Characteristics of Cancer-Related Fatigue and an Efficient Model to Identify Patients with Gynecological Cancer Seeking Fatigue-Related Management

Abstract

Cancer-related fatigue (CRF) is the most common somatic discomfort in patients with gynecological cancers. CRF is often overlooked; however, it can impair the patients’ quality of life considerably. This cross-sectional study aimed to identify the clinical characteristics of CRF in gynecological cancer patients. Questionnaires and the International Classification of Diseases 10th Revision (ICD-10) criteria were used to identify CRF. The enrolled patients were further categorized according to the amount of fatigue-related management received. Of the enrolled 190 patients, 40.0% had endometrial cancer, 28.9% had cervical cancer, and 31.1% had ovarian cancer. On the basis of the ICD-10 diagnostic criteria, 42.6% had non-cancer-related fatigue, 10% had CRF, and 51% had BFI-T questionnaire-based fatigue. Moreover, 77.9% of the study cohort had ever received fatigue-related management. Further analysis showed that patients with endometrial/cervical cancer, International Federation of Gynecology and Obstetrics stage >1, Eastern Cooperative Oncology Group performance status score ≥1, inadequate cancer treatment response, and receiving cancer treatment in the past week had a higher probability of receiving more fatigue-related management. The five-item predictive model developed from these factors may help physicians recognize patients seeking more fatigue-related management more efficiently. This is important as they may suffer from a more profound CRF.

Full text

Citation: Wang, Y.-W.; Ou, Y.-C.; Lin,
H.; Huang, K.-S.; Fu, H.-C.; Wu,
C.-H.; Chen, Y.-Y.; Huang, S.-W.; Tu,
H.-P.; Tsai, C.-C. Characteristics of
Cancer-Related Fatigue and an
Efficient Model to Identify Patients
with Gynecological Cancer Seeking
Fatigue-Related Management.
Cancers 2023,15, 2181. https://
doi.org/10.3390/cancers15072181
Academic Editor: Daniela M.
Dinulescu
Received: 29 January 2023
Revised: 26 March 2023
Accepted: 1 April 2023
Published: 6 April 2023
Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
cancers
Article
Characteristics of Cancer-Related Fatigue and an Efficient
Model to Identify Patients with Gynecological Cancer Seeking
Fatigue-Related Management
Ying-Wen Wang 1,† , Yu-Che Ou 2,†, Hao Lin 1, Kun-Siang Huang 3, Hung-Chun Fu 1, Chen-Hsuan Wu 1,
Ying-Yi Chen 1, Szu-Wei Huang 1, Hung-Pin Tu 4and Ching-Chou Tsai 1, *
1Department of Obstetrics and Gynecology, Chang Gung Memorial Hospital-Kaohsiung Medical Center,
Kaohsiung 833, Taiwan
2Department of Obstetrics and Gynecology, Chia-Yi Chang Gung Memorial Hospital, Chiayi 613, Taiwan
3Department of Family Medicine, Chang Gung Memorial Hospital-Kaohsiung Medical Center,
Kaohsiung 833, Taiwan
4Department of Public Health and Environmental Medicine, School of Medicine, College of Medicine,
Kaohsiung Medical University, Kaohsiung 807, Taiwan
*Correspondence: nick@cgmh.org.tw
† These authors contributed equally to this work.
Simple Summary:
Cancer-related fatigue (CRF) is a common somatic discomfort in gynecological cancer
patients; however, it is usually overlooked by physicians. We aimed to explore the prevalence of CRF’s
and the clinical characteristics of gynecological cancer patients. The results showed that 77.9% of patients
had received related CRF management. A five-item predictive model was developed from the identified
risk factors contributing to CRF. The risk factors included (1) diagnosis of endometrial/cervical cancer,
(2) International Federation of Gynecology and Obstetrics (FIGO) stage >1, (3) Eastern Cooperative
Oncology Group (ECOG) status score
≥
1, (4) inadequate treatment response, and (5) having received
cancer treatment in the past 1 week. The predictive model may help physicians more promptly identify
high-risk patients in clinical practice.
Abstract:
Cancer-related fatigue (CRF) is the most common somatic discomfort in patients with
gynecological cancers. CRF is often overlooked; however, it can impair the patients’ quality of
life considerably. This cross-sectional study aimed to identify the clinical characteristics of CRF in
gynecological cancer patients. Questionnaires and the International Classification of Diseases 10th
Revision (ICD-10) criteria were used to identify CRF. The enrolled patients were further categorized
according to the amount of fatigue-related management received. Of the enrolled 190 patients,
40.0% had endometrial cancer, 28.9% had cervical cancer, and 31.1% had ovarian cancer. On the
basis of the ICD-10 diagnostic criteria, 42.6% had non-cancer-related fatigue, 10% had CRF, and
51% had BFI-T questionnaire-based fatigue. Moreover, 77.9% of the study cohort had ever received
fatigue-related management. Further analysis showed that patients with endometrial/cervical cancer,
International Federation of Gynecology and Obstetrics stage >1, Eastern Cooperative Oncology
Group performance status score
≥
1, inadequate cancer treatment response, and receiving cancer
treatment in the past week had a higher probability of receiving more fatigue-related management.
The five-item predictive model developed from these factors may help physicians recognize patients
seeking more fatigue-related management more efficiently. This is important as they may suffer from
a more profound CRF.
