Preoperative ultrasound combined with routine blood tests in predicting the malignant risk of pancreatic cystic neoplasms

Objective:Accurate preoperative identification of benign or malignant pancreatic cystic neoplasms (PCN) may help clinicians make better intervention choices and will be essential for individualized treatment.Methods:Preoperative ultrasound and laboratory examination findings, and demographic characteristics were collected from patients. Multiple logistic regression was used to identify independent risk factors associated with malignant PCN, which were then included in the nomogram and validated with an external cohort. The Net Reclassification Index (NRI) and Integrated Discrimination Improvement (IDI) were calculated to evaluate the improvement in the predictive power of the new model with respect to that of a combined imaging and tumor marker prediction model.Results:Malignant PCN were found in 83 (40.7%) and 33 (38.7%) of the model and validation cohorts, respectively. Multivariate analysis identified age, tumor location, imaging of tumor boundary, blood type, mean hemoglobin concentration, neutrophil-to-lymphocyte ratio, carbohydrate antigen 19-9, and carcinoembryonic antigen as independent risk factors for malignant PCN. The calibration curve indicated that the predictions based on the nomogram were in excellent agreement with the actual observations. A nomogram score cutoff of 192.5 classified patients as having low vs. high risk of malignant PCN. The model achieved good C-statistics of 0.929 (95% CI 0.890–0.968, P < 0.05) and 0.951 (95% CI 0.903–0.998, P < 0.05) in predicting malignancy in the development and validation cohorts, respectively. NRI = 0.268; IDI = 0.271 (P < 0.001 for improvement). The DCA curve indicated that our model yielded greater clinical benefits than the comparator model.Conclusions:The nomogram showed excellent performance in predicting malignant PCN and may help surgeons select patients for detailed examination and surgery. The nomogram is freely available at https://wangjunjinnomogram.shinyapps.io/DynNomapp/.     

High expression of PYK2 is associated with poor prognosis and cancer progression in early-stage cervical carcinoma

Proline-rich tyrosine kinase-2 (PYK2), also known as calcium dependent tyrosine kinase, regulates different signal transduction cascades that control cell proliferation, migration, and invasion. However, the role of PYK2 in cervical cancer remains to be elucidated. The current study retrospectively included 134 patients with cervical cancer from December 2007 to September 2014. PYK2 expression was detected in tissue microarray and cervical cancer cell lines. Statistical analysis was performed to evaluate its clinicopathological significance. Small interfering RNA (siRNA) was employed to suppress endogenous PYK2 expression in cervical cancer cells to observe the biological function. PYK2 expression was up-regulated in cervical cancer specimens compared with paired adjacent normal cervical tissue samples. Statistical analyses indicated that PYK2 expression might be an independent prognostic indicator for patients with early-stage cervical cancer. A nomogram model was constructed based on PYK2 expression and other clinicopathological risk factors, and it performed well in predicting patients survival. In cellular studies, down-regulation of PYK2 remarkably inhibited cellular proliferation, migration and invasion. PYK2 expression possessed the potential to serve as a novel prognostic marker in cervical cancer patients.     

Predicting in-hospital mortality for MIMIC-III patients: A nomogram combined with SOFA score

Predicting the mortality of patients provides a reference for doctors to judge their physical condition. This study aimed to construct a nomogram to improve the prediction accuracy of patients’ mortality. Patients with severe diseases were screened from the Medical Information Mart for Intensive Care (MIMIC) III database; 70% of patients were randomly selected as the training set for the model establishment, while 30% were used as the test set. The least absolute shrinkage and selection operator (LASSO) regression method was used to filter variables and select predictors. A multivariable logistic regression fit was used to determine the association between in-hospital mortality and risk factors and to construct a nomogram. A total of 9276 patients were included. The area under the curve (AUC) for the clinical nomogram based on risk factors selected by LASSO and multivariable logistic regressions were 0.849 (95% confidence interval [CI]: 0.835–0.863) and 0.821 (95% CI: 0.795–0.846) in the training and test sets, respectively. Therefore, this nomogram might help predict the in-hospital mortality of patients admitted to the intensive care unit (ICU).     

