Validation Of Nomogram Research Articles

Development and Internal Validation of Nomograms for Survival of Advanced Epithelial Ovarian Cancer Based on Established Prognostic Factors and Hematologic Parameters.

Objective: To assess the association between pretreatment thrombocytosis, anemia, and leukocytosis and overall survival (OS) of advanced-stage EOC. Furthermore, to develop nomograms using established prognostic factors and pretreatment hematologic parameters to predict the OS of advanced EOC patients. Methods: Advanced-stage EOC patients treated between January 1996 and January 2010 in eastern Netherlands were included. Survival outcomes were compared between patients with and without pretreatment thrombocytosis (≥450,000 platelets/µL), anemia (hemoglobin level of <7.5 mmol/L), or leukocytosis (≥11.0 × 109 leukocytes/L). Three nomograms (for ≤3-, ≥5-, and ≥10-year OS) were developed. Candidate predictors were fitted into multivariable logistic regression models. Multiple imputation was conducted. Model performance was assessed on calibration, discrimination, and Brier scores. Bootstrap validation was used to correct for model optimism. Results: A total of 773 advanced-stage (i.e., FIGO stages IIB-IV) EOC patients were included. The median [interquartile range, IQR] OS was 2.3 [1.3-4.2] and 3.0 [1.4-7.0] years for patients with and without pretreatment thrombocytosis (p < 0.01). The median OS was not notably different for patients with and without pretreatment leukocytosis (p = 0.58) or patients with and without pretreatment anemia (p = 0.07). The final nomograms comprised established predictors with either pretreatment leukocyte or platelet count. The ≥5- and ≥10-year OS models demonstrated good calibration and adequate discrimination with optimism-corrected c-indices [95%-CI] of 0.76 [0.72-0.80] and 0.78 [0.73-0.83], respectively. The ≤3-year OS model demonstrated suboptimal performance with an optimism-corrected c-index of 0.71 [0.66-0.75]. Conclusions: Pretreatment thrombocytosis is associated with poorer EOC survival. Two well-performing models predictive of ≥5-year and ≥10-year OS in advanced-stage EOC were developed and internally validated.

Development and Validation of Prognostic Nomogram to Predict Overall Survival for Older Patients Who Age 75 Years or Older With Urothelial Carcinoma of Bladder

Purpose: Age is an important factor for prognostic factor in bladder cancer. However, most clinical studies targeting bladder cancer have limited the upper age limit to 75 years. With the higher age of diagnosis of older patients with bladder cancer, there is a lack of prognostic data for bladder cancer at advanced ages. In this study, we analyzed the prognostic factors of older patients with bladder cancer 75 years or older using Surveillance, Epidemiology, and End Results (SEER) data. Materials and Methods: Clinical parameters that constitute potential risk factors were analyzed by downloading a total of 13,505 patients with bladder cancer from the SEER database using univariate and multifactorial Cox proportional risk regression. Nomogram was constructed based on parameters significantly associated with overall survival (OS). The consistency index (c-index) and area under the curve were used to evaluate the predictive performance of the model. Results: From the SEER database, we analyzed 13,505 cases and found that the median survival time for the whole cohort was 52 months. However, the median survival was 11 months, 8 months, and 6 months in 75 to 79 years subgroup, 80 to 84 years subgroup, and older than 85 years subgroup, when patients were diagnosed with stage IV. OS was poorer in the high histological grade patients. Furthermore, it was found that patients with lymph node metastasis had a significantly unfavorable prognosis than those with negative lymph nodes. The more advanced stage and distant metastases of patients had the shorter survival. Patients who underwent surgery had better overall survival than nonsurgical patients, and chemotherapy had prolonged survival. Conversely, survival was documented to be shorter with radiotherapy than those without radiotherapy. Conclusions: The overall prognosis of older patients with bladder cancer was poor, especially in patients with lymph node metastasis, high histological grade, and advanced stage, while a combination of surgery and chemotherapy could prolong survival.

Development and validation of nomograms to predict survival and recurrence after hepatectomy for intermediate/advanced (BCLC stage B/C) hepatocellular carcinoma

