Objective:To construct a machine learning model based on localization CT-based radiomics, dosiomics and clinical features for predicting radiotherapy response in non-small cell lung cancer (NSCLC) and validate its application value.Methods:A retrospective case series study was conducted. A total of 138 NSCLC patients who received radiotherapy at the Affiliated Hospital of Jining Medical University from January 2016 to December 2022 were selected. The efficacy was evaluated according to the Response Evaluation Criteria in Solid Tumors (RECIST) 1.1, and the patients were stratified according to the objective remission (complete remission+partial remission). Random stratified sampling was used to divide the 138 patients into a training group (96 cases) and an internal validation group (42 cases) at a ratio of 7∶3. Additionally, 33 patients who received radiotherapy at Jining Cancer Hospital from January 2019 to December 2022 were included as the external validation group. Based on the pre-radiotherapy data of the radiotherapy planning system, PyRadiomics software package was used to extract 107 radiomics features and 107 dosiomics features for each patient. Pearson correlation analysis and LASSO regression analysis were used for dimensionality reduction screening; the final selected features were weighted and integrated to generate radiomics-dosiomics scores (RDS), which were then input into logistic regression (LR), support vector machine (SVM), extremely randomized forest (Extra Trees), K-nearest neighbor algorithm (KNN), lightweight gradient boosting machine (Light GBM), and multi-layer perceptron (MLP) machine learning algorithms to construct 6 radiomics-dosiomics models (RDM) for predicting the objective remission. RECIST 1.1 standard was used to evaluate objective remission as the gold standard, receiver operating characteristic (ROC) curve of 6 RDM for predicting objective remission was plotted, and the optimal algorithm for RDM was selected. Univariate and multivariate logistic regression were performed on demographic characteristics, hematological indicators and radiotherapy parameters of the training group to screen independent risk factors for NSCLC patients who received radiotherapy but did not achieve objective remission. These factors were input into the optimal machine learning algorithm to construct a clinical model (CM). Combined with features from RDS and CM, the clinical feature-radiomics-dosiomics combined model (CRDM) was established, and the nomogram of the model for predicting objective remission in NSCLC patients with radiotherapy was drawn. ROC curves were used to evaluate the efficacy of CM, RDM and CRDM in predicting the objective remission in NSCLC patients with radiotherapy in the training group, internal validation group and external validation group.Results:Four radiomics features (including grayscale variance, low grayscale long-range operation emphasis, low grayscale area emphasis, and small area low grayscale area emphasis, all of which were texture features) and 6 dosiomics features [including 1 first-order feature (robust mean absolute deviation), 4 texture features (grayscale non-uniformity, large area emphasis, large area high grayscale emphasis, contrast) and 1 shape feature (shortest axis length)] were selected. ROC curve analysis showed that the area under the curve (AUC) of the RDM constructed using SVM algorithm for judging the objective remission in the training group and the internal validation group was 0.907 (95% CI: 0.836-0.977) and 0.822 (95% CI: 0.685-0.959), which were higher than RDM constructed using other algorithms, and the sensitivity (96.2% and 91.7%), specificity (78.6% and 76.7%) and accuracy (83.3% and 81.0%) at the optimal cut-off values were all higher. Considering the stability and generalization ability of the model, SVM algorithm was ultimately used to construct RDM, CM and CRDM uniformly. Based on training group data, univariate and multivariate logistic regression analysis showed that elevated platelet-to-lymphocyte ratio (PLR) ( OR = 1.001, 95% CI: 1.000-1.003, P = 0.035) and increased target volume of radiotherapy plan ( OR = 1.001, 95% CI: 1.000-1.001, P = 0.008) were independent risk factors for failure to achieve objective remission. ROC curve analysis showed that in the training group and the internal validation group, the AUC of CRDM predicting objective remission were 0.914 (95% CI: 0.856-0.972) and 0.864 (95% CI: 0.754-0.974), respectively, which were better than CM [AUC were 0.735 (95% CI: 0.612-0.857) and 0.697 (95% CI: 0.507-0.888)] and RDM, respectively. In the external validation group, the AUC of CRDM, CM and RDM were 0.778 (95% CI: 0.500-1.000), 0.667 (95% CI: 0.434-0.899) and 0.741 (95% CI: 0.463-1.000), respectively. Conclusions:The CRDM constructed by combining radiomics, dosiomics and clinical features can comprehensively and accurately evaluate the radiotherapy response of NSCLC patients, and may have important clinical application value in achieving precision medicine and optimizing treatment strategies.

