Score for the Risk and Overall Survival of Lung Metastasis in Patients First Diagnosed With Soft Tissue Sarcoma: A Novel Nomogram-Based Risk Assessment System

Background: Metastatic soft tissue sarcoma (STS) patients have a poor prognosis with a 3-year survival rate of 25%. About 30% of them present lung metastases (LM). This study aimed to construct 2 nomograms to predict the risk of LM and overall survival of STS patients with LM. Materials and Methods: The data of patients were derived from the Surveillance, Epidemiology, and End Results database during the period of 2010 to 2015. Logistic and Cox analysis was performed to determine the independent risk factors and prognostic factors of STS patients with LM, respectively. Afterward, 2 nomograms were, respectively, established based on these factors. The performance of the developed nomogram was evaluated with receiver operating characteristic curves, area under the curve (AUC) calibration curves, and decision curve analysis (DCA). Results: A total of 7643 patients with STS were included in this study. The independent predictors of LM in first-diagnosed STS patients were N stage, grade, histologic type, and tumor size. The independent prognostic factors for STS patients with LM were age, N stage, surgery, and chemotherapy. The AUCs of the diagnostic nomogram were 0.806 in the training set and 0.799 in the testing set. For the prognostic nomogram, the time-dependent AUC values of the training and testing set suggested a favorable performance and discrimination of the nomogram. The 1-, 2-, and 3-year AUC values were 0.698, 0.718, and 0.715 in the training set, and 0.669, 0.612, and 0717 in the testing set, respectively. Furthermore, for the 2 nomograms, calibration curves indicated satisfactory agreement between prediction and actual survival, and DCA indicated its clinical usefulness. Conclusion: In this study, grade, histology, N stage, and tumor size were identified as independent risk factors of LM in STS patients, age, chemotherapy surgery, and N stage were identified as independent prognostic factors of STS patients with LM, these developed nomograms may be an effective tool for accurately predicting the risk and prognosis of newly diagnosed patients with LM.

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A Web-Based Prediction Model for Cancer-Specific Survival of Elderly Patients With Hypopharyngeal Squamous Cell Carcinomas: A Population-Based Study

Frontiers in Public Health ◽

10.3389/fpubh.2021.815631 ◽

2022 ◽

Vol 9 ◽

Author(s):

JinKui Wang ◽

XiaoZhu Liu ◽

Jie Tang ◽

Qingquan Zhang ◽

Yuanyang Zhao

Keyword(s):

Risk Factors ◽

Elderly Patients ◽

Calibration Curve ◽

Tumor Size ◽

Squamous Cell Carcinomas ◽

Training Set ◽

Cancer Specific Survival ◽

N Stage ◽

Independent Risk Factors ◽

Validation Set

Background: Hypopharyngeal squamous cell carcinomas (HPSCC) is one of the causes of death in elderly patients, an accurate prediction of survival can effectively improve the prognosis of patients. However, there is no accurate assessment of the survival prognosis of elderly patients with HPSCC. The purpose of this study is to establish a nomogram to predict the cancer-specific survival (CSS) of elderly patients with HPSCC.Methods: The clinicopathological data of all patients from 2004 to 2018 were downloaded from the SEER database. These patients were randomly divided into a training set (70%) and a validation set (30%). The univariate and multivariate Cox regression analysis confirmed independent risk factors for the prognosis of elderly patients with HPSCC. A new nomogram was constructed to predict 1-, 3-, and 5-year CSS in elderly patients with HPSCC. Then used the consistency index (C-index), the calibration curve, and the area under the receiver operating curve (AUC) to evaluate the accuracy and discrimination of the prediction model. Decision curve analysis (DCA) was used to assess the clinical value of the model.Results: A total of 3,172 patients were included in the study, and they were randomly divided into a training set (N = 2,219) and a validation set (N = 953). Univariate and multivariate analysis suggested that age, T stage, N stage, M stage, tumor size, surgery, radiotherapy, chemotherapy, and marriage were independent risk factors for patient prognosis. These nine variables are included in the nomogram to predict the CSS of patients. The C-index for the training set and validation was 0.713 (95% CI, 0.697–0.729) and 0.703 (95% CI, 0.678–0.729), respectively. The AUC results of the training and validation set indicate that this nomogram has good accuracy. The calibration curve indicates that the observed and predicted values are highly consistent. DCA indicated that the nomogram has a better clinical application value than the traditional TNM staging system.Conclusion: This study identified risk factors for survival in elderly patients with HPSCC. We found that age, T stage, N stage, M stage, tumor size, surgery, radiotherapy, chemotherapy, and marriage are independent prognostic factors. A new nomogram for predicting the CSS of elderly HPSCC patients was established. This model has good clinical application value and can help patients and doctors make clinical decisions.

