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PubMedDate not supplied

Trends and predictions of cancer incidence cases by site and sex for Mumbai.

In order to estimate the resources needed for the diagnosis, treatment, follow-up and rehabilitation services needed for cancer patients, it is important to know the magnitude of common cancers at present and in future. Keeping this view in mind an attempt has been made to predict cancer incidence cases for most common cancers for Greater Bombay upto the year 2002. The trend analysis is carried out for major 30 cancer sites for both the sexes using age incidence data of Greater Bombay for the period 1968 to 1987. The age-period-cohort model which was fitted to the data for studying trends for each site and sex has also been utilized for prediction also. Prediction was based on the assumption that the characteristic features of the model estimated from the observed rates would continue to hold during the prediction periods. Present trend analysis showed that cancers of the tongue, mouth, oropharynx, oesophagus, stomach and larynx in both sexes and cervix for females have registered a decline in incidence over a period of observation. While, during the same period, cancers of the liver, pancreas, bladder, brain and thyroid in both the sexes, breast, endometrium and ovary in females, and testis in males showed increasing trends in incidence, while cancers of the hypopharynx, lung, bone, connective tissue and lymphomas in both the sexes did not show any significant change in the incidence. In males in 1968-1972 cancer of the oesophagus was the leading cancer, followed by lung, larynx, tongue and stomach, while in 1988-2002 cancer of the lung will be the most predominant cancer, followed by hypopharynx, oesophagus, prostate and tongue. It is believed that the results of the present study will provide a sound basis for planning the cancer control, prevention, diagnostics, treatment and rehabilitation in Mumbai for the years to come.

Adolescent↗
PubMed2006

Prospective breast cancer risk prediction model for women undergoing screening mammography.

BACKGROUND: Risk prediction models for breast cancer can be improved by the addition of recently identified risk factors, including breast density and use of hormone therapy. We used prospective risk information to predict a diagnosis of breast cancer in a cohort of 1 million women undergoing screening mammography. METHODS: There were 2,392,998 eligible screening mammograms from women without previously diagnosed breast cancer who had had a prior mammogram in the preceding 5 years. Within 1 year of the screening mammogram, 11,638 women were diagnosed with breast cancer. Separate logistic regression risk models were constructed for premenopausal and postmenopausal examinations by use of a stringent (P<.0001) criterion for the inclusion of risk factors. Risk models were constructed with 75% of the data and validated with the remaining 25%. Concordance of the predicted with the observed outcomes was assessed by a concordance (c) statistic after logistic regression model fit. All statistical tests were two-sided. RESULTS: Statistically significant risk factors for breast cancer diagnosis among premenopausal women included age, breast density, family history of breast cancer, and a prior breast procedure. For postmenopausal women, the statistically significant factors included age, breast density, race, ethnicity, family history of breast cancer, a prior breast procedure, body mass index, natural menopause, hormone therapy, and a prior false-positive mammogram. The model may identify high-risk women better than the Gail model, although predictive accuracy was only moderate. The c statistics were 0.631 (95% confidence interval [CI] = 0.618 to 0.644) for premenopausal women and 0.624 (95% CI = 0.619 to 0.630) for postmenopausal women. CONCLUSION: Breast density is a strong additional risk factor for breast cancer, although it is unknown whether reduction in breast density would reduce risk. Our risk model may be able to identify women at high risk for breast cancer for preventive interventions or more intensive surveillance.

Adult↗
PubMed2025

ASGCL: Adaptive Sparse Mapping-based graph contrastive learning network for cancer drug response prediction.

Personalized cancer drug treatment is emerging as a frontier issue in modern medical research. Considering the genomic differences among cancer patients, determining the most effective drug treatment plan is a complex and crucial task. In response to these challenges, this study introduces the Adaptive Sparse Graph Contrastive Learning Network (ASGCL), an innovative approach to unraveling latent interactions in the complex context of cancer cell lines and drugs. The core of ASGCL is the GraphMorpher module, an innovative component that enhances the input graph structure via strategic node attribute masking and topological pruning. By contrasting the augmented graph with the original input, the model delineates distinct positive and negative sample sets at both node and graph levels. This dual-level contrastive approach significantly amplifies the model's discriminatory prowess in identifying nuanced drug responses. Leveraging a synergistic combination of supervised and contrastive loss, ASGCL accomplishes end-to-end learning of feature representations, substantially outperforming existing methodologies. Comprehensive ablation studies underscore the efficacy of each component, corroborating the model's robustness. Experimental evaluations further illuminate ASGCL's proficiency in predicting drug responses, offering a potent tool for guiding clinical decision-making in cancer therapy.