Keywords:
cancer-related fatigue; gynecological cancer; quality of life; endometrial cancer;
cross-sectional study; fatigue; cervical cancer; ovarian cancer
Cancers 2023,15, 2181. https://doi.org/10.3390/cancers15072181 https://www.mdpi.com/journal/cancers

Cancers 2023,15, 2181 2 of 13
1. Introduction
According to the Taiwan Cancer Registry Annual Report 2019, endometrial cancer is
the fifth most common cancer, while ovarian and cervical cancers hold the seventh and ninth
positions, respectively [
1
], with the epidemiology of gynecological cancer being similar in
other developed countries [
2
]. To achieve a better outcome, patients with gynecological
cancer receive multimodal management, including surgery, chemotherapy, radiotherapy,
targeted therapy, immunotherapy, or a combination of these [3–6].
The common side effects in patients receiving cancer treatment are as follows: fatigue,
hair loss, nausea, pain, and sleep disturbances [
7
]. Among these, fatigue is the most
common symptom associated with cancer and its treatment. The estimated prevalence of
cancer-related fatigue (CRF) ranges from 25% to 99% [
8
]. CRF may appear to be a trivial
symptom; however, it can impact negatively on the patients’ quality of life and make them
prone to mental disorders [
9
,
10
]. Hofman et al. summarized the impact of CRF, which
includes inferior daily living performance, increased mood disturbances, and decreased
social activities [
11
]. Curt et al. surveyed cancer patients and indicated that patients
with CRF experience frequent mood disturbances, such as mental exhaustion, diminished
interest in daily activities, and frustration with CRF. The study showed that patients with
CRF also experienced problems maintaining daily routine activities, such as cleaning
the house, climbing stairs, preparing food, exercising, and shopping. In addition, the
study indicated that CRF may also result in an economic and occupational impact because
75% of the patients mentioned that they adjusted their employment status because of
CRF [
12
]. Some studies have also indicated that CRF may eventually impact patients’
survival negatively [11].
The definition of CRF proposed by The National Comprehensive Cancer Network
(NCCN) practice guidelines is “a persistent subjective sense of tiredness related to cancer
or cancer treatment that interferes with usual functioning” [
13
]. Several validated tools or
questionnaires have been developed for the evaluation of CRF [
14
,
15
]. A physician can use
the diagnostic criteria of the International Classification of Diseases 10th Revision (ICD-10)
and a simple rating scale developed in the Common Terminology Criteria for Adverse
Events (CTCAE) in clinical practice. Patients can also be evaluated by self-reporting the
severity using questionnaires such as Brief Fatigue Inventory (BFI), Multidimensional
Fatigue Inventory (MFI), and the Functional Assessment of Chronic Illness Therapy—
Fatigue (FACIT-F) [16].
Aside from the CRF resulting from cancer treatment, patients with certain risk factors
are prone to CRF. Bower systemic reviewed CRF and indicated that genetic factors, espe-
cially those promoting inflammatory processes, pretreatment fatigue, pre-existing mood
disturbances, low levels of physical activity, and an elevated body mass index (BMI) are
recognized risk factors for CRF. The study also showed a significant correlation between
sleep disturbance and CRF; however, the relationship needs further studies to clarify the
causality [
17
]. Additionally, Agarwal et al. indicated that pain, physical function, the
ECOG performance status score, tiredness, and albumin levels were associated highly with
CRF [
18
]. Schultz et al. suggested that a higher tumor grade and insomnia also contributed
to CRF in breast cancer patients [
19
], and Hinz et al. indicated that being female, having
an advanced tumor stage, with the presence of metastases, and a poor ECOG performance
status are risk factors for fatigue in cancer patients [20].
As there is limited information on CRF in patients with gynecological cancer, this
study aimed to explore the clinical characteristics of patients with CRF and a more efficient
approach to help physicians identify these patients in clinical practice promptly.
2. Materials and Methods
2.1. Participants
This cross-sectional study was performed at the Department of Obstetrics and Gy-
necology, Kaohsiung Chang Gung Memorial Hospital (KCGMH), and was approved by
the Ethics Committee and the Institutional Review Board of KCGMH (IRB201900669B0).

Cancers 2023,15, 2181 3 of 13
The eligible patients diagnosed with cervical, endometrial, or ovarian cancer receiving
cancer-related management or surveillance at the inpatient or outpatient department in
KCGMH between 1 June 2019, and 31 August 2020, were enrolled for analysis. The other
inclusion criteria were age
≥
20 years, the ability to communicate verbally, and the ability
to complete the questionnaires. The patients diagnosed with cognitive impairment and
those who were unable to complete the questionnaires were excluded. Written informed
consent was obtained from all the patients.
2.2. Measures
The demographic and clinical information, such as the age, cancer type, International
Federation of Gynecology and Obstetrics (FIGO) stage, Eastern Cooperative Oncology
Group (ECOG) performance status, and current disease condition, were extracted from
electronic medical records. We assessed CRF using the BFI-Taiwan (BFI-T) and ICD-10
diagnostic criteria. The ICD-10 diagnostic criteria refer to a structural interview for CRF,
a commonly used physician-oriented diagnostic approach that is used in our institute. The
BFI-T is a Chinese-translated version of the original BFI [
21
]. The BFI-T provides a patient-
oriented approach to CRF. The questionnaire can help patients self-report CRF’s severity
and illustrate possible functional impairment following CRF. It has three items that are used
for evaluating fatigue severity and six for evaluating fatigue-related interference in daily
functioning during the past 24 h. All the items are scored from 0 (no fatigue/no interference)
to 10 (extreme fatigue/complete interference). The final BFI-T score is calculated from the
average of the nine items [22].
The Functional Assessment of Cancer Therapy–General–7 Item Version (FACT-G7) is
a shortened, seven-item version of the Functional Assessment of Cancer Therapy–General
(FACT-G) [
23
]. Unlike the BFI-T, which focuses on illustrating the severity of CRF, the FACT-
G7 reports primarily on the quality of life associated with CRF. The FACT-G7 questionnaire
includes seven common cancer-related symptoms and concerns endorsed by the patients.
