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PubMed1992

Levels of expression of breast epithelial mucin detected by monoclonal antibody BrE-3 in breast-cancer prognosis.

Taking into consideration the relationship of breast neoplasia with recent knowledge obtained on the molecular structure and biosynthesis of the breast epithelial mucin, an epitope on this molecule detected by monoclonal antibody (MAb) BrE-3 was chosen as a marker to study the correlation of expression of the mucin and prognosis in infiltrating ductal carcinomas of the breast. Strong statistical validation was obtained in the use of BrE-3 in immunohistochemical procedures where scores for the expression of the mucin on paraffin-embedded sections of the primary breast tumors were studied. Four different immunohistochemical variables measuring levels of expression (intensity or prevalence) in cytoplasm or membrane were obtained for each of 227 patients' breast tumors and subjected to Kaplan-Meier univariate and Cox proportional-hazards multi-variate analysis. Additionally, traditional prognostic variables for breast-cancer prognosis (grade of differentiation, age, tumor size, axillary-lymph-node involvement and estrogen receptors) were subjected to identical analyses. In uni-variate analysis, low cytoplasmic intensity, high membrane prevalence, and high membrane intensity of mucin expression were each found to be significantly associated with good prognosis in relation to both survival or relapse time. In multi-variate analysis, all 4 immunohistochemical parameters were significantly associated with both survival and relapse time in these patients. Among the traditional variables, 3 (axillary-node involvement, grade of differentiation and tumor size) were also found to be statistically significant at the uni-variate and multi-variate level. A multi-variate analysis of the combined immunohistochemical and traditional variables identified the 4 immunohistochemical parameters, tumor size and axillary-node involvement as having the highest level of association with either survival or relapse time. These variables were then combined and served to define a prognostic model [ILCPS(Comb)], which was found to have the capacity to separate the patient population studied into 4 prognostic groups in terms of survival and 3 groups in terms of relapse. As expected, the ILCPS(Comb) was shown to have a higher level of prognostic association with both survival and relapse than the individual variables themselves, the traditional variables together or the immunohistochemical variables together. Our approach develops a theoretical framework and a statistical model, employing levels of expression of the breast epithelial mucin and 3 traditional variables, which identifies, in terms of prognosis, distinct sub-populations of patients with infiltrating breast carcinoma with defined risk functions.

Adolescent↗

PubMed2026

A machine learning-derived intratumoral heterogeneity-related signature predicts the prognosis for and therapeutic response in patients with skin cutaneous melanoma.

BACKGROUND: Reliable biomarkers for predicting prognosis and therapeutic response in skin cutaneous melanoma (SKCM) remain limited. This study aimed to develop an intratumoral heterogeneity (ITH)-related prognostic signature for SKCM using integrative machine learning. METHODS: RNA sequencing (RNA-seq) data from 472 SKCM patients in The Cancer Genome Atlas (TCGA) and 214 patients in the GSE65904 cohort were analyzed. ITH scores were calculated using the DEPTH2 algorithm. Differentially expressed genes (DEGs) were identified between high- and low-ITH groups [|log2fold change (FC)| ≥1, false discovery rate (FDR) <0.05]. Based on 38 prognostic DEGs identified by univariate Cox regression, we employed an integrative framework of 101 machine learning algorithm combinations to construct prognostic models in the TCGA training cohort. The model with the highest average concordance index (C-index) was validated in the GSE65904 cohort and selected as the prognostic ITH-related signature (PIRS). Associations of the PIRS risk score with tumor mutational burden (TMB), immune cell infiltration, immune checkpoint gene expression, and drug sensitivity were systematically evaluated. Model performance was assessed using receiver operating characteristic (ROC) curves and Cox regression analyses. RESULTS: A 38-gene PIRS was constructed using the plsRcox algorithm. Patients with high PIRS risk scores exhibited significantly poorer overall survival (OS) in both the TCGA and Gene Expression Omnibus (GEO) cohorts. The PIRS was identified as an independent prognostic factor, with area under the curve (AUC) values of 0.779, 0.734, and 0.756 for 1-, 3-, and 5-year survival, respectively. High-risk samples displayed significantly lower TMB (P<0.05), reduced immune and stromal cell infiltration (P<0.001), downregulated immune function, and decreased expression of immune checkpoint genes. Additionally, high- and low-PIRS risk score groups exhibited distinct sensitivity patterns to different classes of targeted agents. CONCLUSIONS: The machine learning-derived PIRS robustly predicts prognosis in SKCM patients. Its clinical application is promising for optimizing patient risk stratification and treatment decisions, though further prospective validation is warranted.