Humans↗
PubMed2005

Cancer risk prediction models: a workshop on development, evaluation, and application.

Cancer researchers, clinicians, and the public are increasingly interested in statistical models designed to predict the occurrence of cancer. As the number and sophistication of cancer risk prediction models have grown, so too has interest in ensuring that they are appropriately applied, correctly developed, and rigorously evaluated. On May 20-21, 2004, the National Cancer Institute sponsored a workshop in which experts identified strengths and limitations of cancer and genetic susceptibility prediction models that were currently in use and under development and explored methodologic issues related to their development, evaluation, and validation. Participants also identified research priorities and resources in the areas of 1) revising existing breast cancer risk assessment models and developing new models, 2) encouraging the development of new risk models, 3) obtaining data to develop more accurate risk models, 4) supporting validation mechanisms and resources, 5) strengthening model development efforts and encouraging coordination, and 6) promoting effective cancer risk communication and decision-making.

Breast Neoplasms↗
PubMed2006

Chromosomal aberrations and SCEs as biomarkers of cancer risk.

Previous studies have suggested that the frequency of chromosomal aberrations (CAs), but not of sister chromatid exchanges (SCEs), predicts cancer risk. We have further examined this relationship in European cohorts comprising altogether almost 22,000 subjects, in the framework of a European collaborative project (CancerRiskBiomarkers). The present paper gives an overview of some of the results of the project, especially as regards CAs and SCEs. The results confirm that a high level of CAs is associated with an increased risk of cancer and indicate that this association does not depend on the time between CA analysis and cancer detection, i.e., is obviously not explained by undetected cancer. The present evidence indicates that both chromatid-type and chromosome-type CAs predict cancer, even though some data suggest that chromosome-type CAs may have a more pronounced predictive value than chromatid-type CAs. CA frequency appears to predict cancers at various sites, although there seems to be a particular association with gastrointestinal cancers. SCE frequency does not appear to have cancer predictive value, at least partly due to uncontrollable technical variation. A number of genetic polymorphisms of xenobiotic metabolism, DNA repair, and folate metabolism affect the level of CAs and might collectively contribute to the cancer predictivity of CAs. Other factors that may influence the association between CAs and cancer include, e.g., exposure to genotoxic carcinogens and internal generation of genotoxic species. Although the association between CA level and cancer is seen at the group level, an association probably also exists for the individual, although it is not known if an individual approach could be feasible. However, group level evidence should be enough to support the use of CA analysis as a tool in screening programs and prevention policies in occupational and environmental health.

Chromosome Aberrations↗
PubMed2003

Prediction of cancer incidence in the Nordic countries: empirical comparison of different approaches.

Prediction of the future number of cancer cases is of great interest to society. The classical approach is to use the age-period-cohort model for making cancer incidence predictions. We made an empirical comparison of different versions of this model, using data from cancer registries in the Nordic countries for the period 1958-1997. We have applied 15 different methods to 20 sites for each sex in Denmark, Finland, Norway and Sweden. Median absolute value of the relative difference between observed and predicted numbers of cases for these 160 combinations of site, sex and country was calculated. The medians varied between 10.4 per cent and 15.3 per cent in predictions 10 years ahead, and between 15.1 per cent and 32.0 per cent for 20 year predictions. We have four main conclusions: (i) projecting current trends worked better than assuming that future rates are equal to present rates; (ii) the method based on the multiplicative APC model often overestimated the number of cancer cases due to its exponential growth over time, but using a power function to level off this growth improved the predictions; (iii) projecting only half of the trend after the first 10 years also gave better long-term predictions; (iv) methods that emphasize trends in the last decade seem to perform better than those that include earlier time trends.

Cohort Studies↗
PubMed2005

The use of chromosome aberrations in predicting breast cancer risk.

In order to assess the usefulness of chromosome aberrations in predicting breast cancer risk, 10 patients with breast cancer diagnosis and appropriately matching 10 healthy controls were chosen. Spontaneous and radiation induced unstable chromosome aberrations in peripheral blood lymphocytes were compared in the two groups. When the spontaneous aberration frequencies were compared, acentric chromosome frequency, scored in the group of patients was significantly higher than that found in the control group (p<0.01). Absolute aberration frequencies as a determinant of radiosensitivity were calculated by subtracting spontaneous aberration frequencies from the frequencies that were obtained following 2 Gy of Co-60 gamma irradiation. Absolute dicentric chromosome frequency significantly increased in the patients1 group (p<0.01) as compared to that observed in the control group. Increases in either spontaneous acentric chromosome frequency or dicentric chromosome frequency as a determinant of an enhanced radiosensitivity in the group of patients may be valuable in predicting breast cancer risk. The studies involving unstable chromosome aberrations can be easily performed and can facilitate cancer diagnosis with minor effort and low cost.

