ライフサイエンスコーパス: ROC analysis

1 control samples (Area under the curve 0.851, ROC-analysis).

2 y to evaluate a model with binary data using ROC analysis.

3 A cut-off value for ONSD was derived using ROC analysis.

4 stic markers through iterative combinatorial ROC analysis.

5 ating characteristic (ROC) and free-response ROC analysis.

6 0.645 and 0.660, respectively, according to ROC analysis.

7 arker was assessed using chi-square test and ROC analysis.

8 lidated in an external dataset using AUC and ROC analysis.

9 is screening method were established through ROC analysis.

10 ore than 11.5 mIU/mL based on results of the ROC analysis.

11 cantly outperform visual assessment based on ROC analysis.

12 sed using receiver operating characteristic (ROC) analysis.

13 come of a receiver operating characteristic (ROC) analysis.

14 ed using receiver operating characteristics (ROC) analysis.

15 ined with receiver operating characteristic (ROC) analysis.

16 test, and receiver operating characteristic (ROC) analysis.

17 gnosis by receiver operating characteristic (ROC) analysis.

18 by using receiver operating characteristic (ROC) analysis.

19 ted using receiver operating characteristic (ROC) analysis.

20 rmined by receiver operating characteristic (ROC) analysis.

21 multicase receiver operating characteristic (ROC) analysis.

22 d by using receiver operator characteristic (ROC) analysis.

23 ured with receiver operating characteristic (ROC) analysis.

24 ated with receiver operating characteristic (ROC) analysis.

25 based on receiver operating characteristic (ROC) analysis.

26 imated by receiver operating characteristic (ROC) analysis.

27 ated with receiver operating characteristic (ROC) analysis.

28 by using receiver operating characteristic (ROC) analysis.

29 means of receiver operating characteristic (ROC) analysis.

30 f-fit and receiver operating characteristic (ROC) analysis.

31 means of receiver operating characteristic (ROC) analysis.

32 ing receiver-operating characteristic curve (ROC) analysis.

33 mputed by receiver operating characteristic (ROC) analysis.

34 ded using receiver operating characteristic (ROC) analysis.

35 ed by the receiver operating characteristic (ROC) analysis.

36 ression models and receiver operating curve (ROC) analysis.

37 ted using receiver operating characteristic (ROC) analysis.

38 ssed using Receiver Operator Characteristic (ROC) analysis.

39 ntrols by receiver operating characteristic (ROC) analysis.

40 zed using Receiver Operating Characteristic (ROC) analysis.

41 ted using receiver operating characteristic (ROC) analysis.

42 and receiver operating characteristic curve (ROC) analysis.

43 d through receiver operating characteristic (ROC) analysis.

44 sessed by receiver operating characteristic (ROC) analysis.

45 ted using receiver operating characteristic (ROC) analysis.

46 essed by receiver operating characteristics (ROC) analysis.

47 0.924 in receiver operating characteristic (ROC) analysis.

48 -qPCR and receiver operating characteristic (ROC) analysis.

49 odels and receiver operating characteristic (ROC) analysis.

50 for the receiver operating characteristics (ROC) analysis.

51 ysis, and receiver operating characteristic (ROC) analysis.

52 ssed with receiver operating characteristic (ROC) analysis.

53 ined with receiver operating characteristic (ROC) analysis.

54 means of receiver operating characteristic (ROC) analysis.

55 y using a receiver operating characteristic (ROC) analysis.

56 According to the ROC analysis, 13 of the tested parameters differentiate

57 compression levels was demonstrated by using ROC analysis, a significant decrease in sensitivity was

58 Based on receiver operating characteristic (ROC) analysis, a model value > - 0.19 was selected as th

59 On ROC analysis, AET threshold of 0.5% modestly predicted n

60 Receiver operating characteristic (ROC) analysis along with the calibration test was used t

61 rkers had an area under the curve of 0.8 for ROC analysis and a sensitivity and specificity of 0.7 an

62 We then used these lead candidates for ROC analysis and found multiple biomarkers with values a

63 ROC analysis and other statistical studies revealed that

64 sessed according to various thresholds using ROC analysis and time-to-event regression.