Skin cutaneous melanoma (SKCM)↗

PubMed2005

Comparison of the Radiation Therapy Oncology Group recursive partitioning classification and Union Internationale Contre le Cancer TNM classification for patients with head and neck carcinoma.

BACKGROUND: Prognostic models need to be tested in external validation studies to assess generalizability. Recursive partitioning analysis (RPA), a prognostic system based on the creation of a classification tree, has been proposed as a classification method in patients with head and neck carcinoma. The aim of this study was to compare the RPA and Union Internationale Contre le Cancer (UICC) TNM classification systems in patients with head and neck carcinoma treated consecutively in a single center. METHODS: A total of 2166 patients with carcinomas of the oral cavity, oropharynx, hypopharynx, and larynx was classified according to both the RPA and the TNM classification systems, and the results were compared. The endpoints considered were observed survival and survival free of locoregional tumor. The two methods of classification were evaluated objectively by use of measures of intrastage homogeneity (hazard consistency), interstage heterogeneity (hazard discrimination), predictive power (outcome prediction), and patient distribution between stages (balance). RESULTS: When the endpoint considered was observed survival, there were no clinically relevant differences between the two classifications. However, when the endpoint was locoregional control, the RPA system was sensitive to the type of treatment used, and it was not generalizable. CONCLUSIONS: To evaluate generalizability, new classification proposals need external validation studies that objectively measure the quality of the model. The performance of the RPA system was not reproducible in our cohort of patients when the endpoint evaluated was locoregional control.

Head and Neck Neoplasms↗

PubMed1994

Age as a prognostic factor in the malignant melanoma population.

BACKGROUND: The incidence of malignant melanoma is increasing faster than any other cancer, and the state of Florida has one of the highest incidence of melanoma in the United States. This increased incidence is thought to be due to the intense sunlight exposure and ultraviolet radiation exposure in the elderly population. With the increased emphasis on issues of aging, it is appropriate to study the role of age as a prognostic factor for malignant melanoma in the Florida population. METHODS: A retrospective, computer-aided search identified 442 consecutively registered patients with malignant melanoma at the Cutaneous Oncology Program. All patients had stage 1 or 2 disease (cutaneous disease only) at diagnosis. Prognostic variables analyzed included the most powerful factors for stage 1 and 2 melanoma, tumor thickness, ulceration, and Clark level of invasion. Other prognostic variables included in the analysis were the clinical variables of sex and primary site (axial vs. extremity). The population was divided into patients < or = 65 and > 65 years of age. RESULTS: Significant disease-free survival differences were encountered in the older population, with only 55% of the elderly population being disease free at 5 years compared with 65% for the younger population (p = 0.0073). However, a greater percentage of patients with melanoma who were > 65 years of age had ulcerated lesions (17.5% vs. 12.9%) and a greater percentage of thick lesions at diagnosis (67.2% vs. 62.7%). Both of these prognostic factors would bias the older population with a poorer survival. A stepwise regression analysis of the entire population was performed, treating age as a continuous variable. Surprisingly, increasing age along with tumor thickness were the only significant predictors for disease-free survival. After inclusion of these two prognostic variables, none of the other prognostic factors, including Clark level, ulceration, sex, and primary site, added to the prognostic model. CONCLUSIONS: From this analysis, it is apparent that geriatric patients with melanoma have a worse prognosis than a younger control population, even after the correction for the more commonly cited prognostic factors. This information should be used in mathematical modeling to identify high-risk populations who are candidates for perhaps more aggressive primary or adjuvant therapies.