BackgroundDespite the Barcelona Clinic Liver Cancer system discouraging hepatectomy for intermediate/advanced hepatocellular carcinoma, the procedure is still performed worldwide, particularly in Asia. This study aimed to develop and validate nomograms for predicting survival and recurrence for these patients. MethodsWe analyzed patients who underwent curative-intent hepatectomy for intermediate/advanced hepatocellular carcinoma between 2010 and 2020 across 3 Chinese hospitals. The Eastern Hepatobiliary Surgery Hospital cohort was used as the training cohort for the nomogram construction, and the Jilin First Hospital and Fujian Mengchao Hepatobiliary Hospital cohorts served as the external validation cohorts. Independent preoperative predictors for survival and recurrence were identified through univariable and multivariable Cox regression analyses. Predictive accuracy was measured using the concordance index and calibration curves. The predictive performance between nomograms and conventional hepatocellular carcinoma staging systems was compared. ResultsA total of 1,328 patients met the inclusion criteria. The nomograms for predicting survival and recurrence were developed using 10 and 6 independent variables, respectively. Nomograms’ concordance indices in the training cohort were 0.777 (95% confidence interval 0.759–0.800) and 0.719 (95% confidence interval 0.697–0.742) for survival and recurrence, outperforming 4 conventional staging systems (P < .001). Nomograms accurately stratified risk into low, intermediate, and high subgroups. These results were validated well by 2 external validation cohorts. ConclusionWe developed and validated nomograms predicting survival and recurrence for patients with intermediate/advanced hepatocellular carcinoma, contradicting Barcelona Clinic Liver Cancer surgical guidelines. These nomograms may facilitate clinicians to formulate personalized surgical decisions, estimate long-term prognosis, and strategize neoadjuvant/adjuvant anti-recurrence therapy.

Development and validation of machine learning models and nomograms for predicting the surgical difficulty of laparoscopic resection in rectal cancer

BackgroundThe objective of this study is to develop and validate a machine learning (ML) prediction model for the assessment of laparoscopic total mesorectal excision (LaTME) surgery difficulty, as well as to identify independent risk factors that influence surgical difficulty. Establishing a nomogram aims to assist clinical practitioners in formulating more effective surgical plans before the procedure.MethodsThis study included 186 patients with rectal cancer who underwent LaTME from January 2018 to December 2020. They were divided into a training cohort (n = 131) versus a validation cohort (n = 55). The difficulty of LaTME was defined based on Escal’s et al. scoring criteria with modifications. We utilized Lasso regression to screen the preoperative clinical characteristic variables and intraoperative information most relevant to surgical difficulty for the development and validation of four ML models: logistic regression (LR), support vector machine (SVM), random forest (RF), and decision tree (DT). The performance of the model was assessed based on the area under the receiver operating characteristic curve(AUC), sensitivity, specificity, and accuracy. Logistic regression-based column-line plots were created to visualize the predictive model. Consistency statistics (C-statistic) and calibration curves were used to discriminate and calibrate the nomogram, respectively.ResultsIn the validation cohort, all four ML models demonstrate good performance: SVM AUC = 0.987, RF AUC = 0.953, LR AUC = 0.950, and DT AUC = 0.904. To enhance visual evaluation, a logistic regression-based nomogram has been established. Predictive factors included in the nomogram are body mass index (BMI), distance between the tumor to the dentate line ≤ 10 cm, radiodensity of visceral adipose tissue (VAT), area of subcutaneous adipose tissue (SAT), tumor diameter >3 cm, and comorbid hypertension.ConclusionIn this study, four ML models based on intraoperative and preoperative risk factors and a nomogram based on logistic regression may be of help to surgeons in evaluating the surgical difficulty before operation and adopting appropriate responses and surgical protocols.

Construction and Validation of Nomograms for Predicting Overall Survival and Cancer-Specific Survival in Patients with Primary Anaplastic Oligodendroglioma

Anaplastic oligodendroglioma (AOD) is a rare high-grade central nervous system tumor. The current research on prognostic prediction of AOD remains limited. This study aimed to identify prognostic factors and establish the nomograms to predict overall survival (OS) and cancer-specific survival (CSS) for patients with AOD. Patients diagnosed with AOD between 1992 and 2020 were extracted from the Surveillance, Epidemiology, and End Result database. We performed univariate and multivariate Cox regression analyses to identify independent prognostic factors based on the training group. Kaplan-Meier survival curves were used to compare the impact of various independent factors on patient prognosis. For OS and CSS, the nomograms were constructed and verified by the validation group. Harrell''s concordance index, receiver operating characteristic curves, calibration curves, and decision curve analyses were used to assess the discrimination, consistency, and clinical value of the nomograms. A total of 1202 AOD patients were enrolled, being randomly divided into training (n= 841) and validation (n= 361) groups (7:3 ratio). Univariate and multivariate Cox analysis identified 4 significant independent factors (tumor site, age, surgery, and chemotherapy). For OS and CSS, Harrell''s concordance index were 0.731 (0.705-0.757) and 0.728 (0.701-0.754) in the training group, 0.688 (0.646-0.731) and 0.684 (0.639-0.729) in the validation group, respectively. Receiver operating characteristic curves and Calibration curves showed good discrimination and consistency, respectively. In addition, the decision curve analyses curves showed the nomograms have good clinical benefits. We successfully established the nomograms to predict the OS and CSS for AOD patients. The nomograms showed good performance in prognostic prediction, assisting clinicians in evaluating patient prognosis and personalizing treatment plans.

