OBJECTIVE: To evaluate the early effectiveness of navigation-free robot-assisted total knee arthroplasty (TKA) compared to traditional TKA in the treatment of knee osteoarthritis combined with extra-articular deformities. METHODS: The clinical data of 30 patients with knee osteoarthritis combined with extra-articular deformities who met the selection criteria between June 2019 and January 2024 were retrospectively analyzed. Fifteen patients underwent CORI navigation-free robot-assisted TKA and intra-articular osteotomy (robot group) and 15 patients underwent traditional TKA and intra-articular osteotomy (traditional group). There was no significant difference in age, gender, body mass index, affected knee side, extra-articular deformity angle, deformity position, deformity type, and preoperative knee range of motion, American Knee Society (KSS) knee score and KSS function score, and lower limb alignment deviation between the two groups ( P>0.05). The operation time, intraoperative blood loss, and complications of the two groups were recorded and compared. The knee range of motion and lower limb alignment deviation were recorded before operation and at 6 months after operation, and the knee joint function was evaluated by KSS knee score and function score. RESULTS: There was no significant difference in operation time between the two groups ( P>0.05); the intraoperative blood loss in the robot group was significantly less than that in the traditional group ( P<0.05). Patients in both groups were followed up 6-12 months, with an average of 8.7 months. The incisions of all patients healed well, and there was no postoperative complication such as thrombosis or infection. At 6 months after operation, X-ray examination showed that the position of the prosthesis was good in both groups, and there was no loosening or dislocation of the prosthesis. The knee joint range of motion, the lower limb alignment deviation, and the KSS knee score and KSS function score significantly improved in both groups ( P<0.05) compared to preoperative ones. The changes of lower limb alignment deviation and KSS function score between pre- and post-operation in the robot group were significantly better than those in the traditional group ( P<0.05), while the changes of other indicators between pre- and post-operation in the two groups were not significant ( P>0.05). CONCLUSION Compared to traditional TKA, navigation-free robot-assisted TKA for knee osteoarthritis with extra-articular deformities results in less intraoperative blood loss, more precise reconstruction of lower limb alignment, and better early effectiveness. However, long-term effectiveness require further investigation.
Humans ; Arthroplasty, Replacement, Knee/methods* ; Osteoarthritis, Knee/surgery* ; Robotic Surgical Procedures/methods* ; Male ; Female ; Retrospective Studies ; Range of Motion, Articular ; Middle Aged ; Aged ; Treatment Outcome ; Osteotomy/methods* ; Knee Joint/physiopathology* ; Operative Time

