4  Results

We summarize cross-validated training performance of class metrics in the training set. The accuracy, F1-score, and kappa, are the metrics of interest. Workflows are ordered by their mean estimates across the outer folds of the nested CV for each metric.

4.1 Training Set

Accuracy

Table 4.1: Training Set Mean Accuracy

			Histotypes
Subsampling	Algorithms	Overall	HGSOC	CCOC	ENOC	MUOC	LGSOC
none	rf	0.922	0.948	0.983	0.953	0.975	0.984
	svm	0.92	0.947	0.982	0.955	0.975	0.982
	xgb	0.807	0.809	0.936	0.933	0.955	0.982
	mr	0.807	0.807	0.936	0.935	0.955	0.982
down	rf	0.804	0.853	0.975	0.903	0.959	0.92
	svm	0.793	0.833	0.973	0.884	0.969	0.928
	xgb	0.784	0.829	0.968	0.893	0.968	0.911
	mr	0.827	0.869	0.977	0.911	0.961	0.937
up	rf	0.928	0.955	0.983	0.959	0.979	0.982
	svm	0.92	0.951	0.974	0.951	0.979	0.985
	xgb	0.933	0.96	0.985	0.963	0.977	0.981
	mr	0.89	0.924	0.975	0.948	0.965	0.968
smote	rf	0.921	0.955	0.98	0.953	0.97	0.984
	svm	0.92	0.95	0.979	0.954	0.978	0.981
	xgb	0.924	0.955	0.982	0.956	0.974	0.982
	mr	0.898	0.932	0.979	0.948	0.968	0.968
hybrid	rf	0.922	0.953	0.98	0.952	0.975	0.983
	svm	0.916	0.944	0.982	0.951	0.975	0.979
	xgb	0.923	0.957	0.979	0.954	0.974	0.982
	mr	0.895	0.931	0.979	0.947	0.967	0.967

Figure 4.1: Training Set Mean Accuracy

Sensitivity

Table 4.2: Training Set Mean Sensitivity

			Histotypes
Subsampling	Algorithms	Overall	HGSOC	CCOC	ENOC	MUOC	LGSOC
none	rf	0.636	0.994	0.794	0.498	0.728	0.167
	svm	0.615	0.989	0.762	0.576	0.751	0
	xgb	0.2	1	0	0	0	0
	mr	0.2	1	0	0	0	0
down	rf	0.742	0.83	0.827	0.565	0.657	0.833
	svm	0.779	0.805	0.759	0.611	0.836	0.883
	xgb	0.753	0.798	0.82	0.545	0.837	0.767
	mr	0.775	0.846	0.831	0.62	0.77	0.808
up	rf	0.673	0.988	0.793	0.635	0.766	0.183
	svm	0.714	0.976	0.735	0.625	0.71	0.525
	xgb	0.725	0.981	0.817	0.679	0.8	0.35
	mr	0.787	0.92	0.845	0.666	0.786	0.717
smote	rf	0.658	0.984	0.748	0.618	0.75	0.192
	svm	0.759	0.966	0.757	0.681	0.748	0.642
	xgb	0.741	0.974	0.829	0.606	0.781	0.517
	mr	0.784	0.932	0.821	0.666	0.786	0.717
hybrid	rf	0.72	0.972	0.805	0.638	0.804	0.383
	svm	0.764	0.957	0.793	0.696	0.733	0.642
	xgb	0.747	0.971	0.815	0.604	0.78	0.567
	mr	0.784	0.93	0.809	0.666	0.75	0.767

