Diagnostic Performance of Risk of Malignancy Algorithm (ROMA), Risk of Malignancy Index (RMI) and Expert Ultrasound Assessment in a Pelvic Mass Classified as Inconclusive by International Ovarian Tumour Analysis (IOTA) Simple Rules
Abstract
:Simple Summary
Abstract
1. Introduction
2. Materials and Methods
2.1. Study Design
2.2. Sample Size Calculation
2.3. Statistical Analysis
3. Results
3.1. Study Population
3.2. Amongst Women with an Inconclusive Result from the IOTA Simple Rules
3.3. Amongst Women with a Conclusive Result from the IOTA Simple Rules
3.4. Amongst the Whole Population of Women with an Ovarian Pathology
3.5. Performance in Pre- and Postmenopausal Women
3.6. Performance in a Cancer Centre vs. General Hospitals (for Patients with an Ovarian Pathology)
3.7. Sensitivity in Diagnosing Early-Stage (Stage 1) Cancer
3.8. Performance for Different Histological Types of Ovarian Cancer
4. Discussion
Strengths and Weaknesses
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Engelen, M.J.; Kos, H.E.; Willemse, P.H.; Aalders, J.G.; de Vries, E.G.; Schaapveld, M.; Otter, R.; van der Zee, A.G. Surgery by consultant gynecologic oncologists improves survival in patients with ovarian carcinoma. Cancer 2006, 106, 589–598. [Google Scholar] [CrossRef] [PubMed]
- Woo, Y.L.; Kyrgiou, M.; Bryant, A.; Everett, T.; Dickinson, H.O. Centralisation of services for gynaecological cancers—A Cochrane systematic review. Gynecol. Oncol. 2012, 126, 286–290. [Google Scholar] [CrossRef] [PubMed]
- Meys, E.M.; Kaijser, J.; Kruitwagen, R.F.; Slangen, B.F.; Van Calster, B.; Aertgeerts, B.; Verbakel, J.Y.; Timmerman, D.; Van Gorp, T. Subjective assessment versus ultrasound models to diagnose ovarian cancer: A systematic review and meta-analysis. Eur. J. Cancer 2016, 58, 17–29. [Google Scholar] [CrossRef] [PubMed]
- Van Calster, B.; Valentin, L.; Froyman, W.; Landolfo, C.; Ceusters, J.; Testa, A.C.; Wynants, L.; Sladkevicius, P.; Van Holsbeke, C.; Domali, E.; et al. Validation of models to diagnose ovarian cancer in patients managed surgically or conservatively: Multicentre cohort study. BMJ 2020, 370, m2614. [Google Scholar] [CrossRef] [PubMed]
- Westwood, M.; Ramaekers, B.; Lang, S.; Grimm, S.; Deshpande, S.; de Kock, S.; Armstrong, N.; Joore, M.; Kleijnen, J. Risk scores to guide referral decisions for people with suspected ovarian cancer in secondary care: A systematic review and cost-effectiveness analysis. Health Technol. Assess. 2018, 22, 1–264. [Google Scholar] [CrossRef]
- Chacon, E.; Dasi, J.; Caballero, C.; Alcazar, J.L. Risk of Ovarian Malignancy Algorithm versus Risk Malignancy Index-I for Preoperative Assessment of Adnexal Masses: A Systematic Review and Meta-Analysis. Gynecol. Obs. Investig. 2019, 84, 591–598. [Google Scholar] [CrossRef]
- Jacobs, I.; Oram, D.; Fairbanks, J.; Turner, J.; Frost, C.; Grudzinskas, J.G. A risk of malignancy index incorporating CA 125, ultrasound and menopausal status for the accurate preoperative diagnosis of ovarian cancer. Br. J. Obs. Gynaecol. 1990, 97, 922–929. [Google Scholar] [CrossRef]
- Khoiwal, K.; Bahadur, A.; Kumari, R.; Bhattacharya, N.; Rao, S.; Chaturvedi, J. Assessment of Diagnostic Value of Serum Ca-125 and Risk of Malignancy Index Scoring in the Evaluation of Adnexal Masses. J. Midlif. Health 2019, 10, 192–196. [Google Scholar] [CrossRef]
- Zhang, S.; Yu, S.; Hou, W.; Li, X.; Ning, C.; Wu, Y.; Zhang, F.; Jiao, Y.F.; Lee, L.T.O.; Sun, L. Diagnostic extended usefulness of RMI: Comparison of four risk of malignancy index in preoperative differentiation of borderline ovarian tumors and benign ovarian tumors. J. Ovarian Res. 2019, 12, 87. [Google Scholar] [CrossRef] [Green Version]
- Royal College of Obstetricians & Gynaecologists. Management of Suspected Ovarian Masses in Premenopausal Women. Green-top Guideline No. 62. Available online: https://www.rcog.org.uk/globalassets/documents/guidelines/gtg_62.pdf (accessed on 2 January 2022).
