Objective To explore the recognition capabilities of electronic nose combined with machine learning in identifying the breath odor map of benign and malignant pulmonary nodules and Traditional Chinese Medicine (TCM) syndrome elements. Methods The study design was a single-center observational study. General data and four diagnostic information were collected from 108 patients with pulmonary nodules admitted to the Department of Cardiothoracic Surgery of Hospital of Chengdu University of TCM from April 2023 to March 2024. The patients' TCM disease location and nature distribution characteristics were analyzed using the syndrome differentiation method. The Cyranose 320 electronic nose was used to collect the odor profiles of oral exhalation, and five machine learning algorithms including random forest (RF), K-nearest neighbor (KNN), logistic regression (LR), support vector machine (SVM), and eXtreme gradient boosting (XGBoost) were employed to identify the exhaled breath profiles of benign and malignant pulmonary nodules and different TCM syndromes. Results (1) The common disease locations in pulmonary nodules were ranked in descending order as liver, lung, and kidney; the common disease natures were ranked in descending order as Yin deficiency, phlegm, dampness, Qi stagnation, and blood deficiency. (2) The electronic nose combined with the RF algorithm had the best efficacy in identifying the exhaled breath profiles of benign and malignant pulmonary nodules, with an AUC of 0.91, accuracy of 86.36%, specificity of 75.00%, and sensitivity of 92.85%. (3) The electronic nose combined with RF, LR, or XGBoost algorithms could effectively identify the different TCM disease locations and natures of pulmonary nodules, with classification accuracy, specificity, and sensitivity generally exceeding 80.00%.Conclusion Electronic nose combined with machine learning not only has the potential capabilities to differentiate the benign and malignant pulmonary nodules, but also provides new technologies and methods for the objective diagnosis of TCM syndromes in pulmonary nodules.

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

Objective To construct a "disease-syndrome combination" mathematical representation model for pulmonary nodules based on oral microbiome data, utilizing a multimodal data algorithm framework centered on dynamic systems theory. Furthermore, to compare predictive models under various algorithmic frameworks and validate the efficacy of the optimal model in predicting the presence of pulmonary nodules. Methods A total of 213 subjects were prospectively enrolled from July 2022 to March 2023 at the Hospital of Chengdu University of Traditional Chinese Medicine, Sichuan Cancer Hospital, and the Chengdu Integrated Traditional Chinese and Western Medicine Hospital. This cohort included 173 patients with pulmonary nodules and 40 healthy subjects. A novel multimodal data algorithm framework centered on dynamic systems theory, termed VAEGANTF (Variational Auto Encoder-Generative Adversarial Network-Transformer), was proposed. Subsequently, based on a multi-dimensional integrated dataset of “clinical features-syndrome elements-microorganisms”, all subjects were divided into training (70%) and testing (30%) sets for model construction and efficacy testing, respectively. Using pulmonary nodules as dependent variables, and combining candidate markers such as clinical features, lesion location, disease nature, and microbial genera, the independent variables were screened based on variable importance ranking after identifying and addressing multicollinearity. Missing values were then imputed, and data were standardized. Eight machine learning algorithms were then employed to construct pulmonary nodule risk prediction models: random forest, least absolute shrinkage and selection operator (LASSO) regression, support vector machine, multilayer perceptron, eXtreme Gradient Boosting (XGBoost), VAE-ViT (Vision Transformer), GAN-ViT, and VAEGANTF. K-fold cross-validation was used for model parameter tuning and optimization. The efficacy of the eight predictive models was evaluated using confusion matrices and receiver operating characteristic (ROC) curves, and the optimal model was selected. Finally, goodness-of-fit testing and decision curve analysis (DCA) were performed to evaluate the optimal model. Results There were no statistically significant differences between the two groups in demographic characteristics such as age and sex. The 213 subjects were randomly divided into training and testing sets (7 : 3), and prediction models were constructed using the eight machine learning algorithms. After excluding potential problems such as multicollinearity, a total of 301 clinical feature information, syndrome elements, and microbial genera markers were included for model construction. The area under the curve (AUC) values of the random forest, LASSO regression, support vector machine, multilayer perceptron, and VAE-ViT models did not reach 0.85, indicating poor efficacy. The AUC values of the XGBoost, GAN-ViT, and VAEGANTF models all reached above 0.85, with the VAEGANTF model exhibiting the highest AUC value (AUC=0.923). Goodness-of-fit testing indicated good calibration ability of the VAEGANTF model, and decision curve analysis showed a high degree of clinical benefit. The nomogram results showed that age, sex, heart, lung, Qixu, blood stasis, dampness, Porphyromonas genus, Granulicatella genus, Neisseria genus, Haemophilus genus, and Actinobacillus genus could be used as predictors. Conclusion The “disease-syndrome combination” risk prediction model for pulmonary nodules based on the VAEGANTF algorithm framework, which incorporates multi-dimensional data features of “clinical features-syndrome elements-microorganisms”, demonstrates better performance compared to other machine learning algorithms and has certain reference value for early non-invasive diagnosis of pulmonary nodules.