Figure 4.2: Training Set Mean Sensitivity

Specificity

Table 4.3: Training Set Mean Specificity

			Histotypes
Subsampling	Algorithms	Overall	HGSOC	CCOC	ENOC	MUOC	LGSOC
none	rf	0.946	0.764	0.996	0.985	0.988	0.999
	svm	0.947	0.775	0.997	0.98	0.986	1
	xgb	0.802	0.009	1	0.998	1	1
	mr	0.8	0	1	1	1	1
down	rf	0.952	0.955	0.984	0.928	0.973	0.921
	svm	0.949	0.952	0.987	0.902	0.976	0.928
	xgb	0.949	0.962	0.978	0.917	0.975	0.913
	mr	0.958	0.963	0.986	0.93	0.971	0.939
up	rf	0.957	0.822	0.996	0.98	0.989	0.997
	svm	0.958	0.844	0.99	0.973	0.992	0.993
	xgb	0.966	0.875	0.996	0.981	0.986	0.993
	mr	0.967	0.937	0.984	0.967	0.974	0.973
smote	rf	0.958	0.839	0.996	0.975	0.981	0.998
	svm	0.964	0.88	0.993	0.971	0.989	0.987
	xgb	0.965	0.878	0.992	0.98	0.983	0.991
	mr	0.968	0.934	0.99	0.967	0.977	0.973
hybrid	rf	0.964	0.874	0.992	0.974	0.984	0.994
	svm	0.965	0.89	0.994	0.968	0.987	0.985
	xgb	0.968	0.9	0.99	0.977	0.983	0.99
	mr	0.967	0.934	0.99	0.965	0.978	0.97

Figure 4.3: Training Set Mean Specificity

F1-Score

Table 4.4: Training Set Mean F1-Score

			Histotypes
Subsampling	Algorithms	Overall	HGSOC	CCOC	ENOC	MUOC	LGSOC
none	rf	0.733	0.968	0.848	0.568	0.723	0.4
	svm	0.782	0.968	0.834	0.607	0.719	NaN
	xgb	0.712	0.894	NaN	0	NaN	NaN
	mr	0.893	0.893	NaN	NaN	NaN	NaN
down	rf	0.594	0.901	0.799	0.419	0.589	0.262
	svm	0.617	0.886	0.778	0.412	0.713	0.295
	xgb	0.597	0.883	0.768	0.399	0.706	0.227
	mr	0.627	0.912	0.817	0.468	0.638	0.299
up	rf	0.755	0.972	0.849	0.652	0.757	0.362
	svm	0.725	0.97	0.778	0.605	0.75	0.521
	xgb	0.729	0.975	0.862	0.685	0.753	0.368
	mr	0.694	0.951	0.808	0.616	0.67	0.428
smote	rf	0.68	0.972	0.82	0.616	0.688	0.304
	svm	0.739	0.969	0.811	0.638	0.752	0.524
	xgb	0.73	0.972	0.848	0.632	0.726	0.474
	mr	0.703	0.957	0.827	0.615	0.688	0.43
hybrid	rf	0.724	0.971	0.829	0.631	0.741	0.45
	svm	0.733	0.965	0.838	0.635	0.72	0.507
	xgb	0.729	0.973	0.823	0.615	0.729	0.503
	mr	0.699	0.956	0.821	0.606	0.675	0.437

Figure 4.4: Training Set Mean F1-Score

Balanced Accuracy

Table 4.5: Training Set Mean Balanced Accuracy

			Histotypes
Subsampling	Algorithms	Overall	HGSOC	CCOC	ENOC	MUOC	LGSOC
none	rf	0.791	0.879	0.895	0.741	0.858	0.583
	svm	0.781	0.882	0.879	0.778	0.868	0.5
	xgb	0.501	0.505	0.5	0.499	0.5	0.5
	mr	0.5	0.5	0.5	0.5	0.5	0.5
down	rf	0.847	0.892	0.905	0.746	0.815	0.877
	svm	0.864	0.879	0.873	0.757	0.906	0.906
	xgb	0.851	0.88	0.899	0.731	0.906	0.84
	mr	0.867	0.905	0.909	0.775	0.87	0.874
up	rf	0.815	0.905	0.894	0.808	0.878	0.59
	svm	0.836	0.91	0.862	0.799	0.851	0.759
	xgb	0.846	0.928	0.906	0.83	0.893	0.671
	mr	0.877	0.929	0.914	0.816	0.88	0.845
smote	rf	0.808	0.911	0.872	0.797	0.865	0.595
	svm	0.862	0.923	0.875	0.826	0.869	0.814
	xgb	0.853	0.926	0.911	0.793	0.882	0.754
	mr	0.876	0.933	0.905	0.816	0.882	0.845
hybrid	rf	0.842	0.923	0.898	0.806	0.894	0.689
	svm	0.864	0.923	0.893	0.832	0.86	0.814
	xgb	0.858	0.936	0.902	0.79	0.882	0.778
	mr	0.876	0.932	0.899	0.815	0.864	0.868

