Identification of women with high grade histopathology results after conisation by artificial neural networks

Marko Mlinaric; Miljenko Krizmaric; Iztok Takac; Alenka Repse Fokter

doi:10.2478/raon-2022-0023

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Identification of women with high grade histopathology results after conisation by artificial neural networks

Radiology and Oncology

Volume 56 (2022): Issue 3 (September 2022)

By: Marko Mlinaric, Miljenko Krizmaric, Iztok Takac and Alenka Repse Fokter

Open Access

|Aug 2022

Figures & Tables

Schematic of simple neural network with input, output and three hidden layers.

Matthews correlation coefficient (MCC) for categorisation squamous intraepithelial lesion (HSIL)-combined for YES and NO prediction for different equalisation methods (no correction of minority class, under-sampling, oversampling and synthetic minority over-sampling technique [SMOTE]) for both RAW and Class settings. Best performance of multi-layer perceptron (MLP) is on dataset with data organised in classes and over-sampling method for minority class – MCC = 0.64. Lowest performance is with original dataset without correction for minority class – MCC = 0.086.

True positive and False positive rate for different settings for prediction Yes and No combined and for different equalisation methods (no correction of minority class, under-sampling, over-sampling and synthetic minority over-sampling technique [SMOTE]) for both RAW and Class settings. Best performance model from Figure 2 has 0.842 true positive rate and 0.182 false positive rate. Lowest performance model from Figure 2 has high 0.814 true positive rate which is almost as high as best performance model but also high false positive rate 0.735. Raw = original settings; Class = class setting; FPR = false positive rate; HSIL = high grade squamous intraepithelial lesion; overs = oversampling; TPR = true positive rate; unders = undersampling; SMOTE = synthetic minority over-sampling technique

Receiver operator characteristic (ROC) curve for multi-layer perceptron (MLP) performance on dataset without grouping in classes and no correction for minority class where X axis represent 1- specificity (false positive rate) and Y axis represents sensitivity (true positive rate). Area under the ROC curve (AUC) = 0.594. AUC for categorisation with random guessing is 0.5. This Figure represents model with lowest performance of MLP from our study.

Receiver operator characteristic (ROC) curve for multi-layer perceptron (MLP) performance on dataset with patients grouping in classes and synthetic minority over-sampling technique (SMOTE) correction for minority class where X axis represent 1- specificity (false positive rate) and Y axis represents sensitivity (true positive rate). Area under the ROC curve (AUC) = 0.802 which is well above classification with random guessing where AUC is 0.5. This Figure represents best performance model of MLP from our study.

Number and percentage of patients according to human papilloma virus (HPV) 16 and 18 statuses in high grade squamous intraepithelial lesion (HSIL) and NO-HSIL group

	HPV 16				HPV 18

	HSIL group		NO-HSIL group		HSIL group		NO-HSIL group

	Frequency	%	Frequency	%	Frequency	%	Frequency	%
not performed	177	14	29	16	172	13	27	15
negative	693	54	106	57	775	60	120	65
positive	419	32	51	27	342	27	39	20
Total	1289	100	186	100	1289	100	186	100

Results of multi-layer perceptron (MLP) classifications for different settings with baseline prediction – ZeroR, percentage of correct classification and Kappa statistic for all analysis_ Results are for prediction high grade squamous intraepithelial lesion (HSIL)-Yes (Y), prediction NO-HSIL (N) and weighted average for whole model (YES and NO combined) – Weighted average (AVG)_ In bold-type letters are results, where prediction by MLP is better than baseline prediction ZeroR