Adult↗

PubMed2026

Efferocytosis related KCTD12 is a clinico-immune target in lung adenocarcinoma.

BACKGROUND: Efferocytosis, the clearance of apoptotic cells by phagocytes, contributes to immune homeostasis but may also promote tumor immune tolerance. However, its transcriptional landscape and clinical relevance in lung adenocarcinoma (LUAD) remain incompletely understood. METHODS: We systematically analyzed efferocytosis-associated genes across TCGA and multiple GEO datasets to classify LUAD subtypes and construct a prognostic risk model. The prognostic and immunological relevance of this model was validated in four independent cohorts and further assessed through immune infiltration, genomic, and immunotherapy datasets. Functional and pharmacogenomic analyses were performed to identify potential therapeutic vulnerabilities, and the efferocytosis-associated signature gene KCTD12 was subsequently validated in vitro. RESULTS: Unsupervised clustering identified two efferocytosis-based LUAD subtypes with distinct prognostic and immune-metabolic characteristics. The derived risk model robustly predicted overall survival across validation cohorts. Among the model genes, KCTD12 emerged as an efferocytosis-associated candidate linked to an immune-active tumor microenvironment. Across the analyzed single-cell, spatial transcriptomic, and immunotherapy-treated cohorts, higher KCTD12 expression was associated with enhanced cytotoxic T-cell activity and more favorable treatment outcomes. Functional experiments confirmed that KCTD12 suppresses tumor cell proliferation, reduces colony formation, and enhances OT-1 CD8+ T-cell activation and cytotoxicity. CONCLUSIONS: Our study identifies an efferocytosis-associated transcriptional program linked to immune heterogeneity and prognosis in LUAD. The efferocytosis-related risk signature provides a framework for prognostic and immune stratification, while KCTD12 represents a candidate biomarker associated with immune activation and clinical outcomes in immunotherapy-treated cohorts. Its treatment-specific predictive value requires prospective validation in appropriately controlled studies.

KCTD12↗

PubMed1999

[Radical prostatectomy in prostate adenocarcinoma. Clinical factors influencing the pathological stage. Diagnostic model].

MATERIAL AND METHODS: Study on the efficacy of stage diagnosis, how to support it based on clinical objective data and description of a prognostic model. Analysis of 160 patients diagnosed with localized prostate adenocarcinoma undergoing radical prostatectomy in the Clínica Universitaria de Navarra between 1988-1997. The statistical study used Fisher's or Pearson's tests for the comparison of qualitative variables. A logistic regression multivariate analysis was run to avoid confounding factors in the pathological stage. RESULTS: 85/160 (53%) were correctly staged. Incorrect staging occurred in patients with higher clinical stage (T1-T2a: 25%; T2bc: 65%). The univariate study shows that the pathological stage is significantly correlated to: a) serum PSA levels (15 ng/mL in P2 vr. 25 ng/mL in P3-4), the most suitable cutoff value being 15 ng/mL. b) digital rectal examination and Gleason. Negatively influencing factors in the multivariate study were: PSA greater than 15 ng/mL, Gleason greater than 5 and a T2bc clinical stage. Risk groups: 4 risk groups are established based on the above factors (inclusion in group 1 involves an 8% risk of having P3, 30% in group 2, 56% in group 3 and 84% in group 4). CONCLUSIONS: The clinical factors with influence in the pathological stage are PSA, Gleason and clinical stage. The reliability of the risk groups established based on these factors is remarkable.

Adenocarcinoma↗

PubMed1990

A histomorphometric analysis of trephine biopsies of bone marrow from 65 patients with chronic myeloid leukemia. Classification of patients into subgroups with different survival patterns.