65 nalized logistic regression, evaluated using ROC analysis and validated in an independent cohort of c

66 Using receiver operating characteristic (ROC) analysis and adjusting the cutoff levels, we improv

67 ted using Receiver Operating Characteristic (ROC) analysis and Area Under the Curve (AUC) values for

68 Receiver operating characteristic (ROC) analysis and continuation ratio logistic regression

69 h using a Receiver Operating Characteristic (ROC) analysis and obtain an area under the ROC curve of

70 d using a receiver operating characteristic (ROC) analysis and was expressed as the area under the cu

71 by using receiver operating characteristic (ROC) analysis and were evaluated in a train, validate, a

72 variance, receiver operating characteristic (ROC) analysis, and exact tests.

73 ated with receiver operating characteristic (ROC) analysis, and interobserver agreement was measured

74 cy was assessed by Receiver-Operating Curve (ROC) analysis, and Positive and Negative Predictive valu

75 On the ROC analysis, APPLE (AUC 0.640, 95%CI 0.602-0.677, p < 0

76 In the ROC analysis, areas under the curve for discriminating l

77 test, chi(2) test, logistic regression, and ROC analysis, as appropriate, with significance set at p

78 ONSD was taken as 4.57 mm, derived using the ROC analysis (AUC was 0.876 suggesting good diagnostic a

79 ROC analysis between PVR (+) and PVR (-) groups demonstr

80 Based on ROC analysis, BHLHE40 and DDIT4 displayed better diagnos

81 sted with receiver operating characteristic (ROC) analysis by using the jackknife method.

82 ly relevant bleeding on Cox regression or on ROC analysis (c-index: 0.50; p = 0.87).

83 By receiver operating characteristics (ROC) analysis, cFFR provided better diagnostic performan

84 A receiver operating characteristic (ROC) analysis compared the distribution of individual ga

85 Receiver operating characteristic (ROC) analysis compared the interval increase with the RV

86 ROC analysis confirmed that HYB significantly increases

87 ROC analysis confirmed the optimal threshold and generat

88 Receiver operating characteristic (ROC) analysis confirmed that both measures detected resp

89 Receiver operating characteristic (ROC) analysis confirmed the predictive power of Klotho a

90 ROC analysis demonstrated a greater area under the curve

91 ROC analysis demonstrated case experience and trainee le

92 ROC analysis demonstrated high accuracy for tumor detect

93 ROC analysis demonstrated significantly higher area unde

94 ROC analysis demonstrated superior NLR predictive capabi

95 ROC analysis demonstrated that the lead blood, CSF and i

96 A receiver operating characteristic (ROC) analysis demonstrated that the SUV distinguished re

97 Receiver operating characteristic (ROC) analysis demonstrated that this integrated EV doubl

98 ROC analysis demonstrates similarities in the distributi

99 An ROC analysis designed to detect hyperopia >5 D in any me

100 Receiver Operating Characteristic (ROC) analysis determined effectiveness of HbA1c and FPG

101 Receiver operator characteristic (ROC) analysis determined that a 45% catheter to vein rat

102 Receiver operating characteristic (ROC) analysis determined the optimal cutoff for the band

103 d t test, receiver operating characteristic (ROC) analysis, discriminant function analysis (DFA), lea

104 means of receiver operating characteristic (ROC) analysis, dual-phase helical computed tomography (C

105 Receiver-operating characteristic (ROC) analysis equated the tests for specificity (80%, 90

106 Receiver Operating Characteristic (ROC) analysis established discriminative cut-off values

107 Receiver operating characteristic (ROC) analysis estimated the predictive ability of cumula

108 ssess associations with PVR development, and ROC analysis evaluated diagnostic performance.

