A predictive model of lymph node metastasis after thoracoscopic surgery for lung adenocarcinoma with a diameter≤3 cm / 中国胸心血管外科临床杂志

@#Objective    To predict the probability of lymph node metastasis after thoracoscopic surgery in patients with lung adenocarcinoma based on nomogram. Methods    We analyzed the clinical data of the patients with lung adenocarcinoma treated in the department of thoracic surgery of our hospital from June 2018 to May 2021. The patients were randomly divided into a training group and a validation group. The variables that may affect the lymph node metastasis of lung adenocarcinoma were screened out by univariate logistic regression, and then the clinical prediction model was constructed by multivariate logistic regression. The nomogram was used to show the model visually, the receiver operating characteristic (ROC) curve, calibration curve and clinical decision curve to evaluate the calibration degree and practicability of the model. Results    Finally 249 patients were collected, including 117 males aged 53.15±13.95 years and 132 females aged 47.36±13.10 years. There were 180 patients in the training group, and 69 patients in the validation group. There was a significant correlation between the 6 clinicopathological characteristics and lymph node metastasis of lung adenocarcinoma in the univariate logistic regression. The area under the ROC curve in the training group was 0.863, suggesting the ability to distinguish lymph node metastasis, which was confirmed in the validation group (area under the ROC curve was 0.847). The nomogram and clinical decision curve also performed well in the follow-up analysis, which proved its potential clinical value. Conclusion    This study provides a nomogram combined with clinicopathological characteristics, which can be used to predict the risk of lymph node metastasis in patients with lung adenocarcinoma with a diameter≤3 cm.

Construction of a individualized model for predicting the risk of recurrence in patients with retinal vein occlusion induced macular edema / 国际眼科杂志(Guoji Yanke Zazhi)

AIM: To analyze the recurrence factors of patients with retinal vein occlusion(RVO)induced macular edema(ME)and construct a nomogram model.METHODS: Retrospective study. A total of 306 patients with RVO induced ME admitted to our hospital from January 2019 to June 2022 were included as study objects, and they were divided into modeling group with 214 cases(214 eyes)and 92 cases(92 eyes)in the verification group by 7:3. All patients were followed up for 1 a after receiving anti-vascular endothelial growth factor(VEGF)treatment, and patients in the modeling group were separated into a recurrence group(n=66)and a non recurrence group(n=148)based on whether they had recurrence. Clinical data were collected and multivariate Logistic regression was applied to analyze and determine the factors affecting recurrence in patients with RVO induced ME; R3.6.3 software was applied to construct a nomogram model for predicting the recurrence risk of patients with RVO induced ME; ROC curve and calibration curve were applied to evaluate the discrimination and consistency of nomogram model in predicting the recurrence risk of patients with RVO induced ME.RESULTS: There were statistically significant differences in central retinal thickness(CRT), course of disease, hyperreflective foci(HF), disorder of retinal inner layer structure, and injection frequency between the non recurrence group and the recurrence group before treatment(all P<0.05). The multivariate Logistic regression analysis showed that pre-treatment CRT(OR=1.011), course of disease(OR=1.104), HF(OR=5.074), retinal inner layer structural disorder(OR=4.640), and injection frequency(OR=4.036)were influencing factors for recurrence in patients with RVO induced ME(all P<0.01). The area under the ROC curve of the modeling group was 0.924(95%CI: 0.882-0.966), the slope of the calibration curve was close to 1, and the results of the Hosmer-Lemeshow goodness of fit test showed that χ2=11.817, P=0.160; the area under the ROC curve of the verification group was 0.939(95%CI: 0.892-0.985), the slope of the calibration curve was close to 1, and the results of the Hosmer-Lemeshow goodness of fit test showed χ2=6.082, P=0.638.CONCLUSION: Pre-treatment CRT, course of disease, HF, disorder of retinal inner layer structure, and injection frequency are independent risk factors for recurrence in patients with RVO induced ME. The nomogram model constructed based on this has a high discrimination and consistency in predicting the recurrence risk of patients with RVO induced ME.