Figure 4.5: Training Set Mean Balanced Accuracy

Kappa

Table 4.6: Training Set Mean Kappa

			Histotypes
Subsampling	Algorithms	Overall	HGSOC	CCOC	ENOC	MUOC	LGSOC
none	rf	0.744	0.822	0.84	0.545	0.711	0.237
	svm	0.741	0.818	0.825	0.583	0.706	0
	xgb	0.007	0.016	0	-0.003	0	0
	mr	0	0	0	0	0	0
down	rf	0.55	0.621	0.785	0.37	0.568	0.24
	svm	0.539	0.584	0.764	0.359	0.697	0.275
	xgb	0.526	0.578	0.752	0.348	0.69	0.204
	mr	0.592	0.654	0.805	0.424	0.618	0.279
up	rf	0.776	0.85	0.84	0.63	0.746	0.213
	svm	0.749	0.835	0.764	0.579	0.739	0.513
	xgb	0.795	0.871	0.854	0.666	0.741	0.36
	mr	0.705	0.775	0.795	0.588	0.652	0.415
smote	rf	0.756	0.853	0.81	0.591	0.673	0.299
	svm	0.761	0.839	0.8	0.614	0.741	0.516
	xgb	0.773	0.855	0.839	0.609	0.712	0.466
	mr	0.721	0.797	0.816	0.588	0.672	0.417
hybrid	rf	0.767	0.85	0.818	0.605	0.728	0.443
	svm	0.753	0.825	0.828	0.609	0.707	0.497
	xgb	0.77	0.863	0.812	0.59	0.715	0.494
	mr	0.713	0.793	0.81	0.577	0.658	0.424

Figure 4.6: Training Set Mean Kappa

4.2 Rank Aggregation

Multi-step methods:

• sequential: sequential algorithm sequence of subsampling methods and algorithms used are:
  • HGSOC vs. non-HGSOC using upsubsampling and XGBoost
  • CCOC vs. non-CCOC using no subsampling and random forest
  • ENOC vs. non-ENOC using no subsampling and support vector machine
  • MUOC vs. LGSOC using SMOTE subsampling and random forest
• two_step: two-step algorithm sequence of subsampling methods and algorithms used are:
  • HGSOC vs. non-HGSOC using upsampling and XGBoost
  • CCOC vs. ENOC vs. MUOC vs. LGSOC using SMOTE subsampling and support vector machine

We conduct rank aggregation using a two-stage nested approach:

1. First we rank aggregate the per-class metrics for F1-score, balanced accuracy and kappa.
2. Then we take the aggregated lists from the three metrics and perform a final rank aggregation.
3. The top workflows from the final rank aggregation are used for gene optimization in the confirmation set