109 y, and predictive values were assessed using ROC analysis, expressed as the area under the curve (AUC

110 on development of myopia was evaluated using ROC-analysis (fast vs slow progressors) and a logistic r

111 When each sign is considered independently, (ROC analysis, followed by binary logistic regression) on

112 ROC analysis for differentiation between HGG and LGG (TB

113 ROC analysis for identification of patients with VT show

114 ROC analysis for SULmax/liver improved test specificity

115 At a specificity of 95%, summary ROC analysis for the 250-microg cosyntropin test yielded

116 ROC analysis for the 4R-like pattern (PSP/CBD vs. all ot

117 ) determine the optimal cut-off points using ROC analysis for the DAS-S, DAS-I, and their subscales;

118 Receiver operator characteristic (ROC) analysis for distinguishing periodontitis from heal

119 Receiver Operator Characteristic (ROC) analysis for full and reduced feature sets revealed

120 Receiver operating characteristic (ROC) analysis for MIP and VIR images demonstrated excell

121 s greater than the optimal volume threshold (ROC analysis) for the prediction of intact MF at referra

122 by using receiver operating characteristic (ROC) analysis, for the capacity to discriminate between

123 Receiver operating characteristic (ROC) analysis further confirms the best performance of t

124 e spermatozoa, a cutoff value established by ROC analysis, had their chance of fathering children by

125 The ROC analysis identified a criterion value of 6.3% for th

126 ROC analysis identified an HRF area >= 0.07 mm2 to predi

127 ROC analysis identified baseline BUN (P = .026, area und

128 ROC analysis identified patients with LS value higher th

129 The ROC analysis identified RBP4 as a sensitive identifier o

130 ROC analysis identified threshold values for ONL->INL pr

131 Receiver operating characteristic (ROC) analysis identified urine white blood cells (WBCs),

132 ROC-analysis identified a NLR of 20.9 as best cut-off va

133 ROC analysis in the independent out-of-sample cohort res

134 d by using receiver operator characteristic (ROC) analysis, including area under the ROC curve (A(z))

135 According to ROC analysis independent predictors of residual flow wer

136 ROC analysis indicated an additional predictive value of

137 The ROC analysis indicated that AUC for lobulated margin and

138 Results of ROC analysis indicated that detection of hardware compli

139 ROC analysis indicated that H1 concentration is potentia

140 ROC analysis indicated that RPR had a maximum predictive

141 The measurement of assay performance by the ROC analysis indicated that there were statistically sig

142 A receiver operating characteristics (ROC) analysis indicated that qCEST/T2w ratio could be us

143 ROC analysis is widely used in radiologic research, conf

144 Receiver operating characteristic (ROC) analysis, Kaplan-Meier curves, and Cox proportional

145 Utilizing ROC analysis, LVMI was found to be a stronger predictor

146 d moderate discriminability of the SOZ using ROC analysis (maximal mean AUC ~ 0.73).

147 At ROC analysis, NGAL showed high sensitivity and specifici

148 ROC analysis (no DR vs STDR) showed that in addition to

149 bone marrow lesions in both knees, and using ROC analysis, no individual structural feature discrimin

150 Based on validation results and ROC analysis, NR4A2 and IGFBP1b were identified as diagn

151 Receiver operating characteristic (ROC) analysis, odds ratios and binary logistic regressio

152 ROC analysis of mean log OTMs, for cancers plus precance

153 The ROC analysis of our models for 1050 studies within our i

154 ROC analysis of relative CBF in the PCC enabled discrimi

155 benignity or malignancy were determined, and ROC analysis of results for the entire nonpheochromocyto

156 ROC analysis of size of non-functioning dNENs to predict

157 An ROC analysis of SUV, SUVRsss, SUVRpit, and SUVRnorm reve

158 ROC analysis of thalamic volumes of the patients with AV

159 ROC analysis of the accuracy of the curvature ratio for

160 the results for the commercial ELISA, as the ROC analysis of the GPI1 test shows 97% specificity and

