Objective To evaluate the diagnostic value of intravoxel incoherent motion (IVIM) and diffusion kurtosis imaging (DKI) parameters in diagnosing prostate cancer(PCa) in transition zone (TZ) and stratifying pathologic Gleason grade of prostate cancer. Methods A total of 55 patients who were undergoing preoperative muti?parameters MRI of T2WI, DWI, IVIM and DKI model for the exploration of prostate cancer (January 2015 to June 2017) with pathologically confirmed by MRI?transrectal ultrasound (TRUS) targeted fusion biopsy were retrospectively included. Parameters were postprocessed by IVIM models including quantitation of perfusion fraction (f), diffusivity (D) and pseudo?diffusivity (D*) and DKI models including the mean diffusivity (MD), mean kurtosis (MK) and fractional anisotropy (FA) by outlining the 3D VOI. Independent sample t?test was used to compare the differences in lesion parameters between prostate cancer and BPH, low?risk (BPH+Gleason score 6 points) and medium?high?risk lesions (Gleason score ≥7 points). Correlation between ADC values, IVIM and DKI parameters and Gleason scores were assessed with Spearman analysis.Receiver operating characteristic curve analysis was used to evaluate the efficacy of various parameters in the differential diagnosis of prostate cancer and BPH with low?risk or high?risk. Results 27 (36 focus) cases of PCa and 28 (40 focus) cases of benign prostatic hyperplasia(BPH) in PZ were included, meanwhile, the cases of GS≥7 and and BPH+(GS=6) were 33,43,respectively. There were significant differences in ADC, D, MD, MK, and FA between patients in PCa?BHP group and high?low risk group in TZ (P<0.05), D*and f had no significant differences (P>0.05). ADC and MD showed relatively higher negativity correlations (r were－0.585 and－0.489, P<0.05) with GS of PCa in TZ. ADC exhibited a higher area under the curve (AUC 0.864) compared with D with area under the curve (AUC 0.853), however, the difference is not significant (P>0.05). Of model DKI in diagnose of PCa and BPH, the highest classification accuracy was MD(AUC 0.796). The AUC derived from multiple model parameters in different combination of ADC+D value, ADC+MD value, and ADC+MD+D value were 0.892, 0.884, and 0.897, respectively. ADC and D of IVIM model showed a significance difference between GS≥7 and BPH+(GS=6) with a higher AUC of 0.826 and 0.743. The AUC was 0.851 of the combination of mean ADC and D, 0.846 of combination of mean ADC and MD, the AUC (0.856) of the combination of ADC, D and MD significant higher than any two combined parameters (P>0.05). Conclusions IVIM and DKI models may help to discriminate prostate cancer from BPH, and predict mid?higher GS PCa in TZ. But there is no significant advantage compared with ADC values. It is feasible to stratify the pathological grade of prostate cancer in TZ by mean ADC and MD.

Objective:To evaluate the application of multiparametric MRI (mpMRI)-transrectal ultrasound (TRUS) fusion guided biopsy in the diagnosis of clinical significant prostate cancer (PCa).Methods:A prospective analysis was performed in 168 patients with suspected PCa from September 2015 to June 2017 in the First Affiliated Hospital of Soochow University. Suspicious areas on mpMRl were defined and graded using prostate imaging reporting and data system version 2 (PI-RADS V2) score. All the patients had the TRUS-guided systematic biopsy, 108 patients with PI-RAD V2 scores ≥ 3 had additional MRI-TRUS targeted biopsies. Taking pathologic results as golden standard, the detection rates were compared between the 2 methods using χ 2 test. Results:Initially, all of the 168 patients underwent TRUS biopsy. PCa was detected in 86 (101 niduses) of 168 patients (51.19%, 86/168), 82 (91 niduses) (48.81%, 82/168) were not prostate cancer. Seventy eight (46.43%, 78/168) cases of PCa were detected by TRUS biopsy, and 63 (58.33%, 63/168) cases of PCa were detected by MRI-TRUS fusion guided biopsy, the difference was statistically significant between TRUS biopsy and MRI-TRUS fusion guided biopsy (χ 2=3.73, P=0.035). The 168 patients were biopsied with a total of 2 300 cores, including TRUS biopsy 2 016 cores and MRI-TRUS fusion targeted biopsy 284 cores. Additionally, the detection rate for per cores for MRI-TRUS fusion targeted biopsy (51.76%, 147/284) was significantly higher than that for TRUS biopsy cores (19.64%, 396/2 016) (χ 2=142.38, P<0.05). Among patients with a positive biopsy for PCa, the biopsy cores for conventional TRUS biopsy was 1 032 comparing to 214 cores for MRI-TRUS biopsy. The suspicious MRI-TRUS fusion targeted biopsy (68.69%, 147/214) detected more PCa compared with TRUS biopsy (38.37%, 396/1 032) (χ 2=66.27, P<0.05). Among patients with a positive biopsy for PCa, MRI-TRUS fusion targeted biopsy [69.74% (106/152)] detected more significant cancer cores than TRUS biopsy [54.50% (351/644) ] (χ 2=11.67, P<0.05). Conclusion:MRI-TRUS fusion biopsy combined with PI-RADS V2 increases positive rate markedly and improves the detection rate of clinical significant PCa.

