Nomograms for staging, prognosis, and predicting treatment outcomes

Nomograms predicting benefit after immunotherapy in oral bifidobacteria supplementation ICC patients: a retrospective study

PURPOSE

The objective of this study was to develop nomograms for predicting outcomes following immunotherapy in patients diagnosed with intrahepatic cholangiocarcinoma (ICC).

PATIENTS AND METHODS

A retrospective analysis was conducted on data from 75 ICC patients who received immunotherapy at Jinling Hospital and Drum Hospital. The discriminative power, accuracy, and clinical applicability of the nomograms were assessed using the concordance index (C-index), calibration curve, and decision curve analysis (DCA). The predictive performance of the nomograms for overall survival (OS) and progression-free survival (PFS) was evaluated using the area under the receiver operating characteristic (ROC) curve. Kaplan-Meier curves were also generated for validation purposes.

RESULTS

Multivariable analysis identified independent prognostic factors for OS, including CA19-9 levels, portal vein tumor thrombus (PVTT) grade, bifidobacteria administration, and surgery. The C-index of the nomogram for OS prediction was 0.722 (95% confidence interval [CI]: 0.661-0.783). Independent prognostic factors for PFS included CA19-9 levels, albumin, and bilirubin, with a C-index of 0.678 (95% CI: 0.612-0.743) for the nomogram predicting PFS. Calibration curves demonstrated strong concordance between predicted and observed outcomes, while DCA and Kaplan-Meier curves further supported the clinical utility of the nomogram.

CONCLUSION

The nomogram developed in this study demonstrated favorable performance in predicting the prognosis of ICC patients undergoing immunotherapy. Additionally, our findings, for the first time, identified probiotics as a potential prognostic marker for immunotherapy. This prognostic model has the potential to enhance patient selection for immunotherapy and improve clinical decision-making.

A Nomogram of Predicting Healthcare-Associated Infections in Burned Children

BACKGROUND

Healthcare-associated infections (HAIs) are a common clinical concern associated with adverse prognosis and mortality in burned children. This study aimed to construct a predictive nomogram of the risk of HAIs in burned children.

METHODS

Children admitted to the burn unit of Wuhan Third Hospital between 2020 and 2022 were included. The univariate and multivariate logistic regression analyses were adopted to ascertain predictors of HAIs. A nomogram was developed to predict the HAI risk of each patient, with receiver operating characteristic curves and calibration curves being generated to assess its predictive ability. Furthermore, decision and impact curves were used to assess the clinical utility.

RESULTS

Of 1122 burned children, 61 (5.5%) patients experienced HAIs. The multivariate analysis indicated that total burn surface area, length of stay, surgery, central venous catheter use and urinary catheter use were the independent risk factors of HAIs. Using these variables, we developed a predictive nomogram of the occurrence of HAIs in burned children, and the internal validation results demonstrated good discrimination and calibration of the nomogram. The area under the curve values of the nomogram was 0.926 (95% CI, 0.896-0.957). The calibration curve showed high consistency between the actual and predicted HAIs. The decision and impact curve indicated that the nomogram was of good clinical utility and more credible net clinical benefits in predicting HAIs.

CONCLUSIONS

The present study constructed a nomogram for predicting the risk of HAIs in burned children. This nomogram may strengthen the effective screening of patients at high risk of HAIs.

Pan-immune-inflammation value: a new prognostic index in epithelial ovarian cancer

BACKGROUND

Epithelial ovarian cancer (EOC) is one of the deadliest gynaecological malignancies worldwide. The aim of this retrospective study was to create a predictive scoring model based on simple immunological and inflammatory parameters to predict overall survival (OS) and progression-free survival (PFS) in patients with EOC.

METHODS

We obtained 576 EOC patients and randomly assigned them to the training set (n = 405) and the validation set (n = 171) in a ratio of 7:3. We retrospectively evaluated the association between PIV and OS and PFS using a novel immunoinflammatory marker, according to the optihmal treshold of PIV, we divided the patients into two different subgroups, high PIV (PIV > 254.9) and low PIV (PIV ≤ 254.9). Pan-immune Inflammatory Value (PIV) was computed as follows: neutrophil count (109/L) × platelet count (109/L) × monocyte count (109/L)/lymphocyte count (109/L). Then developed a simple score prediction model based on several independent prognostic parameters using Cox regression analysis. We used receiver operator characteristic (ROC) curves, calibration plots, and decision analysis (DCA) curves to evaluate the performance of the model. Finally, we used Kaplan-Meier curves to ensure that the model could distinguish well between low- and high-risk groups.

RESULTS

There was a significant difference in survival outcomes between high PIV (PIV > 310.2) and low PIV (PIV ≤ PIV310.2) (3-year survival rates of 61.34% and 76.71%, respectively); 5-year OS, 25.21% and 51.14%, respectively; 3-year PFS, 40.90% and 65.30%; 5-year PFS, 19.33% and 39.73%, respectively). Column plots of OS and PFS were constructed using independent prognostic factors. In the training module, the 3-, 5-, and 10-year AUCs for OS and PFS column charts were 0.713, 0.796, 0.839, and 0.730, 0.799, 0.826, respectively.In the validation cohort, the 3-, 5-, and 10-year AUCs for OS and PFS column charts were 0.676, 0.803, 0.685, and 0.700, respectively, 0.754, 0.727. The calibration curves showed good agreement between predicted survival and actual observations. The decision analysis curves also showed that the current model has good accuracy and clinical applicability. 3-year OS was 61.34% and 76.71%, respectively; 5-year OS was 25.21% and 51.14%, respectively; 3-year PFS was 40.90% and 65.30%, respectively; 5-year PFS was 19.33% and 39.73%, respectively.

CONCLUSIONS

We constructed and validated a PIV-based nomogram to predict OS and PFS in EOC patients, with a view to helping gynaecologists converge on oncologists in their treatment and follow-up expertise in epithelial ovarian cancer.

Novel prognostic nomograms for postoperative patients with oral cavity squamous cell carcinoma in the central region of China

BACKGROUND

Oral cavity squamous cell carcinoma (OCSCC) is the most common pathological type in oral tumors. This study intends to construct a novel prognostic nomogram model based on China populations for these resectable OCSCC patients, and then validate these nomograms.

METHODS

A total of 607 postoperative patients with OCSCC diagnosed between June 2012 and June 2018 were obtained from two tertiary medical institutions in Xinxiang and Zhengzhou. Then, 70% of all the cases were randomly assigned to the training group and the rest to the validation group. The endpoint time was defined as overall survival (OS) and disease-free survival (DFS). The nomograms for predicting the 3-, and 5-year OS and DFS in postoperative OCSCC patients were established based on the independent prognostic factors, which were identified by the univariate analysis and multivariate analysis. A series of indexes were utilized to assess the performance and net benefit of these two newly constructed nomograms. Finally, the discrimination capability of OS and DFS was compared between the new risk stratification and the American Joint Committee on Cancer (AJCC) stage by Kaplan-Meier curves.

RESULTS

607 postoperative patients with OCSCC were selected and randomly assigned to the training cohort (n = 425) and validation cohort (n = 182). The nomograms for predicting OS and DFS in postoperative OCSCC patients had been established based on the independent prognostic factors. Moreover, dynamic nomograms were also established for more convenient clinical application. The C-index for predicting OS and DFS were 0.691, 0.674 in the training group, and 0.722, 0.680 in the validation group, respectively. Besides, the calibration curve displayed good consistency between the predicted survival probability and actual observations. Finally, the excellent performance of these two nomograms was verified by the NRI, IDI, and DCA curves in comparison to the AJCC stage system.

CONCLUSION

The newly established and validated nomograms for predicting OS and DFS in postoperative patients with OCSCC perform well, which can be helpful for clinicians and contribute to clinical decision-making.

A novel conditional survival nomogram for monitoring real-time prognosis of non-metastatic colorectal cancer

AIMS

The aim of this study is to enhance the accuracy of monitoring and treatment information for patients diagnosed with colorectal cancer (CRC).

METHODS

Utilizing the Surveillance, Epidemiology, and End Results (SEER) database, a cohort of 335,948 eligible CRC patients was included in this investigation. Conditional survival probability and actuarial overall survival were employed as methodologies to investigate the association between clinicopathological characteristics and cancer prognosis.

RESULTS

Among CRC patients, the 5-year survival rate was 59%, while the 10-year survival rate was 42%. Over time, conditional survival showed a consistent increase, with rates reaching 45% and 48% for individuals surviving 1 and 2 years, respectively. Notably, patients with unfavorable tumor stages exhibited substantial improvements in conditional survival, thereby narrowing the disparity with actuarial overall survival over time.

CONCLUSION

This study underscores the significance of time-dependent conditional survival probability, particularly for patients with a poorer prognosis. The findings suggest that long-term CRC survivors may experience improved cancer prognosis over time.

Construction and validation of a nomogram model to predict the poor prognosis in patients with pulmonary cryptococcosis

Background

Patients with poor prognosis of pulmonary cryptococcosis (PC) are prone to other complications such as meningeal infection, recurrence or even death. Therefore, this study aims to analyze the influencing factors in the poor prognosis of patients with PC, so as to build a predictive nomograph model of poor prognosis of PC, and verify the predictive performance of the model.

Methods

This retrospective study included 410 patients (78.1%) with improved prognosis of PC and 115 patients (21.9%) with poor prognosis of PC. The 525 patients with PC were randomly divided into the training set and validation set according to the ratio of 7:3. The Least Absolute Shrinkage and Selection Operator (LASSO) algorithm was used to screen the demographic information, including clinical characteristics, laboratory test indicators, comorbidity and treatment methods of patients, and other independent factors that affect the prognosis of PC. These factors were included in the multivariable logistic regression model to build a predictive nomograph. The receiver operating characteristic curve (ROC), calibration curve and decision curve analysis (DCA) were used to verify the accuracy and application value of the model.

Results

It was finally confirmed that psychological symptoms, cytotoxic drugs, white blood cell count, hematocrit, platelet count, CRP, PCT, albumin, and CD4/CD8 were independent predictors of poor prognosis of PC patients. The area under the curve (AUC) of the predictive model for poor prognosis in the training set and validation set were 0.851 (95% CI: 0.818-0.881) and 0.949, respectively. At the same time, calibration curve and DCA results confirmed the excellent performance of the nomogram in predicting poor prognosis of PC.

Conclusion

The nomograph model for predicting the poor prognosis of PC constructed in this study has good prediction ability, which is helpful for improving the prognosis of PC and further optimizing the clinical management strategy.