All the items are scored from 0 (not at all) to 4 (very much). The first to fourth item scores
are reverse-calculated (four-item response = item score), but the fifth to seventh item scores
are calculated directly (0 + item response = item score). The total FACT-G7 score is the sum
of the scores of the seven items. A higher total score indicates a better quality of life [23].
In the cancer symptoms survey, the participants were evaluated for all the associated
symptoms they had experienced in the past week, whether related to cancer itself, treatment,
or other causes. The score ranged from 0 (no symptoms) to 10 (as bad as one can imagine).
Symptoms included pain, fatigue, nausea, vomiting, depression, constipation, hair loss,
diarrhea, sleep disturbance, shortness of breath, lack of appetite, weight loss, and nutritional
imbalance. The items used in the cancer symptoms survey are modified from the Symptom
Distress Scale proposed by McCorkle et al. [24,25].
The different types of fatigue-related management included self-monitoring of fatigue
level, energy conservation, and physical activity as well as the use of psychosocial interven-
tions, cognitive behavioral therapy for sleep, nutritional consultations, physically based
therapies (such as massage, yoga, acupuncture), astragalus polysaccharide supplements,
psychostimulants, steroids, blood transfusion, Chinese medicine, and others [26,27].
2.3. Statistics
The data were analyzed using SPSS (version 19.0; IBM Corp., Armonk, NY, USA).
The continuous parameters were expressed as the means and standard deviations, and
categorical variables were presented as absolute numbers or percentages. The analysis of
the continuous variables was performed using the general linear model or the Kruskal–
Wallis test, as appropriate. The analysis of the categorical variables was tested using the
Fisher’s exact test. The adjusted Ls-Mean was calculated after adjusting for age, cancer type,
FIGO stage, ECOG status, and current disease condition using the generalized linear model.
Post hoc tests were performed using Dunnett’s multiple comparison test. Dunnett’s test is
ideal for examining two or more experimental groups against a single control group [
28
].

Cancers 2023,15, 2181 4 of 13
In our study, “patients receiving limited types of fatigue-related management (
≤
5)” and
“patients receiving multiple types of fatigue-related management (>5)” were considered
the two experimental groups, and “patients never receiving fatigue-related management”
was the control group. Therefore, Dunnett’s test was adopted in the post hoc analysis. The
performance of the binary logistic regression model was assessed using the area under the
receiver operating characteristic curve. The statistical significance was set at p≤0.05.
3. Results
One-hundred and ninety patients completed the survey; their demographic character-
istics are described in Table 1. The median age of the patients was 56.9 years. Among them,
76 patients (40.0%) had endometrial cancer, 55 (28.9%) had cervical cancer, and
59 (31.1%)
had ovarian cancer. One-hundred and seventy-seven patients (93.1%) had ECOG perfor-
mance status scores of 0 and 1. Most of the patients (97.4%) had the disease under control,
while only five patients (2.6%) had a progressive cancer status. When evaluating CRF
with different approaches among these patients, the ICD-10 diagnostic criteria identified
19 patients
(10%) with CRF and 81 patients (42.6%) with non-cancer-related fatigue. Sixty-
one patients (32.1%) had mild CRF, and 36 patients (18.9%) had moderate to severe CRF
according to the results of the BFI-T survey. A total of 148 patients (77.9%) had ever received
fatigue-related management previously, while 83 patients (43.7%) had received multiple
types of fatigue-related management (>5).
Table 1. Demographic characteristics of the enrolled patients.
Patients
(N= 190)
Age, mean ±SD 56.87 ±11.89
Cancer type, n(%)
Endometrium cancer 76 (40.0)
Cervical cancer 55 (28.9)
Ovarian cancer 59 (31.1)
FIGO stage, n(%)
I 95 (50.0)
II 29 (15.3)
III 45 (23.7)
IV 21 (11.1)
ECOG performance status, n(%)
0 55 (28.9)
1 122 (64.2)
2 12 (6.3)
3 1 (0.5)
Current disease condition, n(%)
Complete response 48 (25.3)
Partial response 3 (1.6)
Stable disease 134 (70.5)
Progressive disease 5 (2.6)
ICD-10 diagnosed fatigue, n(%)
No fatigue 90 (47.4)
Non-cancer-related fatigue 81 (42.6)
CRF 19 (10.0)
BFI-T questionnaire-based fatigue, n(%)
No: 0 93 (48.9)
Mild: 1–3 61 (32.1)
Moderate to severe: ≥4 36 (18.9)
Fatigue-related management, n(%)

Cancers 2023,15, 2181 5 of 13
Table 1. Cont.
Patients
(N= 190)
Never 42 (22.1)
Receive limited (≤5) managements 65 (34.2)
Receive multiple (>5) managements 83 (43.7)
SD, standard deviation; FIGO, Federation International of Gynecology and Obstetrics; ECOG, Eastern Cooperative
Oncology Group; ICD-10, International Classification of Diseases 10th Revision; CRF, cancer-related fatigue; BFI-T,
Brief Fatigue Inventory-Taiwan.
When categorizing the patients by the quantity of the types of fatigue-related manage-
ment that was received, they were classified into three groups (Table 2): 0 (never received
fatigue-related management), 1 (received limited types of fatigue-related management
(
≤
5)), and 2 (received multiple types of fatigue-related management (>5)). According to the
statistical results, the number of patients who received fatigue-related management was
significantly lower in patients who had ovarian cancer, stage I disease, ECOG performance
status score 0, controlled current disease condition (complete response or partial response),
and not receiving cancer treatment in the last week (p< 0.0001).
Table 2. Clinical characteristics for patients’ frequency of seeking fatigue-related management.