Construction and validation of a prediction model for public acceptance of kidney xenotransplantation in China / 器官移植

Objective To explore the public attitude towards kidney xenotransplantation in China by constructing and validating the prediction model based on xenotransplantation questionnaire. Methods A convenient sampling survey was conducted among the public in China with the platform of Wenjuanxing to analyze public acceptance of kidney xenotransplantation and influencing factors. Using random distribution method, all included questionnaires (n=2 280) were divided into the training and validation sets according to a ratio of 7:3. A prediction model was constructed and validated. Results A total of 2 280 questionnaires were included. The public acceptance rate of xenotransplantation was 71.3%. Multivariate analysis showed that gender, marital status, resident area, medical insurance coverage, religious belief, vegetarianism, awareness of kidney xenotransplantation and whether on the waiting list for kidney transplantation were the independent influencing factors for public acceptance of kidney xenotransplantation (all P<0.05). The area under the curve (AUC) of receiver operating characteristic (ROC) of the prediction model in the training set was 0.773, and 0.785 in the validation set. The calibration curves in the training and validation sets indicated that the prediction models yielded good prediction value. Decision curve analysis (DCA) suggested that the prediction efficiency of the model was high. Conclusions In China, public acceptance of kidney xenotransplantation is relatively high, whereas it remains to be significantly enhanced. The prediction model based on questionnaire survey has favorable prediction efficiency, which provides reference for subsequent research.

Establishment and validation of nomogram prediction model of cefoperazone/sulbactam-induced thrombocytopenia / 中国药房

OBJECTIVE To explore the predictive factors of cefoperazone/sulbactam-induced thrombocytopenia in adult inpatients， and to establish and validate the nomogram prediction model. METHODS Data of adult inpatients treated with cefoperazone/sulbactam in Xi’an Central Hospital from Jun. 30th， 2021 to Jun. 30th， 2023 were retrospectively collected. The training set and internal validation set were randomly constructed in a 7∶3 ratio. Singler factor and multifactor Logistic regression analysis were used to screen the independent predictors of cefoperazone/sulbactam-induced thrombocytopenia. The nomogram was drawn by using “RMS” of R 4.0.3 software， and the predictive performance of the model was evaluated by the receiver operating characteristic curve and C-index curve. Hosmer-Lemeshow goodness-of-fit test was used to evaluate the calibration degree of the model. Using the same standard， the clinical data of hospitalized patients receiving cefoperazone/sulbactam in Xi’an First Hospital in the same period were collected for external validation of the nomogram prediction model. RESULTS A total of 1 045 patients in Xi’an Central Hospital were included in this study， among which 67 patients suffered from cefoperazone/sulbactam-induced thrombocytopenia， with an incidence of 6.41%. After the false positive patients were excluded， 473 patients were included finally， including 331 in the training set and 142 in theinternal validation set. Multifactor Logistic regression analysis showed that age [OR＝1.043， 95%CI （1.017， 1.070）]， estimated glomerular filtration rate （eGFR） [OR＝0.988，95%CI（0.977， 0.998）]， baseline platelet （PLT） [OR＝0.989， 95%CI（0.982， 0.996）]， nutritional risk [OR＝3.863， 95%CI（1.884， 7.921）] and cumulative defined daily doses （DDDs） [OR＝1.082， 95%CI（1.020， 1.147）] were independent predictors for cefoperazone/sulbactam-induced thrombocytopenia （P＜0.05）. The C-index values of the training set and the internal validation set were 0.824 [95%CI （0.759， 0.890）] and 0.828 [95%CI （0.749， 0.933）]， respectively. The results of the Hosmer-Lemeshow test showed that χ 2 values were 0.441 （P＝0.802） and 1.804 （P＝0.406）. In the external validation set， the C-index value was 0.808 [95%CI （0.672， 0.945）]， the χ 2 value of the Hosmer-Lemeshow test was 0.899 （P＝0.638）. CONCLUSIONS The independent predictors of cefoperazone/sulbactam-induced thrombocytopenia include age， baseline PLT， eGFR， nutritional risk and cumulative DDDs. The model has good predictive efficacy and extrapolation ability， which can help clinic identify the potential risk of cefoperazone/sulbactam-induced thrombocytopenia quickly and accurately.

Nomogram including serum ferritin to predict the occurrence of diabetic retinopathy / 国际眼科杂志(Guoji Yanke Zazhi)

AIM:To establish a nomogram model to predict the effect of serum ferritin on diabetic retinopathy and evaluate the model.METHODS:A total of 21 variables, including ferritin, were screened by univariate and multivariate regression analysis to determine the risk factors of diabetic retinopathy. A nomogram prediction model was established for evaluation and calibration.RESULTS:Ferritin, duration of diabetes, hemoglobin, urine microalbumin, regularity of medication and body mass index were included in the nomogram model. The consistency index of the prediction model with serum ferritin was 0.813(95%CI: 0.748-0.879). The calibration curves of internal and external verification showed good performance, and the probability of the threshold suggested by the decision curve was in the range 10% to 90%. The model had a high net profit value.CONCLUSIONS:Serum ferritin is an important risk factor for diabetic retinopathy. A new nomogram model, which includes body mass index, duration of diabetes, ferritin, hemoglobin, urine microalbumin and regularity of medication, has a high predictive accuracy and could provide early prediction for clinicians.

