The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
Clinical Model to Predict Survival in Chemonaive Patients With Advanced Non–Small-Cell Lung Cancer Treated With Third-Generation Chemotherapy Regimens Based on Eastern Cooperative Oncology Group Data
Abstract
Purpose: (1) Identify clinical factors that can be used to predict survival in chemotherapy-naive patients with advanced non–small-cell lung cancer (NSCLC) treated with third-generation chemotherapy regimens, and (2) build a clinical model to predict survival in this patient population. Patients and Methods: Using data from two randomized, phase III Eastern Cooperative Oncology Group (ECOG) trials (E5592/E1594), we performed univariate and multivariate stepwise Cox regression analyses to identify survival prognostic factors. We used 75% of randomly sampled data to build a prediction model for survival, and the remaining 25% of data to validate the model. Results: From 1993 to 1999, 1,436 patients with stage IV or IIIB NSCLC with effusion were treated with platinum-based doublets (involving either paclitaxel, docetaxel, or gemcitabine). The response rate and median survival time were 20% and 8.2 months, respectively. One- and 2-year survivals were 33% and 11%, respectively. In multivariate analysis, six independent poor prognostic factors were identified: skin metastasis (hazard ratio [HR], 1.88), lower performance status (ECOG 1 or 2; HR, 1.46), loss of appetite (HR, 1.62), liver metastasis (HR, 1.32), ≥ four metastatic sites (HR, 1.20), and no prior surgery (HR, 1.16). A nomogram using six pretreatment prognostic factors was built to predict 1- and 2-year survival. Conclusion: Six pretreatment factors can be used to predict survival in chemotherapy-naive NSCLC patients treated with standard chemotherapy. Using our prognostic nomogram, 1- and 2-year survival probability of NSCLC patients can be estimated before treatment. This prognostic model may help clinicians and patients in clinical decision making, as well as investigators in research planning.
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Comparison of Four Chemotherapy Regimens for Advanced Non–Small-Cell Lung Cancer
Schiller, J. H., Harrington, D., Belani, C. P., Langer, C., Sandler, A., Krook, J., Zhu, J., & Johnson, D. H. (2002) · New England Journal of Medicine · doi:10.1056/nejmoa011954 · Cited by 4,955
Self-citationCOMPARISON OF FOUR CHEMOTHERAPY REGIMENS FOR ADVANCED NON-SMALL-CELL LUNG CANCER
Chiller, J. O. H. S., Arrington, D. A. H., Elani, C. H. P. B., Anger, C., Andler, A. L. S., Rook, J. A. K., Hu, J., & Ohnson, D. A. H. J. (2002) · Cited by 3,730
Pfister, D. G., Johnson, D. H., Azzoli, C. G., Sause, W., Smith, T. J., Baker Jr, S., Olak, J., Stover, D., Strawn, J. R., Turrisi, A. T., et al. (2004) · Journal of Clinical Oncology · doi:10.1200/jco.2004.09.053 · Cited by 1,413
Self-citationKelly, K., Crowley, J., Bunn, P. A., Presant, C. A., Grevstad, P. K., Moinpour, C. M., Ramsey, S. D., Wozniak, A. J., Weiss, G. R., Moore, D. F., et al. (2001) · Journal of Clinical Oncology · doi:10.1200/jco.2001.19.13.3210 · Cited by 1,123
Postoperative Nomogram for Disease Recurrence After Radical Prostatectomy for Prostate Cancer
Kattan, M. W., Wheeler, T. M., & Scardino, P. T. (1999) · Journal of Clinical Oncology · doi:10.1200/jco.1999.17.5.1499 · Cited by 741
Albain, K. S., Crowley, J. J., LeBlanc, M., & Livingston, R. B. (1991) · Journal of Clinical Oncology · doi:10.1200/jco.1991.9.9.1618 · Cited by 728
Bonomi, P., Kim, K., Fairclough, D., Cella, D., Kugler, J., Rowinsky, E., Jiroutek, M., & Johnson, D. (2000) · Journal of Clinical Oncology · doi:10.1200/jco.2000.18.3.623 · Cited by 589
Self-citationPrognostic Factors for Survival in Patients With Inoperable Lung Cancer 2
(1980) · JNCI: Journal of the National Cancer Institute · doi:10.1093/jnci/65.1.25 · Cited by 564
Prognostic Factors in Non-small Cell Lung Cancer
Brundage, M. D., Davies, D., & Mackillop, W. J. (2002) · Chest · doi:10.1378/chest.122.3.1037 · Cited by 564
Langer, C. J. (2002) · CancerSpectrum Knowledge Environment · doi:10.1093/jnci/94.3.173 · Cited by 502
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Machine Learning methods for Quantitative Radiomic Biomarkers
Parmar, C., Grossmann, P., Bussink, J., Lambin, P., & Aerts, H. J. W. L. (2015) · Scientific Reports · doi:10.1038/srep13087 · Cited by 1,019
Deep learning for lung cancer prognostication: A retrospective multi-cohort radiomics study
Hosny, A., Parmar, C., Coroller, T. P., Grossmann, P., Zeleznik, R., Kumar, A., Bussink, J., Gillies, R. J., Mak, R. H., & Aerts, H. J. W. L. (2018) · PLOS Medicine · doi:10.1371/journal.pmed.1002711 · Cited by 579
Sculier, J.-P., Chansky, K., Crowley, J. J., Van Meerbeeck, J., & Goldstraw, P. (2008) · Journal of Thoracic Oncology · doi:10.1097/jto.0b013e31816de2b8 · Cited by 337
Reporting methods in studies developing prognostic models in cancer: a review
Mallett, S., Royston, P., Dutton, S., Waters, R., & Altman, D. G. (2010) · BMC Medicine · doi:10.1186/1741-7015-8-20 · Cited by 206
“Patients recruited from RCTs in 11% (5) of the studies [14,38,40,44,47].”
BackgroundMethodInfluentialReporting performance of prognostic models in cancer: a review.
Mallett, S., Royston, P., Waters, R., Dutton, S., & Altman, D. G. (2010) · BMC Medicine · doi:10.1186/1741-7015-8-21 · Cited by 195
Metadata
- Authors
- Hoang, T., Xu, R., Schiller, J. H., Bonomi, P., & Johnson, D. H.
- Venue
- Journal of Clinical Oncology
- Published
- 2005-01-01
- Volume
- 23
- Issue
- 1
- Pages
- 175-183
- ISSN
- 0732-183X (ISSN-L)
- 1527-7755
- OpenAlex
- W2128990677
- Cited by
- 263
- References
- 18
- FWCI
- 10.15
- Type
- journal article
- Language
- en
- Open access
- closed
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- Hoang, T., Xu, R., Schiller, J. H., Bonomi, P., & Johnson, D. H. (2005). Clinical Model to Predict Survival in Chemonaive Patients With Advanced Non–Small-Cell Lung Cancer Treated With Third-Generation Chemotherapy Regimens Based on Eastern Cooperative Oncology Group Data. Journal of Clinical Oncology. https://doi.org/10.1200/jco.2005.04.177
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