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Prognostic Factors for Children and Adolescents With Surgically Resected Nonrhabdomyosarcoma Soft Tissue Sarcoma: An Analysis of 121 Patients Treated at St Jude Children's Research Hospital

Journal of Clinical Oncology ◽

10.1200/jco.1999.17.12.3697 ◽

1999 ◽

Vol 17 (12) ◽

pp. 3697-3705 ◽

Cited By ~ 114

Author(s):

Sheri L. Spunt ◽

Catherine A. Poquette ◽

Yasmeen S. Hurt ◽

Alvida M. Cain ◽

Bhaskar N. Rao ◽

...

Keyword(s):

Prognostic Factors ◽

Soft Tissue ◽

Soft Tissue Sarcoma ◽

Tumor Size ◽

Primary Tumor ◽

Distant Recurrence ◽

High Grade ◽

Tumor Site ◽

Primary Tumor Site ◽

Positive Surgical Margins

PURPOSE: The rarity and heterogeneity of pediatric nonrhabdomyosarcoma soft tissue sarcoma (NRSTS) has precluded meaningful analysis of prognostic factors associated with surgically resected disease. To define a population of patients at high risk of treatment failure who might benefit from adjuvant therapies, we evaluated the relationship between various clinicopathologic factors and clinical outcome of children and adolescents with resected NRSTS over a 27-year period at our institution. PATIENTS AND METHODS: We analyzed the records of 121 consecutive patients with NRSTS who underwent surgical resection between August 1969 and December 1996. Demographic data, tumor characteristics, treatment, and outcomes were recorded. Univariate and multivariate analyses of prognostic factors for survival, event-free survival (EFS), and local and distant recurrence were performed. RESULTS: At a median follow-up of 9.2 years, 5-year survival and EFS rates for the entire cohort were 89% ± 3% and 77% ± 4%, respectively. In univariate models, positive surgical margins (P = .004), tumor size ≥ 5 cm (P < .001), invasiveness (P = .002), high grade (P = .028), and intra-abdominal primary tumor site (P = .055) adversely affected EFS. All of these factors except invasiveness remained prognostic of EFS and survival in multivariate models. Positive surgical margins (P = .003), intra-abdominal primary tumor site (P = .028), and the omission of radiation therapy (P = .043) predicted local recurrence, whereas tumor size ≥ 5 cm (P < .001), invasiveness (P < .001), and high grade (P = .004) predicted distant recurrence. CONCLUSION: In this largest single-institution analysis of pediatric patients with surgically resected NRSTS, we identified clinicopathologic features predictive of poor outcome. These variables should be prospectively evaluated as risk-adapted therapies are developed.

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Development and validation of a prognostic model for overall survival in chemotherapy-naive men with metastatic castration-resistant prostate cancer (mCRPC) from the phase 3 prevail clinical trial.

Journal of Clinical Oncology ◽

10.1200/jco.2017.35.15_suppl.5022 ◽

2017 ◽

Vol 35 (15_suppl) ◽

pp. 5022-5022

Author(s):

Andrew J. Armstrong ◽

Ping Lin ◽

Celestia S. Higano ◽

Cora N. Sternberg ◽

Guru Sonpavde ◽

...

Keyword(s):

Clinical Trial ◽

Overall Survival ◽

Prognostic Factors ◽

High Risk ◽

Prognostic Model ◽

Risk Groups ◽

Multivariable Model ◽

Training Set ◽

Phase 3 ◽

Testing Set

5022 Background: Prognostic models require updating to reflect contemporary medical practice. In a post hoc analysis of the phase 3 PREVAIL trial (enzalutamide vs placebo), we identified prognostic factors for overall survival (OS) in chemotherapy-naive men with mCRPC. Methods: Patients were randomly divided 2:1 into training (n = 1159) and testing (n = 550) sets. Using the training set, 23 predefined candidate prognostic factors (including treatment) were analyzed in a multivariable Cox model with stepwise procedures and in a penalized Cox proportional hazards model using the adaptive least absolute shrinkage and selection operator (LASSO) penalty (data cutoff June 1, 2014). A multivariable model predicting OS was developed using the training set; the predictive accuracy was assessed in the testing set using time-dependent area under the curve (tAUC). The testing set was stratified based on risk score tertiles (low, intermediate, high), and OS was analyzed using Kaplan-Meier methodology. Results: Demographics, disease characteristics, and OS were balanced between the training and testing sets; median OS was 32.7 months for both datasets. There were no enzalutamide treatment-prognostic factor interactions (predictors). The final multivariable model included 11 prognostic factors: prostate-specific antigen, treatment, hemoglobin, neutrophil-lymphocyte ratio, liver metastases, time from diagnosis to randomization, lactate dehydrogenase, ≥ 10 bone metastases, pain, albumin, and alkaline phosphatase. The tAUC was 0.74 in the testing set. Median (95% confidence interval [CI]) OS for the low-, intermediate-, and high-risk groups (testing set) were not yet reached (NYR) (NYR–NYR), 34.2 months (31.5–NYR), and 21.1 months (17.5–25.0). The hazard ratios (95% CI) for OS in the low- and intermediate-risk groups vs the high-risk group were 0.20 (0.14–0.29) and 0.40 (0.30–0.53), respectively. Conclusions: Our validated prognostic model incorporates factors routinely collected in chemotherapy-naive men with mCRPC treated with enzalutamide and identifies subsets of men with widely differing survival times. Clinical trial information: NCT01212991.