161 ROC analysis of the rubella MBA using ELISA as the compa

162 Receiver Operator Characteristic (ROC) analysis of an artificial test set allows the optim

163 Receiver-operating characteristic (ROC) analysis of classifier score measured by real-time

164 Receiver operating characteristic (ROC) analysis of test samples showed sensitivity/specifi

165 Receiver operating curve (ROC) analysis of the EM properties for cancers among sub

166 ned using receiver operating characteristic (ROC) analysis of the spike-count distribution at each IT

167 Receiver operating characteristic (ROC) analysis of tumor size and transfer constant change

168 multicase receiver operating characteristic (ROC) analysis of variance.

169 The SEM and ROC analysis predicted that Ralstonia invaded rhizospher

170 ROC analysis predictors of critically ill status: 87.5th

171 The ROC analysis produced an area under the curve of 0.87, i

172 RR(2)HAGES and ATRIA scores, as reflected by ROC analysis, reclassification analysis, and decision-cu

173 ROC analysis requires the choice of a reference ITD from

174 ROC analysis revealed 4 grades of total severity score o

175 ROC analysis revealed a list of novel EV proteins that e

176 ROC analysis revealed accurate identification (compared

177 ROC analysis revealed an area under the ROC curve of 0.8

178 The ROC analysis revealed an inflection point of plots of th

179 ROC analysis revealed MYD88 and NFKB1 transcripts to be

180 ROC analysis revealed suboptimal diagnostic performance

181 ROC analysis revealed that CTRP7 and CTRP15 may serve as

182 ROC analysis revealed that methanol lacked diagnostic se

183 The ROC analysis revealed that protein glycoxidation product

184 The Receiver operating characteristic (ROC) analysis revealed AUCs of 0.803 for KS and 0.791 fo

185 Receiver operating characteristic (ROC) analysis revealed that MACC1 mRNA abundance is a go

186 NOVA) and receiver operating characteristic (ROC) analysis revealed that the peak height ratios were

187 Receiver operating characteristic (ROC) analysis revealed the best predictability for APO w

188 Receiver operating characteristic (ROC) analysis, revealed no significant difference in the

189 ROC analysis reveals that the count ratio of immature ne

190 ROC analysis showed a significant discriminatory accurac

191 ROC analysis showed AUC of D(K), K, ADC(0-2000), and ADC

192 ROC analysis showed excellent diagnostic accuracy for lo

193 ROC analysis showed excellent discriminating accuracy of

194 Specifically, for complete SCIs (AIS A), ROC analysis showed impressive specificity and sensitivi

195 ROC analysis showed that cathepsin Z mRNA has strong dia

196 ROC analysis showed that creatinine, lactate, MELD, BiLE

197 ROC analysis showed that miR-23b expression can distingu

198 The ROC analysis showed that ReHo value had high accuracy in

199 ROC analysis showed that the areas under the ROC curve (

200 In the forme fruste eyes, the ROC analysis showed that the AUC values of the mean K, t

201 In fibromyalgia, a ROC analysis showed that these cutoffs could discriminat

202 ROC analysis showed the area under the curve for classif

203 The receiver operating characteristic curve (ROC) analysis showed that the circulating emRNA-based sc

204 A receiver operator characteristics (ROC) analysis showed the -HbO* cutoffs of - 0.175 at lef

205 evaluation strategy disallowed the use of a ROC analysis, so instead we compared the fraction of act

206 d 70% (angiographic source images) (P = .04, ROC analysis); specificity was 100% for both.

