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Cervical cancer prognosis and diagnosis using electrical impedance spectroscopy

Ping Li
Ping Li
, 
Peter E. Highfield
Peter E. Highfield
, 
Zi-Qiang Lang
Zi-Qiang Lang
 und 
Darren Kell
Darren Kell
   | 27. Dez. 2021
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Online veröffentlicht: 27. Dez. 2021
Seitenbereich: 153 - 162
Eingereicht: 25. Sept. 2021
DOI: https://doi.org/10.2478/joeb-2021-0018
Schlüsselwörter
© 2021 Ping Li, Peter E. Highfield , Zi-Qiang Lang, and Darren Kell, published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.

Fig.1

The ZedScan handset for making the EIS measurements used in this paper. The handset is shown placed on the base.
The ZedScan handset for making the EIS measurements used in this paper. The handset is shown placed on the base.

Fig.2

Comparison between measured and model fitted EIS
Comparison between measured and model fitted EIS

Fig.3

ROC comparison between new method, template match method and colposcopy only.
ROC comparison between new method, template match method and colposcopy only.

Fig.4

2-D histogram of α̅-Δα data points from two groups
2-D histogram of α̅-Δα data points from two groups

Fig.5

An ROC curve of final model for separating two groups with OOP and the associated performance indices
An ROC curve of final model for separating two groups with OOP and the associated performance indices

AUC values for testing sets from 10 repeated two-fold cross validation runs with three logistic regression models

Repetitions R¯0,α¯,ΔR0,  CI,Ref $\begin{align}& {{{\bar{R}}}_{0}},\bar{\alpha },\Delta {{R}_{0}}, \\ & \,\,\text{CI,Ref} \\ \end{align}$ R¯02,α¯2,ΔR02,       CI,Ref $\begin{align}& \bar{R}_{0}^{2},{{{\bar{\alpha }}}^{2}},\Delta R_{0}^{2}, \\ & \,\,\,\,\,\,\,\text{CI,Ref} \\ \end{align}$ R¯03,α¯3,ΔR03,      CI, Ref $\begin{align}& \bar{R}_{0}^{3},{{{\bar{\alpha }}}^{3}},\Delta R_{0}^{3}, \\ & \,\,\,\,\,\,\text{CI, Ref} \\ \end{align}$
1 0.9127 0.9160 0.9165
2 0.9177 0.9178 0.9190
3 0.9013 0.9034 0.9045
4 0.9165 0.9210 0.9238
5 0.8830 0.8840 0.8858
6 0.9206 0.9222 0.9230
7 0.9164 0.9165 0.9172
8 0.9053 0.9061 0.9075
9 0.9122 0.9146 0.9155
10 0.9181 0.9215 0.9222
Mean AUC 0.9104 0.9123 0.9135

Mean AUC values from 100 5-fold cross validation runs with linear logistic regression models

Feature combinations Mean AUC Feature combinations Mean AUC
α¯,Δα $\bar{\alpha },\Delta \alpha $ 0.5870 R¯0, α, Δα ${{\bar{R}}_{0}},\,\alpha ,\,\Delta \alpha $ 0.5723
α¯,ΔR∞ $\bar{\alpha },\Delta {{R}_{\infty }}$ 0.5777 f¯c,α,Δα ${{\bar{f}}_{c}},\alpha ,\Delta \alpha $ 0.5716
f¯c,α¯ ${{\bar{f}}_{c}},\bar{\alpha }$ 0.5745 α¯,ΔR0 $\bar{\alpha },\Delta {{R}_{0}}$ 0.5715
fc¯, Δα $\overline{{{f}_{c}}},\,\Delta \alpha $ 0.5744 α¯,Δfc $\bar{\alpha },\Delta {{f}_{c}}$ 0.5686
f¯c,ΔR∞,Δα ${{\bar{f}}_{c}},\Delta {{R}_{\infty }},\Delta \alpha $ 0.5736 R¯0,α¯ $$\bar{R}_{0}, \bar{\alpha}$$ 0.5678

