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.

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

Construction and evaluation of an artificial intelligence-based risk prediction model for death in patients with nasopharyngeal cancer / 南方医科大学学报

OBJECTIVE@#To screen the risk factors for death in patients with nasopharyngeal carcinoma (NPC) using artificial intelligence (AI) technology and establish a risk prediction model.@*METHODS@#The clinical data of NPC patients obtained from SEER database (1973-2015). The patients were randomly divided into model building and verification group at a 7∶3 ratio. Based on the data in the model building group, R software was used to identify the risk factors for death in NPC patients using 4 AI algorithms, namely eXtreme Gradient Boosting (XGBoost), Decision Tree (DT), Least absolute shrinkage and selection operator (LASSO) and random forest (RF), and a risk prediction model was constructed based on the risk factor identified. The C-Index, decision curve analysis (DCA), receiver operating characteristic (ROC) curve and calibration curve (CC) were used for internal validation of the model; the data in the validation group and clinical data of 96 NPC patients (collected from First Affiliated Hospital of Bengbu Medical College) were used for internal and external validation of the model.@*RESULTS@#The clinical data of a total of 2116 NPC patients were included (1484 in model building group and 632 in verification group). Risk factor screening showed that age, race, gender, stage M, stage T, and stage N were all risk factors of death in NPC patients. The risk prediction model for NPC-related death constructed based on these factors had a C-index of 0.76 for internal evaluation, an AUC of 0.74 and a net benefit rate of DCA of 9%-93%. The C-index of the model in internal verification was 0.740 with an AUC of 0.749 and a net benefit rate of DCA of 3%-89%, suggesting a high consistency of the two calibration curves. In external verification, the C-index of this model was 0.943 with a net benefit rate of DCA of 3%-97% and an AUC of 0.851, and the predicted value was consistent with the actual value.@*CONCLUSIONS@#Gender, age, race and TNM stage are risk factors of death of NPC patients, and the risk prediction model based on these factors can accurately predict the risks of death in NPC patients.

Establishment and validation of a nomogram for predicting prognosis of gastric neuroendocrine neoplasms based on data from 490 cases in a single center / 南方医科大学学报

OBJECTIVE@#To develop and validate a nomogram for predicting outcomes of patients with gastric neuroendocrine neoplasms (G-NENs).@*METHODS@#We retrospectively collected the clinical data from 490 patients with the diagnosis of G-NEN at our medical center from 2000 to 2021. Log-rank test was used to analyze the overall survival (OS) of the patients. The independent risk factors affecting the prognosis of G-NEN were identified by Cox regression analysis to construct the prognostic nomogram, whose performance was evaluated using the C-index, receiver-operating characteristic (ROC) curve, area under the ROC curve (AUC), calibration curve, DCA, and AUDC.@*RESULTS@#Among the 490 G-NEN patients (mean age of 58.6±10.92 years, including 346 male and 144 female patients), 130 (26.5%) had NET G1, 54 (11.0%) had NET G2, 206 (42.0%) had NEC, and 100 (20.5%) had MiNEN. None of the patients had NET G3. The numbers of patients in stage Ⅰ-Ⅳ were 222 (45.3%), 75 (15.3%), 130 (26.5%), and 63 (12.9%), respectively. Univariate and multivariate analyses identified age, pathological grade, tumor location, depth of invasion, lymph node metastasis, distant metastasis, and F-NLR as independent risk factors affecting the survival of the patients (P < 0.05). The C-index of the prognostic nomogram was 0.829 (95% CI: 0.800-0.858), and its AUC for predicting 1-, 3- and 5-year OS were 0.883, 0.895 and 0.944, respectively. The calibration curve confirmed a good consistency between the model prediction results and the actual observations. For predicting 1-year, 3-year and 5-year OS, the TNM staging system and the nomogram had AUC of 0.033 vs 0.0218, 0.191 vs 0.148, and 0.248 vs 0.197, respectively, suggesting higher net benefit and better clinical utility of the nomogram.@*CONCLUSION@#The prognostic nomogram established in this study has good predictive performance and clinical value to facilitate prognostic evaluation of individual patients with G-NEN.

