Identification of Effective Model for Prediction of Ovarian Malignancy Risk using Models like Risk of Malignancy Index, Logistic Regression, International Ovarian Tumour Analysis- Simple Rules
Introduction: Ovarian tumour is not a single entity it is a spectrum of neoplasm involving variety of histological tissues. Use of mathematical formula as malignancy index which is based on logistic model, menopausal status, serum levels of Cancer Antigen 125 (CA-125) and ultrasound findings in a sc...
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| Format: | Article |
| Language: | English |
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JCDR Research and Publications Private Limited
2022-04-01
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| Series: | Journal of Clinical and Diagnostic Research |
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| Online Access: | https://www.jcdr.net/articles/PDF/16196/53286_CE[Ra1]_F[SH]_PF1(SC_SS)_PFA_PB(SC_KM)_PN(KM).pdf |
| _version_ | 1811168293458804736 |
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| author | SR Ramya |
| author_facet | SR Ramya |
| author_sort | SR Ramya |
| collection | DOAJ |
| description | Introduction: Ovarian tumour is not a single entity it is a spectrum of neoplasm involving variety of histological tissues. Use of mathematical formula as malignancy index which is based on logistic model, menopausal status, serum levels of Cancer Antigen 125 (CA-125) and ultrasound findings in a score system is not so popular which can be a useful predictor for diagnosing and monitoring the progression of ovarian malignancy.
Aim: To determine the effectiveness of the three models i.e., Risk of Malignancy Index (RMI-1,2,3 and 4), Logistic Regression 2 (LR-2), International Ovarian Tumour Analysis (IOTA) - simple rules in predicting ovarian malignancy.
Materials and Methods: This prospective cohort observational study was conducted in Department of Obstetrics and Gynaecology at Saveetha Medical College and Hospital, Tamil Nadu, India, from June 2017 to July 2018. The study included a total of 70 female subjects with ovarian mass. Information obtained by investigations, ultrasound was used to predict the risk of malignancy by using the three models {Risk of Malignancy Index (RMI-1, 2, 3 and 4), Logistic Regression 2 (LR-2), International Ovarian Tumour Analysis (IOTA)- simple rules}. CA-125 level was considered as primary outcome variable. Study group histopathology impression (malignant vs benign) was considered as primary explanatory variable. The result from the above models was compared with the postoperative histopathological report. The sensitivity and specificity of each model was also identified.
Results: Majority of the study participants 49 (70%) were in premenopausal status and only 21 (30%) were in menopausal status. The mean CA-125 level was 108.82±233.13 in the study population (95% CI: 53.23-164.41). Among the 70 study subjects, 53 (75.70%) patients were RMI-1 benign and only 17 (24.30%) were RMI-1 malignant. Majority of the study participants 44 (60%) were IOTA impression benign and only 23 (40%) were IOTA malignant. The difference in the proportion of IOTA-simple rules between histopathology impression was statistically significant (p-value <0.001). The sensitivity of IOTA-simple rules in predicting malignant histopathology was 92%, specificity was 90.48%, diagnostic accuracy was 91.04%.
Conclusion: For early risk stratification of adnexal masses, IOTA-simple rules can be used as a screening tool due to its high sensitivity, specificity, positive predictive value, negative predictive value and diagnostic accuracy. |
| first_indexed | 2024-04-10T16:23:44Z |
| format | Article |
| id | doaj.art-a2b16bb52ddc482a8e1e90e565dc5693 |
| institution | Directory Open Access Journal |
| issn | 2249-782X 0973-709X |
| language | English |
| last_indexed | 2024-04-10T16:23:44Z |
| publishDate | 2022-04-01 |
| publisher | JCDR Research and Publications Private Limited |
| record_format | Article |
| series | Journal of Clinical and Diagnostic Research |
