Outcome Prediction in Primary Resected Retroperitoneal Soft Tissue Sarcoma: Histology-Specific Overall Survival and Disease-Free Survival Nomograms Built on Major Sarcoma Center Data Sets
01 May 2013-Journal of Clinical Oncology (American Society of Clinical Oncology)-Vol. 31, Iss: 13, pp 1649-1655
TL;DR: These nomograms accurately predict postoperative overall survival (OS) and disease-free survival (DFS) in patients with primary RPS and should be used for patient counseling in clinical practice and stratification in clinical trials.
Abstract: Purpose Integration of numerous prognostic variables not included in the conventional staging of retroperitoneal soft tissue sarcomas (RPS) is essential in providing effective treatment. The purpose of this study was to build a specific nomogram for predicting postoperative overall survival (OS) and disease-free survival (DFS) in patients with primary RPS. Patients and Methods Data registered in three institutional prospective sarcoma databases were used. We included patients with primary localized RPS resected between 1999 and 2009. Univariate (Kaplan and Meier plots) and multivariate (Cox model) analyses were carried out. The a priori chosen prognostic covariates were age, tumor size, grade, histologic subtype, multifocality, quality of surgery, and radiation therapy. External validation was performed by applying the nomograms to the patients of an external cohort. The model's discriminative ability was estimated by means of the bootstrap-corrected Harrell C statistic. Results In all, 523 patients were ...
Citations
More filters
University of Milan1, Instituto Português de Oncologia Francisco Gentil2, Curie Institute3, Leiden University Medical Center4, Vienna General Hospital5, University College London6, Leiden University7, Institut Jules Bordet8, Netherlands Cancer Institute9, Turku University Hospital10, University of Oxford11, University of Mannheim12, Ludwig Maximilian University of Munich13, Helsinki University Central Hospital14, The Royal Marsden NHS Foundation Trust15, Institute of Cancer Research16, University Medical Center Groningen17, Radboud University Nijmegen18, Institut Gustave Roussy19, Tel Aviv Sourasky Medical Center20, University Hospital of Lausanne21, University of Bologna22, University of Turin23, Weston Park Hospital24, Aarhus University Hospital25, Katholieke Universiteit Leuven26, Erasmus University Rotterdam27, Oslo University Hospital28, University College Hospital29, Claude Bernard University Lyon 130
1,150 citations
Journal Article•
TL;DR: In this paper, solitary fibrous tumors (SFTs) are rare fibrous neoplasms arising from the pleura and have been reported at a wide range of anatomic sites.
543 citations
01 Jan 2018
TL;DR: Fondazione IRCCS Istituto Nazionale dei Tumori and University of Milan, Milan, Italy; Instituto Portugues de Oncologia de Lisboa Francisco Gentil, EPE, Lisbon, Portugal; University HospitalEssen, Essen, Germany; Department of Oncological Orthopedics, Musculoskeletal Tissue Bank, IFO, Regina Elena National Cancer Institute, Rome.
Abstract: Fondazione IRCCS Istituto Nazionale dei Tumori and University of Milan, Milan, Italy; Instituto Portugues de Oncologia de Lisboa Francisco Gentil, EPE, Lisbon, Portugal; University Hospital Essen, Essen, Germany; Department of Oncological Orthopedics, Musculoskeletal Tissue Bank, IFO, Regina Elena National Cancer Institute, Rome, Italy; Klinikum Stuttgart-Olgahospital, Stuttgart, Germany; Institut Curie, Paris, France; NORDIX, Athens, Greece; Department of Pathology, Leiden University Medical Center, Leiden, The Netherlands; Vienna General Hospital (AKH), Medizinische Universität Wien, Vienna, Austria; Hospital Universitario Virgen del Rocio-CIBERONC, Seville, Spain; Centro di Riferimento Oncologico di Aviano, Aviano; Ospedale Regionale di Treviso “S.Maria di Cà Foncello”, Treviso, Italy; Integrated Unit ICO Hospitalet, HUB, Barcelona, Spain; Sarcoma Unit, University College London Hospitals, London, UK; Skane University