Advances in post-operative prognostic models for hepatocellular carcinoma / 浙江大学学报（英文版）（B辑：生物医学和生物技术）

Hepatocellular carcinoma (HCC) is one of the most common malignancies and a leading cause of cancer-related death worldwide. Surgery remains the primary and most successful therapy option for the treatment of early- and mid-stage HCCs, but the high heterogeneity of HCC renders prognostic prediction challenging. The construction of relevant prognostic models helps to stratify the prognosis of surgically treated patients and guide personalized clinical decision-making, thereby improving patient survival rates. Currently, the prognostic assessment of HCC is based on several commonly used staging systems, such as Tumor-Node-Metastasis (TNM), Cancer of the Liver Italian Program (CLIP), and Barcelona Clinic Liver Cancer (BCLC). Given the insufficiency of these staging systems and the aim to improve the accuracy of prognostic prediction, researchers have incorporated further prognostic factors, such as microvascular infiltration, and proposed some new prognostic models for HCC. To provide insights into the prospects of clinical oncology research, this review describes the commonly used HCC staging systems and new models proposed in recent years.

Nomogram-Based Prediction of Risk of Cervical Lymph Node Metastasis in Differentiated Thyroid Carcinoma / 中国医学科学院学报

Objective To establish a nomogram for predicting the risk of cervical lymph node metastasis in differentiated thyroid carcinoma (DTC). Methods The patients with complete clinical data of DTC and cervical lymph node ultrasound and diagnosed based on pathological evidence from January 2019 to December 2021 were assigned into a training group (n=444) and a validation group (n=125).Lasso regression was performed to screen the data with differences between groups,and multivariate Logistic regression to establish a prediction model with the factors screened out by Lasso regression.C-index and calibration chart were employed to evaluate the prediction performance of the established model. Results The predictive factors for establishing the model were lymph node short diameter≥0.5 cm,long-to-short-axis ratio<2,disappearance of lymph node hilum,cystic transformation,hyperechogenicity,calcification,and abnormal blood flow (all P<0.001).The established model demonstrated a good discriminative ability,with the C index of 0.938 (95%CI=0.926-0.961) in the training group. Conclusion The nomogram established based on the ultrasound image features of cervical lymph nodes in DTC can accurately predict the risk of cervical lymph node metastasis in DTC.

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.

Prognostic value of pre-treatment prognostic nutrition index in patients with cervical and thoracic upper esophageal squamous cell carcinoma and radiation induced esophagitis / 中华放射肿瘤学杂志

Objective:To investigate the prognostic value of Onodera's prognostic nutrition index (PNI) before treatment in patients with cervical and upper thoracic esophageal squamous cell carcinoma (CUTESCC) undergoing definitive chemoradiotherapy (dCRT) and its predictive value in the occurrence of ≥ grade 2 radiation esophagitis (RE).Methods:The data of 163 CUTESCC patients eligible for inclusion criteria admitted to the Fourth Hospital of Hebei Medical University from January 2012 to December 2017 were retrospectively analyzed. The receiver operating characteristic (ROC) curve was used to calculate the best cut-off value of PNI for predicting the prognosis of patients. The prognosis of patients was analyzed by univariate and Cox multivariate analyses. Logistics binary regression model was adopted to analyze the risk factors of ≥ grade 2 RE in univariate and multivariate analyses. The significant factors in logistic multivariate analysis were used to construct nomogram for predicting ≥ grade 2 RE.Results:The optimal cut-off value of PNI was 48.57 [area under the curve (AUC): 0.653, P<0.001]. The median overall survival (OS) and progression-free survival (PFS) were 26.1 and 19.4 months, respectively. The OS ( χ2=6.900, P=0.009) and PFS ( χ2=9.902, P=0.003) of patients in the PNI ≥ 48.57 group ( n=47) were significantly better than those in the PNI < 48.57 group ( n=116). Cox multivariate analysis showed that cTNM stage and PNI were the independent predictors of OS ( HR=1.513, 95% CI: 1.193-1.920, P=0.001; HR=1.807, 95% CI: 1.164-2.807, P=0.008) and PFS ( HR=1.595, 95% CI: 1.247-2.039, P<0.001; HR=2.260, 95% CI: 1.439-3.550, P<0.001). Short-term efficacy was another independent index affecting PFS ( HR=2.072, 95% CI: 1.072-4.003, P=0.030). Logistic multivariate analysis showed that the maximum transverse diameter of the lesion ( OR=3.026, 95% CI: 1.266-7.229, P=0.013), gross tumor volume (GTV) ( OR=3.456, 95% CI: 1.373-8.699, P=0.008), prescription dose ( OR=3.124, 95% CI: 1.346-7.246, P=0.009) and PNI ( OR=2.072, 95% CI: 1.072-4.003, P=0.030) were the independent factors affecting the occurrence of ≥ grade 2 RE. These four indicators were included in the nomogram model, and ROC curve analysis showed that the model could properly predict the occurrence of ≥ grade 2 RE (AUC=0.686, 95% CI: 0.585-0.787). The calibration curve indicated that the actually observed values were in good agreement with the predicted RE. Decision curve analysis (DCA) demonstrated satisfactory nomogram positive net returns in most threshold probabilities. Conclusions:PNI before treatment is an independent prognostic factor for patients with CUTESCC who received definitive chemoradiotherapy. The maximum transverse diameter of the lesion, GTV, prescription dose and PNI are the risk factors for ≥ grade 2 RE in this cohort. Establishing a prediction model including these factors has greater predictive value.