A histomorphometric (planimetric) study was performed on trephine biopsies of the bone marrow taken at presentation from 65 patients (31 males and 34 females, with a median age of 48 years) with chronic myeloid leukemia (CML). Specimens from 20 patients (9 males and 11 females, with a median age of 53 years) without any hematologic disorders served as controls. Of the various histologic variables tested, only the counts of neutrophilic granulocytes per 1 sq mm, the ratio of granulocytopoiesis to megakaryopoiesis and the density of reticulin (argyrophilic) fibers revealed a significant correlation with the prognosis. The CML patients were separated into two groups with different survival patterns. Group I (34 patients with a median survival of 24 months) mostly contained cases with the so-called "megakaryocytic subtype" of CML, which is accompanied by variable degrees of fibrosis; group II (31 patients with a median survival of 36 months) mainly contained cases with the "granulocytic subtype," which is not accompanied by myelofibrosis. Among the morphometric parameters, a positive correlation existed between the megakaryocyte count and the reticulin fiber density, which underlines the important role of that cell lineage in fibrillogenesis. There were multiple interrelationships between the histomorphometric variables and the laboratory data. Consequently, multivariate regression methods (using Cox's proportional hazards model) were applied to assess the relative predictive value of the patient characteristics for survival. The derived prognostic model divided the patients into two risk groups, with median survivals of 14 and 41 months, respectively. In order of their entry into the regression model, these variables were percentage of neutrophils in the differential blood count, amount of granulopoiesis, liver size, percentage of peripheral myeloblasts and density of reticulin fibers in the bone marrow. In comparing the two patient groups, based on bone marrow histomorphometric parameters, this model revealed that two of those factors (amount of granulopoiesis and density of reticulin fibers) had a significant correlation with the prognosis.

Biopsy↗

PubMed2006

An approach to estimating prognosis using fractional polynomials in metastatic renal carcinoma.

We present a prognostic model for metastatic renal cell carcinoma based on fractional polynomials. We retrospectively analysed 425 metastatic renal cell carcinoma patients treated with subcutaneous recombinant cytokine-based home therapies in consecutive trials. In our approach, we categorised a continuous prognostic index produced by the multivariable fractional polynomial (MFP) algorithm, using a strategy in which continuous predictors are kept continuous. The MFP algorithm selected five prognostic factors as significant at the 5% level in a multivariable model: lymph node metastases, liver metastases, bone metastases, age, C-reactive protein and neutrophils. The MFP model allowed us to divide patients into four risk groups achieving median overall survivals of 38 months (low risk), 23 months (low intermediate risk), 15 months (high intermediate risk) and 5.6 months (high risk). Our approach, based on categorising a continuous prognostic index produced by the MFP algorithm, allowed more flexibility in the determination of risk groups than traditional approaches.

Algorithms↗

PubMed2003

Defining community acquired pneumonia severity on presentation to hospital: an international derivation and validation study.

BACKGROUND: In the assessment of severity in community acquired pneumonia (CAP), the modified British Thoracic Society (mBTS) rule identifies patients with severe pneumonia but not patients who might be suitable for home management. A multicentre study was conducted to derive and validate a practical severity assessment model for stratifying adults hospitalised with CAP into different management groups. METHODS: Data from three prospective studies of CAP conducted in the UK, New Zealand, and the Netherlands were combined. A derivation cohort comprising 80% of the data was used to develop the model. Prognostic variables were identified using multiple logistic regression with 30 day mortality as the outcome measure. The final model was tested against the validation cohort. RESULTS: 1068 patients were studied (mean age 64 years, 51.5% male, 30 day mortality 9%). Age >/=65 years (OR 3.5, 95% CI 1.6 to 8.0) and albumin <30 g/dl (OR 4.7, 95% CI 2.5 to 8.7) were independently associated with mortality over and above the mBTS rule (OR 5.2, 95% CI 2.7 to 10). A six point score, one point for each of Confusion, Urea >7 mmol/l, Respiratory rate >/=30/min, low systolic(<90 mm Hg) or diastolic (</=60 mm Hg) Blood pressure), age >/=65 years (CURB-65 score) based on information available at initial hospital assessment, enabled patients to be stratified according to increasing risk of mortality: score 0, 0.7%; score 1, 3.2%; score 2, 3%; score 3, 17%; score 4, 41.5% and score 5, 57%. The validation cohort confirmed a similar pattern. CONCLUSIONS: A simple six point score based on confusion, urea, respiratory rate, blood pressure, and age can be used to stratify patients with CAP into different management groups.