207 Receiver operating analysis (ROC) analysis suggested DHI's great capability in distin

208 In the ROC analysis, the AUCs for discriminative performance of

209 Using ROC analysis, the highest value of area under curve (AUC

210 In ROC analysis, the magnetic resonance (MR) imaging and MR

211 Using ROC analysis, the OKAP examination taken at the third ye

212 In ROC analysis, the use of ADC with a threshold value of 1

213 means of receiver operating characteristic (ROC) analysis, the ability of 11 observers to detect pat

214 Based on the ROC analysis, there were no satisfactory cut-off values

215 Adding K(trans) to ADC in ROC analysis to differentiate CG carcinoma from SH incre

216 zed with conditional logistic regression and ROC analysis to investigate changes in interpretation.

217 applied a receiver operating characteristic (ROC) analysis to assess the role of intraindividual vari

218 red using receiver operating characteristic (ROC) analysis to calculate the area under the ROC curve

219 ed using receiver operating characteristics (ROC) analysis to determine the area under the curve (AUC

220 also used receiver operator characteristics (ROC) analysis to estimate the ability of ultrasound to p

221 We used receiver operating characteristic (ROC) analysis to evaluate the discriminative ability of

222 ssed with receiver operating characteristic (ROC) analysis to generate an area under the ROC curve (A

223 performed receiver operating characteristic (ROC) analysis to identify the optimal cutoff value for t

224 accuracy of 66% on Receiver Operator Curve (ROC) analysis to predict for successful SWL outcome.

225 luated by receiver operating characteristic (ROC) analysis to quantify diagnostic accuracy.

226 sion, and receiver operating characteristic (ROC) analysis to quantify the relationship between ring

227 ated with receiver operating characteristic (ROC) analysis, using tumor response at 6 months accordin

228 By ROC analysis, V(alt) = 2.6 microV in late repolarization

229 y of 56%, specificity was 93% and the AUC on ROC analysis was 0.86.

230 ROC analysis was carried out with the task of discrimina

231 ROC analysis was performed to compare the diagnostic per

232 ROC analysis was performed to determine the optimal rCBV

233 th detectable salbutamol (p(corr) > 0.5) and ROC analysis was performed to measure the predictive pot

234 acterized as a dysplastic nodule or HCC, and ROC analysis was performed.

235 ROC analysis was used to determine the predictive value

236 ROC analysis was used to evaluate the diagnostic value o

237 Receiver operating characteristics (ROC) analysis was applied for a combination of 4 VOCs (h

238 Receiver operating characteristics (ROC) analysis was applied for a combination of six VOCs

239 Receiver operating characteristics (ROC) analysis was applied for the combined VOCs of aceta

240 Receiver operating characteristic (ROC) analysis was conducted to assess the sensitivity an

241 Receiver operating characteristic (ROC) analysis was done, and areas under the curves were

242 orrelated receiver operating characteristic (ROC) analysis was employed to assess radiologist perform

243 Receiver operating curve (ROC) analysis was employed to identify pretreatment feat

244 Receiver operating characteristic (ROC) analysis was generated to set optimal thresholds; a

245 ified and receiver operating characteristic (ROC) analysis was performed to assess sensitivity and sp

246 Receiver operating characteristic (ROC) analysis was performed to assess the accuracy of DW

247 Receiver operating characteristic (ROC) analysis was performed to assess the diagnostic val

248 Receiver operating characteristic (ROC) analysis was performed to compare fluorescein angio

249 Receiver operating characteristic (ROC) analysis was performed to compare mean attenuation

250 Receiver operating characteristic (ROC) analysis was performed to compare the accuracy of t

251 Receiver operating characteristic (ROC) analysis was performed to compare the detection of

252 A receiver-operator characteristic curve (ROC) analysis was performed to determine an optimal GCB

253 Receiver operating characteristic (ROC) analysis was performed to determine how well each m

254 Receiver-operating-characteristic (ROC) analysis was performed to determine optimal cut-off

255 ax) Receiver-operating-characteristic curve (ROC) analysis was performed to determine predictive capa