- Geomini, P.; Kruitwagen, R.; Bremer, G.L.; Cnossen, J.; Mol, B.W. The accuracy of risk scores in predicting ovarian malignancy: A systematic review. Obs. Gynecol. 2009, 113, 384–394. [Google Scholar] [CrossRef]
- Zurawski, V.R., Jr.; Orjaseter, H.; Andersen, A.; Jellum, E. Elevated serum CA 125 levels prior to diagnosis of ovarian neoplasia: Relevance for early detection of ovarian cancer. Int. J. Cancer 1988, 42, 677–680. [Google Scholar] [CrossRef]
- Hellstrom, I.; Raycraft, J.; Hayden-Ledbetter, M.; Ledbetter, J.A.; Schummer, M.; McIntosh, M.; Drescher, C.; Urban, N.; Hellstrom, K.E. The HE4 (WFDC2) protein is a biomarker for ovarian carcinoma. Cancer Res. 2003, 63, 3695–3700. [Google Scholar] [PubMed]
- Moore, R.G.; Brown, A.K.; Miller, M.C.; Skates, S.; Allard, W.J.; Verch, T.; Steinhoff, M.; Messerlian, G.; DiSilvestro, P.; Granai, C.O.; et al. The use of multiple novel tumor biomarkers for the detection of ovarian carcinoma in patients with a pelvic mass. Gynecol. Oncol. 2008, 108, 402–408. [Google Scholar] [CrossRef]
- Moore, R.G.; McMeekin, D.S.; Brown, A.K.; DiSilvestro, P.; Miller, M.C.; Allard, W.J.; Gajewski, W.; Kurman, R.; Bast, R.C., Jr.; Skates, S.J. A novel multiple marker bioassay utilizing HE4 and CA125 for the prediction of ovarian cancer in patients with a pelvic mass. Gynecol. Oncol. 2009, 112, 40–46. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Van Gorp, T.; Cadron, I.; Despierre, E.; Daemen, A.; Leunen, K.; Amant, F.; Timmerman, D.; De Moor, B.; Vergote, I. HE4 and CA125 as a diagnostic test in ovarian cancer: Prospective validation of the Risk of Ovarian Malignancy Algorithm. Br. J. Cancer 2011, 104, 863–870. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Chan, K.K.; Chen, C.A.; Nam, J.H.; Ochiai, K.; Wilailak, S.; Choon, A.T.; Sabaratnam, S.; Hebbar, S.; Sickan, J.; Schodin, B.A.; et al. The use of HE4 in the prediction of ovarian cancer in Asian women with a pelvic mass. Gynecol. Oncol. 2013, 128, 239–244. [Google Scholar] [CrossRef]
- Wang, J.; Gao, J.; Yao, H.; Wu, Z.; Wang, M.; Qi, J. Diagnostic accuracy of serum HE4, CA125 and ROMA in patients with ovarian cancer: A meta-analysis. Tumour. Biol. 2014, 35, 6127–6138. [Google Scholar] [CrossRef] [PubMed]
- Wilailak, S.; Chan, K.K.; Chen, C.A.; Nam, J.H.; Ochiai, K.; Aw, T.C.; Sabaratnam, S.; Hebbar, S.; Sickan, J.; Schodin, B.A.; et al. Distinguishing benign from malignant pelvic mass utilizing an algorithm with HE4, menopausal status, and ultrasound findings. J. Gynecol. Oncol. 2015, 26, 46–53. [Google Scholar] [CrossRef] [Green Version]
- Timmerman, D.; Ameye, L.; Fischerova, D.; Epstein, E.; Melis, G.B.; Guerriero, S.; Van Holsbeke, C.; Savelli, L.; Fruscio, R.; Lissoni, A.A.; et al. Simple ultrasound rules to distinguish between benign and malignant adnexal masses before surgery: Prospective validation by IOTA group. BMJ 2010, 341, c6839. [Google Scholar] [CrossRef] [Green Version]
