Introduction: Appropriately stratifying the risk of adnexal masses is of great importance. Many diagnostic algorithms have been devised, most of which rely on ultrasound features. However, some remote areas lack trained sonographers. This study aimed to develop an alternative model to distinguish between malignant and benign adnexal masses in resource-constrained settings using clinical information rather than ultrasound data. Methods: The study included women diagnosed with an adnexal tumor and scheduled for surgery between 2020 and 2023. Participants were divided into two groups based on histopathology reports: those with malignant adnexal masses and those with benign ones. Univariate and multivariate logistic regression analyses were used to identify independent predictors of adnexal mass malignancy. The training set yielded a nomogram model, which was then validated in the validation set. The model’s effectiveness was evaluated using receiver operating characteristic (ROC), calibration, and clinical decision curve analysis (DCA) curves. Results: We randomly assigned 550 participants to the training and the validation sets in an 8:2 ratio. Logistic regression analyses identified age (OR = 1.044, p = 0.003), abdominal distension (OR = 0.139, p < 0.001), serum CA125 (OR = 1.007, p < 0.001), and serum carcinoembryonic antigen (CEA) (OR = 1.291, p = 0.004) as independent risk factors for predicting malignant adnexal tumors. A nomogram was constructed using these factors. The ROC curve showed an area under the curve of 0.846 (95% confidence interval [CI]: 0.783, 0.908) in the training set and 0.817 (95% CI: 0.668, 0.966) in the validation set. The calibration curve showed good consistency between model predictions and actual outcomes. The DCA curve demonstrated a considerable clinical advantage afforded by the model. Conclusion: The logistic regression model can aid gynecologists – particularly those in areas with limited access to skilled sonographers – in identifying patients at high risk and implementing appropriate management strategies.

Appropriately stratifying the risk of adnexal masses is of great importance. Many diagnostic algorithms have been devised, most of which rely on ultrasound features. However, some remote areas lack trained sonographers. Our retrospective study developed an alternative model to distinguish between malignant and benign adnexal masses in resource-constrained settings using clinical information rather than ultrasound data. Univariate and multivariate logistic regression analyses were used to identify independent predictors of adnexal mass malignancy, and we found age, abdominal distension, serum CA125, and serum carcinoembryonic antigen (CEA) were independent risk factors for predicting malignant adnexal tumors and a nomogram was then developed. The ROC curve showed an area under the curve of 0.817 in the validation set. The calibration curve showed good consistency between model predictions and actual outcomes. The DCA curve demonstrated a considerable clinical advantage afforded by the model.

1.
Timmerman
D
,
Planchamp
F
,
Bourne
T
,
Landolfo
C
,
du Bois
A
,
Chiva
L
, et al
.
ESGO/ISUOG/IOTA/ESGE Consensus Statement on pre-operative diagnosis of ovarian tumors
.
Int J Gynecol Cancer
.
2021
;
31
(
7
):
961
82
.
2.
Stein
EB
,
Roseland
ME
,
Shampain
KL
,
Wasnik
AP
,
Maturen
KE
.
Contemporary guidelines for adnexal mass. Imaging: a 2020 update
.
Abdom Radiol
.
2021
;
46
(
5
):
2127
39
.
3.
Andreotti
RF
,
Timmerman
D
,
Strachowski
LM
,
Froyman
W
,
Benacerraf
BR
,
Bennett
GL
, et al
.
O-RADS US risk stratification and management system: a consensus guideline from the ACR ovarian-adnexal reporting and data system committee
.
Radiology
.
2020
;
294
(
1
):
168
85
.
4.
Høgdall
EV
,
Christensen
L
,
Kjaer
SK
,
Blaakaer
J
,
Kjaerbye-Thygesen
A
,
Gayther
S
, et al
.
CA125 expression pattern, prognosis and correlation with serum CA125 in ovarian tumor patients. From the Danish “MALOVA” Ovarian Cancer Study
.
Gynecol Oncol
.
2007
;
104
(
3
):
508
15
.
5.
Funston
G
,
Mounce
LT
,
Price
S
,
Rous
B
,
Crosbie
EJ
,
Hamilton
W
, et al
.
CA125 test result, test-to-diagnosis interval, and stage in ovarian cancer at diagnosis: a retrospective cohort study using electronic health records
.
Br J Gen Pract
.
2021
;
71
(
707
):
e465
72
.
6.
Ghaemmaghami
F
,
Akhavan
S
.
Is postoperative CA125 level in patients with epithelial ovarian cancer reliable to guess the optimality of surgery
.
Eur J Gynaecol Oncol
.
2011
;
32
(
2
):
192
5
.
7.