Construction of a column-line graph for predicting the probability of survival in early-stage elderly HER2-positive breast cancer based on the SEER database / 中国临床药理学与治疗学

AIM: To construct column-line plots to predict survival in elderly patients with early-stage HER2-positive breast cancer using the Surveillance, Epidemiology and End Results (SEER) database. METHODS: 5 220 (based on the era of single-targeted therapy) and 1 176 (based on the era of dual-targeted therapy) patients screened in the SEER database were randomized into a training group and an internal validation group. COX proportional risk regression was used to screen survival-related predictors and build a column-line graphical model, and the accuracy and utility of the model were tested using the consistency index (C-index), calibration curves, and time-dependent ROC curves. Patients receiving chemotherapy and non-chemotherapy were statistically paired using two-group propensity score matching, and subgroup analyses were performed on the screened variables. RESULTS: The single-targeted therapy era line graph was constructed from seven variables: age, marital status, T-stage, N-stage, surgery, chemotherapy, and radiotherapy. The dual-targeted therapy era line graph was constructed from five variables: age, AJCC staging, surgery, chemotherapy, and radiotherapy. The results of the subgroup analysis showed that older HER2-positive breast cancer patients who received chemotherapy had better OS. CONCLUSION: Based on the SEER database, an accurate column-line graph predicting survival in elderly patients with early-stage HER2-positive breast cancer was established and validated. This study suggests that chemotherapy increases survival benefit in elderly patients.

Classification of bladder cancer based on immune cell infiltration and construction of a risk prediction model for prognosis / 浙江大学学报·医学版

OBJECTIVES@#To classify bladder cancer based on immune cell infiltration score and to construct a risk assessment model for prognosis of patients.@*METHODS@#The transcriptome data and data of breast cancer patients were obtained from the TCGA database. The single sample gene set enrichment analysis was used to calculate the infiltration scores of 16 immune cells. The classification of breast cancer patients was realized by unsupervised clustering, and the sensitivity of patients with different types to immunotherapy and chemotherapy was analyzed. The key modules significantly related to the infiltration of key immune cells were identified by weighted correlation network analysis (WGCNA), and the key genes in the modules were extracted. A risk scoring model and a nomogram for risk assessment of prognosis for bladder cancer patients were constructed and verified.@*RESULTS@#The immune cell infiltration scores of normal tissues and tumor tissues were calculated, and B cells, mast cells, neutrophils, T helper cells and tumor infiltrating lymphocytes were determined to be the key immune cells of bladder cancer. Breast cancer patients were clustered into two groups (Cluster 1 and Custer 2) based on immune cell infiltration scores. Compared with patients with Cluster 1, patients with Cluster 2 were more likely to benefit from immunotherapy (P<0.05), and patients with Cluster 2 were more sensitive to Enbeaten, Docetaxel, Cyclopamine, and Akadixin (P<0.05). WGCNA screened out 35 genes related to key immune cells, and 4 genes (GPR171, HOXB3, HOXB5 and HOXB6) related to the prognosis of bladder cancer were further screened by LASSO Cox regression. The areas under the ROC curve (AUC) of the bladder cancer prognosis risk scoring model based on these 4 genes to predict the 1-, 3- and 5-year survival of patients were 0.735, 0.765 and 0.799, respectively. The nomogram constructed by combining risk score and clinical parameters has high accuracy in predicting the 1-, 3-, and 5-year overall survival of bladder cancer patients.@*CONCLUSIONS@#According to the immune cell infiltration score, bladder cancer patients can be classified. And the bladder cancer prognosis risk scoring model and nomogram based on key immune cell-related genes have high accuracy in predicting the prognosis of bladder cancer patients.

Risk factors for ketoacidosis in children/adolescents with type 1 diabetes mellitus and establishment of a predictive model / 中国当代儿科杂志

OBJECTIVES@#To investigate the risk factors for diabetic ketoacidosis (DKA) in children/adolescents with type 1 diabetes mellitus (T1DM) and to establish a model for predicting the risk of DKA.@*METHODS@#A retrospective analysis was performed on 217 children/adolescents with T1DM who were admitted to General Hospital of Ningxia Medical University from January 2018 to December 2021. Among the 217 children/adolescents,169 cases with DKA were included as the DKA group and 48 cases without DKA were included as the non-DKA group. The risk factors for DKA in the children/adolescents with T1DM were analyzed, and a nomogram model was established for predicting the risk of DKA in children/adolescents with T1DM.@*RESULTS@#For the 217 children/adolescents with T1DM, the incidence rate of DKA was 77.9% (169/217). The multivariate logistic regression analysis showed that high levels of random blood glucose, hemoglobin A1c (HbA1c), blood ketone body, and triglyceride on admission were closely associated with the development of DKA in the children/adolescents with T1DM (OR=1.156, 3.2031015, 20.131, and 9.519 respectively; P<0.05). The nomogram prediction model had a C-statistic of 0.95, with a mean absolute error of 0.004 between the risk of DKA predicted by the nomogram model and the actual risk of DKA, indicating that the model had a good overall prediction ability.@*CONCLUSIONS@#High levels of random blood glucose, HbA1c, blood ketone body, and triglyceride on admission are closely associated with the development of DKA in children/adolescents with T1DM, and targeted intervention measures should be developed to reduce the risk of DKA.