4.2.1 Across Classes

F1-Score

Table 4.7: F1-Score Rank Aggregation Summary

Balanced Accuracy

Table 4.8: Balanced Accuracy Rank Aggregation Summary

Kappa

Table 4.9: Kappa Rank Aggregation Summary

4.2.2 Across Metrics

Table 4.10: Rank Aggregation Comparison of Metrics Used

Rank	F1	Balanced Accuracy	Kappa
1	sequential	sequential	sequential
2	two_step	two_step	two_step
3	up_xgb	up_mr	up_xgb
4	up_rf	smote_mr	up_rf
5	smote_svm	up_xgb	smote_svm
6	hybrid_svm	smote_xgb	hybrid_rf
7	smote_xgb	hybrid_mr	smote_xgb
8	hybrid_rf	hybrid_xgb	hybrid_svm
9	up_svm	down_xgb	up_svm
10	hybrid_xgb	smote_svm	hybrid_xgb
11	none_rf	hybrid_rf	smote_rf
12	smote_mr	down_mr	smote_mr
13	smote_rf	hybrid_svm	none_svm
14	hybrid_mr	up_rf	none_rf
15	up_mr	smote_rf	hybrid_mr
16	down_mr	down_rf	up_mr
17	down_rf	none_svm	down_mr
18	down_svm	down_svm	down_rf
19	down_xgb	none_rf	down_svm
20	NA	up_svm	down_xgb
21	NA	none_mr	none_mr
22	NA	none_xgb	none_xgb

Table 4.11: Top 5 Workflows from Final Rank Aggregation

Rank	Workflow
1	sequential
2	two_step
3	up_xgb
4	up_rf
5	smote_svm

4.2.3 Top Workflows

We look at the per-class evaluation metrics of the top 5 workflows.

