Development and Internal Validation of a Novel Risk Prediction Score for Endometrial Malignancy in Premenopausal Women with Abnormal Uterine Bleeding

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Current diagnostic strategies rely on endometrial sampling, which is invasive and often unnecessary. This study aimed to develop and internally validate a novel risk prediction score for endometrial malignancy in premenopausal women with AUB. Methods A retrospective longitudinal analytical study was conducted over 8 years and 11 months (January 2016–November 2024) at Charles Nicolle Hospital, Tunis, Tunisia. Premenopausal women with AUB who underwent endometrial biopsy followed by hysterectomy were included. Comparative analyses, logistic regression, and ROC curve analysis were performed. Significant variables were weighted according to adjusted odds ratios to construct a risk prediction score. Results Among 209 patients, 13 (6.2%) had endometrial malignancy. Independent predictors of endometrial malignancy were: oral contraceptive use (OR 29.9, 95% CI 1.5–587.1, p = 0.025), endometrial thickness >9 mm (OR 25.3, 95% CI 4.3–147.6, p < 0.001), vascularization (OR 98.3, 95% CI 3.7–2594.8, p = 0.006). Protective factors included hemorrhage episode ≤1 (OR 0.20, 95% CI 0.08–0.52, p = 0.001) and lower bleeding abundance (OR 0.30, 95% CI 0.13–0.65, p = 0.002). The final score allocated points as follows: endometrial thickness >9 mm (+3), oral contraceptive use (+3), vascularization (+4), hemorrhage episodes ≤1 (−2), and lower bleeding abundance (−1). A score ≥7 defined high risk. Model discrimination was excellent (AUC 0.901, 95% CI 0.825–0.976, p < 0.001). At a cutoff ≥7, sensitivity was 77%, specificity 90%, positive predictive value 34%, and negative predictive value 98%. Conclusions We developed and internally validated a novel risk prediction score for endometrial malignancy in premenopausal women with AUB. With strong diagnostic performance and high negative predictive value, this score may help clinicians better identify women who truly require invasive sampling. " } { "@context": "http://schema.org", "@type": "BreadcrumbList", "itemListElement": [ { "@type": "ListItem", "position": "1", "item": { "@id": "https://f1000research.com/", "name": "Home" } }, { "@type": "ListItem", "position": "2", "item": { "@id": "https://f1000research.com/browse/articles", "name": "Browse" } }, { "@type": "ListItem", "position": "3", "item": { "@id": "https://f1000research.com/articles/14-1281", "name": "Development and Internal Validation of a Novel Risk Prediction Score..." } } ] } Home Browse Development and Internal Validation of a Novel Risk Prediction Score... ALL Metrics - Views Downloads Get PDF Get XML Cite How to cite this article karmous N, Jemli H, Masmoudi A et al. Development and Internal Validation of a Novel Risk Prediction Score for Endometrial Malignancy in Premenopausal Women with Abnormal Uterine Bleeding [version 1; peer review: 1 not approved] . F1000Research 2025, 14 :1281 ( https://doi.org/10.12688/f1000research.170227.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Close Copy Citation Details Export Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente EXPORT Select a format first Track Share ▬ ✚ Research Article Development and Internal Validation of a Novel Risk Prediction Score for Endometrial Malignancy in Premenopausal Women with Abnormal Uterine Bleeding [version 1; peer review: 1 not approved] Narjes karmous https://orcid.org/0009-0001-9101-7849 1,2 , Hanene Jemli 1 , Abdelwaheb Masmoudi 1 , [...] Badreddine Bouguerra 1,2 , Aymen Mabrouk 2,3 , Anis Ben Dhou 2,3 , Abdennour Karmous 2,4 Narjes karmous https://orcid.org/0009-0001-9101-7849 1,2 , Hanene Jemli 1 , [...] Abdelwaheb Masmoudi 1 , Badreddine Bouguerra 1,2 , Aymen Mabrouk 2,3 , Anis Ben Dhou 2,3 , Abdennour Karmous 2,4 PUBLISHED 18 Nov 2025 Author details Author details 1 Gynaecology and Obstetrics department B, Charles Nicolle Hospital, Tunis, Tunisia 2 Faculty of medicine of Tunis, University Tunis el Manar, Tunis, Tunisia 3 General surgery department B, Charles Nicolle Hospital, Tunis, Tunisia 4 Psychiatric department, Razi Hospital, Manouba, Tunisia Narjes karmous Roles: Conceptualization, Methodology, Writing – Original Draft Preparation Hanene Jemli Roles: Data Curation, Visualization Abdelwaheb Masmoudi Roles: Writing – Review & Editing Badreddine Bouguerra Roles: Validation, Visualization Aymen Mabrouk Roles: Writing – Review & Editing Anis Ben Dhou Roles: Writing – Review & Editing Abdennour Karmous Roles: Formal Analysis, Writing – Review & Editing OPEN PEER REVIEW DETAILS REVIEWER STATUS This article is included in the Oncology gateway. Abstract Background Endometrial malignancy in premenopausal women with abnormal uterine bleeding (AUB) is rare but clinically challenging. Current diagnostic strategies rely on endometrial sampling, which is invasive and often unnecessary. This study aimed to develop and internally validate a novel risk prediction score for endometrial malignancy in premenopausal women with AUB. Methods A retrospective longitudinal analytical study was conducted over 8 years and 11 months (January 2016–November 2024) at Charles Nicolle Hospital, Tunis, Tunisia. Premenopausal women with AUB who underwent endometrial biopsy followed by hysterectomy were included. Comparative analyses, logistic regression, and ROC curve analysis were performed. Significant variables were weighted according to adjusted odds ratios to construct a risk prediction score. Results Among 209 patients, 13 (6.2%) had endometrial malignancy. Independent predictors of endometrial malignancy were: oral contraceptive use (OR 29.9, 95% CI 1.5–587.1, p = 0.025), endometrial thickness >9 mm (OR 25.3, 95% CI 4.3–147.6, p < 0.001), vascularization (OR 98.3, 95% CI 3.7–2594.8, p = 0.006). Protective factors included hemorrhage episode ≤1 (OR 0.20, 95% CI 0.08–0.52, p = 0.001) and lower bleeding abundance (OR 0.30, 95% CI 0.13–0.65, p = 0.002). The final score allocated points as follows: endometrial thickness >9 mm (+3), oral contraceptive use (+3), vascularization (+4), hemorrhage episodes ≤1 (−2), and lower bleeding abundance (−1). A score ≥7 defined high risk. Model discrimination was excellent (AUC 0.901, 95% CI 0.825–0.976, p < 0.001). At a cutoff ≥7, sensitivity was 77%, specificity 90%, positive predictive value 34%, and negative predictive value 98%. Conclusions We developed and internally validated a novel risk prediction score for endometrial malignancy in premenopausal women with AUB. With strong diagnostic performance and high negative predictive value, this score may help clinicians better identify women who truly require invasive sampling. READ ALL READ LESS Keywords Abnormal uterine bleeding; endometrial cancer; premenopause; risk prediction Corresponding Author(s) Narjes karmous ( [email protected] ) Close Corresponding author: Narjes karmous Competing interests: No competing interests were disclosed. Grant information: The author(s) declared that no grants were involved in supporting this work. Copyright: © 2025 karmous N et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. How to cite: karmous N, Jemli H, Masmoudi A et al. Development and Internal Validation of a Novel Risk Prediction Score for Endometrial Malignancy in Premenopausal Women with Abnormal Uterine Bleeding [version 1; peer review: 1 not approved] . F1000Research 2025, 14 :1281 ( https://doi.org/10.12688/f1000research.170227.1 ) First published: 18 Nov 2025, 14 :1281 ( https://doi.org/10.12688/f1000research.170227.1 ) Latest published: 18 Nov 2025, 14 :1281 ( https://doi.org/10.12688/f1000research.170227.1 ) 1. Introduction Abnormal uterine bleeding (AUB) is one of the most frequent reasons for consultation in gynaecology. 