Long-term survival patients with advanced non-small cell lung cancer receiving thoracic radiotherapy: clinical characteristics and the construction of a nomogram prognostic model / 中华放射医学与防护杂志

Objective:To analyze the clinical characteristics of long-term survival patients with advanced non-small cell lung cancer (NSCLC) treated with chemotherapy combined with primary tumor radiotherapy, and to establish a Nomogram prognostic model, aiming to provide a certain reference for making a decision about the treatment of advanced NSCLC.Methods:A retrospective analysis was made on the data of 260 NSCLC patients who participated in two prospective clinical studies from January 2003 to May 2012 and the data of 138 NSCLC patients admitted to the Affiliated Cancer Hospital of Guizhou Medical University from January 2014 to August 2020. The former 260 cases were used as a training set and the latter 138 cases were used as the validation set. The overall survival (OS) of ≥ 18 months was defined as long-term survival (LTS). The clinical characteristics of LTS patients were compared with those with OS less than 18 months. The clinical characteristics and treatment-related parameters between the two types of patients were compared using the χ2 test. A multivariate analysis was made using logistic regression, and a nomogram model was built using RStudio. Results:The median OS of the training set was 13.4 months (95% CI: 11.9-14.9), with 1-, 2-, and 3-year OS rates of 55.4%, 19.1%, and 11.9%, respectively. In the training set, 87 cases had LTS and were classified as the LTS group, while 173 cases had OS less than 18 months and were classified as the non-LTS group. The univariate analysis showed that the prognostic factors affecting LST included the KPS score, T status, the number of metastatic organs, the number of metastatic lesions, brain metastasis, bone metastasis, the number of chemotherapy cycles, the biologically effective dose (BED) to the primary tumor, hemoglobin level, platelet count, plasma D-dimer, fibrinogen level, lactate dehydrogenase, and lung immune prognostic index (LIPI; χ2=4.72-12.63, P < 0.05). The multivariable analysis showed that the independent prognostic factors of LTS included a number of chemotherapy cycles ≥ 4, BED ≥ 70 Gy, platelets ≤ 220×10 9/L, D-dimer ≤ 0.5 mg/L, and a good LIPI score ( P= 0.002, 0.036, 0.005, 0.008, and 0.002). A nomogram model was established using the meaningful parameters obtained in the multivariable analysis, determining that the training and validation sets had a consistency index (C-index) of 0.750 and 0.727, respectively. As shown by the analytical result of the corrected curves, for the advanced NSCLC patients treated with thoracic radiotherapy, their LTS probability predicted using the nomogram prognostic model was highly consistent with their actual LTS probability. Both the analytical result of the receiver operating characteristic (ROC) curves and the decision curve analysis (DCA) result showed that the composite prediction model was more beneficial than a single prediction model. Conclusions:For patients with advanced NSCLC treated with thoracic radiotherapy, the independent prognostic factors of LTS included the number of chemotherapy cycles, BED, platelet count, pre-chemotherapy D-dimer, and LIPI score. The Nomogram prognostic model built based on these prognostic factors is a convenient, intuitive, and personalized prediction model used to screen patients who can benefit from thoracic radiotherapy.