Establishment of a Predictive Model for Chronic Cough after Pulmonary Resection / 中国肺癌杂志

BACKGROUND@#Chronic cough after pulmonary resection is one of the most common complications, which seriously affects the quality of life of patients after surgery. Therefore, the aim of this study is to explore the risk factors of chronic cough after pulmonary resection and construct a prediction model.@*METHODS@#The clinical data and postoperative cough of 499 patients who underwent pneumonectomy or pulmonary resection in The First Affiliated Hospital of University of Science and Technology of China from January 2021 to June 2023 were retrospectively analyzed. The patients were randomly divided into training set (n=348) and validation set (n=151) according to the principle of 7:3 randomization. According to whether the patients in the training set had chronic cough after surgery, they were divided into cough group and non-cough group. The Mandarin Chinese version of Leicester cough questionnare (LCQ-MC) was used to assess the severity of cough and its impact on patients' quality of life before and after surgery. The visual analog scale (VAS) and the self-designed numerical rating scale (NRS) were used to evaluate the postoperative chronic cough. Univariate and multivariate Logistic regression analysis were used to analyze the independent risk factors and construct a model. Receiver operator characteristic (ROC) curve was used to evaluate the discrimination of the model, and calibration curve was used to evaluate the consistency of the model. The clinical application value of the model was evaluated by decision curve analysis (DCA).@*RESULTS@#Multivariate Logistic analysis screened out that preoperative forced expiratory volume in the first second/forced vital capacity (FEV1/FVC), surgical procedure, upper mediastinal lymph node dissection, subcarinal lymph node dissection, and postoperative closed thoracic drainage time were independent risk factors for postoperative chronic cough. Based on the results of multivariate analysis, a Nomogram prediction model was constructed. The area under the ROC curve was 0.954 (95%CI: 0.930-0.978), and the cut-off value corresponding to the maximum Youden index was 0.171, with a sensitivity of 94.7% and a specificity of 86.6%. With a Bootstrap sample of 1000 times, the predicted risk of chronic cough after pulmonary resection by the calibration curve was highly consistent with the actual risk. DCA showed that when the preprobability of the prediction model probability was between 0.1 and 0.9, patients showed a positive net benefit.@*CONCLUSIONS@#Chronic cough after pulmonary resection seriously affects the quality of life of patients. The visual presentation form of the Nomogram is helpful to accurately predict chronic cough after pulmonary resection and provide support for clinical decision-making.

Comprehensive analysis of clinicopathologic and sonographic features in thyroid cancer with skip lymph node metastasis: establish and assessment of a prediction nomogram

Abstract Lateral Lymph Node Metastasis (LLNM) is common in Papillary Thyroid Carcinoma (PTC) and is associated with a poor prognosis. LLNM without central lymph node metastasis as skip metastasis is not common. We aimed to investigate clinicopathologic and sonographic risk factors for skip metastasis in PTC patients, and to establish a nomogram for predicting the possibility of skip metastasis in order to determine the therapeutic strategy. We retrospectively reviewed the data of 1037 PTC patients who underwent surgery from 2016 to 2020 at a single institution. Univariate and multivariate analyses were used to identify the clinicopathologic and preoperative sonographic risk factors of skip metastasis. A nomogram including the risk factors for predicting skip metastasis was further developed and validated. The incidence of skip metastasis was 10.7%. The univariate and multivariate analyses suggested that gender (p = 0.001), tumor location (p = 0.000), extrathyroidal extension (p = 0.000), and calcification (p = 0.000) were independent risk factors. For papillary thyroid microcarcinoma, tumor location (p = 0.000) and calcification (p = 0.001) were independent risk factors. A nomogram according to the clinicopathologic and sonographic predictors was developed. The receiver operating characteristic curve indicated that AUC was 0.824 and had an excellent consistency. The calibration plot analysis showed a good performance and clinical utility of the model. Decision curve analysis revealed it was clinically useful. A nomogram for predicting the probability of skip metastasis was developed, which exhibited a favorable predictive value and consistency. For the female PTC patient, tumor located at the upper pole is more likely to have skip metastasis. Surgeons and sonographers should pay close attention to the patients who have the risk factors. Evidence level: This article's evidence level is 3. Level 3 evidence is derived from nonrandomized, controlled clinical trials. In this study, patients who receive an intervention are compared to a control group. Authors may detect a statistically significant and clinically relevant outcome.

