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  • 1
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    Surgery for primary tumor benefits survival for breast cancer patients with bone metastases: a large cohort retrospective study
    Springer Science and Business Media LLC ; 2021
    In:  BMC Cancer Vol. 21, No. 1 ( 2021-12)
    Details
    In: BMC Cancer, Springer Science and Business Media LLC, Vol. 21, No. 1 ( 2021-12)
    Abstract: The role of surgery for the primary tumor in breast cancer patients with bone metastases (BM) remains unclear. The purpose of this study was to determine the impact of surgery for the primary tumor in breast cancer patients with BM and to develop prognostic nomograms to predict the overall survival (OS) of breast cancer patients with BM. Methods A total of 3956 breast cancer patients with BM from the Surveillance, Epidemiology, and End Results database between 2010 and 2016 were included. Propensity score matching (PSM) was used to eliminate the bias between the surgery and non-surgery groups. The Kaplan-Meier analysis and the log-rank test were performed to compare the OS between two groups. Cox proportional risk regression models were used to identify independent prognostic factors. Two nomograms were constructed for predicting the OS of patients in the surgery and non-surgery groups, respectively. In addition, calibration curve, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA) were used to evaluate the performance of nomograms. Result The survival analysis showed that the surgery of the primary tumor significantly improved the OS for breast cancer patients with BM. Based on independent prognostic factors, separate nomograms were constructed for the surgery and non-surgery groups. The calibration and ROC curves of these nomograms indicated that both two models have high predictive accuracy, with the area under the curve values ≥0.700 on both the training and validation cohorts. Moreover, DCA showed that nomograms have strong clinical utility. Based on the results of the X-tile analysis, all patients were classified in the low-risk-of-death subgroup had a better prognosis. Conclusion The surgery of the primary tumor may provide survival benefits for breast cancer patients with BM. Furthermore, these prognostic nomograms we constructed may be used as a tool to accurately assess the long-term prognosis of patients and help clinicians to develop individualized treatment strategies.
    Type of Medium: Online Resource
    ISSN: 1471-2407
    URL: Article
    DOI: 10.1186/s12885-021-07964-9
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2021
    detail.hit.zdb_id: 2041352-X
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  • 2
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    A Novel Prognostic Nomogram and Risk Classification System for Predicting Cancer-Specific Survival of Postoperative Fibrosarcoma Patients: A Large Cohort Retrospective Study
    Hindawi Limited ; 2022
    In:  Journal of Oncology Vol. 2022 ( 2022-8-27), p. 1-11
    Details
    In: Journal of Oncology, Hindawi Limited, Vol. 2022 ( 2022-8-27), p. 1-11
    Abstract: Background. Fibrosarcoma (FS) is a typically invasive sarcoma formed by fibroblasts and collagen fibers. Currently, the standard treatment for FS is the surgical resection, but the high recurrence rate and poor prognosis limit the benefits of postoperative patients. Exploring what factors affect the benefit of postoperative patients is significant for guiding the implementation of surgical resection. Therefore, this study aims to construct a novel nomogram to predict the cancer-specific survival (CSS) of postoperative fibrosarcoma (POFS) patients. Methods. The included patients were randomly assigned to the training and validation sets at a ratio of 7 : 3. CSS was indexed as the research endpoint. Firstly, univariate and multivariate Cox regression analyses were used on the training set to determine independent prognostic predictors and build a nomogram for predicting the 1-, 3-, and 5-year CSS of POFS patients. Secondly, the nomogram's discriminative power and prediction accuracy were evaluated by receiver operating characteristic (ROC) and the calibration curve, and a risk classification system for POFS patients was constructed. Finally, the nomogram's clinical utility was evaluated using decision curve analysis (DCA). Results. Our study included 346 POFS patients, divided into the training (244) and validation sets (102). Multivariate Cox regression analysis demonstrated that tumor size, SEER stage, and tumor grade were independent prognostic predictors of CSS for POFS patients. They were used to create a nomogram. In the training and validation sets, the ROC curve showed that the 1-, 3-, and 5-year area under the curve (AUC) were higher than 0.700, indicating that the nomogram had good reliability and accuracy. DCA also showed that the nomogram has high application value in clinical practice. Conclusion. The larger tumor size, higher tumor grade, and distant metastasis were independently related to the poor prognosis of POFS patients. The nomogram constructed based on the above variables could accurately predict the 1-, 3-, and 5-year CSS of POFS patients. So, the nomogram and risk classification system we built might help make accurate judgments in clinical practice, optimize patient treatment decisions, maximize postoperative benefits, and ultimately improve the prognosis of POFS patients.