1 It can be caused by structural or non-structural disorders of the uterus. According to the PALM-COEIN classification system of the International Federation of Gynecology and Obstetrics (FIGO), the causes include polyps, adenomyosis, leiomyomas, malignancy and hyperplasia, coagulopathy, ovulatory dysfunction, endometrial disorders, and iatrogenic or not yet classified causes. 2 Although most cases of AUB are unrelated to pre-cancerous or cancerous endometrial pathologies, their seriousness should not be underestimated. 3 It is established that in postmenopausal women with AUB, the risk of endometrial cancer rises to 10%. 4 , 5 However, this risk drops to less than 1% when transvaginal ultrasound shows an endometrial thickness (ET) of less than 4 mm. 6 For premenopausal women, such risk stratification is difficult, as the predictive value of ET assessment has yielded controversial results in the literature. 7 – 10 In this group, other clinical factors are considered to assess the risk of endometrial hyperplasia (EH) or cancer: obesity, nulliparity, age, infertility, intermenstrual bleeding, anovulation, and diabetes. 11 Based on these factors, guidelines recommend endometrial biopsy for women over 40 years, and for those under 40 with comorbidities. 12 , 13 Despite these recommendations, many studies have not provided conclusive results regarding the impact of the mentioned risk factors. 6 , 14 , 15 Consequently, the optimal management of premenopausal patients suffering from AUB remains unclear, hence the interest of our work to develop a predictive model based on clinical variables to assess the risk of pre-cancerous or cancerous endometrial pathologies in cases of AUB in premenopausal women. 2. Methods 2.1 Study design and setting Retrospective, longitudinal and analytical study was conducted over a period of 8 years and 11 months, from January 2016 to November 2024, at Gynaecology and Obstetrics Department B, Charles Nicolle Hospital, Tunis, Tunisia. 2.2 Study population Premenopausal women presenting with AUB who were referred to our department were identified. An endometrial biopsy was performed during a diagnostic hysteroscopy or a fractional curettage and hemostasis procedure in cases of heavy AUB. All these women subsequently underwent surgical treatment (hysterectomy). AUB was defined by the presence of bleeding from the uterine corpus that was abnormal in volume, regularity, and/or timing, according to what was reported by women. 2 Patients meeting the following criteria were included: Inclusion criteria: - Endometrial pathology revealed by endometrial biopsy: atypical endometrial hyperplasia (AEH), endometrial cancer … with a definitive histological diagnosis on the hysterectomy specimen (considered the reference standard). - Complete and available medical and surgical records. Exclusion criteria: - Postmenopausal women (absence of menstruation for at least 12 months after the age of 40 5 ). - Hysterectomy not performed (No definitive histological diagnosis). - Incomplete or missing data. A study flowchart detailing case selection and exclusions has been developed ( Figure 1 ). Figure 1. Flowchart for the study. 2.3 Variables Data were retrospectively extracted from electronic medical records and focused on: • Included women characteristics: Age, family cancer history (mainly endometrial, breast, ovarian, cervical, colon, rectal, and stomach cancer), personal medical history (diabetes, hypertension, obesity, thyroid disorders…), personal surgical history, gynaecological and obstetric history (gravidity, parity, contraception …). • Bleeding characteristics: Type, abundance, number of haemorrhage episodes, associated symptoms (anorexia, pelvic pain, abdominal mass …). • Additional examinations: Pelvic ultrasound (endometrial thickness, intra-cavitary image …), method of uterine cavity exploration (hysteroscopy or curettage), date of sampling and abundance of the sample, anatomopathological diagnosis from the biopsy, and definitive anatomopathological diagnosis from the hysterectomy specimen. 2.4 Statistical analysis Data were entered and analysed with SPSS software (version 26.0, IBM Corp). Microsoft Office Excel was used to create the tables and graphs ( https://www.office.com/?omkt=fr-FR ). For comparative analysis, variables significantly associated with endometrial malignancy were assessed using the chi-square test or Fisher’s exact test for categorical variables and Student’s t test or Mann-Whitney U test for continuous variables. Multivariate logistic regression models were then constructed to identify independent predictors of endometrial malignancy. Variables with a p value ≤ 0.20 in the univariate analysis were entered into the model. Adjusted odds ratios (ORs) with 95% confidence intervals (CIs) were reported and a p value ≤ 0.05 was considered statistically significant. 16 , 17 The diagnostic performance of predictors was evaluated using sensitivity, specificity, positive predictive value, and negative predictive value. 18 Finally, a risk score for predicting endometrial malignancy was developed based on the variables that remained statistically significant in multivariate analysis. 2.5 Ethical considerations The study protocol was approved on 6 Mars 2025 by the institutional ethics committee of Charles Nicolle Hospital, Tunis, Tunisia before conducting the study with approval number FWA 00032748- IORG0011243. As this was a retrospective study using anonymized data, informed consent was waived. 3. Results During the study period, a total of 209 patients were included; 13 cases (6.22%) had endometrial malignancy. 3.1 Descriptive study The mean age was 45.7 ± 3.2 years (range 38–56). The mean body mass index (BMI) was 26.0 ± 2.2 kg/m 2 (range 23–35). A family cancer history was reported in 6% of cases. Regarding medical history, diabetes was reported in 6% of patients, hypertension in 15%, thyroid disorders in 8%, and polycystic ovary syndrome (PCOS) in 1%. Other medical conditions included dyslipidemia (13%), lupus (2%), depressive syndrome (1%), and peptic ulcer disease (3%). Previous surgical history included appendectomy in 7%, cholecystectomy in 12%, and other procedures in 6%. Tobacco use was found in 3% and coffee consumption in 11%. Regarding gynaecological history, the mean age at menarche was 12.2 ± 0.7 years. The median gravidity was 3 (range 0–7) and the median parity 3 (range 0–5). Contraceptive methods included intrauterine device (IUD) use in 29% of patients, oral contraceptives in 3%, and progestin-only pills in 4%. Hormone replacement therapy was reported in 43% of cases. A history of infertility was present in 6%. Screening procedures included Pap smear (24%) and mammography (18%), colposcopy was rarely performed (0.5%). The majority (158 cases, 75.6%) had not undergone a Pap smear. Among those tested, 40 cases (19.1%) were normal. Abnormal results were observed in a minority of cases: 6 (2.9%) presented atypical squamous cells of undetermined significance (ASC-US), 2 (1.0%) had low-grade intraepithelial lesions (LIEB), and 3 (1.4%) atypical glandular cells (AGC). Fibroids were the most frequent gynaecological condition, present in 76% of patients followed by ovarian cysts (10%). Previous uterine endoscopic procedures were performed in 11% of patients. The most frequent clinical presentations were pelvic pain (55%) and abdominal mass (35%). Fatigue was reported in 10% of cases. Profuse bleeding was noted in 61%. The median delay before consultation was 12 months (range 1–72). On physical and imaging assessment, an increased uterine size was observed in 77% of cases, vascularization in 49%, an abnormal endometrial–myometrial interface in 57%, and an intracavitary image in 23%. Hemostatic curettage was performed in 10% of cases, while hysteroscopy was carried out in 90%. 