Impact of crossplay between ocular aberrations and depth of focus in topo-guided laser-assisted in situ keratomileusis outcomes

Purpose: To develop a nomogram in cases with mismatch between subjective and Topolyzer cylinder, and based on the magnitude of the mismatch, customize a treatment plan to attain good visual outcomes post?laser?assisted in situ keratomileusis (LASIK) surgery. Methods: The patients were evaluated preoperatively using corneal tomography with Pentacam. Five optimal corneal topography scans were obtained from the Topolyzer Vario were used for planning the LASIK treatment. For the nomogram purpose, the patients were divided into three categories based on the difference between the subjective cylinder and Topolyzer (corneal) cylinder. The first group (group 1) consisted of eyes of patients, where the difference was less than or equal to 0.4 D. The second group (group 2) consisted of eyes, where the difference was more than 0.4 D and the subjective cylinder was lesser than the Topolyzer cylinder. The third group (group 3) included eyes where the difference was more than 0.4 D but the subjective cylinder was greater than the Topolyzer cylinder. LASIK was performed with the WaveLight FS 200 femtosecond laser and WaveLight EX500 excimer laser. Assessment of astigmatism correction for the three groups was done using Aplins vector analysis. For comparison of proportions, Chi?square test was used. A P value less than 0.05 was considered statistically significant. Results: The UDVA was statistically significantly different when compared between groups 1 and 2 (P = 0.02). However, the corrected distance visual acuity (CDVA) was similar among all the three groups (P = 0.1). Group 3 showed an increase of residual cylinder by ?0.25 D, which was significant at intermediate and near reading distances (P < 0.05). Group 3 showed significantly higher target?induced astigmatism (TIA) compared to groups 1 and 2 (P = 0.01). The mean surgically induced astigmatism (SIA) was the least in group 2, which was statistically significant (P < 0.01). Conclusion: The outcomes for distance vision using our nomogram postoperatively were excellent, but further refinement for improving the near vision outcomes is required

Prognostic Model Based on Preoperative FAR and SII Versus TNM Staging System in Evaluating Prognosis of Patients with Pancreatic Cancer After Radical Resection / 肿瘤防治研究

Objective To investigate the predictive value of preoperative fibrinogen/albumin ratio (FAR) and systemic immune inflammation index (SII) on the postoperative prognosis of patients with pancreatic ductal adenocarcinoma. Methods An ROC curve was used in determining the best cutoff values of FAR and SII and then grouped. The Cox proportional hazards model was used in analyzing the prognostic factors of radical pancreatic cancer surgery, and then a Nomogram prognostic model was established. C-index, AUC, and calibration curve were used in evaluating the discrimination and calibration ability of the Nomogram. DCA curves were used in assessing the clinical validity of the Nomograms. Results The optimal cutoff values for preoperative FAR and SII were 0.095 and 532.945, respectively. FAR≥ 0.095, SII≥ 532.945, CA199≥ 450.9 U/ml, maximum tumor diameter≥ 4 cm, and the absence of postoperative chemotherapy were independent risk factors for the poor prognosis of pancreatic cancer (P<0.05). The discrimination ability, calibration ability, and clinical effectiveness of Nomogram prognostic model were better than those of the TNM staging system. Conclusion The constructed Nomogram prognostic model has higher accuracy and level of discrimination and more clinical benefits than the TNM staging prognostic model.

Nomogram for Predicting Early Death in Patients with Metastatic Colon Cancer Based on SEER Database / 肿瘤防治研究

Objective To construct a Nomogram model that can accurately predict early death of metastatic colon cancer (mCC). Methods A total of 6 669 patients from the SEER database were identified using inclusion and exclusion criteria. Multivariate logistic regression was used to identify risk factors for early mortality and to construct a Nomogram. The predictive performance of the Nomogram was evaluated by C-index, calibration curve, and decision curve analysis (DCA). Results Primary tumor location, differentiation grade, T stage, M stage, bone metastases, brain metastases, CEA, tumor size, age and marital status were independent factors for early death in patients with mCC. A Nomogram was constructed based on these variables. The C-index and the calibration curve of the Nomogram showed the good predictive ability of the nomogram. DCA showed that the Nomogram had a superior clinical net benefit in predicting early death compared with TNM stage. Conclusion The developed Nomogram has good predictive ability and can help guide clinicians to identify patients with high-risk mCC for individualized diagnosis and treatment.