Objective:To develop and validate a combined model integrating radiomics, dosiomics, and clinical parameters based on CT simulation and dosimetric images in order to predict the occurrence of radiation pneumonitis (RP) in patients with non-small cell lung cancer (NSCLC).Methods:A retrospective study was conducted on the clinic data of 143 NSCLC patients who received radiotherapy at the Affiliated Hospital of Jining Medical University from January 2016 to December 2022. Patients were randomly stratified into a training group ( n = 100) and an internal validation group ( n = 43) at a 7∶3 ratio. Moreover, clinic data were collected from 34 NSCLC patients who received radiotherapy at the Jining Cancer Hospital between January 2019 and December 2022 as an external validation group. All three groups (the training group, internal validation, and external validation groups) were further categorized into two groups based on the RP severity (i.e., RP ≥ grade 2 and RP < grade 2). Their radiotherapy dose, CT simulation, and 3D dose distribution images were collected. Then, the total lung minus planning target volume (TL-PTV) was defined as the region of interest (ROI) for radiomics and dosiomic feature extraction, followed by feature dimensionality reduction. Consequently, key features associated with RP were determined. Four predictive models were developed using machine learning approaches (especially multilayer perceptron, MLP): a clinical model (CM), a radiomics model (RM), a dosiomics model (DM), and a radiomics and dosiomics nomogram (RDN), with a nomogram subsequently constructed. Ultimately, the performance and clinical feasibility of these models were assessed using receiver operating characteristic (ROC), area under the curve (AUC), and decision curve analysis (DCA). Results:A total of 1 834 radiomic features and 1 834 dosiomic features were extracted. Using the occurrence of RP ≥ grade 2 as the marker variable, 14 radiomic features, 15 dosiomic features, and three clinical features were selected from the training group to construct the prediction models (CM, RM, DM, and RDN). The performance and generalizability of these models were subsequently validated in both the internal validation and external validation groups. Specifically, the RDN exhibited AUCs of 0.915 (95% CI: 0.852-0.978), 0.879 (95% CI: 0.777-0.982), and 0.838 (95% CI: 0.701-0.975) in the three groups, respectively. A nomogram was established for RDN by integrating the radiomics score (R-score), dosiomics score (D-score), mean lung dose (MLD), V20, and V30. This nomogram allowed for individualized risk estimation of RP and facilitated personalized radiotherapy planning. Conclusions:The RDN model that is developed based on CT simulation and 3D dose distribution images and integrates radiomics, dosiomics, and clinical features can effectively predict the RP risk of NSCLC patients. The integration of multidimensional data contributes to the formation of the optimal predictive model, offering guidance for clinicians.