256 A receiver operator characteristic (ROC) analysis was performed to determine the accuracy of

257 The receiver operating characteristic curve (ROC) analysis was performed to determine the best cutoff

258 Receiver operating characteristic (ROC) analysis was performed to determine the optimal inh

259 Receiver operating characteristic (ROC) analysis was performed to determine the optimal thr

260 Receiver operating characteristic (ROC) analysis was performed to differentiate between the

261 and receiver operating characteristic curve (ROC) analysis was performed to evaluate the performance

262 A receiver operating characteristic (ROC) analysis was performed to evaluate the SIRI diagnos

263 W and RPW receiver operating characteristic (ROC) analysis was performed to evaluate the strength of

264 Receiver operator characteristic (ROC) analysis was performed to identify cutoff SUV value

265 Receiver operating characteristic (ROC) analysis was performed to identify optimal threshol

266 Receiver operating characteristic (ROC) analysis was performed using the average score for

267 cale, and receiver operating characteristic (ROC) analysis was performed.

268 Receiver operating characteristic (ROC) analysis was performed.

269 cision in receiver operating characteristic (ROC) analysis was reached with an AUC(ROC) of 0.994 (CI

270 Receiver operating characteristic (ROC) analysis was then used to test the discriminatory p

271 Receiver operating characteristic (ROC) analysis was used to assess diagnostic performance

272 Receiver operating characteristic (ROC) analysis was used to assess the model performances.

273 Receiver operating characteristic (ROC) analysis was used to compare accuracies of paramete

274 Receiver operating characteristic (ROC) analysis was used to compare the accuracy of type I

275 Receiver operating characteristics (ROC) analysis was used to determine the area under the R

276 Receiver operating characteristic (ROC) analysis was used to determine the diagnostic accur

277 Receiver operating characteristic (ROC) analysis was used to evaluate observer performance.

278 Receiver operating characteristic (ROC) analysis was used to evaluate performance differenc

279 Receiver operating characteristic (ROC) analysis was used to evaluate the accuracy of these

280 Receiver-operating-characteristic (ROC) analysis was used to evaluate the results.

281 Receiver operating characteristic curve (ROC) analysis was used to examine the sensitivity and sp

282 d summary receiver operating characteristic (ROC) analysis was used to generate a summary area under

283 mined, and receiver operator characteristic (ROC) analysis was used to identify an optimal threshold.

284 Receiver-operating characteristic (ROC) analysis was used to quantify prediction accuracy.

285 analysis, receiver operating characteristic [ROC] analysis) was performed.

286 For the ROC analysis we combined the malignant and indeterminate

287 The optimal cutoff values determined by ROC analysis were 69.16% (human) and 58.76% (swine); the

288 dels, and receiver operating characteristic (ROC) analysis were used to assess associations of kynure

289 t compounds, with 130 agonists identified by ROC analysis when seeded in 2175 non-agonist ligands of

290 the receiver-operating characteristic plot (ROC) analysis which indicated that 16.95 units was the m

291 Compared with bioluminescence, ROC analysis with 25% and 30% weight loss as thresholds

292 ted in the unit square) values obtained from ROC analysis with and without CAD output were 0.940 and

293 radiologists' performance was evaluated with ROC analysis with two different methods (independent tes

294 ated with receiver operating characteristic (ROC) analysis, with the area under the ROC curve (AUC) a

295 spleens and pNETs with specificity 100%, the ROC analysis yielded an AUC of 0.742 (sensitivity 56%)/0

296 ROC analysis yielded an optimal cut-off value of 2.245 (

297 ROC analysis yielded an optimal cutoff of 2.5 for TBR(ma

298 ROC analysis yielded diaschisis thresholds of 0.62 for T

299 iPD, the receiver operating characteristic (ROC) analysis yielded an area under the curve (AUC) of 0

300 Receiver Operating Characteristic (ROC) analysis yielded the following precisions for model