Development of a predictive nomogram for postembolization syndrome after transcatheter arterial chemoembolization of hepatocellular carcinoma

Post-embolization syndrome (PES) is a frequent complication after receiving transcatheter arterial chemoembolization (TACE) in patients with hepatocellular carcinoma (HCC), but only a few studies have focused on the factors influencing PES in those patients. In this study, the impact factors of PES were explored and a nomogram was constructed to predict the occurrence of PES in HCC patients with TACE. This was a retrospective cohort study of HCC patients who underwent TACE obtained from the third affiliated Hospital of Kunming Medical University between January 1, 2020, and September 1, 2022. T‑test and Chi‑square test were used to search for factors influencing PES occurrence, and then the nomogram was further established based on multivariable logistic regression analysis. Validation of the predictive nomogram was also evaluated by calibration curve, concordance index (C-index), and receiver operating characteristic (ROC) curves. The enrolled patients (n = 258) were randomly assigned to the primary cohort (n = 180) and validation cohort (n = 78) in a 7:3 ratio. Among 180 patients in the primary cohort, 106 (58.89%) experienced PES. TACE types (P = 0.015), embolization degree (P = 0.008), and tumor number (P = 0.026) were identified as predictors by the logistic regression analysis and were used to develop the predictive nomogram. The internally validated and externally validated C-indexes were 0.713 and 0.703, respectively. The calibration curves presented good consistency between actual and predictive survival. Types of embolic agents, embolization degree, and tumor number were found to be the predictors of PES after TACE. The nomogram could reliably predict PES in HCC patients with TACE. This predictive model might be considered for clinical practice.

Combined the surgery, radiation, and chemotherapy for predicting overall survival in patients with gastroenteropancreatic neuroendocrine tumors

BACKGROUND

Over the last few decades, the annual global incidence of gastroenteropancreatic neuroendocrine tumors (GEP-NETs) has steadily increased. Because of the complex and inconsistent treatment of GEP-NETs, the prognosis of patients with GEP-NETs is still difficult to assess. The study aimed to construct and validate the nomograms included treatment data for prediction overall survival (OS) in GEP-NETs patients.

METHODS

GEP-NETs patients determined from the Surveillance, Epidemiology, and End Results (SEER)-13 registry database (1992-2018) and with additional treatment data from the SEER-18 registry database (1975-2016). In order to select independent prognostic factors that contribute significantly to patient survival and can be included in the nomogram, multivariate Cox regression analysis was performed using the minimum value of Akaike information criterion (AIC) and we analyzed the relationship of variables with OS by calculating hazard ratios (HRs) and 95% CIs. In addition, we also comprehensively compared the nomogram using to predict OS with the current 7th American Joint Committee on Cancer (AJCC) staging system.

RESULTS

From 2004 to 2015, a total of 42,662 patients at diagnosis years with GEP-NETs were determined from the SEER database. The results indicated that the increasing incidence of GEP-NETs per year and the highest incidence is in patients aged 50-54. After removing cases lacking adequate clinicopathologic characteristics, the remaining eligible patients (n=7,564) were randomly divided into training (3,782 patients) and testing sets (3,782 patients). In the univariate analysis, sex, age, race, tumor location, SEER historic stage, pathology type, TNM, stage, surgery, radiation, chemotherapy, and CS tumor size were found to be significantly related to OS. Ultimately, the key factors for predicting OS were determined, involving sex, age, race, tumor location, SEER historic stage, M, N, grade, surgery, radiation, and chemotherapy. For internal validation, the C-index of the nomogram used to estimate OS in the training set was 0.816 (0.804-0.828). For external validation, the concordance index (C-index) of the nomogram used to predict OS was 0.822 (0.812-0.832). In the training and testing sets, our nomogram produced minimum AIC values and C-index of OS compared with AJCC stage. Decision curve analysis (DCA) indicated that the nomogram was better than the AJCC staging system because more clinical net benefits were obtained within a wider threshold probability range.

CONCLUSION

A nomogram combined treatment data may be better discrimination in predicting overall survival than AJCC staging system. We highly recommend to use our nomogram to evaluate individual risks based on different clinical features of GEP-NETs, which can improve the diagnosis and treatment outcomes of GEP-NETs patients and improve their quality of life.

Development and validation of a novel risk stratification model and a survival rate calculator for diffuse large B-cell lymphoma in the rituximab era: a multi-institutional cohort study

BACKGROUND

This study aimed to develop and validate a novel risk stratification model and a web-based survival rate calculator to improve discriminative and predictive accuracy for diffuse large B-cell lymphoma (DLBCL) in the rituximab era.

METHODS

We retrospectively collected pre-treatment data from 873 primary DLBCL patients who received R-CHOP-based immunochemotherapy regimens at the Cancer Hospital, Chinese Academy of Medical Sciences, from January 1, 2005, to December 31, 2018. An independent cohort of 175 DLBCL patients from Fujian Cancer Hospital was used for external validation.

FINDINGS

Age, ECOG PS, number of extranodal sites, Ann Arbor stage, bulky disease, and LDH levels were screened to develop the nomogram and web-based survival rate calculator. The C-index of the nomogram in the training, internal validation, and external validation cohorts was 0.761, 0.758, and 0.768, respectively. The risk stratification model generated based on the nomogram effectively stratified patients into three distinct risk groups. K-M survival curves demonstrated that the novel risk stratification model exhibited a superior level of predictive accuracy compared to IPI, R-IPI, and NCCN-IPI both in training and two validation cohorts. Additionally, the area under the curve (AUC) value of the novel model (0.763) for predicting 5-year overall survival rates was higher than those of IPI (0.749), R-IPI (0.725), and NCCN-IPI (0.727) in the training cohort. Similar results were observed in both internal and external validation cohort.

CONCLUSIONS

In conclusion, we have successfully developed and validated a novel risk stratification model and a web-based survival rate calculator that demonstrated superior discriminative and predictive accuracy compared to IPI, R-IPI, and NCCN-IPI in the rituximab era.

Optimizing detection of clinically significant prostate cancer through nomograms incorporating mri, clinical features, and advanced serum biomarkers in biopsy naïve men

PURPOSE

To develop nomograms that predict the detection of clinically significant prostate cancer (csPCa, defined as ≥GG2 [Grade Group 2]) at diagnostic biopsy based on multiparametric prostate MRI (mpMRI), serum biomarkers, and patient clinicodemographic features.

MATERIALS AND METHODS

Nomograms were developed from a cohort of biopsy-naïve men presenting to our 11-hospital system with prostate specific antigen (PSA) of 2-20 ng/mL who underwent pre-biopsy mpMRI from March 2018-June 2021 (n = 1494). The outcomes were the presence of csPCa and high-grade prostate cancer (defined as ≥GG3 prostate cancer). Using significant variables on multivariable logistic regression, individual nomograms were developed for men with total PSA, % free PSA, or prostate health index (PHI) when available. The nomograms were both internally validated and evaluated in an independent cohort of 366 men presenting to our hospital system from July 2021-February 2022.

RESULTS

1031 of 1494 men (69%) underwent biopsy after initial evaluation with mpMRI, 493 (47.8%) of whom were found to have ≥GG2 PCa, and 271 (26.3%) were found to have ≥GG3 PCa. Age, race, highest PIRADS score, prostate health index when available, % free PSA when available, and PSA density were significant predictors of ≥GG2 and ≥GG3 PCa on multivariable analysis and were used for nomogram generation. Accuracy of nomograms in both the training cohort and independent cohort were high, with areas under the curves (AUC) of ≥0.885 in the training cohort and ≥0.896 in the independent validation cohort. In our independent validation cohort, our model for ≥GG2 prostate cancer with PHI saved 39.1% of biopsies (143/366) while only missing 0.8% of csPCa (1/124) with a biopsy threshold of 20% probability of csPCa.

CONCLUSIONS

Here we developed nomograms combining serum testing and mpMRI to help clinicians risk stratify patients with elevated PSA of 2-20 ng/mL who are being considered for biopsy. Our nomograms are available at https://rossnm1.shinyapps.io/MynMRIskCalculator/ to aid with biopsy decisions.

Development and validation of prediction model for overall survival in patients with lymphoma: a prospective cohort study in China

OBJECTIVE

The survival of patients with lymphoma varies greatly among individuals and were affected by various factors. The aim of this study was to develop and validate a prognostic model for predicting overall survival (OS) in patients with lymphoma.

METHODS

We conducted a prospective longitudinal cohort study in China between January 2014 and December 2018 (n = 1,594). After obtaining the follow-up data, we randomly split the cohort into the training cohort (n = 1,116) and the validation cohort (n = 478). The least absolute shrinkage and selection operator (LASSO) regression analysis was used to select the predictors of the model. Cox stepwise regression analysis was used to identify independent prognostic factors, which were finally displayed as static nomogram and web-based dynamic nomogram. We calculated the concordance index(C-index) to describe how the predicted survival of objectively confirmed prognosis. The calibration plot is used to evaluate the prediction accuracy and discrimination ability of the model. Net reclassification index (NRI) and decision curve analysis (DCA) curves were also used to evaluate the prediction ability and net benefit of the model.

RESULTS

Nine variables in the training cohort were considered to be independent risk factors for patients with lymphoma in the final model: age, Ann Arbor Stage, pathologic type, B symptoms, chemotherapy, targeted therapy, lactate dehydrogenase (LDH), β2-microglobulin and C-reactive protein (CRP). The C-indices of OS were 0.749 (95% CI, 0.729-0.769) in the training cohort and 0.731 (95% CI, 0.762-0.700) in the validation cohort. A good agreement between prediction by nomogram and actual observation was shown in the calibration curve for the probability of survival in both the training cohort and validation cohorts. The areas under curve (AUC) of the area under the receiver operating characteristic (ROC) curves for 1-year, 3-year, and 5-year OS were 0.813, 0.800, and 0.762, respectively, in the training cohort, and 0.802, 0.768, and 0.721, respectively, in the validation cohort. Compared with the Ann Arbor Stage system, NRI and DCA showed that the model had a higher predictive capacity and net benefit.

CONCLUSION

The prediction models reliably estimate the outcome of patients with lymphoma. The model had high discrimination and calibration, which provided a simple and reliable tool for the survival prediction of the patients, and it might help patients benefit from personalized intervention.

Nomograms for predicting overall survival and cancer-specific survival in elderly patients with epithelial ovarian cancer

BACKGROUND

Epithelial ovarian cancer (EOC) is one of the most fatal gynecological malignancies among elderly patients. We aim to construct two nomograms to predict the overall survival (OS) and cancer-specific survival (CSS) in elderly EOC patients.

METHODS

Elderly patients with EOC between 2000 and 2019 were selected from the Surveillance, Epidemiology, and End Results (SEER) database. Enrolled patients were randomly divided into the training and validation set at a ratio of 2:1. The OS and CSS were recognized as endpoint times. The independent prognostic factors from the multivariate analysis were used to establish nomograms for predicting the 3-, 5- and 10-year OS and CSS of elderly EOC patients. The improvement of predictive ability and clinical benefits were evaluated by consistency index (C-index), receiver operating characteristic (ROC), calibration curve, decision curve (DCA), net reclassification improvement (NRI), and integrated discrimination improvement (IDI). Finally, the treatment efficacy of surgery and chemotherapy in low-, medium-, and high-risk groups were displayed by Kaplan-Meier curves.