Objective:To investigate how age and nail-tract volumetric bone mineral density (vBMD) are associated with postoperative mechanical performance of proximal femoral nail anti-rotation (PFNA-Ⅱ) fixation for geriatric intertrochanteric fractures using a finite-element analysis.Methods:Fifteen elderly patients with intertrochanteric fracture of the femur were selected for this study. They were 11 females and 4 males and divided into 5 groups based on their ages ( n=3): 55-year-old, 65-year-old, 75-year-old, 85-year-old, and 95-year-old groups. After three-dimensional models of the proximal femur were constructed using the preoperative CT data of their healthy contralateral hip, 31-A1.2 fractures of the AO/OTA type were created and PFNA-Ⅱ fixations simulated. Two loading schemes were created: graded quasi-static axial loads (700 N, 1,400 N, 2,100 N, and 2,800 N) were applied to compute equivalent plastic strain volumes in the femoral head region; displacement-controlled loading was applied to failure to derive load-displacement curves for stiffness and the maximum failure load. Nail-tract vBMD and regional hip vBMDs were measured by quantitative CT. Pearson correlation analysis was conducted to investigate the associations of age and nail-tract vBMD with the aforementioned mechanical indicators. Results:Under the same load, compared with the 55-year-old, 65-year-old, and 75-year-old groups, the plastic strain unit volumes of the fracture models in the 85-year-old and 95-year-old groups increased significantly. Under a load of 700 N, no plastic strain was observed in the fracture models in the 55-year-old, 65-year-old, and 75-year-old groups, while an average plastic strain of approximately 50 mm 3 was observed in the fracture models in the 85-year-old group. Under a load of 2,800 N, the high strain areas in the fracture models in the 85-year-old and 95-year-old groups were mainly concentrated at the tip of the head nail and the junction between the head nail and the main nail. Load-displacement curves showed a marked reduction in the failure load in patients aged ≥85 years. Under loads of 1,400 N, 2,100 N, and 2,800 N, there was a strong association between the nail-tract vBMD and the volume of the plastic strain unit ( r=-0.82, -0.88, -0.89, respectively), which was stronger than those for total-hip vBMD. Conclusions:Finite-element analysis indicates that age and nail-tract vBMD are closely related to local plastic strain and overall stiffness of the proximal femur after PFNA-Ⅱ fixation for the geriatric intertrochanteric fractures. Patients aged ≥85 years old are more prone to plastic yielding, which compromises fixation stability.

OBJECTIVE: To evaluate the dynamic changes of glucocorticoid (GC) dose and the feasibility of GC discontinuation in rheumatoid arthritis (RA) patients under the background of Chinese medicine (CM). METHODS: This multicenter retrospective cohort study included 1,196 RA patients enrolled in the China Rheumatoid Arthritis Registry of Patients with Chinese Medicine (CERTAIN) from September 1, 2019 to December 4, 2023, who initiated GC therapy. Participants were divided into the Western medicine (WM) and integrative medicine (IM, combination of CM and WM) groups based on medication regimen. Follow-up was performed at least every 3 months to assess dynamic changes in GC dose. Changes in GC dose were analyzed by generalized estimator equation, the probability of GC discontinuation was assessed using Kaplan-Meier curve, and predictors of GC discontinuation were analyzed by Cox regression. Patients with <12 months of follow-up were excluded for the sensitivity analysis. RESULTS: Among 1,196 patients (85.4% female; median age 56.4 years), 880 (73.6%) received IM. Over a median 12-month follow-up, 34.3% (410 cases) discontinued GC, with significantly higher rates in the IM group (40.8% vs. 16.1% in WM; P<0.05). GC dose declined progressively, with IM patients demonstrating faster reductions (median 3.75 mg vs. 5.00 mg in WM at 12 months; P<0.05). Multivariate Cox analysis identified age <60 years [P<0.001, hazard ratios (HR)=2.142, 95% confidence interval (CI): 1.523-3.012], IM therapy (P=0.001, HR=2.175, 95% CI: 1.369-3.456), baseline GC dose ⩽7.5 mg (P=0.003, HR=1.637, 95% CI: 1.177-2.275), and absence of non-steroidal anti-inflammatory drugs use (P=0.001, HR=2.546, 95% CI: 1.432-4.527) as significant predictors of GC discontinuation. Sensitivity analysis (545 cases) confirmed these findings. CONCLUSIONS RA patients receiving CM face difficulties in following guideline-recommended GC discontinuation protocols. IM can promote GC discontinuation and is a promising strategy to reduce GC dependency in RA management. (Trial registration: ClinicalTrials.gov, No. NCT05219214).