3.2 Comparative analysis ▪ Categorical variables Table 1 summarizes family cancer history and medical, surgical and exposure histories of the study population. Table 1. Family cancer history, and medical, surgical, and exposure histories of the study population. Variable Category Benign (n, %) Malignant (n, %) Total (n, %) p-value Family cancer history No 186 (94) 11 (6) 197 (100) 0.123 Yes 10 (83) 2 (17) 12 (100) Tobacco use No 189 (94) 13 (6) 202 (100) 0.488 Yes 7 (100) 0 (0) 7 (100) Coffee consumption No 174 (93) 13 (7) 187 (100) 0.202 Yes 22 (100) 0 (0) 22 (100) Diabetes No 183 (93) 13 (7) 196 (100) 0.338 Yes 13 (100) 0 (0) 13 (100) Hypertension No 167 (94) 11 (6) 178 (100) 0.954 Yes 29 (94) 2 (6) 31 (100) Thyroid disorder No 180 (93) 13 (7) 193 (100) 0.284 Yes 16 (100) 0 (0) 16 (100) PCOS * No 194 (94) 12 (6) 206 (100) 0.050 Yes 2 (67) 1 (33) 3 (100) Other medical history No 154 (92) 13 (8) 167 (100) 0.480 Dyslipidemia 28 (100) 0 (0) 28 (100) Lupus 5 (100) 0 (0) 5 (100) Depressive syndrome 3 (100) 0 (0) 3 (100) PUD ** 6 (100) 0 (0) 6 (100) Gastrectomy No 195 (94) 13 (6) 208 (100) 0.796 Yes 1 (100) 0 (0) 1 (100) Colectomy No 196 (94) 13 (6) 209 (100) — Appendectomy No 182 (93) 13 (7) 195 (100) 0.318 Yes 14 (100) 0 (0) 14 (100) Cholecystectomy No 173 (94) 12 (6) 185 (100) 0.658 Yes 23 (96) 1 (4) 24 (100) Other surgeries No 185 (94) 12 (6) 197 (100) 0.350 Cystectomy 4 (80) 1 (20) 5 (100) Amygdalectomy 7 (100) 0 (0) 7 (100) * PCOS: Polycystic Ovary Syndrome. ** PUD: Peptic Ulcer Disease. Most patients had no family cancer history (94% benign vs. 6% malignant, p = 0.123). Most patients did not use tobacco (94% benign vs. 6% malignant, p = 0.488) or coffee (93% benign vs. 7% malignant, p = 0.202). Diabetes (93% benign vs. 7% malignant, p = 0.338), hypertension (94% benign vs. 6% malignant, p = 0.954), and thyroid disorders (93% benign vs. 7% malignant, p = 0.284) were uncommon. Other medical histories such as dyslipidemia, lupus, depressive syndrome, and peptic ulcer disease were rare and not associated with malignancy (all p > 0.05). Previous surgeries—including gastrectomy, colectomy, appendectomy, cholecystectomy, cystectomy, and amygdalectomy—were infrequent and showed no significant association with malignancy. Table 2 presents gynaecological history of the study population. Table 2. Gynaecological history of the study population. Variable Category Benign (n, %) Malignant (n, %) Total (n, %) Chi-Square p Oral contraceptive use No 191 (95) 11 (5) 202 (100) 0.013 Yes 5 (71) 2 (29) 7 (100) IUD * No 137 (93) 11 (7) 148 (100) 0.258 Yes 59 (97) 2 (3) 61 (100) Progestin-only pill No 188 (94) 13 (6) 201 (100) 0.458 Yes 8 (100) 0 (0) 8 (100) Long-term treatment No 180 (94) 12 (6) 192 (100) 0.952 Yes 16 (94) 1 (6) 17 (100) Hormonal replacement therapy No 109 (92) 10 (8) 119 (100) 0.133 Yes 87 (97) 3 (3) 90 (100) Infertility No 181 (93) 13 (7) 194 (100) 0.628 primary 9 (100) 0 (0) 9 (100) secondary 4 (100) 0 (0) 4 (100) Pap smear No 148 (93.7) 10 (6.3) 158 (75.6) <0.001 Normal 39 (97.5) 1 (2.5) 40 (19.1) ASCUS 6 (100) 0 6 (2.9) LIEB 2 (100) 0 2 (1.0) ACG 1 (33.3) 2 (66.7) 3 (1.4) Colposcopy No 195 (93.8) 13 (6.3) 208 (99.5) 0.796 Yes 1 (100) 0 1 (0.5) Fibroids No 43 (86.0) 7 (14.0) 50 (23.9) 0.009 Yes 153 (96.2) 6 (3.8) 159 (76.1) Previous uterine endoscopic procedure No 177 (95.2) 9 (4.8) 186 (89.0) 0.035 Myomectomy 7 (87.5) 1 (12.5) 8 (3.8) Polypectomy 9 (75.0) 3 (25.0) 12 (5.7) hemostatic curettage 3 (100) 0 3 (1.4) PCOS ** No 194 (94) 12 (6) 206 (100) 0.050 Yes 2 (67) 1 (33) 3 (100) Ovarian cyst No 176 (93.6) 12 (6.4) 188 (90.4) 0.808 Yes 19 (95.0) 1 (5.0) 20 (9.6) Mammogram No 162 (93.1) 12 (6.9) 174 (83.3) 0.805 ACR2 25 (96.2) 1 (3.8) 26 (12.4) ACR3 7 (100) 0 7 (3.3) ACR4 2 (100) 0 2 (1.0) Interval delay 1 7 (87.5) 1 (12.5) 8 (3.8) 0.040 2 53 (86.9) 8 (13.1) 61 (29.3) 3 105 (97.2) 3 (2.8) 108 (51.9) 4 24 (100) 0 24 (11.5) 5 6 (85.7) 1 (14.3) 7 (3.4) Breast neoplasia No 196 (94) 13 (6) 209 (100) – Tamoxifen No 195 (94) 13 (6) 208 (100) – Pelvic irradiation No 196 (94) 13 (6) 209 (100) – * IUD: Intrauterine Device. ** PCOS: Polycystic Ovary Syndrome. Oral contraceptive use was significantly associated with malignancy (29% malignant in users vs. 5% in non-users, p = 0.013), whereas use of IUDs, progestin-only pills, long-term treatments, and hormonal replacement therapy showed no significant differences. Infertility—primary or secondary—was also rare and not associated with malignancy (p > 0.05). Pap smear results were significantly associated with malignancy (p < 0.001), with the highest proportion of malignant cases observed in patients with ACG (67%). Colposcopy showed no significant association with malignancy (p = 0.796). Presence of fibroids was associated with a lower risk of malignancy (3.8% malignant in patients with fibroids vs. 14% in those without, p = 0.009). Previous uterine endoscopic procedures were significantly associated with malignancy (p = 0.035), with higher malignant rates in patients who underwent polypectomy (25%) or myomectomy (12.5%). Polycystic ovary syndrome (PCOS) was observed in three patients, with one malignant case (p = 0.050). Ovarian cysts and mammogram findings did not show a significant association with malignancy (p = 0.808 and p = 0.805, respectively). Breast neoplasia, tamoxifen use, and pelvic irradiation were not associated with malignancy. Interval delay was significantly related to malignancy (p = 0.04). Table 3 summarizes the bleeding characteristics and associated symptoms. Table 3. Bleeding characteristics and associated symptoms. Variable Category Benign n (%) Malignant n (%) Total n (%) Chi-square p Bleeding abundance Minimal 15 (94) 1 (6) 16 (100) 0.963 Moderate 156 (94) 10 (6) 166 (100) Abundant 25 (93) 2 (7) 27 (100) Type of bleeding Metrorrhagia 4 (100) 0 (0) 4 (100) 0.732 Menorrhagia 90 (95) 5 (5) 95 (100) Menometrorrhagia 102 (93) 8 (7) 110 (100) Number of hemorrhage episodes 0 4 (67) 2 (33) 6 (100) 0.027 1 3 (75) 1 (25) 4 (100) 2 4 (100) 0 (0) 4 (100) 3 7 (100) 0 (0) 7 (100) ≥4 178 (95) 10 (5) 188 (100) Other associated symptoms No sign 52 (93) 4 (7) 56 (100) 0.001 Leucorrhea 2 (100) 0 (0) 2 (100) Hydrorrhea 1 (100) 0 (0) 1 (100) Weight loss 1 (100) 0 (0) 1 (100) Anorexia 0 (0) 2 (100) 2 (100) Pelvic pain No 89 (95) 5 (5) 94 (100) 0.626 Yes 107 (93) 8 (7) 115 (100) Abdominal mass No 124 (91) 12 (9) 136 (100) 0.033 Yes 72 (99) 1 (1) 73 (100) Fatigue No 178 (95) 10 (5) 188 (100) 0.107 Yes 18 (86) 3 (14) 21 (100) Profuse 114 (90) 13 (10) 127 (100) Bleeding characteristics—including abundance and type—showed limited association, except for the number of hemorrhage episodes (p = 0.027), where patients with 0–1 episode had higher malignancy rates (25–33%) than those with ≥2 episodes. Other associated symptoms were significantly related to malignancy (p = 0.001), with anorexia present in 100% of malignant cases. Pelvic pain, fatigue, and uterine size were not significantly associated with malignancy. Presence of an abdominal mass was inversely associated with malignancy (1% malignant if present vs. 9% if absent, p = 0.033). The complementary examination findings are summarized in Table 4 . Table 4. Complementary examination findings among the study population. Variable Category Benign n (%) Malignant n (%) Total n (%) Chi-square p Uterine size Normal 45 (94) 3 (6) 48 (100) 0.992 Increased 151 (94) 10 (6) 161 (100) Vascularization No 97 (98) 2 (2) 99 (100) 0.000 Score 1 97 (94) 6 (6) 103 (100) Score 2 2 (29) 5 (71) 7 (100) Endometrial–myometrial interface Unseen 84 (93) 6 (7) 90 (100) 0.828 Seen 111 (94) 7 (6) 118 (100) Intracavitary image No 149 (93) 11 (7) 160 (100) 0.871 Polyp 15 (94) 1 (6) 16 (100) Fibroma 31 (97) 1 (3) 32 (100) Other 1 (100) 0 (0) 1 (100) Hemostatic curettage No 177 (94) 12 (6) 189 (100) 0.812 Yes 19 (95) 1 (5) 20 (100) Hysteroscopy No 20 (95) 1 (5) 21 (100) 0.771 Yes 176 (94) 12 (6) 188 (100) Quantity of curettage specimen Scanty 81 (100) 0 (0) 81 (100) 0.003 Profuse 114 (90) 13 (10) 127 (100) Ultrasound features revealed strong associations for vascularization (p < 0.001), with Score 2 showing 71% malignancy, whereas normal vascularization or Score 1 had much lower rates. The endometrial–myometrial interface, intracavitary images, hemostatic curettage, and