Nomograms Predict Cancer‐Specific and Overall Survival of Patients With Primary Limb Leiomyosarcoma

Wang, Wei; Hong, Jianqiao; Meng, Jinghui; Wu, Haobo; Shi, Mude; Yan, Shigui; Huang, Yi‐Ting

doi:10.1002/jor.24298

Cited by 10 publications

(14 citation statements)

References 23 publications

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“…Most of the CP values ranged from 0.58 to 0.84 [ 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 ]. There are also several nomogram models for soft tissue sarcoma but none was specific to uLMS [ 49 , 50 , 51 , 52 , 53 , 54 ].…”

Section: Discussionmentioning

confidence: 99%

Development of a Multi-Institutional Prediction Model for Three-Year Survival Status in Patients with Uterine Leiomyosarcoma (AGOG11-022/QCGC1302 Study)

Tse

Wong

Chao

et al. 2021

Cancers

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Background: The existing staging systems of uterine leiomyosarcoma (uLMS) cannot classify the patients into four non-overlapping prognostic groups. This study aimed to develop a prediction model to predict the three-year survival status of uLMS. Methods: In total, 201 patients with uLMS who had been treated between June 1993 and January 2014, were analyzed. Potential prognostic indicators were identified by univariate models followed by multivariate analyses. Prediction models were constructed by binomial regression with 3-year survival status as a binary outcome, and the final model was validated by internal cross-validation. Results: Nine potential parameters, including age, log tumor diameter, log mitotic count, cervical involvement, parametrial involvement, lymph node metastasis, distant metastasis, tumor circumscription and lymphovascular space invasion were identified. 110 patients had complete data to build the prediction models. Age, log tumor diameter, log mitotic count, distant metastasis, and circumscription were significantly correlated with the 3-year survival status. The final model with the lowest Akaike’s Information Criterion (117.56) was chosen and the cross validation estimated prediction accuracy was 0.745. Conclusion: We developed a prediction model for uLMS based on five readily available clinicopathologic parameters. This might provide a personalized prediction of the 3-year survival status and guide the use of adjuvant therapy, a cancer surveillance program, and future studies.

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Section: Discussionmentioning

confidence: 99%

Development of a Multi-Institutional Prediction Model for Three-Year Survival Status in Patients with Uterine Leiomyosarcoma (AGOG11-022/QCGC1302 Study)

Tse

Wong

Chao

et al. 2021

Cancers

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“…developed the first nomogram predicting SSS for patients with STS ( 32 ). This model was subsequently externally validated ( 33 ) and followed by many studies developing STS-specific models predicting SSS ( 12 , 14 – 16 , 19 , 22 ) ( Table 4 ). Whereas the model by Kattan et al.…”

Section: Discussionmentioning

confidence: 99%

“…Recently, several nomograms have been developed for predicting long-term outcomes (relapse, overall, and sarcomaspecific survival) in patients with STS (11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23). Many of them focused on specific sites (i.e., primary STS of the trunk and extremity) (14,18,19) and/or on specific histological subtypes (14-16, 19, 20).…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Nomograms predicting local and distant recurrence and disease-specific mortality for R0/R1 soft tissue sarcomas of the extremities

