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N Bitterlich

Publications and source records attributed to N Bitterlich.

4 recordsLinked to original sources
PubMed2003

Fuzzy logic-based tumor marker profiles improved sensitivity of the detection of progression in small-cell lung cancer patients.

Tumor markers were used for disease monitoring in small-cell lung cancer patients. The aim of this study was to improve diagnostic efficiency in the detection of tumor progression in small-cell lung cancer patients by using fuzzy logic modeling in combination with a tumor marker panel (NSE, ProGRP, Tumor M2-PK, CYFRA 21-1, and CEA). Thirty-three consecutive small-cell lung cancer patients were included in a prospective study. The changes in blood levels of tumor markers and their analysis by fuzzy logic modeling were compared with the clinical evaluation of response versus non-response to therapy. Clinical monitoring was performed according to the standard criteria of the WHO. Tumor M2-PK was measured in plasma with an ELISA, all other markers were measured in sera. At 90% specificity, clinically detected tumor progression was found by the best single marker, NSE, in 32% of all cases. A fuzzy logic rule-based system employing a tumor marker panel increased the sensitivity significantly (P>0.0001) in small-cell carcinomas to 67% with the threemarker combination NSE/ProGRP/Tumor M2-PK and to 56% with the best two-marker combination ProGRP/Tumor M2-PK, respectively. An improvement of sensitivity was also observed using the two-marker combination of ProGRP/NSE (sensitivity 49%) or NSE/Tumor M2-PK (sensitivity 52%). The fuzzy classifier was able to detect a higher rate of progression in small-cell lung cancer patients compared with the multiple logistic regression analysis using the marker combination NSE/ProGRP/Tumor M2-PK (sensitivity 44%; AUC=0.76). With the fuzzy logic method and different tumor marker panels (NSE, ProGRP and Tumor M2-PK), a new diagnostic tool for the detection of progression in patients with small-cell lung cancer is available.

Aged↗
PubMed2001

Value of combinations of pancreatic function tests to predict mild or moderate chronic pancreatitis.

UNLABELLED: Pancreatic function tests share an insufficient accuracy concerning the detection of mild or moderate forms of chronic pancreatitis. It was evaluated here whether by combination of different assays the prediction or exclusion of chronic pancreatitis could be improved. METHODS: 62 patients with chronic abdominal pain and suspected chronic pancreatitis underwent an endoscopic retrograde pancreaticography. The duct alterations were classified according to the Cambridge criteria. In all individuals the pancreolauryl test in serum (PLT-S) and urine (PLT-U) was performed and elastase 1-immunoreactivity (E) as well as chymotrypsin (Chy) activity in stool were measured. Sensitivities, specificities, receiver-operator-curves as well as cut-off points at optimal accuracies were calculated for each single assay and all test combinations. Cut offs were optimized by a mathematical model to achieve highest accuracies. RESULTS: In 30 patients the pancreatic duct was normal and in 32 patients alterations of the duct system were found. These were classified as mild in 10 patients, as moderate in 8 patients and as severe in 14 patients. In those with mild and moderate disease all pancreatic function tests showed sensitivities/specificities of 60-65% and 65-70%, respectively. Only in severe chronic pancreatitis elastase was superior to the other tests. Combinations of function tests did not lead to improved accuracy. After mathematical optimization the accuracy (sensitivity 80%, specificity 80%) was best for the combination of PLT-S (cut off 4.7 micrograms/ml) and E (cut off 500 micrograms/g). Both parameters had to be below these newly defined cut offs to diagnose chronic pancreatitis. CONCLUSIONS: The accuracy of pancreatic function tests may be improved by use of altered cut offs and a combination of serum pancreolauryl test and elastase. These newly defined cut offs will have to be evaluated in a much larger study.

Adult↗
PubMed1998

Tumour markers in the diagnosis of bronchial carcinoma: new options using fuzzy logic-based tumour marker profiles.

The diagnosis of lung cancer and early knowledge of its histological type are very important; however, this is still a difficult subject for the physician. The aim of this study was to improve the diagnostic efficiency of tumour markers in the diagnosis of bronchial carcinoma by mathematical evaluation of a tumour marker profile employing fuzzy logic modeling. A panel of five tumour markers, including CYFRA 21-1, CEA, NSE, and five additional parameters was determined in 281 patients with confirmed primary diagnosis of bronchial carcinoma of different histology and stage. A further 131 persons, who had acute and chronic benign lung diseases, served as a control group. A classificator was developed using a fuzzy-logic rule-based system. The diagnostic value of the combined tumour markers was significantly better than that of the individual markers and of a combination of CYFRA 21-1, CEA, and NSE. The discrimination of malignant vs benign diseases was realized with a sensitivity of 87.5% and specificity of 85.5%. The rate of correct classification of small-cell vs non-small-cell lung carcinoma was 90.6% and 91.1%, respectively; for squamous cell carcinoma vs adenocarcinoma it was 76.8% and 78.8%, respectively. Our detailed analysis has shown that the fuzzy logic system improves diagnostic accuracy up to a rate of 20%, especially in early stages and in patients with all marker levels in the grey area. Our concept proved to be more powerful than measurement of single markers or the combination of CEA, CYFRA 21-1, and NSE. Its use may help in distinguishing between malignant and benign disease and make it possible to define different subgroups of patients earlier in the course of their disease.

Adolescent↗