Establishment of a prognostic nomogram and discussion on optimal treatment for cervical adenocarcinoma:a retrospective study based on SEER database and Chinese single-center data

Objective: To establish and validate a predicting nomogram for cervical adenocarcinoma based on surveillance, epidemiology and end results (SEER) database and Chinese single-center data, and to explore the optimal treatment for cervical adenocarcinoma. Methods: This study selected 2 478 cervical adenocarcinoma patients from the SEER database as the training cohort, and 195 cervical adenocarcinoma patients from Cancer Hospital of Dalian University of Technology, Liaouing Cancer Hospital and Institute as an external validation cohort. Clinicopathological information and follow-up data of the two cohorts were collected. The radiotherapy group was defined as receiving comprehensive treatment based on concurrent chemoradiotherapy after initial diagnosis, while the surgery group was defined as receiving comprehensive treatment based on radical surgery. Log-rank test and cox regression were used to evaluate factors affecting the prognosis of cervical adenocarcinoma patients. A nomogram was drawn to predict the 3-year and 5-year overall survival rates of cervical adenocarcinoma patients, and then internal validation of the training cohort from SEER database and external validation of the hospital cohort were conducted. Results: (1) In the SEER database training cohort, there were 385 patients (15.54%, 385/2 478) in the radiotherapy group and 2 093 patients (84.46%, 2 093/2 478) in the surgery group. Overall survival time of the radiotherapy group was (55.8±51.3) months, while that of the surgery roup was (94.4±61.7) months, the difference between the two groups was statistically significant (χ2=256.44, P<0.001). Log-rank test showed that age, marital status, maximum of tumor diameters, pathological grade, International Federation of Gynecology and Obstetrics (FIGO) stage, and treatments were all significant factors affecting the overall survival time of cervical adenocarcinoma patients (all P<0.001). Multivariate Cox regression analysis showed that elder (>50 years old), single status, huge tumors (>4 cm), high pathological grades (G2, G3), and advanced FIGO stages (≥Ⅱa2 stage) were independent risk factors for the overall survival time of cervical adenocarcinoma patients (all P<0.05); compared with radiotherapy, surgery was a protective factor for the prognosis of cervical adenocarcinoma patients (HR=0.619, 95%CI: 0.494-0.777; P<0.001). Further analysis of locally advanced stage and Ⅲc stage of patients showed that surgery was a protective factor for the prognosis of cervical adenocarcinoma patients with a maximum tumor diameter >4 to <6 cm (HR=0.414, 95%CI: 0.182-0.942; P=0.036) in locally advanced stage and Ⅲc T1 to T2 stage (HR=0.473, 95%CI: 0.307-0.728; P=0.001). (2) The external validation cohort consisted of 39 patients (20.00%, 39/195) in the radiotherapy group and 156 patients (80.00%, 156/195) in the surgery group. The overall survival time of patients in the radiotherapy group was (51.7±34.3) months, while that of the surgery group was (63.1±26.6) months (χ2=28.41, P<0.001). Further analysis was conducted on locally advanced stage and Ⅲc stage patients, and multivariate Cox regression analysis was performed after propensity score matching, which showed that surgery was a protective factor for the prognosis of cervical adenocarcinoma patients with a maximum tumor diameter >4 to <6 cm in locally advanced stage (HR=0.141, 95%CI: 0.023-0.843; P=0.032) and Ⅲc T1 to T2 stage (HR=0.184, 95%CI: 0.036-0.947; P=0.043). (3) Establishment and internal and external validation of nomogram: based on the six factors screened out by the multivariate Cox regression model, the nomogram was developed to predict the prognosis of cervical adenocarcinoma patients. The consistency index of the internal and external validation were 0.801 and 0.766, respectively, and the calibration curves matched well with the ideal fitting line. Conclusions: The key to the treatment of cervical adenocarcinoma is to prioritize radical surgery for patients with conditions for radical tumor resection. Compared with concurrent chemoradiotherapy, patients with locally advanced stages (Ⅰb3, Ⅱa2), and Ⅲc (T1, T2) stages cervical adenocarcinoma could benefit from comprehensive treatment based on radical surgery. The nomogram of this study has been validated internally and externally, and show good survival prediction efficacy for cervical adenocarcinoma patients.