RESULTS

Five thousand five hundred eighty-eight elderly EOC patients were obtained and randomly assigned to the training set (n = 3724) and validation set (n = 1864). The independent prognostic factors were utilized to construct nomograms for OS and CSS. Dynamic nomograms were also developed. The C-index of the OS nomogram and CSS nomogram were 0.713 and 0.729 in the training cohort. In the validation cohort, the C-index of the OS nomogram and CSS nomogram were 0.751 and 0.702. The calibration curve demonstrated good concordance between the predicted survival rates and actual observations. Moreover, the NRI, IDI, and DCA curves determined the outperformance of the nomogram compared with the AJCC stage system. Besides, local tumor resection had a higher benefit on the prognosis in all patients. Chemotherapy had a better prognosis in the high-risk groups, but not for the medium- risk and low-risk groups.

CONCLUSIONS

We developed and validated nomograms for predicting OS and CSS in elderly EOC patients to help gynecologists to develop an appropriate individualized therapeutic schedule.

Development and validation of a clinical risk score to predict the occurrence of critical illness in hospitalized patients with SFTS

BACKGROUND

Severe fever with thrombocytopenia syndrome (SFTS) is an emerging infectious disease with high mortality. Early identification of patients who may advance to critical stages is crucial. This investigation aimed to establish models to predict SFTS before it reaches the critical illness stage.

METHODS

Between January 2016 and September 2022, 278 cases have been included in this study. There were 87 demographic and systemic chosen variables. For selecting the predictive variables from the cohort, the LASSO was utilized, and for identifying independent predictors, multivariate logistic regression was performed. Based on these factors, a nomogram was established for critical illness. Concordance index values, decision curve analysis and the area under the curve (AUC) were also examined.

RESULTS

Multivariate logistic regression demonstrated the most important differentiating factors as;> 65 years old (P < 0.001, OR 3.388, 95 % CI 1.767-6.696), elevated serum PT (P = 0.011, OR 6.641, 95 % CI 1.584-31.934), elevated serum TT (P = 0.005, OR 3.384, 95 % CI 1.503-8.491), and elevated serum bicarbonate (P = 0.014, OR 0.242, 95 % CI 0.070-0.707). The C-index of the nomogram was 0.812 (95 % CI: 0.754-0.869), representing good discrimination. The model also showed excellent calibration. The AUC of the nomogram established based on four factors, as mentioned earlier, was 0.806. Furthermore, the model had the excellent net benefit, as revealed by the decision curve analysis.

CONCLUSION

An accurate risk score system built on manifestations noted in patients with SFTS upon admission to hospital, might be advantageous in managing SFTS.

The Role of Nomogram Based on the Combination of Ultrasound Parameters and Clinical Indicators in the Degree of Pathological Remission of Breast Cancer

Background

The mortality rate of breast cancer (BC) ranks first among female tumors worldwide and presents a trend of younger age, which poses a great threat to women's health and life. Neoadjuvant chemotherapy (NAC) for breast cancer is defined as the first step of treatment for breast cancer patients without distant metastasis before planned surgical treatment or local treatment with surgery and radiotherapy. According to the current NCCN guidelines, patients with different molecular types of BC should receive neoadjuvant chemotherapy (NAC), which can not only achieve tumor downstaging, increase the chance of surgery, and improve the breast-conserving rate. In addition, it can identify new genetic pathways and drugs related to cancer, improve patient survival rate, and make new progress in breast cancer management.

Objective

To explore the role of the nomogram established by the combination of ultrasound parameters and clinical indicators in the degree of pathological remission of breast cancer.

Methods

A total of 147 breast cancer patients who received neoadjuvant chemotherapy and elective surgery in the Department of Ultrasound, Nantong Cancer Hospital, from May 2014 to August 2021 were retrospectively included. Postoperative pathological remission was divided into two groups according to Miller-Payne classification: no significant remission group (NMHR group, n = 93) and significant remission group (MHR group, n = 54). Clinical characteristics of patients were recorded and collected. The multivariate logistic regression model was used to screen the information features related to the MHR group, and then, a nomogram model was constructed; ROC curve area, consistency index (C-index, CI), calibration curve, and H-L test were used to evaluate the model. And the decision curve is used to compare the net income of the single model and composite model.

Results

Among 147 breast cancer patients, 54 (36.7%) had pathological remission. Multivariate logistic regression showed that ER, reduction/disappearance of strong echo halo, Adler classification after NAC, PR + CR, and morphological changes were independent risk factors for pathological remission (P < 0.05). Based on these factors, the nomogram was constructed and verified. The area under the curve (AUC) and CI were 0.966, the sensitivity and specificity were 96.15% and 92.31%, and the positive predictive value (PPV) and negative predictive value (NPV) were 87.72% and 97.15%, respectively. The mean absolute error of the agreement between the predicted value and the real value is 0.026, and the predicted risk is close to the actual risk. In the range of HRT of about 0.0∼0.9, the net benefit of the composite evaluation model is higher than that of the single model. H-L test results showed that χ ² = 8.430, P=0.393 > 0.05.

Conclusion

The nomogram model established by combining the changes of ultrasound parameters and clinical indicators is a practical and convenient prediction model, which has a certain value in predicting the degree of pathological remission after neoadjuvant chemotherapy.

Radiomic and clinical nomogram for cognitive impairment prediction in Wilson's disease

Objective

To investigate potential biomarkers for the early detection of cognitive impairment in patients with Wilson's disease (WD), we developed a computer-assisted radiomics model to distinguish between WD and WD cognitive impairment.

Methods

Overall, 136 T1-weighted MR images were retrieved from the First Affiliated Hospital of Anhui University of Chinese Medicine, including 77 from patients with WD and 59 from patients with WD cognitive impairment. The images were divided into training and test groups at a ratio of 70:30. The radiomic features of each T1-weighted image were extracted using 3D Slicer software. R software was used to establish clinical and radiomic models based on clinical characteristics and radiomic features, respectively. The receiver operating characteristic profiles of the three models were evaluated to assess their diagnostic accuracy and reliability in distinguishing between WD and WD cognitive impairment. We combined relevant neuropsychological test scores of prospective memory to construct an integrated predictive model and visual nomogram to effectively assess the risk of cognitive decline in patients with WD.

Results

The area under the curve values for distinguishing WD and WD cognitive impairment for the clinical, radiomic, and integrated models were 0.863, 0.922, and 0.935 respectively, indicative of excellent performance. The nomogram based on the integrated model successfully differentiated between WD and WD cognitive impairment.

Conclusion

The nomogram developed in the current study may assist clinicians in the early identification of cognitive impairment in patients with WD. Early intervention following such identification may help improve long-term prognosis and quality of life of these patients.

Identification of a Prognostic Colorectal Cancer Model Including LncRNA FOXP4-AS1 and LncRNA BBOX1-AS1 Based on Bioinformatics Analysis

Background: Knowledge about the prognostic role of long noncoding RNA (lncRNA) in colorectal cancer (CRC) is limited. Therefore, we constructed a lncRNA-related prognostic model based on data from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). Materials and Methods: CRC transcriptome and clinical data were downloaded from the GSE20916 dataset and the TCGA database, respectively. R software was used for data processing and analysis. The differential lncRNA expression within the two datasets was first screened, and then intersections were measured. Cox regression and the Kaplan-Meier method were used to evaluate the effects of various factors on prognosis. The area under the curve (AUC) of the receiver operating characteristic curve and a nomogram based on multivariate Cox analysis were used to estimate the prognostic value of the lncRNA-related model. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were applied to elucidate the significantly involved biological functions and pathways. Results: A total of 11 lncRNAs were crossed. The univariate Cox analysis screened out two lncRNAs, which were analyzed in the multivariate Cox analysis. A nomogram based on the two lncRNAs and other clinicopathological risk factors was constructed. The AUC of the nomogram was 0.56 at 3 years and 0.71 at 5 years. The 3-year nomogram model was compared with the ideal model, which showed that some indices of the 3-year model were consistent with the ideal model, suggesting that our model was highly accurate. The GO and KEGG enrichment analyses showed that positive regulation of secretion by cells, positive regulation of secretion, positive regulation of exocytosis, endocytosis, and the calcium signaling pathway were differentially enriched in the two-lncRNA-associated phenotype. Conclusions: A two-lncRNA prognostic model of CRC was constructed by bioinformatics analysis. The model had moderate prediction accuracy. LncRNA BBOX1-AS1 and lncRNA FOXP4-AS1 were identified as prognostic biomarkers.

A risk prediction model for acute kidney injury in patients with pulmonary tuberculosis during anti-tuberculosis treatment

BACKGROUND

Acute kidney injury (AKI) is not a rare complication during anti-tuberculosis treatment in some patients with pulmonary tuberculosis (PTB). We aimed to develop a risk prediction model for early recognition of patients with PTB at high risk for AKI during anti-TB treatment.

METHODS

This retrospective cohort study assessed the clinical baseline, and laboratory test data of 315 inpatients with active PTB who were screened for predictive factors from January 2019 to June 2020. The elements were analyzed by logistic regression analysis. A nomogram was established by the results of the logistic regression analysis. The prediction model discrimination and calibration were evaluated by the concordance index (C-index), ROC curve, and Hosmer-Lemeshow analysis.

RESULTS

A total of 315 patients with PTB were enrolled (67 patients with AKI and 248 patients without AKI). Seven factors, including microalbuminuria, hematuria, cystatin-C (CYS-C), albumin (ALB), creatinine-based estimated glomerular filtration rates (eGFRs), body mass index (BMI), and CA-125 were acquired to develop the predictive model. According to the logistic regression, microalbuminuria (OR = 3.038, 95%CI 1.168-7.904), hematuria (OR = 3.656, 95%CI 1.325-10.083), CYS-C (OR = 4.416, 95%CI 2.296-8.491), and CA-125 (OR = 3.93, 95%CI 1.436-10.756) were risk parameter, while ALB (OR = 0.741, 95%CI 0.650-0.844) was protective parameter. The nomogram demonstrated good prediction in estimating AKI (C-index= 0.967, AUC = 0.967, 95%CI (0.941-0.984), sensitivity = 91.04%, specificity = 93.95%, Hosmer-Lemeshow analysis SD = 0.00054, and quantile of absolute error = 0.049).

CONCLUSIONS

Microalbuminuria, hematuria, ALB reduction, elevated CYS-C, and CA-125 are predictive factors for the development of AKI in patients with PTB during anti-TB treatments. The predictive nomogram based on five predictive factors is achieved good risk prediction for AKI during anti-TB treatments.

Construction and validation of a prognostic nomogram in metastatic breast cancer patients of childbearing age: A study based on the SEER database and a Chinese cohort

Introduction

Cancer in patients of childbearing age continues to become increasingly common. The purpose of this study was to explore the impact of metastatic breast cancer (MBC) on overall survival (OS) and cancer-specifific survival (CSS) in patients of childbearing age and to construct prognostic nomograms to predict OS and CSS.