hysteroscopy did not show significant differences. Quantity of curettage specimen was also significant (p = 0.003), with abundant specimens associated with 10% malignancy compared to 0% for scanty specimens. ▪ Continuous variables ( Table 5 ) Table 5. Continuous variables of the study population. Benign Malignant p Age (years) 46 48 0,217 Body Mass Index (Kg/m 2 ) 25 26 0,576 Gravidity 3 2 0,018 Parity 3 2 0,060 Menarche 12 12 0,603 Delay before consultation (months) 12 6 0,011 Endometrial Thickness (mm) 5 12 0,000 Hemoglobin (g/dcl) 10 10 0,487 Delay before biopsy (months) 1 1 0,756 The median age was 46 years in benign cases and 48 years in malignant cases (p = 0.217). The median BMI was 25 for benign and 26 for malignant cases (p = 0.576). Gravidity and parity differed, with median gravidity of 3 versus 2 (p = 0.018) and median parity of 3 versus 2 (p = 0.060). Median age at menarche was 12 years in both groups (p = 0.603). The median delay before consultation was significantly shorter in malignant cases (6 months) compared to benign cases (12 months, p = 0.011). Endometrial thickness was markedly higher in malignant cases (median 12 mm) than in benign cases (median 5 mm, p < 0.001). Hemoglobin levels were similar between groups (median 10 g/dL, p = 0.487), and the median delay before biopsy was 1 month in both groups (p = 0.756). 3.3 Receiver operating characteristic (ROC) curve analysis The predictive performance of several clinical and ultrasound variables for endometrial malignancy was evaluated using ROC analysis: ( Figure 2 ) ( Table 6 ). ▪ Endometrial thickness demonstrated the highest discriminative ability, with an AUC of 0.842 (p 9 mm was identified as the optimal threshold, yielding a sensitivity of 69%, specificity of 87%, a positive predictive value of 26.5%, and a negative predictive value of 97.7%. This indicates that increased endometrial thickness is a strong predictor of malignancy, particularly useful for ruling out disease when the measurement is below the threshold. ▪ Gravidity showed a moderate predictive value with an AUC of 0.690 (p = 0.022). A gravidity of ≤1 was associated with high specificity (96%) but low sensitivity (23%), suggesting that low gravidity may indicate increased risk of malignancy, though many cases could be missed if this criterion is used alone. ▪ The number of hemorrhage episodes had limited predictive value, with an AUC of 0.576 (p = 0.360). Using ≤2 episodes as a threshold, the sensitivity was 62% and specificity 69%, indicating that this variable alone is a weak predictor of malignancy. Figure 2. Predictive performance of gravidity, number of hemorrhage episodes and endometrial thickness for endometrial malignancy evaluated using ROC analysis. Table 6. Predictive performance of gravidity, number of hemorrhage episodes and endometrial thickness for endometrial malignancy was evaluated using ROC analysis. Variable AUC p-value 95% CI Criterion Sensitivity (%) Specificity (%) PPV * (%) NPV ** (%) Gravidity 0.690 0.022 0.542–0.838 ≤1 23.08 96.43 30 95 Number of hemorrhage episodes 0.576 0.360 0.398–0.754 ≤2 61.54 68.88 11.6 96.4 Endometrial thickness 0.842 9 69.23 87.11 26.5 97.7 * PPV: Predictive positive value. ** NPV: Negative predictive value. Overall, endometrial thickness is the most significant individual predictor of malignancy, while low gravidity adds specificity but is limited by poor sensitivity. The number of hemorrhage episodes does not reliably predict malignancy. 3.4 Logistic regression analysis Logistic regression analysis identified several independent predictors of endometrial malignancy ( Table 7 ). ▪ Oral contraceptive use was significantly associated with increased risk (OR = 29.87, 95% CI: 1.52–587.08, p = 0.025). ▪ Endometrial thickness greater than 9 mm was a strong predictor of malignancy (OR = 25.25, 95% CI: 4.32–147.64, p < 0.001). ▪ The presence of vascularization significantly increased the risk (OR = 98.34, 95% CI: 3.73–2594.79, p = 0.006). Table 7. Independent predictors of endometrial malignancy identified by logistic regression. p-value OR 95% CI for OR Lower Upper Oral contraceptive use 0,025 29,866 1,519 587,080 Previous uterine endoscopic procedure 0,054 6,180 0,970 39,392 Number of hemorrhage episode ≤1 0,001 0,202 0,079 0,520 Vascularization 0,006 98,343 3,727 2594,786 Thickness >9 <0,001 25,254 4,320 147,642 Bleeding abundance 0,002 0,295 0,134 0,648 Conversely: ▪ A low number of hemorrhage episodes (≤1) was protective (OR = 0.202, 95% CI: 0.079–0.520, p = 0.001). ▪ Lower bleeding abundance decreased the likelihood of malignancy (OR = 0.295, 95% CI: 0.134–0.648, p = 0.002). ▪ Previous uterine endoscopic procedures showed a borderline association (p = 0.054) but did not reach statistical significance. 3.5 Predictive score for endometrial malignancy A risk score for predicting endometrial malignancy was developed using the variables found to be statistically significant in multivariate analysis. The included predictors were: - Endometrial thickness greater than 9 mm, - Oral contraceptive use, - Presence of vascularization, - Low number of hemorrhage episodes (≤1), - And lower bleeding abundance. Each predictor was weighted according to its odds ratio: - Endometrial thickness >9 mm and oral contraceptive use were assigned 3 points each, - Vascularization was assigned 4 points, - while a low number of hemorrhage episodes and lower bleeding abundance were considered protective and assigned −2 and −1 points, respectively. The total score allows stratification of patients into risk categories: scores of 7 or higher indicate a high risk of malignancy. The predictive score for endometrial malignancy demonstrated excellent discriminative ability ( Figure 3 ): - The area under the ROC curve (AUC) was 0.901 (95% CI: 0.825–0.976, p 7, the score showed a sensitivity of 77% and a specificity of 90%, with a positive predictive value of 34% and a negative predictive value of 98%, suggesting strong effectiveness in ruling out malignancy among low-risk patients. Figure 3. ROC curve associated with the prediction model of endometrial cancer in premonopausal women with abnormal uterine bleeding. 4. Discussion The evaluation of women presenting with AUB to exclude endometrial malignancy relies on a combination of clinical risk assessment, imaging, and histopathological sampling. The presented results, when compared to the broader literature, reveal a consistent emphasis on endometrial thickness as a pivotal triage tool. They also highlight the development and variable utility of integrated risk prediction models. ▪ The primacy of endometrial thickness Evidence consistently highlights the crucial role of transvaginal ultrasound–derived endometrial thickness, which demonstrates robust diagnostic accuracy in predicting endometrial malignancy across studies. Endometrial thickness among postmenopausal women with AUB below 4 mm seems to be associated with a very low risk of endometrial cancer. 19 Unfortunately, there is no established consensus on the threshold for endometrial thickness for premenopausal women. In accordance with the results of other studies, our data indicated that patients with thicker endometrium exhibited a higher risk of endometrial malignancy. 