Adult ; Aged ; Female ; Humans ; Male ; Middle Aged ; Arthritis, Rheumatoid/drug therapy* ; Glucocorticoids/therapeutic use* ; Medicine, Chinese Traditional ; Retrospective Studies

Objective:To explore the impact of a three-dimensional management based on a perinatal one-day clinic on pregnancy outcomes in overweight and obese pregnant women.Methods:It was a randomized controlled trial. A simple random sampling method was used to select 460 singleton pregnant women with a pre-pregnancy body mass index≥24 kg/m2 who had regular prenatal check-ups at the Obstetrics and Gynecology Center of the Third Affiliated Hospital of Chongqing Medical University from June 1, 2018, to December 31, 2022. The women were randomly divided into an experimental group and a control group (230 cases each) using a computer-generated random number table. The control group received regular prenatal check-ups according to the pregnancy care guidelines (once every 4 weeks during mid-pregnancy, once every 2 weeks during late pregnancy, with additional frequency as needed based on the condition). The control group also received a one-time body composition analysis and dietary guidance from a nutritionist at the time of registration. In addition to the control group′s interventions, the experimental group received three-dimensional management based on a perinatal one-day clinic. It included an intensive one-day clinic session, a traditional plus internet-based re-education model (as needed based on the condition), individualized guidance from obstetrics and clinical nutrition clinics (once every 2 weeks), a free body composition test at 24 weeks of pregnancy, and weekly WeChat group push of health care knowledge during pregnancy. A total of 55 cases dropped out, leaving 200 cases in the experimental group and 205 cases in the control group for analysis. General information, pregnancy-related, and postpartum indicators were collected in the two groups. The effect of three-dimensional management based on a perinatal one-day clinic on pregnancy outcomes in overweight and obese pregnant women was analyzed using t-tests and chi-square tests. Results:There was no significant differences in baseline age, pre-pregnancy body mass index, initial blood glucose, initial glycated hemoglobin, or initial gestational age between the two groups (all P>0.05). The experimental group showed significantly lower gestational weight gain, neonatal weight, and proportion of excessive pregnancy weight gain compared to those in the control group [(11.41±5.23) vs (13.22±4.51) kg, (3 352.1±465.3) vs (3 489.5±464.0) g, 48.00% vs 73.17%] (all P<0.05). There were no significant differences in hospitalization days, gestational age at delivery, incidence of gestational diabetes, incidence of hypertensive disorders of pregnancy, incidence of premature rupture of membranes, incidence of preterm birth, incidence of macrosomia, vaginal delivery rate and rate of neonatal transfer to the pediatric department between the two groups (all P>0.05). Conclusion:Early intervention with the three-dimensional management based on the one-day perinatal clinic can effectively control gestational weight gain and neonatal weight in overweight and obese pregnant women.

Continuous manufacturing, as an innovative pharmaceutical production model, offers advantages such as high production efficiency and ease of control compared to traditional batch production, aligning with the future trend of drug production moving toward greater efficiency and intelligence. However, the development of continuous manufacturing technology in wet granulation has been slow. On one hand, this is closely related to its high technical complexity, substantial equipment investment costs, and stringent process control requirements. On the other hand, the long-term use of the traditional batch production model has created strong path dependence, and the lack of mature standardized processes further increases the difficulty of technological transformation. To promote the deep integration of wet granulation technology with continuous manufacturing, this review systematically outlines the current application of wet granulation in continuous manufacturing. It focuses on the development of key technologies such as online detection, process modeling, and process scale-up, with the aim of providing a reference for process innovation and application in wet granulation.