Methods

Data from MBC patients of childbearing age were obtained from the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2015, and the patients were randomly assigned into the training and validation cohorts. Univariate and multivariate Cox analyses were used to search for independent prognostic factors impacting OS and CSS, and these data were used to construct nomograms. The concordance index (C-index), area under the curve (AUC), and calibration curves were used to determine the predictive accuracy and discriminative ability of the nomograms. Additional data were obtained from patients at the Yunnan Cancer Hospital to further verify the accuracy of the nomograms.

Results

A total of 1,700 MBC patients of childbearing age were identifified from the SEER database, and an additional 92 eligible patients were enrolled at the Yunnan Cancer Hospital. Multivariate Cox analyses identifified 10 prognostic factors for OS and CSS that were used to construct the nomograms. The calibration curve for the probabilities of OS and CSS showed good agreement between nomogram prediction and clinical observations. The C-index of the nomogram for OS was 0.735 (95% CI = 0.725-0.744); the AUC at 3 years was 0.806 and 0.794 at 5 years.The nomogram predicted that the C-index of the CSS was 0.740 (95% CI = 0.730- 0.750); the AUC at 3 years was 0.811 and 0.789 at 5 years. The same results were observed in the validation cohort. Kaplan- Meier curves comparing the low-,medium-, and high-risk groups showed strong prediction results for the prognostic nomogram.

Conclusion

We identifified several independent prognostic factors and constructed nomograms to predict the OS and CSS for MBC patients of childbearing age.These prognostic models should be considered in clinical practice to individualize treatments for this group of patients.

Development and Validation of a nomogram for forecasting survival of alcohol related hepatocellular carcinoma patients

BACKGROUND

With the increasing incidence and prevalence of alcoholic liver disease, alcohol-related hepatocellular carcinoma has become a serious public health problem worthy of attention in China. However, there is currently no prognostic prediction model for alcohol-related hepatocellular carcinoma.

METHODS

The retrospective analysis research of alcohol related hepatocellular carcinoma patients was conducted from January 2010 to December 2014. Independent prognostic factors of alcohol related hepatocellular carcinoma were identified by Lasso regression and multivariate COX proportional model analysis, and the nomogram model was constructed. The reliability and accuracy of the model were assessed using the concordance index(C-Index), receiver operating characteristic (ROC) curve and calibration curve. Evaluate the clinical benefit and application value of the model through clinical decision curve analysis (DCA). The prognosis was assessed by the Kaplan-Meier (KM) survival curve.

RESULTS

In sum, 383 patients were included in our study. Patients were stochastically assigned to training cohort (n=271) and validation cohort (n=112) according to 7:3 ratio. The predictors included in the nomogram were splenectomy, platelet count (PLT), creatinine (CRE), Prealbumin (PA), mean erythrocyte hemoglobin concentration (MCHC), red blood cell distribution width (RDW) and TNM. Our nomogram demonstrated excellent discriminatory power (C-index) and good calibration at 1-year, 3-year and 5- year overall survival (OS). Compared to TNM and Child-Pugh model, the nomogram had better discriminative ability and higher accuracy. DCA showed high clinical benefit and application value of the model.

CONCLUSION

The nomogram model we established can precisely forcasting the prognosis of alcohol related hepatocellular carcinoma patients, which would be helpful for the early warning of alcohol related hepatocellular carcinoma and predict prognosis in patients with alcoholic hepatocellular carcinoma.

Prognostic nomogram for acute pancreatitis after percutaneous biliary stent insertion in patients with malignant obstruction

Objective

This study aimed to develop and validate a nomogram to predict the risk of pancreatitis after percutaneous transhepatic biliary stent insertion (PTBS) in patients with malignant biliary obstruction (MBO).

Materials and methods

We enrolled 314 patients who underwent PTBS for MBO from March 2016 to July 2021 in this retrospective study. We used univariate analysis to identify potential risk factors, while a multivariate logistic regression model was employed to establish a nomogram for predicting the risk of pancreatitis. The discrimination and calibration of the nomogram were evaluated by estimating the area under the receiver operator characteristic curve (AUC) and by bootstrap resampling and visual inspection of the calibration curve. The clinical utility of the nomogram was assessed using decision curve analysis (DCA).

Results

After the procedure, 41 (13.1%) patients developed pancreatitis. Based on multivariate logistic regression analysis, young age (OR = 2.57, 95% CI 1.16 to 5.69), stent insertion across the papilla (OR = 6.47, 95% CI 2.66 to 15.70), and visualization of the pancreatic duct (OR = 15.40, 95% CI 6.07 to 39.03) were associated with an elevated risk of pancreatitis. Importantly, the performance of the nomogram was satisfactory, with an identical AUC (0.807, 95% CI 0.730 to 0.883) and high-level agreement between predicted and observed probabilities as suggested in calibration curves. The DCA curve subsequently confirmed the clinical utility.

Conclusion

A predictive nomogram for pancreatitis after PTBS in patients with MBO was successfully established in the present study.

Development and validation of a prognostic nomogram for decompensated liver cirrhosis

BACKGROUND

Decompensated liver cirrhosis (DLC) is a stage in the progression of liver cirrhosis and has a high mortality.

AIM

To establish and validate a novel and simple-to-use predictive nomogram for evaluating the prognosis of DLC patients.

METHODS

A total of 493 patients with confirmed DLC were enrolled from The First Affiliated Hospital of Nanchang University (Nanchang, Jiangxi Province, China) between December 2013 and August 2019. The patients were divided into two groups: a derivation group (n = 329) and a validation group (n = 164). Univariate and multivariate Cox regression analyses were performed to assess prognostic factors. The performance of the nomogram was determined by its calibration, discrimination, and clinical usefulness.

RESULTS

Age, mechanical ventilation application, model for end-stage liver disease (MELD) score, mean arterial blood pressure, and arterial oxygen partial pressure/inhaled oxygen concentration were used to construct the model. The C-indexes of the nomogram in the derivation and validation groups were 0.780 (95%CI: 0.670-0.889) and 0.792 (95%CI: 0.698-0.886), respectively. The calibration curve exhibited good consistency with the actual observation curve in both sets. In addition, decision curve analysis indicated that our nomogram was useful in clinical practice.

CONCLUSION

A simple-to-use novel nomogram based on a large Asian cohort was established and validated and exhibited improved performance compared with the Child-Turcotte-Pugh and MELD scores. For patients with DLC, the proposed nomogram may be helpful in guiding clinicians in treatment allocation and may assist in prognosis prediction.

Development and validation of safety and efficacy-associated risk calculator for hepatocellular carcinoma in the elderly after resection (SEARCHER): A multi-institutional observational study

BACKGROUND

Increased life expectancy and improved perioperative management have resulted in increased utilization of hepatectomy for hepatocellular carcinoma (HCC) among elderly patients. However, individualized model for predicting the surgical safety and efficacy is lacking. The present study aimed to develop a safety and efficacy-associated risk calculator for HCC in the elderly after resection (SEARCHER).

METHODS

From an international multicenter database, elderly patients who underwent curative-intent hepatectomy for HCC were stratified by patient age: 65-69 years, 70-74 years, 75-79 years, and ≥80 years. Short- and long-term outcomes among the 4 groups were compared. Univariate and multivariate analyses of risk factors of postoperative major morbidity, cancer-specific survival (CSS) and overall survival (OS) were performed in the training cohort. A nomogram-based online calculator was then constructed and validated in the validation cohort.

RESULTS

With increasing age, the risk of postoperative major morbidity and worse OS increased (P = 0.001 and 0.020), but not postoperative mortality and CSS (P = 0.577 and 0.890) among patients across the 4 groups. Based on three nomograms to predict major morbidity, CSS and OS, the SEARCHER model was constructed and made available at https://elderlyhcc.shinyapps.io/SEARCHER. The model demonstrated excellent calibration and optimal performance in both the training and validation cohorts, and performed better than the several commonly-used conventional scoring and staging systems of HCC.

CONCLUSIONS

With higher potential postoperative major morbidity and worse OS as patients age, the decision of whether to perform a hepatectomy for HCC needs to be comprehensively considered in the elderly. The proposed SEARCHER model demonstrated good performance to individually predict safety and efficacy of hepatectomy in elderly patients with HCC.

Construction of a nomogram to predict the survival of metastatic gastric cancer patients that received immunotherapy

Background

Immunotherapy has shown promising results for metastatic gastric cancer (MGC) patients. Nevertheless, not all patients can benefit from anti-PD-1 treatment. Thus, this study aimed to develop and validate a prognostic nomogram for MGC patients that received immunotherapy.

Methods

Herein, MGC patients treated with anti-PD-1 between 1 October 2016 and 1 June 2022 at two separate Chinese PLA General Hospital centers were enrolled and randomly divided into training and validation sets (186 and 80 patients, respectively). The nomogram was constructed based on a multivariable Cox model using baseline variables from the training cohort. Its predictive accuracy was validated by the validation set. The consistency index (C-index) and calibration plots were used to evaluate the discriminative ability and accuracy of the nomogram. The net benefit of the nomogram was evaluated using decision curve analysis (DCA). Finally, we stratified patients by median total nomogram scores and performed Kaplan–Meier survival analyses.

Results

We developed the nomogram based on the multivariate analysis of the training cohort, including four parameters: surgery history, treatment line, lung immune prognostic index (LIPI), and platelet-to-lymphocyte ratio (PLR). The C-index of the nomogram was 0.745 in the training set. The calibration curve for 1- and 2-year survival showed good agreement between nomogram predictions and actual observations. In the validation group, the calibration curves demonstrated good performance of the nomogram, with a C-index for overall survival (OS) prediction of 0.713. The OS of patients with a score greater than the median nomogram score was significantly longer than patients with a score lower or equal to the median (p < 0.001).

Conclusion

We constructed a nomogram to predict the outcomes of MGC patients that received immunotherapy. This nomogram might facilitate individualized survival predictions and be helpful during clinical decision-making for MGC patients under anti-PD-1 therapy.

Development and validation of a prediction model of catheter-related thrombosis in patients with cancer undergoing chemotherapy based on ultrasonography results and clinical information

Central venous catheters can be used conveniently to deliver medications and improve comfort in patients with cancer. However, they can cause major complications. The current study aimed to develop and validate an individualized nomogram for early prediction of the risk of catheter-related thrombosis (CRT) in patients with cancer receiving chemotherapy. In total, 647 patients were included in the analysis. They were randomly assigned to the training (n = 431) and validation (n = 216) cohorts. A nomogram for predicting the risk of CRT in the training cohort was developed based on logistic regression analysis results. The accuracy and discriminatory ability of the model were determined using area under the receiver operating characteristic curve (AUROC) values and calibration plots. Multivariate logistic regression analysis showed that body mass index, risk of cancer-related thrombosis, D-dimer level, and blood flow velocity were independent risk factors of CRT. The calibration plot showed an acceptable agreement between the predicted and actual probabilities of CRT. The AUROC values of the nomogram were 0.757 (95% confidence interval: 0.717-0.809) and 0.761 (95% confidence interval: 0.701-0.821) for the training and validation cohorts, respectively. Our model presents a novel, user-friendly tool for predicting the risk of CRT in patients with cancer receiving chemotherapy. Moreover, it can contribute to clinical decision-making.