Development and validation of an ultrasound-based deep learning radiomics nomogram for predicting the malignant risk of ovarian tumours

BackgroundThe timely identification and management of ovarian cancer are critical determinants of patient prognosis. In this study, we developed and validated a deep learning radiomics nomogram (DLR_Nomogram) based on ultrasound (US) imaging to accurately predict the malignant risk of ovarian tumours and compared the diagnostic performance of the DLR_Nomogram to that of the ovarian-adnexal reporting and data system (O-RADS).MethodsThis study encompasses two research tasks. Patients were randomly divided into training and testing sets in an 8:2 ratio for both tasks. In task 1, we assessed the malignancy risk of 849 patients with ovarian tumours. In task 2, we evaluated the malignancy risk of 391 patients with O-RADS 4 and O-RADS 5 ovarian neoplasms. Three models were developed and validated to predict the risk of malignancy in ovarian tumours. The predicted outcomes of the models for each sample were merged to form a new feature set that was utilised as an input for the logistic regression (LR) model for constructing a combined model, visualised as the DLR_Nomogram. Then, the diagnostic performance of these models was evaluated by the receiver operating characteristic curve (ROC).ResultsThe DLR_Nomogram demonstrated superior predictive performance in predicting the malignant risk of ovarian tumours, as evidenced by area under the ROC curve (AUC) values of 0.985 and 0.928 for the training and testing sets of task 1, respectively. The AUC value of its testing set was lower than that of the O-RADS; however, the difference was not statistically significant. The DLR_Nomogram exhibited the highest AUC values of 0.955 and 0.869 in the training and testing sets of task 2, respectively. The DLR_Nomogram showed satisfactory fitting performance for both tasks in Hosmer–Lemeshow testing. Decision curve analysis demonstrated that the DLR_Nomogram yielded greater net clinical benefits for predicting malignant ovarian tumours within a specific range of threshold values.ConclusionsThe US-based DLR_Nomogram has shown the capability to accurately predict the malignant risk of ovarian tumours, exhibiting a predictive efficacy comparable to that of O-RADS.

Development and validation of survival nomograms in elder triple-negative invasive ductal breast carcinoma patients

ABSTRACT Background We aimed to develop a nomogram to predict the overall survival of elderly patients with Triple-negative invasive ductal breast carcinoma (TNIDC). Research design and methods 12165 elderly patients with nonmetastatic TNIDC were retrieved from the SEER database from 2010 to 2019 and were randomly assigned to training and validation cohorts. Stepwise Cox regression analysis was used to select variables for the nomogram based on the training cohort. Univariate and multivariate Cox analyses were used to calculate the correlation between variables and prognosis of the patients. Survival analysis was performed for high- and low-risk subgroups based on risk score. Results Eleven predictive factors were identified to construct our nomograms. Compared with the TNM stage, the discrimination of the nomogram revealed good prognostic accuracy and clinical applicability as indicated by C-index values of 0.741 (95% CI 0.728–0.754) against 0.708 (95% CI 0.694–0.721) and 0.765 (95% CI 0.747–0.783) against 0.725 (95% CI 0.705–0.744) for the training and validation cohorts, respectively. Differences in OS were also observed between the high- and low-risk groups (p < 0.001). Conclusion The proposed nomogram provides a convenient and reliable tool for individual evaluations for elderly patients with M0_stage TNIDC. However, the model may only for Americans.

Construction and validation of nomogram for the cancer-specific mortality for HER2-positive breast cancer patients.

The cancer-specific mortality (CSM) of patients with human epidermal growth factor receptor 2 positive (HER2+) breast cancer remains dismal and varies widely from person to person. Therefore, we aim to construct a nomogram to predict CSM in HER2+ breast cancer using data from the surveillance, epidemiology, and end results (SEER) database. The clinicopathological data of patients diagnosed with HER2+ breast cancer from 2000 to 2019 were selected from the SEER database. Independent prognostic factors for CSM of patients were identified by competing risk model. Subsequently, we constructed a new predicting nomogram. Calibration curves, receiver operating characteristic curve, and decision curve were used to evaluate the efficiency of the nomogram. A total of 45,362 breast cancer patients in the SEER database were selected for study and randomly separated into training (n = 31,753) and validation (n = 13,609) cohorts. Univariate and multivariate analysis showed that age, race, tumor grade, clinical stage, T stage, surgery status, radiotherapy, chemotherapy, and regional nodes examined were independent risk factors for CSM of HER2+ breast cancer patients. Receiver operating characteristic curves for the prediction nomogram of the CSM for breast cancer patients indicated that the 1-, 3- and 5-year AUCs were 0.874, 0.843, and 0.820 in the training cohort and 0.861, 0.845, and 0.825 in the validation cohort, respectively. The c-index was 0.817 and 0.821 in training cohort and validation cohort, respectively. Moreover, a good agreement was seen between the observed outcome and the predicted probabilities in the calibration curves of the nomogram in training cohort and validation cohort. Further decision curve analysis demonstrated good clinical utilities of the nomogram in training cohort and validation cohort. The nomogram shows good accuracy and reliability in predicting the CSM of breast cancer patients, and it could provide some theoretical support for clinicians to make decisions.

Establishment and validation of nomograms to predict the overall survival and cancer-specific survival for non-metastatic bladder cancer patients: A large population-based cohort study and external validation.

