Prediction of clinical behaviour and treatment for cancers.
Prediction of clinical behaviour and treatment for cancers is based on the integration of clinical and pathological parameters. Recent reports have demonstrated that gene expression profiling provides a powerful new approach for determining disease outcome. If clinical and microarray data each contain independent information then it should be possible to combine these datasets to gain more accurate prognostic information. Here, we have used existing clinical information and microarray data to generate a combined prognostic model for outcome prediction for diffuse large B-cell lymphoma (DLBCL). A prediction accuracy of 87.5% was achieved. This constitutes a significant improvement compared to the previously most accurate prognostic model with an accuracy of 77.6%. The model introduced here may be generally applicable to the combination of various types of molecular and clinical data for improving medical decision support systems and individualising patient care.
Futschik
Matthias E
ME
Departments of Information Science and Biochemistry, University of Otago, Dunedin, New Zealand.
Sullivan
Mike
M
Reeve
Anthony
A
Kasabov
Nikola
N
D000465:Algorithms; D000970:Antineoplastic Agents / Q000627:therapeutic use*; D001185:Artificial Intelligence; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D017143:Fuzzy Logic; D006801:Humans; D016393:Lymphoma, B-Cell / Q000175:diagnosis* / Q000188:drug therapy* / Q000235:genetics; D016571:Neural Networks, Computer; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010363:Pattern Recognition, Automated; D011379:Prognosis; D015203:Reproducibility of Results; D018570:Risk Assessment / Q000379:methods*; D012680:Sensitivity and Specificity
D000970:Antineoplastic Agents