Fatigue-Related Management
0
(n= 42)
1
(n= 65)
2
(n= 83) p-Value
Age, years, mean ±SD 57.96 ±8.97 58.1 ±12.96
55.35
±
12.25
0.3021
≥60, n(%) 22 (52.4) 33 (50.8) 52 (62.7)
<60, n(%) 20 (47.6) 32 (49.2) 31 (37.3) 0.2965
Cancer type, n(%)
Endometrial cancer 5 (11.9) 26 (40.0) 45 (54.2)
Cervical cancer 8 (19.0) 22 (33.8) 25 (30.1)
Ovarian cancer 29 (69.0) 17 (26.2) 13 (15.7) <0.0001
FIGO stage, n(%)
I 39 (92.9) 29 (44.6) 27 (32.5) <0.0001
II 1 (2.4) 15 (23.1) 13 (15.7)
III 1 (2.4) 15 (23.1) 29 (34.9)
IV 1 (2.4) 6 (9.2) 14 (16.9)
ECOG, n(%)
0 26 (61.9) 15 (23.1) 14 (16.9) <0.0001
1 16 (38.1) 46 (70.8) 60 (72.3)
2 0 (0.0) 3 (4.6) 9 (10.8)
3 0 (0.0) 1 (1.5) 0 (0.0)
Current disease condition, n(%)
Complete response + partial
response 26 (61.9) 11 (16.9) 14 (16.9) <0.0001
Stable disease + progressive
disease 16 (38.1) 54 (83.1) 69 (83.1)
ICD-10-diagnosed fatigue, n(%)
No fatigue 27 (64.3) 29 (44.6) 34 (41.0)
Non-cancer-related fatigue 13 (31.0) 27 (41.5) 41 (49.4)
CRF 2 (4.8) 9 (13.8) 8 (9.6) 0.1016
BFI-T questionnaire-based
fatigue, n(%)
No: 0 27 (64.3) 28 (43.1) 38 (45.8)
Mild: 1–3 13 (31.0) 21 (32.3) 27 (32.5)
Moderate to severe: ≥4 2 (4.8) 16 (24.6) 18 (21.7) 0.0731
FACT-G7, mean ±SD
Total score 24.00 ±3.13 20.28 ±4.67 20.90 ±5.62 0.0004

Cancers 2023,15, 2181 6 of 13
Table 2. Cont.
Fatigue-Related Management
0
(n= 42)
1
(n= 65)
2
(n= 83) p-Value
Physical well-being 10.38 ±1.74 9.03 ±2.23 9.10 ±2.63 0.0061
Emotional well-being 3.33 ±0.69 2.58 ±0.95 2.78 ±1.12 0.0006
Functional well-being 10.29 ±1.38 8.66 ±2.28 9.02 ±2.63 0.0014
Cancer treatment in recent
1 week, n(%)
No 42 (100.0) 42 (64.6) 54 (65.1)
Yes 0 (0.0) 23 (35.4) 29 (34.9) <0.0001
0: never receive fatigue-related management; 1: receive limited (
≤
5) fatigue-related management; 2: receive
multiple (>5) fatigue-related managements. SD, standard deviation; FIGO, International Federation of Gynecology
and Obstetrics; ECOG, Eastern Cooperative Oncology Group; ICD-10, International Classification of Diseases 10th
Revision; CRF, cancer-related fatigue; BFI-T, Brief Fatigue Inventory-Taiwan; FACT-G7, Functional Assessment of
Cancer Therapy–General–7 Item Version. Statistical method: Fischer’s exact test for the comparison of categorical
variables; Kruskal–Wallis test for the comparison of continuous variables.
The results of the cancer symptoms survey, FACT-G7 score, and the number of patients
receiving fatigue-related management are shown in Table 3. According to the results,
patients not receiving any fatigue-related management tended to have a lower total score
(5.74
±
8.62) in the cancer symptoms survey (p< 0.0004). Among all the items in the
cancer symptoms survey, fatigue (2.21
±
2.63) and insomnia (2.07
±
2.84) were the leading
two symptoms in the 190 patients. The patients who did not receive any fatigue-related
management tended to have a significantly lower score for fatigue, nausea, vomiting,
alopecia, anorexia, and weight loss. Additionally, the FACT-G7 score in patients who
did not receive any fatigue-related management was significantly higher (24
±
3.13 vs.
20.28 ±4.67
and 20.9
±
5.62, p= 0.0004), which suggested a better quality of life. By setting
the cutoff value at 22 for the FACT-G7 score, we were able to distinguish the patients who
did not receive fatigue-related management from others (p= 0.0002).
On the basis of the results shown in Tables 2and 3, the factors that significantly influ-
enced the patients’ amount of receiving fatigue-related management are summarized and
highlighted in Table 4. When comparing the patients who received multiple types of fatigue-
related management (>5) to patients who never received any fatigue-related management, the
following were found to be the predictive performance of individual factors, as illustrated in
the area under the receiver operating characteristic curve (AUC): endometrial cancer/cervical
cancer (AUC, 0.8), FIGO stage > I (AUC, 0.8), ECOG performance status score
≥
1 (AUC,
0.73), inadequate treatment response (stable disease or progressive disease; AUC, 0.73), low
FACT-G7 score (<22; AUC, 0.65), and received cancer treatment in the past week (AUC, 0.67).
When incorporating these six factors into a six-item predictive model, the overall AUC became
0.95. When comparing patients who received limited types of fatigue-related management
(
≤
5) to patients who never received any fatigue-related management, the following were
found to be the predictive performance of individual factors: endometrial cancer/cervical
cancer (AUC, 0.73), FIGO stage > 1 (AUC, 0.74), ECOG performance status score
≥
1 (AUC,
0.69), inadequate treatment response (stable disease or progressive disease; AUC, 0.73), low
FACT-G7 score (<22; AUC, 0.7), and received cancer treatment in the past week (AUC, 0.68).