This study aimed to develop nomograms to accurately predict the overall survival (OS) and cancer-specific survival (CSS) of non-metastatic bladder cancer (BC) patients. Clinicopathological information of 260,412 non-metastatic BC patients was downloaded from the Surveillance, Epidemiology, and End Results (SEER) database from 2000 to 2020. LASSO method and Cox proportional hazard regression analysis were utilized to discover the independent risk factors, which were used to develop nomograms. The accuracy and discrimination of models were tested by the consistency index (C-index), the area under the subject operating characteristic curve (AUC) and the calibration curve. Decision curve analysis (DCA) was used to test the clinical value of nomograms compared with the TNM staging system. Nomograms predicting OS and CSS were constructed after identifying independent prognostic factors. The C-index of the training, internal validation and external validation cohort for OS was 0.722 (95%CI: 0.720-0.724), 0.723 (95%CI: 0.721-0.725) and 0.744 (95%CI: 0.677-0.811). The C-index of the training, internal validation and external validation cohort for CSS was 0.794 (95%CI: 0.792-0.796), 0.793 (95%CI: 0.789-0.797) and 0.879 (95%CI: 0.814-0.944). The AUC and the calibration curves showed good accuracy and discriminability. The DCA showed favorable clinical potential value of nomograms. Kaplan-Meier curve and log-rank test uncovered statistically significance survival difference between high- and low-risk groups. We developed nomograms to predict OS and CSS for non-metastatic BC patients. The models have been internally and externally validated with accuracy and discrimination and can assist clinicians to make better clinical decisions.

Development and validation of nomograms to predict clinical outcomes of preeclampsia.

Preeclampsia (PE) is one of the most severe pregnancy-related diseases; however, there is still a lack of reliable biomarkers. In this study, we aimed to develop models for predicting early-onset PE, severe PE, and the gestation duration of patients with PE. Eligible patients with PE were enrolled and divided into a training (n = 253) and a validation (n = 108) cohort. Multivariate logistic and Cox models were used to identify factors associated with early-onset PE, severe PE, and the gestation duration of patients with PE. Based on significant factors, nomograms were developed and evaluated using the area under the curve (AUC) and a calibration curve. In the training cohort, multiple gravidity experience (p = 0.005), lower albumin (ALB; p < 0.001), and higher lactate dehydrogenase (LDH; p < 0.001) were significantly associated with early-onset PE. Abortion history (p = 0.017), prolonged thrombin time (TT; p < 0.001), and higher aspartate aminotransferase (p = 0.002) and LDH (p = 0.003) were significantly associated with severe PE. Abortion history (p < 0.001), gemellary pregnancy (p < 0.001), prolonged TT (p < 0.001), higher mean platelet volume (p = 0.014) and LDH (p < 0.001), and lower ALB (p < 0.001) were significantly associated with shorter gestation duration. Three nomograms were developed and validated to predict the probability of early-onset PE, severe PE, and delivery time for each patient with PE. The AUC showed good predictive performance, and the calibration curve and decision curve analysis demonstrated clinical practicability. Based on the clinical features and peripheral blood laboratory indicators, we identified significant factors and developed models to predict early-onset PE, severe PE, and the gestation duration of pregnant women with PE, which could help clinicians assess the clinical outcomes early and design appropriate strategies for patients.

Development and validation of comprehensive nomograms from the SEER database for predicting early mortality in metastatic rectal cancer patients

BackgroundMetastatic rectal cancer is an incurable malignancy, which is prone to early mortality. We aimed to establish nomograms for predicting the risk of early mortality in patients with metastatic rectal cancer.MethodsIn this study, clinical data were obtained from the Surveillance, Epidemiology, and End Results (SEER) database.We utilized X-tile software to determine the optimal cut-off points of age and tumor size in diagnosis. Significant independent risk factors for all-cause and cancer-specific early mortality were determined by the univariate and multivariate logistic regression analyses, then we construct two practical nomograms. In order to assess the predictive performance of nomograms, we performed calibration plots, time-dependent receiver-operating characteristic curve (ROC), decision curve analysis (DCA) and clinical impact curve (CIC).ResultsA total of 2570 metastatic rectal cancer patients were included in the study. Multivariate logistic regression analyses revealed that age at diagnosis, CEA level, tumor size, surgical intervention, chemotherapy, radiotherapy, and metastases to bone, brain, liver, and lung were independently associated with early mortality of metastatic rectal cancer patients in the training cohort. The area under the curve (AUC) values of nomograms for all-cause and cancer-specific early mortality were all higher than 0.700. Calibration curves indicated that the nomograms accurately predicted early mortality and exhibited excellent discrimination. DCA and CIC showed moderately positive net benefits.ConclusionsThis study successfully generated applicable nomograms that predicted the high-risk early mortality of metastatic rectal cancer patients, which can assist clinicians in tailoring more effective treatment regimens.