p-values from MANOVA using EIS data taken from 1704 women for HG CIN detection

Feature combinations p-values Feature combinations p-values
R¯0,α¯,ΔR0 ${{\bar{R}}_{0}},\bar{\alpha },\Delta {{R}_{0}}$ 1.1003 × 10−31 R¯0,α¯,ΔR0,Δfc ${{\bar{R}}_{0}},\bar{\alpha },\Delta {{R}_{0}},\Delta {{f}_{c}}$ 5.0124 × 10−31
R¯0,ΔR0 ${{\bar{R}}_{0}},\Delta {{R}_{0}}$ 1.5861 × 10−31 R¯∞,R¯0,ΔR∞,ΔR0 ${{\bar{R}}_{\infty }},{{\bar{R}}_{0}},\Delta {{R}_{\infty }},\Delta {{R}_{0}}$ 5.3276 × 10−31
R¯0,α¯,ΔR∞,ΔR0 ${{\bar{R}}_{0}},\bar{\alpha },\Delta {{R}_{\infty }},\Delta {{R}_{0}}$ 2.6287 × 10−31 R¯0,ΔR0,Δfc ${{\bar{R}}_{0}},\Delta {{R}_{0}},\Delta {{f}_{c}}$ 6.5955 × 10−31
R¯0,ΔR∞,ΔR0 ${{\bar{R}}_{0}},\Delta {{R}_{\infty }},\Delta {{R}_{0}}$ 3.1665 × 10−31 R¯0,ΔR0,Δα ${{\bar{R}}_{0}},\Delta {{R}_{0}},\Delta \alpha $ 7.2683 × 10−31
R¯0,α¯,ΔR0,Δα ${{\bar{R}}_{0}},\bar{\alpha },\Delta {{R}_{0}},\Delta \alpha $ 3.4687 × 10−31 R¯0,f¯c,α¯,ΔR0 ${{\bar{R}}_{0}},{{\bar{f}}_{c}},\bar{\alpha },\Delta {{R}_{0}}$ 7.4318 × 10−31

Mean AUC values from 100 5-fold cross validation runs with nonlinear logistic regression models

Feature combinations Mean AUC Feature combinations Mean AUC
α¯2,Δα2 ${{\bar{\alpha }}^{2}},\Delta {{\alpha }^{2}}$ 0.6103 f¯c,α¯2,Δα2 ${{\bar{f}}_{c}},{{\bar{\alpha }}^{2}},\Delta {{\alpha }^{2}}$ 0.5911
Δα,α¯2 $\Delta \alpha ,{{\bar{\alpha }}^{2}}$ 0.5992 R¯02,α2,Δα2 $\bar{R}_{0}^{2},{{\alpha }^{2}},\Delta {{\alpha }^{2}}$ 0.5899
α¯,Δα2 $\bar{\alpha },\Delta {{\alpha }^{2}}$ 0.5989 ΔR∞,α¯2,Δα2 $\Delta {{R}_{\infty }},{{\bar{\alpha }}^{2}},\Delta {{\alpha }^{2}}$ 0.5895
α¯2,α¯⋅Δα,Δα2 ${{\bar{\alpha }}^{2}},\bar{\alpha }\cdot \Delta \alpha ,\Delta {{\alpha }^{2}}$ 0.5946 ΔR∞2,α¯2,Δα2 $\Delta R_{\infty }^{2},{{\bar{\alpha }}^{2}},\Delta {{\alpha }^{2}}$ 0.5891
α, Δα, Δα2 $\alpha ,\,\Delta \alpha ,\,\Delta {{\alpha }^{2}}$ 0.5939 α¯2,Δfc,Δα2 ${{\bar{\alpha }}^{2}},\Delta {{f}_{c}},\Delta {{\alpha }^{2}}$ 0.5885

Regression coefficient estimates and the associated p-values for the final logistic regression model

β estimates p-values
βo -2.9619 3.9518 × 10−32
β1 −7.4684 × 10−11 0.0047
β2 3.3987 0.0090
β1 3.0025 × 10−11 0.0044
βCI 2.3621 3.9281 × 10−47
βRef 2.2241 5.8068 × 10−35

Classification performance comparison between the new classifier developed and the previous classifiers

Classifier AUC Sensitivity Specificity
Logistic regression (α¯2,Δα2) $\left( {{{\bar{\alpha }}}^{2}},\Delta {{\alpha }^{2}} \right)$ 0.628 45.714% 82.022%
Impedance at 152Hz 0.621 38.7% 83.4%
Slope (between 1.22 0.596 45.2% 70.1%
and 2.44kHz) as α

p-values from MANOVA using EIS data taken at initial colposcopy of 569 women for evaluation of prognostic value of EIS

Feature combinations p-values Feature combinations p-values
α¯,Δα $\bar{\alpha },\Delta \alpha $ 0.0168 f¯c,Δα ${{\bar{f}}_{c}},\Delta \alpha $ 0.0286
α¯,ΔR0 $\bar{\alpha },\Delta {{R}_{0}}$ 0.0231 R¯0,α¯ ${{\bar{R}}_{0}},\bar{\alpha }$ 0.0295
f¯c,α¯ ${{\bar{f}}_{c}},\bar{\alpha }$ 0.0256 R¯∞,α¯ ${{\bar{R}}_{\infty }},\bar{\alpha }$ 0.0296
α¯,ΔR∞ $\bar{\alpha },\Delta {{R}_{\infty }}$ 0.0274 f¯c,α¯,Δα ${{\bar{f}}_{c}},\bar{\alpha },\Delta \alpha $ 0.0314
α¯,Δfc $\bar{\alpha },\Delta {{f}_{c}}$ 0.0275 R¯0,α¯,Δα ${{\bar{R}}_{0}},\bar{\alpha },\Delta \alpha $ 0.0335
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Sprache:
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Technik, Bioingenieurwesen, Biomedizinische Elektronik, Biologie, Biophysik, Medizin, Biomedizinische Technik, Physik, Spektroskopie und Metrologie
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