The predictive performance of the six-item model was 0.91.
The recognition of patients who might seek multiple cancer-related management
in clinical practice by physicians is crucial as it indicates that these patients suffer from
a profound CRF. Considering that the evaluation by FACT-G7 is still time-consuming in
clinical practice, we aimed to identify a more direct and efficient predictive model; therefore,
a five-item predictive model was developed without using FACT-G7. With an AUC of
0.9438 in the five-item predictive model (Figure 1), there was no significant statistical
difference when comparing the AUC of the six-item and the five-item predictive models
for CRF (p= 0.5924).

Cancers 2023,15, 2181 7 of 13
Table 3. Association between cancer-related symptoms, FACT-G7, and the frequency of seeking fatigue-related management.
Fatigue-Related Management 1 vs. 0 2 vs. 0 2 vs. 1
Total
(N= 190)
0
(n= 42)
1
(n= 65)
2
(n= 83) p-Value * Difference
Ls-Mean (95% CI) p-Value Difference
Ls-Mean (95% CI) p-Value Difference
Ls-Mean (95% CI) p-Value
Cancer-related symptoms,
mean ±SD 13.04 ±16.17 5.74 ±8.62 15.17 ±14.97 15.07 ±18.85 0.0004 4.41 (−2.83, 11.64) 0.2816 2.77 (−4.75, 10.28) 0.5929 −1.64 (−7.25, 3.96) 0.7541
Pain 0.86 ±2.04 0.29 ±1.29 1.03 ±2.25 1.02 ±2.15 0.0471 0.29 (−0.65, 1.24) 0.6812 0.07 (−0.91, 1.06) 0.9765 −0.22 (−0.96, 0.51) 0.7412
Fatigue 2.21 ±2.63 0.83 ±1.86 2.69 ±2.62 2.53 ±2.76 0.0002 1.56 (0.36, 2.76) 0.0082 1.35 (0.10, 2.59) 0.0317 −0.21 (−1.14, 0.71) 0.8390
Nausea 0.88 ±2.13 0.07 ±0.46 0.82 ±2.04 1.35 ±2.56 0.0015 0.19 (−0.74, 1.12) 0.8423 0.50 (−0.47, 1.46) 0.3887 0.31 (−0.41, 1.03) 0.5519
Vomiting 0.55 ±1.70 0.00 ±0.00 0.46 ±1.56 0.89 ±2.12 0.0086 0.11 (−0.65, 0.86) 0.9158 0.37 (−0.42, 1.15) 0.4506 0.26 (−0.33, 0.84) 0.5337
Depression 1.41 ±2.35 1.00 ±1.85 1.66 ±2.66 1.41 ±2.32 0.8007 0.43 (−0.67, 1.54) 0.5557 0.07 (−1.07, 1.22) 0.9830 −0.36 (−1.22, 0.49) 0.5598
Constipation 1.01 ±2.19 0.88 ±2.05 1.49 ±2.68 0.69 ±1.75 0.3448 0.32 (−0.72, 1.37) 0.6801 −0.61 (−1.69, 0.48) 0.3340 −0.93 (−1.74, −0.12) 0.0200
Alopecia 1.15 ±2.46 0.33 ±1.22 1.88 ±3.13 0.99 ±2.19 0.0194 0.94 (−0.19, 2.07) 0.1156 −0.01 (−1.18, 1.17) 0.9998 −0.95 (−1.82, −0.07) 0.0317
Diarrhea 0.53 ±1.49 0.48 ±1.40 0.45 ±1.24 0.63 ±1.71 0.8727 −0.34 (−1.02, 0.35) 0.4175 −0.32 (−1.03, 0.39) 0.4739 0.02 (−0.51, 0.55) 0.9966
Insomnia 2.07 ±2.84 1.50 ±2.42 2.46 ±3.12 2.05 ±2.78 0.1922 0.65 (−0.71, 2.00) 0.4359 0.19 (−1.22, 1.59) 0.9282 −0.46 (−1.51, 0.59) 0.5348
Shortness of breath 0.62 ±1.59 0.33 ±1.18 0.45 ±1.20 0.90 ±1.96 0.2743 −0.03 (−0.78, 0.73) 0.9934 0.46 (−0.32, 1.25) 0.3013 0.49 (−0.09, 1.08) 0.1146
Anorexia 0.92 ±2.16 0.02 ±0.15 1.08 ±2.41 1.24 ±2.38 0.0012 0.38 (−0.60, 1.37) 0.5588 0.34 (−0.68, 1.36) 0.6472 −0.04 (−0.80, 0.72) 0.9887
Weight loss 0.41 ±1.53 0.00 ±0.00 0.25 ±0.83 0.73 ±2.15 0.0247 −0.12 (−0.81, 0.58) 0.8907 0.3 (−0.42, 1.02) 0.5240 0.42 (−0.12, 0.95) 0.1609
Nutrition imbalance 0.44 ±1.65 0.00 ±0.00 0.46 ±1.74 0.64 ±1.95 0.0506 0.01 (−0.77, 0.79) 0.9991 0.06 (−0.75, 0.87) 0.9773 0.05 (−0.56, 0.65) 0.9794
FACT-G7, mean ±SD
Total score 21.37 ±5.03 24.00 ±3.13 20.28 ±4.67 20.90 ±5.62 0.0004 −2.25 (−4.48, −0.03) 0.0467 −1.26 (−3.57, 1.06) 0.3537 1.00 (−0.73, 2.72) 0.3446
Physical well-being 9.36 ±2.37 10.38 ±1.74 9.03 ±2.23 9.10 ±2.63 0.0061 −0.67 (−1.77, 0.43) 0.2848 −0.41 (−1.56, 0.73) 0.6035 0.25 (−0.60, 1.11) 0.7465
Emotional well-being 2.84 ±1.01 3.33 ±0.69 2.58 ±0.95 2.78 ±1.12 0.0006 −0.62 (−1.07, −0.17) 0.0053 −0.38 (−0.85, 0.09) 0.1210 0.23 (−0.12, 0.58) 0.2451
Functional well-being 9.18 ±2.35 10.29 ±1.38 8.66 ±2.28 9.02 ±2.63 0.0014 −0.97 (−2.00, 0.06) 0.0668 −0.46 (−1.53, 0.61) 0.5010 0.51 (−0.29, 1.31) 0.2740
0: never receive fatigue-related management; 1: receive limited (
≤
5) fatigue-related management; 2: receive multiple (>5) fatigue-related managements. SD, standard deviation; Ls-Mean,
least squares mean; CI, confidence interval; FACT-G7, Functional Assessment of Cancer Therapy–General–7 Item Version. * p-values were calculated using the general linear model or
Kruskal–Wallis test, as appropriate. Adjusted Ls-Mean was calculated after adjusting for age group, cancer type, stage, ECOG, and current disease condition using the generalized linear
model. Post hoc tests were performed using Dunnett’s multiple comparison test.

