[Clinical value of nomogram model in evaluating the prognosis of cholangiocarcinoma after interventional therapy].

Objective: To investigate the clinical value and efficacy of the nomogram model in evaluating the prognosis of cholangiocarcinoma after interventional therapy. Methods: The clinical data of 259 patients with cholangiocarcinoma who received interventional therapy at the First Affiliated Hospital of zhengzhou University from January 2014 to June 2021 were retrospectively analyzed, including 148 males and 111 females, aged from 26 to 91 (65±12) years. They were randomly divided into a training group (181 cases) and a validation group (78 cases) in a ratio of 7∶3. Cox regression analysis was performed in the training group, independent risk factors affecting the prognosis of patients were screened, and a nomogram for 6-month, 1-year, and 2-year survival was constructed. The performance of the nomogram was analyzed by calculating the area under the receiver operating characteristic curve (AUC) value, calibration curve, and decision curve, and the predictive efficacy of the model was evaluated in the validation group. Results: There was no significant difference in baseline data between the training group and the validation group, which was comparable. Regression analysis showed that T stage (T2: HR=0.147,95%CI: 0.077-0.281;T3: HR=0.207,95%CI: 0.122-0.351;T4: HR=0.864,95%CI: 0.537-1.393), tumor diameter (17-33 mm: HR=0.201,95%CI: 0.119-0.341;≥33 mm: HR=0.795,95%CI: 0.521-1.211) and differentiation degree(middle differentiation: HR=3.318,95%CI: 2.082-5.289;highly differentiation: HR=1.842,95%CI: 1.184-2.867) were risk factors affecting the prognosis of interventional therapy for cholangiocarcinoma. The AUC values of the survival curve prediction models were generally consistent between the training and validation groups, and the AUC values of the training group at 6 months, 1 year, and 2 years were 0.925 (95%CI: 0.888-0.963), 0.921 (95%CI: 0.877-0.964) and 0.974 (95%CI: 0.957-0.993), respectively. In the validation group, the 6-month, 1-year, and 2-year AUC values were 0.951 (95%CI: 0.911-0.991), 0.917 (95%CI: 0.857-0.977) and 0.848 (95%CI: 0.737-0.959), respectively, and the AUC values were all greater than 0.8, suggesting that the nomogram had better discrimination ability. The calibration curves of the prediction models of the two groups were basically consistent, and the shape of the calibration curves at 6 months and 1 year fitted the ideal curve, while the fitting degree of the calibration curves at 2 years was relatively poor. The decision curve showed the high clinical utility of this nomogram in predicting the 6-month, 1-year survival of patients with cholangiocarcinoma. Conclusions: T stage, tumor diameter, and differentiation are independent risk factors affecting the prognosis of patients with interventional cholangiocarcinoma, and the nomogram model proposed in this study has good distinguishing ability and exact clinical value for prognosis evaluation.

[Comparative study of CT relative enhancement value and subjective visual evaluation for intestinal ischemia in patients with closed loop obstruction].

Objective: To compare and evaluate the diagnostic performance of visual evaluation and CT maximal density relative enhancement value in the diagnosis of intestinal ischemia complication in patients with closed loop obstruction and to explore the feasibility of CT maximal density relative enhancement value in quantifying the degrees of intestinal ischemia. Methods: The clinical and CT imaging data of 82 patients, 46 males and 36 females, aged from 19 to 78(52±18) years, with closed loop obstruction were retrospectively analyzed in the First Affiliated Hospital of Zhengzhou University from July 2017 to July 2019. All patients were classified into three groups: necrosis group (28 cases), ischemia group (22 cases), non-ischemia group(32 cases) using clinicopathologic results as reference standard. CT visual evaluation was performed by two experienced radiologists. The sensitivity, specificity, positive and negative predictive values and accuracy of the two observers were calculated respectively. The inter-observer agreement was analyzed by kappa analysis. Maximal density relative enhancement value was defined as the difference CT value of an ROI at dilated obstructed loops between contrast-enhanced and unenhanced CT images. The differences among groups were compared by one-way analysis of variance. Diagnostic performances were evaluated by receiver operating characteristic (ROC) curve analysis. Results: The sensitivity, specificity, positive and negative predictive values and accuracy of observer1 were 62.0%, 87.5%, 88.6%, 59.6%, 72.0%, and 58.0%, 93.8%, 93.5%, 58.8%, 72.0%for observer2, respectively. The kappa value of inter-observer agreement was 0.747. The unenhanced CT value of necrosis group, ischemia group and non-ischemia group was (53.7±9.7), (45.7±7.2) and (44.7±7.0) HU, enhanced CT value was (60.5±10.1), (65.0±11.6) and (87.0±15.8) HU, relative enhancement value was(6.8±8.4), (19.2±12.4) and(44.7±16.2)HU, respectively. All had a statistical difference among three groups (all P<0.05). The unenhanced CT value of necrosis group was higher than that of ischemia group and un-ischemia group (both P<0.05). The enhanced CT value of non-ischemia group was higher than that of ischemia group and necrosis (both P<0.05). The relative enhancement value all had a significant difference between groups (all P<0.05). Taking maximal density relative enhancement value below 19.5 HU as diagnosis threshold, the sensitivity, specificity and area under curve(AUC) were 96.9%, 74.0% and 0.947, respectively. Taking enhanced CT value below 66.5 HU as diagnosis threshold, the sensitivity, specificity and AUC were 93.8%, 60.0% and 0.903, respectively; the sensitivity was higher than that of objective visual evaluation. Conclusion: Maximal density relative enhancement value can quantize the bowel wall enhancement, and is a more reliable way in the diagnosis of intestinal ischemia than visual evaluation.