Adult↗
PubMed2001

Helix pomatia agglutinin lectin-binding oligosaccharides of aggressive breast cancer.

Predicting long-term outcome after breast-cancer diagnosis remains problematic, particularly for patients with clinically small, axillary lymph node- negative tumours. Evidence suggests that the lectin Helix pomatia agglutinin (HPA) identifies oligosaccharides associated with poor-prognosis cancer. Our aim was to identify oligosaccharides that bind HPA in aggressive breast cancers. Breast-cancer cell lines (MCF-7, BT-549 and BT-20) and a cell line from human milk (HBL-100), which showed a range of HPA-binding intensities, were used to extract HPA-binding glycoproteins. Oligosaccharides were released using anhydrous hydrazine and separated on a range of HPLC matrices. We investigated whether HPA-binding oligosaccharides from cell lines were present in human breast-cancer tissues, using 69 breast-cancer specimens from patients with between 5 and 10 years' follow-up. A monosialylated oligosaccharide was over-expressed in the cell line that bound HPA strongly. Further analysis by normal-phase HPLC showed that the 2-aminobenzamide-conjugated oligosaccharide had a hydrodynamic volume of 4.58 glucose units (HPAgly1). Increased expression of HPAgly1 was associated with HPA staining of breast-cancer specimens (Student's t-test p = 0.025). Analysis of oligosaccharide levels and disease-free survival after treatment for breast cancer indicated a shorter disease-free interval for patients with elevated levels of HPAgly1. This is the first time that histochemical lectin staining has been correlated with biochemical mapping of oligosaccharides. Using this approach, we have identified a monosialylated HPA lectin-binding oligosaccharide present in breast-cancer cells grown in vitro which is elevated in breast-cancer specimens that bind the lectin.

Adult↗
PubMed2006

Evaluation of an inflammation-based prognostic score (GPS) in patients with metastatic breast cancer.

Prediction of outcome in patients with metastatic breast cancer remains problematical. The present study evaluated the value of an inflammation-based score (Glasgow Prognostic Score, GPS) in patients with metastatic breast cancer. The GPS was constructed as follows: patients with both an elevated C-reactive protein (>10 mg l(-1)) and hypoalbuminaemia (<35 g l(-1)) were allocated a score of 2. Patients in whom only one or none of these biochemical abnormalities was present were allocated a score of 1 or 0, respectively. In total, 96 patients were studied. During follow-up 51 patients died of their cancer. On multivariate analysis of the GPS and treatment received, only the GPS (HR 2.26, 95% CI 1.45-3.52, P<0.001) remained significantly associated with cancer-specific survival. The presence of a systemic inflammatory response (the GPS) appears to be a useful indicator of poor outcome independent of treatment in patients with metastatic breast cancer.

Breast Neoplasms↗
PubMed2002

The accuracy of the increased prostate specific antigen level (greater than or equal to 20 ng./ml.) in predicting prostate cancer: is biopsy always required?

PURPOSE: Urologists are often referred patients who initially present with an extremely high serum prostate specific antigen (PSA) level. Despite a presumptive diagnosis of prostate cancer, many of these men undergo biopsy to obtain a tissue diagnosis before treatment with androgen ablative therapy. We examined a data base of men undergoing prostate biopsy to determine the accuracy of high PSA levels (greater than or equal to 20 ng./ml.) in predicting prostate cancer. MATERIALS AND METHODS: We reviewed the records of 1,250 consecutive patients undergoing transrectal ultrasound guided prostate biopsy at 1 institution. From this data base we identified all patients with PSA greater than or equal to 20 ng./ml. at the time of prostate biopsy. The accuracy of PSA in predicting cancer was determined by calculating positive predictive values for PSA ranges and PSA cutoffs. RESULTS: We identified 187 men (15%) presenting with PSA greater than or equal to 20 ng./ml. Of these 187 men 157 (84.0%) were diagnosed with prostate cancer on initial biopsy. Due to a negative initial biopsy, yet a high suspicion of cancer, 12 (6.4%) patients underwent at least 1 repeat biopsy. Of these 12 men 6 (50%) were diagnosed with cancer on repeat biopsy. Overall, 163 of the 187 men (87.2%) were diagnosed with prostate cancer by biopsy. Stratified by PSA ranges, positive predictive values were 73.6% for 20 to 29.9, 90.3% for 30 to 39.9, 93.8% for 40 to 49.9, 100% for 50 to 99.9, 95% for 100 to 199.9 and 100% for greater than or equal to 200 ng./ml. Using PSA cutoffs positive predictive values were 95.7% for PSA greater than or equal to 30, 97.6% for PSA greater than or equal to 40 and 98.5% for PSA greater than or equal to 50 ng./ml. CONCLUSIONS: Serum PSA, when increased above 50 ng./ml., is 98.5% accurate in predicting the presence of prostate cancer on tissue biopsy. Nonetheless, since transrectal prostate biopsy has a low complication rate and is relatively well tolerated, we recommend continuing to biopsy most patients with high PSA levels. However, carefully selected elderly patients on chronic anticoagulation, with severe co-morbidities or presenting with spinal cord compression may not require biopsy before androgen ablative therapy since PSA is highly accurate in diagnosing prostate cancer at levels greater than 50 ng./ml.