Drug Compounding/instrumentation* ; Technology, Pharmaceutical/methods* ; Drugs, Chinese Herbal/chemistry* ; Models, Theoretical

Objective:To explore the impact of a three-dimensional management based on a perinatal one-day clinic on pregnancy outcomes in overweight and obese pregnant women.Methods:It was a randomized controlled trial. A simple random sampling method was used to select 460 singleton pregnant women with a pre-pregnancy body mass index≥24 kg/m2 who had regular prenatal check-ups at the Obstetrics and Gynecology Center of the Third Affiliated Hospital of Chongqing Medical University from June 1, 2018, to December 31, 2022. The women were randomly divided into an experimental group and a control group (230 cases each) using a computer-generated random number table. The control group received regular prenatal check-ups according to the pregnancy care guidelines (once every 4 weeks during mid-pregnancy, once every 2 weeks during late pregnancy, with additional frequency as needed based on the condition). The control group also received a one-time body composition analysis and dietary guidance from a nutritionist at the time of registration. In addition to the control group′s interventions, the experimental group received three-dimensional management based on a perinatal one-day clinic. It included an intensive one-day clinic session, a traditional plus internet-based re-education model (as needed based on the condition), individualized guidance from obstetrics and clinical nutrition clinics (once every 2 weeks), a free body composition test at 24 weeks of pregnancy, and weekly WeChat group push of health care knowledge during pregnancy. A total of 55 cases dropped out, leaving 200 cases in the experimental group and 205 cases in the control group for analysis. General information, pregnancy-related, and postpartum indicators were collected in the two groups. The effect of three-dimensional management based on a perinatal one-day clinic on pregnancy outcomes in overweight and obese pregnant women was analyzed using t-tests and chi-square tests. Results:There was no significant differences in baseline age, pre-pregnancy body mass index, initial blood glucose, initial glycated hemoglobin, or initial gestational age between the two groups (all P>0.05). The experimental group showed significantly lower gestational weight gain, neonatal weight, and proportion of excessive pregnancy weight gain compared to those in the control group [(11.41±5.23) vs (13.22±4.51) kg, (3 352.1±465.3) vs (3 489.5±464.0) g, 48.00% vs 73.17%] (all P<0.05). There were no significant differences in hospitalization days, gestational age at delivery, incidence of gestational diabetes, incidence of hypertensive disorders of pregnancy, incidence of premature rupture of membranes, incidence of preterm birth, incidence of macrosomia, vaginal delivery rate and rate of neonatal transfer to the pediatric department between the two groups (all P>0.05). Conclusion:Early intervention with the three-dimensional management based on the one-day perinatal clinic can effectively control gestational weight gain and neonatal weight in overweight and obese pregnant women.

Objective:To investigate how age and nail-tract volumetric bone mineral density (vBMD) are associated with postoperative mechanical performance of proximal femoral nail anti-rotation (PFNA-Ⅱ) fixation for geriatric intertrochanteric fractures using a finite-element analysis.Methods:Fifteen elderly patients with intertrochanteric fracture of the femur were selected for this study. They were 11 females and 4 males and divided into 5 groups based on their ages ( n=3): 55-year-old, 65-year-old, 75-year-old, 85-year-old, and 95-year-old groups. After three-dimensional models of the proximal femur were constructed using the preoperative CT data of their healthy contralateral hip, 31-A1.2 fractures of the AO/OTA type were created and PFNA-Ⅱ fixations simulated. Two loading schemes were created: graded quasi-static axial loads (700 N, 1,400 N, 2,100 N, and 2,800 N) were applied to compute equivalent plastic strain volumes in the femoral head region; displacement-controlled loading was applied to failure to derive load-displacement curves for stiffness and the maximum failure load. Nail-tract vBMD and regional hip vBMDs were measured by quantitative CT. Pearson correlation analysis was conducted to investigate the associations of age and nail-tract vBMD with the aforementioned mechanical indicators. Results:Under the same load, compared with the 55-year-old, 65-year-old, and 75-year-old groups, the plastic strain unit volumes of the fracture models in the 85-year-old and 95-year-old groups increased significantly. Under a load of 700 N, no plastic strain was observed in the fracture models in the 55-year-old, 65-year-old, and 75-year-old groups, while an average plastic strain of approximately 50 mm 3 was observed in the fracture models in the 85-year-old group. Under a load of 2,800 N, the high strain areas in the fracture models in the 85-year-old and 95-year-old groups were mainly concentrated at the tip of the head nail and the junction between the head nail and the main nail. Load-displacement curves showed a marked reduction in the failure load in patients aged ≥85 years. Under loads of 1,400 N, 2,100 N, and 2,800 N, there was a strong association between the nail-tract vBMD and the volume of the plastic strain unit ( r=-0.82, -0.88, -0.89, respectively), which was stronger than those for total-hip vBMD. Conclusions:Finite-element analysis indicates that age and nail-tract vBMD are closely related to local plastic strain and overall stiffness of the proximal femur after PFNA-Ⅱ fixation for the geriatric intertrochanteric fractures. Patients aged ≥85 years old are more prone to plastic yielding, which compromises fixation stability.