Adult↗

PubMed1990

[Surgical risk in old age. Prospective evaluation of a prognosis index].

A prognostic index of octogenarians was calculated by a critical retrospective analysis of our results of the outcome after operations. In the years 1986-1988 this index was prospective validated at 381 patients over 80 years old. The accuracy was 85%. The other quality parameter (sensitivity, specificity, negative and positive predictive values) of the decision point of our prognostic model reached good or very good values too. This results showed the possibility of application for the recommended prognostic index. In the last three years the postoperative mortality has fallen essential in general surgery. At present the mortality estimate 20% for emergency and elective operations for the whole hospital stay of the patients. The direct clinical utility of risk analyses to work out prognostically statements was proved by this study.

Aged↗

PubMed2025

An anti-androgen resistance-related gene signature acts as a prognostic marker and increases enzalutamide efficacy via PLK1 inhibition in prostate cancer.

BACKGROUND: Anti-androgen resistance remains a major clinical challenge in the treatment of prostate cancer (PCa), leading to disease progression and treatment failure. Despite extensive research on resistance mechanisms, a reliable prognostic model for predicting patient outcomes and guiding therapeutic strategies is still lacking. This study aimed to develop a novel gene signature related to anti-androgen resistance and evaluate its prognostic and therapeutic implications. METHODS: Anti-androgen resistance-related differentially expressed genes (ARRDEGs) were identified through transcriptomic analysis of enzalutamide- and dual enzalutamide abiraterone-resistant PCa cell lines from the GEO database. Functional enrichment analysis was performed to determine the biological roles of these genes. A prognostic gene signature was developed using univariate Cox regression, LASSO, and multivariate Cox regression models. The model was validated in independent PCa cohorts from The Cancer Genome Atlas (TCGA). Additionally, we assessed the correlation between the signature, immune infiltration, immune checkpoint expression, and drug sensitivity. The efficacy of PLK1 inhibition combined with enzalutamide was further explored using in vitro and in vivo experiments. RESULTS: We identified 304 ARRDEGs, from which three key genes (LMNB1, SSPO, and PLK1) were selected to construct a prognostic signature. This gene signature effectively stratified PCa patients into high- and low-risk groups, with the high-risk group exhibiting shorter recurrence-free survival and distinct immune characteristics. High-risk patients demonstrated elevated immune checkpoint expression (B7H3, CTLA-4, B7-1, and TIGIT), increased M2 macrophage infiltration, and enhanced sensitivity to chemotherapy and targeted therapy. Mechanistically, PLK1 inhibition potentiated the antitumor effect of enzalutamide by downregulating SLC7A11 and inducing ferroptosis, providing a potential therapeutic strategy to overcome anti-androgen resistance. CONCLUSION: We established a novel ARRDEGs-based prognostic signature that predicts PCa progression and response to chemotherapy and targeted therapy. The integration of this signature with immune profiling and drug sensitivity analysis provides a valuable tool for precision oncology in PCa. Our findings highlight the potential of PLK1 inhibition as a therapeutic strategy to enhance enzalutamide efficacy and overcome resistance.

Humans↗

PubMed2003

[New scoring systems for severity outcome of liver cirrhosis and hepatocellular carcinoma: current issues concerning the Child-Turcotte-Pugh score and the Model of End-Stage Liver Disease (MELD) score].

