Accurate and dynamic predictive model for better prediction in medicine and healthcare.

INTRODUCTION: Information and communication technologies (ICTs) have changed the trend into new integrated operations and methods in all fields of life. The health sector has also adopted new technologies to improve the systems and provide better services to customers. Predictive models in health care are also influenced from new technologies to predict the different disease outcomes. However, still, existing predictive models have suffered from some limitations in terms of predictive outcomes performance. AIMS AND OBJECTIVES: In order to improve predictive model performance, this paper proposed a predictive model by classifying the disease predictions into different categories. To achieve this model performance, this paper uses traumatic brain injury (TBI) datasets. TBI is one of the serious diseases worldwide and needs more attention due to its seriousness and serious impacts on human life. CONCLUSION: The proposed predictive model improves the predictive performance of TBI. The TBI data set is developed and approved by neurologists to set its features. The experiment results show that the proposed model has achieved significant results including accuracy, sensitivity, and specificity.

Combined p21WAF1/CIP1 and p53 overexpression predict improved survival in muscle-invasive bladder cancer treated by radical radiotherapy.

PURPOSE: The prognostic value of p21 and p53 expression was evaluated for patients with muscle-invasive bladder cancer treated by radical radiotherapy. METHODS AND MATERIALS: Sixty-eight paraffin-embedded sections from surgically resected tumors taken prior to irradiation were immunostained for p21 and p53. RESULTS: Nuclear staining for p21 and p53 was demonstrated in 32/68 (47%) and 46/68 (68%) tumors, respectively. There was no correlation between p21 and p53 immunopositivity in this group (r = 0.067, p = 0.56). Patients were stratified into four distinct groups depending on staining for p21 and p53: p21+p53+, p21+p53-, p21-p53+, and p21-p53-. Patients with p21+p53+ tumors had the best prognosis with a 3-year survival of 82% compared to 12% for p21-p53+ tumors (p = 0.0031), 29% for p21+p53- tumors (p = 0.0108); and 45% for p21-p53- tumors (p = 0.0375). The p21+p53+ group also demonstrated significantly improved survival when a combined analysis was performed of p21-p53+, p21-p53-, and p21+p53- tumors (3-year survival = 30%, p = 0.0062). In a multivariate model, p21+p53+ tumors (p = 0.0108, relative risk [RR] = 5.18) and complete/partial response (p = 0.0019, RR = 3.76) were the only independent predictors of improved survival. CONCLUSIONS: With muscle-invasive bladder tumors treated by radical radiotherapy, stratification for p21 and p53 identifies distinct prognostic groups, with p21+p53+ tumors being associated with the best survival and p21-p53+ the worst.

Neural network analysis of clinicopathological and molecular markers in bladder cancer.

PURPOSE: To evaluate retrospectively the ability of an artificial neural network (ANN) to predict bladder cancer recurrence within 6 months of diagnosis and stage progression in patients with Ta/T1 bladder cancer, and 12-month cancer-specific survival in patients with T2-T4 bladder cancer. MATERIALS AND METHODS: Data were analyzed using a NeuralWorks Professional II/Plus software package. The input neural data consisted of clinicopathological and molecular characteristics. Distinct patient groups were used for the prediction of stage progression and tumor recurrence in Ta/T1 bladder cancers, and 12-month cancer-specific survival for patients with T2-T4 tumors. ANN predictions were compared with those of four consultant urologists. RESULTS: The accuracy of the neural network in predicting stage progression and recurrence within 6 months for Ta/T1 tumors and 12-month cancer-specific survival for T2-T4 cancers was 80%, 75% and 82% respectively; with corresponding figures for clinicians being 74%, 79% and 65%. On restricting the validation subset to patients with T1G3 tumors in relation to stage progression, the sensitivity of the ANN analysis increased to 100% with a specificity of 78% and an overall accuracy of 82%. The performance of the ANN in predicting stage progression in T1G3 tumors was significantly higher than that of clinicians (p = 0.25 for the ANN and p = 0.008 for clinicians, McNemar test). CONCLUSIONS: Data analysis using an ANN has been shown to be a useful adjunct in predicting outcomes in patients with bladder cancer and out-performs clinicians' predictions of stage progression in the high risk group of patients with T1G3 disease.

TP53 accumulation predicts improved survival in patients resistant to systemic cisplatin-based chemotherapy for muscle-invasive bladder cancer.

To examine retrospectively the prognostic significance of TP53 immunoreactivity for both tumor response and patient survival in 83 patients with nonmetastatic muscle-invasive bladder cancer treated with a single transurethral resection (TUR) of tumor and combined cisplatin-based systemic chemotherapy followed by repeat TUR, paraffin-embedded sections of a bladder tumor obtained at TUR before chemotherapy (1 T2, 52 T3, and 30 T4) were immunostained for TP53 using monoclonal PAb1801 and DO-7 antibodies. For the entire cohort, TP53 immunopositivity (PAb1801 or DO-7) did not predict complete response (CR), complete or partial response (PR), progressive disease, or time to death from bladder cancer. There was a highly significant correlation between PAb1801 and DO-7 nuclear immunoreactivity (r = 0.8242; P<0.0001). In 76 patients in which complete clinical data were available, tumor stage (T2/T3; P = 0.0499), CR and PR (P = 0.0016) and CR (P<0.0001) were associated with patient survival. In a multivariate model, CR (P<0.0001) was the only independent predictor of improved survival. In complete responders, neither TP53 immunostaining nor clinicopathological factors stratified patients into prognostic groups. However, in the subset of patients (n = 38) who were chemoresistant (PR or progressive disease), improved survival was associated with > or =20% TP53 immunoreactivity (PAb1801; P = 0.0191) and tumor stage (T2/T3; P = 0.0358). TP53 immunopositivity (PAb1801 or DO-7) did not predict overall survival or response to systemic chemotherapy in patients with nonmetastatic but predominantly clinical stage > or =T3 bladder cancer, but it had prognostic significance within the chemoresistant subgroup.

Molecular biological changes in bladder cancer.

A large number of potential molecular markers of bladder cancer have been identified, although only a few are truly independent prognostic factors. A number of markers may need to be measured in a single tumour and used as a combination for use in the diagnosis and prognosis of transitional cell carcinoma (TCC). Epidermal growth factor receptor immunoreactivity has been shown to be an independent predictor of survival and stage progression. TP53 may be an independent predictor of recurrence and overall survival in TCC confined to the bladder, and TP53 alterations may predict chemosensitivity in patients who have had TCC treated by radical cystectomy. At present molecular techniques such as fluorescence in situ hybridization and the polymerase chain reaction are restricted to the laboratory, but immunohistochemical methods are available in most hospital pathology departments. There are some discrepancies and conflicting reports of the usefulness of molecular markers in different studies, and these need to be addressed in large, prospective, multi-institutional studies using standardized molecular techniques.

Acute upper limb ischemia resulting from cleidocranial dysostosis.

A case is reported of a woman with an ischemic arm as a result of damage to the subclavian artery by the clavicular anomaly associated with cleidocranial dysostasis. Only one similar case has been reported previously.