Identifying the Best Candidate for Primary Tumor Resection in Patients With Advanced Osteosarcoma
Objectives Not all patients with stage III and IV osteosarcoma who undergo surgery to remove the primary tumor will benefit from surgery; therefore, we developed a nomogram model to test the hypothesis that only a subset of patients will benefit from surgery. Methods 412 patients were screened from...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2024-03-01
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| Series: | Cancer Control |
| Online Access: | https://doi.org/10.1177/10732748241242244 |
| _version_ | 1797213082282360832 |
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| author | Zhengjiang Li Xingyao Yang Shuxing Xing |
| author_facet | Zhengjiang Li Xingyao Yang Shuxing Xing |
| author_sort | Zhengjiang Li |
| collection | DOAJ |
| description | Objectives Not all patients with stage III and IV osteosarcoma who undergo surgery to remove the primary tumor will benefit from surgery; therefore, we developed a nomogram model to test the hypothesis that only a subset of patients will benefit from surgery. Methods 412 patients were screened from the Surveillance, Epidemiology and End Results (SEER) database. Subsequently, 1:1 propensity score matching (PSM) was used to screen and balance confounders. We first made the hypothesis that patients who underwent the procedure would benefit more. A multivariate Cox model was used to explore the independent influencing factors of CSS in two groups (benefit group and non-benefit group) and constructed nomograms with predicted prognosis. Finally, receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) were used to verify the performance of the nomogram. Results Of these patients, approximately 110 did not undergo primary tumour resection. After passing PSM, they were divided into a surgical group and a non-surgical group. Age, primary site and chemotherapy as calculated independent factors were used to construct a nomogra. The predicted nomogram showed good consistency in terms of the ROC curve and the calibration curve, and the DCA curve showed a certain clinical utility. Finally, dividing the surgical patients into surgical beneficiaries and surgical non-beneficiaries, a Kaplan–Meier analysis showed that the nomogram can identify patients with osteosarcoma who can benefit from surgery. Conclusion A practical predictive model was established to determine whether patients with stage III or IV osteosarcoma would benefit from surgery. |
| first_indexed | 2024-04-24T10:52:37Z |
| format | Article |
| id | doaj.art-e1dadd880a9642c4a46976b095b2c76f |
| institution | Directory Open Access Journal |
| issn | 1526-2359 |
| language | English |
| last_indexed | 2024-04-24T10:52:37Z |
| publishDate | 2024-03-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Cancer Control |
| spelling | doaj.art-e1dadd880a9642c4a46976b095b2c76f2024-04-12T10:03:19ZengSAGE PublishingCancer Control1526-23592024-03-013110.1177/10732748241242244Identifying the Best Candidate for Primary Tumor Resection in Patients With Advanced OsteosarcomaZhengjiang LiXingyao YangShuxing XingObjectives Not all patients with stage III and IV osteosarcoma who undergo surgery to remove the primary tumor will benefit from surgery; therefore, we developed a nomogram model to test the hypothesis that only a subset of patients will benefit from surgery. Methods 412 patients were screened from the Surveillance, Epidemiology and End Results (SEER) database. Subsequently, 1:1 propensity score matching (PSM) was used to screen and balance confounders. We first made the hypothesis that patients who underwent the procedure would benefit more. A multivariate Cox model was used to explore the independent influencing factors of CSS in two groups (benefit group and non-benefit group) and constructed nomograms with predicted prognosis. Finally, receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) were used to verify the performance of the nomogram. Results Of these patients, approximately 110 did not undergo primary tumour resection. After passing PSM, they were divided into a surgical group and a non-surgical group. Age, primary site and chemotherapy as calculated independent factors were used to construct a nomogra. The predicted nomogram showed good consistency in terms of the ROC curve and the calibration curve, and the DCA curve showed a certain clinical utility. Finally, dividing the surgical patients into surgical beneficiaries and surgical non-beneficiaries, a Kaplan–Meier analysis showed that the nomogram can identify patients with osteosarcoma who can benefit from surgery. Conclusion A practical predictive model was established to determine whether patients with stage III or IV osteosarcoma would benefit from surgery.https://doi.org/10.1177/10732748241242244 |
| spellingShingle | Zhengjiang Li Xingyao Yang Shuxing Xing Identifying the Best Candidate for Primary Tumor Resection in Patients With Advanced Osteosarcoma Cancer Control |
| title | Identifying the Best Candidate for Primary Tumor Resection in Patients With Advanced Osteosarcoma |
| title_full | Identifying the Best Candidate for Primary Tumor Resection in Patients With Advanced Osteosarcoma |
| title_fullStr | Identifying the Best Candidate for Primary Tumor Resection in Patients With Advanced Osteosarcoma |
| title_full_unstemmed | Identifying the Best Candidate for Primary Tumor Resection in Patients With Advanced Osteosarcoma |
| title_short | Identifying the Best Candidate for Primary Tumor Resection in Patients With Advanced Osteosarcoma |
| title_sort | identifying the best candidate for primary tumor resection in patients with advanced osteosarcoma |
| url | https://doi.org/10.1177/10732748241242244 |
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