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PubMed2007

Age, period and cohort effects in Bayesian smoothing of spatial cancer survival with geoadditive models.

Based on the example of data on breast cancer survival in a specific area in France, this paper describes a Bayesian approach to analysing individual continuous-time spatially referenced survival data. Starting from the well-known Cox model we develop a geoadditive survival model with a baseline effect, spatial effects, age, period and cohort effects as well as the effect of the number of metastases at the time of diagnosis. Furthermore, we also investigate temporal and spatial variations in the effect of the number of metastases. Our approach is particularly useful since we find clear hints for a violation of the proportional hazards assumption and the existence of different spatial patterns for patients with no, one and more than one metastasis. The reliability of our approach is attested by comparison with a parametric model and with several simulated data sets with known risk profiles.

Adult↗
PubMed2004

Assessment of clonal relationships in ipsilateral and bilateral multiple breast carcinomas by comparative genomic hybridisation and hierarchical clustering analysis.

The issue of whether multiple, ipsilateral or bilateral, breast carcinomas represent multiple primary tumours or dissemination of a single carcinomatous process has been difficult to resolve, especially for individual patients. We have addressed the problem by comparative genomic hybridisation analysis of 26 tumours from 12 breast cancer patients with multiple ipsilateral and/or bilateral carcinoma lesions. Genomic imbalances were detected in 25 of the 26 (96%) tumours. Using the genomic imbalances detected in these 26 lesions as well as those previously found by us in an independent series of 35 unifocal breast carcinomas, we compared a probabilistic model for likelihood of independence with unsupervised hierarchical clustering methodologies to determine the clonal relatedness of multiple tumours in breast cancer patients. We conclude that CGH analysis of multiple breast carcinomas followed by unsupervised hierarchical clustering of the genomic imbalances is more reliable than previous criteria to determine the tumours' clonal relationship in individual patients, that most ipsilateral breast carcinomas arise through intramammary spreading of a single breast cancer, and that most patients with bilateral breast carcinomas have two different diseases.

Adult↗
PubMed2001

A global optimization method for three-dimensional conformal radiotherapy treatment planning.

Multiple local minima exist in almost every coplanar or non-coplanar radiotherapy treatment planning problem. We used a global optimization method based on topographical information about the distribution of local minima to find all local minima and select the best as the global minimum. We uniformly select N random points from search regions and construct a topographical graph, from which M (M << N) starting points are selected to launch a local search. Because every seed point was at or near the local minimum, the solutions found by the local search could be used as the final optimization results. We verified this algorithm by applying it to three different clinical cases and comparing the results with those obtained by a local optimization method (sequential quadratic programming). The results show that this algorithm is feasible and efficient.

Algorithms↗
PubMed2004

Superior feature-set ranking for small samples using bolstered error estimation.

MOTIVATION: Ranking feature sets is a key issue for classification, for instance, phenotype classification based on gene expression. Since ranking is often based on error estimation, and error estimators suffer to differing degrees of imprecision in small-sample settings, it is important to choose a computationally feasible error estimator that yields good feature-set ranking. RESULTS: This paper examines the feature-ranking performance of several kinds of error estimators: resubstitution, cross-validation, bootstrap and bolstered error estimation. It does so for three classification rules: linear discriminant analysis, three-nearest-neighbor classification and classification trees. Two measures of performance are considered. One counts the number of the truly best feature sets appearing among the best feature sets discovered by the error estimator and the other computes the mean absolute error between the top ranks of the truly best feature sets and their ranks as given by the error estimator. Our results indicate that bolstering is superior to bootstrap, and bootstrap is better than cross-validation, for discovering top-performing feature sets for classification when using small samples. A key issue is that bolstered error estimation is tens of times faster than bootstrap, and faster than cross-validation, and is therefore feasible for feature-set ranking when the number of feature sets is extremely large.

Algorithms↗
PubMed2006

Convergence of the proteomic pattern in cancer.