Aged↗
PubMedDate not supplied

Determining the main risk factors and high-risk groups of breast cancer using a predictive model for breast cancer risk assessment in South Korea.

This study was aimed at developing a predictive model for assessing the breast cancer risk of Korean women under the assumption of differences in the risk factors between Westerners and Koreans. The cohort comprised 384 breast cancer patients and 2 control groups: one comprising 166 hospitalized patients and the other comprising 104 nurses and teachers. Two initial models were produced by comparing cases and the 2 control groups, and the final equations were established by selecting highly significant variables of the initial models to test the accuracy of the models in terms of disease probability and predictability. Both the initial models and the final disease-probability models were confirmed to exhibit high degrees of accuracy and predictability. Major risk factors determined by comparing the patients with hospitalized controls were a family history, menstrual regularity, total menstrual duration, age at first full-term pregnancy, and duration of breastfeeding. Major risk factors determined by comparing patients with nurse/teacher controls were age, education level, menstrual regularity, drinking status, and smoking status. The predictive model developed here shows that risk factors for breast cancer differ between Korean and Western subjects in the aspect of breastfeeding behavior. However, identifying the relationship between genetic susceptibility and breast cancer will require further studies with larger samples. In a model with nurse/teacher controls, drinking and higher education were found to be protective variables, whereas smoking was a risk factor. Hence the predictive model in this group could not be generalized to the Korean population; instead, breast cancer incidence needs to be compared among nurses and teachers in a nurse-and-teacher cohort.

Adult↗
PubMed2006

French claims data as a source of information to describe cancer incidence: predictive values of two identification methods of incident prostate cancers.

Claims data from the "Programme de Médicalisation du Système d'Information" (PMSI) have been commonly used for several years to complement cancer registries and describe cancer incidence in France. It is less clear whether or not it is possible to use these data as an independent source of information to assess cancer incidence, in the absence of a regional cancer registry. Following a similar study on breast cancer, we present a study which aimed to evaluate two methods of identifying incident prostate cancer using claims data. These methods were developed using claims data from the Hospices Civils de Lyon (HCL) and their validity was tested against medical records. The first method (M1) identified incident patients as those who had at least one stay with a principal diagnosis of prostate cancer. The second method (M2) had a prostate cancer treatment code in addition to the criteria for the first method. Both methods of identification had similar results, indicating a low rate of false negatives (negative predictive values: M1 = 100 [CI95: 93.8-100], M2 = 98.6 [CI95: 90.1-99.6]) and a high rate of false positives (positive predictive values: M1 = 33.3 [CI95: 23.2-42.1], M2 = 33.7 [CI95: 24.2-43.2]). The sample size did not allow us to produce consistent estimates of sensitivity and specificity. Our results showed that an estimation of the number of incident cases of prostate cancer using both methods of identification would be biased because of the high rate of false positives. Statistical methods that correct identification errors should be used.

France↗
PubMed2002

Molecular markers in prognosis of colorectal cancer and prediction of response to treatment.

Colorectal cancer is the third most common human malignancy. Direct observation and biopsy sampling by colonoscopy have provided unique opportunities to study the natural history of the disease. As a consequence, advances in the understanding of colorectal cancer pathogenesis have evolved more rapidly than with most other solid tumours. Numerous molecular events arising during the development of colorectal cancer have been sorted out over the past two decades. Despite these advances, predicting outcome and response to therapy is still a major challenge in the management of patients with colorectal cancer. Molecular and biochemical markers of colorectal cancer are greatly needed for diagnosis and prognosis as well as for the selection and monitoring of treatments. The purpose of this chapter is to provide a critical assessment of the usefulness of markers in the prognosis and prediction of response to treatment in colorectal cancer.