Hospital-Lund, Lund, Sweden; N. N. Blokhin Russian Cancer Research Center, Moscow, Russian Federation; Institute of Scientific Hospital Care (IRCCS), Regina Elena National Cancer Institute, Rome; Pediatric Oncology Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan; Istituto Ortopedico Rizzoli, Bologna; Azienda Ospedaliera Universitaria Careggi Firenze, Florence, Italy; Department of Medical Oncology, Leiden University Medical Centre, Leiden, The Netherlands; Institut Jules Bordet, Brussels, Belgium; Candiolo Cancer Institute, FPO IRCCS, Candiolo, Italy; Department of Radiotherapy, The Netherlands Cancer Institute, Amsterdam and Department of Radiotherapy, Leiden University Medical Centre, Leiden, The Netherlands; Turku University Hospital (Turun Yliopistollinen Keskussairaala), Turlu, Finland; Oxford University Hospitals NHS Foundation Trust, Oxford, UK; Mannheim University Medical Center, Mannheim; Department of Medicine III, University Hospital, Ludwig-Maximilians-University Munich, Munich, Germany; Helsinki University Central Hospital (HUCH), Helsinki, Finland; Royal Marsden Hospital, London; The Institute of Cancer Research, London, UK; University Medical Center Groningen, Groningen; Radboud University Medical Center, Nijmegen, The Netherlands; University Hospital Motol, Prague; Masaryk Memorial Cancer Institute, Brno, Czech Republic; Gustave Roussy Cancer Campus, Villejuif, France; Maria Skłodowska Curie Institute, Oncology Centre, Warsaw, Poland; Tel Aviv Sourasky Medical Center (Ichilov), Tel Aviv, Israel; Medical Oncology, University Hospital of Lausanne, Lausanne, Switzerland; Azienda Ospedaliera, Universitaria, Policlinico S Orsola-Malpighi Università di Bologna, Bologna; Azienda Ospedaliero, Universitaria Cita della Salute e della Scienza di Torino, Turin, Italy; Fundacio de Gestio Sanitaria de L’hospital de la SANTA CREU I Sant Pau, Barcelona, Spain; Helios Klinikum Berlin Buch, Berlin, Germany; YCRC Department of Clinical Oncology, Weston Park Hospital NHS Trust, Sheffield, UK; Aarhus University Hospital, Aarhus, Finland; Leuven Cancer Institute, Leuven, Belgium; Department of Medical Oncology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands; Fondazione Istituto di Ricovero e Cura a Carattere Scientifico, Istituto Nazionale dei Tumori, Milan, Italy; Department of Oncology, Oslo University Hospital, The Norwegian Radium Hospital, Oslo, Norway; Institute of Oncology of Ljubljana, Ljubljana, Slovenia; Netherlands Cancer Institute Antoni van Leeuwenhoek, Amsterdam, The Netherlands; University College Hospital, London, UK; Gerhard-Domagk-Institut für Pathologie, Universitätsklinikum Münster, Münster, Germany; Oslo University Hospital, Norwegian Radium Hospital, Oslo, Norway; Centre Leon Bernard and UCBL1, Lyon, France
440 citations
Cites background from "Outcome Prediction in Primary Resec..."
...Nomograms are available, which can help personalise risk assessment and thus clinical decision making, especially on adjuvant/neoadjuvant treatments [8, 9]....
[...]
...Histology-specific nomograms for RPS patients are available that can help personalise risk assessment and clinical decision making [9]....
[...]
TL;DR: Reference centers are critical to outcomes of RPS patients, as the management strategy requires specific expertise, and Histologic subtype predicts patterns of recurrence and should inform management decision.
Abstract: Background:Retroperitoneal sarcomas (RPS) are rare tumors composed of several well defined histologic subtypes. The aim of this study was to analyze patterns of recurrence and treatment variations in a large population of patients, treated at reference centers.Methods:All consecutive patients with p
367 citations
TL;DR: The authors' nomograms are reliable prognostic methods that can be used to predict overall survival and distant metastases in patients after surgical resection of soft-tissue sarcoma of the extremities and can be offered to clinicians to improve their abilities to assess patient prognosis.