20 Our findings, which identified endometrial thickness >9 mm as an independent risk factor for AUB in perimenopausal women, are consistent with previously published evidence. Tian et al. 21 similarly reported that an endometrial thickness ≥10 mm was an independent risk factor for AUB, while Sahu et al. 22 observed that the majority of perimenopausal women with AUB had an endometrial thickness of 10–12 mm (35.7%), followed by 7–9 mm (27.1%). In contrast, Getpook et al. 23 demonstrated that an endometrial thickness ≤8 mm was unlikely to be associated with malignant pathology in premenopausal AUB. Collectively, these concordant findings reinforce the clinical significance of transvaginal ultrasonography in assessing endometrial thickness as a key predictor of endometrial pathology in premenopausal women with AUB. This principle holds strong in clinical guidelines, confirming endometrial thickness as the most robust initial screening parameter. ▪ Risk-scoring prediction models for endometrial malignancy Several studies have developed integrated risk prediction models to address the limitations of using endometrial thickness alone. Giannella et al. 3 proposed a model including BMI ≥30 kg/m 2 , diabetes, and endometrial thickness >11 mm, which achieved robust accuracy (AUC 0.854; sensitivity 75.0%, specificity 90.8%; PPV 30.0%, NPV 98.6%). Similarly, the PAD30 score developed by Bagepalli Srinivas et al., 24 based on anovulatory bleeding pattern, age ≥45 years, BMI ≥30 kg/m 2 , and diabetes mellitus, showed good diagnostic performance (AUC 0.84; sensitivity 85.7%, specificity 87.6%). Ruan et al. 20 incorporated metabolic diseases, family history, age ≥40 years, resistance index of endometrial vasculature ≤0.5, and endometrial thickness ≥10 mm into a nomogram, which demonstrated good discrimination with an AUC 0.837 (95% CI 0.800-0.874) and calibration in both the development and validation cohorts. Compared with these models, our predictive score demonstrated even stronger discriminative ability, with an AUC of 0.901 (95% CI 0.825–0.976, p 7, the model achieved a sensitivity of 77% and specificity of 90%, with a particularly high negative predictive value of 98%, supporting its effectiveness in reliably excluding malignancy in low-risk patients. These results suggest that our score may provide an accurate and clinically useful tool, comparable or superior to previously published models, for guiding the selective use of invasive diagnostic procedures. ▪ Consistency and divergence in clinical risk factors The evaluation of patient-specific risk factors shows both consistency and variation. The divergence underscores that while certain risk factors are epidemiologically important, their utility in a specific predictive algorithm can vary based on the study population and the other variables in the model. In our study, oral contraceptive use emerged as an independent predictor of endometrial malignancy (OR 29.87, 95% CI 1.52–587.08, p = 0.025). This result contrasts with extensive evidence from large-scale epidemiological studies and meta-analyses, which consistently report a protective effect of oral contraceptives against endometrial cancer. The Collaborative Group on Epidemiological Studies on Endometrial Cancer, through an individual participant meta-analysis including over 27,000 women with endometrial cancer across 36 studies, demonstrated a substantial and sustained risk reduction associated with oral contraceptive use. 25 Similarly, Michels et al. 26 and Harajka et al. 27 confirmed these findings in systematic reviews and meta-analyses, while Karlsson et al. 28 further highlighted the time-dependent protective effect, with longer duration of use conferring greater benefit. The discrepancy with our findings may be explained by the short duration of oral contraceptive use among women in our cohort, which might have been insufficient to exert a protective effect. Additionally, the small sample size and wide confidence interval suggest caution in interpretation. Nevertheless, this unexpected association highlights the importance of considering duration and patterns of oral contraceptive exposure when assessing their relationship with endometrial cancer risk. Our findings demonstrated that vascularization on transvaginal ultrasound was found to be one of the strongest predictors of endometrial malignancy. Logistic regression analysis showed that the presence of abnormal vascularization significantly increased the risk, with an odds ratio of 98.34 (95% CI: 3.73–2594.79, p = 0.006). Notably, patients with a vascular score of 2 had a malignancy rate of 71%, whereas normal vascularization or a score of 1 was associated with substantially lower rates. These findings are in line with recent evidence supporting the role of Doppler ultrasound vascular scoring in differentiating benign from malignant endometrial lesions. Tirnovanu et al. 29 reported that a vascular score of 1 typically excludes endometrial cancer, with high sensitivity (87.5%) and specificity (79%). Conversely, a cutoff score of 2 provided excellent discriminative performance, yielding 100% sensitivity and 86.3% specificity, which reflects the increased neovascularization commonly observed in malignant tumors. Such results are also consistent with the International Endometrial Tumor Analysis (IETA) consensus, 30 which emphasizes abnormal vascular patterns as a key ultrasound feature suggestive of malignancy. Taken together, our findings reinforce the clinical value of color Doppler vascular assessment in women with abnormal uterine bleeding. The strong association between vascular score ≥2 and malignancy suggests that integrating vascularization into risk prediction models may substantially improve diagnostic accuracy. This parameter could therefore serve as a non-invasive adjunct to guide clinical decision-making and reduce unnecessary invasive procedures. In our cohort, two clinical characteristics of bleeding were independently protective: having ≤1 hemorrhagic episode (OR 0.20, 95% CI 0.08–0.52, p = 0.001) and lower bleeding abundance (OR 0.30, 95% CI 0.13–0.65, p = 0.002). Clinically, this pattern is plausible: malignant endometrial lesions often generate recurrent and profuse bleeding due to fragile neovascularization and disordered repair, whereas isolated or low-volume events are more typical of benign etiologies (e.g., anovulatory dysfunction, polyps, simple hyperplasia). Most of the available studies have not addressed the predictive value of bleeding frequency or volume. Specifically, several key works—including Giannella et al., 3 Timmermans et al., 19 Sahu et al., 22 and Getpook et al. 23 —focused primarily on endometrial thickness or metabolic and clinical factors, without evaluating bleeding characteristics as independent predictors of malignancy. Similarly, Tian et al. 21 did not report hemorrhage count or abundance in relation to cancer risk. Bleeding burden was not included as a variable into the nomogram of Ruan et al. 20 The closest conceptual overlap is PAD30’s anovulatory pattern, 24 which acknowledges that bleeding characteristics carry predictive signal, indirectly supporting our observation that clinical bleeding features can refine risk. Our findings highlight the potential value of bleeding characteristics as predictive markers in premenopausal AUB. Specifically, the number of hemorrhagic episodes and the abundance of bleeding represent simple, clinically accessible variables that can be systematically collected during patient history. As our data suggest, fewer episodes and lower bleeding abundance are associated with a reduced likelihood of malignancy, underscoring the importance of detailed bleeding history in refining risk stratification. 4.1 Study strengths and limitations This study has several strengths. It is, to our knowledge, one of the few to focus exclusively on premenopausal women with AUB, a population for whom risk stratification for endometrial malignancy remains uncertain. The use of a robust reference standard—hysterectomy specimen histology—provides high diagnostic accuracy for outcome classification. Furthermore, the study covered a relatively long period (almost nine years), enhancing the reliability of findings. By incorporating bleeding patterns—simple, cost-free, and universally available clinical variables—our model complements existing approaches and may enhance decision-making regarding endometrial sampling in premenopausal women with AUB. However, some limitations should be acknowledged. First, the retrospective single-center design may limit generalizability. Second, external validation was not performed, and the predictive score requires further confirmation in independent cohorts before clinical application. Finally, some variables of potential interest, such as molecular or hormonal markers, were not available in the dataset. 