A nomogram based on CT enterography signs for prediction of intestinal penetrating lesions in patients with Crohn disease / 中华放射学杂志

Objective:To explore the value of a nomogram model based on the CT enterography (CTE) signs for prediction of intestinal penetrating lesions in patients with Crohn disease (CD).Methods:The clinical and CTE data of CD patients who underwent at least two CTE examinations from January 2010 to June 2020 in the First Affiliated Hospital of Sun Yat-sen University were retrospectively collected. A total of 112 patients were enrolled, and according to whether there was intestinal wall penetration in the last CTE observation were divided into non-penetration group (84 cases) and penetration group (28 cases). First, the clinical and CTE data for the first examination was analyzed by using univariate and multivariate Cox proportional hazards regression to screen out high-risk factors that could effectively predict intestinal wall penetrating lesions in CD patients and established a nomogram model. Then the change trend of CTE data (ΔCTE) between the first and last clinical and CTE signs was analyzed by using univariate and multivariate Cox proportional hazards regression, and built a nomogram model to sort out ΔCTE that may accompany the development of penetrating lesions in CD patients. The Harrell concordance index was used to evaluate the discriminative ability of the nomogram model.Results:In the first time clinical and CTE signs, multivariate Cox proportional hazards regression results showed that numbers of diseased bowel segments (HR=0.686, 95%CI 0.475-0.991, P=0.045) and the shortest diameter of the largest lymph node (HR=0.751, 95%CI 0.593-0.949, P=0.017) were independent protection factors for penetrating lesions, and rough bowel wall surface (HR=5.626, 95%CI 2.466-12.839, P<0.001) was an independent risk factor for penetrating lesions. The specificity and sensitivity of the nomogram model to predict non-penetration lesions were 82.1% and 59.5% respectively, and the Harrell concordance index was 0.810 (95%CI 0.732-0.888). In the ΔCTE signs, multivariate Cox proportional hazards regression showed that Δrough bowel wall surface (always rough bowel wall surface HR=12.344, 95%CI 2.042-74.625, P=0.006; slide bowel wall surface becomes rough bowel wall surface HR=28.720, 95%CI 4.580-180.112, P<0.001) and Δthe shortest diameter of the largest lymph node (HR=1.534, 95%CI 1.091-2.157, P=0.014) were independent risk factors for penetrating lesions. The specificity and sensitivity of the nomogram model were 89.3% and 79.2% respectively, and the Harrell concordance index was 0.876 (95%CI 0.818-0.934). Conclusion:The nomogram based on CTE signs of numbers of diseased bowel segments, the shortest diameter of the largest lymph node and rough bowel wall surface and ΔCTE can effectively predict the intestinal wall penetrating lesions of CD patients.

Nomogram based on CT texture analysis and morphological characteristics for differentiating Borrmann Ⅳ type gastric cancer from gastric diffuse large B-cell lymphoma / 中华放射学杂志