Objective:To compare the early efficacy of proximal femoral bionic nail (PFBN) and proximal femoral nail anti-rotation (PFNA) in the treatment of intertrochanteric fracture in the elderly.Methods:A retrospective cohort study was conducted to analyze the clinical data of 45 elderly patients with intertrochanteric fracture treated at First Affiliated Hospital of Henan Polytechnic University from March 2020 to December 2021, including 13 males and 32 females; aged 70-96 years [(78.6±7.3)years]. According to the AO classification, there were 10 patients with type 31-A1, 16 type 31-A2 and 19 type 31-A3. There were 20 patients treated with PFBN (PFBN group) and 25 with PFNA (PFNA group). The operation time, intraoperaive blood loss, bone healing time, and time to full weight-bearing were compared between the two groups. The tip-apex distance, distance from tail of lag screw to lateral side of intramedllary nail, proximal femoral length and femur neck-shaft angle were compared between the two groups at day 1 postoperatively and at the last follow-up, and their amount of changes between the two time points were also measured. Harris hip score was evaluated at 3, 6 months postoperatively and at the last follow-up. Complications were observed as well.Results:All patients were followed up for 6-21 months [(14.7±3.6)months]. There were no significant differences in operation time, intraoperative blood loss and bone healing time between the two groups (all P>0.05). The time to full weight-bearing was significantly earlier in PFBN group [(7.9±1.2)weeks] than that in PFNA group [(9.1±0.9)weeks] ( P<0.05). At day 1 postoperatively and at last follow-up, the tip-apex distance was (23.4±1.7)mm and (23.3±1.6)mm in PFBN group ( P>0.05), and was (24.5±2.1)mm and (24.3±2.3)mm in PFNA group ( P>0.05); the distance from tail of lag screw to lateral side of intramedllary nail was (8.1±1.1)mm and (11.8±0.9)mm in PFBN group ( P<0.01), and was (7.7±1.0)mm and (12.6±1.6)mm in PFNA group ( P<0.01); the proximal femoral length was (91.3±1.6)mm and (88.5±2.6)mm in PFBN group ( P<0.01), and was (91.4±2.0)mm and (87.6±2.0)mm in PFNA group ( P<0.01); the femur neck-shaft angle was (127.2±2.9)°and (125.7±3.0)° in PFBN group ( P>0.05), and was (128.5±3.0)° and (127.2±3.2)° in PFNA group ( P>0.05). There were no significant differences in the above indicators between the two groups at day 1 postoperatively and at the last follow-up (all P>0.05). The amount of changes in the tip-apex distance and femur neck-shaft angle were (-0.2±0.1)mm and (-1.6±0.7)° in PFBN group, similar with (-0.2±0.2)mm and (-1.5±1.0)° in PFNA group (all P>0.05). However, the amount of changes in the distance from tail of lag screw to lateral side of intramedllary nail and proximal femoral length were (3.6±1.4)mm and (2.7±1.2)mm in PFBN group, significantly lower than (5.2±1.1)mm and (4.0±1.1)mm in PFNA group (all P<0.01). There were no significant differences in Harris hip score between the two groups at 3, 6 months postoperatively or at the last follow-up (all P>0.05). PFBN group had 1 patient with fracture displacement, 2 with compression screw backup and 2 with thigh pain. PFNA group had 1 patient with fracture displacement and 4 with thigh pain. Conclusion:For elderly patients with intertrochanteric fracture, PFBN provides not only earlier full weight-bearing, but also stronger stability of fracture fixation than PFNA.