A dynamic online nomogram for predicting the heterogeneity trajectories of frailty among elderly gastric cancer survivors

BackgroundFrailty is very common among older people with gastric cancer and seriously affects their prognosis. The development of frailty is continuous and dynamic, increasing the difficulty and burden of care. ObjectivesThe aims of this study were to delineate the developmental trajectory of frailty in older people with gastric cancer 1 year after surgery, identify heterogeneous frailty trajectories, and further explore their predictors to construct a nomogram for prediction. DesignWe conducted a prospective longitudinal observation study. Clinical evaluation and data collection were performed at discharge, and at 1, 3, 6, and 12 months. Setting and participantsThis study was conducted in a tertiary hospital and 381 gastric cancer patients over 60 years who underwent radical gastrectomy completed the 1-year follow-up. MethodsA growth mixture model (GMM) was used to delineate the frailty trajectories, and identify heterogeneous trajectories. A regression model was performed to determine their predictors and further construct a nomogram based on the predictors. Bootstrap with 1000 resamples was used for internal validation of nomogram, a receiver operating characteristic (ROC) curve to evaluate discrimination, calibration curves to evaluate calibration and decision curve analysis (DCA) to evaluate the clinical value. ResultsGMM identified three classes of frailty trajectories: “frailty improving”, “frailty persisting” and “frailty deteriorating”. The latter two were referred to as heterogeneous frailty trajectories. Regression analysis showed 8 independent predictors of heterogeneous frailty trajectories and a nomogram was constructed based on these predictors. The area under ROC curve (AUC) of the nomogram was 0.731 (95 % CI = 0.679–0.781), the calibration curves demonstrated that probabilities predicted by the nomogram agreed well with the actual observation with a mean absolute error of 0.025, and the DCA of nomogram indicated that the net benefits were higher than that of the other eight single factors. ConclusionsOlder gastric cancer patients have heterogeneous frailty trajectories of poor prognosis during one-year postoperative survival. Therefore, early assessment to predict the occurrence of heterogeneous frailty trajectories is essential to improve the outcomes of elderly gastric cancer patients. Scientific and effective frailty interventions should be further explored in the future to improve the prognosis of older gastric cancer patients. Contribution of the paper statementsThis study constructed a static and dynamic online nomogram with good discrimination and calibration, which can help to screen high-risk patients, implement preoperative risk stratification easily and promote the rational allocation of medical resources greatly. RegistrationClinicalTrials.gov (Number: NCT05982899). Tweetable abstractOur findings identified three frailty trajectories and constructed a nomogram to implement preoperative risk stratification and improve patient outcomes.

Development and validation of predictive nomogram for postoperative non-union of closed femoral shaft fracture

Closed femoral shaft fracture is caused by high-energy injuries, and non-union exists after operation, which can significantly damage patients’ body and mind. This study aimed to explore the factors influencing postoperative non-union of closed femoral shaft fractures and establish a predictive nomogram. Patients with closed femoral shaft fractures treated at Hebei Medical University Third Hospital between January 2015 and December 2021 were retrospectively enrolled. A total of 729 patients met the inclusion criteria; of them, those treated in 2015–2019 comprised the training cohort (n = 617), while those treated in 2020–2021 comprised the external validation cohort (n = 112). According to multivariate logistic regression analysis, complex fractures, bone defects, smoking, and postoperative infection were independent risk factors. Based on the factors, a predictive nomogram was constructed and validated. The C-indices in training and external validation cohorts were 0.818 and 0.781, respectively; and the C-index of internal validation via bootstrap resampling was 0.804. The Hosmer–Lemeshow test showed good fit of the nomogram (P > 0.05) consistent with the calibration plot results. The clinical effectiveness was best at a threshold probability of 0.10–0.40 in decision curve analysis. The risk prediction for patients with fractures using this nomogram may aid targeted prevention and rehabilitation programs.

Establishment and Validation of Prognostic Nomograms for Patients with Metastatic Pulmonary Large Cell Neuroendocrine Carcinoma.