Prognostic significance of the preoperative hemoglobin to albumin ratio for the short-term survival of gastric cancer patients

BACKGROUND

Hemoglobin and albumin are associated with the prognosis of gastric cancer (GC) patients. However, the prognostic value of the hemoglobin to albumin ratio (HAR) for the short-term survival of GC patients with D2 radical resection has not been studied.

AIM

To investigate the significance of the HAR in evaluating the short-term survival of GC patients after D2 radical resection and to construct a nomogram to predict the prognosis in GC patients after surgery, thus providing a reference for the development of postoperative individualized treatment and follow-up plans.

METHODS

Cox regression and Kaplan-Meier analysis was used for prognostic analysis. Logistic regression was used to analyze the relationships between HAR and the clinicopathological characteristics of the GC patients. A prognostic nomogram model for the short-term survival of GC patients was constructed by R software.

RESULTS

HAR was an independent risk factor for the short-term survival of GC patients. GC patients with a low HAR had a poor prognosis (P < 0.001). Low HAR was markedly related to high stage [odds ratio (OR) = 0.45 for II vs I; OR = 0.48 for III vs I], T classification (OR = 0.52 for T4 vs T1) and large tumor size (OR = 0.51 for ≥ 4 cm vs < 4 cm) (all P < 0.05). The nomogram model was based on HAR, age, CA19-9, CA125 and stage, and the C-index was 0.820.

CONCLUSION

Preoperative low HAR was associated with short-term survival in GC patients. The prognostic nomogram model can accurately predict the short-term survival of GC patients with D2 radical resection.

Development of a prognostic nomogram for metastatic pancreatic ductal adenocarcinoma integrating marital status

Previous studies have shown that marital status can affect the overall survival (OS) of cancer patients yet its role in metastatic pancreatic ductal adenocarcinoma (mPDAC) remains unclear. This study aimed to explore the impact of marital status on the OS of mPDAC patients and to construct a prognostic nomogram to predict OS outcomes. Data from patients diagnosed with mPDAC were obtained from the Surveillance, Epidemiology, and End Results database between 1973 and 2015. The patients were randomized into primary and validation cohorts. Kaplan-Meier survival analysis was performed to compare differences in survival depending on marital status. Univariate and multivariate analyses were conducted to identify independent prognostic factors and a nomogram was established based using Cox regression analyses. Validation of the prognostic nomogram was evaluated with a calibration curve and concordance index (C-index). Our data showed significant differences in the OS of mPDAC patients with different marital status by Kaplan-Meier analysis (P < 0.05). Univariate and multivariate analyses confirmed that marital status was an independent OS-related factor in mPDAC patients. Based on the multivariate models of the primary cohort, a nomogram was developed that combined marital status, age, grade, tumor size, surgery of primary site, surgery of lymph node and metastatic. The nomogram showed that marital status had a moderate influence on predicting the OS of mPDAC patients. Moreover, the internally and externally validated C-indexes were 0.633 and 0.619, respectively. A calibration curve confirmed favorable consistency between the observed and predicted outcomes. Marital status was identified as an independent prognostic factor for OS of mPDAC patients and is a reliable and valid parameter to predict the survival of patients with mPDAC. This prognostic model has value and may be integrated as a tool to inform decision-making in the clinic.

A nomogram for predicting the risk of postoperative recurrence of hepatitis B virus-related hepatocellular carcinoma in patients with high preoperative serum glutamyl transpeptidase

BACKGROUND

Recurrence is a major risk factor affecting the postoperative survival of patients with hepatocellular carcinoma (HCC), especially those with high preoperative serum γ-glutamyl transpeptidase (GGT) levels. This study had the aim of developing a personalized predictive tool to accurately determine the risk of postoperative recurrence of hepatitis B-virus (HBV)-related HCC in patients with high preoperative serum GGT levels.

METHODS

Patients who underwent curative liver resection of HBV-related HCC and had high preoperative GGT levels were consecutively enrolled between 2008 and 2011. Prognostic indicators for recurrence were determined using Cox regression analysis. A nomogram was then developed and assessed by integrating the independent risk factors into the model.

RESULTS

A total of 603 eligible patients were included. The final nomogram for predicting HCC recurrence in patients with high preoperative GGT levels consisted of five independent prognostic factors: α-fetoprotein (AFP), HBV-DNA, satellite nodules, microvascular invasion, and tumor grade. The C-index of the nomogram for predicting recurrence was 0.759, and validation showed high accuracy and discriminatory.

CONCLUSIONS

The predictive nomogram developed and validated in this study performs well in predicting postoperative recurrence of HBV-related HCC in patients with high preoperative GGT levels. It can provide personalized assessments to inform the development of surveillance strategies and allows patients with a high risk of recurrence to be selected for further adjuvant treatment.

Development and External Validation of a Nomogram to Predict Cancer-Specific Survival in Patients with Primary Intestinal Non-Hodgkin Lymphomas

Purpose

Primary intestinal non-Hodgkin lymphoma (PINHL) is a biologically and clinically heterogeneous disease. Few individual prediction models are available to establish prognoses for PINHL patients. Herein, a novel nomogram was developed and verified to predict long-term cancer-specific survival (CSS) rates in PINHL patients, and a convenient online risk calculator was created using the nomogram.

Materials and Methods

Data on PINHL patients from January 1, 2004, to December 31, 2015, obtained from the Surveillance, Epidemiology, and End Results (SEER) database (n = 2372; training cohort), were analyzed by Cox regression to identify independent prognostic parameters for CSS. The nomogram was internally and externally validated in a SEER cohort (n = 1014) and a First Affiliated Hospital of Guangzhou University of Chinese Medicine (FAHGUCM) cohort (n = 37), respectively. Area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCA) were used to evaluate nomogram performance.

Results

Five independent predictors were identified, namely, age, marital status, Ann Arbor Stage, B symptoms, and histologic type. The nomogram showed good performance in discrimination and calibration, with C-indices of 0.772 (95% CI: 0.754–0.790), 0.763 (95% CI: 0.734–0.792), and 0.851 (95% CI: 0.755–0.947) in the training, internal validation, and external validation cohorts, respectively. The calibration curve indicated that the nomogram was accurate, and DCA showed that the nomogram had a high clinical application value. AUC values indicated that the prediction accuracy of the nomogram was higher than that of Ann Arbor Stage (training cohort: 0.804 vs 0.630; internal validation cohort: 0.800 vs 0.637; external validation cohort: 0.811 vs 0.598), and Kaplan–Meier curves indicated the same.

Conclusion

A nomogram was developed to assist clinicians in predicting the survival of PINHL patients and in making optimal treatment decisions. An online calculator based on the nomogram was made available at https://cuifenzhang.shinyapps.io/DynNomapp/.

Predicting Pathological Complete Response After Neoadjuvant Chemotherapy in Advanced Breast Cancer by Ultrasound and Clinicopathological Features Using a Nomogram

Background and Aims

Prediction of pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) for breast cancer is critical for surgical planning and evaluation of NAC efficacy. The purpose of this project was to assess the efficiency of a novel nomogram based on ultrasound and clinicopathological features for predicting pCR after NAC.

Methods

This retrospective study included 282 patients with advanced breast cancer treated with NAC from two centers. Patients received breast ultrasound before NAC and after two cycles of NAC; and the ultrasound, clinicopathological features and feature changes after two cycles of NAC were recorded. A multivariate logistic regression model was combined with bootstrapping screened for informative features associated with pCR. Then, we constructed two nomograms: an initial-baseline nomogram and a two-cycle response nomogram. Sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV) were analyzed. The C-index was used to evaluate predictive accuracy.

Results

Sixty (60/282, 21.28%) patients achieved pCR. Triple-negative breast cancer (TNBC) and HER2-amplified types were more likely to obtain pCR. Size shrinkage, posterior acoustic pattern, and elasticity score were identified as independent factors by multivariate logistic regression. In the validation cohort, the two-cycle response nomogram showed better discrimination than the initial-baseline nomogram, with the C-index reaching 0.79. The sensitivity, specificity, and NPV of the two-cycle response nomogram were 0.77, 0.77, and 0.92, respectively.

Conclusion

The two-cycle response nomogram exhibited satisfactory efficiency, which means that the nomogram was a reliable method to predict pCR after NAC. Size shrinkage after two cycles of NAC was an important in dependent factor in predicting pCR.

A Novel Clinical-Radiomics Model Based on Sarcopenia and Radiomics for Predicting the Prognosis of Intrahepatic Cholangiocarcinoma After Radical Hepatectomy

Background

Intrahepatic cholangiocarcinoma (ICC) is a highly aggressive malignant tumor with a poor prognosis. This study aimed to establish a novel clinical-radiomics model for predicting the prognosis of ICC after radical hepatectomy.

Methods

A clinical-radiomics model was established for 82 cases of ICC treated with radical hepatectomy in our hospital from May 2011 to December 2020. Radiomics features were extracted from venous-phase and arterial-phase images of computed tomography. Kaplan-Meier survival analysis was generated to compare overall survival (OS) between different groups. The independent factors were identified by univariate and multivariate Cox regression analyses. Nomogram performance was evaluated regarding discrimination, calibration, and clinical utility. C-index and area under the curve (AUC) were utilized to compare the predictive performance between the clinical-radiomics model and conventional staging systems.

Results

The radiomics model included five features. The AUC of the radiomics model was 0.817 in the training cohort, and 0.684 in the validation cohort. The clinical-radiomics model included psoas muscle index, radiomics score, hepatolithiasis, carcinoembryonic antigen, and neutrophil/lymphocyte ratio. The reliable C-index of the model was 0.768, which was higher than that of other models. The AUC of the model for predicting OS at 1, and 3 years was 0.809 and 0.886, which was significantly higher than that of the American Joint Committee on Cancer 8^th staging system (0.594 and 0.619), radiomics model (0.743 and 0.770), and tumor differentiation (0.645 and 0.628). After stratification according to the constructed model, the median OS was 59.8 months for low-risk ICC patients and 10.1 months for high-risk patients (p < 0.0001).

Conclusion

The clinical-radiomics model integrating sarcopenia, clinical features, and radiomics score was accurate for prognostic prediction for mass-forming ICC patients. It provided an individualized prognostic evaluation in patients with mass-forming ICC and could helped surgeons with clinical decision-making.

Second Primary Malignancies in Patients with Pancreatic Neuroendocrine Neoplasms: A Population-Based Study on Occurrence, Risk Factors, and Prognosis

Background

This study aimed to evaluate the risk factors of developing second primary malignancies (SPMs) among patients with pancreatic neuroendocrine neoplasms (pNENs) and the prognosis of pNENs patients with SPMs (pSPMs) using data from the Surveillance, Epidemiology, and End Results (SEER) database.

Methods

Data from patients diagnosed with pNENs between 1988 and 2016 were extracted. A case-control study was conducted to investigate the risk factors of developing SPMs among patients with pNENs. Meanwhile, cox regression analysis was also conducted to obtain the independent prognostic factors in pSPMs.