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Development and validation of a prognostic model for overall survival in chemotherapy-naïve men with metastatic castration-resistant prostate cancer (mCRPC) from the phase III PREVAIL clinical trial.

Journal of Clinical Oncology ◽

10.1200/jco.2017.35.6_suppl.138 ◽

2017 ◽

Vol 35 (6_suppl) ◽

pp. 138-138

Author(s):

Andrew J. Armstrong ◽

Ping Lin ◽

Celestia S. Higano ◽

Cora N. Sternberg ◽

Guru Sonpavde ◽

...

Keyword(s):

Overall Survival ◽

Prognostic Factors ◽

High Risk ◽

Prognostic Model ◽

Specific Antigen ◽

Risk Groups ◽

Phase Iii ◽

Multivariable Model ◽

Training Set ◽

Testing Set

138 Background: Prognostic models require updating to reflect contemporary medical practice. In a post hoc analysis of the phase 3 PREVAIL trial (enzalutamide vs placebo), we identified prognostic factors for overall survival (OS) in chemotherapy-naïve men with mCRPC. Methods: Patients were randomly divided 2:1 into training (n = 1159) and testing (n = 550) sets. Using the training set, 23 predefined candidate prognostic factors (including treatment) were analyzed in a multivariable Cox model with stepwise procedures and in a penalized Cox proportional hazards model using the adaptive least absolute shrinkage and selection operator (LASSO) penalty (data cutoff June 1, 2014). A multivariable model predicting OS was developed using the training set; the predictive accuracy was assessed in the testing set using time-dependent area under the curve (tAUC). The testing set was stratified based on risk score tertiles (low, intermediate, high), and OS was analyzed using Kaplan-Meier methodology. Results: Demographics, disease characteristics, and OS were balanced between the training and testing sets; median OS was 32.7 months for both datasets. There were no enzalutamide treatment-prognostic factor interactions (predictors). The final multivariable model included 11 prognostic factors: prostate-specific antigen, treatment, hemoglobin, neutrophil-lymphocyte ratio, liver metastases, time from diagnosis to randomization, lactate dehydrogenase, ≥ 10 bone metastases, pain, albumin, and alkaline phosphatase. The tAUC was 0.74 in the testing set. Median (95% confidence interval [CI]) OS for the low-, intermediate-, and high-risk groups (testing set) were not yet reached (NYR) (NYR–NYR), 34.2 months (31.5–NYR), and 21.1 months (17.5–25.0). The hazard ratios (95% CI) for OS in the low- and intermediate-risk groups vs the high-risk group were 0.20 (0.14–0.29) and 0.40 (0.30–0.53), respectively. Conclusions: Our validated prognostic model incorporates factors routinely collected in chemotherapy-naïve men with mCRPC treated with enzalutamide and identifies subsets of men with widely differing survival times.

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Diabetes mellitus indicate poor prognosis in uterine serous cancer patients

10.21203/rs.3.rs-261282/v1 ◽

2021 ◽

Author(s):

Yunyun Liu ◽

Jing Li ◽

Zhibo Cheng ◽

Guocai Xu ◽

Yongpai Peng ◽

...

