Decision-tree sensitivity analysis for cost-effectiveness of whole-body FDG PET in the management of patients with non-small-cell lung carcinoma in Japan

Kosuda, Shigeru; Ichihara, Kiyoshi; Watanabe, Masahiro; Kobayashi, Hideo; Kusano, Shoichi

doi:10.1007/bf03000105

Cited by 25 publications

(14 citation statements)

References 38 publications

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“…Intensity values for each peak were then averaged for each duplicate sample pair analyzed and input into BioMarker Patterns software (Ciphergen Biosystems) for classification tree analysis as described previously. 29,30,36,37 Briefly, classification trees split the data into two nodes, using one rule at a time in the form of a question. The splitting decisions in this case were based on the normalized intensity levels of peaks from the SELDI protein expression profile.…”

Section: Methodsmentioning

confidence: 99%

SELDI‐TOF MS profiling of serum for detection of the progression of chronic hepatitis C to hepatocellular carcinoma†

et al. 2005

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Proteomic profiling of serum is an emerging technique to identify new biomarkers indicative of disease severity and progression. The objective of our study was to assess the use of surfaceenhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS) to identify multiple serum protein biomarkers for detection of liver disease progression to hepatocellular carcinoma (HCC). A cohort of 170 serum samples obtained from subjects in the United States with no liver disease (n ‫؍‬ 39), liver diseases not associated with cirrhosis (n ‫؍‬ 36), cirrhosis (n ‫؍‬ 38), or HCC (n ‫؍‬ 57) were applied to metal affinity protein chips for protein profiling by SELDI-TOF MS. Across the four test groups, 38 differentially expressed proteins were used to generate multiple decision classification trees to distinguish the known disease states. Analysis of a subset of samples with only hepatitis C virus (HCV)-related disease was emphasized. The serum protein profiles of control patients were readily distinguished from each HCV-associated disease state. Two-way comparisons of chronic hepatitis C, HCV cirrhosis, or HCV-HCC versus healthy had a sensitivity/specificity range of 74% to 95%. For distinguishing chronic HCV from HCV-HCC, a sensitivity of 61% and a specificity of 76% were obtained. However, when the values of known serum markers ␣ fetoprotein, des-gamma carboxyprothrombin, and GP73 were combined with the SELDI peak values, the sensitivity and specifity improved to 75% and 92%, respectively. In conclusion, SELDI-TOF MS serum profiling is able to distinguish HCC from liver disease before cirrhosis as well as cirrhosis, especially in patients with HCV infection compared with other etiologies. (HEPATOLOGY 2005;41:634-642.) T he incidence of hepatocellular carcinoma (HCC) continues to increase in the United States, 1 while, unfortunately, patient survival with HCC has only marginally improved over the last 20 years. Between 1981 and 1998, the 5-year survival rate only rose from 2% to 5%. 2 The poor survival rate is in part related to the diagnosis of HCC at advanced stages, where effective therapies are lacking. 3 Surveillance of patients at the highest risk for developing HCC (i.e., patients with cirrhosis) is an important strategy that can potentially decrease the cancer-related mortality rate. Although HCC meets the criteria of a tumor that would benefit from a surveillance program, the poor sensitivity and specificity of currently available tools has prevented widespread implementation of HCC surveillance. For example, ␣ fetoprotein (AFP) has been the serum marker that is most widely used for diagnosis as well as surveillance of HCC. 4,5 However, AFP levels may be normal in up to 40% of patients with HCC, particularly during the early stages (low sensitivity). 6 Furthermore, elevated AFP levels may be seen in patients with cirrhosis or exacerbations of chronic hepatitis (low specificity). 7 Prospective studies evaluating the performance characteristics of AFP for HCC surveillance

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Section: Methodsmentioning

confidence: 99%

SELDI‐TOF MS profiling of serum for detection of the progression of chronic hepatitis C to hepatocellular carcinoma†

et al. 2005

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“…Details regarding CART and the computational algorithms incorporated within the BioMarker Patterns software program have been described elsewhere (15,16). Briefly, classification trees split the data into two nodes, using one rule at a time in the form of a question.…”

Section: Methodsmentioning

confidence: 99%

Serum Protein Profiles to Identify Head and Neck Cancer

Wadsworth

Somers

Cazares

et al. 2004

Clinical Cancer Research

108

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Purpose: New and more consistent biomarkers of head and neck squamous cell carcinoma (HNSCC) are needed to improve early detection of disease and to monitor successful patient management. The purpose of this study was to determine whether a new proteomic technology could correctly identify protein expression profiles for cancer in patient serum samples.Experimental Design: Surface-enhanced laser desorption/ionization-time of flight-mass spectrometry ProteinChip system was used to screen for differentially expressed proteins in serum from 99 patients with HNSCC and 102 normal controls. Protein peak clustering and classification analyses of the surface-enhanced laser desorption/ionization spectral data were performed using the Biomarker Wizard and Biomarker Patterns software (version 3.0), respectively (Ciphergen Biosystems, Fremont, CA).Results: Several proteins, with masses ranging from 2,778 to 20,800 Da, were differentially expressed between HNSCC and the healthy controls. The serum protein expression profiles were used to develop and train a classification and regression tree algorithm, which reliably achieved a sensitivity of 83.3% and a specificity of 100% in discriminating HNSCC from normal controls.Conclusions: We propose that this technique has potential for the development of a screening test for the detection of HNSCC.

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“…18,19 Briefly, classification trees split the data into 2 nodes, using one rule at a time in the form of a question. The splitting decisions in this case were based on the normalized intensity levels of peaks from the SELDI protein expression profile.…”

Section: Classification and Regression Tree Analysismentioning

confidence: 99%

Identification of Patients With Head and Neck Cancer Using Serum Protein Profiles

Wadsworth

Somers

Stack

et al. 2004

Arch Otolaryngol Head Neck Surg

102

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Background: New and more consistent biomarkers of head and neck squamous cell carcinoma (HNSCC) are needed to improve early detection of disease and to monitor successful patient management.Objective: To determine if a new proteomic technology can correctly identify protein expression profiles for cancer in patient serum samples as well as detect the presence of a known tumor marker.Design: Direct proteomic analysis and comparison. Methods:The surface-enhanced laser desorption/ ionization time of flight mass spectrometry (SELDI-TOF) ProteinChip system was used to screen for differentially expressed proteins in serum samples from 99 patients with HNSCC, 25 "healthy" smokers, and 102 healthy (normal) controls. Protein peak clustering and classification analyses of the SELDI spectral data were performed.Results: Several proteins, with masses ranging from 2778 to 20 800 Da, were differentially expressed Conclusion:We propose that this technique may allow for the development of a reliable screening test for the early detection and diagnosis of HNSCC, as well as the potential identification of tumor biomarkers.

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Decision-tree sensitivity analysis for cost-effectiveness of whole-body FDG PET in the management of patients with non-small-cell lung carcinoma in Japan

Abstract: The introduction of a WB-PET strategy in place of CI for managing NSCLC patients is potentially cost-effective in Japan.

Cited by 25 publications

References 38 publications

SELDI‐TOF MS profiling of serum for detection of the progression of chronic hepatitis C to hepatocellular carcinoma†

SELDI‐TOF MS profiling of serum for detection of the progression of chronic hepatitis C to hepatocellular carcinoma†

Serum Protein Profiles to Identify Head and Neck Cancer

Identification of Patients With Head and Neck Cancer Using Serum Protein Profiles

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