Cancers 2023,15, 2181 8 of 13
Table 4. Predictors for patients seeking multiple types of fatigue-related management (>5).
Fatigue-Related Management 1 vs. 0 2 vs. 0 1 vs. 0 2 vs. 0
0
(n= 42)
1
(n= 65)
2
(n= 83) p-Value AUC
(95% CI)
AUC
(95% CI)
Adjusted OR
(95% CI) p-Value Adjusted OR
(95% CI) p-Value
Cancer type, n(%) 0.73 (0.64–0.82) 0.80 (0.72–0.88)
Ovarian cancer 29 (69.0) 17 (26.2) 13 (15.7) <0.0001 1.00 1.00
Cervical cancer 8 (19.0) 22 (33.8) 25 (30.1) 2.99 (0.85–10.49) 0.7327 3.64 (1.03–12.94) 0.9058
Endometrial cancer 5 (11.9) 26 (40.0) 45 (54.2) 5.85 (1.52–22.51) 0.0610 11.49 (3.04–43.48) 0.0049
FIGO Stage, n(%) 0.74 (0.67–0.81) 0.80 (0.74–0.87)
I 39 (92.9) 29 (44.6) 27 (32.5) 1.00 1.00
>I 3 (7.1) 36 (55.4) 56 (67.5) <0.0001 10.92 (2.64–45.16) 0.0010 15.42 (3.80–62.65) 0.0001
ECOG performance status,
n(%) 0.69 (0.60–0.78) 0.73 (0.64–0.81)
0 26 (61.9) 15 (23.1) 14 (16.9) 1.00 1.00
≥1 16 (38.1) 50 (76.9) 69 (83.1) <0.0001 1.00 (0.18–5.50) 0.9987 2.58 (0.44–15.15) 0.2934
Current disease condition 0.73 (0.64–0.81) 0.73 (0.65–0.82)
Complete response
+ partial response 26 (61.9) 11 (16.9) 14 (16.9) 1.00 1.00
Stable disease
+ progressive disease 16 (38.1) 54 (83.1) 69 (83.1) <0.0001 4.59 (0.82–25.82) 0.9844 1.89 (0.32–11.17) 0.9788
Total score (FACT-G7) 0.70 (0.62–0.78) 0.65 (0.57–0.73)
≥22 35 (83.3) 28 (43.1) 44 (53.0) 1.00 1.00
<22 7 (16.7) 37 (56.9) 39 (47.0) 0.0002 9.09 (2.82–29.28) 0.0002 5.63 (1.70–18.64) 0.0047
Cancer treatment in recent
1 week 0.68 (0.62–0.74) 0.67 (0.62–0.73)
No 42
(100.0) 42 (64.6) 54 (65.1) 1.00 1.00
Yes * 0 (0.0) † 23 (35.4) 29 (34.9) <0.0001
AUC (95% CI): combined
factors ‡ 0.91 (0.86–0.97) 0.95 (0.91–0.98)
FIGO, International Federation of Gynecology and Obstetrics; ECOG, Eastern Cooperative Oncology Group; FACT-G7, Functional Assessment of Cancer Therapy–General–7 Item
Version; AUC, area under the receiver operating characteristic curve; CI, confidence interval; OR, odds ratio. The adjusted OR with 95% CI was calculated using a multiple multinomial
logistic regression model. * Cell contains 0 by trend analysis (as a continuous variable per category).
†
Cells containing 0, not calculated.
‡
The performance of the binary logistic
regression model was assessed using the AUC.

Cancers 2023,15, 2181 9 of 13
Cancers 2023, 15, x FOR PEER REVIEW 10 of 14
Cancers 2023, 15, x. https://doi.org/10.3390/xxxxx www.mdpi.com/journal/cancers
The recognition of patients who might seek multiple cancer-related management in
clinical practice by physicians is crucial as it indicates that these patients suffer from a
profound CRF. Considering that the evaluation by FACT-G7 is still time-consuming in
clinical practice, we aimed to identify a more direct and efficient predictive model; there-
fore, a five-item predictive model was developed without using FACT-G7. With an AUC
of 0.9438 in the five-item predictive model (Figure 1), there was no significant statistical
difference when comparing the AUC of the six-item and the five-item predictive models
for CRF (p = 0.5924).