    Type of Medium: Online Resource
    ISSN: 1687-8469 , 1687-8450
    URL: Article
    DOI: 10.1155/2022/7831001
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2022
    detail.hit.zdb_id: 2461349-6
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  • 3
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    Clinical Features, Risk Factors, and Prediction Nomogram for Primary Spinal Osteosarcoma: A Large-Cohort Retrospective Study
    SAGE Publications ; 2024
    In:  Global Spine Journal Vol. 14, No. 3 ( 2024-04), p. 930-940
    Details
    In: Global Spine Journal, SAGE Publications, Vol. 14, No. 3 ( 2024-04), p. 930-940
    Abstract: Retrospective cohort study Objectives The goal of this study was to determine the clinical characteristics of patients with primary spinal osteosarcoma and to construct a practical clinical prediction model for patients to achieve an accurate prediction of overall survival. Methods This study included 230 patients diagnosed between 2004-2015 from the Surveillance, Epidemiology, and End Results database. Independent risk factors were screened in the training set using Cox regression algorithms, and a prognostic model was developed. Internal and external validation sets were used to test the nomogram model's calibration, discrimination, and clinical utility. A risk classification system based on the nomogram was developed and validated. Results Four independent prognostic factors were identified, and based on this a nomogram model was developed for predicting patient prognosis. The C-index of the training set was .737, while that of the validation set was .693. The time-varying area under the curve values was greater than .720 in both cohorts. The calibration curves proved that the prediction model has high prediction accuracy. The decision curve analysis showed that the nomogram is clinically useful. A risk classification system was established, which allows all patients to be divided into two different risk groups. Conclusions A nomogram and risk classification system was developed for patients with primary spinal osteosarcoma to accurately predict overall patient survival and achieve risk stratification of patient mortality. These tools are expected to play an important role in clinical practice, informing clinicians in making decisions.
    Type of Medium: Online Resource
    ISSN: 2192-5682 , 2192-5690
    URL: Article
    DOI: 10.1177/21925682221129219
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2024
    detail.hit.zdb_id: 2648287-3
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  • 4
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    A Novel Clinical Tool to Predict Cancer-specific Survival in Postoperative Patients With Primary Spinal and Pelvic Sarcomas: A Large Population-Based Retrospective Cohort Study
    SAGE Publications ; 2024
    In:  Global Spine Journal Vol. 14, No. 3 ( 2024-04), p. 776-788
    Details
    In: Global Spine Journal, SAGE Publications, Vol. 14, No. 3 ( 2024-04), p. 776-788
    Abstract: Retrospective cohort study. Objective Primary osseous sarcomas originating from the spine and pelvis are rare and usually portend inferior prognoses. Currently, the standard treatment for spinal and pelvic sarcomas is surgical resection, but the poor prognosis limits the benefits to postoperative patients. This study aims to identify the independent prognostic factors of cancer-specific survival (CSS) in postoperative patients with primary spinal and pelvic sarcomas and construct a nomogram for predicting these patients’ 3-, 5-, and 10-year CSS probability. Methods A total of 452 patients were enrolled from the Surveillance, Epidemiology, and End Results (SEER) database. They were divided into a training cohort and a validation cohort. Univariate and multivariate Cox regression analyses were used to identify these patients’ CSS-related independent prognostic factors. Then, those factors were used to construct a prognostic nomogram for predicting the 3-, 5-, and 10-year CSS probability, whose predictive performance and clinical value were verified by the calibration curve, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA). Finally, a mortality risk stratification system was constructed. Results Sex, histological type, tumor stage, and tumor grade were identified as CSS-related independent prognostic factors. A nomogram with high predictive performance and good clinical value to predict the 3-, 5-, and 10-year CSS probability was constructed, on which a mortality risk stratification system was constructed based to divide these patients into 3 mortality risk subgroups effectively. Conclusions This study constructed and validated a clinical nomogram to predict CSS in postoperative patients with primary spinal and pelvic sarcomas. It could assist clinicians in classifying these patients into different mortality risk subgroups and realize sarcoma-specific management.