Objective:To develop and validate a combined model integrating radiomics, dosiomics, and clinical parameters based on CT simulation and dosimetric images in order to predict the occurrence of radiation pneumonitis (RP) in patients with non-small cell lung cancer (NSCLC).Methods:A retrospective study was conducted on the clinic data of 143 NSCLC patients who received radiotherapy at the Affiliated Hospital of Jining Medical University from January 2016 to December 2022. Patients were randomly stratified into a training group ( n = 100) and an internal validation group ( n = 43) at a 7∶3 ratio. Moreover, clinic data were collected from 34 NSCLC patients who received radiotherapy at the Jining Cancer Hospital between January 2019 and December 2022 as an external validation group. All three groups (the training group, internal validation, and external validation groups) were further categorized into two groups based on the RP severity (i.e., RP ≥ grade 2 and RP < grade 2). Their radiotherapy dose, CT simulation, and 3D dose distribution images were collected. Then, the total lung minus planning target volume (TL-PTV) was defined as the region of interest (ROI) for radiomics and dosiomic feature extraction, followed by feature dimensionality reduction. Consequently, key features associated with RP were determined. Four predictive models were developed using machine learning approaches (especially multilayer perceptron, MLP): a clinical model (CM), a radiomics model (RM), a dosiomics model (DM), and a radiomics and dosiomics nomogram (RDN), with a nomogram subsequently constructed. Ultimately, the performance and clinical feasibility of these models were assessed using receiver operating characteristic (ROC), area under the curve (AUC), and decision curve analysis (DCA). Results:A total of 1 834 radiomic features and 1 834 dosiomic features were extracted. Using the occurrence of RP ≥ grade 2 as the marker variable, 14 radiomic features, 15 dosiomic features, and three clinical features were selected from the training group to construct the prediction models (CM, RM, DM, and RDN). The performance and generalizability of these models were subsequently validated in both the internal validation and external validation groups. Specifically, the RDN exhibited AUCs of 0.915 (95% CI: 0.852-0.978), 0.879 (95% CI: 0.777-0.982), and 0.838 (95% CI: 0.701-0.975) in the three groups, respectively. A nomogram was established for RDN by integrating the radiomics score (R-score), dosiomics score (D-score), mean lung dose (MLD), V20, and V30. This nomogram allowed for individualized risk estimation of RP and facilitated personalized radiotherapy planning. Conclusions:The RDN model that is developed based on CT simulation and 3D dose distribution images and integrates radiomics, dosiomics, and clinical features can effectively predict the RP risk of NSCLC patients. The integration of multidimensional data contributes to the formation of the optimal predictive model, offering guidance for clinicians.

