Identifying Optimal Candidates for Trimodality Therapy among Nonmetastatic Muscle-Invasive Bladder Cancer Patients
(1) Background: This research aims to identify candidates for trimodality therapy (TMT) or radical cystectomy (RC) by using a predictive model. (2) Methods: Patients with nonmetastatic muscle-invasive bladder cancer (MIBC) in the Surveillance, Epidemiology, and End Results (SEER) database were enrol...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-11-01
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| Series: | Current Oncology |
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| Online Access: | https://www.mdpi.com/1718-7729/30/12/740 |
| _version_ | 1797381491777339392 |
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| author | Shengming Ran Jingtian Yang Jintao Hu Liekui Fang Wang He |
| author_facet | Shengming Ran Jingtian Yang Jintao Hu Liekui Fang Wang He |
| author_sort | Shengming Ran |
| collection | DOAJ |
| description | (1) Background: This research aims to identify candidates for trimodality therapy (TMT) or radical cystectomy (RC) by using a predictive model. (2) Methods: Patients with nonmetastatic muscle-invasive bladder cancer (MIBC) in the Surveillance, Epidemiology, and End Results (SEER) database were enrolled. The clinical data of 2174 eligible patients were extracted and separated into RC and TMT groups. To control for confounding bias, propensity score matching (PSM) was carried out. A nomogram was established via multivariable logistic regression. The area under the receiver operating characteristic curve (AUC) and calibration curves were used to assess the nomogram’s prediction capacity. Decision curve analysis (DCA) was carried out to determine the nomogram’s clinical applicability. (3) Results: After being processed with PSM, the OS of the RC group was significantly longer compared with the TMT group (<i>p</i> < 0.001). This remarkable capacity for discrimination was exhibited in the training (AUC: 0.717) and validation (AUC: 0.774) sets. The calibration curves suggested acceptable uniformity. Excellent clinical utility was shown in the DCA curve. The RC and RC-Beneficial group survived significantly longer than the RC and TMT-Beneficial group (<i>p</i> < 0.001) or the TMT group (<i>p</i> < 0.001). However, no significant difference was found between the RC and TMT-Beneficial group and the TMT group (<i>p</i> = 0.321). (4) Conclusions: A predictive model with excellent discrimination and clinical application value was established to identify the optimal patients for TMT among nonmetastatic MIBC patients. |
| first_indexed | 2024-03-08T20:52:19Z |
| format | Article |
| id | doaj.art-faf91f34985b483aba0631a8e82501c2 |
| institution | Directory Open Access Journal |
| issn | 1198-0052 1718-7729 |
| language | English |
| last_indexed | 2024-03-08T20:52:19Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Current Oncology |
| spelling | doaj.art-faf91f34985b483aba0631a8e82501c22023-12-22T14:02:20ZengMDPI AGCurrent Oncology1198-00521718-77292023-11-013012101661017810.3390/curroncol30120740Identifying Optimal Candidates for Trimodality Therapy among Nonmetastatic Muscle-Invasive Bladder Cancer PatientsShengming Ran0Jingtian Yang1Jintao Hu2Liekui Fang3Wang He4Department of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou 510289, ChinaDepartment of Urology, The Third People’s Hospital of Shenzhen, Southern University of Science and Technology, Shenzhen 518116, ChinaDepartment of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou 510289, ChinaDepartment of Urology, The Third People’s Hospital of Shenzhen, Southern University of Science and Technology, Shenzhen 518116, ChinaDepartment of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou 510289, China(1) Background: This research aims to identify candidates for trimodality therapy (TMT) or radical cystectomy (RC) by using a predictive model. (2) Methods: Patients with nonmetastatic muscle-invasive bladder cancer (MIBC) in the Surveillance, Epidemiology, and End Results (SEER) database were enrolled. The clinical data of 2174 eligible patients were extracted and separated into RC and TMT groups. To control for confounding bias, propensity score matching (PSM) was carried out. A nomogram was established via multivariable logistic regression. The area under the receiver operating characteristic curve (AUC) and calibration curves were used to assess the nomogram’s prediction capacity. Decision curve analysis (DCA) was carried out to determine the nomogram’s clinical applicability. (3) Results: After being processed with PSM, the OS of the RC group was significantly longer compared with the TMT group (<i>p</i> < 0.001). This remarkable capacity for discrimination was exhibited in the training (AUC: 0.717) and validation (AUC: 0.774) sets. The calibration curves suggested acceptable uniformity. Excellent clinical utility was shown in the DCA curve. The RC and RC-Beneficial group survived significantly longer than the RC and TMT-Beneficial group (<i>p</i> < 0.001) or the TMT group (<i>p</i> < 0.001). However, no significant difference was found between the RC and TMT-Beneficial group and the TMT group (<i>p</i> = 0.321). (4) Conclusions: A predictive model with excellent discrimination and clinical application value was established to identify the optimal patients for TMT among nonmetastatic MIBC patients.https://www.mdpi.com/1718-7729/30/12/740nonmetastatic muscle-invasive bladder cancertrimodality therapypredictive modelSEER databasenomogram |
| spellingShingle | Shengming Ran Jingtian Yang Jintao Hu Liekui Fang Wang He Identifying Optimal Candidates for Trimodality Therapy among Nonmetastatic Muscle-Invasive Bladder Cancer Patients Current Oncology nonmetastatic muscle-invasive bladder cancer trimodality therapy predictive model SEER database nomogram |
| title | Identifying Optimal Candidates for Trimodality Therapy among Nonmetastatic Muscle-Invasive Bladder Cancer Patients |
| title_full | Identifying Optimal Candidates for Trimodality Therapy among Nonmetastatic Muscle-Invasive Bladder Cancer Patients |
| title_fullStr | Identifying Optimal Candidates for Trimodality Therapy among Nonmetastatic Muscle-Invasive Bladder Cancer Patients |
| title_full_unstemmed | Identifying Optimal Candidates for Trimodality Therapy among Nonmetastatic Muscle-Invasive Bladder Cancer Patients |
| title_short | Identifying Optimal Candidates for Trimodality Therapy among Nonmetastatic Muscle-Invasive Bladder Cancer Patients |
| title_sort | identifying optimal candidates for trimodality therapy among nonmetastatic muscle invasive bladder cancer patients |
| topic | nonmetastatic muscle-invasive bladder cancer trimodality therapy predictive model SEER database nomogram |
| url | https://www.mdpi.com/1718-7729/30/12/740 |
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