Accuracy of detecting vertical root fractures in non-root filled teeth using cone beam computed tomography: effect of voxel size and fracture width.

AIM: To investigate simultaneously the effect of voxel size and fracture width on the accuracy of detecting vertical root fractures (VRFs) in non-root filled teeth when using cone beam computed tomography. METHODOLOGY: Fifty-one of 161 extracted human permanent teeth (16 anterior teeth, 132 premolars and 13 mandibular molars) were selected randomly for VRF induction with two fracture widths. All teeth were scanned with four CBCT units at different voxel sizes provided by the units. Three observers classified the presence or absence of VRF using a 5-point scale. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and area under the ROC curve (AUC) were calculated. AUCs amongst voxel sizes and between the fracture widths were compared using the Z test. Intra- and inter-observer agreement was assessed using weighted Cohen kappa. RESULTS: For the NewTom VGi and ProMax 3D Mid CBCT unit, no significant differences were found amongst voxel sizes for the AUCs, irrespective of the fracture width (P > 0.05). There were significant differences between images scanned with voxel size 250 and 160 µm (P = 0.02), and images scanned with voxel size 250 and 80 µm for AUCs in the narrow VRF group for the 3D Accuitomo 170 unit (P = 0.03). For i-CAT FLX, significant differences were found between the voxel protocols of 300 µm and of the other three voxel sizes for AUC, sensitivity and NPV (P < 0.05). Significant differences between the wide and the narrow VRF groups for AUCs were found for 3D Accuitomo 170 (P = 0.01) and ProMax 3D Mid (P < 0.01). CONCLUSIONS: Cone beam computed tomography was accurate for detecting VRF in non-root filled teeth. Fracture width had an effect on the detection of VRF. The effect of the voxel size on the detection of VRF depended on the CBCT unit used.

[Condition and future of robotics in stomatology].

The successful research and application of robotic techniques will promote more dental practitioners to operate robotic simulation systems in various aspects as clinical practice, teaching and scientific research. This paper reviews the history and development of dental robotics in a systematic way. We specifically introduce the application conditions and estimate the future development of dental robotics. Besides, we also foresee the potential impact and change brought by dental robotic techniques.

[Preliminary study on the automatic preparation of dental implant socket controlled by micro-robot].