Clinical Prediction of Lung Cancer Complicated with Pulmonary Infection After Thoracoscopic Surgery Based on Nomogram Model / 肿瘤防治研究

Objective To analyze the risk factors of lung cancer patients complicated with pulmonary infection after thoracoscopic surgery and establish a predictive nomogram model. Methods A total of 315 patients with primary lung cancer who had undergone thoracoscopic surgery from January 2018 to October 2021 in our hospital were divided into two groups according to the incidence of pulmonary infection. Two groups of clinical data were collected for single-factor and regression analyses, and independent predictors were obtained. On this basis, a risk model was constructed and its predictive effectiveness was evaluated. Results The independent risk factors of lung cancer patients complicated with pulmonary infection after thoracoscopic radical operation were as follows: age≥62.5 years, smoking index≥100, PEF≤72.1 ml/s, TNM stage Ⅲ/Ⅳ, and operation time≥188.5 min (P < 0.05). Based on the above factors, the risk model of the column chart was established. Model-verification results showed that the C-index of the model was 0.909, and the correction curve showed that the column chart model had good differentiation and consistency. Conclusion Lung cancer patients' age, smoking index, TNM stage, PEF, and operation time are closely related to pulmonary infection after thoracoscopic radical operation. The nomogram model is useful for identifying high-risk patients and reducing postoperative complications.

Predictive Value of Pan-immune-inflammation Value for Prognosis of Patients with Resectable Colorectal Cancer / 肿瘤防治研究

Objective To explore the correlation of the pan-immune-inflammation value (PIV) and the prognosis of patients with resectable colorectal cancer (CRC) and establish a predictive model. Methods A total of 753 patients who underwent primary lesion resection and were pathologically diagnosed with CRC were enrolled. They were randomly divided into training (n=527) and test (n=226) cohorts. The best cutoff value of PIV was determined by the time-dependent receiver operator characteristics curve, and patients were divided into high- and low-level groups to analyze the relationship between the high- and low-level groups of PIV and the clinicopathological characteristics and survival of patients. Chi-square test, Kaplan-Meier survival analysis, and Cox regression analysis were used to evaluate the prognosis. The accuracy of the model was evaluated by C index and Brier score. Results In the univariate model of overall survival (OS), high (> 231) baseline PIV (HR=1.627; 95%CI: 1.155-2.292, P=0.005) suggested that PIV level might be an independent prognostic factor for OS. The nomogram plotted according to PIV had a C index of 0.823. Its calibration curve showed good agreement between predicted and observed outcomes for one- and three-year OS probabilities, with Brier score of 0.035 and 0.068 for OS, respectively. Conclusion PIV can be used as a prognostic marker in patients with resectable CRC, and a novel prognostic model to guide clinical decision-making in CRC is successfully established.

Based on CT radiomics model for predicting the response to first-line chemotherapy of diffuse large B-cell lymphoma / 中华肿瘤杂志

Objective: To investigate the potential value of CT Radiomics model in predicting the response to first-line chemotherapy in diffuse large B-cell lymphoma (DLBCL). Methods: Pre-treatment CT images and clinical data of DLBCL patients treated at Shanxi Cancer Hospital from January 2013 to May 2018 were retrospectively analyzed and divided into refractory patients (73 cases) and non-refractory patients (57 cases) according to the Lugano 2014 efficacy evaluation criteria. The least absolute shrinkage and selection operator (LASSO) regression algorithm, univariate and multivariate logistic regression analyses were used to screen out clinical factors and CT radiomics features associated with efficacy response, followed by radiomics model and nomogram model. Receiver operating characteristic (ROC) curve, calibration curve and clinical decision curve were used to evaluate the models in terms of the diagnostic efficacy, calibration and clinical value in predicting chemotherapy response. Results: Based on pre-chemotherapy CT images, 850 CT texture features were extracted from each patient, and 6 features highly correlated with the first-line chemotherapy effect of DLBCL were selected, including 1 first order feature, 1 gray level co-occurence matrix, 3 grey level dependence matrix, 1 neighboring grey tone difference matrix. Then, the corresponding radiomics model was established, whose ROC curves showed AUC values of 0.82 (95% CI: 0.76-0.89) and 0.73 (95% CI: 0.60-0.86) in the training and validation groups, respectively. The nomogram model, built by combining validated clinical factors (Ann Arbor stage, serum LDH level) and CT radiomics features, showed an AUC of 0.95 (95% CI: 0.90-0.99) and 0.91 (95% CI: 0.82-1.00) in the training group and the validation group, respectively, with significantly better diagnostic efficacy than that of the radiomics model. In addition, the calibration curve and clinical decision curve showed that the nomogram model had good consistency and high clinical value in the assessment of DLBCL efficacy. Conclusion: The nomogram model based on clinical factors and radiomics features shows potential clinical value in predicting the response to first-line chemotherapy of DLBCL patients.