Objective:To compare the efficacy of cannulated screw internal fixation combined with quadratus femoris bone flap with preservation of the posterior superior retinaculum and cannulated screw internal fixation alone in the treatment of femoral neck fracture in young and middle-aged patients.Methods:A retrospective cohort study was conducted to analyze the clinical data of 83 young and middle-aged patients with femoral neck fracture admitted to the 920th Hospital of Joint Logistic Support Force of PLA from January 2018 to January 2023, including 56 males and 27 females, aged 28-55 years [(42.7±3.2)years]. According to Garden classification, the fractures were classified as type III in 22 patients and type IV in 61. Based on Pauwels classification, the fractures were classified as type I in 15 patients, type II in 38 and type III in 30. Forty patients were treated with cannulated screw internal fixation combined with modified quadratus femoris bone flap (cannulated screw combined with bone flap group) and 43 with cannulated screw internal fixation alone (cannulated screw group). The two groups were compared in terms of the operation time, intraoperative blood loss, time to weight-bearing, length of hospital stay, and wound healing. The visual analogue scale (VAS) scores and Harris hip function scores at 1, 3, 6, 12 months after surgery and at the last follow-up. The postoperative complication rate was detected.Results:All the patients were followed up for 20-70 months [(40.0±1.2)months]. The operation time and intraoperative blood loss were (105.2±2.7)minutes and (100.6±16.3)ml in the cannulated screw combined with bone flap group, which were longer or more than (92.4±4.7)minutes and (92.5±14.6)ml in the cannulated screw group ( P<0.01). The time to weight-bearing was (12.1±1.4)weeks in the cannulated screw combined with bone flap group, shorter than (23.6±1.2)weeks in the cannulated screw group ( P<0.01). There was no statistically significant difference in the length of hospital stay between the two groups (P>0.05). The incisions in both groups were healed by first intention. At 1 month after surgery, no statistically significant difference was observed in VAS scores between the two groups ( P>0.05); at 3, 6, 12 months after surgery and at the last follow-up, the VAS scores were (6.6±0.2)points, (4.5±0.3)points, (3.2±0.5)points, and (2.6±0.4)points in the cannulated screw combined with bone flap group, lower than (7.0±0.1)points, (5.2±0.2)points, (3.9±0.4)points, and (3.3±0.1)points in the cannulated screw group ( P<0.05 or 0.01). At 1 and 3 months after surgery, no statistically significant difference was observed in the Harris hip function scores between the two groups ( P>0.05); at 6, 12 months after surgery and at the last follow-up, the Harris hip function scores were (82.2±1.7)points, (90.0±1.4)points, and (91.6±1.0)points in the cannulated screw combined with bone flap group, higher than (75.2±1.7)points, (83.4±1.9)points, and (85.2±0.7)points in the cannulated screw group ( P<0.01). At the last follow-up, in the cannulated screw combined with bone flap group, the Harris hip function was rated excellent in 32 patients, good in 5, and fair in 3, with an excellent and good rate of 92.5%, while in the cannulated screw group, the Harris hip function was rated excellent in 20 patients, good in 13, and fair in 10, with an excellent and good rate of 76.7% ( P<0.05). The postoperative complication rate was 5.0% (2/40) in the cannulated screw combined with bone flap group, significantly lower than 23.2% (10/43) in the cannulated screw group ( P<0.05). Conclusion:Compared with cannulated screw internal fixation alone, cannulated screw internal fixation combined with quadratus femoris bone flap with preservation of the posterior superior retinaculum has the advantages of earlier weight-bearing, less pain, better recovery of hip joint function, and lower incidence of postoperative complications in the treatment of femoral neck fracture in young and middle-aged patients, despite longer operation time and more intraoperative blood loss.

Objective:To explore the application of machine learning (ML) models based on radiomics and dosiomics to the assessment of hematologic toxicity (HT) in patients with locally advanced cervical cancer, and to preliminarily explore the comprehensive application of multi-omics features.Methods:A retrospective study was conducted on the clinical data, planning computed tomography (CT) images, and dose files of 205 patients with locally advanced cervical cancer who received concurrent chemoradiotherapy at the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, from January 2022 to June 2023. Patients were categorized according to the severity of HT. Radiomics and dosiomics features were extracted from the same regions of interest (ROIs), followed by feature selection utilizing a random forest algorithm. Then, radiomics, dosiomics, and hybrid models were established based on extreme gradient boosting (XGBoost). The classification performance of these models was assessed by calculating their sensitivity, specificity, and area under the receiver operating characteristic curve (AUC).Results:The radiomics model yielded sensitivity, specificity, and AUC of 0.42, 0.86, and 0.78, respectively. The dosiomics model exhibited sensitivity, specificity, and AUC of 0.50, 0.90, and 0.74, respectively. In contrast, the hybrid model achieved sensitivity, specificity, and AUC of 0.50, 0.83, and 0.83, respectively. These findings suggest that the hybrid model possessed an enhanced classification capability compared to the individual radiomics and dosiomics models.Conclusions:It is feasible to use ML models based on radiomics and dosiomics to conduct the classification and prediction of HT in patients with locally advanced cervical cancer treated with concurrent chemoradiotherapy. Furthermore, integrating both radiomics features and dosiomics features can improve the classification performance of relevant prediction models, thus holding application potentials to optimize treatment strategies for patients with locally advanced cervical cancer.