Gu
Z
,
He
Y
,
Zhang
Y
,
Chen
M
,
Song
K
,
Huang
Y
, et al
.
Postprandial increase in serum CA125 as a surrogate biomarker for early diagnosis of ovarian cancer
.
J Transl Med
.
2018
;
16
(
1
):
114
.
8.
Wilailak
S
,
Chan
KK
,
Chen
CA
,
Nam
JH
,
Ochiai
K
,
Aw
TC
, et al
.
Distinguishing benign from malignant pelvic mass utilizing an algorithm with HE4, menopausal status, and ultrasound findings
.
J Gynecol Oncol
.
2015
;
26
(
1
):
46
53
.
9.
Bolstad
N
,
Øijordsbakken
M
,
Nustad
K
,
Bjerner
J
.
Human epididymis protein 4 reference limits and natural variation in a Nordic reference population
.
Tumour Biol
.
2012
;
33
(
1
):
141
8
.
10.
Moore
RG
,
Miller
MC
,
Eklund
EE
,
Lu
KH
,
Bast
RC
Jr
,
Lambert-Messerlian
G
.
Serum levels of the ovarian cancer biomarker HE4 are decreased in pregnancy and increase with age
.
Am J Obstet Gynecol
.
2012
;
206
(
4
):
349.e1
349.e3497
.
11.
Ferraro
S
,
Schiumarini
D
,
Panteghini
M
.
Human epididymis protein 4: factors of variation
.
Clin Chim Acta
.
2015
;
438
:
171
7
.
12.
Urban
N
,
Thorpe
J
,
Karlan
BY
,
McIntosh
MW
,
Palomares
MR
,
Daly
MB
, et al
.
Interpretation of single and serial measures of HE4 and CA125 in asymptomatic women at high risk for ovarian cancer
.
Cancer Epidemiol Biomarkers Prev
.
2012
;
21
(
11
):
2087
94
.
13.
Urban
N
,
Thorpe
JD
,
Bergan
LA
,
Forrest
RM
,
Kampani
AV
,
Scholler
N
, et al
.
Potential role of HE4 in multimodal screening for epithelial ovarian cancer
.
J Natl Cancer Inst
.
2011
;
103
(
21
):
1630
4
.
14.
Ali
MA
,
Sweed
MS
,
NasrElDin
EA
,
Ahmed
WE
,
ElHawwary
GE
.
Risk of ovarian malignancy algorithm and pelvic mass score for the prediction of malignant ovarian tumors: a prospective comparative study
.
J Ultrason
.
2024
;
24
(
94
):
1
8
.
15.
Landolfo
C
,
Ceusters
J
,
Valentin
L
,
Froyman
W
,
Van Gorp
T
,
Heremans
R
, et al
.
Comparison of the ADNEX and ROMA risk prediction models for the diagnosis of ovarian cancer: a multicentre external validation in patients who underwent surgery
.
Br J Cancer
.
2024
;
130
(
6
):
934
40
.
16.
Suri
A
,
Perumal
V
,
Ammalli
P
,
Suryan
V
,
Bansal
SK
.
Diagnostic measures comparison for ovarian malignancy risk in Epithelial ovarian cancer patients: a meta-analysis
.
Sci Rep
.
2021
;
11
(
1
):
17308
.
17.
Romagnolo
C
,
Leon
AE
,
Fabricio
ASC
,
Taborelli
M
,
Polesel
J
,
Del Pup
L
, et al
.
HE4, CA125 and risk of ovarian malignancy algorithm (ROMA) as diagnostic tools for ovarian cancer in patients with a pelvic mass: an Italian multicenter study
.
Gynecol Oncol
.
2016
;
141
(
2
):
303
11
.
18.
Shridhar
V
,
Lee
J
,
Pandita
A
,
Iturria
S
,
Avula
R
,
Staub
J
, et al
.
Genetic analysis of early-versus late-stage ovarian tumors
.
Cancer Res
.
2001
;
61
(
15
):
5895
904
.
19.
Lin
J
,
Qin
J
,
Sangvatanakul
V
.
Human epididymis protein 4 for differential diagnosis between benign gynecologic disease and ovarian cancer: a systematic review and meta-analysis
.
Eur J Obstet Gynecol Reprod Biol
.
2013
;
167
(
1
):
81
5
.
20.
Dunton
C
,
Bullock
RG
,
Fritsche
H
.
Multivariate index assay is superior to CA125 and HE4 testing in detection of ovarian malignancy in African-American women
.
Biomark Cancer
.
2019
;
11
:
1179299X19853785
.
21.
Winarto
H
,
Laihad
BJ
,
Nuranna
L
.