2022

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BackgroundPrognostic models for patients with soft tissue sarcoma (STS) of the extremities have been developed from large multi-institutional datasets with mixed results. We aimed to develop predictive nomograms for sarcoma-specific survival (SSS) and, for the first time, long-term local recurrence (LR) and distant recurrence (DR) in patients with STS of the extremities treated at our institution.Patients and methodsData from patients treated at Humanitas Cancer Center from 1997 to 2015 were analyzed. Variable selection was based on the clinical knowledge and multivariable regression splines algorithm. Perioperative treatments were always included in the model. Prognostic models were developed using Cox proportional hazards model, and model estimates were plotted in nomograms predicting SSS at 5 and 10 years and LR and DR at 2, 5, and 10 years. Model performance was estimated internally via bootstrapping, in terms of optimism-corrected discrimination (Harrell C-index) and calibration (calibration plots).ResultsData on 517 patients were analyzed. At 5 and 10 years, SSS was 68.1% [95% confidence interval (CI), 63.8–72.1] and 55.6% (50.5–60.3), respectively. LR was 79.1% (95% CI, 75.3–82.4), 71.1% (95% CI, 66.7–75.1), and 66.0% (95% CI, 60.7–70.7) at 2, 5, and 10 years, respectively, whereas DR was 65.9% (95% CI, 61.6–69.9), 57.5% (95% CI, 53.0–61.8), and 52.1% (95% CI, 47.1–56.8) at 2, 5, and 10 years, respectively. SSS nomogram included age, gender, margins, tumor size, grading, and histotype. LR and DR nomograms incorporated mostly the same variables, except for age for DR; LR nomogram did not include gender but included anatomic site. The optimism-corrected C-indexes were 0.73 and 0.72 for SSS at 5 and 10 years, respectively; 0.65, 0.64, and 0.64 for LR at 2, 5, and 10 years, respectively; and 0.68 for DR at 2, 5, and 10 years. Predicted probabilities were close to the observed ones for all outcomes.ConclusionsWe developed and validated three nomograms for STS of the extremities predicting the probability of SSS at 5 and 10 years and LR and DR at 2, 5, and 10 years. By accounting for the perioperative treatment, these models allow prediction for future patients who had no perioperative treatment, thus being useful in the clinical decision-making process.

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“…The SEER database includes data on approximately 34.6% of the American population and provides large-scale data support for rare disease studies. All SEER research data are publicly available and de-identified, which exempted this study from the need for informed consent and ethical approval ( 10 ).…”

Section: Methodsmentioning

confidence: 99%

Establishment and validation of a novel nomogram for survival prediction of ovarian carcinosarcoma

Liu¹,

Lin²,

Xue³

et al. 2022

Transl Cancer Res TCR

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Background: Ovarian carcinosarcoma (OCS) is a rare and aggressive histological type of ovarian cancer.Current prognostic methods for OCS are insufficient. This study was undertaken to establish and validate a novel nomogram for predicting the overall survival (OS) of OCS patients. Methods:We extracted 820 patients with OCS from the Surveillance, Epidemiology, and End Results (SEER) database and further randomly assigned them to a training set (n=574) and a validation set (n=246) at a ratio of 7-to-3. Univariate and multivariate regression analyses were utilized to verity independent prognostic factors, and a prognostic nomogram was constructed based on the results. The performance of the new model was compared with that of the AJCC staging system using the area under the receiver operating characteristic curve (AUC), net reclassification improvement (NRI), integrated discrimination improvement (IDI), calibration curve, and decision curve analysis (DCA).Results: Cox regression analysis suggested that age, grade, tumor size, AJCC stage, surgery, and chemotherapy were the independent prognostic factors. These factors were integrated into a prognostic nomogram to determine the 1-, 3-, and 5-year OS of OCS patients. The AUC, NRI, IDI, calibration curves, and DCA data demonstrated that our nomogram had better discriminative ability than the AJCC staging system alone. The calibration curves indicate that the nomogram was well-calibrated. The DCA verified the clinical applicability of the nomogram.Conclusions: Our current study is the first to establish and internally validate a novel nomogram for predicting the 1-, 3-, and 5-year OS probabilities of OCS patients. Our prognostic nomogram was of good performance and can be an accurate tool to predict individualized survival time of OCS in clinical work.

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Nomograms Predict Cancer‐Specific and Overall Survival of Patients With Primary Limb Leiomyosarcoma

Cited by 10 publications

References 23 publications

Development of a Multi-Institutional Prediction Model for Three-Year Survival Status in Patients with Uterine Leiomyosarcoma (AGOG11-022/QCGC1302 Study)

Development of a Multi-Institutional Prediction Model for Three-Year Survival Status in Patients with Uterine Leiomyosarcoma (AGOG11-022/QCGC1302 Study)

Nomograms predicting local and distant recurrence and disease-specific mortality for R0/R1 soft tissue sarcomas of the extremities

Establishment and validation of a novel nomogram for survival prediction of ovarian carcinosarcoma

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