Objective:To explore the application of machine learning (ML) models based on radiomics and dosiomics to the assessment of hematologic toxicity (HT) in patients with locally advanced cervical cancer, and to preliminarily explore the comprehensive application of multi-omics features.Methods:A retrospective study was conducted on the clinical data, planning computed tomography (CT) images, and dose files of 205 patients with locally advanced cervical cancer who received concurrent chemoradiotherapy at the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, from January 2022 to June 2023. Patients were categorized according to the severity of HT. Radiomics and dosiomics features were extracted from the same regions of interest (ROIs), followed by feature selection utilizing a random forest algorithm. Then, radiomics, dosiomics, and hybrid models were established based on extreme gradient boosting (XGBoost). The classification performance of these models was assessed by calculating their sensitivity, specificity, and area under the receiver operating characteristic curve (AUC).Results:The radiomics model yielded sensitivity, specificity, and AUC of 0.42, 0.86, and 0.78, respectively. The dosiomics model exhibited sensitivity, specificity, and AUC of 0.50, 0.90, and 0.74, respectively. In contrast, the hybrid model achieved sensitivity, specificity, and AUC of 0.50, 0.83, and 0.83, respectively. These findings suggest that the hybrid model possessed an enhanced classification capability compared to the individual radiomics and dosiomics models.Conclusions:It is feasible to use ML models based on radiomics and dosiomics to conduct the classification and prediction of HT in patients with locally advanced cervical cancer treated with concurrent chemoradiotherapy. Furthermore, integrating both radiomics features and dosiomics features can improve the classification performance of relevant prediction models, thus holding application potentials to optimize treatment strategies for patients with locally advanced cervical cancer.

Objective:To compare the efficacy of cannulated screw internal fixation combined with quadratus femoris bone flap with preservation of the posterior superior retinaculum and cannulated screw internal fixation alone in the treatment of femoral neck fracture in young and middle-aged patients.Methods:A retrospective cohort study was conducted to analyze the clinical data of 83 young and middle-aged patients with femoral neck fracture admitted to the 920th Hospital of Joint Logistic Support Force of PLA from January 2018 to January 2023, including 56 males and 27 females, aged 28-55 years [(42.7±3.2)years]. According to Garden classification, the fractures were classified as type III in 22 patients and type IV in 61. Based on Pauwels classification, the fractures were classified as type I in 15 patients, type II in 38 and type III in 30. Forty patients were treated with cannulated screw internal fixation combined with modified quadratus femoris bone flap (cannulated screw combined with bone flap group) and 43 with cannulated screw internal fixation alone (cannulated screw group). The two groups were compared in terms of the operation time, intraoperative blood loss, time to weight-bearing, length of hospital stay, and wound healing. The visual analogue scale (VAS) scores and Harris hip function scores at 1, 3, 6, 12 months after surgery and at the last follow-up. The postoperative complication rate was detected.Results:All the patients were followed up for 20-70 months [(40.0±1.2)months]. The operation time and intraoperative blood loss were (105.2±2.7)minutes and (100.6±16.3)ml in the cannulated screw combined with bone flap group, which were longer or more than (92.4±4.7)minutes and (92.5±14.6)ml in the cannulated screw group ( P<0.01). The time to weight-bearing was (12.1±1.4)weeks in the cannulated screw combined with bone flap group, shorter than (23.6±1.2)weeks in the cannulated screw group ( P<0.01). There was no statistically significant difference in the length of hospital stay between the two groups (P>0.05). The incisions in both groups were healed by first intention. At 1 month after surgery, no statistically significant difference was observed in VAS scores between the two groups ( P>0.05); at 3, 6, 12 months after surgery and at the last follow-up, the VAS scores were (6.6±0.2)points, (4.5±0.3)points, (3.2±0.5)points, and (2.6±0.4)points in the cannulated screw combined with bone flap group, lower than (7.0±0.1)points, (5.2±0.2)points, (3.9±0.4)points, and (3.3±0.1)points in the cannulated screw group ( P<0.05 or 0.01). At 1 and 3 months after surgery, no statistically significant difference was observed in the Harris hip function scores between the two groups ( P>0.05); at 6, 12 months after surgery and at the last follow-up, the Harris hip function scores were (82.2±1.7)points, (90.0±1.4)points, and (91.6±1.0)points in the cannulated screw combined with bone flap group, higher than (75.2±1.7)points, (83.4±1.9)points, and (85.2±0.7)points in the cannulated screw group ( P<0.01). At the last follow-up, in the cannulated screw combined with bone flap group, the Harris hip function was rated excellent in 32 patients, good in 5, and fair in 3, with an excellent and good rate of 92.5%, while in the cannulated screw group, the Harris hip function was rated excellent in 20 patients, good in 13, and fair in 10, with an excellent and good rate of 76.7% ( P<0.05). The postoperative complication rate was 5.0% (2/40) in the cannulated screw combined with bone flap group, significantly lower than 23.2% (10/43) in the cannulated screw group ( P<0.05). Conclusion:Compared with cannulated screw internal fixation alone, cannulated screw internal fixation combined with quadratus femoris bone flap with preservation of the posterior superior retinaculum has the advantages of earlier weight-bearing, less pain, better recovery of hip joint function, and lower incidence of postoperative complications in the treatment of femoral neck fracture in young and middle-aged patients, despite longer operation time and more intraoperative blood loss.