- Testa, A.; Kaijser, J.; Wynants, L.; Fischerova, D.; Van Holsbeke, C.; Franchi, D.; Savelli, L.; Epstein, E.; Czekierdowski, A.; Guerriero, S.; et al. Strategies to diagnose ovarian cancer: New evidence from phase 3 of the multicentre international IOTA study. Br. J. Cancer 2014, 111, 680–688. [Google Scholar] [CrossRef] [Green Version]
- Knafel, A.; Banas, T.; Nocun, A.; Wiechec, M.; Jach, R.; Ludwin, A.; Kabzinska-Turek, M.; Pietrus, M.; Pitynski, K. The Prospective External Validation of International Ovarian Tumor Analysis (IOTA) Simple Rules in the Hands of Level I and II Examiners. Ultraschall Med. 2016, 37, 516–523. [Google Scholar] [CrossRef] [PubMed]
- Nunes, N.; Ambler, G.; Foo, X.; Naftalin, J.; Widschwendter, M.; Jurkovic, D. Use of IOTA simple rules for diagnosis of ovarian cancer: Meta-analysis. Ultrasound Obs. Gynecol. 2014, 44, 503–514. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Peces Rama, A.; Llanos Llanos, M.C.; Sanchez Ferrer, M.L.; Alcazar Zambrano, J.L.; Martinez Mendoza, A.; Nieto Diaz, A. Simple descriptors and simple rules of the International Ovarian Tumor Analysis (IOTA) Group: A prospective study of combined use for the description of adnexal masses. Eur. J. Obs. Gynecol. Reprod Biol. 2015, 195, 7–11. [Google Scholar] [CrossRef] [PubMed]
- Ruiz de Gauna, B.; Rodriguez, D.; Olartecoechea, B.; Auba, M.; Jurado, M.; Gomez Roig, M.D.; Alcazar, J.L. Diagnostic performance of IOTA simple rules for adnexal masses classification: A comparison between two centers with different ovarian cancer prevalence. Eur. J. Obs. Gynecol. Reprod Biol. 2015, 191, 10–14. [Google Scholar] [CrossRef]
- Sayasneh, A.; Wynants, L.; Preisler, J.; Kaijser, J.; Johnson, S.; Stalder, C.; Husicka, R.; Abdallah, Y.; Raslan, F.; Drought, A.; et al. Multicentre external validation of IOTA prediction models and RMI by operators with varied training. Br. J. Cancer 2013, 108, 2448–2454. [Google Scholar] [CrossRef]
- Kaijser, J.; Sayasneh, A.; Van Hoorde, K.; Ghaem-Maghami, S.; Bourne, T.; Timmerman, D.; Van Calster, B. Presurgical diagnosis of adnexal tumours using mathematical models and scoring systems: A systematic review and meta-analysis. Hum. Reprod Update 2014, 20, 449–462. [Google Scholar] [CrossRef] [Green Version]
- Kaijser, J.; Van Gorp, T.; Smet, M.E.; Van Holsbeke, C.; Sayasneh, A.; Epstein, E.; Bourne, T.; Vergote, I.; Van Calster, B.; Timmerman, D. Are serum HE4 or ROMA scores useful to experienced examiners for improving characterization of adnexal masses after transvaginal ultrasonography? Ultrasound Obs. Gynecol. 2014, 43, 89–97. [Google Scholar] [CrossRef]