4.2 Clinical implications The proposed risk score offers a clinically useful, non-invasive tool to stratify premenopausal women with AUB according to their likelihood of endometrial malignancy: ▪ Low-risk group (score <7): These women are characterized by protective factors such as a low number of hemorrhagic episodes (≤1) and lower bleeding abundance. The score demonstrated an an AUC of 0.901 and an excellent negative predictive value (98%) in this group, suggesting that unnecessary endometrial biopsies could be safely avoided, thereby reducing patient morbidity and healthcare costs. ▪ High-risk group (score ≥7): Patients presenting with predictors such as endometrial thickness >9 mm, oral contraceptive use, and the presence of vascularization fall into this category. With a sensitivity of 77% and a specificity of 90%, this group should be prioritized for invasive diagnostic procedures (endometrial sampling or hysteroscopy) to ensure timely detection and management of endometrial malignancy. This stratification may improve patient-centered care by tailoring diagnostic strategies, optimizing resource allocation, and minimizing unnecessary interventions. 4.3 Recommendations for further research To confirm and expand the clinical applicability of this score, further research should address the following: ▪ External validation: Conduct multicenter studies across diverse populations to confirm reproducibility and generalizability. ▪ Prospective evaluation: Implement prospective trials to assess real-world impact on reducing unnecessary biopsies without delaying cancer detection. ▪ Comparative assessment: Benchmark the score against existing guidelines and other predictive models to establish relative performance. ▪ Model refinement: Explore the addition of serum biomarkers, molecular markers, or advanced imaging parameters to enhance predictive accuracy. 5. Conclusions This study developed and internally validated a novel risk prediction score for endometrial malignancy in premenopausal women presenting with AUB. The score based on five key predictors: endometrial thickness greater than 9 mm, oral contraceptive use, and the presence of vascularization as risk factors, while a low number of hemorrhage episodes and lower bleeding abundance were protective. A cutoff value of 7 or higher identified high risk of endometrial malignancy, with excellent diagnostic accuracy (AUC 0.901). At this threshold, the score achieved a sensitivity of 77% and specificity of 90%, with a particularly high negative predictive value of 98%, making it especially useful for ruling out malignancy among low-risk patients. These findings suggest that the proposed score could serve as a valuable non-invasive decision-support tool to guide the selective use of endometrial biopsy. While external validation in larger and more diverse populations remains necessary, the model has the potential to improve patient-centered care by reducing unnecessary invasive procedures and ensuring timely diagnosis in women at elevated risk. Ethical considerations We confirm that we have read the Journal’s position on issues involved in ethical publication and affirm that this report is consistent with those guidelines. The study protocol was approved on 6 Mars 2025 by the institutional ethics committee of Charles Nicolle Hospital, Tunis, Tunisia before conducting the study (approval number: FWA 00032748- IORG0011243). Consent to participate As this was a retrospective study using anonymized data, informed consent was waived. Data availability statement All data sets can be assessed and all study findings reported in the article are shared via Harvard Dataverse: “Development and Internal Validation of a Novel Risk Prediction Score for Endometrial Malignancy in Premenopausal Women with Abnormal Uterine Bleeding”, https://doi.org/10.7910/DVN/BTRWYC . 31 This project contains the following: • Dataset Novel Risk Prediction Score • Study findings Extended data Harvard Dataverse: “Development and Internal Validation of a Novel Risk Prediction Score for Endometrial Malignancy in Premenopausal Women with Abnormal Uterine Bleeding”, https://doi.org/10.7910/DVN/BTRWYC . 31 This project contains the following: • Canvas (English) Reporting guidelines This work has been reported in line with the STROBE guidelines. 32 Harvard Dataverse: “Development and Internal Validation of a Novel Risk Prediction Score for Endometrial Malignancy in Premenopausal Women with Abnormal Uterine Bleeding”, https://doi.org/10.7910/DVN/BTRWYC . 31 This project contains the following: • STROBE Checklist Data are available under the terms of the Creative Commons Zero “No rights reserved” data waiver (CC0 1.0 Public domain dedication). References 1. Sweet MG, Schmidt-Dalton TA, Weiss PM, et al. : Evaluation and management of abnormal uterine bleeding in premenopausal women. Am. Fam. Physician. 2012; 85 (1): 35–43. PubMed Abstract 2. Fraser IS, Critchley HO, Broder M, et al. : The FIGO recommendations on terminologies and definitions for normal and abnormal uterine bleeding. Semin. Reprod. Med. 2011; 29 (5): 383–390. PubMed Abstract | Publisher Full Text 3. Giannella L, Cerami LB, Setti T, et al. : Prediction of Endometrial Hyperplasia and Cancer among Premenopausal Women with Abnormal Uterine Bleeding. Biomed. Res. Int. 2019; 2019 : 8598152. 4. Clarke MA, Long BJ, Del Mar MA, et al. : Association of Endometrial Cancer Risk With Postmenopausal Bleeding in Women: A Systematic Review and Meta-analysis. JAMA Intern. Med. 2018; 178 (9): 1210–1222. PubMed Abstract | Publisher Full Text | Free Full Text 5. Saccardi C, Vitagliano A, Marchetti M, et al. : Endometrial Cancer Risk Prediction According to Indication of Diagnostic Hysteroscopy in Post-Menopausal Women. Diagnostics (Basel). 2020; 10 (5): 257. PubMed Abstract | Publisher Full Text | Free Full Text 6. Saccardi C, Spagnol G, Bonaldo G, et al. : New Light on Endometrial Thickness as a Risk Factor of Cancer: What Do Clinicians Need to Know? Cancer Manag. Res. 2022; 14 : 1331–1340. PubMed Abstract | Publisher Full Text | Free Full Text 7. Kim MJ, Kim JJ, Kim SM: Endometrial evaluation with transvaginal ultrasonography for the screening of endometrial hyperplasia or cancer in premenopausal and perimenopausal women. Obstet Gynecol Sci. 2016; 59 (3): 192–200. PubMed Abstract | Publisher Full Text | Free Full Text 8. El Agwany AS: Sonographic Criteria for Uterine Curettage: Suspecting Endometrial Neoplasia. Indian J. Surg. Oncol. 2019; 10 (4): 679–684. PubMed Abstract | Publisher Full Text | Free Full Text 9. Jha S, Singh A, Sinha HH, et al. : Rate of premalignant and malignant endometrial lesion in "low-risk" premenopausal women with abnormal uterine bleeding undergoing endometrial biopsy. Obstet Gynecol Sci. 2021; 64 (6): 517–523. PubMed Abstract | Publisher Full Text | Free Full Text 10. Park YR, Lee SW, Kim Y, et al. : Endometrial thickness cut-off value by transvaginal ultrasonography for screening of endometrial pathology in premenopausal and postmenopausal women. Obstet Gynecol Sci. 2019; 62 (6): 445–453. PubMed Abstract | Publisher Full Text | Free Full Text 11. Wang L, Wei W, Cai M: A Review of the Risk Factors Associated with Endometrial Hyperplasia During Perimenopause. Int. J. Women’s Health. 2024; 16 : 1475–1482. PubMed Abstract | Publisher Full Text | Free Full Text 12. Singh S, Best C, Dunn S, et al. : SOGC clinical practice guideline no 292—abnormal uterine bleeding in Pre-Menopausal women. J. Obstet. Gynaecol. Can. 2018; 40 (5): e391–e415. Publisher Full Text 13. National Institute for Health and Care Excellence (NICE): Heavy menstrual bleeding: assessment and management. NICE guideline [NG88]. London: NICE; 2018 Mar 14 [cited 2021 May 24]. 