Development and validation of prognostic nomogram for malignant pleural mesothelioma / 中华肿瘤杂志

Objective: To development the prognostic nomogram for malignant pleural mesothelioma (MPM). Methods: Two hundred and ten patients pathologically confirmed as MPM were enrolled in this retrospective study from 2007 to 2020 in the People's Hospital of Chuxiong Yi Autonomous Prefecture, the First and Third Affiliated Hospital of Kunming Medical University, and divided into training (n=112) and test (n=98) sets according to the admission time. The observation factors included demography, symptoms, history, clinical score and stage, blood cell and biochemistry, tumor markers, pathology and treatment. The Cox proportional risk model was used to analyze the prognostic factors of 112 patients in the training set. According to the results of multivariate Cox regression analysis, the prognostic prediction nomogram was established. C-Index and calibration curve were used to evaluate the model's discrimination and consistency in raining and test sets, respectively. Patients were stratified according to the median risk score of nomogram in the training set. Log rank test was performed to compare the survival differences between the high and low risk groups in the two sets. Results: The median overall survival (OS) of 210 MPM patients was 384 days (IQR=472 days), and the 6-month, 1-year, 2-year, and 3-year survival rates were 75.7%, 52.6%, 19.7%, and 13.0%, respectively. Cox multivariate regression analysis showed that residence (HR=2.127, 95% CI: 1.154-3.920), serum albumin (HR=1.583, 95% CI: 1.017-2.464), clinical stage (stage Ⅳ: HR=3.073, 95% CI: 1.366-6.910) and the chemotherapy (HR=0.476, 95% CI: 0.292-0.777) were independent prognostic factors for MPM patients. The C-index of the nomogram established based on the results of Cox multivariate regression analysis in the training and test sets were 0.662 and 0.613, respectively. Calibration curves for both the training and test sets showed moderate consistency between the predicted and actual survival probabilities of MPM patients at 6 months, 1 year, and 2 years. The low-risk group had better outcomes than the high-risk group in both training (P=0.001) and test (P=0.003) sets. Conclusion: The survival prediction nomogram established based on routine clinical indicators of MPM patients provides a reliable tool for prognostic prediction and risk stratification.

Treatment of palbociclib in hormone receptor positive breast cancer: a real-world study and efficacy prediction model / 中华肿瘤杂志

Objective: To summarize the clinical use of palbociclib and evaluate its efficacy and safety in hormone-receptor (HR)-positive advanced breast cancer patients. Methods: We retrospectively analyzed data from 66 HR-positive metastatic breast cancer patients treated with palbociclib and endocrine therapy at the Department of Oncology in the First Affiliated Hospital with Nanjing Medical University between 2018 and 2020. We evaluated the factors affecting the efficacy of palbociclib using Kaplan-Meier method and Log-rank test for survival analysis and Cox regressions for multivariate analysis. Nomogram model was built for predicting prognosis among HR-positive breast cancer patients who received palbociclib. Concordance index (C-index) and calibration curve were used for internal validation to assess the predictive ability and conformity of the model. Results: Of the 66 patients treated with palbociclib, 33.3%(22), 42.4%(28) and 24.2%(16) patients were treated without endocrine therapy, first-line endocrine therapy, second-line or above endocrine therapy after recurrence, respectively. 36.4%(24) patients had hepatic metastasis, 16.7% (11) patients were sensitive to previous endocrine therapy, 27.3%(18/66) patients had primary resistance to endocrine therapy, while 56.1% (37) patients had secondary resistance to endocrine therapy. The overall response rate was 14.3% (95% CI: 6.7%, 25.4%) and clinical benefit rate was 58.7% (95% CI: 45.6%, 71.0%). Better clinical outcomes were associated with non-hepatic metastasis (P=0.001), sensitive/secondary resistant to previous endocrine therapy (P=0.004), no or only one line of chemotherapy for metastatic breast cancer (P=0.004), recent pathological confirmation of immunohistochemical analysis (P=0.025). Hepatic metastasis (P=0.005) and primary resistance to endocrine therapy (P=0.016) were the independent risk factors of progression free survival. The C-index of predictive probability for the nomogram constructed from the patient clinical characteristics (whether liver metastasis, whether primary endocrine resistance, lines of chemotherapy after metastasis, lines of endocrine therapy, number of metastatic sites, and time to last immunohistochemistry) to predict the progression-free survival at 6 and 12 months for patients was 69.7% and 72.1%, respectively. The most common adverse events were hematologic toxicities. Conclusions: Our report indicates that palbociclib combined with endocrine therapy for HR-positive recurrent metastatic breast cancer is effective and safe; patients with hepatic metastases and primary resistance to endocrine therapy have worse prognoses and are independent risk factors for progression after palbociclib therapy. The constructed nomogram could help predict the survival and guide the use of palbociclib.