Figure 1. Receiver operating characteristic (ROC) curve and area under the ROC curve (AUC) for
predicting the possibility of seeking multiple fatigue-related managements. (a) Six-item predictive
model. (b) Five-item predictive model. (c) Comparison between the six-item and five-item models.
4. Discussion
4.1. Summary of the Main Results
The discrepancy between the identification of CRF by either the ICD-10 diagnostic
criteria or the BFI-T and the number of patients receiving fatigue-related management
may be due to an imperfect diagnostic algorithm for CRF. In our study cohort, 48.9% of
the patients denied having fatigue according to the the BFI-T questionnaire. When evalu-
ating the same cohort using the ICD-10 diagnostic criteria, 47.4% denied having fatigue.
Moreover, if analyzed in the same cohort regarding ever receiving “fatigue-related man-
agement,” only 22.1% of patients never received fatigue-related management.
Although there are established diagnostic criteria for CRF according to the ICD-10 or
the NCCN guidelines [27], it can be difficult for cancer patients to distinguish CRF from
general fatigue, mental discomfort, or sleep disturbances in a precise manner. In our co-
hort, 52.6% of the patients had ICD-10-diagnosed fatigue when counting non-cancer-re-
lated fatigue and CRF together. This ratio is compatible with the proposed ratio from other
relevant studies [8]. Regardless of the CRF identified by either diagnostic approach, a phy-
sician should provide resources or assistance actively, for those patients who seek multi-
ple types of fatigue-related management (>5), as these patients may suffer from more pro-
found CRF than others.
Poort et al. found no significant differences in the development of CRF between pa-
tients with endometrial and ovarian cancer [29], and Sekse et al. did not notice a consid-
erable difference between the gynecological cancer types and the development of CRF
[10]. Moreover, there has been limited research on the relationship between cancer type
and CRF, and most of the results that have been obtained have been inconclusive.
Our study proposed a five-item predictive model for helping physicians identify
those patients that may seek more fatigue-related management. The five-item predictive
model demonstrated an outstanding performance with an AUC of 0.9438. Our predictive
model may be the first model to identify gynecological cancer patients who require more
Figure 1.
Receiver operating characteristic (ROC) curve and area under the ROC curve (AUC) for
predicting the possibility of seeking multiple fatigue-related managements. (
a
) Six-item predictive
model. (b) Five-item predictive model. (c) Comparison between the six-item and five-item models.
4. Discussion
4.1. Summary of the Main Results
The discrepancy between the identification of CRF by either the ICD-10 diagnostic
criteria or the BFI-T and the number of patients receiving fatigue-related management
may be due to an imperfect diagnostic algorithm for CRF. In our study cohort, 48.9%
of the patients denied having fatigue according to the the BFI-T questionnaire. When
evaluating the same cohort using the ICD-10 diagnostic criteria, 47.4% denied having
fatigue. Moreover, if analyzed in the same cohort regarding ever receiving “fatigue-related
management,” only 22.1% of patients never received fatigue-related management.
Although there are established diagnostic criteria for CRF according to the ICD-10
or the NCCN guidelines [
27
], it can be difficult for cancer patients to distinguish CRF
from general fatigue, mental discomfort, or sleep disturbances in a precise manner. In our
cohort, 52.6% of the patients had ICD-10-diagnosed fatigue when counting non-cancer-
related fatigue and CRF together. This ratio is compatible with the proposed ratio from
other relevant studies [
8
]. Regardless of the CRF identified by either diagnostic approach,
a physician should provide resources or assistance actively, for those patients who seek
multiple types of fatigue-related management (>5), as these patients may suffer from more
profound CRF than others.
Poort et al. found no significant differences in the development of CRF between
patients with endometrial and ovarian cancer [
29
], and Sekse et al. did not notice a consid-
erable difference between the gynecological cancer types and the development of CRF [
10
].
Moreover, there has been limited research on the relationship between cancer type and
CRF, and most of the results that have been obtained have been inconclusive.
Our study proposed a five-item predictive model for helping physicians identify those
patients that may seek more fatigue-related management. The five-item predictive model
demonstrated an outstanding performance with an AUC of 0.9438. Our predictive model
may be the first model to identify gynecological cancer patients who require more fatigue-
related management. Our study also showed that patients with endometrial and cervical
cancers had a significantly higher probability of developing CRF.
4.2. The Other Predictive Model
There has been limited research on predictive models for CRF. Among female cancer
patients, most of the proposed predictors for CRF were developed for breast cancer patients.
Haghighat et al. conducted a prospective study with 112 breast cancer patients, suggesting
that depression, anxiety, and pain were significant predictors of CRF [
30
]. Von Ah et al.
proposed that mood disturbance was the most important predictor of CRF before, during,

Cancers 2023,15, 2181 10 of 13
and after adjuvant therapy in breast cancer patients [
31
]. Schultz et al. indicated that
a higher tumor grade and insomnia were predictors of significant CRF in breast cancer
patients [
19
]. On the other hand, a predictive CRF model has also been proposed for
patients of all cancer types. Hwang et al. proposed a multidimensional predictive model
for CRF, including sadness, drowsiness, pain, poor appetite, irritability, and dyspnea. The
AUC of the multidimensional model was 0.88 [
32
]. Agarwal et al. evaluated 110 patients
with advanced cancer and suggested that pain, physical function, ECOG performance
status score, tiredness, and albumin levels were independent predictors of CRF [
18
]. Unlike
the above predictors, we proposed a predictive model that incorporated data that can
be collected easily from the patients’ clinical information without the use of additional
questionnaires., which significantly facilitates the evaluation of CRF in clinical practice.
