Can a Nomogram Help to Predict the Overall and Cancer-specific Survival of Patients With Chondrosarcoma?

Song, Kunpeng; Shi, Xiao; Wang, Hongli; Zou, Fei; Lu, Feizhou; Ma, Xin; Xia, Xinlei; Jiang, Jianyuan

doi:10.1007/s11999.0000000000000152

Cited by 55 publications

(62 citation statements)

References 40 publications

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“…In our series, we discovered that patients ≥65 years had poorer OS and CSS. Prior to our series, many studies drew the same conclusion. The subgroup analysis implied that elder patients were prone to reject surgery (Table S4, P = 0.005), probably because they could not tolerate surgery well.…”

Section: Discussionsupporting

confidence: 92%

“…Our training cohort contained 721 (56.9%) males and 546 (43.1%) females, with a mean age of 50.50 (range, 4.00 to 91.00) years, similar to prior studies. In our series, we discovered that patients ≥65 years had poorer OS and CSS.…”

Section: Discussionmentioning

confidence: 82%

“…In our study, 20.3% of chondrosarcomas occurred in the trunk (pelvis, spine, and scapula), suggesting poor prognosis. Previously, primary sites were often divided into two groups: axial and appendicular groups. Chondrosarcomas in the pelvis and spine had a worse prognosis due to anatomic constraints hindering the efforts of complete tumor resection, which resulted in a high rate of tumor recurrence.…”

Section: Discussionmentioning

confidence: 99%

“…To improve the predictive capability of survival models, previous studies constructed several nomograms, but most nomograms were derived from small samples in different research centers, which might diminish the accuracy of the predictors. Moreover, it is critical to apply external validation to evaluate generalizability and avoid overfitting, but none of them tried to validate externally with multi‐institutional databases, which decreases the credibility of the predication models.…”

Section: Introductionmentioning

confidence: 99%

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Identifying the Prognosis Factors and Predicting the Survival Probability in Patients with Non‐Metastatic Chondrosarcoma from the SEER Database

Huang

Sun

Zheng

et al. 2019

Orthopaedic Surgery

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ObjectiveTo identify prognostic factors and establish nomograms for predicting overall survival (OS) and cause specific survival (CSS) of patients with non‐metastatic chondrosarcoma.MethodsWe collected information on patients with non‐metastatic chondrosarcoma from the Surveillance, Epidemiology, and End Results (SEER) database between 2005 and 2014, together with data from the First Affiliated Hospital of Zhengzhou University from 2011 to 2016. Variables including patients’ baseline demographics (age, race, and gender), tumor characteristics (tumor size and extension, histology subtype, primary site, and American Joint Committee on Cancer [AJCC] stage), therapy (surgery, chemotherapy, and radiotherapy), and socioeconomic status (SES) were extracted for further analysis. OS and CSS were retrieved as our researching endpoints. Patients from the database were regarded as the training set, and univariate analysis, Lasso regression and multivariate analysis as well as the random forest were used to explore the predictors and establish nomograms. To validate nomograms internally and externally, we applied bootstrapped validation internally with the training dataset, while the dataset for external validation was obtained from the First Affiliated Hospital of Zhengzhou University. We estimated the discriminative ability of nomograms based on Cox proportional hazard regression models by means of calibration curves and the concordance index (C‐index) of internal and external validation.ResultsAfter the implementation of exclusion criteria, there were 1267 patients in the training set and 72 patients in the testing set with non‐metastatic chondrosarcomas. Age, gender, grade, histological subtype, primary site, surgery, radiation, chemotherapy, being employed/unemployed, tumor size, and tumor extension were significantly associated with prognosis in the univariate analysis. Age, gender, tumor size and extension, primary site, surgery, radiotherapy, chemotherapy, histological grade, and subtype were independent prognostic factors in the Cox models. The C‐index of nomograms (internal: OS, 0.787; CSS, 0.821; external: OS, 0.777; CSS, 0.821) were higher than following conventional systems: AJCC sixth (OS, 0.640; CSS, 0.673) and seventh edition (OS, 0.675; CSS, 0.711).ConclusionsAge, gender, tumor size and extension, surgery, histological grade, and subtype were independent prognostic factors for both OS and CSS. In addition, we revealed that chondrosarcomas in the trunk, radiotherapy, and chemotherapy were correlated with poor prognosis. Our nomograms based on significant clinicopathologic features can well predict the 3‐year and 5‐year survival probability of patients with non‐metastatic chondrosarcoma and assist oncologists in making accurate survival evaluation.