Results

Of 7,630 patients with pNENs, 326 developed SPMs. Patients with pNENs who had not undergone surgery and had been diagnosed in recent periods had a higher risk of developing SPMs. The following independent prognostic predictors for pSPMs were identified: age, latency period, SEER stage, radiotherapy, and surgery.

Conclusions

These findings may improve the surveillance of risk factors for developing SPMs in patients with pNENs and the prognostic risk factors in pSPMs.

Development and validation of a SEER-based prognostic nomogram for cervical cancer patients below the age of 45 years

In this study, we established a nomogram for the prognostic prediction of patients with early-onset cervical cancer (EOCC) for both overall survival (OS) and cancer-specific survival (CSS). The Surveillance, Epidemiology, and End Results (SEER) database was used to identify 10,079 patients diagnosed with EOCC between 2004 and 2015; these cases were then randomly divided into training and validation sets. The independent prognostic factors were identified in a retrospective study of 7,055 patients from the training set. A prognostic nomogram was developed using R software according to the results of multivariable Cox regression analysis. Furthermore, the model was externally validated using the data from the remaining 3,024 patients diagnosed at different times and enrolled in the SEER database. For the training set, the C-indexes for OS and CSS prediction were determined to be 0.831 (95 % confidence interval [CI]: 0.815–0.847) and 0.855 (95 % CI: 0.839–0.871), respectively. Receiver operating characteristic (ROC) analysis has revealed that the nomograms were a superior predictor compared with TNM stage and SEER stage. The areas under the curve (AUC) of the nomogram for OS and CSS prediction in the ROC analysis were 0.855 (95 % CI: 0.847–0.864) and 0.782 (95 % CI: 0.760–0.804), respectively. In addition, calibration curves indicated a perfect agreement between the nomogram-predicted and the actual 1-, 3-, and 5-year OS and CSS rates in the validation cohort. Thus, in this study, we established and validated a prognostic nomogram that provides an accurate prediction for 3-, 5-, and 10-year OS and CSS of EOCC patients. This will be useful for clinicians in guiding counseling and clinical trial design for cervical cancer patients.

Prognostic Score System Using Preoperative Inflammatory, Nutritional and Tumor Markers to Predict Prognosis for Gastric Cancer: A Two-Center Cohort Study

INTRODUCTION

Gastric cancer (GC) is the fourth leading cause of cancer-related death worldwide. Our study aimed to investigate the prognostic value of preoperative inflammatory, nutritional and tumor markers and develop an effective prognostic score system to predict the prognosis of GC patients.

METHODS

We retrospectively analyzed 1587 consecutive GC patients who received curative gastrectomy from two medical centers. A novel prognostic score system was proposed based on independently preoperative markers associated with overall survival (OS) of GC patients. A nomogram based on prognostic score system was further established and validated internally and externally.

RESULTS

Based on multivariate analysis in the training set, a novel BLC (body mass index-lymphocyte-carbohydrate antigen 19-9) score system was proposed, which showed an effective predictability of OS in GC patients (log-rank P < 0.001). Moreover, receiver-operating characteristic (ROC) analysis showed that BLC had better performance in predicting OS than the traditional prognostic markers. The C-index of the BLC based-nomogram was 0.710 (95% CI 0.686-0.734), and the areas under ROC curves for predicting 3- and 5-year OS were 0.781 (95% CI 0.750-0.813) and 0.755 (95% CI 0.723-0.786), respectively, which were higher than those of tumor node metastasis (TNM) staging system alone. The calibration curve for probability of 3- and 5-year OS rate showed a good fitting effect between prediction by nomogram and actual observation. Verification in the internal and external validation sets showed results consistent with those in the training set.

CONCLUSIONS

The BLC combining inflammatory, nutritional and tumor markers was an independent prognostic predictor for GC patients, and the nomogram based on BLC could accurately predict the personalized survival of patients with GC.

Risk-score model to predict prognosis of malignant airway obstruction after interventional bronchoscopy

Background

Interventional bronchoscopy exhibits substantial effects for patients with malignant airway obstruction (MAO), while little information is available regarding the potential prognostic factors for these patients.

Methods

Between October 31, 2016, and July 31, 2019, a total of 150 patients undergoing interventional bronchoscopy and histologically-confirmed MAO were collected, in which 112 eligible participants formed the cohort for survival study. External validation cohort from another independent institution comprised 33 MAO patients with therapeutic bronchoscopy. The least absolute shrinkage and selection operator regression (LASSO) was applied to the model development dataset for selecting features correlated with MAO survival for inclusion in the Cox regression from which we elaborated the risk score system. A nomogram algorithm was also utilized.

Results

In our study, we observed a significant decline of stenosis rate after interventional bronchoscopy from 71.7%±2.1% to 36.6%±2.7% (P<0.001) and interventional bronchoscopy dilated airway effectively. Patients in our study undergoing interventional bronchoscopy had a median survival time of 614.000 days (95% CI: 269.876–958.124). Patients receiving distinct therapeutic methods of interventional bronchoscopy had different prognosis (P=0.022), and patients receiving treatment of electrocoagulation in combination with stenting and electrosurgical snare had worse survival than those receiving other options. Multivariate Cox analysis revealed that nonsmoking status, adenoid cystic carcinoma, and low preoperative stenosis length, as independent predictive factors for better overall survival (OS) of MAO patients. Then, the nomogram based on Cox regression and risk score system based on results from LASSO regression were elaborated respectively. Importantly, this risk score system was proved to have better performance than the nomogram and other single biomarkers such as traditional staging system (area under the curve 0.855 vs. 0.392–0.739). Survival curves showed that patients with the higher risk-score had poorer prognosis than those with lower risk-score (third quantile of OS: 126.000 days, 95% CI: 73.588–178.412 vs. 532.000 days, 95% CI: 0.000–1,110.372; P<0.001).

Conclusions

Nonsmoking status, adenoid cystic carcinoma, and low preoperative stenosis length, were independent predictive factors for better OS of MAO patients. We proposed a nomogram and risk score system for survival prediction of MAO patients undergoing interventional bronchoscopy with good performance.

Emergency admission parameters for predicting in-hospital mortality in patients with acute exacerbations of chronic obstructive pulmonary disease with hypercapnic respiratory failure

Background

Acute exacerbation of chronic obstructive pulmonary disease (AECOPD) is a common presentation in emergency departments (ED) that can be fatal. This study aimed to develop a mortality risk assessment model for patients presenting to the ED with AECOPD and hypercapnic respiratory failure.

Methods

We analysed 601 participants who were presented to an ED of a tertiary hospital with AECOPD between 2018 and 2020. Patient demographics, vital signs, and altered mental status were assessed on admission; moreover, the initial laboratory findings and major comorbidities were assessed. We used least absolute shrinkage and selection operator (LASSO) regression to identify predictors for establishing a nomogram for in-hospital mortality. Predictive ability was assessed using the area under the receiver operating curve (AUC). A 500 bootstrap method was applied for internal validation; moreover, the model’s clinical utility was evaluated using decision curve analysis (DCA). Additionally, the nomogram was compared with other prognostic models, including CRB65, CURB65, BAP65, and NEWS.

Results

Among the 601 patients, 19 (3.16%) died during hospitalization. LASSO regression analysis identified 7 variables, including respiratory rate, PCO₂, lactic acid, blood urea nitrogen, haemoglobin, platelet distribution width, and platelet count. These 7 variables and the variable of concomitant pneumonia were used to establish a predictive model. The nomogram showed good calibration and discrimination for mortality (AUC 0.940; 95% CI 0.895–0.985), which was higher than that of previous models. The DCA showed that our nomogram had clinical utility.

Conclusions

Our nomogram, which is based on clinical variables that can be easily obtained at presentation, showed favourable predictive accuracy for mortality in patients with AECOPD with hypercapnic respiratory failure.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12890-021-01624-1.

Prediction model of emergency mortality risk in patients with acute upper gastrointestinal bleeding: a retrospective study

Background

Upper gastrointestinal bleeding is a common presentation in emergency departments and carries significant morbidity worldwide. It is paramount that treating physicians have access to tools that can effectively evaluate the patient risk, allowing quick and effective treatments to ultimately improve their prognosis. This study aims to establish a mortality risk assessment model for patients with acute upper gastrointestinal bleeding at an emergency department.

Methods

A total of 991 patients presenting with acute upper gastrointestinal bleeding between July 2016 and June 2019 were enrolled in this retrospective single-center cohort study. Patient demographics, parameters assessed at admission, laboratory test, and clinical interventions were extracted. We used the least absolute shrinkage and selection operator regression to identify predictors for establishing a nomogram for death in the emergency department or within 24 h after leaving the emergency department and a corresponding nomogram. The area under the curve of the model was calculated. A bootstrap resampling method was used to internal validation, and decision curve analysis was applied for evaluate the clinical utility of the model. We also compared our predictive model with other prognostic models, such as AIMS65, Glasgow-Blatchford bleeding score, modified Glasgow-Blatchford bleeding score, and Pre-Endoscopic Rockall Score.

Results

Among 991 patients, 41 (4.14%) died in the emergency department or within 24 h after leaving the emergency department. Five non-zero coefficient variables (transfusion of plasma, D-dimer, albumin, potassium, age) were filtered by the least absolute shrinkage and selection operator regression analysis and used to establish a predictive model. The area under the curve for the model was 0.847 (95% confidence interval [0.794–0.900]), which is higher than that of previous models for mortality of patients with acute upper gastrointestinal bleeding. The decision curve analysis indicated the clinical usefulness of the model.

Conclusions

The nomogram based on transfusion of plasma, D-dimer, albumin, potassium, and age effectively assessed the prognosis of patients with acute upper gastrointestinal bleeding presenting at the emergency department.

A nomogram based on CENPP expression for survival prediction in breast cancer

Background

In recent years, it has been found that the expression of 17 centromere proteins (CENPs) was closely related to malignant tumors, however, the role of CENPs in breast cancer (BC) has not been fully investigated. This study intends to investigate the prognostic value of CENPs in BC and establish nomogram based on expression of CENPs to predict BC patients' prognosis.

Methods

A total of 800 BC patients with complete relevant data were included from the TCGA database and were further randomly divided into training set (N=480) and validation set (N=320). Univariate and multivariate Cox regression analysis were used to screen independent factors for overall survival (OS) prediction of BC patients in the training set. Then, the nomogram was established based on these independent predictors and further validated by receiver-operating characteristic (ROC) curves and calibration plots. The GEPIA and bcGenExMiner v4.4 databases were utilized to analyze mRNA expression of candidate gene in BC patients with different clinicopathological features, respectively.

Results

Multivariate Cox regression analysis showed that age, Her2 status, pathologic_T stage, pathologic_M stage and CENPP expression were of independent prognostic value for BC. CENPP was overexpressed in BC tissues (P<0.01) and lower expression of CENPP was associated with worse OS (P=0.005, HR =2.35; 95% CI: 1.30-4.23). We then established a nomogram based on those independent predictors, and the calibration curve demonstrated good fitness of the nomogram for OS prediction. In the training set, the AUCs of 3- and 5-year survival were 0.757 and 0.797, respectively. In the validation set, the AUCs of 3- and 5-year survival were 0.727 and 0.71, respectively.