ObjectiveTo investigate the effects of Aconiti Coreani Radix and Typhonii Rhizoma on the urinary metabolites of gerbils with stroke by non-targeted metabolomics technique， and then to clarify the mechanism of the two， as well as their similarities and differences. MethodTwenty-four gerbils were randomly divided into control group（CG）， model group（MG）， Aconiti Coreani Radix group（RA） and Typhonii Rhizoma group（RT）. Except for the CG， ischemic stroke model was constructed using right unilateral ligation of gerbil carotid artery in the remaining groups. Except for the CG and MG， rats in the other groups received whole powder suspension（0.586 mg·g^-1） was administered for 14 days. The neurological deficit in each group was scored by Longa scoring on days 0， 3， 7 and 14. After the end of administration， the serum， brain tissue and urine of gerbils in each group were collected， and the rate of cerebral infarction was detected by 2，3，5-triphenyltetrazolium chloride（TTC）， and the levels of interleukin（IL）-6， tumor necrosis factor（TNF）-α， malondialdehyde（MDA）， superoxide dismutase（SOD）， glutathione（GSH）， and nitric oxide（NO） in serum and brain tissue were determined by enzyme-linked immunosorbent assay（ELISA）. The urine metabolomics of gerbils in each group was studied by ultra performance liquid chromatography-quadrupole-electrostatic field orbitrap high resolution mass spectrometry（UPLC-Q-Orbitrap-MS）， and the data were processed by multivariate statistical analysis， and differential metabolites were screened based on value of variable importance in the projection（VIP） of the first principal component>1 and t-test P<0.05. Metabolic pathway analysis of the screened differential metabolites was performed using Kyoto Encyclopedia of Genes and Genomes（KEGG） database and Metaboanalyst 5.0. ResultCompared with the CG， the neurological deficit score was significantly increased in the MG（P<0.05）， compared with the MG， the neurological deficit scores in the RA and RT were significantly reduced after 7 d and 14 d（P<0.05）. Compared with the CG， the rate of cerebral infarction was significantly increased in the MG（P<0.05）， compared with the MG， the rates of cerebral infarction in the RA and RT were significantly reduced（P<0.05）. Compared with the CG， the levels of IL-6， TNF-α， and MDA in the serum and brain tissue of gerbils from the MG were significantly increased（P<0.05）， and the levels of SOD， GSH and NO were significantly reduced（P<0.05）. Compared with the MG， Aconiti Coreani Radix and Typhonii Rhizoma could down-regulate the levels of IL-6， TNF-α and MDA， and up-regulated the levels of SOD， GSH and NO. A total of 112 endogenous differential metabolites were screened by urine metabolomics， of which 16 and 26 metabolites were called back by Aconiti Coreani Radix and Typhonii Rhizoma， and could be used as potential biomarkers for both treatments in stroke gerbils， respectively. The results of the pathway analysis showed that both Aconiti Coreani Radix and Typhonii Rhizoma had regulatory effects on arginine and proline metabolism， pyrimidine metabolism， and aminoacyl-tRNA biosynthesis. In addition， Aconiti Coreani Radix could also regulate riboflavin metabolism， Typhonii Rhizoma could also regulate purine metabolism， glycine， serine and threonine metabolism， arachidonic acid metabolism， biosynthesis of pantothenate and coenzyme A， and β-alanine metabolism. ConclusionBoth Aconiti Coreani Radix and Typhonii Rhizoma have better therapeutic effects on stroke， with Aconiti Coreani Radix having stronger effects. From the metabolomics results， the main metabolic pathways regulated by Aconiti Coreani Radix involve amino acid metabolism， oxidative stress and so on， while Typhonii Rhizoma mainly involve amino acid metabolism， lipid metabolism， energy metabolism， etc.