    Type of Medium: Online Resource
    ISSN: 2192-5682 , 2192-5690
    URL: Article
    DOI: 10.1177/21925682221121269
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2024
    detail.hit.zdb_id: 2648287-3
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  • 5
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    Bone Metastasis in Renal Cell Carcinoma Patients: Risk and Prognostic Factors and Nomograms
    Hindawi Limited ; 2021
    In:  Journal of Oncology Vol. 2021 ( 2021-5-12), p. 1-17
    Details
    In: Journal of Oncology, Hindawi Limited, Vol. 2021 ( 2021-5-12), p. 1-17
    Abstract: Background. Bone metastasis (BM) is one of the common sites of renal cell carcinoma (RCC), and patients with BM have a poorer prognosis. We aimed to develop two nomograms to quantify the risk of BM and predict the prognosis of RCC patients with BM. Methods. We reviewed patients with diagnosed RCC with BM in the Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2015. Multivariate logistic regression analysis was used to determine independent factors to predict BM in RCC patients. Univariate and multivariate Cox proportional hazards regression analyses were used to determine independent prognostic factors for BM in RCC patients. Two nomograms were established and evaluated by calibration curve, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA). Results. The study included 37,554 patients diagnosed with RCC in the SEER database, 537 of whom were BM patients. BM’s risk factors included sex, tumor size, liver metastasis, lung metastasis, brain metastasis, N stage, T stage, histologic type, and grade in RCC patients. Currently, independent prognostic factors for RCC with BM included grade, histologic type, N stage, surgery, brain metastasis, and lung metastasis. The calibration curve, ROC curve, and DCA showed good performance for diagnostic and prognostic nomograms. Conclusions. Nomograms were established to predict the risk of BM in RCC and the prognosis of RCC with BM, separately. These nomograms strengthen each patient’s prognosis-based decision making, which is critical in improving the prognosis of patients.
    Type of Medium: Online Resource
    ISSN: 1687-8469 , 1687-8450
    URL: Article
    DOI: 10.1155/2021/5575295
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2021
    detail.hit.zdb_id: 2461349-6
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  • 6
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    Sites of Synchronous Distant Metastases, Prognosis, and Nomogram for Small Cell Lung Cancer Patients with Bone Metastasis: A Large Cohort Retrospective Study
    Hindawi Limited ; 2021
    In:  Journal of Oncology Vol. 2021 ( 2021-7-28), p. 1-16
    Details
    In: Journal of Oncology, Hindawi Limited, Vol. 2021 ( 2021-7-28), p. 1-16
    Abstract: Background. Small cell lung cancer (SCLC) is often associated with metastases at the time of diagnosis, and the bone is one of the most common sites. The primary aim of this study was to investigate the site of synchronous distant metastasis to other organs in SCLC patients with bone metastasis (BM) and develop a robust predictive prognostic model. Methods. We retrospectively analyzed the data from patients diagnosed with SCLC with BM in the Surveillance, Epidemiology, and End Results database. Univariate and multivariate Cox analyses were used to identify independent prognostic factors. A prognostic nomogram was constructed and evaluated by calibration curves, receiver operating characteristic (ROC) curves, and decision curve analysis (DCA). Then, according to the sites of metastasis and treatment modality, all patients were stratified into several subgroups. The relationship among sites of metastasis, treatment modality, and overall survival was then analyzed. Results. A total of 6253 patients were included. Independent prognostic factors for SCLC with BM were age, sex, primary site, radiotherapy, chemotherapy, brain metastasis, liver metastasis, and marital status. Calibration, ROC curves, and DCA indicated the excellent performance of the prognostic nomogram. The liver is the most common organ for extraskeletal metastases, followed by the lung. Patients with only BM had the longest mean survival time (9.30 ± 0.31 months). In the subgroup analysis, chemotherapy was an independent prognostic factor for all subgroups. In contrast, radiotherapy showed a positive effect on the prognosis of patients in all subgroups except those with bone and brain metastases and those with bone, lung, and brain metastases. Conclusions. The prognostic nomogram is expected to be an accurate and personalized tool for predicting the prognosis of SCLC patients with BM. Additionally, the determination of the sites of synchronous extraskeletal metastases and the associated prognosis helps in treatment selection.
    Type of Medium: Online Resource
    ISSN: 1687-8469 , 1687-8450
    URL: Article
    DOI: 10.1155/2021/9949714
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2021
    detail.hit.zdb_id: 2461349-6
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  • 7
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    Comprehensive analysis of a 14 immune-related gene pair signature to predict the prognosis and immune features of gastric cancer
    Elsevier BV ; 2020
    In:  International Immunopharmacology Vol. 89 ( 2020-12), p. 107074-
    Details
    In: International Immunopharmacology, Elsevier BV, Vol. 89 ( 2020-12), p. 107074-
    Type of Medium: Online Resource
    ISSN: 1567-5769
    URL: Article
    DOI: 10.1016/j.intimp.2020.107074
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2020
    detail.hit.zdb_id: 2049924-3
    SSG: 15,3
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  • 8
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    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
    SAGE Publications ; 2022
    In:  Technology in Cancer Research & Treatment Vol. 21 ( 2022-01), p. 153303382110662-
    Details
    In: Technology in Cancer Research & Treatment, SAGE Publications, Vol. 21 ( 2022-01), p. 153303382110662-
    Abstract: 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.