Modification of cutoff values for HE4, CA125, the risk of malignancy index, and the risk of malignancy algorithm for ovarian cancer detection in Jakarta, Indonesia
.
Asian Pac J Cancer Prev
.
2014
;
15
(
5
):
1949
53
.
22.
Zhao
X
,
Zhao
M
,
Gao
B
,
Zhang
A
,
Xu
D
,
Modified HE4, CA125, and ROMA cut-off values and predicted probability of ovarian tumor in Chinese patients
.
Gland Surg
.
2021
;
10
(
11
):
3097
105
.
23.
American College of Obstetricians and Gynecologists’ Committee on Practice Bulletins: Gynecology
.
Practice bulletin No. 174: evaluation and management of adnexal masses
.
Obstet Gynecol
.
2016
;
128
(
5
):
e210
26
.
24.
Jacobs
I
,
Oram
D
,
Fairbanks
J
,
Turner
J
,
Frost
C
,
Grudzinskas
JG
.
A risk of malignancy index incorporating CA 125, ultrasound and menopausal status for the accurate preoperative diagnosis of ovarian cancer
.
Br J Obstet Gynaecol
.
1990
;
97
(
10
):
922
9
.
25.
Amor
F
,
Vaccaro
H
,
Alcázar
JL
,
León
M
,
Craig
JM
,
Martinez
J
.
Gynecologic imaging reporting and data system: a new proposal for classifying adnexal masses on the basis of sonographic findings
.
J Ultrasound Med
.
2009
;
28
(
3
):
285
91
.
26.
Won
D
,
Walker
J
,
Horowitz
R
,
Bharadwaj
S
,
Carlton
E
,
Gabriel
H
.
Sound the alarm: the sonographer shortage is echoing across healthcare
.
J Ultrasound Med
.
2024
;
43
(
7
):
1289
301
.
27.
Luntsi
G
,
Ugwu
AC
,
Nkubli
FB
,
Emmanuel
R
,
Ochie
K
,
Nwobi
CI
.
Achieving universal access to obstetric ultrasound in resource constrained settings: a narrative review
.
Radiography
.
2021
;
27
(
2
):
709
15
.
28.
Harrison
G
,
Beardmore
C
.
Ultrasound clinical teaching capacity in England: a scoping exercise
.
Radiography
.
2020
;
26
(
1
):
3
8
.
29.
Giri
SK
,
Nayak
B
.
Management of ovarian cancer in elderly
.
Rev Recent Clin Trials
.
2015
;
10
(
4
):
270
5
.
30.
Yancik
R
.
Ovarian cancer. Age contrasts in incidence, histology, disease stage at diagnosis, and mortality
.
Cancer
.
1993
;
71
(
2 Suppl
):
517
23
.
31.
Roett
MA
,
Evans
P
.
Ovarian cancer: an overview
.
Am Fam Physician
.
2009
;
80
(
6
):
609
16
.
32.
Sundar
S
,
Agarwal
R
,
Davenport
C
,
Scandrett
K
,
Johnson
S
,
Sengupta
P
, et al
.
Risk-prediction models in postmenopausal patients with symptoms of suspected ovarian cancer in the UK (ROCkeTS): a multicentre, prospective diagnostic accuracy study
.
Lancet Oncol
.
2024
;
25
(
10
):
1371
86
.
33.
Iizuka
M
,
Hamada
Y
,
Matsushima
J
,
Ichikawa
T
,
Irie
T
,
Yamaguchi
N
, et al
.
Comparison of the risk of ovarian malignancy algorithm and Copenhagen Index for the preoperative assessment of Japanese women with ovarian tumors
.
J Obstet Gynaecol Res
.
2023
;
49
(
11
):
2717
27
.
34.
Goff
B
.
Symptoms associated with ovarian cancer
.
Clin Obstet Gynecol
.
2012
;
55
(
1
):
36
42
.
35.
Behnamfar
F
,
Zafarbakhsh
A
,
Ahmadian
N
.
Are ROMA and HE4 more accurate than CA-125, in predicting of ovarian epithelial carcinoma
.
Adv Biomed Res
.
2023
;
12
:
156
.
36.
Liu
CC
,
Yang
H
,
Zhang
R
,
Zhao
JJ
,
Hao
DJ
.
Tumour-associated antigens and their anti-cancer applications
.
Eur J Cancer Care
.
2017
;
26
(
5
):
e12446
.
37.
Cui
R
,
Wang
Y
,
Li
Y
,
Li
Y
.
Clinical value of ROMA index in diagnosis of ovarian cancer: meta-analysis
.
Cancer Manag Res
.
2019
;
11
:
2545
51
.
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