MOTIVATION: On the histological level the differentiation of normal epithelial tissues is well known. The phenomenon of dedifferentiation, which occurs as cells develop towards malignancy is also well described. To identify an epithelial tumor-specific proteomic profile as well as to measure the proximities between we used data from tumor tissue and adjacent normal tissue microdissected from head and neck and colon cancer samples which were analyzed using ProteinChip technology and performed a bioinformatic meta-analysis on the resulting four complex datasets. RESULTS: All four groups could be identified based on their proteomic signatures and the tumor tissues were found to be more similar to one another than to the normal epithelial tissue from which they progressed. This study shows at the proteomic level that changes in the histological features of tumors as compared to the tissues from which they arise are reflected in the convergence of proteomic pattern during the development to cancer.

Cell Differentiation↗
PubMed2006

Distance-based clustering of CGH data.

MOTIVATION: We consider the problem of clustering a population of Comparative Genomic Hybridization (CGH) data samples. The goal is to develop a systematic way of placing patients with similar CGH imbalance profiles into the same cluster. Our expectation is that patients with the same cancer types will generally belong to the same cluster as their underlying CGH profiles will be similar. RESULTS: We focus on distance-based clustering strategies. We do this in two steps. (1) Distances of all pairs of CGH samples are computed. (2) CGH samples are clustered based on this distance. We develop three pairwise distance/similarity measures, namely raw, cosine and sim. Raw measure disregards correlation between contiguous genomic intervals. It compares the aberrations in each genomic interval separately. The remaining measures assume that consecutive genomic intervals may be correlated. Cosine maps pairs of CGH samples into vectors in a high-dimensional space and measures the angle between them. Sim measures the number of independent common aberrations. We test our distance/similarity measures on three well known clustering algorithms, bottom-up, top-down and k-means with and without centroid shrinking. Our results show that sim consistently performs better than the remaining measures. This indicates that the correlation of neighboring genomic intervals should be considered in the structural analysis of CGH datasets. The combination of sim with top-down clustering emerged as the best approach. AVAILABILITY: All software developed in this article and all the datasets are available from the authors upon request. CONTACT: juliu@cise.ufl.edu.

Algorithms↗
PubMed2006

The University of Rochester model of breast cancer detection and survival.

This paper presents a biologically motivated model of breast cancer development and detection allowing for arbitrary screening schedules and the effects of clinical covariates recorded at the time of diagnosis on posttreatment survival. Biologically meaningful parameters of the model are estimated by the method of maximum likelihood from the data on age and tumor size at detection that resulted from two randomized trials known as the Canadian National Breast Screening Studies. When properly calibrated, the model provides a good description of the U.S. national trends in breast cancer incidence and mortality. The model was validated by predicting some quantitative characteristics obtained from the Surveillance, Epidemiology, and End Results data. In particular, the model provides an excellent prediction of the size-specific age-adjusted incidence of invasive breast cancer as a function of calendar time for 1975-1999. Predictive properties of the model are also illustrated with an application to the dynamics of age-specific incidence and stage-specific age-adjusted incidence over 1975-1999.

Age Factors↗
PubMed2001

A cost--utility analysis comparing second-line chemotherapy schemes in patients with metastatic breast cancer.

A cost-utility analysis has been performed comparing taxanes, vinorelbine and standard therapy for metastatic breast cancer considering clinical efficacy, quality-adjusted-life-years (QALYs) and costs. A decision model has been built. Clinical efficacy data were collected by literature review. Utility data and cost data were collected from previous studies and Dutch wholesale prices. Except for the MV standard therapy, VM has the lowest C/E ratio of $17,114/QALY, followed by paclitaxel ($30,270/QALY) and docetaxel ($49,739/QALY). VM yields the highest number of QALYs (0.47), compared to paclitaxel (0.35), docetaxel (0.34) and MV (0.29). Compared to the MV standard therapy, the incremental C/E of VM is $23,046/QALY, which is the lowest of all alternatives. We conclude that compared to paclitaxel, docetaxel and MV standard chemotherapy, VM is the most cost-effective second-line chemotherapy for metastatic breast cancer patients. There is a considerable variation in utility scores, depending on the methods or the data sources used. The C/E ratios were influenced most strongly by drug prices, utility and efficacy (in descending order of importance).

Adult↗
PubMed2001

The privatization of risk.