BACKGROUND:There is currently no anatomic locking plate suitable for the anteromedial platform,so the medial locking plate of the tibial plateau is usually placed forward to fix anteromedial compression fractures caused by hyperextension varus injury.Due to the inability of the locking screw to achieve vertical fixation of the fracture line,coupled with the influence of the patellar ligament,the clinical results are still unsatisfactory. OBJECTIVE:To compare the biomechanical performance of a new type of plate with traditional internal fixation methods in treating hyperextension varus tibial plateau fractures through finite element analysis. METHODS:CT data of 20 cases of hyperextension varus tibial plateau fractures were collected,and their morphological characteristics,such as medial posterior tibial slope,the medial articular fracture angle,surface area,and anterior cortical height were measured.A 24-year-old male volunteer with a height of 175 cm and a weight of 65 kg was selected,and his tibial CT data were imported into Mimics 21.0 software to generate a 3D model.Then,internal fixation models were imported into SolidWorks 2017 software.New type of plate,medial locking plate,posterior medial locking plate,and 6.5 mm hollow screws fixed data models were established based on the measured morphological data.Ansys 17.0 software was used to load stress on the four fixation models and compare their biomechanical performance. RESULTS AND CONCLUSION:(1)With the increase of axial load,the peak stresses of different internal fixation models approximately increased proportionally.At 500 N,the peak stress values were as follows:screw group(6.973 7 MPa)

Objective:To explore the clinical characteristics and prognostic influencing factors of multiple primary lung cancer (MPLC), and to construct a prognostic prediction model.Methods:The clinical data and prognostic information of MPLC patients diagnosed by pathological examination included in the Surveillance, Epidemiology, and End Results (SEER) database from January 2010 to December 2020 were retrospectively analyzed. Patients were randomly divided into training and validation sets according to a 7:3 ratio using R software. Survival curves were plotted by using the Kaplan-Meier method and log-rank test was used for comparison between groups. The independent influencing factors of overall survival (OS) of MPLC patients in the training set were screened using univariate and multivariate Cox proportional hazards models, and accordingly, the nomogram predicting the survival rate of patients at 3, 5 and 8 years were plotted. In the training and validation sets, using the actual survival as the gold standard, the receiver operating characteristic (ROC) curves of the constructed models for predicting the patients' 3-, 5- and 8-year OS rates were plotted, the area under the curve (AUC) was obtained, and C-index of the model was analyzed by using R software. The calibration curves of 3-, 5- and 8-year OS rates predicted by the models and the actual OS rates were plotted.Results:A total of 5 495 MPLC patients were included, 3 846 in the training set and 1 649 in the validation set. The differences in the composition of patients of different ages and AJCC stages between the training and validation sets were statistically significant (both P < 0.05), and the differences in the comparison of other clinicopathological characteristics were not statistically significant (all P > 0.05). The results of multivariate Cox regression analysis showed that males (compared with females, HR = 1.256, 95% CI: 1.144-1.379, P < 0.001), age ≥ 70 years old (compared with 50-59 years old, HR = 1.201, 95% CI: 1.030-1.400, P = 0.019), FPLC with pathological types of squamous cell carcinoma or other types (compared with adenocarcinoma, HR = 1.275, 95% CI: 1.137-1.431, P < 0.001; HR = 1.208, 95% CI: 1.041-1.403, P = 0.013), and SPLC with pathological types of squamous cell carcinoma, small cell lung carcinoma, or other types (compared with adenocarcinoma, HR = 1.270, 95% CI: 1.121-1.440, P < 0.001; HR = 1.978, 95% CI: 1.642-2.384, P < 0.001; HR = 1.246, 95% CI: 1.090-1.424, P = 0.001), and AJCC stage Ⅲ and Ⅳ (compared with stage Ⅰ, HR = 1.645, 95% CI: 1.447-1.869, P < 0.001; HR = 2.078, 95% CI: 1.669-2.587, P < 0.001), FPLC without operation (compared with operation, HR = 1.263, 95% CI: 1.038-1.536, P = 0.020), SPLC without operation (operation vs. no operation, HR = 0.680, 95% CI: 0.579-0.799, P < 0.001), FPLC without lymph node dissection or with clearance of 1-3 regional lymph nodes (compared with clearance of ≥4, HR = 1.225, 95% CI: 1.016-1.477, P = 0.034; HR = 1.314, 95% CI: 1.103-1.566, P = 0.002), FPLC with maximum diameter 3-5 cm or >5 cm (compared with <3 cm, HR = 1.181, 95% CI: 1.053-1.324, P = 0.005; HR = 1.232, 95% CI: 1.069-1.420, P = 0.004), and SPLC with maximum diameter 3-5 cm or >5 cm (compared with <3 cm, HR = 1.560, 95% CI: 1.362-1.786, P < 0.001; HR = 1.727, 95% CI: 1.451-2.054, P < 0.001), and FPLC without chemotherapy (chemotherapy vs. no chemotherapy or unknown, HR = 0.744, 95% CI: 0.655-0.845, P < 0.001) were the independent risk factors of patients' poor OS (all P < 0.05). The results of Kaplan-Meier survival analysis showed that the OS of patients with different gender, race, age, two tumor locations, AJCC staging, pathological type of two lung tumors, maximum diameter of two tumors, and whether two tumors were treated surgically or not, and whether two tumors were treated with chemotherapy or not in the training set were compared, and the differences were all statistically significant (all P < 0.05). Based on the independent factors affecting the OS of MPLC patients screened by the results of multivariate Cox regression analysis, nomogram predicting the 3-, 5- and 8-year OS rates of MPLC were plotted. The results of ROC curve analysis showed that the C-index of the training set's nomogram was 0.679 (95% CI: 0.649-0.701), and the AUC values for predicting the 3-, 5- and 8-year OS rates were 0.601, 0.595 and 0.586, respectively; the C-index of the validation set was 0.678 (95% CI: 0.633-0.720), and the AUC values for predicting 3-, 5- and 8-year OS rates were 0.643, 0.631 and 0.626, respectively. The calibration curves showed that the 3-, 5- and 8-year OS rates of patients predicted by the nomogram models in both the training and validation sets were in good agreement with the actual results with a high goodness-of-fit. Conclusions:The established prognostic model has good predictive value and can effectively assess the prognosis of patients.

Objective: To evaluate the diagnostic value of intravoxel incoherent motion (IVIM) and diffusion kurtosis imaging (DKI) parameters in diagnosing prostate cancer(PCa) in transition zone (TZ) and stratifying pathologic Gleason grade of prostate cancer. Methods: A total of 55 patients who were undergoing preoperative muti-parameters MRI of T₂WI, DWI, IVIM and DKI model for the exploration of prostate cancer (January 2015 to June 2017) with pathologically confirmed by MRI-transrectal ultrasound (TRUS) targeted fusion biopsy were retrospectively included. Parameters were postprocessed by IVIM models including quantitation of perfusion fraction (f), diffusivity (D) and pseudo-diffusivity (D^*) and DKI models including the mean diffusivity (MD), mean kurtosis (MK) and fractional anisotropy (FA) by outlining the 3D VOI. Independent sample t-test was used to compare the differences in lesion parameters between prostate cancer and BPH, low-risk (BPH+Gleason score 6 points) and medium-high-risk lesions (Gleason score ≥7 points). Correlation between ADC values, IVIM and DKI parameters and Gleason scores were assessed with Spearman analysis. Receiver operating characteristic curve analysis was used to evaluate the efficacy of various parameters in the differential diagnosis of prostate cancer and BPH with low-risk or high-risk. Results: 27 (36 focus) cases of PCa and 28 (40 focus) cases of benign prostatic hyperplasia (BPH) in PZ were included, meanwhile, the cases of GS ≥7 and and BPH+(GS=6) were 33,43,respectively. There were significant differences in ADC, D, MD, MK, and FA between patients in PCa-BHP group and high-low risk group in TZ (P<0.05), D^* and f had no significant differences (P>0.05). ADC and MD showed relatively higher negativity correlations (r were -0.585 and -0.489, P<0.05) with GS of PCa in TZ. ADC exhibited a higher area under the curve (AUC 0.864) compared with D with area under the curve (AUC 0.853), however, the difference is not significant (P>0.05). Of model DKI in diagnose of PCa and BPH, the highest classification accuracy was MD(AUC 0.796). The AUC derived from multiple model parameters in different combination of ADC+D value, ADC+MD value, and ADC+MD+D value were 0.892, 0.884, and 0.897, respectively. ADC and D of IVIM model showed a significance difference between GS ≥7 and BPH+(GS=6) with a higher AUC of 0.826 and 0.743. The AUC was 0.851 of the combination of mean ADC and D, 0.846 of combination of mean ADC and MD, the AUC (0.856) of the combination of ADC, D and MD significant higher than any two combined parameters (P>0.05). Conclusions IVIM and DKI models may help to discriminate prostate cancer from BPH, and predict mid-higher GS PCa in TZ. But there is no significant advantage compared with ADC values. It is feasible to stratify the pathological grade of prostate cancer in TZ by mean ADC and MD.