Metastatic pulmonary large cell neuroendocrine carcinoma (LCNEC) is an aggressive cancer with generally poor outcomes. Effective methods for predicting survival in patients with metastatic LCNEC are needed. This study aimed to identify independent survival predictors and develop nomograms for predicting survival in patients with metastatic LCNEC. We conducted a retrospective analysis using the Surveillance, Epidemiology, and End Results (SEER) database, identifying patients with metastatic LCNEC diagnosed between 2010 and 2017. To find independent predictors of cancer-specific survival (CSS), we performed Cox regression analysis. A nomogram was developed to predict the 6-, 12-, and 18-month CSS rates of patients with metastatic LCNEC. The concordance index (C-index), area under the receiver operating characteristic (ROC) curves (AUC), and calibration curves were adopted with the aim of assessing whether the model can be discriminative and reliable. Decision curve analyses (DCAs) were used to assess the model's utility and benefits from a clinical perspective. This study enrolled a total of 616 patients, of whom 432 were allocated to the training cohort and 184 to the validation cohort. Age, T staging, N staging, metastatic sites, radiotherapy, and chemotherapy were identified as independent prognostic factors for patients with metastatic LCNEC based on multivariable Cox regression analysis results. The nomogram showed strong performance with C-index values of 0.733 and 0.728 for the training and validation cohorts, respectively. ROC curves indicated good predictive performance of the model, with AUC values of 0.796, 0.735, and 0.736 for predicting the 6-, 12-, and 18-month CSS rates of patients with metastatic LCNEC in the training cohort, and 0.795, 0.801, and 0.780 in the validation cohort, respectively. Calibration curves and DCAs confirmed the nomogram's reliability and clinical utility. The new nomogram was developed for predicting CSS in patients with metastatic LCNEC, providing personalized risk evaluation and aiding clinical decision-making.

Development and validation of nomograms using photoacoustic imaging and 2D ultrasound to predict breast nodule benignity and malignancy.

The application of photoacoustic imaging (PAI), utilizing laser-induced ultrasound, shows potential in assessing blood oxygenation in breast nodules. However, its effectiveness in distinguishing between malignant and benign nodules remains insufficiently explored. This study aims to develop nomogram models for predicting the benign or malignant nature of breast nodules using PAI. A prospective cohort study enrolled 369 breast nodules, subjecting them to PAI and ultrasound examination. The training and testing cohorts were randomly divided into two cohorts in a ratio of 3:1. Based on the source of the variables, three models were developed, Model 1: photoacoustic-BIRADS+BMI + blood oxygenation, Model 2: BIRADS+Shape+Intranodal blood (Doppler) + BMI, Model 3: photoacoustic-BIRADS+BIRADS+ Shape+Intranodal blood (Doppler) + BMI + blood oxygenation. Risk factors were identified through logistic regression, resulting in the creation of three predictive models. These models were evaluated using calibration curves, subject receiver operating characteristic (ROC), and decision curve analysis. The area under the ROC curve for the training cohort was 0.91 (95% confidence interval, 95% CI: 0.88-0.95), 0.92 (95% CI: 0.89-0.95), and 0.97 (95% CI: 0.96-0.99) for Models 1-3, and the ROC curve for the testing cohort was 0.95 (95% CI: 0.91-0.98), 0.89 (95% CI: 0.83-0.96), and 0.97 (95% CI: 0.95-0.99) for Models 1-3. The calibration curves demonstrate that the model's predictions agree with the actual values. Decision curve analysis suggests a good clinical application.

Establishment and Validation of Nomograms and Web Calculators for Different Cement Leakage Risk Types in Pedicle Screw Augmentation for Degenerative Lumbar Stenosis in Osteoporotic Vertebrae

BackgroundThe use of cement in pedicle screw augmentation (PSA) enhances the pullout force of pedicle screws in vertebrae affected by osteoporosis. Risks involved in the use of cement for PSA include nerve injury and vascular damage caused by cement leakage. MethodsOur study included all individuals who received PSA for degenerative lumbar stenosis (DLS) in osteoporotic vertebrae from January 2014 to May 2022. Postoperative CT scan will be used to assess cement leakage. Correlation analysis and logistic regression analyses were utilized to establish the associated clinical or radiological factors, which were then used to construct Nomograms and web calculators. ResultsOur study included a total of 181 patients, with 886 screws being inserted into 443 vertebrae. Perivertebral cement leakage was significantly associated with female, decreased BMD, solid screws, and scattered cement distribution. The majority of cement leakage happened through segmental veins (type S, 72.1%), with basivertebral veins (type B, 23.9%) and instrument-related leakage (type I, 13.9%) following suit. Lower BMD and scattered cement distribution were more likely to experience type S or type B leakage. Our analysis data showed that cement augmentation with cannulated and fenestrated screws tended toward concentrated cement distribution. The creation and verification of each Nomogram additionally showcased the prognostic capability and medical significance of the corresponding model. ConclusionNomograms and web-based calculators can accurately forecast the probability of cement leakage. We should regularly perform the procedure using cannulated and fenestrated screws, along with a moderate amount of high-viscosity cement, while continuously monitoring with fluoroscopy during PSA.