Table 4.12: Top Workflow Per-Class Evaluation Metrics

		Histotypes
Metric	Workflow	HGSOC	CCOC	ENOC	MUOC	LGSOC
Accuracy	sequential	0.963 (0.956, 0.968)	0.917 (0.896, 0.958)	0.856 (0.774, 0.909)	0.951 (0.882, 1)	0.951 (0.882, 1)
	2-STEP	0.963 (0.956, 0.968)	0.934 (0.896, 0.958)	0.839 (0.729, 0.896)	0.892 (0.812, 0.96)	0.971 (0.938, 1)
	Up-XGB	0.96 (0.944, 0.976)	0.985 (0.968, 0.996)	0.963 (0.956, 0.976)	0.977 (0.968, 0.988)	0.981 (0.976, 0.988)
	Up-RF	0.955 (0.92, 0.976)	0.983 (0.972, 0.992)	0.959 (0.948, 0.972)	0.979 (0.964, 0.988)	0.982 (0.968, 0.988)
	SMOTE-SVM	0.95 (0.936, 0.968)	0.979 (0.968, 0.988)	0.954 (0.94, 0.964)	0.978 (0.96, 0.984)	0.981 (0.972, 0.984)
Sensitivity	sequential	0.978 (0.966, 0.99)	0.814 (0.75, 0.882)	0.865 (0.812, 0.941)	0.965 (0.909, 1)	0.92 (0.6, 1)
	2-STEP	0.978 (0.966, 0.99)	0.866 (0.824, 0.933)	0.735 (0.556, 0.824)	0.842 (0.75, 0.923)	0.767 (0, 1)
	Up-XGB	0.981 (0.976, 0.986)	0.817 (0.571, 0.941)	0.679 (0.444, 0.818)	0.8 (0.538, 1)	0.35 (0.25, 0.667)
	Up-RF	0.988 (0.979, 0.995)	0.793 (0.571, 0.882)	0.635 (0.444, 0.812)	0.766 (0.462, 0.909)	0.183 (0, 0.333)
	SMOTE-SVM	0.966 (0.955, 0.977)	0.757 (0.571, 0.85)	0.681 (0.5, 0.864)	0.748 (0.538, 0.909)	0.642 (0.5, 0.75)
Specificity	sequential	0.897 (0.875, 0.918)	0.969 (0.938, 1)	0.847 (0.733, 0.875)	0.92 (0.6, 1)	0.965 (0.909, 1)
	2-STEP	0.897 (0.875, 0.918)	0.969 (0.935, 1)	0.893 (0.833, 0.935)	0.908 (0.833, 0.973)	0.977 (0.953, 1)
	Up-XGB	0.875 (0.826, 0.919)	0.996 (0.992, 1)	0.981 (0.97, 0.991)	0.986 (0.979, 0.992)	0.993 (0.984, 1)
	Up-RF	0.822 (0.738, 0.892)	0.996 (0.992, 1)	0.98 (0.966, 0.987)	0.989 (0.983, 0.996)	0.997 (0.988, 1)
	SMOTE-SVM	0.88 (0.804, 0.919)	0.993 (0.987, 1)	0.971 (0.962, 0.975)	0.989 (0.983, 0.996)	0.987 (0.976, 0.996)
F1-Score	sequential	0.977 (0.973, 0.98)	0.868 (0.828, 0.933)	0.86 (0.788, 0.914)	0.966 (0.923, 1)	0.91 (0.75, 1)
	2-STEP	0.977 (0.973, 0.98)	0.899 (0.848, 0.933)	0.755 (0.606, 0.848)	0.788 (0.667, 0.923)	0.733 (0, 1)
	Up-XGB	0.975 (0.964, 0.986)	0.862 (0.667, 0.97)	0.685 (0.471, 0.857)	0.753 (0.636, 0.87)	0.368 (0.25, 0.571)
	Up-RF	0.972 (0.949, 0.986)	0.849 (0.696, 0.938)	0.652 (0.471, 0.788)	0.757 (0.571, 0.87)	0.362 (0.286, 0.4)
	SMOTE-SVM	0.969 (0.96, 0.981)	0.811 (0.667, 0.919)	0.638 (0.5, 0.809)	0.752 (0.583, 0.833)	0.524 (0.364, 0.714)
Balanced Accuracy	sequential	0.938 (0.928, 0.947)	0.892 (0.859, 0.938)	0.856 (0.773, 0.908)	0.943 (0.8, 1)	0.943 (0.8, 1)
	2-STEP	0.938 (0.928, 0.947)	0.917 (0.88, 0.952)	0.814 (0.694, 0.88)	0.875 (0.792, 0.948)	0.872 (0.479, 1)
	Up-XGB	0.928 (0.901, 0.952)	0.906 (0.781, 0.971)	0.83 (0.714, 0.905)	0.893 (0.765, 0.99)	0.671 (0.621, 0.829)
	Up-RF	0.905 (0.858, 0.941)	0.894 (0.784, 0.941)	0.808 (0.714, 0.898)	0.878 (0.727, 0.95)	0.59 (0.5, 0.665)
	SMOTE-SVM	0.923 (0.885, 0.948)	0.875 (0.781, 0.925)	0.826 (0.735, 0.919)	0.869 (0.761, 0.948)	0.814 (0.746, 0.869)
Kappa	sequential	0.879 (0.862, 0.895)	0.808 (0.754, 0.903)	0.712 (0.547, 0.818)	0.877 (0.679, 1)	0.877 (0.679, 1)
	2-STEP	0.879 (0.862, 0.895)	0.85 (0.769, 0.903)	0.635 (0.402, 0.769)	0.716 (0.538, 0.896)	0.718 (-0.029, 1)
	Up-XGB	0.871 (0.822, 0.905)	0.854 (0.65, 0.968)	0.666 (0.452, 0.844)	0.741 (0.62, 0.863)	0.36 (0.239, 0.566)
	Up-RF	0.85 (0.768, 0.903)	0.84 (0.682, 0.933)	0.63 (0.452, 0.773)	0.746 (0.554, 0.863)	0.213 (0, 0.396)
	SMOTE-SVM	0.839 (0.783, 0.876)	0.8 (0.65, 0.913)	0.614 (0.48, 0.789)	0.741 (0.563, 0.825)	0.516 (0.352, 0.706)