- Hartman, C.A.; Juliato, C.R.; Sarian, L.O.; Toledo, M.C.; Jales, R.M.; Morais, S.S.; Pitta, D.D.; Marussi, E.F.; Derchain, S. Ultrasound criteria and CA 125 as predictive variables of ovarian cancer in women with adnexal tumors. Ultrasound Obstet. Gynecol. Off. J. Int. Soc. Ultrasound Obstet. Gynecol. 2012, 40, 360–366. [Google Scholar] [CrossRef]
- Alcazar, J.L.; Pascual, M.A.; Olartecoechea, B.; Graupera, B.; Auba, M.; Ajossa, S.; Hereter, L.; Julve, R.; Gaston, B.; Peddes, C.; et al. IOTA simple rules for discriminating between benign and malignant adnexal masses: Prospective external validation. Ultrasound Obstet. Gynecol. Off. J. Int. Soc. Ultrasound Obstet. Gynecol. 2013, 42, 467–471. [Google Scholar] [CrossRef] [Green Version]
- Knafel, A.N.; Nocun, A.; Banas, T.; Wiechec, M.; Jach, R.; Pietrus, M. Iota simple ultrasound-based rules: Why do we have inconclusive results? Int. J. Gynecol. Cancer 2013, 23, 155–156. [Google Scholar]
- Piovano, E.; Cavallero, C.; Fuso, L.; Viora, E.; Ferrero, A.; Gregori, G.; Grillo, C.; Macchi, C.; Mengozzi, G.; Mitidieri, M.; et al. Diagnostic accuracy and cost-effectiveness of different strategies to triage women with adnexal masses: A prospective study. Ultrasound Obstet. Gynecol. Off. J. Int. Soc. Ultrasound Obstet. Gynecol. 2017, 50, 395–403. [Google Scholar] [CrossRef]
- Lycke, M.; Kristjansdottir, B.; Sundfeldt, K. A multicenter clinical trial validating the performance of HE4, CA125, risk of ovarian malignancy algorithm and risk of malignancy index. Gynecol. Oncol. 2018, 151, 159–165. [Google Scholar] [CrossRef] [PubMed]
- Karlsen, M.A.; Sandhu, N.; Hogdall, C.; Christensen, I.J.; Nedergaard, L.; Lundvall, L.; Engelholm, S.A.; Pedersen, A.T.; Hartwell, D.; Lydolph, M.; et al. Evaluation of HE4, CA125, risk of ovarian malignancy algorithm (ROMA) and risk of malignancy index (RMI) as diagnostic tools of epithelial ovarian cancer in patients with a pelvic mass. Gynecol. Oncol. 2012, 127, 379–383. [Google Scholar] [CrossRef] [PubMed]
- Education and Practical Standards Committee. Minimum training recommendations for the practice of medical ultrasound. Ultraschall Med. 2006, 27, 79–105. [Google Scholar] [CrossRef] [Green Version]
- Timmerman, D.; Testa, A.C.; Bourne, T.; Ferrazzi, E.; Ameye, L.; Konstantinovic, M.L.; Van Calster, B.; Collins, W.P.; Vergote, I.; Van Huffel, S.; et al. Logistic regression model to distinguish between the benign and malignant adnexal mass before surgery: A multicenter study by the International Ovarian Tumor Analysis Group. J. Clin. Oncol. 2005, 23, 8794–8801. [Google Scholar] [CrossRef] [PubMed]