Development and validation of nomogram for predicting early recurrence after radical gastrectomy of gastric cancer

BackgroundAfter radical surgery, early detection of recurrence and metastasis is a crucial factor in enhancing the prognosis and survival of patients with gastric cancer (GC). Therefore, assessing the risk of recurrence in gastric cancer patients and determining the timing for postoperative recurrence is crucial.MethodsThe clinicopathological data of 521 patients with recurrent gastric cancer, who underwent radical gastrectomy at Zhejiang Cancer Hospital between January 2010 and January 2017, were retrospectively analyzed. These patients were randomly divided into two groups: a training group (n = 365) and a validation group (n = 156). In the training set, patients were further categorized into early recurrence (n = 263) and late recurrence (n = 102) groups based on a 2-year boundary. Comparative analyses of clinicopathological features and prognoses were conducted between these two groups. Subsequently, a nomogram for predicting early recurrence was developed and validated.ResultsIn this study, the developed nomogram incorporated age, serous infiltration, lymph node metastasis, recurrence mode, and the tumour marker CA19-9. In the training cohort, the area under the curve (AUC value) was 0.739 (95% CI, 0.682–0.798), with a corresponding C-index of 0.739. This nomogram was subsequently validated in an independent validation cohort, yielding an AUC of 0.743 (95% CI, 0.652–0.833) and a C-index of 0.743. Furthermore, independent risk factors for prognosis were identified, including age, absence of postoperative chemotherapy, early recurrence, lymph node metastasis, abdominal metastasis, and vascular cancer embolus.ConclusionIndependent risk factors for gastric cancer recurrence following radical surgery were utilized to construct a nomogram for predicting early relapse. This nomogram effectively assesses the risk of recurrence, aids in treatment decision-making and follow-up planning in clinical settings, and demonstrated strong performance in the validation cohort.

Development and validation of web-based nomograms for predicting survival status in patients with intrahepatic cholangiocarcinoma depending on the surgical status: a SEER database analysis

This study aimed to develop and validate prognostic nomograms that can estimate the probability of 1-, 3- and 5-year overall survival (OS) as well as cancer-specific survival (CSS) for Intrahepatic cholangiocarcinoma (ICCA) patients. Clinical data of 1446 patients diagnosed with ICCA between 2010 and 2017 from the Surveillance, Epidemiology, and End Results (SEER) database were analyzed. In both the OS and the CSS group, the training cohort and validation cohort were divided into a 7:3 ratio. Age, sex, AJCC T stage, AJCC N stage, AJCC M stage, surgical status, and tumor grade were selected as independent prognostic risk factors to build the nomograms. To compare the efficacy of predicting 1-, 3-, and 5-year OS and CSS rates of the nomogram with the 8th edition of the American Joint Committee on Cancer (AJCC) staging system, we evaluated the Harrell’s index of concordance (C-index), area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA) in both cohorts. The results showed the nomogram for 1-, 3-, and 5-year OS and CSS prediction performed better than the AJCC staging system. In the subgroup analysis for patients could not receive surgery as the primary treatment. We developed two nomograms for predicting the 1-, and 2-year OS and CSS rates following the same analysis procedure. Results indicate that the performance of both nomograms, which contained sex, AJCC T stage, AJCC M stage, chemotherapy, and tumor grade and prognostic factors, was also superior to the AJCC staging system. Meanwhile, four dynamic network-based nomograms were published. The survival analysis showed the survival rate of patients classified as high-risk based on the nomogram score was significantly lower compared to those categorized as low-risk (P < 0.0001). Finally, accurate and convenient nomograms were established to assist clinicians in making more personalized prognosis predictions for ICCA patients.

Development and validation of prognostic nomograms for adult with papillary renal cell carcinoma: A retrospective study

ObjectiveThe aim of the study was to create two consensus nomograms for predicting Overall Survival (OS) and Cancer-Specific Survival (CSS) in adults with papillary Renal Cell Carcinoma (pRCC). MethodsUsing the Surveillance, Epidemiology, and End Results databases, a retrospective analysis of 1,074 adults with pRCC from 2004 to 2015 was performed. These patients were then randomly divided into two independent cohorts with a ratio of 7:3 (training cohort: 752; validation cohort: 322). In a retrospective analysis of 752 patients from the training cohort, independent prognostic variables affecting OS and CSS were found. R software was used to create prognostic nomograms based on the findings of Cox regression analysis. The performance of the nomograms was assessed using the Concordance Index (C-index), the Area Under Curve (AUC), a calibration curve, and Decision Curve Analysis (DCA). Data from the 107 postoperative pRCC patients at the Affiliated Hospital of Xuzhou Medical University were used for external validation of the nomogram. ResultsFor OS and CSS, the C-indices and AUCs of the training cohort and the validation cohort indicated that the model had excellent discrimination. The DCA demonstrated that the model was clinically applicable, and the calibration curves in the internal and external validations showed that the model's accuracy was high. ConclusionThe authors developed and validated a prognostic nomogram that accurately predicted the 3-, 5-, and 8-year OS and CSS of adults with pRCC. Clinicians can use this knowledge to direct the clinical management and counseling of patients with pRCC.