Keyword(s):

Diabetes Mellitus ◽

Risk Factors ◽

Overall Survival ◽

Prognostic Factors ◽

Poor Prognosis ◽

Cox Regression ◽

Figo Stage ◽

Chi Square ◽

Institutional Review ◽

Independent Risk Factors

Abstract Purpose. We aimed to find prognostic factors for uterine serous cancer(USC) patients in a retrospective study.Methods. 51 USC patients between 2010-2020 were enrolled. All pathological specimens were reviewd. The research protocol was approved by Institutional Review Board and all patients were informed consent before the study began. Statistics were done using SPSS 25.0, T test and chi-square analyses were used to compare differences, the overall survival(OS) was estimated with Kaplan-Meier(KM) analysis, univariate and multivariate Cox regression analyses were utilized to find prognostic factors.Results. The median overall survival(OS) and progressive free survival(PFS) were 75.94 and 63.49 months, respectively. Diagnosed with diabetes mellitus(P=0.006, HR=6.792, 95%CI=1.726-26.722) and CA125＞28U/ml(P=0.006, HR=7.136, 95%CI=1.780-28.607) before surgery were independent risk factors for OS, advanced FIGO stage(P=0.001, HR=10.628, 95%CI=2.894-39.026) and DM(P=0.003, HR=6.327, 95%CI=1.875-21.354) were independent factors for PFS. Age≤52, , tumor size≥2.5cm and cervical mucosal infiltration may indicate poor prognosis but were not independent risk factors. Hypertension patients with routine medical treatment tend to survive longer, but there was no statistical differences in OS and PFS compared to patients with normal blood pressure.Conclusion. In addition to surgical and adjuvant treatments, gynecologists should focus more on the comorbid conditions of USC patiens, especially for DM.

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Nomograms to predict lung metastasis probability and lung metastasis subgroup survival in malignant bone tumors

Future Oncology ◽

10.2217/fon-2020-0553 ◽

2021 ◽

Vol 17 (6) ◽

pp. 649-661

Author(s):

Jie Wang ◽

Yonggang Fan ◽

Lei Xia

Keyword(s):

Risk Factors ◽

Overall Survival ◽

Prognostic Factors ◽

High Risk ◽

Lung Metastasis ◽

Bone Tumors ◽

Malignant Bone Tumors ◽

Tumor Extension ◽

N Stage ◽

Selection Operator

The aim of this study was to construct and validate nomograms for predicting lung metastasis and lung metastasis subgroup overall survival in malignant primary osseous neoplasms. Least absolute shrinkage and selection operator, logistic and Cox analyses were used to identify risk factors for lung metastasis in malignant primary osseous neoplasms and prognostic factors for overall survival in the lung metastasis subgroup. Further, nomograms were established and validated. A total of 3184 patients were collected. Variables including age, histology type, American Joint Committee on Cancer T and N stage, other site metastasis, tumor extension and surgery were extracted for the nomograms. The authors found that nomograms could provide an effective approach for clinicians to identify patients with a high risk of lung metastasis in malignant primary osseous neoplasms and perform a personalized overall survival evaluation for the lung metastasis subgroup.

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Localized Extremity Soft Tissue Sarcoma: Improved Knowledge With Unchanged Survival Over Time

Journal of Clinical Oncology ◽

10.1200/jco.2003.02.026 ◽

2003 ◽

Vol 21 (14) ◽

pp. 2719-2725 ◽

Cited By ~ 213

Author(s):

Jürgen Weitz ◽

Christina R. Antonescu ◽

Murray F. Brennan

Keyword(s):

Risk Factors ◽

Prognostic Factors ◽

Soft Tissue ◽

Soft Tissue Sarcoma ◽

Current Therapy ◽

Disease Specific Survival ◽

Actuarial Survival ◽

Extremity Soft Tissue Sarcoma ◽

Risk Patients ◽

Disease Specific

Purpose: The objective of this study was to define whether survival of patients with extremity soft tissue sarcoma (STS), stratified for known risk factors, has improved over the last 20 years. Patients and Methods: From January 1982 to December 2001, 1,706 patients with primary and recurrent STS of the extremities were treated at our institution and were prospectively followed. From this cohort, we selected 1,261 patients who underwent complete macroscopic resection and had one of the following histopathologies: fibrosarcoma, liposarcoma, leiomyosarcoma, malignant fibrous histiocytoma, or synovial sarcoma. Median follow-up was 55 months. Patient, tumor, and treatment factors were analyzed as prognostic factors. Results: The 5-year disease-specific actuarial survival was 79% (78% for patients treated from 1982 to 1986, 79% for patients treated from 1986 to 1991, 79% for patients treated from 1992 to 1996, and 85% for patients treated from 1997 to 2001; P = not significant). For high-risk patients (high-grade, > 10 cm, deep tumors; n = 247), 5-year disease-specific survival was 51% (50% for patients treated from 1982 to 1986, 45% for patients treated from 1986 to 1991, 52% for patients treated from 1992 to 1996, and 61% for patients treated from 1997 to 2001; P = not significant). Tumor depth, size, grade, microscopic margin status, patient age, presentation status (primary tumor versus local recurrence), location (proximal versus distal), and certain histopathologic subtypes were significant prognostic factors for disease-specific survival on multivariate analysis; however, time period of treatment was not. Conclusion: Prognosis of patients with extremity STS, stratified for known risk factors, has not improved over the last 20 years, indicating that current therapy has reached the limits of efficacy.