- Timmerman, D.; Van Calster, B.; Testa, A.; Savelli, L.; Fischerova, D.; Froyman, W.; Wynants, L.; Van Holsbeke, C.; Epstein, E.; Franchi, D.; et al. Predicting the risk of malignancy in adnexal masses based on the Simple Rules from the International Ovarian Tumor Analysis group. Am. J. Obs. Gynecol. 2016, 214, 424–437. [Google Scholar] [CrossRef] [Green Version]
- Van Calster, B.; Van Hoorde, K.; Valentin, L.; Testa, A.C.; Fischerova, D.; Van Holsbeke, C.; Savelli, L.; Franchi, D.; Epstein, E.; Kaijser, J.; et al. Evaluating the risk of ovarian cancer before surgery using the ADNEX model to differentiate between benign, borderline, early and advanced stage invasive, and secondary metastatic tumours: Prospective multicentre diagnostic study. BMJ 2014, 349, g5920. [Google Scholar] [CrossRef] [Green Version]
- Timmerman, D.; Planchamp, F.; Bourne, T.; Landolfo, C.; du Bois, A.; Chiva, L.; Cibula, D.; Concin, N.; Fischerova, D.; Froyman, W.; et al. ESGO/ISUOG/IOTA/ESGE Consensus Statement on preoperative diagnosis of ovarian tumors. Ultrasound Obs. Gynecol. 2021, 58, 148–168. [Google Scholar] [CrossRef]
Methods | Components | Risk of Malignancy | |
---|---|---|---|
High Risk | Low Risk | ||
IOTA simple rules (IOTA) | Ultrasound assessment using 5 benign (B-features) and 5 malignant features (M-features) | Presence of >1 M-features and absence of B-features | Presence of >1 B-features and absence of M-features |
Risk of malignancy algorithm (ROMA) | Calculation of risk by an algorithm taking into account the menopausal status, CA125 and HE4 levels Premenopausal Predictive Index (PI) = −12.0 + 2.38 × LN (natural log) [HE4] + 0.0626 × LN[CA125] Postmenopausal PI = −8.09 + 1.04 × LN[HE4] + 0.732 × LN[CA125] ROMA = exp(PI)/[1 + exp(PI)] × 100 | Premenopausal: ROMA ≥ 7 .4 Postmenopausal: ROMA ≥ 25.3 | Premenopausal: ROMA < 7.4 Postmenopausal: ROMA < 25.3 |
Risk of malignancy index (RMI) | Calculation of risk by ultrasound score (U), menopausal status (M) and CA125 level RMI = U × M × CA125 | RMI ≥ 200 | RMI < 200 |
Expert ultrasound | Subjective assessment by expert sonographer | Assessment suggestive of malignancy | Assessment suggestive of benign tumour |
(a) | ||||
Cancer Centre | General Units | Total | ||
No. of patients | 341 (49.4%) * | 349 (50.6%) * | 690 (100%) * | |
Age (median) | 47 (18–85) | 45 (19–89) | 46 (18–89) | |
Menopausal status | ||||
Postmenopausal | 113 (33.1%) | 99 (28.4%) | 212 (30.7%) | |
Pre-menopausal | 228 (66.9%) | 250 (71.6%) | 478 (69.3%) | |
No. of ovarian malignancies (%) | 112 (32.8%) | 30 (8.6%) | 142 (20.6%) | |
Histology | ||||
Ovarian | ||||
Benign | 184 (54.0%) | 275 (78.8%) | 459 (66.5%) | |
Endometriotic cyst | 84 (45.7%) | 97 (35.3%) | 181 (39.4%) | |
Dermoid | 32 (17.4%) | 71 25.8%) | 103 (22.4%) | |
Serous/mucinous cystadenoma | 34 (18.5%) | 57 (20.7%) | 91 (19.8%) | |
Fibroma | 5 (2.7%) | 7 (2.5%) | 12 (2.6%) | |
Functional cyst | 6 (3.3%) | 8 (2.9%) | 14 (3.1%) | |