Figure 4.7: Top 5 Workflow Per-Class Evaluation Metrics by Metric

Table 4.13: Top Workflow Per-Class Evaluation Metrics and Ranks

Workflow	Rank	HGSOC	CCOC	ENOC	MUOC	LGSOC
F1-Score
sequential	1	0.977	0.868	0.860	0.966	0.910
2-STEP	2	0.977	0.899	0.755	0.788	0.733
Up-XGB	3	0.975	0.862	0.685	0.753	0.368
Up-RF	4	0.972	0.849	0.652	0.757	0.362
SMOTE-SVM	5	0.969	0.811	0.638	0.752	0.524
Balanced Accuracy
sequential	1	0.938	0.892	0.856	0.943	0.943
2-STEP	2	0.938	0.917	0.814	0.875	0.872
Up-XGB	5	0.928	0.906	0.830	0.893	0.671
SMOTE-SVM	10	0.923	0.875	0.826	0.869	0.814
Up-RF	14	0.905	0.894	0.808	0.878	0.590
Kappa
sequential	1	0.879	0.808	0.712	0.877	0.877
2-STEP	2	0.879	0.850	0.635	0.716	0.718
Up-XGB	3	0.871	0.854	0.666	0.741	0.360
Up-RF	4	0.850	0.840	0.630	0.746	0.213
SMOTE-SVM	5	0.839	0.800	0.614	0.741	0.516

Figure 4.8: Top 5 Workflow Per-Class Evaluation Metrics by Metric

Misclassified cases from a previous step of the sequence of classifiers are not included in subsequent steps of the training set CV folds. Thus, we cannot piece together the test set predictions from the sequential and two-step algorithms to obtain overall metrics.

4.3 Confirmation Set

Now we’d like to see how our best five workflows perform in the confirmation set. The class-specific F1-scores will be used. The top performing method will be selected for gene optimization.

Table 4.14: Evaluation Metrics on Confirmation Set Models

			Histotypes
Method	Metric	Overall	HGSOC	CCOC	ENOC	MUOC	LGSOC
Sequential	Accuracy	0.830	0.860	0.977	0.882	0.969	0.974
	Sensitivity	0.592	0.950	0.847	0.486	0.593	0.083
	Specificity	0.923	0.683	0.993	0.961	0.985	0.990
	F1-Score	0.618	0.900	0.891	0.578	0.615	0.105
	Balanced Accuracy	0.757	0.817	0.920	0.723	0.789	0.537
	Kappa	0.648	0.670	0.877	0.512	0.599	0.093
2-STEP	Accuracy	0.833	0.860	0.970	0.889	0.978	0.969
	Sensitivity	0.608	0.950	0.861	0.477	0.667	0.083
	Specificity	0.923	0.683	0.984	0.972	0.992	0.986
	F1-Score	0.633	0.900	0.867	0.590	0.720	0.091
	Balanced Accuracy	0.766	0.817	0.923	0.724	0.829	0.535
	Kappa	0.655	0.670	0.850	0.530	0.709	0.075
Up-XGB	Accuracy	0.844	0.868	0.970	0.896	0.980	0.975
	Sensitivity	0.658	0.958	0.875	0.467	0.741	0.250
	Specificity	0.927	0.693	0.982	0.981	0.990	0.989
	F1-Score	0.680	0.905	0.869	0.599	0.755	0.273
	Balanced Accuracy	0.793	0.825	0.929	0.724	0.865	0.619
	Kappa	0.678	0.688	0.852	0.544	0.744	0.260
Up-RF	Accuracy	0.835	0.857	0.975	0.883	0.974	0.981
	Sensitivity	0.613	0.972	0.875	0.383	0.667	0.167
	Specificity	0.918	0.633	0.988	0.983	0.987	0.997
	F1-Score	0.648	0.900	0.887	0.522	0.679	0.250
	Balanced Accuracy	0.765	0.802	0.931	0.683	0.827	0.582
	Kappa	0.646	0.654	0.873	0.466	0.665	0.243
SMOTE-SVM	Accuracy	0.827	0.866	0.958	0.888	0.972	0.970
	Sensitivity	0.650	0.939	0.861	0.477	0.556	0.417
	Specificity	0.927	0.725	0.970	0.970	0.990	0.981
	F1-Score	0.656	0.902	0.821	0.586	0.625	0.345
	Balanced Accuracy	0.788	0.832	0.916	0.723	0.773	0.699
	Kappa	0.651	0.690	0.797	0.525	0.611	0.330