Conclusions

Our study showed that CENPP was a novel prognostic factor for patients with BC, and the established nomogram could provide valuable information on prognostic prediction for patients with BC.

Who do we miss when screening for postpartum depression? A population-based study in a Swedish region

BACKGROUND

Universal screening for postpartum depression is crucial for early detection, interventions and support. The aim of this study was to describe the proportion of, and explore risk factors for, women not being offered screening, as well as for declining an offer or not being screened due to any other unknown reason.

METHODS

Socioeconomic, obstetrical and neonatal data, extracted from the Swedish Pregnancy Registry, for 9,959 pregnancies recorded for the Östergötland county between 2016 and 2018 were linked to Edinburgh Postnatal Depression Scale (EPDS) screening results at 6-8 weeks postpartum, extracted from medical records. Risk factors were assessed using logistic regression models and with a nomogram for easy visualization.

RESULTS

In total, there were no recorded offers of EPDS screening in the medical records for 30.0% of women at the postpartum follow-up. Women born outside of Sweden and women reporting poor self-rated health were at increased risk of not being offered screening for postpartum depression.

LIMITATIONS

There is a possibility that women were offered screening or were screened, but this was incorrectly or never recorded in medical records.

CONCLUSIONS

The majority of women were offered screening for postpartum depression, but there is room for improvement in order to achieve universal screening. Awareness among healthcare providers of the risk factors for not screening might increase adherence to guidelines for universal screening. Overcoming barriers for screening and raising the topic of mental-health issues for postpartum women should be prioritized.

Nomograms to Predict Overall and Cancer-Specific Survival in Gastric Signet-Ring Cell Carcinoma

BACKGROUND

The objective of our study was to develop and validate nomograms to predict the overall survival (OS) and cancer-specific survival (CSS) of patients with signet-ring cell carcinoma (SRCC) of the stomach.

METHODS

Data were collected from the Surveillance, Epidemiology, and End Results (SEER) database. A total of 1781 patients were randomly allocated to a training set (n = 1335) and a validation set (n = 446). Univariate and multivariate analyses were used to determine the prognostic effect of variables. Nomograms were developed to estimate OS and CSS and assessed using the concordance index (C-index), calibration curves, receiver operating characteristic (ROC), and decision curve analyses (DCA). DCA was utilized to compare the nomograms and the Tumor-Node-Metastasis (TNM) staging system.

RESULTS

Age, race, tumor size, T, N, M stage, and use of surgery and/or radiotherapy were included in the nomograms. C-indexes for OS and CSS were 0.74 and 0.75 in the training set, respectively. C-indexes for OS and CSS were 0.76 and 0.76 in the validation set. Calibration plots and receiver operating characteristic (ROC) curves showed good predictive accuracy. According to the decision curve analyses (DCA), the new model was more useful than the TNM staging system.

CONCLUSIONS

We developed nomograms to predict OS and CSS in patients with SRCC of the stomach. Nomograms may be a valuable clinical supplement of the conventional TNM staging system.

Association Between Oncotype DX Genomic Prostate Score and Adverse Tumor Pathology After Radical Prostatectomy

BACKGROUND

The Oncotype DX assay is a clinically validated 17-gene genomic assay that provides a genomic prostate score (GPS; scale 0-100) measuring the heterogeneous nature of prostate tumors. The test is performed on prostate tissue collected during biopsy. There is a lack of data on the association between the GPS and tumor pathology after radical prostatectomy (RP).

OBJECTIVE

To investigate the association between GPS and final pathology, including extraprostatic extension (EPE), positive surgical margin (PSM), and seminal vesicle invasion (SVI).

DESIGN, SETTING, AND PARTICIPANTS

Data for the 749 patients who underwent Oncotype DX assay and RP at a referral prostate cancer center between 2015 and 2019 were retrospectively assessed to evaluate the association between GPS and unfavorable pathology parameters.

INTERVENTION

After a GPS genetic test, patients underwent robotic RP performed by the same surgeon.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Multivariable logistic regression analyses were performed to assess the association between GPS and EPE, PSM, and SVI. The models were adjusted for age, clinical stage, prostate-specific antigen (PSA) level, Gleason score, and time between the genomic assay and surgery. The median time between Oncotype DX assay and surgery was 176 d (interquartile range [IQR] 141-226). The median age was 63 yr (IQR 58-68), median GPS was 29 (IQR 21-39), and median PSA was 5.7 ng/ml (IQR 4.6-7.7). In multivariable analyses assessing the odds ratio (OR) per 20-point change in GPS, GPS was an independent predictor of EPE (OR 1.8, 95% confidence interval [CI] 1.4-2.3) and SVI (OR 2.1, 95% CI 1.3-3.4). In addition, when patients were grouped by GPS quartile, the percentage of cases with EPE and SVI increased with the GPS quartile.

CONCLUSIONS

We provide evidence that the Oncotype DX GPS is significantly associated with adverse pathology after RP. Specifically, the risk of EPE and SVI increases with the GPS. Therefore, use of the Oncotype DX GPS may help clinicians to improve preoperative patient counseling and develop surgical strategies for patients with a higher chance of EPE or unfavorable pathological features.

PATIENT SUMMARY

We studied whether the score for a prostate genetic test was associated with prostate cancer pathology findings for patients who had their prostate removed. We found that the risk of prostate cancer spread outside the gland and to the seminal vesicle increases with higher test scores. These findings may help surgeons in counseling patients on surgical options for prostate cancer.

Individualized predictions of early isolated distal deep vein thrombosis in patients with acute ischemic stroke: a retrospective study

Background

Although isolated distal deep vein thrombosis (IDDVT) is a clinical complication for acute ischemic stroke (AIS) patients, very few clinicians value it and few methods can predict early IDDVT. This study aimed to establish and validate an individualized predictive nomogram for the risk of early IDDVT in AIS patients.

Methods

This study enrolled 647 consecutive AIS patients who were randomly divided into a training cohort (n = 431) and a validation cohort (n = 216). Based on logistic analyses in training cohort, a nomogram was constructed to predict early IDDVT. The nomogram was then validated using area under the receiver operating characteristic curve (AUROC) and calibration plots.

Results

The multivariate logistic regression analysis revealed that age, gender, lower limb paralysis, current pneumonia, atrial fibrillation and malignant tumor were independent risk factors of early IDDVT; these variables were integrated to construct the nomogram. Calibration plots revealed acceptable agreement between the predicted and actual IDDVT probabilities in both the training and validation cohorts. The nomogram had AUROC values of 0.767 (95% CI: 0.742–0.806) and 0.820 (95% CI: 0.762–0.869) in the training and validation cohorts, respectively. Additionally, in the validation cohort, the AUROC of the nomogram was higher than those of the other scores for predicting IDDVT.

Conclusions

The present nomogram provides clinicians with a novel and easy-to-use tool for the prediction of the individualized risk of IDDVT in the early stages of AIS, which would be helpful to initiate imaging examination and interventions timely.

A nomogram for the predicting of survival in patients with esophageal squamous cell carcinoma undergoing definitive chemoradiotherapy

Background

Definitive chemoradiotherapy (dCRT) is widely accepted for esophageal squamous cell carcinoma (ESCC), although the outcomes can vary. Therefore, we aimed to develop a nomogram for the pre-treatment prediction of survival after dCRT for ESCC.

Methods

This retrospective study evaluated 204 patients (169 patients in a primary cohort and 35 patients in a validation cohort) who received dCRT for ESCC between July 2013 and June 2017.

Results

Pre-treatment parameters that predicted long-term survival in this setting were body mass index (BMI), absolute lymphocyte count (ALC), neutrophil-to-lymphocyte ratio (NLR), wall thickness, concurrent chemoradiotherapy, radiotherapy modality, and American Joint Committee on Cancer (AJCC) stage. The nomogram incorporated these factors and provided C-index values of 0.691 [95% confidence interval (CI): 0.641-0.740] in the primary cohort and 0.816 (95% CI: 0.700-0.932) in the validation cohort. The calibration curve analysis revealed that the nomogram had good ability to predict 2-year progression-free survival (PFS). The nomogram also performed better than the AJCC staging system by the C-index values (0.691 vs. 0.560) and the area under the curve values (0.702 vs. 0.576). Decision curve analysis (DCA) also indicated that the nomogram had better clinical utility.

Conclusions

These results suggest that pre-treatment parameters may help predict the efficacy of dCRT for ESCC. Furthermore, as the nomogram provided better prognostic accuracy than the AJCC staging system, the nomogram may be useful in clinical practice for prognostication among patients who are going to receive dCRT for ESCC.

Development and Validation of the Nomograms for Predicting Overall Survival and Cancer-Specific Survival in Patients With Synovial Sarcoma

BACKGROUND

The study aimed to build and validate practical nomograms to predict overall survival (OS) and cancer-specific survival (CSS) for patients with synovial sarcoma (SyS).

METHODS

A total of 893 eligible patients confirmed to have SyS between 2007 and 2015 were selected from the Surveillance, Epidemiology, and End Results (SEER) database. Patients were randomly divided into the training cohort (n = 448) and validation cohort (n = 445). Clinically independent prognostic and important factors were determined according to the Akaike information criterion in multivariate Cox regression models when developing the nomograms with the training cohort. The predictive accuracy of nomograms was bootstrapped validated internally and externally with the concordance index (C-index) and calibration curve. Decision curve analysis (DCA) was performed to compare the clinical usefulness between nomograms and American Joint Commission on Cancer (AJCC) staging system.

RESULTS

Two nomograms shared common indicators including age, insurance status, tumor site, tumor size, SEER stage, surgery, and radiation, while marital status and tumor site were only included into the OS nomogram. The C-index of nomograms for predicting OS and CSS was 0.819 (0.873-0.764) and 0.821 (0.876-0.766), respectively, suggesting satisfactory predictive performance. Internal and external calibration curves exhibited optimal agreement between the nomogram prediction and the actual survival. Additionally, DCA demonstrated that our nomograms had obvious superiority over the AJCC staging system with more clinical net benefits.

CONCLUSIONS

Two nomograms predicting 3- and 5-year OS and CSS of SyS patients were successfully constructed and validated for the first time, with higher predictive accuracy and clinical values than the AJCC staging system regarding OS and CSS.

Prognostic Inflammatory Index Based on Preoperative Peripheral Blood for Predicting the Prognosis of Colorectal Cancer Patients

Simple Summary

Inflammation plays a critical role in the progression of colorectal cancer (CRC). Peripheral blood cell counts could reflect the extent of systemic inflammation and are readily available in clinical practice. The aim of our study was to construct a novel prognostic inflammatory index (PII) by integrating the blood cell counts associated with prognosis and to evaluate and validate the prognostic value of PII in two independent CRC cohorts. Multivariate Cox analyses in the training cohort of 4154 CRC patients indicated that high OS-PII (>4.27) and high DFS-PII (>4.47) were significantly associated with worse OS (HR: 1.330, p < 0.001) and worse DFS (HR: 1.366, p < 0.001), which has been validated in the external validation cohort of 5161 patients. Both OS-PII and DFS-PII have a stable prognostic performance at various follow-up times, and the nomograms based on OS-PII and DFS-PII achieved good accuracy in personalized survival prediction of patients with CRC.