A nomogram for individualized prediction of lower extremity deep venous thrombosis in stroke patients: A retrospective study

To develop and validate a nomogram for individualized prediction of lower extremity deep venous thrombosis (DVT) in stroke patients based on extremity function and daily living ability of stroke patients. In this study, 423 stroke patients admitted to the Rehabilitation Medical Center of the First Affiliated Hospital of Nanjing Medical University from December 2015 to February 2019 were taken as the subjects, who were divided into the DVT group (110) and No-DVT group (313) based on the existence of DVT. Inter-group comparison of baseline data was performed by 1-way Analysis of Variance, Kruskal-Wallis rank-sum test, or Pearson chi-square test. Data dimensions and predictive variables were selected by least absolute shrinkage and selection operator (LASSO); the prediction model was developed and the nomogram was prepared by binary logistics regression analysis; the performance of the nomogram was identified by the area under the receiver operating characteristic curve (AUC), Harrell’s concordance index, and calibration curve; and the clinical effectiveness of the model was analyzed by clinical decision curve analysis. Age, Brunnstrom stage (lower extremity), and D-dimer were determined to be the independent predictors affecting DVT. The independent predictors mentioned above were developed and presented as a nomogram, with AUC and concordance index of 0.724 (95% confidence interval [CI]: 0.670–0.777), indicating the satisfactory discrimination ability of the nomogram. The P value of the results of the Hosmer-Lemeshow test was 0.732, indicating good fitting of the prediction model. Decision curve analysis showed that the clinical net benefit of this model was 6% to 50%. We developed a nomogram to predict lower extremity deep venous thrombosis in stroke patients, and the results showed that the nomogram had satisfactory prediction performance and clinical efficacy.     

A novel Anoikis and immune-related genes marked prognostic signature for colorectal cancer

Colorectal cancer (CRC) is second most commonly diagnosed cancer with high morbidity and mortality. The heterogeneity of CRC makes clinical treatment tremendously challenging. Here, we aimed to comprehensively analyze the prognosis of CRC patients based on ANOIKIS- and immune-related genes. ANOIKIS-related genes were identified by differentially analysis of high anoikis score group (ANOIKIS_high group) and low anoikis score group (ANOIKIS_low group) divided by the cutoff value of anoikis score. Immune-related genes were screened by differentially analysis of high immune score group (ImmuneScore_high group) and low immune score group (ImmuneScore_low group) classified by the cutoff value of ImmuneScore. Prognostic ANOIKIS- and immune-related genes were identified by univariate Cox regression analysis. Multivariate Cox regression analysis were used for prognostic model construction. Ferroptosis expression profiles, the infiltration of immune cells, and the somatic mutation status were analyzed and compared. Univariate and multivariate Cox-regression analyses were performed to identify independent prognostic factors for CRC patient. Nomogram that contained the independent prognostic factors was established to predict 1-, 3-, and 5-year OS probability of CRC patients. Three ANOIKIS- and immune-related signatures were applied to construct a prognostic model, which divided the CRC patients into high-risk and low-risk groups. The patients with high-risk scores had obviously shorter OSs than those with low-risk scores. The time dependent ROC curve indicated that the risk score model had a stable performance to predict survival rates. Notably, the age, pathologic T, and risk score could be used independent indicators for CRC prognosis prediction. A nomogram containing the independent prognostic factors showed that the nomogram accurately predicted 1-, 3-, and 5-year survival rates of CRC patients. In our research, a novel prognostic model was developed based on ANOIKIS- and immune-related genes in CRC, which could be used for prognostic prediction of CRC patients.     