Objective: To analyze the quantitative relationship between the number of layers of laser pulses and the amount of step in ultra-short pulse laser cutting of cortical bone, optimize the robot's vertical single stepping parameters, and to explore the feasibility of automatic preparation of dental implant cavity using robot controlling ultra-short pulse laser, in order to lay the foundation for automated dental implant surgery. Methods: Eight pig ribs were segmented into to make 16 specimens. Using the robotic surgical system and path planning software independently developed by our group, circular holes with a diameter of 4 mm were cut two-dimensionally in the rib segments to obtain the quantification relationship of the number of laser pulse layers (n) and the depth of two-dimensional (2D) cutting (d). When conducting the three-dimensional (3D) cutting procedure, the number of pulse layers were set to 5, 10, 15, 20, 25, 30, 35, 40, 45, 50 layers, the vertical single step amount was an integer value corresponding to the results of 2D cutting depth, and the number of pulses (n') corresponding to the minimum difference between the theoretical depth of cut and the actual depth of cut was obtained. The n' was taken as the most suitable single step pulse layer, the rib segment was cut, and the depth of single cut was measured while the integer value was taken as the most appropriate vertical single step amount (d'). The vertical parameters of laser single stepping were set as n' layer pulse and d' µm step size. The 3D cutting produces a cylindrical cavity with a diameter of 4 mm and a height of 2 mm to evaluate the 3D cutting accuracy (the difference between the measured value and the theoretical value of cutting diameter or depth). Ten 4 mm×3 mm implant holes were automatically prepared on the bilateral femurs of 5 Japanese big white rabbits, and ten 4 mm×3 mm implants made by 3D printer were artificially implanted, and the preparation effect of the implant cavities was evaluated. Results: The quantitative relationship curve between the number of laser pulses (n) and 2D depth of cut (d) showed a linear upward trend. The linear fitting obtained the quantitative relation function formula d=9.278 4 n±26.763 0, R(2)=0.988 9. The optimum number of single step pulse layers was 5 layers, and the vertical single step amount was 50 µm, so as to set the vertical parameters of a single step of a 3D cutting, and the 3D cutting diameter accuracy was (3.98±2.87) µm, with a depth accuracy of (15.42±5.44) µm. Automated preparation of 10 implant cavities on the femur of the rabbit were completed. When the implants were placed into the implant cavities, there was resistance, but they were fully seated and primary stability has been achieved after seating implant placement. Conclusions: The method of non-contact automatic preparation of dental implant cavities using robot controlling ultra-short pulse laser is feasible. By optimizing the single cutting process parameters, precise control of laser cutting cortical bone can be realized.

[Study on the appropriate parameters of automatic full crown tooth preparation for dental tooth preparation robot].

Objective: To further study the most suitable parameters for automatic full crown preparation using oral clinical micro robot. Its purpose is to improve the quality of automated tooth preparing for the system and to lay the foundation for clinical application. Methods: Twenty selected artificial resin teeth were used as sample teeth. The micro robot automatic tooth preparation system was used in dental clinic to control the picosecond laser beam to complete two dimensional cutting on the resin tooth sample according to the motion planning path. Using the laser scanning measuring microscope, each layer of cutting depth values was obtained and the average value was calculated. The monolayer cutting depth was determined. The three-dimensional (3D) data of the target resin teeth was obtained using internal scanner, and the CAD data of full-crown tooth preparation was designed by CAD self-develged software. According to the depth of the single layer, 11 complete resin teeth in phantom head were automatically prepared by the robot controlling the laser focused spot in accordance with the layer-cutting way. And the accuracy of resin tooth preparation was evaluated with the software. Using the same method, monolayer cutting depth parameter for cutting dental hard tissue was obtained. Then 15 extracted mandibular and maxillary first molars went through automatic full crown tooth preparation. And the 3D data of tooth preparations were obtained with intra oral scanner. The software was used to evaluate the accuracy of tooth preparation. Results: The results indicated that the single cutting depth of cutting resin teeth and in vitro teeth by picosecond laser were (60.0±2.6) and (45.0±3.6) µm, respectively. Using the tooth preparation robot, 11 artificial resin teeth and 15 complete natural teeth were automatically prepared, and the average time were (13.0±0.7), (17.0±1.8) min respectively. Through software evaluation, the average preparation depth of the occlusal surface of 11 resin teeth was approximately (2.089±0.026) mm, the error was about (0.089±0.026) mm; the average convergence angle was about 6.56°±0.30°, the error was about 0.56°±0.30°. Compared with the target preparation shape, the average shape error of the 11 resin tooth preparations was about 0.02-0.11 mm. And the average preparation depth of the occlusal surface of 15 natural teeth was approximately (2.097±0.022) mm, the error was about (0.097±0.022) mm; the average convergence angle was about 6.98°±0.35°, the error was about 0.98°±0.35°. Compared with the target preparation shape, the average shape error of the 15 natural tooth preparations was about 0.05-0.17 mm. Conclusions: The experimental results indicate that the automatic tooth preparation for resin teeth and the teeth were completed according to the specific parameters of the single cutting depth by the micro robot controlling picosecond laser respectively, its preparation accuracy met the clinical needs. And the suitability of the parameter was confirmed.