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Rami-Porta R.,Hospital Universitari Mutua Terrassa | Bolejack V.,Cancer Research and Biostatistics | Goldstraw P.,Imperial College London
Seminars in Respiratory and Critical Care Medicine | Year: 2011

The seventh edition of the tumor, node, and metastasis (TNM) classification is based on the proposals of the Staging Project of the International Association for the Study of Lung Cancer (IASLC). The analyses of the IASLC international database of 81,015 patients diagnosed with lung cancer between 1990 and 2000 were used to validate the TNM descriptors. The changes include: the subclassification of T1 and T2 tumors into T1a (≤2 cm) and T1b (>2 and ≤3 cm), and T2a (>3 and ≤5 cm) and T2b (>5 and ≤7 cm), respectively; the reclassification of T2 tumors >7 cm as T3; the reclassification of T4 tumors by additional nodules in the same lobe of the primary tumor as T3; the reclassification of M1 tumors by additional nodules in another ipsilateral lobe as T4; the reclassification of pleural and pericardial dissemination, and contralateral M1 nodules as M1a; and the separation of intrathoracic (M1a) and extrathoracic (M1b) metastases. Other innovations include the emphasis on the use of the TNM classification for small cell carcinoma, the inclusion of bronchopulmonary carcinoids into this staging system, the proposal of a new lymph node map, and the adoption of a new, internationally agreed definition of visceral pleura invasion. All these changes improve the separation of tumors with significantly different prognosis. © Georg Thieme Verlag KG Stuttgart. New York.

Othus M.,Fred Hutchinson Cancer Research Center | Barlogie B.,University of Arkansas for Medical Sciences | LeBlanc M.L.,Fred Hutchinson Cancer Research Center | Crowley J.J.,Cancer Research and Biostatistics
Clinical Cancer Research | Year: 2012

Cure models are a popular topic within statistical literature but are not as widely known in the clinical literature. Many patients with cancer can be long-term survivors of their disease, and cure models can be a useful tool to analyze and describe cancer survival data. The goal of this article is to review what a cure model is, explain when cure models can be used, and use cure models to describe multiple myeloma survival trends. Multiple myeloma is generally considered an incurable disease, and this article shows that by using cure models, rather than the standard Cox proportional hazards model, we can evaluate whether there is evidence that therapies at the University of Arkansas for Medical Sciences induce a proportion of patients to be long-term survivors. ©2012 ACCR.

Korn R.L.,Imaging Endpoints Core Laboratory | Crowley J.J.,Cancer Research and Biostatistics
Clinical Cancer Research | Year: 2013

Progression-free survival (PFS) is increasingly used as an important and even a primary endpoint in randomized cancer clinical trials in the evaluation of patients with solid tumors for both practical and clinical considerations. Although in its simplest form, PFS is the time from randomization to a predefined endpoint, there are many factors that can influence the exact moment of when disease progression is recorded. In this overview, we review the circumstances that can devalue the use of PFS as a primary endpoint and attempt to provide a pathway for a future desired state when PFS will become not just a secondary alternative to overall survival but rather an endpoint of choice. © 2013 AACR.

Janes H.,Fred Hutchinson Cancer Research Center | Pepe M.S.,Fred Hutchinson Cancer Research Center | Bossuyt P.M.,University of Amsterdam | Barlow W.E.,Cancer Research and Biostatistics
Annals of Internal Medicine | Year: 2011

Treatment selection markers, sometimes called predictive markers, are factors that help clinicians select therapies that maximize good outcomes and minimize adverse outcomes for patients. Existing statistical methods for evaluating a treatment selection marker include assessing its prognostic value, evaluating treatment effects in patients with a restricted range of marker values, and testing for a statistical interaction between marker value and treatment. These methods are inadequate, because they give misleading measures of performance that do not answer key clinical questions about how the marker might help patients choose treatment, how treatment decisions should be made on the basis of a continuous marker measurement, what effect using the marker to select treatment would have on the population, or what proportion of patients would have treatment changes on the basis of marker measurement. Marker-by-treatment predictiveness curves are proposed as a more useful aid to answering these clinically relevant questions, because they illustrate treatment effects as a function of marker value, outcomes when using or not using the marker to select treatment, and the proportion of patients for whom treatment recommendations change after marker measurement. Randomized therapeutic clinical trials, in which entry criteria and treatment regimens are not restricted by the marker, are also proposed as the basis for constructing the curves and evaluating and comparing markers, © 2011 American College of Physicians.

Fenton J.J.,University of California at Davis | Abraham L.,Group Health Research Institute | Taplin S.H.,U.S. National Cancer Institute | Geller B.M.,University of Vermont | And 4 more authors.
Journal of the National Cancer Institute | Year: 2011

Background Computer-aided detection (CAD) is applied during screening mammography for millions of US women annually, although it is uncertain whether CAD improves breast cancer detection when used by community radiologists. Methods We investigated the association between CAD use during film-screen screening mammography and specificity, sensitivity, positive predictive value, cancer detection rates, and prognostic characteristics of breast cancers (stage, size, and node involvement). Records from 684 956 women who received more than 1.6 million filmscreen mammograms at Breast Cancer Surveillance Consortium facilities in seven states in the United States from 1998 to 2006 were analyzed. We used random-effects logistic regression to estimate associations between CAD and specificity (true-negative examinations among women without breast cancer), sensitivity (true-positive examinations among women with breast cancer diagnosed within 1 year of mammography), and positive predictive value (breast cancer diagnosed after positive mammograms) while adjusting for mammography registry, patient age, time since previous mammography, breast density, use of hormone replacement therapy, and year of examination (1998-2002 vs 2003-2006). All statistical tests were two-sided. Results Of 90 total facilities, 25 (27.8%) adopted CAD and used it for an average of 27.5 study months. In adjusted analyses, CAD use was associated with statistically significantly lower specificity (OR = 0.87, 95% confidence interval [CI] = 0.85 to 0.89, P < .001) and positive predictive value (OR = 0.89, 95% CI = 0.80 to 0.99, P = .03). A non-statistically significant increase in overall sensitivity with CAD (OR = 1.06, 95% CI = 0.84 to 1.33, P = .62) was attributed to increased sensitivity for ductal carcinoma in situ (OR = 1.55, 95% CI = 0.83 to 2.91; P = .17), although sensitivity for invasive cancer was similar with or without CAD (OR = 0.96, 95% CI = 0.75 to 1.24; P = .77). CAD was not associated with higher breast cancer detection rates or more favorable stage, size or lymph node status of invasive breast cancer. Conclusion CAD use during film-screen screening mammography in the United States is associated with decreased specificity but not with improvement in the detection rate or prognostic characteristics of invasive breast cancer. © The Author 2011. Published by Oxford University Press. All rights reserved.

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