A Prognostic Model Based on Nutritional Risk Index in Operative Breast Cancer

Background: The nutritional risk index (NRI) is an independent prognostic factor for overall survival in various cancers, but its prognostic value in breast cancer remains unclear. This study aimed to explore the relationship between the NRI and overall survival (OS) in breast cancer and to develop a predictive nomogram. Methods: We retrospectively enrolled 1347 breast cancer patients who underwent mastectomy or lumpectomy between January 2011 and November 2012. Using a cutoff value of 110.59, patients were divided into a high-NRI group and a low-NRI group. OS was compared between the two groups. Clinicopathological factors independently associated with survival were used to construct a predictive nomogram. Results: Of the 1347 patients, 534 patients were classified as high NRI and 813 as low NRI. OS was significantly shorter in low-NRI patients. The 3- and 5-year OS rates were 87.3% and 73.4%, respectively, in the high-NRI group whereas they were 83.0% and 67.2%, respectively, in the low-NRI group. Cox regression analysis found that histopathological type, tumor size, lymph node status, progesterone receptor (PR) status, Ki-67, and NRI were independently associated with OS. Conclusions: NRI is an independent prognostic factor of OS in breast cancer patients. The proposed nomogram model may be a useful tool for individualized survival prediction.     

Development and validation of prognostic nomogram for patients with metastatic gastric adenocarcinoma based on the SEER database

The aim of this study was to investigate the prognostic factors affecting overall survival in patients with metastatic gastric adenocarcinoma and to establish a nomogram prediction model for comprehensive clinical application. Data from 2370 patients with metastatic gastric adenocarcinoma between 2010 and 2017 were retrieved from the surveillance, epidemiology, and end results database. They were randomly divided into a training set (70%) and a validation set (30%), univariate and multivariate Cox proportional hazards regressions were used to screen important variables that may affect overall survival and to establish the nomogram. The nomogram model was evaluated using a receiver operating characteristic curve, calibration plot, and decision curve analysis. Internal validation was performed to test the accuracy and validity of the nomogram. Univariate and multivariate Cox regression analyses revealed that, age, primary site, grade, and American joint committee on cancer. T, bone metastasis, liver metastasis, lung metastasis, tumor Size, and chemotherapy were identified as independent prognostic factors for overall survival and were included in the prognostic model to construct a nomogram. The prognostic nomogram showed good overall survival risk stratification ability for the area under the curve, calibration plots, and decision curve analysis in both the training and validation sets. Kaplan–Meier curves further showed that patients in the low-risk group had better overall survival. This study synthesizes the clinical, pathological, therapeutic characteristics of patients with metastatic gastric adenocarcinoma, establishes a clinically effective prognostic model, and that can help clinicians to better evaluate the patient’s condition and provide accurate treatment.     

Evaluation of serum B7‐H3 expression,ultrasound and clinical characteristics to predict the risk of cervical lymph node metastases in papillary thyroid carcinoma by nomogram

BackgroundImproving the preoperative diagnosis of cervical lymph node metastasis (LNM) will help improve the clinical outcomes of papillary thyroid carcinoma (PTC) patients. B7‐H3, as an immune checkpoint of the B7 family, is highly expressed in PTC tissues and related to LNM and prognosis. We aimed to explore the clinical values of serum B7‐H3 (sB7‐H3) in predicting LNM in PTC by a nomogram prediction model.MethodsFrom September 2019 to May 2021, a total of 344 PTC patients with primary surgery in our hospital were enrolled in this research. Enzyme‐linked Immunosorbent Assay (ELISA) was used to detect sB7‐H3 from the peripheral blood of PTC patients and normal controls. We created a nomogram prediction model in combination with sB7‐H3 expression, clinical and ultrasound characteristics to predict LNM in the early stage.ResultsGender (p = 0.001), age (p = 0.015), tumor size (p < 0.001), number of tumors (p = 0.021) and sB7‐H3 expression (p = 0.003) were independent risk factors for LNM in PTC. All the factors were included in the nomogram. The area under the curve (AUC) was 73.9% (95% CI, 68.12%–79.69%).ConclusionThe nomogram is helpful in assessing the risk of LNM in PTC. sB7‐H3 has excellent potential in predicting LNM in patients with PTC as an adjunctive ultrasound tool.     