Abstract: Summary Background The current American Joint Committee on Cancer/Union for International Cancer Control (AJCC/UICC) staging system does not have sufficient details to encompass the variety of soft-tissue sarcomas, and available prognostic methods need refinement. We aimed to develop and externally validate two prediction nomograms for overall survival and distant metastases in patients with soft-tissue sarcoma in their extremities. Methods Consecutive patients who had had an operation at the Istituto Nazionale Tumori (Milan, Italy), from Jan 1, 1994, to Dec 31, 2013, formed the development cohort. Three cohorts of patient data from the Institut Gustave Roussy (Villejuif, France; from Jan 1, 1996, to May 15, 2012), Mount Sinai Hospital (Toronto, ON, Canada; from Jan 1, 1994, to Dec 31, 2013), and the Royal Marsden Hospital (London, UK; from Jan 1, 2006, to Dec 31, 2013) formed the external validation cohorts. We developed the nomogram for overall survival using a Cox multivariable model, and a Fine and Gray multivariable model for the distant metastases nomogram. We applied a backward procedure for variables selection for both nomograms. We assessed nomogram model performance by examining overall accuracy (Brier score), calibration (calibration plots and Hosmer–Lemeshow calibration test), and discrimination (Harrell C index). We plotted decision curves to evaluate the clinical usefulness of the two nomograms. Findings 1452 patients were included in the development cohort, with 420 patients included in the French validation cohort, 1436 patients in the Canadian validation cohort, and 444 patients in the UK validation cohort. In the development cohort, 10-year overall survival was 72·9% (95% CI 70·2–75·7) and 10-year crude cumulative incidence of distant metastases was 25·0% (95% CI 22·7–27·5). For the overall survival nomogram, the variables selected applying a backward procedure in the multivariable Cox model (patient's age, tumour size, Federation Francaise des Centres de Lutte Contre le Cancer [FNCLCC] grade, and histological subtype) had a significant effect on overall survival. The same variables, except for patient age, were selected for the distant metastases nomogram. In the development cohort, the Harrell C index for overall survival was 0·767 (95% CI 0·743–0·789) and for distant metastases was 0·759 (0·736–0·781). In the validation cohorts, the Harrell C index for overall survival and distant metastases were 0·698 (0·638–0·754) and 0·652 (0·605–0·699; French), 0·775 (0·754–0·796) and 0·744 (0·720–0·768; Canadian), and 0·762 (0·720–0·806) and 0·749 (0·707–0·791; UK). The two nomograms both performed well in terms of discrimination (ability to distinguish between patients who have had an event from those who have not) and calibration (accuracy of nomogram prediction) when applied to the validation cohorts. Interpretation Our nomograms are reliable prognostic methods that can be used to predict overall survival and distant metastases in patients after surgical resection of soft-tissue sarcoma of the extremities. These nomograms can be offered to clinicians to improve their abilities to assess patient prognosis, strengthen the prognosis-based decision making, enhance patient stratification, and inform patients in the clinic. Funding None.
285 citations
References
More filters
Book•
01 Jan 1993TL;DR: This article presents bootstrap methods for estimation, using simple arguments, with Minitab macros for implementing these methods, as well as some examples of how these methods could be used for estimation purposes.
Abstract: This article presents bootstrap methods for estimation, using simple arguments. Minitab macros for implementing these methods are given.
37,183 citations
Proceedings Article•
01 Jan 1973
TL;DR: The classical maximum likelihood principle can be considered to be a method of asymptotic realization of an optimum estimate with respect to a very general information theoretic criterion to provide answers to many practical problems of statistical model fitting.
Abstract: In this paper it is shown that the classical maximum likelihood principle can be considered to be a method of asymptotic realization of an optimum estimate with respect to a very general information theoretic criterion. This observation shows an extension of the principle to provide answers to many practical problems of statistical model fitting.
18,539 citations
01 Jan 1973
TL;DR: In this paper, it is shown that the classical maximum likelihood principle can be considered to be a method of asymptotic realization of an optimum estimate with respect to a very general information theoretic criterion.
Abstract: In this paper it is shown that the classical maximum likelihood principle can be considered to be a method of asymptotic realization of an optimum estimate with respect to a very general information theoretic criterion. This observation shows an extension of the principle to provide answers to many practical problems of statistical model fitting.
15,424 citations
TL;DR: In this article, an easily interpretable index of predictive discrimination as well as methods for assessing calibration of predicted survival probabilities are discussed, which are particularly needed for binary, ordinal, and time-to-event outcomes.
Abstract: Multivariable regression models are powerful tools that are used frequently in studies of clinical outcomes. These models can use a mixture of categorical and continuous variables and can handle partially observed (censored) responses. However, uncritical application of modelling techniques can result in models that poorly fit the dataset at hand, or, even more likely, inaccurately predict outcomes on new subjects. One must know how to measure qualities of a model's fit in order to avoid poorly fitted or overfitted models. Measurement of predictive accuracy can be difficult for survival time data in the presence of censoring. We discuss an easily interpretable index of predictive discrimination as well as methods for assessing calibration of predicted survival probabilities. Both types of predictive accuracy should be unbiasedly validated using bootstrapping or cross-validation, before using predictions in a new data series. We discuss some of the hazards of poorly fitted and overfitted regression models and present one modelling strategy that avoids many of the problems discussed. The methods described are applicable to all regression models, but are particularly needed for binary, ordinal, and time-to-event outcomes. Methods are illustrated with a survival analysis in prostate cancer using Cox regression.
7,879 citations