A Nomogram Model for Predicting Recurrence of Stage I–III Endometrial Cancer Based on Inflammation-Immunity-Nutrition Score (IINS) and Traditional Classical Predictors

Figures & data

Figure 1 Study design and the flow chart of patient inclusion.

Table 1 Baseline Characteristics of the Training Cohort and the Validation Cohort

Variable	Training Cohort N = 775	%	Validation Cohort N = 491	%	P-value*
Age (yrs)					0.279
Mean (±SD)	53.84 (±9.32)		53.25 (±9.49)
Median (range)	53.00 (24–81)		52.00 (25–83)
BMI (kg/m^2)					0.369
Mean (±SD)	24.65 (±3.72)		24.84 (±3.82)
Median (range)	24.34 (16.35–45.72)		24.44 (16.53–41.84)
FIGO stage					0.914
I	548	70.7	348	70.9
II	73	9.4	49	10.0
III	154	19.9	94	19.1
Pathological type					0.791
I	555	71.6	355	72.3
II	220	28.4	136	27.7
Myometrial invasion					0.862
<1/2	541	69.8	345	70.3
≥1/2	234	30.2	146	29.7
Cervical stromal invasion					0.799
No	642	82.8	404	82.3
Yes	133	17.2	87	17.7
LVSI					0.734
LVSI negative	587	75.7	376	76.6
LVSI positive	188	24.3	115	23.4
Lymphadenectomy					0.540
Yes	651	84.0	406	82.7
No	124	16.0	85	17.3
Serum Ca125(U/mL)					0.586
<35	575	74.2	371	75.6
≥35	200	25.8	120	24.4
Ki67 expression					0.815
Low	455	58.7	285	58.0
High	320	41.3	206	42.0
ER expression					0.840
Positive	639	82.5	407	82.9
Negative	136	17.5	84	17.1
PR expression					0.670
Positive	611	78.8	392	79.8
Negative	164	21.2	99	20.2
P53 expression					0.978
Normal	482	62.2	305	62.1
Abnormal	293	37.8	186	37.9
Adjuvant treatment					0.928
Follow-up or HT	298	38.5	180	36.7
Only radiotherapy	259	33.4	169	34.4
Only chemotherapy	26	3.4	18	3.7
Chemoradiotherapy	192	24.8	124	25.3
RFS time (months)					0.458
Mean (±SD)	51.06 (±20.01)		51.92 (±20.20)
Median (range)	49.00 (6–91)		49.00 (6–91)
Follow-up (months)					0.615
Mean (±SD)	53.59 (±18.10)		54.12 (±18.32)
Median (range)	51.00 (7–91)		50.00 (7–91)

Note: *The comparison of the parameters between the training cohort and the validation cohort.

Abbreviations: BMI, body mass index; FIGO, International Federation of Gynecology and Obstetrics; LVSI, lymphovascular space invasion; ER, estrogen receptor; PR, progesterone receptor; HT, hormonal treatment; RFS, recurrence-free survival.

Table 2 The Distribution of Inflammation-Immunity-Nutrition Score (IINS) of Two Cohorts

Variable	Training Cohort N =775	%	Validation Cohort N = 491	%	P value*
C-reactive protein (mg/L)					0.589
Mean (±SD)	4.86 (±4.45)		5.00 (±4.47)
Median (range)	3.35 (1.00–50.80)		3.48 (1.00–39.75)
<4.76	493	63.6	310	63.1
≥4.76	282	36.4	181	36.9
Albumin (g/L)					0.370
Mean (±SD)	41.41 (±5.05)		41.67 (±4.98)
Median (range)	42.00 (24.00–64.00)		42.00 (24.00–58.00)
<37	124	16.0	70	14.3
≥37	651	84.0	421	85.7
Lymphocyte (10^9/L)					0.720
Mean (±SD)	1.72 (±0.55)		1.71 (±0.53)
Median (range)	1.68 (0.32–3.89)		1.67 (0.35–3.55)
<1.37	223	28.8	139	28.3
≥1.37	552	71.2	352	71.7
Inflammation-immunity-nutrition score
0	338	43.6	207	42.2	0.493
1	285	36.8	196	39.9
2	112	14.5	70	14.3
3	40	5.2	18	3.7

Note: *The comparison of the parameters between the training cohort and the validation cohort.