Objective:To explore the value in differentiating Borrmann Ⅳ type gastric cancer (BT4-GC) from gastric diffuse large B-cell lymphoma (DLBCL) using a nomogram based on CT texture analysis (CTTA) and morphological characteristics.Methods:From June 2011 to December 2020, a total of 60 patients with BT4-GC and 24 patients with DLBCL were retrospectively collected in Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University. Morphological characteristics were evaluated, including major location, long axis range, circumferential range, mucosal line status, and perigastric enlarged lymph nodes. CTTA parameters were calculated using venous CT images with a manual region of interest. The morphological characteristics and CTTA parameters between BT4-GC and DLBCL were compared by χ 2 test, Fisher exact test or Mann-Whitney U test. The multivariate binary logistic regression analysis was used to filter factors into the diagnostic model and construct a nomogram. The receiver operating characteristic (ROC) curve was used to evaluate the diagnostic performance of CTTA parameters and the diagnostic model in differentiating BT4-GC from DLBCL. Results:For morphological characteristics, mucosal line status showed a significant difference between BT4-GC and DLBCL (χ 2=12.99, P<0.001). For CTTA parameters, 16 parameters showed significant differences between BT4-GC and DLBCL (all P<0.05). The area under the ROC curve (AUC) of 16 CTTA parameters in differentiating BT4-GC from DLBCL was 0.662-0.833. Percentile 90 showed the highest AUC of 0.833 (95%CI 0.736-0.906). The mucosal line status (OR 4.82, 95%CI 1.21-19.25, P=0.026) and percentile 90 (OR 1.09, 95%CI 1.04-1.15, P=0.001) were brought into the diagnostic model and constructed a nomogram. The AUC of the model in differentiating BT4-GC from DLBCL was 0.898 (95%CI 0.813-0.953), sensitivity was 0.833, and specificity was 0.817. Conclusions:The nomogram based on CTTA percentile 90 and morphological characteristics mucosal line status can effectively distinguish BT4-GC from DLBCL and shows high diagnostic efficacy.

Value of nomogram based on preoperative ultrasound and inflammatory indexes in predicting axillary high nodal burden in early breast cancer / 中华超声影像学杂志

Objective:To explore the values of ultrasound, pathology combined with inflammatory indicators in predicting high nodal burden (HNB) in patients with early breast cancer and to construct a nomogram to provide reference for individualized diagnosis and treatment.Methods:The ultrasonographic, pathological features and preoperative inflammatory indicators of 378 female patients diagnosed with early breast cancer confirmed by pathology in the South Hospital of the Sixth People′s Hospital Affiliated to Shanghai Jiaotong University from January 2014 to July 2022 were retrospectively analyzed. They were randomly divided into training set ( n=302) and test set ( n=76) in a ratio of 8∶2, and the baseline data of the two groups were compared. The optimal cutoff values of neutrophil to lymphocyte ratio (NLR), platelet to lymphocyte ratio (PLR) and lymphocyte to monocyte ratio (LMR) were obtained by ROC curve. In the training set, with axillary high lymph node load (≥3 metastatic lymph nodes) as the dependent variable, independent influencing factors of HNB were identified by univariate and multivariate Logistic regression analyses, and the nomogram was established. The test set data were used to verify the model. The discrimination, calibration and clinical applicability of the model were assessed by the area under the ROC curve (AUC), C-index, the calibration curve, Brier score and the decision curve analysis, respectively. Results:There were no significant differences in all variables between the training set and the test set (all P>0.05). ROC curve analysis results showed that AUCs of NLR, PLR and LMR were 0.578, 0.547 and 0.516, respectively, and the optimal cut-off values were 2.184, 150 and 3.042, respectively. Univariate Logistic regression analysis showed that age, pathological type, histological grade, Ki-67, lymphovascular invasion, NLR, PLR, ultrasonic characteristics (maximum diameter of primary tumor, shape, long/short diameter of lymph node, cortical thickness, cortical and medullary boundary, lymph node hilum, lymph node blood flow pattern) were correlated with HNB of early breast cancer (all P<0.05). Multivariate Logistic regression analysis showed that ultrasonic characteristics (maximum diameter of primary tumor >2 cm, effacement of lymph node hilum, non-lymphatic portal blood flow), lymphovascular invasion, Ki-67>14% and NLR>2.184 were independent risk factors for HNB in early breast cancer ( OR=7.258, 8.784, 6.120, 8.031, 3.394 and 3.767, respectively; all P<0.05) and were used to construct the nomogram model. The AUC of the training set was 0.914 (95% CI=0.878-0.949), C-index was 0.914; The AUC of the test set was 0.871 (95% CI=0.769-0.973), C-index was 0.871, indicating good discrimination. Calibration curve and Brier score were 0.090, indicating high calibration degree of the model. The clinical decision curve indicated good clinical benefit. Conclusions:The nomogram based on ultrasonic characteristics (maximum diameter of primary tumor, lymph node hilum, lymph node blood flow pattern), lymphovascular invasion, Ki-67 and NLR can effectively predict the risk of HNB in patients with early breast cancer, and provide a reference for precision diagnosis and treatment to avoid excessive or insufficient treatment.