Figure 4.9: Evaluation Metrics on Confirmation Set Models

Figure 4.10: Entropy vs. Predicted Probability in Confirmation Set

Figure 4.11: Gene Optimized Workflows Per-Class Metrics in Confirmation Set

Figure 4.12: Confusion Matrices for Confirmation Set Models

Sequential

Figure 4.13: ROC Curves for Sequential Model in Confirmation Set

2-STEP

Figure 4.14: ROC Curves for 2-STEP Model in Confirmation Set

Up-XGB

Figure 4.15: ROC Curves for Up-XGB Model in Confirmation Set

Up-RF

Figure 4.16: ROC Curves for Up-RF Model in Confirmation Set

SMOTE-SVM

Figure 4.17: ROC Curves for SMOTE-SVM Model in Confirmation Set

4.4 Gene Optimization

4.4.1 Up-XGB

Figure 4.18: Gene Optimization for Up-XGB Classifier using Balanced Accuracy

In the Up-XGB classifier, the mean balanced accuracy is highest when we reach 12 genes added, hence the optimal number of genes used will be n=28+12=40 The added genes are: HNF1B, TPX2, TFF1, CYP2C18, TFF3, WT1, GPR64, KLK7, SLC3A1, IGFBP1, GAD1 and LGALS4.

Table 4.15: Gene Profile of Optimal Set in Up-XGB Workflow

Set	Genes	PrOTYPE	SPOT	Optimal Set	Candidate Rank
Base	COL11A1	✔		◉
	CD74	✔		◉
	CD2	✔		◉
	TIMP3	✔		◉
	LUM	✔		◉
	CYTIP	✔		◉
	COL3A1	✔		◉
	THBS2	✔		◉
	TCF7L1	✔	✔	◉
	HMGA2	✔		◉
	FN1	✔		◉
	POSTN	✔		◉
	COL1A2	✔		◉
	COL5A2	✔		◉
	PDZK1IP1	✔		◉
	FBN1	✔		◉
	HIF1A		✔	◉
	CXCL10		✔	◉
	DUSP4		✔	◉
	SOX17		✔	◉
	MITF		✔	◉
	CDKN3		✔	◉
	BRCA2		✔	◉
	CEACAM5		✔	◉
	ANXA4		✔	◉
	SERPINE1		✔	◉
	CRABP2		✔	◉
	DNAJC9		✔	◉
Candidates	HNF1B			◉	1
	TPX2			◉	2
	TFF1			◉	3
	CYP2C18			◉	4
	TFF3			◉	5
	WT1			◉	6
	GPR64			◉	7
	KLK7			◉	8
	SLC3A1			◉	9
	IGFBP1			◉	10
	GAD1			◉	11
	LGALS4			◉	12
	MET			ⓧ	13
	GCNT3			ⓧ	14
	FUT3			ⓧ	15
	C1orf173			ⓧ	16
	EGFL6			ⓧ	17
	MUC5B			ⓧ	18
	C10orf116			ⓧ	19
	DKK4			ⓧ	20
	IL6			ⓧ	21
	CAPN2			ⓧ	22
	KGFLP2			ⓧ	23
	BRCA1			ⓧ	24
	CYP4B1			ⓧ	25
	IGKC			ⓧ	26
	PBX1			ⓧ	27
	TSPAN8			ⓧ	28
	SEMA6A			ⓧ	29
	SENP8			ⓧ	30
	PAX8			ⓧ	31
	TP53			ⓧ	32
	SERPINA5			ⓧ	33
	ATP5G3			ⓧ	34
	CPNE8			ⓧ	35
	LIN28B			ⓧ	36
	STC1			ⓧ	37
	EPAS1			ⓧ	38
	BCL2			ⓧ	39
	MAP1LC3A			ⓧ	40
	SCGB1D2			ⓧ	41
	ADCYAP1R1			ⓧ	42
	IGJ			ⓧ	43
	ZBED1			ⓧ	44