4.3. Results in the Context of Published Literature
Poort et al. reported that 48.0% of patients with gynecological cancer experienced
clinically significant fatigue after surgery. Although the prevalence slightly decreased
1 year
later, it was still 39.0% [
29
]. Jewett et al. also illustrated that 53.6% of the patients
with gynecological cancer considered fatigue to be the most common problem in their
lives [
33
]. CRF can significantly impair the patients’ quality of life. Liavaag et al. reported
that CRF may contribute to more somatic and mental morbidities. These patients also had
significantly higher anxiety scores and poorer body image, contributing to an increased
use of sedatives and antidepressants [
34
]. Moreover, there is a connection between CRF
and sleep disturbance; however, the exact mechanism or causality remains unknown.
While it is easy to infer that patients with CRF can fall asleep easily, several factors can
influence their sleep quality negatively. Factors that may impair sleep quality include
somatic discomfort, cancer-related treatment, emotional discomfort, and an imbalance
between sleep opportunities and sleep ability [35].
4.4. Treatment for Cancer-Related Fatigue
Available treatment for CRF includes exercise, psychological management, and the use
of pharmacological agents. The patients who perceive that physical activity may increase
their discomfort following cancer treatment may be inhibited from regular activity, which
is called the “fear of movement,” with the decreased activity further triggering CRF [
36
]. It
has been established that exercise can lead to the most considerable symptom improvement
in patients with CRF [
37
]. Meneses-Echávez et al. reviewed nine studies that addressed
the efficacy of exercise for treating CRF and proposed that supervised aerobic exercise was
effective in patients with CRF [
38
]. Cramp et al. also indicate that more significant benefits
can be achieved when exercise is done during or after adjuvant cancer therapy. Patients
with breast cancer and prostate cancer may benefit from exercise; however, there are no
significant benefits for patients with hematological cancer [
39
]. Carroll et al. showed that
hematopoietic agents, corticosteroids, and psychostimulants were commonly used pharma-
cological agents for patients with CRF [
40
]. Minton et al. indicate that methylphenidate was
the only identified beneficial psychostimulant for patients with CRF in the meta-analysis,
resulting in a mean decreased fatigue score of 0.30 (95% CI =
−
0.54 to
−
0.05; p= 0.02). They
also indicated that erythropoietin may benefit CRF patients with anemia. It was found that
compared to the use of a placebo, erythropoietin resulted in a mean decreased fatigue score
of 0.3 (95% CI =
−
0.46 to
−
0.29; p= 0.008) [
41
]. On the basis of current evidence, the efficacy
of psychological interventions for CRF is controversial. Jacobsen et al. have suggested
that psychological interventions are helpful for patients with CRF [
42
], while Poort et al.
systemically reviewed a wide range of psychosocial interventions for CRF, which included
education, cognitive-behavioral therapies (such as changing thoughts and emotions), and
supportive group therapies; however, this investigation provided inconclusive results
due to study bias, the heterogeneity of the study design, and the small sample size of the
enrolled studies [43].

Cancers 2023,15, 2181 11 of 13
4.5. Strengths and Weaknesses
Although the proposed prediction model demonstrated outstanding performance,
there were still some limitations to this study. Only 190 patients from one tertiary medical
center were enrolled in this study. The limited number of cases may have yielded bias
in the analysis and the development of the model for identifying patients seeking more
fatigue-related management. Despite these limitations, our study had several strengths.
This study focused only on CRF in patients with gynecological cancer. This study identified
the risk factors of patients seeking multiple fatigue-related management and proposed
a predictive model aimed at helping physicians recognize these patients. This model was
efficient and not time-consuming for physicians because the information required was
based primarily on patients’ clinical information.
4.6. Implications for Practice and Future Research
A prospective study may be necessary in the future to validate the actual performance
of the proposed prediction model. If patients have all the risk factors for seeking more
fatigue-related management, resources or assistance should be actively provided to them
to alleviate their poor quality of life.
5. Conclusions
There is a discrepancy among the different diagnostic approaches for CRF. In our study
cohort, on the basis of the ICD-10 diagnostic criteria, 42.6% of the patients had non-cancer-
related fatigue, and 10% had CRF, while on the basis of the BFI-T questionnaire, 51.0%
had fatigue. However, 77.9% of the cohort had ever received fatigue-related management.
Further analysis showed that the patients with endometrial/cervical cancer, FIGO stage > I,
ECOG performance status score
≥
1, an inadequate cancer treatment response, and those
who received cancer treatment in the past week had a higher probability of receiving more
fatigue-related management. The five-item predictive model that was developed from
these factors may help physicians recognize patients who might seek more fatigue-related
management efficiently, indicating their profound symptoms of CRF, and that management
for alleviating CRF should be provided promptly for these patients.
Author Contributions:
Conceptualization and design of the work, Y.-W.W., Y.-C.O., H.L., K.-S.H.,
H.-C.F., C.-H.W., Y.-Y.C., S.-W.H., H.-P.T. and C.-C.T.; data acquisition, analysis, and interpretation,
Y.-C.O., H.L., K.-S.H., H.-C.F., C.-H.W. and C.-C.T.; drafting the manuscript and revising it critically
for important intellectual content, Y.-W.W., K.-S.H., S.-W.H. and C.-C.T.; final approval of the version
to be published, Y.-W.W., H.-P.T. and C.-C.T. All authors have read and agreed to the published
version of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement:
The study was conducted in accordance with the Declaration
of Helsinki. The present study was approved by the Ethics Committee and the Institutional Review
Board of KCGMH (study number: CRF-CGMH01, IRB number: 201900669B0).
Informed Consent Statement: Informed consent was obtained from all enrolled patients.
Data Availability Statement:
The datasets used in the current study are available from the corre-
sponding author upon reasonable request.
Conflicts of Interest: The authors declare no conflict of interest.
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