Abstract

Host inflammation is a critical component of tumor progression and its status can be indicated by peripheral blood cell counts. We aimed to construct a comprehensively prognostic inflammatory index (PII) based on preoperative peripheral blood cell counts and further evaluate its prognostic value for patients with colorectal cancer (CRC). A total of 9315 patients with stage II and III CRC from training and external validation cohorts were included. The PII was constructed by integrating all the peripheral blood cell counts associated with prognosis in the training cohort. Cox analyses were performed to evaluate the association between PII and overall survival (OS) and disease-free survival (DFS). In the training cohort, multivariate Cox analyses indicated that high OS-PII (>4.27) was significantly associated with worse OS (HR: 1.330, 95% CI: 1.189–1.489, p < 0.001); and high DFS-PII (>4.47) was significantly associated with worse DFS (HR: 1.366, 95% CI: 1.206–1.548, p < 0.001). The prognostic values of both OS-PII and DFS-PII were validated in the external validation cohort. The nomograms achieved good accuracy in predicting both OS and DFS. Time-dependent ROC analyses showed that both OS-PII and DFS-PII have a stable prognostic performance at various follow-up times. The prognostic value of tumor-node-metastasis staging could be enhanced by combining it with either OS-PII or DFS-PII. We demonstrated that PIIs are independent prognostic predictors for CRC patients, and the nomograms based on PIIs can be recommended for personalized survival prediction of patients with CRC.

Operable gastric adenocarcinoma with different histological subtypes: Cancer-specific survival in the United States

BACKGROUND/AIMS

Gastric signet ring cell carcinoma (GSRC), a subtype of adenocarcinoma, has been considered a histological type with poor survival. We aimed to compare the survival outcomes between patients with GSRC and patients with gastric non-signet ring cell adenocarcinoma (NGSRC) and constructed a nomogram to predict gastric adenocarcinoma-specific survival (GCSS).

PATIENTS AND METHODS

We identified 10,031 patients with gastric adenocarcinoma (GA) from the surveillance, epidemiology, and end results (SEER) database and stratified them into two histological type groups: GSRC and NGSRC. We used propensity score matching and identified 4304 patients (training cohort) to assess the effect of the histological type on GCSS with Kaplan-Meier curves, and constructed a predictive nomogram. The accuracy of the nomogram was tested on the remaining 5727 patients (validation cohort) with concordance index (C-index) values, calibration curves, and receiver operating characteristic (ROC) curve analysis.

RESULTS

We found that the histological type SRC was not associated with significantly poor survival (5-year survival rate: 46.1% vs 46.7%, P = 0.822). GSRC patients had similar GCSS rates compared to those with NGSRC in each tumor, node, and metastasis (TNM) stage (allP > 0.05). The nomogram showed that histological type was a relatively weak predictor of survival. The C-index value of the nomogram for predicting survival was 0.720, similar to that in the validation cohort (0.724).

CONCLUSIONS

Patients with GSRC had a similar prognosis to those with NGSRC. The proposed nomogram allowed a relatively accurate survival prediction for operable GA patients after gastrectomy.

Development and Validation of Prognostic Nomograms for Patients with Primary Gastrointestinal Non-Hodgkin Lymphomas

BACKGROUND AND AIMS

The objective of this study was to construct and authenticate nomograms to project overall survival (OS) and cancer-specific survival (CSS) in primary gastrointestinal non-Hodgkin lymphomas (PGINHL).

METHODS

Suitable patients were chosen from the Surveillance, Epidemiology and End Results database and Wannan Medical College Yijishan Hospital. The Cox regression model was used to acquire independent predictive factors to develop nomograms for projecting OS and CSS. The performance of the nomograms was validated using the Harrell's concordance index (C-index), calibration curves, and decision curve analysis (DCA) and was compared with that of the AJCC 7th staging system. Survival curves were obtained using the Kaplan-Meier method, while the log-rank test was used to compare the difference among the groups.

RESULTS

The C-index of the nomograms for OS and CSS was 0.735 (95% CI = 0.719-0.751) and 0.761 (95% CI = 0.739-0.783), respectively, signifying substantial predictive accuracy. These outcomes were reproducible when the nomograms were used for the internal and external validation cohorts. Moreover, assessments of the C-index, AUC, and DCA between the nomogram results and the AJCC 7th staging system showed that the former was better for evaluation and was more clinically useful.

CONCLUSIONS

We constructed the nomogram which could predict 1-, 3-, and 5-year OS and CSS of patients with PGINHL. Our nomogram showed good performance, suggesting that it can be used as an efficacious instrument for predictive assessment of patients with PGINHL.

Neuromyelitis optica spectrum disorder: pregnancy-related attack and predictive risk factors

OBJECTIVES

To investigate the influence of pregnancy on patients with neuromyelitis optica spectrum disorder (NMOSD) and to identify risk factors that predict pregnancy-related attack.

METHODS

From January 2015 to April 2019, 418 female patients with NMOSD were registered at Huashan Hospital. We retrospectively reviewed their medical records and identified 110 patients with 136 informative pregnancies, of whom 83 were aquaporin-4 antibody (AQP4-ab)-positive and 21 were myelin oligodendrocyte glycoprotein-antibody-positive. Pregnancy-related attack was defined as an attack that occurred during pregnancy or within 1 year after delivery/abortion. We compared annualised relapse rate (ARR) during 12 months before pregnancy with that during every trimester of pregnancy and after delivery/abortion. Multivariate analyses were used to explore the independent risk factors involved and a nomogram was generated for the prediction of pregnancy-related attack. Thirty-five female patients from 3 other centres formed an external cohort to validate this nomogram.

RESULTS

ARR increased significantly during the first trimester after delivery (p<0.001) or abortion (p=0.019) compared with that before pregnancy. Independent risk factors predicting pregnancy-related attack included age at delivery/abortion (20-26.5, p=0.018; 26.5-33, p=0.001), AQP4-ab titre (≥1:100, p=0.049) and inadequate treatment during pregnancy and postpartum period (p=0.004). The concordance index of nomogram was 0.87 and 0.77 using bootstrap resampling in internal and external validation.

CONCLUSIONS

The first trimester post partum is a high-risk period for NMOSD recurrence. Patients with younger age, higher AQP4-ab titre and inadequate treatment are at higher risk for pregnancy-related attack.

Development and validation of prognostic nomogram for germ cell testicular cancer patients

The purpose of our study was to establish a reliable and practical nomogram based on significant clinical factors to predict the overall survival (OS) and cancer-specific survival (CSS) of patients with germ cell testicular cancer (GCTC). Patients diagnosed with GCTC between 2004 and 2015 were obtained from the SEER database. Nomograms were constructed using the R software to predict the OS and CSS probabilities and the constructed nomograms were validated and calibrated. A total of 22,165 GCTC patients were enrolled in the study, including the training cohort (15,515 patients) and the validation cohort (6,650 patients). In the training cohort, multivariate Cox regression showed that age, race, AJCC stage, SEER stage and surgery were independent prognostic factors for OS, while age, race, AJCC stage, TM stage, SEER stage and radiotherapy were independent prognostic factors for CSS. Based on the above Cox regression results, we constructed prognostic nomograms of OS and CSS in GCTC patients and found that the OS nomograms had higher C-index and AUC compared to TNM stage in the training and validation cohorts. In addition, in the training and external validation cohorts, the calibration curves showed a good consistency between the predicted and actual 3-, 5- and 10-year OS and CSS rates of the nomogram. The current prognostic nomogram can provide a personalized risk assessment for the survival of GCTC patients.

ARHGEF11 promotes proliferation and epithelial-mesenchymal transition of hepatocellular carcinoma through activation of β-catenin pathway

Rho guanine nucleotide exchange factor 11 (ARHGEF11) has been proved to promote tumor metastasis in glioblastoma and ovarian carcinoma. However, the role of ARHGEF11 in hepatocellular carcinoma (HCC) progression is largely unknown. Here, we found that ARHGEF11 was upregulated in HCC samples and highly metastatic hepatoma cell lines. Knockdown of ARHGEFF11 inhibited the cell proliferation and invasion in both HCCLM3 and SKHEP1 cell lines. Subsequent mechanistic investigation showed that downregulation of ARHGEF11 significantly attenuated β-catenin nuclear translocation, thereafter repressed the expression of ZEB1 and cyclinD1, finally contributing to inhibition of epithelial-mesenchymal transition (EMT) and cell cycle arrest. Moreover, high levels of ARHGEF11 were found to be associated with shorter disease free and overall survival. A prognostic nomogram model that integrated ARHGEF11, tumor size and BCLC classification showed good performance in predicting clinical outcomes of HCC patients. Overall, this study demonstrated that ARHGEF11 could promote proliferation and metastasis of HCC via activating β-catenin pathway, suggesting that ARHGEF11 might serve as a potential prognostic biomarker for HCC.

The Usefulness of a Preoperative Nomogram for Predicting the Probability of Conversion from Laparoscopic to Open Distal Pancreatectomy: A Single-Center Experience

Background

Laparoscopic distal pancreatectomy (LDP) represents a challenging procedure with a high conversion rate. A nomogram is a simple statistical predictive tool which is superior to risk groups. The aim of this study was to develop and validate a preoperative nomogram for predicting the probability of conversion from laparoscopic to open distal pancreatectomy.

Methods

This is a retrospective study of 100 consecutive patients who underwent LDP. For each patient demographic, pre-intra- and postoperative data were collected. Univariate and multivariate analyses were carried out to identify the factors significantly influencing the conversion rate. The effect of each factor was weighted using the beta coefficient (β), and a nomogram was built. Finally, a logistic regression between the score and the conversion rate was carried out to calibrate the nomogram.

Results

The conversion rate was 19.0%. At multivariate analysis, female (β =  − 1.8 ± 0.9; P = 0.047) and tail location of the tumor (β =  − 2.1 ± 1.1; P = 0.050) were significantly related to a low probability of conversion. Body mass index (BMI) (β = 0.2 ± 0.1; P = 0.011) and subtotal pancreatectomy (β = 2.4 ± 0.9; P = 0.006) were factors independently related to a high probability of conversion. The nomogram constructed had a minimum value of 4 and a maximum value of 18 points. The probability of conversion increased significantly starting from a minimum score of 6 points (P = 0.029; conversion probability 14.4%; 95%CI, 1.5–27.3%) up to 16 (P = 0.048; 27.8%; 95%CI, 0.2–48.7%).

Conclusion

The nomogram proposed could serve as an effective preoperative tool capable of assessing the probability of conversion, allowing to take reliable decisions regarding indications and adequate stepwise training program of LDP.