Construction of predictive model for renal allograft rejection based on clinical multimodal ultrasound features / 中华超声影像学杂志

Objective:To construct a nomogram for predicting the occurrence of renal allograft rejection based on the combination of multimodal ultrasound features and clinical data.Methods:The ultrasound findings and clinical characteristics of 102 patients with transplanted kidneys who underwent renal biopsy in the General Hospital of Eastern Theater Command from January 2021 to March 2022 were analyzed retrospectively. Patients were divided into rejection group and nephropathy group according to Banff transplant kidney pathological diagnostic criteria (2017 edition). Multivariate Logistic regression was used to screen independent predictors related to the status of rejection, and nomograms were drawn based on the independent predictors. The internal validation of the nomogram was carried out by Bootstrap method, and the ROC curve and calibration curve were utilized to evaluate the diagnostic efficacy of the nomogram.Results:Blood urea nitrogen concentration, renal aortic resistance index, absolute time to peak and cortical echo were independent predictors of rejection( OR=1.073, 1.078, 0.843, 0.205; all P<0.05). Incorporating blood urea nitrogen concentration, renal aortic resistance index, absolute peak time and cortical echo, the nomogram was constructed. The AUC of the predictive model was 0.814(95% CI=0.722-0.905) and the cutoff value was 0.67(corresponding to a total score of about 157 points). Both internal verification (AUC=0.788) and calibration curve demonstrated the clinical usefulness of the nomogram. Conclusions:The nomogram for predicting the occurrence of rejection in renal allograft patients based on multimodal ultrasound features and clinical data can guide the individualized treatment of patients with renal dysfunction.

Establishment and evaluation of early in-hospital death prediction model for patients with acute pancreatitis in intensive care unit / 中华危重病急救医学

Objective:To investigate the death risk prediction factors of acute pancreatitis (AP) patients in intensive care unit (ICU), and to establish a death prediction model and evaluate its efficacy.Methods:A retrospective cohort study was conducted using the data in the Medical Information Mart for Intensive Care-Ⅲ (MIMIC-Ⅲ). The clinical data of 285 AP patients admitted to the ICU in the database were collected, including age, gender, blood routine and blood biochemical indicators, comorbidities, simplified acute physiology score Ⅲ (SAPS Ⅲ) and hospital prognosis. By using univariate analysis, the differences in the clinical data of the patients were compared between the two groups. Binary multivariate Logistic regression analysis was used to screen out independent predictors of in-hospital death in AP patients. A death prediction model was established, and the nomogram was drawn. The receiver operator characteristic curve (ROC curve) was plotted, and the area under the ROC curve (AUC) was used to test the discrimination of the prediction model. In addition, the prediction model was compared with the SAPSⅢ score in predicting in-hospital death. The calibration ability of the prediction model was evaluated by the Hosmer-Lemeshow goodness of fit test, and a calibration map was drawn to show the calibration degree of the prediction model.Results:Among 285 patients with AP, 29 patients died in the hospital and 256 patients survived. Univariate analysis showed that the patients in the death group were older than those in the survival group (years old: 70±17 vs. 58±16), and had higher white blood cell count (WBC), total bilirubin (TBil), serum creatinine (SCr), blood urea nitrogen (BUN), red blood cell volume distribution width (RDW), proportion of congestive heart failure and SAPSⅢ score [WBC (×10 9/L): 18.5 (13.9, 24.3) vs. 13.2 (9.3, 17.9), TBil (μmol/L): 29.1 (15.4, 66.7) vs. 16.2 (10.3, 29.1), SCr (μmol/L): 114.9 (88.4, 300.6) vs. 79.6 (53.0, 114.9), BUN (mmol/L): 13.9 (9.3, 17.8) vs. 6.1 (3.7, 9.6), RDW: 0.152 (0.141, 0.165) vs. 0.141 (0.134, 0.150), congestive heart failure: 34.5% vs. 14.8%, SAPSⅢ score: 66 (52, 90) vs. 39 (30, 48), all P < 0.05]. Multivariate Logistic regression analysis showed that age [odds ratio ( OR) = 1.038, 95% confidence interval (95% CI) was 1.005-1.073], WBC ( OR = 1.103, 95% CI was 1.038-1.172), TBil ( OR = 1.247, 95% CI was 1.066-1.459), BUN ( OR = 1.034, 95% CI was 1.014-1.055) and RDW ( OR = 1.344, 95% CI was 1.024-1.764) were independent risk predictors of in-hospital death in patients with AP. Logistic regression model was established: Logit ( P) = 0.037×age+0.098×WBC+0.221×TBil+0.033×BUN+0.296×RDW-12.133. ROC curve analysis showed that the AUC of the Logistic regression model for predicting the in-hospital death of patients with AP was 0.870 (95% CI was 0.794-0.946), the sensitivity was 86.2%, and the specificity was 78.5%, indicating that the model had good predictive performance, and it was superior to the SAPSⅢ score [AUC was 0.831 (95% CI was 0.754-0.907), the sensitivity was 82.8%, and the specificity was 75.4%]. A nomogram model was established based on the result of multivariate Logistic regression analysis. The calibration map showed that the calibration curve of the nomogram model was very close to the standard curve, with the goodness of fit test: χ 2 = 6.986, P = 0.538, indicating that the consistency between the predicted death risk of the nomogram model and the actual occurrence risk was relatively high. Conclusions:The older the AP patient is, the higher the WBC, TBil, BUN, and RDW, and the greater the risk of hospital death. The death prediction Logistic regression model and nomogram model constructed based on the above indicators have good discrimination ability and high accuracy for high-risk patients with hospital death, which can accurately predict the probability of death in AP patients and provide a basis for prognosis judgment and clinical treatment of AP patients.