Figure 2 The ROC curve of (A) CRP, (B) ALB, and (C) LYM for predicting the recurrence of EC. Description: “black dot” represents the area under the curve (AUC) at this point is the largest, which suggests that the value of this point is the optimal threshold of the indicator for predicting the recurrence of EC.

Figure 3 Kaplan–Meier survival curve of IINS in two cohorts. Description: (A) RFS curve of IINS in the training cohort; (B) OS curve of IINS in the training cohort. (C) RFS curve of IINS in the validation cohort. (D) OS curve of IINS in the validation cohort.

Table 3 Univariate and Multivariate Analysis of Factors Predicting EC Recurrence in the Training Cohort

Variables	Univariate Analysis			Multivariate Analysis
	Hazard Ratio	95% CI	P-value	Hazard Ratio	95% CI	P-value
Age (≥60 vs <60)	2.404	1.640–3.525	<0.001	1.570	1.044–2.361	0.030
FIGO stage
I	1.000		<0.001	1.000		0.038
II	2.203	1.091–4.449	0.028	1.157	0.491–2.728	0.739
III	7.809	5.149–11.842	<0.001	1.959	1.123–3.417	0.018
Pathological type (II vs I)	5.718	3.824–8.550	<0.001	1.837	1.107–3.046	0.019
Myometrial invasion (≥1/2 vs <1/2)	3.715	2.523–5.469	<0.001	1.628	1.048–2.528	0.030
Cervical stromal invasion (Yes vs No)	2.935	1.968–4.377	<0.001	1.265	0.759–2.109	0.367
LVSI (Yes vs No)	4.562	3.108–6.696	<0.001	1.879	1.181–2.989	0.008
Adjuvant treatment (Yes vs No)	2.376	1.497–3.771	<0.001	0.677	0.397–1.153	0.151
Serum Ca125 (≥35 vs <35)	2.406	1.639–3.533	<0.001	1.201	0.786–1.835	0.397
Ki67 expression (High vs Low)	3.001	2.007–4.486	<0.001	1.670	1.090–2.561	0.019
ER expression (Negative vs Positive)	6.038	4.123–8.844	<0.001	1.902	1.036–3.492	0.038
PR expression (Negative vs Positive)	4.541	3.102–6.649	<0.001	1.037	0.571–1.883	0.905
P53 expression (Abnormal vs Normal)	1.932	1.319–2.829	0.001	1.560	1.049–2.318	0.028
IINS
0	1.000		<0.001	1.000		<0.001
1	4.075	2.237–7.425	<0.001	3.128	1.701–5.755	<0.001
2	7.418	3.946–13.946	<0.001	4.557	2.385–8.707	<0.001
3	12.234	5.968–25.076	<0.001	5.286	2.448–11.412	<0.001

Abbreviations: CI, confidence interval; FIGO, International Federation of Gynecology and Obstetrics; ER, estrogen receptor; PR, progesterone receptor; LVSI, lymphovascular space invasion; IINS, inflammation-immunity-nutrition score.

Figure 4 Nomogram model for predicting the 1-year, 3-year, and 5-year RFS rates of EC patients. Description: to predict the 1-year, 3-year, and 5-year RFS rates of EC patients, locate the patient’s age on the “age” axis. Draw a straight line up to the “point” axis to determine the points for “age”. Repeat the process for each of the remaining axes, drawing a straight line each time to the “point” axis. Add the points received from each variable and locate this point on the “total point” axis. A straight line is drawn down from the “total point” axis to the “1-year RFS”, “3-year RFS”, and “5-year RFS” axis to determine the 1-year, 3-year, and 5-year RFS rates of EC patients.

Figure 5 The calibration curve for internal and external validation of the nomogram model. Description: (A–C) the internal calibration curve of the nomogram for predicting the 1-year, 3-year, and 5-year RFS rates of EC patients, respectively; (D–F) the external calibration curve of the nomogram for predicting the 1-year, 3-year, and 5-year RFS rates of EC patients, respectively.