    Type of Medium: Online Resource
    ISSN: 1533-0346 , 1533-0338
    URL: Article
    DOI: 10.1177/15330338211066240
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2022
    detail.hit.zdb_id: 2146365-7
    detail.hit.zdb_id: 2220436-2
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  • 9
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    An Artificial Intelligence Model for Predicting 1-Year Survival of Bone Metastases in Non-Small-Cell Lung Cancer Patients Based on XGBoost Algorithm
    Hindawi Limited ; 2020
    In:  BioMed Research International Vol. 2020 ( 2020-06-28), p. 1-13
    Details
    In: BioMed Research International, Hindawi Limited, Vol. 2020 ( 2020-06-28), p. 1-13
    Abstract: Non-small-cell lung cancer (NSCLC) patients often develop bone metastases (BM), and the overall survival for these patients is usually perishing. However, a model with high accuracy for predicting the survival of NSCLC with BM is still lacking. Here, we aimed to establish a model based on artificial intelligence for predicting the 1-year survival rate of NSCLC with BM by using extreme gradient boosting (XGBoost), a large-scale machine learning algorithm. We selected NSCLC patients with BM between 2010 and 2015 from the Surveillance, Epidemiology, and End Results database. In total, 5973 cases were enrolled and divided into the training ( n = 4183 ) and validation ( n = 1790 ) sets. XGBoost, random forest, support vector machine, and logistic algorithms were used to generate predictive models. Receiver operating characteristic curves were used to evaluate and compare the predictive performance of each model. The parameters including tumor size, age, race, sex, primary site, histological subtype, grade, laterality, T stage, N stage, surgery, radiotherapy, chemotherapy, distant metastases to other sites (lung, brain, and liver), and marital status were selected to construct all predictive models. The XGBoost model had a better performance in both training and validation sets as compared with other models in terms of accuracy. Our data suggested that the XGBoost model is the most precise and personalized tool for predicting the 1-year survival rate for NSCLC patients with BM. This model can help the clinicians to design more rational and effective therapeutic strategies.
    Type of Medium: Online Resource
    ISSN: 2314-6133 , 2314-6141
    URL: Article
    DOI: 10.1155/2020/3462363
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2020
    detail.hit.zdb_id: 2698540-8
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  • 10
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    Individualized assessment of risk and overall survival in patients newly diagnosed with primary osseous spinal neoplasms with synchronous distant metastasis
    Frontiers Media SA ; 2022
    In:  Frontiers in Public Health Vol. 10 ( 2022-8-22)
    Details
    In: Frontiers in Public Health, Frontiers Media SA, Vol. 10 ( 2022-8-22)
    Abstract: The prognosis of patients with primary osseous spinal neoplasms (POSNs) presented with distant metastases (DMs) is still poor. This study aimed to evaluate the independent risk and prognostic factors in this population and then develop two web-based models to predict the probability of DM in patients with POSNs and the overall survival (OS) rate of patients with DM. Methods The data of patients with POSNs diagnosed between 2004 and 2017 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate logistics regression analyses were used to study the risk factors of DM. Based on independent DM-related variables, we developed a diagnostic nomogram to estimate the risk of DM in patients with POSNs. Among all patients with POSNs, those who had synchronous DM were included in the prognostic cohort for investigating the prognostic factors by using Cox regression analysis, and then a nomogram incorporating predictors was developed to predict the OS of patients with POSNs with DM. Kaplan–Meier (K-M) survival analysis was conducted to study the survival difference. In addition, validation of these nomograms were performed by using receiver operating characteristic (ROC) curves, the area under curves (AUCs), calibration curves, and decision curve analysis (DCA). Results A total of 1345 patients with POSNs were included in the study, of which 238 cases (17.70%) had synchronous DM at the initial diagnosis. K-M survival analysis and multivariate Cox regression analysis showed that patients with DM had poorer prognosis. Grade, T stage, N stage, and histological type were found to be significantly associated with DM in patients with POSNs. Age, surgery, and histological type were identified as independent prognostic factors of patients with POSNs with DM. Subsequently, two nomograms and their online versions ( https://yxyx.shinyapps.io/RiskofDMin/ and https://yxyx.shinyapps.io/SurvivalPOSNs/ ) were developed. The results of ROC curves, calibration curves, DCA, and K-M survival analysis together showed the excellent predictive accuracy and clinical utility of these newly proposed nomograms. Conclusion We developed two well-validated nomograms to accurately quantify the probability of DM in patients with POSNs and predict the OS rate in patients with DM, which were expected to be useful tools to facilitate individualized clinical management of these patients.
    Type of Medium: Online Resource
    ISSN: 2296-2565
    URL: Article
    DOI: 10.3389/fpubh.2022.955427
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2022
    detail.hit.zdb_id: 2711781-9
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