Objective:To construct classification prediction models for gamma passing rate using radiomics-based machine learning approaches and data from multiple radiotherapy institutions and evaluate the models′ performance.Methods:The data from 572 volumetric-modulated arc therapy (VMAT) patients across three radiotherapy institutions (514 for training and 58 for testing)were retrospectively collected. Additionally, 45 VMAT plans were collected from a single institution as an independent external validation set. For all the data, a three-dimensional dose validation approach based on actual measurements of phantoms was utilized, and gamma analysis was performed at the 3%/2 mm criterion using a dose threshold of 10%, absolute doses, and global normalization. After radiomic features were extracted from dose files, feature selection was performed using the random forest (RF) method and RF combined with Shapley Additive exPlanation (SHAP). Then, feature subsets of varying sizes (10, 20, 30, 40, and 50) were selected based on feature rankings. Using these subsets as inputs, data training was conducted using the Extreme Gradient Boosting (XGBoost) algorithm. Finally, the models′ classification performance was assessed using the area under the curve (AUC) values and F1-score.Results:Under the 3%/2 mm criterion, all models performed the best in the case of 20 feature subsets. The optimal prediction model established based on the feature selection using RF exhibited AUC and F1-score of 0.88 and 0.89, respectively on the testing set and 0.82 and 0.90, respectively, on the validation set. The optimal prediction model built based on the feature selection using RF combined with SHAP yielded AUC and F1-score of 0.86 and 0.92 on the testing set and 0.87 and 0.89, respectively, on the validation set, along with superior robustness. Therefore, the second model possessed certain advantages over the first model.Conclusions:For multicenter dose verification result, it is feasible to construct a machine learning prediction model with high classification performance using radiomic features derived from dose files, combined with feature selection based on SHAP. This approach can assist in advancing the clinical applications and implementation of gamma passing rate prediction models.

Objective:To construct classification prediction models for gamma passing rate using radiomics-based machine learning approaches and data from multiple radiotherapy institutions and evaluate the models′ performance.Methods:The data from 572 volumetric-modulated arc therapy (VMAT) patients across three radiotherapy institutions (514 for training and 58 for testing)were retrospectively collected. Additionally, 45 VMAT plans were collected from a single institution as an independent external validation set. For all the data, a three-dimensional dose validation approach based on actual measurements of phantoms was utilized, and gamma analysis was performed at the 3%/2 mm criterion using a dose threshold of 10%, absolute doses, and global normalization. After radiomic features were extracted from dose files, feature selection was performed using the random forest (RF) method and RF combined with Shapley Additive exPlanation (SHAP). Then, feature subsets of varying sizes (10, 20, 30, 40, and 50) were selected based on feature rankings. Using these subsets as inputs, data training was conducted using the Extreme Gradient Boosting (XGBoost) algorithm. Finally, the models′ classification performance was assessed using the area under the curve (AUC) values and F1-score.Results:Under the 3%/2 mm criterion, all models performed the best in the case of 20 feature subsets. The optimal prediction model established based on the feature selection using RF exhibited AUC and F1-score of 0.88 and 0.89, respectively on the testing set and 0.82 and 0.90, respectively, on the validation set. The optimal prediction model built based on the feature selection using RF combined with SHAP yielded AUC and F1-score of 0.86 and 0.92 on the testing set and 0.87 and 0.89, respectively, on the validation set, along with superior robustness. Therefore, the second model possessed certain advantages over the first model.Conclusions:For multicenter dose verification result, it is feasible to construct a machine learning prediction model with high classification performance using radiomic features derived from dose files, combined with feature selection based on SHAP. This approach can assist in advancing the clinical applications and implementation of gamma passing rate prediction models.