Objective:To explore the survival prognosis of hepatocellular carcinoma patients over 60 years old with comorbidities treated with microwave ablation.Methods:A retrospective analysis of 267 patients with hepatocellular carcinoma aged 60 years or older admitted to the PLA General Hospital from April 2012 to September 2022 were analyzed, including 179 patients with preoperative comorbidities and 88 patients without comorbidities. The overall survival (OS) and progression-free survival (PFS) of the two groups were compared by the Log-rank test, and the Cox proportional hazards regression model was used to evaluate ablation-related risk factors.Results:A total of 267 patients were included (comorbidity group, n=179; no comorbidity group, n=88). There were no statistical differences in OS and PFS between the two groups (all P>0.05). Multivariate Cox regression analysis showed that comorbidities were not risk factors that affected the survival prognosis (OS and PFS) of patients with hepatocellular carcinoma after microwave ablation ( P>0.05). Total bilirubin (hazard ratio 0.356, 95% CI=0.174-0.731, P=0.005) was a risk factor affecting OS; tumor number (hazard ratio 0.538, 95% CI=0.365-0.793, P=0.002) and international coagulation normalized ratio (hazard ratio 1.022, 95% CI=1.001-1.043, P=0.040) were risk factors affecting PFS. Subgroup analysis showed that patients with a maximum diameter of >3 cm and female patients, the OS of the comorbidity group was significantly lower than that of the non-comorbidity group( P<0.05). Conclusions:Microwave ablation therapy remains an effective treatment modality in hepatocellular carcinoma patients over 60 years of age with comorbidities, and its survival prognosis is not inferior to patients with hepatocellular carcinoma without comorbidities.

Objective:To investigate the value of transanal multipoint full-layer puncture biopsy (TMFP) in predicting pathological complete response (pCR) after neoadjuvant radiotherapy and chemotherapy (nCRT) in patients with locally advanced rectal cancer (LARC) and to establish a predictive model for providing clinical guidance regarding the treatment of LARC.Methods:In this multicenter, prospective, cohort study, we collected data on 110 LARC patients from four hospitals between April 2020 and March 2023: Beijing Chaoyang Hospital of Capital Medical University (50 patients), Beijing Friendship Hospital of Capital Medical University (41 patients), Qilu Hospital of Shandong University (16 patients), and Zhongnan Hospital of Wuhan University (three patients). The patients had all received TMFP after completing standard nCRT. The variables studied included (1) clinicopathological characteristics; (2) clinical complete remission (cCR) and efficacy of TMFP in determining pCR after NCRT in LARC patients; and (3) hospital attended, sex, age, clinical T- and N-stages, distance between the lower margin of the tumor and the anal verge, baseline and post-radiotherapy serum carcinoembryonic antigen (CEA) and carbohydrate antigen (CA)19-9 concentrations, chemotherapy regimen, use of immunosuppressants with or without radiotherapy, radiation therapy dosage, interval between surgery and radiotherapy, surgical procedure, clinical T/N stage after radiotherapy, cCR, pathological results of TMFP, puncture method (endoscopic or percutaneous), and number and timing of punctures. Single-factor and multifactorial logistic regression analysis were used to determine the factors affecting pCR after NCRT in LARC patients. A prediction model was constructed based on the results of multivariat analysis and the performance of this model evaluated by analyzing subject work characteristics (ROC), calibration, and clinical decision-making (DCA) curves. pCR was defined as complete absence of tumor cells on microscopic examination of the surgical specimens of rectal cancer (including lymph node dissection) after NCRT, that is, ypT0+N0. cCR was defined according to the Chinese Neoadjuvant Rectal Cancer Waiting Watch Database Study Collaborative Group criteria after treatment, which specify an absence of ulceration and nodules on endoscopy; negative rectal palpation; no tumor signals on rectal MRI T2 and DWI sequences; normal serum CEA concentrations, and no evidence of recurrence on pelvic computed tomography/magnetic resonance imaging.Results:Of the 110 patients, 45 (40.9%) achieved pCR after nCRT, which was combined with immune checkpoint inhibitors in 34 (30.9%). cCR was diagnosed before puncture in 38 (34.5%) patients, 43 (39.1%) of the punctures being endoscopic. There were no complications of puncture such as enterocutaneous fistulae, vaginal injury, prostatic injury, or presacral bleeding . Only one (2.3%) patient had a small amount of blood in the stools, which was relieved by anal pressure. cCR had a sensitivity of 57.8% (26/45) for determining pCR, specificity of 81.5% (53/65), accuracy of 71.8% (79/110), positive predictive value 68.4% (26/38), and negative predictive value of 73.6% (53/72). In contrast, the sensitivity of TMFP pathology in determining pCR was 100% (45/45), specificity 66.2% (43/65), accuracy 80.0% (88/110), positive predictive value 67.2% (45/67), and negative predictive value 100.0% (43/43). In this study, the sensitivity of TMFP for pCR (100.0% vs. 57.8%, χ 2=24.09, P<0.001) was significantly higher than that for cCR. However, the accuracy of pCR did not differ significantly (80.0% vs. 71.8%, χ 2=2.01, P=0.156). Univariate and multivariate logistic regression analyses showed that a ≥4 cm distance between the lower edge of the tumor and the anal verge (OR=7.84, 95%CI: 1.48-41.45, P=0.015), non-cCR (OR=4.81, 95%CI: 1.39-16.69, P=0.013), and pathological diagnosis by TMFP (OR=114.29, the 95%CI: 11.07-1180.28, P<0.001) were risk factors for pCR after NCRT in LARC patients. Additionally, endoscopic puncture (OR=0.02, 95%CI: 0.05-0.77, P=0.020) was a protective factor for pCR after NCRT in LARC patients. The area under the ROC curve of the established prediction model was 0.934 (95%CI: 0.892-0.977), suggesting that the model has good discrimination. The calibration curve was relatively close to the ideal 45° reference line, indicating that the predicted values of the model were in good agreement with the actual values. A decision-making curve showed that the model had a good net clinical benefit. Conclusion:Our predictive model, which incorporates TMFP, has considerable accuracy in predicting pCR after nCRT in patients with locally advanced rectal cancer. This may provide a basis for more precisely selecting individualized therapy.

Objective:To investigate the value of transanal multipoint full-layer puncture biopsy (TMFP) in predicting pathological complete response (pCR) after neoadjuvant radiotherapy and chemotherapy (nCRT) in patients with locally advanced rectal cancer (LARC) and to establish a predictive model for providing clinical guidance regarding the treatment of LARC.Methods:In this multicenter, prospective, cohort study, we collected data on 110 LARC patients from four hospitals between April 2020 and March 2023: Beijing Chaoyang Hospital of Capital Medical University (50 patients), Beijing Friendship Hospital of Capital Medical University (41 patients), Qilu Hospital of Shandong University (16 patients), and Zhongnan Hospital of Wuhan University (three patients). The patients had all received TMFP after completing standard nCRT. The variables studied included (1) clinicopathological characteristics; (2) clinical complete remission (cCR) and efficacy of TMFP in determining pCR after NCRT in LARC patients; and (3) hospital attended, sex, age, clinical T- and N-stages, distance between the lower margin of the tumor and the anal verge, baseline and post-radiotherapy serum carcinoembryonic antigen (CEA) and carbohydrate antigen (CA)19-9 concentrations, chemotherapy regimen, use of immunosuppressants with or without radiotherapy, radiation therapy dosage, interval between surgery and radiotherapy, surgical procedure, clinical T/N stage after radiotherapy, cCR, pathological results of TMFP, puncture method (endoscopic or percutaneous), and number and timing of punctures. Single-factor and multifactorial logistic regression analysis were used to determine the factors affecting pCR after NCRT in LARC patients. A prediction model was constructed based on the results of multivariat analysis and the performance of this model evaluated by analyzing subject work characteristics (ROC), calibration, and clinical decision-making (DCA) curves. pCR was defined as complete absence of tumor cells on microscopic examination of the surgical specimens of rectal cancer (including lymph node dissection) after NCRT, that is, ypT0+N0. cCR was defined according to the Chinese Neoadjuvant Rectal Cancer Waiting Watch Database Study Collaborative Group criteria after treatment, which specify an absence of ulceration and nodules on endoscopy; negative rectal palpation; no tumor signals on rectal MRI T2 and DWI sequences; normal serum