Antineoplastic Agents↗
PubMed2006

Influence of alternative mammographic screening scenarios on breast cancer incidence predictions (Finland).

BACKGROUND: A population-based early detection programme for breast cancer has been in progress in Finland since 1987. Recently, detailed information about actual screening invitation schemes in 1987-2001 has become available in electronic form, which enables more specific modeling of breast cancer incidence. OBJECTIVES: To present a methodology for taking into account historical municipality-specific schemes of mass screening when constructing predictions for breast cancer incidence. To provide predictions for numbers of new cancer cases and incidence rates according to alternative future screening policies. METHODS: Observed municipality-specific screening invitation schemes in Finland during 1987-2001 were linked together with breast cancer data. The incidence rate during the observation period was analyzed using Poisson regression, and this was done separately for localized and non-localized cancers. For modeling, the screening programme was divided into seven different components. Alternative screening scenarios for future mass-screening practices in Finland were created and an appropriate model for incidence prediction was defined. RESULTS AND CONCLUSION: Expanding the screening programme would increase the incidence of localized breast cancers; the biggest increase would be obtained by expanding from women aged 50-59 to 50-69. The impacts of changes in the screening practices on predictions for non-localized cancers would be minor.

Adult↗
PubMed2005

Microarray analysis identifies a death-from-cancer signature predicting therapy failure in patients with multiple types of cancer.

Activation in transformed cells of normal stem cells' self-renewal pathways might contribute to the survival life cycle of cancer stem cells and promote tumor progression. The BMI-1 oncogene-driven gene expression pathway is essential for the self-renewal of hematopoietic and neural stem cells. We applied a mouse/human comparative translational genomics approach to identify an 11-gene signature that consistently displays a stem cell-resembling expression profile in distant metastatic lesions as revealed by the analysis of metastases and primary tumors from a transgenic mouse model of prostate cancer and cancer patients. To further validate these results, we examined the prognostic power of the 11-gene signature in several independent therapy-outcome sets of clinical samples obtained from 1,153 cancer patients diagnosed with 11 different types of cancer, including 5 epithelial malignancies (prostate, breast, lung, ovarian, and bladder cancers) and 5 nonepithelial malignancies (lymphoma, mesothelioma, medulloblastoma, glioma, and acute myeloid leukemia). Kaplan-Meier analysis demonstrated that a stem cell-like expression profile of the 11-gene signature in primary tumors is a consistent powerful predictor of a short interval to disease recurrence, distant metastasis, and death after therapy in cancer patients diagnosed with 11 distinct types of cancer. These data suggest the presence of a conserved BMI-1-driven pathway, which is similarly engaged in both normal stem cells and a highly malignant subset of human cancers diagnosed in a wide range of organs and uniformly exhibiting a marked propensity toward metastatic dissemination as well as a high probability of unfavorable therapy outcome.

Animals↗
PubMedDate not supplied

Multistage prediction of cancer in serially dosed animals with application to the ED01 study.

The ED01 study is a large-scale experiment in which mice were exposed to the known carcinogen 2-acetylaminofluorene (2-AAF). The dosing was continuous until time of death for most of the mice. However, for some mice, dosing was terminated at specified intervals prior to sacrifice time. Exploratory model fitting of the continuous dosing data of sacrificed mice using liver neoplasms as the endpoint was conducted by Brown and Hoel (1982). They report that the multistage model of Armitage and Doll (1954) fits the data moderately well, however a much improved fit with fewer parameters is attainable by a modified multistage model. These two models, with parameter estimates obtained from the data on continuous dosing, are referred to in this paper as Model 1 and Model 2, respectively. Models 1 and 2 are extended to provide for termination of dosing prior to time of sacrifice. The resultant equations are used to predict cancer responses in the serial dosing part of the ED01 study, and then are compared to the actual observed tumor frequencies. The objective is to evaluate the change in cancer risk over time after dosing is terminated, and to establish which stages in the multistage process are affected by dose. The predictive capabilities of the two models are compared and contrasted. Model 1, which presumes four stages of which two are affected by dose, predicts only moderately well and is inconclusive as to which two of the stages are the ones affected. Model 2, which differs from the multistage model by using a "J-shaped" curve instead of a polynomial for the dose metameter, provides an improvement over Model 1.(ABSTRACT TRUNCATED AT 250 WORDS)

2-Acetylaminofluorene↗