Objective:Based on radiomics characteristics, different machine learning classification models are constructed to predict the gamma pass rate of dose verification in intensity-modulated radiotherapy for pelvic tumors, and to explore the best prediction model.Methods:The results of three-dimensional dose verification based on phantom measurement were retrospectively analyzed in 196 patients with pelvic tumor intensity-modulated radiotherapy plans. The gamma pass rate standard was 3%/2 mm and 10% dose threshold. Prediction models were constructed by extracting radiomic features based on dose documentation. Four machine learning algorithms, random forest, support vector machine, adaptive boosting, and gradient boosting decision tree were used to calculate the AUC value, sensitivity, and specificity respectively. The classification performance of the four prediction models was evaluated.Results:The sensitivity and specificity of the random forest, support vector machine, adaptive boosting, and gradient boosting decision tree models were 0.93, 0.85, 0.93, 0.96, 0.38, 0.69, 0.46, and 0.46, respectively. The AUC values were 0.81 and 0.82 for the random forest and adaptive boosting models, respectively, and 0.87 for the support vector machine and gradient boosting decision tree models.Conclusions:Machine learning method based on radiomics can be used to construct a prediction model of gamma pass rate for specific dosimetric verification of pelvic intensity-modulated radiotherapy. The classification performance of the support vector machine model and gradient boosting decision tree model is better than that of the random forest model and adaptive boosting model.

Objective:To explore the feasibility of a classification prediction model for gamma pass rates (GPRs) under different intensity-modulated radiation therapy techniques for pelvic tumors using a radiomics-based machine learning approach, and compare the classification performance of four integrated tree models.Methods:With a retrospective collection of 409 plans using different IMRT techniques, the three-dimensional dose validation results were adopted based on modality measurements, with a GPR criterion of 3%/2 mm and 10% dose threshold. Then prediction were built models by extracting radiomics features based on dose documentation. Four machine learning algorithms were used, namely random forest (RF), adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM). Their classification performance was evaluated by calculating sensitivity, specificity, F1 score, and AUC value. Results:The RF, AdaBoost, XGBoost, and LightGBM models had sensitivities of 0.96, 0.82, 0.93, and 0.89, specificities of 0.38, 0.54, 0.62, and 0.62, F1 scores of 0.86, 0.81, 0.88, and 0.86, and AUC values of 0.81, 0.77, 0.85, and 0.83, respectively. XGBoost model showed the highest sensitivity, specificity, F1 score, and AUC value, outperforming the other three models. Conclusions:To build a GPR classification prediction model using a radiomics-based machine learning approach is feasible for plans using different intensity-modulated radiotherapy techniques for pelvic tumors, providing a basis for future multi-institutional collaborative research on GPR prediction.

Objective:To explore the feasibility and validity of constructing an intensity-modulated radiotherapy gamma pass rate prediction model after combining the SHAP values with the extreme gradient boosting tree (XGBoost) algorithm feature selection technique, and to deliver corresponding model interpretation.Methods:The dose validation results of 196 patients with pelvic tumors receiving fixed-field intensity-modulated radiotherapy using modality-based measurements with a gamma pass rate criterion of 3%/2 mm and 10% dose threshold in Hunan Provincial Tumor Hospital from November 2020 to November 2021 were retrospectively analyzed. Prediction models were constructed by extracting radiomic features based on dose files and using SHAP values combined with the XGBoost algorithm for feature filtering. Four machine learning classification models were constructed when the number of features was 50, 80, 110 and 140, respectively. The area under the receiver operating characteristic curve (AUC), recall rate and F1 score were calculated to assess the classification performance of the prediction models.Results:The AUC of prediction model constructed with 110 features selected based on the SHAP-valued features was 0.81, the recall rate was 0.93 and the F1 score was 0.82, which were all better than the other 3 models.Conclusion:For intensity-modulated radiotherapy of pelvic tumor, SHAP values can be used in combination with the XGBoost algorithm to select the optimal subset of radiomic features to construct predictive models of gamma pass rates, and deliver an interpretation of the model output by SHAP values, which may provide value in understanding the prediction by machine learning-dependent models.