| spelling | doaj.art-a2b16bb52ddc482a8e1e90e565dc56932023-02-09T08:58:12ZengJCDR Research and Publications Private LimitedJournal of Clinical and Diagnostic Research2249-782X0973-709X2022-04-01164QC01QC0510.7860/JCDR/2022/53286.16196Identification of Effective Model for Prediction of Ovarian Malignancy Risk using Models like Risk of Malignancy Index, Logistic Regression, International Ovarian Tumour Analysis- Simple RulesSR Ramya0Assistant Professor, Department of Obstetrics and Gynaecology, Karpaga Vinayaga Institute of Medical Sciences and Research Centre, Madhuranthagam, Tamil Nadu, India.Introduction: Ovarian tumour is not a single entity it is a spectrum of neoplasm involving variety of histological tissues. Use of mathematical formula as malignancy index which is based on logistic model, menopausal status, serum levels of Cancer Antigen 125 (CA-125) and ultrasound findings in a score system is not so popular which can be a useful predictor for diagnosing and monitoring the progression of ovarian malignancy. Aim: To determine the effectiveness of the three models i.e., Risk of Malignancy Index (RMI-1,2,3 and 4), Logistic Regression 2 (LR-2), International Ovarian Tumour Analysis (IOTA) - simple rules in predicting ovarian malignancy. Materials and Methods: This prospective cohort observational study was conducted in Department of Obstetrics and Gynaecology at Saveetha Medical College and Hospital, Tamil Nadu, India, from June 2017 to July 2018. The study included a total of 70 female subjects with ovarian mass. Information obtained by investigations, ultrasound was used to predict the risk of malignancy by using the three models {Risk of Malignancy Index (RMI-1, 2, 3 and 4), Logistic Regression 2 (LR-2), International Ovarian Tumour Analysis (IOTA)- simple rules}. CA-125 level was considered as primary outcome variable. Study group histopathology impression (malignant vs benign) was considered as primary explanatory variable. The result from the above models was compared with the postoperative histopathological report. The sensitivity and specificity of each model was also identified. Results: Majority of the study participants 49 (70%) were in premenopausal status and only 21 (30%) were in menopausal status. The mean CA-125 level was 108.82±233.13 in the study population (95% CI: 53.23-164.41). Among the 70 study subjects, 53 (75.70%) patients were RMI-1 benign and only 17 (24.30%) were RMI-1 malignant. Majority of the study participants 44 (60%) were IOTA impression benign and only 23 (40%) were IOTA malignant. The difference in the proportion of IOTA-simple rules between histopathology impression was statistically significant (p-value <0.001). The sensitivity of IOTA-simple rules in predicting malignant histopathology was 92%, specificity was 90.48%, diagnostic accuracy was 91.04%. Conclusion: For early risk stratification of adnexal masses, IOTA-simple rules can be used as a screening tool due to its high sensitivity, specificity, positive predictive value, negative predictive value and diagnostic accuracy.https://www.jcdr.net/articles/PDF/16196/53286_CE[Ra1]_F[SH]_PF1(SC_SS)_PFA_PB(SC_KM)_PN(KM).pdfca-125hysterectomyindexmalignant tumoursmenopausal status |
| spellingShingle | SR Ramya Identification of Effective Model for Prediction of Ovarian Malignancy Risk using Models like Risk of Malignancy Index, Logistic Regression, International Ovarian Tumour Analysis- Simple Rules Journal of Clinical and Diagnostic Research ca-125 hysterectomy index malignant tumours menopausal status |
| title | Identification of Effective Model for Prediction of Ovarian Malignancy Risk using Models like Risk of Malignancy Index, Logistic Regression, International Ovarian Tumour Analysis- Simple Rules |
| title_full | Identification of Effective Model for Prediction of Ovarian Malignancy Risk using Models like Risk of Malignancy Index, Logistic Regression, International Ovarian Tumour Analysis- Simple Rules |
| title_fullStr | Identification of Effective Model for Prediction of Ovarian Malignancy Risk using Models like Risk of Malignancy Index, Logistic Regression, International Ovarian Tumour Analysis- Simple Rules |
| title_full_unstemmed | Identification of Effective Model for Prediction of Ovarian Malignancy Risk using Models like Risk of Malignancy Index, Logistic Regression, International Ovarian Tumour Analysis- Simple Rules |
| title_short | Identification of Effective Model for Prediction of Ovarian Malignancy Risk using Models like Risk of Malignancy Index, Logistic Regression, International Ovarian Tumour Analysis- Simple Rules |
| title_sort | identification of effective model for prediction of ovarian malignancy risk using models like risk of malignancy index logistic regression international ovarian tumour analysis simple rules |
| topic | ca-125 hysterectomy index malignant tumours menopausal status |
| url | https://www.jcdr.net/articles/PDF/16196/53286_CE[Ra1]_F[SH]_PF1(SC_SS)_PFA_PB(SC_KM)_PN(KM).pdf |
| work_keys_str_mv | AT srramya identificationofeffectivemodelforpredictionofovarianmalignancyriskusingmodelslikeriskofmalignancyindexlogisticregressioninternationalovariantumouranalysissimplerules |