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A Prediction Rule for Overall Survival in Non-Small-Cell Lung Cancer Patients with a Pathological Tumor Size Less Than 30 mm

Disease Markers ◽

10.1155/2019/8435893 ◽

2019 ◽

Vol 2019 ◽

pp. 1-9 ◽

Cited By ~ 1

Author(s):

Wang-Yu Zhu ◽

Ke-xin Fang ◽

Jian-ying He ◽

Ri Cui ◽

Yong-Kui Zhang ◽

...

Keyword(s):

Lung Cancer ◽

Overall Survival ◽

Prognostic Factors ◽

Small Cell Lung Cancer ◽

Cell Lung Cancer ◽

Tumor Size ◽

Small Cell ◽

Small Cell Lung ◽

Training Set ◽

Nsclc Patients

We sought to develop and validate a clinical nomogram model for predicting overall survival (OS) in non-small-cell lung cancer (NSCLC) patients with resected tumors that were 30 mm or smaller, using clinical data and molecular marker findings. We retrospectively analyzed 786 NSCLC patients with a pathological tumor size less than 30 mm who underwent surgery between 2007 and 2017 at our institution. We identified and integrated significant prognostic factors to build the nomogram model using the training set, which was subjected to the internal data validation. The prognostic performance was calibrated and evaluated by the concordance index (C-index) and risk group stratification. Multivariable analysis identified the pathological tumor size, lymph node metastasis, and Ki-67 expression as independent prognostic factors, which were entered into the nomogram model. The nomogram-predicted probabilities of OS at 1 year, 3 years, and 5 years posttreatment represented optimal concordance with the actual observations. Harrell’s C-index of the constructed nomogram with the training set was 0.856 (95% CI: 0.804-0.908), whereas TNM staging was 0.814 (95% CI: 0.742-0.886, P=5.280221e−13). Survival analysis demonstrated that NSCLC subgroups showed significant differences in the training and validation sets (P<0.001). A nomogram model was established for predicting survival in NSCLC patients with a pathological tumor size less than 30 mm, which would be further validated using demographic and clinicopathological data. In the future, this prognostic model may assist clinicians during treatment planning and clinical studies.

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Score for the Overall Survival Probability Scores of Fibrosarcoma Patients after Surgery: A Novel Nomogram-Based Risk Assessment System

Journal of Oncology ◽

10.1155/2021/4533175 ◽

2021 ◽

Vol 2021 ◽

pp. 1-9

Author(s):

Yuyuan Chen ◽

Changxing Chi ◽

Dedian Chen ◽

Sanjun Chen ◽

Binbin Yang ◽

...

Keyword(s):

Risk Factors ◽

Overall Survival ◽

Receiver Operating Characteristic ◽

Operating Characteristic ◽

Curve Analysis ◽

Training Set ◽

Characteristic Curves ◽

Decision Curve Analysis ◽

Receiver Operating Characteristic Curves ◽

Independent Risk Factors

Background. The primary purpose of this study was to determine the risk factors affecting overall survival (OS) in patients with fibrosarcoma after surgery and to develop a prognostic nomogram in these patients. Methods. Data were collected from the Surveillance, Epidemiology, and End Results database on 439 postoperative patients with fibrosarcoma who underwent surgical resection from 2004 to 2015. Independent risk factors were identified by performing Cox regression analysis on the training set, and based on this, a prognostic nomogram was created. The accuracy of the prognostic model in terms of survival was demonstrated by the area under the curve (AUC) of the receiver operating characteristic curves. In addition, the prediction consistency and clinical value of the nomogram were validated by calibration curves and decision curve analysis. Results. All included patients were divided into a training set (n = 308) and a validation set (n = 131). Based on univariate and multivariate analyses, we determined that age, race, grade, and historic stage were independent risk factors for overall survival after surgery in patients with fibrosarcoma. The AUC of the receiver operating characteristic curves demonstrated the high predictive accuracy of the prognostic nomogram, while the decision curve analysis revealed the high clinical application of the model. The calibration curves showed good agreement between predicted and observed survival rates. Conclusion. We developed a new nomogram to estimate 1-year, 3-year, and 5-year OS based on the independent risk factors. The model has good discriminatory performance and calibration ability for predicting the prognosis of patients with fibrosarcoma after surgery.

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