It has been approximately 30 years since Child-Turcotte-Pugh score has been used as a predictor of mortality in patients with liver cirrhosis and hepatocellular carcinoma (HCC). Recently, new prognostic models such as Model for End-Stage Liver disease (MELD), Short- and Long-term Prognostic Indices (STPI and LTPI), Rockall score, and Emory score were proposed for predicting survival in patients with liver cirrhosis treated by transjugular intrahepatic portosystemic shunt (TIPS). In MELD scoring, three independent variables which showed a wide range of results including serum creatinine, serum bilirubin and international normalization ratio (INR) of prothrombin time were evaluated in log(e) scale in comparison with simply categorized-into-three scoring system of Child-Turcotte-Pugh. The etiology of liver cirrhosis was applied to the score of MELD: alcoholic or cholestatic, 0; viral or others, 1. Concurrent statistic (C-statistic) of MELD (0.73-0.84) was slightly superior or insignificantly different to that (0.67-0.809) of Child-Turcotte-Pugh score. In February 2002, UNOS status 2a and 2b were replaced with MELD score for priority allocation of liver transplantation. MELD score does not reflect the severity of patients with HCC or metabolic disorders. For assessing prognosis in patients with liver cirrhosis or HCC, there seems little reason to replace the well established Child-Turcotte-Pugh score. Herein the literatures was briefly reviewed.

Bilirubin↗

PubMed2006

Clinical prognostic rules for severe acute respiratory syndrome in low- and high-resource settings.

BACKGROUND: An accurate prognostic model for patients with severe acute respiratory syndrome (SARS) could provide a practical clinical decision aid. We developed and validated prognostic rules for both high- and low-resource settings based on data available at the time of admission. METHODS: We analyzed data on all 1755 and 291 patients with SARS in Hong Kong (derivation cohort) and Toronto (validation cohort), respectively, using a multivariable logistic scoring method with internal and external validation. Scores were assigned on the basis of patient history in a basic model, and a full model additionally incorporated radiological and laboratory results. The main outcome measure was death. RESULTS: Predictors for mortality in the basic model included older age, male sex, and the presence of comorbid conditions. Additional predictors in the full model included haziness or infiltrates on chest radiography, less than 95% oxygen saturation on room air, high lactate dehydrogenase level, and high neutrophil and low platelet counts. The basic model had an area under the receiver operating characteristic (ROC) curve of 0.860 in the derivation cohort, which was maintained on external validation with an area under the ROC curve of 0.882. The full model improved discrimination with areas under the ROC curve of 0.877 and 0.892 in the derivation and validation cohorts, respectively. CONCLUSION: The model performs well and could be useful in assessing prognosis for patients who are infected with re-emergent SARS.

Adult↗

PubMed2006

Post-chemotherapy gross tumor volume is predictive of survival in patients with stage III non-small cell lung cancer treated with combined modality therapy.

PURPOSE: To evaluate the influence of clinical covariates, particularly pre-chemotherapy gross tumor volume (GTV), post-chemotherapy GTV, on overall survival in the treatment of stage III non-small cell lung cancer (NSCLC). METHODS AND MATERIALS: We retrospectively analyzed 102 patients who were enrolled on three consecutive clinical trials, which employed the treatment paradigm of two cycles of induction chemotherapy followed by thoracic radiation therapy. The pre-chemotherapy GTV, post-chemotherapy GTV, change in GTV, histology, disease stage, performance status, age, race, treatment with concurrent chemoradiotherapy versus radiotherapy alone were evaluated to determine their impact on overall survival. The log10 of the GTV was used to normalize the data and thereby reduce the impact of exceptionally large values. RESULTS: Both the log10 of the post-chemotherapy GTV and Eastern Cooperative Oncology Group (ECOG) performance status covariates were highly prognostic for overall survival (p = 0.006 and p = 0.008, respectively). Disease stage (at diagnosis) was also significant (p = 0.048). The log10 pre-chemotherapy GTV covariate was borderline significant (p = 0.067). The strongest prognostic model was the two-covariate model, which contained the log10 post-chemotherapy GTV and ECOG performance status covariates, (model chi2 of 18.67, with p = 0.001 for each covariate). CONCLUSIONS: The log10 post-chemotherapy GTV has significant prognostic survival value when the strategy of induction chemotherapy is employed in the treatment on stage III NSCLC. ECOG performance status and stage were also significant prognostic factors for survival.

Adult↗

PubMed1998

Ethanol administration delays recovery from acute pancreatitis induced by exocrine hyperstimulation.