Table 4 Analysis of Survival Differences Between High- and Low-Risk Groups in Training Cohort and Validation Cohort

Cohort	Group	Number of Recurrences	3-Year RFS Rate (95% CI)	5-Year RFS Rate (95% CI)	P-value^a	Number of Deaths	3-Year OS Rate (95% CI)	5-Year OS Rate (95% CI)	P-value^b
Training Cohort (N=775)	High-risk group (N=195, 25.2%)	85 (80.2%)	59.0% (52.1–65.9%)	54.6% (47.2–62.0%)	<0.001	57 (76.0%)	74.4% (68.3–80.5%)	69.5% (62.8–76.2%)	<0.001
	Low-risk group (N=580, 74.8%)	21 (9.8%)	96.5% (94.9–98.1%)	96.4% (94.8–98.0%)		18 (24.0%)	97.2% (95.8–98.6%)	96.8% (95.4–98.2%)
Validation Cohort (N=491)	High-risk group (N=131, 26.7%)	50 (83.3%)	64.1% (55.9–72.3%)	60.4% (51.6–69.2%)	<0.001	32 (76.2%)	79.4% (72.5–86.3%)	74.5% (66.7–82.3%)	<0.001
	Low-risk group (N=360, 73.3%)	10 (16.7%)	97.5% (95.9–99.1%)	97.2% (95.4–99.0%)		10 (23.8%)	97.5% (95.9–99.1%)	97.1% (95.3–98.9%)

Note: ^aLog rank test of RFS; ^bLog rank test of OS.

Abbreviation: CI, confidence interval.

Figure 6 The ROC curve of the 3-year RFS rates (predicted by the nomogram model) for predicting the recurrence of EC. Description: “black dot” represents the area under the curve (AUC) at this point is the largest, which suggests that the optimal threshold of the 3-year RFS rate (risk threshold of the model) for predicting the recurrence of EC is 0.85 (area under the curve = 0.887; sensitivity, 80.2%; specificity, 83.6%).

Figure 7 Kaplan–Meier survival curve of low- and high-risk groups in two cohorts. Description: (A) RFS curve of low- and high-risk groups in the training cohort; (B) OS curve of low- and high-risk groups in the training cohort. (C) RFS curve of low- and high-risk groups in the validation cohort. (D) OS curve of low- and high-risk groups in the validation cohort.

Table 5 The Predictive Performance (C-Index and NRI) of Different Models for Predicting EC Recurrence in the Training Cohort and Validation Cohort

Model	Author	Composition of the Model	Key Predictors of the Model	C-Index (95% CI)		NRI*
				Training Cohort	Validation Cohort	Training Cohort	Validation Cohort
Model A^Citation31	Lobna Ouldamer et al 2016	Clinicopathological parameters.	A nomogram model including age, surgical nodal staging, histological type, histological grade, LVSI, FIGO staging.	0.820 (0.750–0.850)	0.818 (0.748–0.889)	0.106	0.142
Model B^Citation26	Kenta Takahashi et al 2019	Clinicopathological parameters.	A scoring system based on age, pathological type, cervical stromal invasion, peritoneal cytology.	0.755 (0.704–0.807)	0.778 (0.710–0.847)	0.175	0.179
Model C^Citation32	Mingzhu Jia et al 2020	Molecular prognostic markers.	The combined ratio of ER, PR, Ki67, and P53 ([ER + PR]/[P53 + Ki67]).	0.767 (0.712–0.823)	0.779 (0.705–0.852)	0.219	0.225
Model D^Citation33	Peng Jiang et al 2020	Clinicopathological parameters + Molecular prognostic makers.	A nomogram model including pathological type, myometrial invasion, cervical stromal invasion, Ki67, ER and P53 expression.	0.818 (0.762–0.873)	0.838 (0.764–0.912)	0.070	0.078
Model proposed in this study		Clinicopathological parameters + Molecular prognostic makers + IINS.	A nomogram model including FIGO staging, myometrial invasion, LVSI, pathological type, ER expression, Ki67 expression, P53 expression and IINS.	0.887 (0.855–0.918)	0.883 (0.838–0.928)	——	——

Note: NRI* indicates the improvement ratio of the prediction ability of the model proposed in this study compared with other models (model A, model B, model C and model D).

Abbreviations: LVSI, lymphovascular space invasion; ER, estrogen receptor; PR, progesterone receptor.

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