Establishment and Verification of Benign and Malignant Prediction Model of Subcentimeter Pulmonary Ground Glass Nodules Based on HRCT / 中国肺癌杂志

BACKGROUND@#Pre-operative accuracy of subcentimeter ground glass nodules (SGGNs) is a difficult problem in clinical practice, but there are few clinical studies on the benign and malignant prediction model of SGGNs. The aim of this study was to help identify benign and malignant lesions of SGGNs based on the imaging features of high resolution computed tomography (HRCT) and the general clinical data of patients, and to build a risk prediction model.@*METHODS@#This study retrospectively analyzed the clinical data of 483 patients with SGGNs who underwent surgical resection and were confirmed by histology from the First Affiliated Hospital of University of Science and Technology of China from August 2020 to December 2021. The patients were divided into the training set (n=338) and the validation set (n=145) according to 7:3 random assignment. According to the postoperative histology, they were divided into adenocarcinoma group and benign lesion group. The independent risk factors and models were analyzed by univariate analysis and multivariate Logistic regression. The receiver operator characteristic (ROC) curve was constructed to evaluate the model differentiation, and the calibration curve was used to evaluate the model consistency. The clinical application value of the decision curve analysis (DCA) evaluation model was drawn, and the validation set data was substituted for external verification.@*RESULTS@#Multivariate Logistic analysis screened out patients' age, vascular sign, lobular sign, nodule volume and mean-CT value as independent risk factors for SGGNs. Based on the results of multivariate analysis, Nomogram prediction model was constructed, and the area under ROC curve was 0.836 (95%CI: 0.794-0.879). The critical value corresponding to the maximum approximate entry index was 0.483. The sensitivity was 76.6%, and the specificity was 80.1%. The positive predictive value was 86.5%, and the negative predictive value was 68.7%. The benign and malignant risk of SGGNs predicted by the calibration curve was highly consistent with the actual occurrence risk after sampling 1,000 times using Bootstrap method. DCA showed that patients showed a positive net benefit when the predictive probability of the predicted model probability was 0.2 to 0.9.@*CONCLUSIONS@#Based on preoperative medical history and preoperative HRCT examination indicators, the benign and malignant risk prediction model of SGGNs was established to have good predictive efficacy and clinical application value. The visualization of Nomogram can help to screen out high-risk groups of SGGNs, providing support for clinical decision-making.

Establishment and Clinical Significance of Prognostic Nomogram Model for Diffuse Large B-Cell Lymphoma Based on Immunohistochemistry Markers and International Prognostic Index Scores / 中国实验血液学杂志

OBJECTIVE@#To retrospectively analyze clinical characteristics and survival time of patients with diffuse large B-cell lymphoma (DLBCL), detect prognosis-related markers, and establish a nomogram prognostic model of clinical factors combined with biomarkers.@*METHODS@#One hundred and thirty-seven patients with DLBCL were included in this study from January 2014 to March 2019 in the First Affiliated Hospital of Nanchang University. The expression of GCET1, LMO2, BCL-6, BCL-2 and MYC protein were detected by immunohistochemistry (IHC), then the influences of these proteins on the survival and prognosis of the patients were analyzed. Univariate and multivariate Cox regression analysis were used to gradually screen the prognostic factors in nomogram model. Finally, nomogram model was established according to the result of multivariate analysis.@*RESULTS@#The positive expression of GCET1 protein was more common in patients with Ann Arbor staging I/II (P =0.011). Compared with negative patients, patients with positive expression of LMO2 protein did not often show B symptoms (P =0.042), and could achieve better short-term curative effect (P =0.005). The overall survival (OS) time of patients with positive expression of LMO2 protein was significantly longer than those with negative expression of LMO2 protein (P =0.018), though the expression of LMO2 protein did not correlate with progression-free survival (PFS) (P >0.05). However, the expression of GCET1 protein had no significant correlation with OS and PFS. Multivariate Cox regression analysis showed that nomogram model consisted of 5 prognostic factors, including international prognostic index (IPI), LMO2 protein, BCL-2 protein, MYC protein and rituximab. The C-index applied to the nomogram model for predicting 4-year OS rate was 0.847. Moreover, the calibrated curve of 4-year OS showed that nomogram prediction had good agreement with actual prognosis.@*CONCLUSION@#The nomogram model incorporating clinical characteristics and IHC biomarkers has good discrimination and calibration, which provides a useful tool for the risk stratification of DLBCL.