	TP Rate	FP Rate	Precision	Recall	F-Measure	MCC	ROC Area	PRC Area	Class	% Correct	Kappa	ZeroR %
Class_orig–Y	0.751	0.634	0.739	0.751	0.745	0.118	0.567	0.735	Yes	82.10	0.0965	87.39
Class_orig–N	0.366	0.249	0.308	0.366	0.373	0.118	0.567	0.377	No
Class_orig–AVG	0.637	0.521	0.633	0.637	0.635	0.118	0.567	0.629	Weighted Avg
Class_overs–Y	0.860	0.201	0.908	0.860	0.884	0.640	0.870	0.920	Yes	84.19	0.6376	69.79
Class_overs–N	0.799	0.140	0.712	0.799	0.753	0.640	0.870	0.703	No
Class_overs–AVG	0.842	0.182	0.849	0.842	0.844	0.640	0.870	0.855	Weighted Avg
Class_SMOTE–Y	0.797	0.274	0.834	0.797	0.815	0.515	0.802	0.850	Yes	77.08	0.5141	63.40
Class_SMOTE–N	0.726	0.203	0.673	0.726	0.699	0.515	0.802	0.669	No
Class_SMOTE–AVG	0.771	0.248	0.775	0.771	0.772	0.515	0.802	0.784	Weighted Avg
Class_unders–Y	0.669	0.559	0.636	0.669	0.652	0.112	0.542	0.608	Yes	57.64	0.1113	59.39
Class_unders–N	0.441	0.331	0.477	0.441	0.458	0.112	0.542	0.448	No
Class_unders–AVG	0.576	0.466	0.572	0.576	0.573	0.112	0.542	0.543	Weighted Avg
RAW_orig–Y	0.907	0.828	0.884	0.907	0.895	0.086	0.594	0.905	Yes	81.42	0.0856	87.39
RAW_orig–N	0.172	0.093	0.211	0.172	0.189	0.086	0.594	0.174	No
RAW_orig–AVG	0.814	0.735	0.799	0.814	0.806	0.086	0.594	0.813	Weighted Avg
RAW_overs–Y	0.825	0.285	0.870	0.825	0.847	0.525	0.837	0.905	Yes	79.21	0.523	69.79
RAW_overs–N	0.715	0.175	0.639	0.715	0.675	0.525	0.837	0.661	No
RAW_overs–AVG	0.792	0.252	0.800	0.792	0.795	0.525	0.837	0.831	Weighted Avg
RAW_SMOTE–Y	0.800	0.258	0.843	0.800	0.821	0.533	0.814	0.867	Yes	77.87	0.5318	63.4
RAW_SMOTE–N	0.742	0.200	0.681	0.742	0.710	0.533	0.814	0.691	No
RAW_SMOTE–AVG	0.779	0.237	0.784	0.779	0.780	0.533	0.814	0.802	Weighted Avg
RAW_unders–Y	0.688	0.575	0.636	0.688	0.661	0.115	0.551	0.614	Yes	58.08	0.1144	59.39
RAW_unders–N	0.425	0.313	0.482	0.425	0.451	0.115	0.551	0.466	No
RAW_unders–AVG	0.581	0.469	0.573	0.581	0.576	0.115	0.551	0.554	Weighted Avg

Confusion matrix for classification with all possible outcomes

	Predicted pos (PP)	Predicted neg (PN)
Actual pos (P)	True positives (TP)	False negatives (FN)
Actual neg (N)	False positives (FP)	True negatives (TN)

Final histology of the cone in patients without human papilloma virus (HPV) testing

	Frequency	Percent
NO dysplasia	9	1.8
CIN 1	26	5.3
CIN 1–2	27	5.4
CIN 2	90	18.1
CIN 2–3	55	11.1
CIN 3	223	45.0
CIS	55	11.1
invasive ca	11	2.2
Total	496	100.0

References

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DOI: https://doi.org/10.2478/raon-2022-0023 | Journal eISSN: 1581-3207 | Journal ISSN: 1318-2099

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Language: English

Page range: 355 - 364

Submitted on: Jan 16, 2022

Accepted on: Apr 25, 2022

Published on: Aug 14, 2022

Published by: Association of Radiology and Oncology

In partnership with: Paradigm Publishing Services

Publication frequency: 4 issues per year

Keywords:

uterine cervical dysplasia,

uterine cervical cancer,

conisation,

artificial neural networks

Related subjects:

Medicine,

Clinical medicine,

Internal medicine,

Haematology, oncology,

Radiology

© 2022 Marko Mlinaric, Miljenko Krizmaric, Iztok Takac, Alenka Repse Fokter, published by Association of Radiology and Oncology
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Volume 56 (2022): Issue 3 (September 2022)

Identification of women with high grade histopathology results after conisation by artificial neural networks

Figures & Tables

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Number and percentage of patients according to human papilloma virus (HPV) 16 and 18 statuses in high grade squamous intraepithelial lesion (HSIL) and NO-HSIL group

Confusion matrix for classification with all possible outcomes

Final histology of the cone in patients without human papilloma virus (HPV) testing

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