CEA concentrations, and no evidence of recurrence on pelvic computed tomography/magnetic resonance imaging.Results:Of the 110 patients, 45 (40.9%) achieved pCR after nCRT, which was combined with immune checkpoint inhibitors in 34 (30.9%). cCR was diagnosed before puncture in 38 (34.5%) patients, 43 (39.1%) of the punctures being endoscopic. There were no complications of puncture such as enterocutaneous fistulae, vaginal injury, prostatic injury, or presacral bleeding . Only one (2.3%) patient had a small amount of blood in the stools, which was relieved by anal pressure. cCR had a sensitivity of 57.8% (26/45) for determining pCR, specificity of 81.5% (53/65), accuracy of 71.8% (79/110), positive predictive value 68.4% (26/38), and negative predictive value of 73.6% (53/72). In contrast, the sensitivity of TMFP pathology in determining pCR was 100% (45/45), specificity 66.2% (43/65), accuracy 80.0% (88/110), positive predictive value 67.2% (45/67), and negative predictive value 100.0% (43/43). In this study, the sensitivity of TMFP for pCR (100.0% vs. 57.8%, χ 2=24.09, P<0.001) was significantly higher than that for cCR. However, the accuracy of pCR did not differ significantly (80.0% vs. 71.8%, χ 2=2.01, P=0.156). Univariate and multivariate logistic regression analyses showed that a ≥4 cm distance between the lower edge of the tumor and the anal verge (OR=7.84, 95%CI: 1.48-41.45, P=0.015), non-cCR (OR=4.81, 95%CI: 1.39-16.69, P=0.013), and pathological diagnosis by TMFP (OR=114.29, the 95%CI: 11.07-1180.28, P<0.001) were risk factors for pCR after NCRT in LARC patients. Additionally, endoscopic puncture (OR=0.02, 95%CI: 0.05-0.77, P=0.020) was a protective factor for pCR after NCRT in LARC patients. The area under the ROC curve of the established prediction model was 0.934 (95%CI: 0.892-0.977), suggesting that the model has good discrimination. The calibration curve was relatively close to the ideal 45° reference line, indicating that the predicted values of the model were in good agreement with the actual values. A decision-making curve showed that the model had a good net clinical benefit. Conclusion:Our predictive model, which incorporates TMFP, has considerable accuracy in predicting pCR after nCRT in patients with locally advanced rectal cancer. This may provide a basis for more precisely selecting individualized therapy.

Objective:To investigate the value of nomograms based on clinical parameters, apparent diffusion coefficient (ADC) and MRI-derived radiomics in predicting survival of patients with locally advanced cervical cancer (LACC) after concurrent chemoradiotherapy (CCRT).Methods:Clinical data of 423 patients with IB-IVA cervical cancer treated with CCRT at Anhui Provincial Hospital Affiliated to Anhui Medical University from March 2014 to March 2020 were retrospectively analyzed and randomly divided into the training and validation groups at a ratio of 2∶1 using the simple randomization method. The values of ADC min, ADC mean, ADC max and 3D texture parameters of diffusion weighted imaging (DWI), T 2WI, T 2WI-fat suppression of pre-treatment primary lesions in all patients were measured. The least absolute shrinkage and selection operator (LASSO) algorithm and logistic regression analysis were used to screen the texture features and calculate radiomics score (Rad-score). Cox regression analysis was employed to construct nomogram models for predicting overall survival (OS) and cancer-specific survival (CS) of patients with LACC after CCRT, which were subject to internal and external validation. Results:Squamous cell carcinoma antigen (SCC-Ag), external beam radiotherapy dose, ADCmin and Rad-score were the independent prognostic factors for OS and CS of LACC patients after CCRT and constituted predictive models for OS and CS. The area under the receiver operating characteristic (ROC) curve (AUC) of two models in predicting 1-year, 3-year, 5-year OS and CS was 0.906, 0.917, 0.916 and 0.911, 0.918, 0.920, with internally validated consistency indexes (C-indexes) of 0.897 and 0.900. Then, models were brought into the validation group for external validation with AUC of 0.986, 0.942, 0.932 and 0.986, 0.933, 0.926 in predicting 1-year, 3-year, 5-year OS and CS.Conclusion:The nomograms based on clinical parameters, ADC values and MRI-derived radiomics are of high clinical value in predicting OS and CS of patients with LACC after CCRT, which can be used as prognostic markers for patients with cervical cancer to certain extent.