The role of a simple Nomogram model in evaluating the severity of mycoplasma pneumonia pneumonia in adults / 中国基层医药

Objective:To investigate the role of a simple Nomogram model in evaluating the severity of mycoplasma pneumoniae pneumonia (MPP) in adults.Methods:The clinical data of 162 patients with MPP who received treatment in Wenzhou Central Hospital from March 2015 to October 2022 were retrospectively analyzed. These patients were divided into a severe group ( n = 67) and a common group ( n = 95) according to whether they were diagnosed with severe MPP. The clinical data of patients were recorded. Fourteen clinical variables were screened, including age, sex, onset season, fever, heat peak, fever duration, cough duration, white blood cell count, percentage of neutrophils, percentage of lymphocytes, hemoglobin, platelet count, C-reactive protein, and procalcitonin. Multivariate logistic regression analysis of statistically significant variables in univariate analysis was performed. The Nomogram model was constructed with the R language software package (version 3.6.2). The model was verified with a calibration curve and receiver operating characteristic curve. Results:Univariate analysis results showed that in the severe group, the fever peak ( Z = 5.03, P < 0.001) was higher, fever duration ( χ2 = 27.55, P < 0.001), and cough duration ( χ2 = 28.72, P < 0.001) were longer, white cell count ( t = 2.93, P = 0.004), percentage of neutrophils ( t = 9.08, P < 0.001), C-reactive protein ( t = 35.05, P < 0.001), and procalcitonin level ( t = 15.09, P < 0.001) were greater compared with the common group. The percentage of lymphocytes ( t = 1.16, P < 0.001), hemoglobin level ( t = 1.22, P < 0.001), and platelet count ( t = 2.82, P < 0.001) in the severe group were significantly lower than those in the common group. Multivariate logistic regression analysis results showed that heat peak, cough duration, and C-reactive protein were positively correlated with the severity of MPP (all P < 0.05). The percentage of lymphocytes, hemoglobin concentration, and platelet count were negatively correlated with the severity of MPP (all P < 0.05). The establishment and validation results of the Nomogram model showed that the accuracy of the model was good, with a sensitivity of 88.73%, a specificity of 77.61%, and a C-index of 0.904. Conclusion:Heat peak, cough duration, percentage of lymphocytes, platelet count, and C-reactive protein are closely related to the severity of early MPP. A simple Nomogram model can be one of the tools for early assessment of the severity of MPP.