Acupuncture Points ; Acupuncture Therapy/methods* ; Meridians ; History, Ancient ; Medicine in Literature ; China

ObjectiveTo evaluate the clinical efficacy and safety of acupuncture combined with levodopa in the treatment of Parkinson's disease（PD）. MethodsA total of 60 patients with PD were enrolled and randomly assigned to test group or control group， with 30 patients in each group. The control group received levodopa only， starting at 100 mg per dose， three times daily， with gradual increases not exceeding a maximum daily dose of 800 mg. The test group received acupuncture three times per week in addition to levodopa. Both groups were treated for 12 weeks. Assessments were conducted before treatment， after 6 and 12 weeks treatment， using the Unified Parkinson's Disease Rating Scale（UPDRS）， Wearing-Off Questionnaire-9（WOQ-9）， Montreal Cognitive Assessment（MoCA）， Mini-Mental State Examination（MMSE）， Depression Rating Scale（DRS）， Hamilton Depression Scale（HAMD）， Hamilton Anxiety Scale（HAMA）， PD Questionnaire-39（PDQ-39）， and Pittsburgh Sleep Quality Index（PSQI）. Repeated measures ANOVA was utilized to evaluate the effects of time， group， and their interaction on each index. Spearman correlation analysis was conducted to examine the relationships between combined treatment and outcome scores. Adverse events in both groups were recorded throughout the study. ResultsBoth groups showed significant improvements after 6 and 12 weeks treatment， with decreases in UPDRS total score， WOQ-9 total score， DRS score， HAMD score， HAMA score， PDQ-39 score， and PSQI score， and increases in MoCA and MMSE scores（P＜0.05）. Compared with the control group， the test group demonstrated significantly greater improvements in all the above indicators after 6 and 12 weeks （P＜0.05）. Repeated measures ANOVA showed significant time main effects， group main effects， and their interaction across all outcome measures（P＜0.01）. Spearman correlation analysis revealed that combined therapy was significantly negatively correlated with UPDRS， WOQ-9， DRS， HAMD， HAMA， PDQ-39， and PSQI scores， while positively correlated with MoCA and MMSE scores after 12 weeks of treatment（P＜0.05）. Both groups did not experience any serious adverse events and did not affect treatment. ConclusionAcupuncture combined with levodopa is more effective than levodopa alone in improving motor function， non-motor symptoms， cognitive function， depression and anxiety， quality of life， and sleep quality in patients with PD， with good safety.

Objective To observe the value of transvaginal two-dimensional ultrasonography(2D-TVS)combined with three-dimensional ultrasonography OmniView for diagnosing intrauterine adhesion(IUA).Methods Totally 200 women with suspected IUA were retrospectively enrolled,including 172 cases who were ultimately diagnosed IUA and the rest 28 without IUA.The efficacy of 2D-TVS combined with OmniView for diagnosing IUA was analyzed.Results Among 200 cases,150 cases were diagnosed IUA with 2D-TVS(135 cases were diagnosed correctly),167 cases were diagnosed IUA with OmniView(159 cases were diagnosed correctly),and 170 cases were diagnosed with combination of the above two(165 cases were diagnosed correctly).The area under the curve(AUC)of 2D-TVS,OmniView and 2D-TVS combined with OmniView for diagnosing IUA was 0.625,0.819 and 0.890,respectively,and 2D-TVS combined with OmniView had the highest AUC(both P＜0.05),with sensitivity of 95.93％,specificity of 82.14％,positive predictive value of 97.06％and negative predictive value of 76.67％.Conclusion 2D-TVS combined with OmniView could be helpful for diagnosing IUA.

Purpose To explore the feasibility of the deep learning-based segmentation of extra-pelvic region and metastases in advanced prostate cancer based on metastasis reporting and data system for prostate cancer(MET-RADS-P).Materials and Methods Four datasets(68,91,57 and 263 patients with head,neck,chest and abdomen metastases,respectively)from Jan 2017 to Jan 2022 in Peking University First Hospital were retrospectively collected for the development of the classification model of scanning range and segmentation model of different regions and metastases according to the scanning sites(head,neck,chest and abdomen).In addition,90 patients with prostate cancer confirmed by pathology and underwent whole-body MRI were collected for external validation of the developed model.The manual annotation of the regions and metastases were used as the"reference standard"for the model evaluation.The evaluation indexes included dice similarity coefficient(DSC)and volumetric similarity(VS).Results In the external validation set,the classification accuracy of head,neck,chest and abdomen were 100%(90/90),98.89%(89/90),96.67%(87/90)and 94.44%(85/90),respectively.The range of DSC,VS values of the segmentation model for organs in different regions were(0.86±0.10)-(0.99±0.01),(0.89±0.10)-(0.99±0.01),respectively.The range of DSC,VS values of the segmentation model for metastases in different regions were(0.65±0.07)-(0.72±0.13),(0.74±0.04)-(0.82±0.13),respectively.Conclusion The 3D U-Net model based on deep learning may achieve the segmentation of extra-pelvic region and metastasis in advanced prostate cancer.

Objective:To investigate the diagnostic performance of continuous-time random-walk (CTRW) diffusion model combined with vesical imaging-reporting and data system (VI-RADS) in the diagnosis of muscle invasion of bladder cancer.Methods:In this case-control study, 64 patients with pathologically confirmed bladder urothelial carcinoma in Peking University First Hospital were retrospectively enrolled from August 2022 to March 2023. The patients were divided into the muscle invasive bladder cancer (MIBC) group and the nonmuscle invasive bladder cancer (NMIBC) group (29 cases and 35 cases, respectively) according to the pathological results. All patients underwent bladder MRI within 4 weeks before surgery, including T 2WI, conventional diffusion weighted imaging (DWI), and multi-b-value DWI. The CTRW model was used to obtain three quantitative diffusion parameters, including D m (an anomalous diffusion coefficient), α (related to temporal diffusion heterogeneity), and β (related to spatial diffusion heterogeneity). The apparent diffusion coefficient (ADC) was calculated using a mono-exponential model. The VI-RADS scores were evaluated based on T 2WI and conventional DWI. The Mann-Whitney U test was used to compare the diffusion parameters between the MIBC group and the NMIBC group. The combination of the parameters was investigated with logistic regression analysis. The diagnostic performance for muscle invasion of bladder cancer was evaluated by receiver operating characteristic analysis and the area under the curve (AUC). The difference between AUC was compared using the DeLong test. Results:There were statistically significant differences in ADC, D m, and α between the MIBC group and the NMIBC group ( Z=-2.31, -2.91, -3.97, P=0.021, 0.004,<0.001). No significant difference was found in β between the two groups ( Z=1.69, P=0.091). The AUC (95% CI) of D m and α for diagnosing MIBC were 0.712 (0.587-0.838) and 0.790 (0.676-0.904) respectively, both of which were higher than that of ADC (AUC 0.669, 95% CI 0.537-0.801) with statistically significant differences ( Z=2.86, 2.27, P=0.004, 0.023). The AUC (95% CI) of CTRW (D m+α) was 0.782 (0.661-0.876), which was significantly higher than that of ADC ( Z=2.35, P=0.019). The AUC (95% CI) of VI-RADS score and VI-RADS combined with CTRW parameter (VI-RADS+D m+α) were 0.823 (0.716-0.930) and 0.900 (0.799-0.961) respectively, with a statistically significant difference between them ( Z=2.16, P=0.031). Conclusion:The D m and α parameters in the CTRW diffusion model show better performance than the ADC in the mono-exponential model for muscle-invasive evaluation of bladder cancer, and the CTRW diffusion model can enhance the diagnostic performance of VI-RADS.