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Section: Discussionsupporting

confidence: 92%

Section: Discussionmentioning

confidence: 82%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Identifying the Prognosis Factors and Predicting the Survival Probability in Patients with Non‐Metastatic Chondrosarcoma from the SEER Database

Huang

Sun

Zheng

et al. 2019

Orthopaedic Surgery

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“…Thio et al recently developed different machine‐learning models for predicting the 5‐year survival rates of CS patients; however, further validation is needed . Song et al built a nomogram to predict the 3‐year and 5‐year overall and cancer‐specific survival rates of CS patients based on the Surveillance, Epidemiology, and End Results (SEER) database . However, their nomogram was solely based on clinicopathologic features without considering imaging features.…”

mentioning

confidence: 99%

Can clinical radiomics nomogram based on 3D multiparametric MRI features and clinical characteristics estimate early recurrence of pelvic chondrosarcoma?

Yin

Mao

Liu

et al. 2019

Magnetic Resonance Imaging

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Background Chondrosarcoma (CS) is the second most common primary malignant bone tumor, with a relatively high recurrence rate. However, an effective method that estimates whether pelvic CS will recur after surgery, which influences the formulation of a clinical treatment plan, remains lacking. Purpose To develop and validate a clinical radiomics nomograms based on 3D multiparametric magnetic resonance imaging (mpMRI) features and clinical characteristics that could estimate early recurrence (ER) (≤1 year) of pelvic CS. Study Type Retrospective. Population In all, 103 patients (ER = 41, non‐ER = 62) with histologically proven CS were retrospectively analyzed and divided into a training set (n = 72) and a validation set (n = 31). Field Strength/Sequence 3.0T axial T1‐weighted (T1‐w), T2‐weighted (T2‐w), diffusion weighted imaging (DWI), contrast‐enhanced T1‐weighted (CET1‐w). Assessment Risk factors (sex, age, type, grade, resection margins, etc.) associated with ER were evaluated. Five individual models based on T1‐w, T2‐w, DWI, CET1‐w, and clinical data were built. Then we compared the performance of models based on T1‐w, T2‐w, CET1‐w and their combination. Lastly, two nomograms based on the best model + clinical data and DWI + clinical data were built. Statistical Tests The area under the receiver operating characteristic curve (AUC) and accuracy (ACC) were used to evaluate different models. Results Grade was the most important univariate clinical predictor of ER of pelvic CS patients (odds ratio [OR]1 = 4.616, OR2 = 8.939, P < 0.05). T1‐w + T2‐w + CET1‐w had a significantly higher performance than CET1‐w in the training set (P = 0.01). Radiomics features are more important than clinical characteristics in clinical radiomics nomograms, especially for multisequence combined features (OR = 3.208, P < 0.01). Clinical radiomics nomogram based on combined features (T1‐w + T2‐w + CET1‐w) + clinical data achieved an AUC of 0.891 and ACC of 0.857, followed by DWI + clinical data (AUC = 0.882, ACC = 0.760) in the validation set. Data Conclusion The clinical radiomics nomogram had good performance in estimating ER of pelvic CS patients, which would be helpful in clinical decision‐making. Level of Evidence: 4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:435–445.

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Deep Learning and Prediction of Survival Period for Breast Cancer Patients

Doppalapudi

Yang

Jourquin

et al. 2021

Lecture Notes in Operations Research

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Can a Nomogram Help to Predict the Overall and Cancer-specific Survival of Patients With Chondrosarcoma?

Abstract: Level III, prognostic study.

Cited by 55 publications

References 40 publications

Identifying the Prognosis Factors and Predicting the Survival Probability in Patients with Non‐Metastatic Chondrosarcoma from the SEER Database

Identifying the Prognosis Factors and Predicting the Survival Probability in Patients with Non‐Metastatic Chondrosarcoma from the SEER Database

Can clinical radiomics nomogram based on 3D multiparametric MRI features and clinical characteristics estimate early recurrence of pelvic chondrosarcoma?

Deep Learning and Prediction of Survival Period for Breast Cancer Patients

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