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Pan-cancer analysis of prognostic metastatic phenotypes.
Zaorsky, Nicholas G; Wang, Xi; Garrett, Sara M; Lehrer, Eric J; Lin, Christine; DeGraff, David J; Spratt, Daniel E; Trifiletti, Daniel M; Kishan, Amar U; Showalter, Timothy N; Park, Henry S; Yang, Jonathan T; Chinchilli, Vernon M; Wang, Ming.
  • Zaorsky NG; Department of Radiation Oncology, Penn State Cancer Institute, Hershey, Pennsylvania, USA.
  • Wang X; Department of Public Health Sciences, Penn State College of Medicine, Hershey, Pennsylvania, USA.
  • Garrett SM; Department of Public Health Sciences, Penn State College of Medicine, Hershey, Pennsylvania, USA.
  • Lehrer EJ; Department of Radiation Oncology, Penn State Cancer Institute, Hershey, Pennsylvania, USA.
  • Lin C; Department of Public Health Sciences, Penn State College of Medicine, Hershey, Pennsylvania, USA.
  • DeGraff DJ; Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
  • Spratt DE; Department of Radiation Oncology, Penn State Cancer Institute, Hershey, Pennsylvania, USA.
  • Trifiletti DM; Department of Public Health Sciences, Penn State College of Medicine, Hershey, Pennsylvania, USA.
  • Kishan AU; Division of Experimental Pathology, Department of Pathology and Laboratory Medicine, Penn State College of Medicine, Hershey, Pennsylvania, USA.
  • Showalter TN; Department of Radiation Oncology, University Hospitals Seidman Cancer Center, Case Western Reserve University, Cleveland, OH, USA.
  • Park HS; Department of Radiation Oncology, Mayo Clinic Jacksonville, Jacksonville, Florida, USA.
  • Yang JT; Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, California, USA.
  • Chinchilli VM; Department of Radiation Oncology, University of Virginia, Charlottesville, Virginia, USA.
  • Wang M; Department of Therapeutic Radiology, Yale School of Medicine, New Haven, Connecticut, USA.
Int J Cancer ; 150(1): 132-141, 2022 01 01.
Article en En | MEDLINE | ID: mdl-34287840
ABSTRACT
Although cancer is highly heterogeneous, all metastatic cancer is considered American Joint Committee on Cancer (AJCC) Stage IV disease. The purpose of this project was to redefine staging of metastatic cancer. Internal validation of nationally representative patient data from the National Cancer Database (n = 461 357; 2010-2013), and external validation using the Surveillance, Epidemiology and End Results database (n = 106 595; 2014-2015) were assessed using the concordance index for evaluation of survival prediction. A Cox proportional hazards model was used for overall survival by considering identified phenotypes (latent classes) and other confounding variables. Latent class analysis was performed for phenotype identification, where Bayesian information criterion (BIC) and sample-size-adjusted BIC were used to select the optimal number of distinct clusters. Kappa coefficients assessed external cluster validation. Latent class analysis identified five metastatic phenotypes with differences in overall survival (P < .0001) (Stage IVA) nearly exclusive bone-only metastases (n = 59 049, 12.8%; median survival 12.7 months; common in lung, breast and prostate cancers); (IVB) predominant lung metastases (n = 62 491, 13.5%; 11.4 months; common in breast, stomach, kidney, ovary, uterus, thyroid, cervix and soft tissue cancers); (IVC) predominant liver/lung metastases (n = 130 014, 28.2%; 7.0 months; common in colorectum, pancreatic, lung, esophagus and stomach cancers); (IVD) bone/liver/lung metastases predominant over brain (n = 61 004, 13.2%; 5.9 months; common in lung and breast cancers); and (IVE) brain/lung metastases predominant over bone/liver (n = 148 799, 32.3%; 5.7 months; lung cancer and melanoma). Long-term survivors were identified, particularly in Stages IVA-B. A pan-cancer nomogram model to predict survival (STARS site, tumor, age, race, sex) was created, validated and provides 13% better prognostication than AJCC 1-month concordance index of 0.67 (95% confidence interval [CI] 0.66-0.67) vs 0.61 (95% CI 0.60-0.61). STARS is simple, uses easily accessible variables, better prognosticates survival outcomes and provides a platform to develop novel metastasis-directed clinical trials.
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Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Fenotipo / Nomogramas / Neoplasias Tipo de estudio: Observational_studies / Prognostic_studies Límite: Adult / Female / Humans / Male / Middle aged Idioma: En Año: 2022 Tipo del documento: Article

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Fenotipo / Nomogramas / Neoplasias Tipo de estudio: Observational_studies / Prognostic_studies Límite: Adult / Female / Humans / Male / Middle aged Idioma: En Año: 2022 Tipo del documento: Article