Hydrosalpinx | 1 (0.5%) | 2 (0.7%) | 3 (0.7%) | |
Mixed | 3 (1.6%) | 1 (0.4%) | 4 (0.9%) | |
Others/unspecified | 19 (10.3%) | 32 (11.6%) | 51 (11.1%) | |
Malignant | 112 (32.8%) | 30 (8.6%) | 142 (20.6%) | |
Serous | 28 (25.0%) | 9 (30.0%) | 37 (26.1%) | |
Mucinous | 5 (4.5%) | 2 (6.7%) | 7 (4.9%) | |
Clear cell | 25 (22.3%) | 5 (16.7%) | 30 (21.1%) | |
Endometrioid | 18 (16.1%) | 7 (23.3%) | 25 (17.6%) | |
Mixed | 13 (11.6%) | 0 (0%) | 13 (9.2%) | |
Sex cord stromal/germ cell | 4 (3.6%) | 2 (6.7%) | 6 (4.2%) | |
Metastatic | 10 (8.9%) | 4 (13.3%) | 14 (9.9%) | |
Others | 9 (8.0%) | 1 (3.3%) | 10 (7.0%) | |
Borderline | 16 (4.7%) | 20 (5.7%) | 36 (5.2%) | |
Malignant/borderline | 1 (0.3%) | 0 (0%) | 1 (0.1%) | |
Non-ovarian | ||||
Benign | 10 (2.9%) | 23 (6.6%) | 33 (4.8%) | |
Malignant | 18 (5.3%) | 1 (0.3%) | 19 (2.8%) | |
FIGO staging | ||||
I | 36 (39.6%) | 15 (65.2%) | 51 (44.7%) | |
II | 11 (12.1%) | 4 (17.4%) | 15 (13.2%) | |
III | 23 (25.3%) | 2 (8.7%) | 25 (21.9%) | |
IV | 10 (11.0%) | 2 (8.7%) | 12 (10.5%) | |
Unstaged | 11 (12.1%) | 0 (0%) | 11 (9.6%) | |
No. of inconclusive IOTA (%) | 105 (30.8%) | 66 (18.9%) | 171 (24.8%) | |
(b) | ||||
Cancer Centre | General Units | Total | ||
No. of patients | 105 (61.4%) * | 66 (38.6%) * | 171 (100%) * | |
Age (median) | 49 (21–84) | 46.5 (22–83) | 48 (21–84) | |
Menopausal status | ||||
Postmenopausal | 46 (43.8%) | 21 (31.8%) | 67 (39.2%) | |
Pre-menopausal | 59 (56.2%) | 45 (68.2%) | 104 (60.8%) | |
No. of ovarian malignancies (%) | 46 (43.8%) | 7 (10.6%) | 53 (31.0%) | |
Histology | ||||
Ovarian | ||||
Benign | 37 (35.2%) | 48 (72.7%) | 85 (49.7%) | |
Endometriotic cyst | 11 (29.7%) | 15 (31.3%) | 26 (30.6%) | |
Dermoid | 7 (18.9%) | 15 (31.3%) | 22 (25.9%) | |
Serous/mucinous cystadenoma | 12 (32.4%) | 8 (16.7%) | 20 (23.5%) | |
Fibroma | 2 (5.4%) | 5 (10.4%) | 7 (8.2%) | |
Functional cyst | 1 (2.7%) | 0 (0%) | 1 (1.2%) | |
Others/unspecified | 4 (10.8%) | 5 (10.4%) | 9 (10.6%) | |
Malignant | 46 (43.8%) | 7 (10.6%) | 53 (31.0%) | |
Serous | 8 (17.4%) | 1 (1.5%) | 9 (5.3%) | |
Mucinous | 3 (6.5%) | 0 (0%) | 3 (1.8%) | |
Clear cell | 16 (34.8%) | 1 (1.5%) | 17 (9.9%) | |
Endometrioid | 4 (8.7%) | 2 (3.0%) | 6 (3.5%) | |
Mixed | 4 (8.7%) | 0 (0%) | 4 (2.3%) | |
Sex cord stromal/germ cell | 2 (4.3%) | 1 (1.5%) | 3 (1.8%) | |
Metastatic | 6 (13.0%) | 1 (1.5%) | 7 (4.1%) | |
Others | 3 (6.5%) | 1 (1.5%) | 4 (2.3%) | |
Borderline | 9 (8.6%) | 7 (10.6%) | 16 (9.4%) | |
Malignant/borderline | 1 (1.0%) | 0 (0%) | 1 (0.6%) | |
Non-ovarian | ||||
Benign | 4 (3.8%) | 3 (4.5%) | 7 (4.1%) | |
Malignant | 8 (7.6%) | 1 (1.5%) | 9 (5.3%) | |
FIGO staging | ||||
I | 17 (43.6%) | 3 (50.0%) | 20 (44.4%) | |
II | 2 (5.1%) | 2 (33.3%) | 4 (8.9%) | |