14. Dreisler E, Frandsen CS, Ulrich L: Perimenopausal abnormal uterine bleeding. Maturitas. 2024; 184 : 107944. Publisher Full Text 15. Yılmaz Aksoy MS, Bornaun T: Evolving paradigms in the diagnosis and management of premenopausal women with abnormal uterine bleeding. Eur Res J. July 2024; 10 (4): 414–425. Publisher Full Text 16. Rybak A, Levy C, Ouldali N, et al. : Dynamics of Antibiotic Resistance of Streptococcus pneumoniae in France: A Pediatric Prospective Nasopharyngeal Carriage Study from 2001 to 2022. Antibiotics. 2023 Jun; 12 (6): 1020. PubMed Abstract | Publisher Full Text | Free Full Text 17. Hasan MM, Kamruzzaman M, Anik KS, et al. : Dengue Encephalopathy, its Presentations and Outcome, A Study on 200 Dengue Encephalopathy Patients in Tertiary Level Hospitals in Bangladesh. Sch J App Med Sci. 2024 Nov 11; 12 (11): 1550–1557. Publisher Full Text 18. Ben-Haim Y, Dacso CC: Interpreting PPV and NPV of Diagnostic Tests with Uncertain Prevalence. Rambam Maimonides Med J. 2024 Jul 30; 15 (3): e0013. PubMed Abstract | Publisher Full Text | Free Full Text 19. Timmermans A, Opmeer BC, Khan KS, et al. : Endometrial thickness measurement for detecting endometrial cancer in women with postmenopausal bleeding: a systematic review and meta-analysis. Obstet. Gynecol. 2010; 116 : 160–167. Publisher Full Text 20. Ruan H, Chen S, Li J, et al. : Development and Validation of a Nomogram Prediction Model for Endometrial Malignancy in Patients with Abnormal Uterine Bleeding. Yonsei Med. J. 2023; 64 (3): 197–203. PubMed Abstract | Publisher Full Text | Free Full Text 21. Tian Y, Bai B, Wang L, et al. : Contributing factors related to abnormal uterine bleeding in perimenopausal women: a case-control study. J. Health Popul. Nutr. 2024; 43 (1): 52. PubMed Abstract | Publisher Full Text | Free Full Text 22. Sahu HD, Varma AV, Karmarkar S, et al. : Endometrial histopathology in abnormal uterine bleeding and its relation with thyroid profile and endometrial thickness. Cureus. 2023; 15 (4): e37931. PubMed Abstract | Publisher Full Text 23. Getpook C, Wattanakumtornkul S: Endometrial thickness screening in premenopausal women with abnormal uterine bleeding. J. Obstet. Gynaecol. Res. 2006; 32 (6): 588–592. Publisher Full Text 24. Bagepalli Srinivas S, Kubakaddi SS, Polisetti S, et al. : A Novel Risk-Scoring Model for Prediction of Premalignant and Malignant Lesions of Uterine Endometrium Among Symptomatic Premenopausal Women. Int. J. Women’s Health. 2020; 12 : 883–891. PubMed Abstract | Publisher Full Text | Free Full Text 25. Collaborative Group on Epidemiological Studies on Endometrial Cancer: Endometrial cancer and oral contraceptives: an individual participant meta-analysis of 27 276 women with endometrial cancer from 36 epidemiological studies. Lancet Oncol. 2015; 16 (9): 1061–1070. Publisher Full Text 26. Michels KA, Pfeiffer RM, Brinton LA, et al. : Modification of the Associations Between Duration of Oral Contraceptive Use and Ovarian, Endometrial, Breast, and Colorectal Cancers. JAMA Oncol. 2018; 4 (4): 516–521. PubMed Abstract | Publisher Full Text | Free Full Text 27. Harajka A, Hercsik T, das Virgens IPA , et al. : Association of oral contraceptives and risk of endometrial cancer: A systematic review and meta-analysis. Acta Obstet. Gynecol. Scand. 2025; 104 (4): 591–603. PubMed Abstract | Publisher Full Text | Free Full Text 28. Karlsson T, Johansson T, Höglund J, et al. : Time-Dependent Effects of Oral Contraceptive Use on Breast, Ovarian, and Endometrial Cancers. Cancer Res. 2021; 81 (4): 1153–1162. PubMed Abstract | Publisher Full Text 29. Tîrnovanu MC, Cojocaru E, Tîrnovanu VG, et al. : The Role of Ultrasound in Diagnosing Endometrial Pathologies: Adherence to IETA Group Consensus and Preoperative Assessment of Myometrial Invasion in Endometrial Cancer. Diagnostics (Basel). 2025; 15 (7): 891. PubMed Abstract | Publisher Full Text | Free Full Text 30. Leone FP, Timmerman D, Bourne T, et al. : Terms, definitions and measurements to describe the sonographic features of the endometrium and intrauterine lesions: a consensus opinion from the International Endometrial Tumor Analysis (IETA) group. Ultrasound Obstet. Gynecol. 2010; 35 (1): 103–112. PubMed Abstract | Publisher Full Text 31. Karmous N, Jemli H, Masmoudi A, et al. : Development and internal validation of a novel risk prediction score for endometrial malignancy in premenopausal women with abnormal uterine bleeding. [Dataset]. Harvard Dataverse. 2025. 32. von Elm E , Altman DG, Egger M, et al. : The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. J. Clin. Epidemiol. 2008 Apr; 61 (4): 344–349. Publisher Full Text Comments on this article Comments (0) Version 1 VERSION 1 PUBLISHED 18 Nov 2025 ADD YOUR COMMENT Comment Author details Author details 1 Gynaecology and Obstetrics department B, Charles Nicolle Hospital, Tunis, Tunisia 2 Faculty of medicine of Tunis, University Tunis el Manar, Tunis, Tunisia 3 General surgery department B, Charles Nicolle Hospital, Tunis, Tunisia 4 Psychiatric department, Razi Hospital, Manouba, Tunisia Narjes karmous Roles: Conceptualization, Methodology, Writing – Original Draft Preparation Hanene Jemli Roles: Data Curation, Visualization Abdelwaheb Masmoudi Roles: Writing – Review & Editing Badreddine Bouguerra Roles: Validation, Visualization Aymen Mabrouk Roles: Writing – Review & Editing Anis Ben Dhou Roles: Writing – Review & Editing Abdennour Karmous Roles: Formal Analysis, Writing – Review & Editing Competing interests No competing interests were disclosed. Grant information The author(s) declared that no grants were involved in supporting this work. Article Versions (1) version 1 Published: 18 Nov 2025, 14:1281 https://doi.org/10.12688/f1000research.170227.1 Copyright © 2025 karmous N et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Download Export To Sciwheel Bibtex EndNote ProCite Ref. Manager (RIS) Sente metrics Views Downloads F1000Research - - PubMed Central info_outline Data from PMC are received and updated monthly. - - Citations open_in_new 0 open_in_new 0 open_in_new SEE MORE DETAILS CITE how to cite this article karmous N, Jemli H, Masmoudi A et al. Development and Internal Validation of a Novel Risk Prediction Score for Endometrial Malignancy in Premenopausal Women with Abnormal Uterine Bleeding [version 1; peer review: 1 not approved] . F1000Research 2025, 14 :1281 ( https://doi.org/10.12688/f1000research.170227.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS track receive updates on this article Track an article to receive email alerts on any updates to this article. TRACK THIS ARTICLE Share Open Peer Review Current Reviewer Status: ? Key to Reviewer Statuses VIEW HIDE Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Version 1 VERSION 1 PUBLISHED 18 Nov 2025 Views 0 Cite How to cite this report: Wang X. Reviewer Report For: Development and Internal Validation of a Novel Risk Prediction Score for Endometrial Malignancy in Premenopausal Women with Abnormal Uterine Bleeding [version 1; peer review: 1 not approved] . F1000Research 2025, 14 :1281 ( https://doi.org/10.5256/f1000research.187658.r447598 ) The direct URL for this report is: https://f1000research.com/articles/14-1281/v1#referee-response-447598 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 03 Feb 2026 Xuemin Wang , Institution: The University of Queensland Institute for Molecular Bioscience (Ringgold ID: 85088), Saint Lucia, Queensland, Australia Not Approved VIEWS 0 https://doi.org/10.5256/f1000research.187658.r447598 The authors developed a predictive model in identifying endometrial cancer in 209 premenopausal women with abnormal uterine bleeding. While the study was interesting, more details need to be provided, results can be better presented and summarised. Some of my comments ... Continue reading READ ALL The authors developed a predictive model in identifying endometrial cancer in 209 premenopausal women with abnormal uterine bleeding. While the study was interesting, more details need to be provided, results can be better presented and summarised. Some of my comments are specifically listed below. 