Objective:To construct a machine learning model based on localization CT-based radiomics, dosiomics and clinical features for predicting radiotherapy response in non-small cell lung cancer (NSCLC) and validate its application value.Methods:A retrospective case series study was conducted. A total of 138 NSCLC patients who received radiotherapy at the Affiliated Hospital of Jining Medical University from January 2016 to December 2022 were selected. The efficacy was evaluated according to the Response Evaluation Criteria in Solid Tumors (RECIST) 1.1, and the patients were stratified according to the objective remission (complete remission+partial remission). Random stratified sampling was used to divide the 138 patients into a training group (96 cases) and an internal validation group (42 cases) at a ratio of 7∶3. Additionally, 33 patients who received radiotherapy at Jining Cancer Hospital from January 2019 to December 2022 were included as the external validation group. Based on the pre-radiotherapy data of the radiotherapy planning system, PyRadiomics software package was used to extract 107 radiomics features and 107 dosiomics features for each patient. Pearson correlation analysis and LASSO regression analysis were used for dimensionality reduction screening; the final selected features were weighted and integrated to generate radiomics-dosiomics scores (RDS), which were then input into logistic regression (LR), support vector machine (SVM), extremely randomized forest (Extra Trees), K-nearest neighbor algorithm (KNN), lightweight gradient boosting machine (Light GBM), and multi-layer perceptron (MLP) machine learning algorithms to construct 6 radiomics-dosiomics models (RDM) for predicting the objective remission. RECIST 1.1 standard was used to evaluate objective remission as the gold standard, receiver operating characteristic (ROC) curve of 6 RDM for predicting objective remission was plotted, and the optimal algorithm for RDM was selected. Univariate and multivariate logistic regression were performed on demographic characteristics, hematological indicators and radiotherapy parameters of the training group to screen independent risk factors for NSCLC patients who received radiotherapy but did not achieve objective remission. These factors were input into the optimal machine learning algorithm to construct a clinical model (CM). Combined with features from RDS and CM, the clinical feature-radiomics-dosiomics combined model (CRDM) was established, and the nomogram of the model for predicting objective remission in NSCLC patients with radiotherapy was drawn. ROC curves were used to evaluate the efficacy of CM, RDM and CRDM in predicting the objective remission in NSCLC patients with radiotherapy in the training group, internal validation group and external validation group.Results:Four radiomics features (including grayscale variance, low grayscale long-range operation emphasis, low grayscale area emphasis, and small area low grayscale area emphasis, all of which were texture features) and 6 dosiomics features [including 1 first-order feature (robust mean absolute deviation), 4 texture features (grayscale non-uniformity, large area emphasis, large area high grayscale emphasis, contrast) and 1 shape feature (shortest axis length)] were selected. ROC curve analysis showed that the area under the curve (AUC) of the RDM constructed using SVM algorithm for judging the objective remission in the training group and the internal validation group was 0.907 (95% CI: 0.836-0.977) and 0.822 (95% CI: 0.685-0.959), which were higher than RDM constructed using other algorithms, and the sensitivity (96.2% and 91.7%), specificity (78.6% and 76.7%) and accuracy (83.3% and 81.0%) at the optimal cut-off values were all higher. Considering the stability and generalization ability of the model, SVM algorithm was ultimately used to construct RDM, CM and CRDM uniformly. Based on training group data, univariate and multivariate logistic regression analysis showed that elevated platelet-to-lymphocyte ratio (PLR) ( OR = 1.001, 95% CI: 1.000-1.003, P = 0.035) and increased target volume of radiotherapy plan ( OR = 1.001, 95% CI: 1.000-1.001, P = 0.008) were independent risk factors for failure to achieve objective remission. ROC curve analysis showed that in the training group and the internal validation group, the AUC of CRDM predicting objective remission were 0.914 (95% CI: 0.856-0.972) and 0.864 (95% CI: 0.754-0.974), respectively, which were better than CM [AUC were 0.735 (95% CI: 0.612-0.857) and 0.697 (95% CI: 0.507-0.888)] and RDM, respectively. In the external validation group, the AUC of CRDM, CM and RDM were 0.778 (95% CI: 0.500-1.000), 0.667 (95% CI: 0.434-0.899) and 0.741 (95% CI: 0.463-1.000), respectively. Conclusions:The CRDM constructed by combining radiomics, dosiomics and clinical features can comprehensively and accurately evaluate the radiotherapy response of NSCLC patients, and may have important clinical application value in achieving precision medicine and optimizing treatment strategies.