III | 12 (30.8%) | 1 (16.7%) | 13 (28.9%) | |
IV | 2 (5.1%) | 0 (0%) | 2 (4.4%) | |
Unstaged | 6 (15.4%) | 0 (0%) | 6 (13.3%) |
(a) | |||
Risk of Malignancy | Histology | ||
Malignant | Benign | ||
---|---|---|---|
Expert ultrasound | High risk | 64 | 26 |
Low risk | 15 | 66 | |
ROMA | High risk | 50 | 25 |
Low risk | 29 | 67 | |
(b) | |||
Sensitivity (95% CI) | Specificity (95% CI) | Accuracy (95%CI) | |
Expert ultrasound | 81.0% (70.3–88.6%) | 71.7% (61.2–80.4%) | 76.0% (68.8–82.1%) |
ROMA | 63.3% (51.6–73.6%) | 72.8% (62.4–81.3%) | 68.4% (60.8–75.2%) |
(a) | |||
Risk of Malignancy | Histology | ||
Malignant | Benign | ||
IOTA | High risk | 96 | 10 |
Low risk | 23 | 390 | |
ROMA | High risk | 97 | 59 |
Low risk | 22 | 341 | |
RMI | High risk | 84 | 23 |
Low risk | 35 | 377 | |
(b) | |||
Sensitivity (95% CI) | Specificity (95% CI) | Accuracy (95%CI) | |
IOTA | 80.7% (72.2–87.1%) | 97.5% (95.3–98.7%) | 93.6% (91.1–95.5%) |
ROMA | 81.5% (73.1–87.8%) | 85.3% (81.3–88.5%) | 84.4% (80.9–87.3%) |
RMI | 70.6% (61.4–78.4%) | 94.3% (91.4–96.2%) | 88.8% (85.7–91.3%) |
Sensitivity (95% CI) | Specificity (95% CI) | Accuracy (95%CI) | |
---|---|---|---|
IOTA + expert | 79.9% (73.1–85.3%) | 92.8% (90.0–94.9%) | 89.2% (86.5–91.5%) |
IOTA + ROMA | 73.2% (66.0–79.4%) | 93.7% (91.0–95.7%) | 88.0% (85.1–90.3%) |
IOTA + RMI | 72.1% (64.8–78.4%) | 94.1% (91.5–96.0%) | 88.0% (85.1–90.3%) |
ROMA alone | 74.3% (67.1–80.4%) | 84.4% (80.7–87.5%) | 81.6% (78.3–84.4%) |
RMI alone | 66.5% (59.0–73.2%) | 91.1% (88.0–93.5%) | 84.2% (81.1–86.9%) |
Sensitivity (95% CI) | Specificity (95% CI) | Accuracy (95%CI) | |
---|---|---|---|
Premenopausal | |||
IOTA + expert | 80.9% (70.9–88.2%) | 94.1% (91.0–96.2%) | 91.5% (88.4–93.8%) |
IOTA + ROMA | 73.0% (62.4–81.6%) | 94.4% (91.3–96.4%) | 90.1% (86.9–92.6%) |
IOTA + RMI | 71.9% (61.2–80.7%) | 96.1% (93.3–97.7%) | 91.2% (88.1–93.6%) |
ROMA alone | 76.4% (66.0–84.5%) | 84.0% (79.7–87.6%) | 82.5% (78.5–85.8%) |
RMI alone | 66.3% (55.4–75.8%) | 92.4% (89.0–94.9%) | 87.2% (83.6–90.1%) |
Postmenopausal | |||
IOTA + expert | 78.9% (68.8–86.5%) | 88.6% (80.5–93.7%) | 84.1% (78.0–88.8%) |
IOTA + ROMA | 73.3% (62.8–81.9%) | 91.4% (83.9–95.8%) | 83.1% (76.9–87.9%) |
IOTA + RMI | 72.2% (61.6–80.9%) | 87.6% (79.4–93.0%) | 80.5% (74.1–85.7%) |
ROMA alone | 72.2% (61.6–80.9%) | 85.7% (77.2–91.5%) | 79.5% (73.0–84.8%) |
RMI alone | 66.7% (55.9–76.0%) | 86.7% (78.3–92.3%) | 77.4% (70.8–83.0%) |
Sensitivity (95% CI) | Specificity (95% CI) | Accuracy (95%CI) | ||||
---|---|---|---|---|---|---|
Cancer | General | Cancer | General | Cancer | General | |
IOTA + expert | 82.9% (75.1–88.8%) | 72.0% (57.3–83.3%) | 87.1% (81.2–91.4%) | 96.7% (93.7–98.4%) | 85.4% (80.9–89.0%) | 92.9% (89.4–95.4%) |