4.4.2 SMOTE-SVM

Figure 4.19: Gene Optimization for SMOTE-SVM Classifier using Balanced Accuracy

In the SMOTE-SVM classifier, the mean balanced accuracy is optimal with 0 genes added, hence the optimal number of genes used will be n=28+0=28 The added genes are: .

Table 4.16: Gene Profile of Optimal Set in SMOTE-SVM Workflow

Set	Genes	PrOTYPE	SPOT	Optimal Set	Candidate Rank
Base	COL11A1	✔		◉
	CD74	✔		◉
	CD2	✔		◉
	TIMP3	✔		◉
	LUM	✔		◉
	CYTIP	✔		◉
	COL3A1	✔		◉
	THBS2	✔		◉
	TCF7L1	✔	✔	◉
	HMGA2	✔		◉
	FN1	✔		◉
	POSTN	✔		◉
	COL1A2	✔		◉
	COL5A2	✔		◉
	PDZK1IP1	✔		◉
	FBN1	✔		◉
	HIF1A		✔	◉
	CXCL10		✔	◉
	DUSP4		✔	◉
	SOX17		✔	◉
	MITF		✔	◉
	CDKN3		✔	◉
	BRCA2		✔	◉
	CEACAM5		✔	◉
	ANXA4		✔	◉
	SERPINE1		✔	◉
	CRABP2		✔	◉
	DNAJC9		✔	◉

4.5 Validation Set

Table 4.17: Evaluation Metrics on Validation Set Model, Up-XGB, Full Set

		Histotypes
Metric	Overall	HGSOC	CCOC	ENOC	MUOC	LGSOC
Accuracy	0.902	0.915	0.975	0.951	0.983	0.979
Sensitivity	0.797	0.937	1.000	0.602	0.913	0.533
Specificity	0.954	0.836	0.973	0.989	0.985	0.986
F1-Score	0.742	0.945	0.862	0.707	0.737	0.457
Balanced Accuracy	0.876	0.886	0.987	0.796	0.949	0.760
Kappa	0.743	0.756	0.849	0.681	0.729	0.447

Figure 4.20: Up-XGB, Full Set, Per-Class Metrics in Validation Set

Figure 4.21: Confusion Matrix for Validation Set Model

Figure 4.22: ROC Curves for Up-XGB, Full Set Model in Validation Set

Figure 4.23: Calibration Plots for Up-XGB, Full Set Model in Validation Set

Figure 4.24: Validation Summary

Table 4.18: Clinicopath characteristics between correct and incorrect predictions of ENOC cases

Characteristic	Predicted ENOC Correctly N = 531	Missed ENOC N = 351	p-value2
Age at diagnosis	52 (46, 63)	57 (51, 63)	0.2
Tumour grade			0.002
low grade	41 (93%)	18 (64%)
high grade	3 (6.8%)	10 (36%)
Unknown	9	7
FIGO tumour stage			0.2
I	40 (77%)	22 (63%)
II-IV	12 (23%)	13 (37%)
Unknown	1	0
Race			>0.9
white	48 (92%)	28 (90%)
non-white	4 (7.7%)	3 (9.7%)
Unknown	1	4
ARID1A			0.4
absent/subclonal	11 (21%)	5 (14%)
present	42 (79%)	30 (86%)
1 Median (Q1, Q3); n (%)
2 Wilcoxon rank sum test; Pearson’s Chi-squared test; Fisher’s exact test

Figure 4.25: Volcano Plots of Validation Set Predictions

Figure 4.26: Subtype Prediction Summary among Predicted HGSC Samples