The privatization, or individualization, of risk factor knowledge has been largely responsible for a rising tide of criticism of epidemiology. The current debate seems polarized into 2 sides, those who support and those who attack "risk factor" epidemiology. This commentary aims to reinvigorate some of Geoffrey Rose's central arguments and show that this debate may miss a key point: a risk factor is a probabilistic concept that applies to an aggregate of individuals, not to a specific individual. Risk factor knowledge compels those in public health to seek actions that shift population distributions of these factors and, to do so, to understand their social, economic, and political determinants. The author links Rose's qualitative distinction between the causes of cases and the causes of incidence to an examination of the conceptual and quantitative limits of "individual risk" estimation. The attempt to predict individuals' futures on the basis of risk factor profile is especially prominent now with breast cancer. The author suggests reasons why a policy promoting private decision making about risk, while likely ineffective from a population standpoint, is viewed as the only feasible primary prevention option against this disease.

Breast Neoplasms↗
PubMed1999

A prognostic model that makes quantitative estimates of probability of relapse for breast cancer patients.

Tumor-node-metastasis (TNM) staging is the standard system for the estimation of prognosis of breast cancer patients. However, this system does not exploit information yielded by markers of the biological aggressiveness of breast cancer and is clearly unsatisfactory for optimal-treatment decision-making and for patient counseling. We have developed a prognostic model, based on a few routinely evaluated prognostic variables, that produces quantitative estimates for risk of relapse of individual breast cancer patients. We used data concerning 2441 of 2990 consecutive breast cancer patients to develop an artificial neural network (ANN) for the prediction of the probability of relapse over 5 years. The prognostic variables used were: patient age, tumor size, number of axillary metastases, estrogen and progesterone receptor levels, S-phase fraction, and tumor ploidy. Performances of the model were evaluated in terms of discrimination ability and quantitative precision. Predictions were validated on an independent series of 310 patients from an institution in another country. The ANN discriminated patients according to their risk of relapse better than the TNM classification (P = 0.0015). The quantitative precision of the model's estimates was accurate and was confirmed on the series from the second institution. The 5-year relapse risk yielded by the model varied greatly within the same TNM class, particularly for patients with four or more nodal metastases. The model discriminates prognosis better than the TNM classification and is able to identify patients with strikingly different risks of relapse within each TNM class.

Adult↗
PubMed1994

Comparative Gompertzian analysis of alterations of tumor growth patterns.

We have shown previously that different patterns of growth (including classical Gompertzian, exponential, and hyperexponential) can be described based on the Gompertzian difference equation, In G(t+s) = a+b In G(t) [G(t), tumor size at time t; s, constant time interval of a stroboscopic set of tumor size measurements], by the initial or intrinsic growth rate, a, and the exponential rate of growth deceleration, b (I. D. Bassukas and B. Maurer-Schultze, Growth Dev. Aging, 52: 113-122, 1988). Particularly, the possible effects of antineoplastic treatments on this latter factor have not been adequately appreciated to date. In the present study a new method of comparison of growth process is introduced, which consists of two steps. (a) The corresponding growth patterns are compared with each other on the basis of the Gompertzian difference equation using an analysis of variance algorithm. The result indicates whether or not there is an alteration of the growth pattern and, if there is one, whether it refers to alterations of the a and/or b values for the growth patterns. (b) The ultimate effect of any perturbation is assessed by using the limit (for t-->infinity) of the quotient of the tumor sizes after and before perturbation to characterize it as stimulation or inhibition. The present approach introduces a new quality of tumor growth perturbations; tumor growth may be altered although being neither inhibited nor stimulated, a phenomenon with biological significance still to be evaluated. The application of this method to the treatment with suramin of a human renal cell carcinoma xenograft in nude mice reveals a complex alteration of its growth pattern. Suramin simultaneously inhibits both the initial or intrinsic growth rate and the exponential rate of growth deceleration of this tumor. Consequently, this tumor starts with a lower growth rate in the presence of suramin; however, this rate also decelerates more slowly as a function of tumor growth, so that treated tumors, although initially inhibited, grow larger than controls.

Animals↗
PubMed1985

[Multivariate discriminant analysis--a current method for evaluating the enzyme spectrum in cancer patients].

The study was concerned with the procedure and results of evaluation of the clinical course versus enzymatic activity in blood serum of patients with breast, uterine and bladder tumors. Mathematical and statistical methods used in multivariate discriminant analysis offer more advantage as compared with the standard procedures for evaluation of enzymatic activity in blood serum of cancer patients.

Breast Neoplasms↗