IOTA + ROMA | 76.0% (67.5–82.9%) | 66.0% (51.1–78.4%) | 91.4% (86.2–94.8%) | 95.3% (91.9–97.4%) | 85.1% (80.5–88.7%) | 90.8% (87.0–93.6%) |
IOTA + RMI | 76.7% (68.3–83.5%) | 60.0% (45.2–73.3%) | 91.9% (86.8–95.3%) | 95.6% (92.3–97.6%) | 85.7% (81.2–89.3%) | 90.2% (86.3–93.1%) |
ROMA alone | 79.8% (71.7–86.2%) | 60.0% (45.2–73.3%) | 79.6% (72.9–85.0%) | 87.6% (83.0–91.2%) | 79.7% (74.7–83.9%) | 83.4% (78.8–87.2%) |
RMI alone | 76.0% (67.5–82.9%) | 42.0% (28.5–56.7%) | 87.1% (81.2–91.4%) | 93.8% (90.1–96.2%) | 82.5% (77.8–86.5%) | 85.8% (81.5–89.4%) |
Sensitivity | |
---|---|
IOTA + expert | 80.7% (67.7–89.5%) |
IOTA + ROMA | 71.9% (58.3–82.6%) |
IOTA + RMI | 70.2% (56.4–81.2%) |
ROMA alone | 70.2% (56.4–81.2%) |
RMI alone | 57.9% (44.1–70.6%) |
Number of Patients | Malignancy Prevalence % | Inconclusive IOTA % | Sensitivity % | Specificity % | |
---|---|---|---|---|---|
Timmerman D et al., 2010 [20] | 997 | 28 | 23 | 90 | 93 |
Hartman CA et al., 2012 [29] | 110 | 28 | 17 | 84 | 86 |
Alcazar JL et al., 2013 [30] | 340 | 16 | 21 | 89 | 96 |
Sayasneh A et al., 2013 [26] | 255 | 29 | 17 | 86 | 94 |
Nunes N et al., 2014 [23] | 303 | 44 | 22 | 94 | 89 |
Ruiz de Gauna B et al., 2015 [25] centre A | 114 | 27 | 18 | 100 | 89 |
Ruiz de Gauna B et al., 2015 [25] centre B | 133 | 11 | 18 | 86 | 88 |
Knafel A et al., 2013 [31] | 226 | NA | 18 | 95 | 74 |
Piovano E et al., 2017 [32] | 391 | 21 | 11 | 82 | 92 |
Current study | 640 | 21 | 25 | 80 | 93 |
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Ngu, S.F.; Chai, Y.K.; Choi, K.M.; Leung, T.W.; Li, J.; Kwok, G.S.T.; Chu, M.M.Y.; Tse, K.Y.; Cheung, V.Y.T.; Ngan, H.Y.S.; et al. Diagnostic Performance of Risk of Malignancy Algorithm (ROMA), Risk of Malignancy Index (RMI) and Expert Ultrasound Assessment in a Pelvic Mass Classified as Inconclusive by International Ovarian Tumour Analysis (IOTA) Simple Rules. Cancers 2022, 14, 810. https://doi.org/10.3390/cancers14030810
Ngu SF, Chai YK, Choi KM, Leung TW, Li J, Kwok GST, Chu MMY, Tse KY, Cheung VYT, Ngan HYS, et al. Diagnostic Performance of Risk of Malignancy Algorithm (ROMA), Risk of Malignancy Index (RMI) and Expert Ultrasound Assessment in a Pelvic Mass Classified as Inconclusive by International Ovarian Tumour Analysis (IOTA) Simple Rules. Cancers. 2022; 14(3):810. https://doi.org/10.3390/cancers14030810
Chicago/Turabian StyleNgu, Siew Fei, Yu Ka Chai, Ka Man Choi, Tsin Wah Leung, Justin Li, Gladys S. T. Kwok, Mandy M. Y. Chu, Ka Yu Tse, Vincent Y. T. Cheung, Hextan Y. S. Ngan, and et al. 2022. "Diagnostic Performance of Risk of Malignancy Algorithm (ROMA), Risk of Malignancy Index (RMI) and Expert Ultrasound Assessment in a Pelvic Mass Classified as Inconclusive by International Ovarian Tumour Analysis (IOTA) Simple Rules" Cancers 14, no. 3: 810. https://doi.org/10.3390/cancers14030810