Alcohol consumption causes acute alcohol pancreatitis and worsens the prognosis; however, there is no useful model for elucidation of the mechanism underlying this worsening. The aim of our study was to establish a new prognostic model of acute alcohol pancreatitis in rats. To ascertain the effect of continuous infusion of ethanol on each phase, i.e., progression and recovery, in caerulein-induced pancreatic injury in rats, we infused a physiological or supramaximal dose of caerulein intravenously to conscious Wistar rats for up to 6 h (time: 0-6 h) with or without ethanol infusion for 9 h (time: 3-12 h). Ethanol did not induce the pancreatic injury alone or when combined with a physiological dose of caerulein. In the progression phase, ethanol infusion for 3 h (time: 6 h) did not aggravate the pancreatic injury induced by a supramaximal dose of caerulein in terms of plasma amylase and lipase activities but did increase the pancreatic calcium level. In the recovery phase, however, ethanol infusion for 9 h (time: 12 h) significantly restrained the recovery from pancreatic injury as monitored in terms of these activities. Further, ethanol infusion for 9 h significantly increased the cumulative urinary excretion of amylase from 12 to 27 h but did not do the same from 0 to 12 h. In the histological evaluation at 27 h, the induction of acinar cell vacuolization and dilation of the glandular lumina and ducts were significant in the caerulein plus ethanol-treated group. Our findings suggest that ethanol administration delays the recovery rather than worsens the progression in acute pancreatic injury induced by exocrine hyperstimulation, and we consider our experimental model to be a useful tool for studying the pathogenesis of worsening prognosis in acute alcohol pancreatitis.

Acute Disease↗

PubMed1988

Prospective confirmation of a prognostic classification for Ph+ chronic myeloid leukaemia. The Italian Cooperative Study Group on Chronic Myeloid Leukaemia.

In 1984, Sokal et al described a prognostic model for Ph+ chronic myeloid leukaemia (CML), based on retrospective analysis of data from six series of patients in the United States and Europe. In the same year, a prospective study of prognosis was started by the Italian Cooperative Study Group on CML. The first analysis of the prospective study is based on 508 Ph+, non-blastic, non-transplanted patients registered between January 1984 and December 1986. Application of the hazard ratio formula described in 1984 to this prospective series has shown that the model predicts accurately the frequency and the time-distribution of blastic transformation and death during the first 2 years after diagnosis. These results confirm the earlier conclusion that prognostic discrimination is possible at the time of diagnosis of CML, and indicate that the model described in 1984 can prove useful in evaluation and management of this disease.

Adult↗

PubMed2003

Use of nomograms for predicting survival in patients with castrate prostate cancer.

Given the heterogeneity of prognoses in patients with castrate metastatic prostate cancer, the ability to accurately predict survival is vital for optimal patient counseling, selection of treatments, clinical trial design, and interpretation of clinical data. Over the past 20 years, several prognostic models have been developed in an attempt to refine the clinician's predictive ability. Early models were based on patients with more advanced disease. They included variables that are no longer regularly encountered today and involved cumbersome calculations that were not practical for everyday use in the clinic. Recently, 2 point-based nomograms have been developed based on pretreatment variables measured on a routine basis. These models provide a user-friendly format in which to make sophisticated predictions of survival. These models have improved our ability to predict the outcomes of patients with castrate metastatic disease. However, further work to identify novel prognostic markers to improve the accuracy of these predictions is needed.

Adenocarcinoma↗

PubMedDate not supplied

Neural network applications in physical medicine and rehabilitation.

The purpose of this article is to provide an overview of neural networks and their applications in physical medicine and rehabilitation. Conventional statistical models may present certain limitations that can be overcome by neural networks. We show what neural networks are, how they "learn" regularities from the data, and how they can classify previously unseen cases. We present advantages and disadvantages of using neural networks and compare them with regression models. We explain how neural networks can be used as statistical tools for making inferences using the example of a prognostic model that predicts ambulation after spinal cord injury.

Humans↗