Clinicopathological features and prognostic nomogram of giant cell carcinoma of the lung: A population‐based study

BackgroundDue to its rarity, the features and prognosis of giant cell carcinoma of the lung (GCCL) are not well defined. The present study aimed to describe the clinicopathological features and prognostic analysis of this rare disease, compare it with lung adenocarcinoma (LAC), further determine the prognostic factors and establish a nomogram.MethodsPatients diagnosed with GCCL and LAC were identified from the SEER database between 2004 and 2016. The features and survival between GCCL and LAC were compared in the unmatched and matched cohorts after propensity score matching (PSM) analysis. Univariate and multivariate Cox analyses were used to identify the prognostic factors, and a nomogram was constructed. Area under the curve (AUC), C‐index, calibration curve and decision curve analysis (DCA) were used to confirm the established nomogram.ResultsA total of 295 patient diagnosed with GCCL and 149 082 patients with LAC were identified. Compared with LAC, patients with GCCL tend to be younger, male, black and have pathological Grade III/IV GCCL, more proportion of AJCC‐TNM‐IV, T3/T4 and distant metastases. The 1‐, 2‐ and 5‐year OS rates of the patients with GCCL were 21.7%, 13.4% and 7.9%, respectively. The median OS and CSS were 3 and 4 months, respectively. Patients with GCCL had significantly shorter OS and CSS than those with LAC in the unmatched and matched cohorts after PSM. Multivariate Cox analysis demonstrated that T, N and M stages and use of chemotherapy and surgery were independent of survival. Furthermore, we constructed a prognostic nomogram for OS and CSS by using independent prognostic factors. The C‐index of OS‐specific nomogram is 0.78 (0.74–0.81), and the C‐index of CSS‐specific nomogram is 0.77 (0.73–0.80). The calibration curve and ROC analysis showed good predictive capability of these nomograms. DCA showed that the nomogram had greater clinical practical value in predicting the OS and CSS of GCCL than TNM staging.ConclusionGCCL have distinct clinicopathological characteristics and significantly worse clinical outcomes. Prognostic nomograms for overall survival (OS) and CSS were constructed.     

Survival benefit of resection surgery for lung adenocarcinoma with bone metastases and a post-operative prognosis nomogram establishment and validation

BackgroundSurgical resection is not usually recommended for lung adenocarcinoma (LUAD) patients with bone metastases. However, the criteria for surgery are constantly being adjusted and there is a need to focus on the prognostic role of cancer-directed surgery (CDS) for bone metastatic LUAD patients investigate the factors influencing survival of CDS. We determined the survival benefit of CDS for LUAD patients with bone metastases and to develop a prognostic nomogram to predict overall survival (OS) for patients after surgery.MethodsLUAD patients with bone metastases from the Surveillance, Epidemiology, and End Results (SEER) database between 2010–2015 were included and divided into CDS and non-CDS groups. The propensity score matching (PSM) was used to balance baseline characteristics. We used Kaplan-Meier curves and log-rank tests to compare cancer-specific survival (CSS) and OS between the two groups. Patients underwent CDS were randomly divided into training and validation cohorts to develop and validate a nomogram model to predict postoperative prognosis outcome–OS.ResultsPatients who underwent CDS had a better OS and CSS than those who did not underwent CDS (e.g., 1-year OS rate: 56.9% vs. 30.1%). Independent prognostic factors were selected by Cox regression analysis for CDS patients including age, sex, race, histological grade, N stage, and chemotherapy, and a nomogram was constructed to predict 1-, 2-, and 3-year OS after surgery according to the prognostic factors. The calibration curve and receiver operating characteristic (ROC) curve for the nomogram showed the model had a high predictive accuracy. [Area under the curve (AUC) at 1, 2, and 3 years in the training cohort were 0.735, 0.756 and 0.782, and in the validation cohort were 0.703, 0.758 and 0.836, respectively]. In addition, patients were divided into high-risk and low-risk groups based on prognostic scores, and Kaplan-Meier curves showed significant differences in prognosis between the two groups.ConclusionsThese results indicated that patients with bone metastasis of LUAD received survival benefit from CDS. The prognostic nomograms could assist clinicians in specifying individualized assessments, but further research is needed.