1. There is no internal validation of the predictive model. So, the "internal validation" in the title of the manuscript is not accurate. The authors may want to change it to "Development and performance of ...". 2. the last sentence in the Introduction "..., hence the interest of ..." doesn't read. 3. Was hysterectomy conducted to the participants at the time of recruitment or during the follow up period? 4. can the authors please list the conditions instead of using "..." throughout the text? 5. Figure 1 isn't the flowchart of the study. It showed the recruitment of participants in the current form. 6. In Results section, can the authors please summarise the results more concisely, rather than listing them one by one. The authors can consider merging the tables describing the characteristics of the participants. It's also necessary to present the SD of the continuous variables. 7. change comparative analysis to univariate analysis. 8. Many variables showed p < 0.2 in the univariate analysis. It wasn't mentioned why the authors only looked at 3 variables (gravity, number of haemorrhage episodes, and endometrial thickness) in the ROC analysis? Also, the authors didn't mention why they applied p<0.2 to select variables in the univariate analysis. This p-value threshold looked too lenient. 9. As mentioned above, many variables passed p<0.2 in the univariate analysis and should have been included in the multivariable analysis according to their inclusion criteria. But only the results of six variables were presented. what about the results of the other variables? 10. participants with incomplete or missing data should be excluded according to their exclusion criteria. therefore, all 209 participants included should have non-missing data. However, only 208 participants had tamoxifen medication as shown in Table 2. 11. the authors assigned different points to the five variables that showed significant associations with endometrial malignancy. How were the points determined? for example, why endometrial thickness > 9mm assigned 3 points and vascularisation 4 points? 12. endometrial thickness varies substantially depending on the stage of the menstrual cycle. How was the menstrual cycle adjusted, or was it adjusted at all? Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? No Competing Interests: No competing interests were disclosed. Reviewer Expertise: statistical genetics, genetic epidemiology I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Wang X. Reviewer Report For: Development and Internal Validation of a Novel Risk Prediction Score for Endometrial Malignancy in Premenopausal Women with Abnormal Uterine Bleeding [version 1; peer review: 1 not approved] . F1000Research 2025, 14 :1281 ( https://doi.org/10.5256/f1000research.187658.r447598 ) The direct URL for this report is: https://f1000research.com/articles/14-1281/v1#referee-response-447598 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Comments on this article Comments (0) Version 1 VERSION 1 PUBLISHED 18 Nov 2025 ADD YOUR COMMENT Comment keyboard_arrow_left keyboard_arrow_right Open Peer Review Reviewer Status info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Reviewer Reports Invited Reviewers 1 Version 1 18 Nov 25 read Xuemin Wang , Institution: The University of Queensland Institute for Molecular Bioscience (Ringgold ID: 85088), Saint Lucia, Australia Comments on this article All Comments (0) Add a comment Sign up for content alerts Sign Up You are now signed up to receive this alert Browse by related subjects keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2026 Wang X. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 03 Feb 2026 | for Version 1 Xuemin Wang , Institution: The University of Queensland Institute for Molecular Bioscience (Ringgold ID: 85088), Saint Lucia, Queensland, Australia 0 Views copyright © 2026 Wang X. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Not Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions The authors developed a predictive model in identifying endometrial cancer in 209 premenopausal women with abnormal uterine bleeding. While the study was interesting, more details need to be provided, results can be better presented and summarised. Some of my comments are specifically listed below. 1. There is no internal validation of the predictive model. So, the "internal validation" in the title of the manuscript is not accurate. The authors may want to change it to "Development and performance of ...". 2. the last sentence in the Introduction "..., hence the interest of ..." doesn't read. 3. Was hysterectomy conducted to the participants at the time of recruitment or during the follow up period? 4. can the authors please list the conditions instead of using "..." throughout the text? 5. Figure 1 isn't the flowchart of the study. It showed the recruitment of participants in the current form. 6. In Results section, can the authors please summarise the results more concisely, rather than listing them one by one. The authors can consider merging the tables describing the characteristics of the participants. It's also necessary to present the SD of the continuous variables. 7. change comparative analysis to univariate analysis. 8. Many variables showed p < 0.2 in the univariate analysis. It wasn't mentioned why the authors only looked at 3 variables (gravity, number of haemorrhage episodes, and endometrial thickness) in the ROC analysis? Also, the authors didn't mention why they applied p<0.2 to select variables in the univariate analysis. This p-value threshold looked too lenient. 9. As mentioned above, many variables passed p<0.2 in the univariate analysis and should have been included in the multivariable analysis according to their inclusion criteria. But only the results of six variables were presented. what about the results of the other variables? 10. participants with incomplete or missing data should be excluded according to their exclusion criteria. therefore, all 209 participants included should have non-missing data. However, only 208 participants had tamoxifen medication as shown in Table 2. 11. the authors assigned different points to the five variables that showed significant associations with endometrial malignancy. How were the points determined? for example, why endometrial thickness > 9mm assigned 3 points and vascularisation 4 points? 12. endometrial thickness varies substantially depending on the stage of the menstrual cycle. How was the menstrual cycle adjusted, or was it adjusted at all? Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? No Competing Interests No competing interests were disclosed. Reviewer Expertise statistical genetics, genetic epidemiology I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above. reply Respond to this report Responses (0) Wang X. Peer Review Report For: Development and Internal Validation of a Novel Risk Prediction Score for Endometrial Malignancy in Premenopausal Women with Abnormal Uterine Bleeding [version 1; peer review: 1 not approved] . F1000Research 2025, 14 :1281 ( https://doi.org/10.5256/f1000research.187658.r447598) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-1281/v1#referee-response-447598 Alongside their report, reviewers assign a status to the article: Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved - fundamental flaws in the paper seriously undermine the findings and conclusions Adjust parameters to alter display View on desktop for interactive features Includes Interactive Elements View on desktop for interactive features Competing Interests Policy Provide sufficient details of any financial or non-financial competing interests to enable users to assess whether your comments might lead a reasonable person to question your impartiality. 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