<scp>IOTA</scp> simple rules for discriminating between benign and malignant adnexal masses: prospective external validation

Alcázar, Juan Luis; Pascual, M.; Olartecoechea, Begoña; Graupera, B.; Aubá, M.; Ajossa, Silvia; Hereter, L.; Julve, R; Gastón, Begoña; Peddes, C.; Sedda, Franciscu; Piras, A.; Saba, Luca; Guerriero, S.

doi:10.1002/uog.12485

Cited by 67 publications

(61 citation statements)

References 20 publications

(26 reference statements)

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“…One interesting observation is that in the present study the results for center B were quite similar than those we found in our previous study using IOTA simple rules [10]. In the previous study ultrasound was performed by non-expert examiners under the supervision of an expert examiner.…”

Section: Commentssupporting

confidence: 79%

“…c. Masses removed surgically in another center from recruitment. All women underwent transvaginal color Doppler ultrasound using the same predefined scanning protocol [10]. In both centers, a Voluson E8 machine (GE Healthcare, Milwaukee, Il, USA) equipped with 5-9 MHz transvaginal transducers was used.…”

Section: Methodsmentioning

confidence: 99%

“…To date several studies [7][8][9][10][11] have been reported demonstrating that these Simple Rules can be applied in about 76-89% of adnexal masses and that their diagnostic performance is good in both experienced and non-experienced hands.…”

Section: Commentsmentioning

confidence: 99%

“…Several papers have been reported so far validating these SR, used by expert and non-expert examiners [8][9][10][11]. Nunes et al have recently reported a meta-analysis about the use of IOTA simple rules, which conclude that the simple rules can be used in routine clinical practice being an accurate test [12].…”

Section: Introductionmentioning

confidence: 99%

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Diagnostic performance of IOTA simple rules for adnexal masses classification: a comparison between two centers with different ovarian cancer prevalence

Gauna

Rodríguez

Olartecoechea

et al. 2015

European Journal of Obstetrics & Gynecology and Reproductiv

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Section: Commentssupporting

confidence: 79%

Section: Methodsmentioning

confidence: 99%

Section: Commentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Diagnostic performance of IOTA simple rules for adnexal masses classification: a comparison between two centers with different ovarian cancer prevalence

Gauna

Rodríguez

Olartecoechea

et al. 2015

European Journal of Obstetrics & Gynecology and Reproductiv

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“…Hormone replacement therapy and smoking reduce CA-125 levels in menopausal women (Dehaghani et al, 2007). On the other hand, Alcázar et al (2013) reported false positive results by non-expertized ultrasonography operators to be as high as 12% even in the presence of findings strongly suggestive of malignancy such as ascites, bilaterality, solid component, septa formation and metastasis or even if pattern recognition method has been used.…”

Section: Discussionmentioning

confidence: 99%

Should Cut-Off Values of the Risk of Malignancy Index be Changed for Evaluation of Adnexal Masses in Asian and Pacific Populations?

Yavuzcan¹,

Çağlar²,

Özgü³

et al. 2013

Asian Pacific Journal of Cancer Prevention

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Background: The risk of malignancy index (RMI) for the evaluation of adnexal masses is a sensitive tool in certain populations. The best cut off value for RMI 1, 2 and 3 is 200. The cut off value of RMI-4 to differentiate benign from malignant lesions is 450. Our aim was to evaluate the efficiency of four different malignancy indexes (RMI1-4) in a homogeneous population. Materials and Methods: We evaluated a total of 153 non-pregnant women with adnexal masses who did not have a history of malignancy and who were above 18 years of age. Results: A cut-off value of 250 for RMI-1 provided 95.9% inter-observer agreement, yielding 95.9% specificity, 93.5% negative predictive value, 75.0% sensitivity and 82.8% positive predictive value. A cut-off value of 250 for RMI-1 showed high performance in preoperative diagnosis of invasive malignant lesions than cut-off value of 200 in our population. A cut-off value of 350 for RMI-2 provided 94.5% inter-observed agreement, yielding 94.2% specificity, 93.4% negative predictive value, 75.0% sensitivity and 77.4% positive predictive value. RMI-2 showed the higher performance when the cut-off value was set at 350 in our population. A cut-off value of 250 provided 95.2% inter-observer agreement, yielding 95.0% specificity, 93.2% negative predictive value, 75.0% sensitivity, and 88.0% positive predictive value. RMI-3 showed the highest performance to diagnose malignant adnexal masses when the cut-off value was set at 250. In our study, RMI-4 showed similar statistical performance when the cut-off value was set at 400 [(Kappa: 0.684/p=0.000), yielding 93.8% inter-observer agreement, 93.4% specificity, 93.4% negative predictive value, 75.0% sensitivity, and 75.0% negative predictive value]. Conclusions: We showed successful utilization of RMIs in preoperative differentiation of benign from malignant masses. Many studies conducted in Asian and Pacific countries have reported different cut-off values as was the case in our study. We think that it is difficult to determine universally accepted cut-off values for RMIs for common use around the globe.

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An explainable machine learning model to solid adnexal masses diagnosis based on clinical data and qualitative ultrasound indicators

Fanizzi,

Arezzo,

Cormio

et al. 2024

Cancer Medicine

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BackgroundAccurate characterization of newly diagnosed a solid adnexal lesion is a key step in defining the most appropriate therapeutic approach. Despite guidance from the International Ovarian Tumor Analyzes Panel, the evaluation of these lesions can be challenging. Recent studies have demonstrated how machine learning techniques can be applied to clinical data to solve this diagnostic problem. However, ML models can often consider as black‐boxes due to the difficulty of understanding the decision‐making process used by the algorithm to obtain a specific result.AimsFor this purpose, we propose an Explainable Artificial Intelligence model trained on clinical characteristics and qualitative ultrasound indicators to predict solid adnexal masses diagnosis.Materials & MethodsSince the diagnostic task was a three‐class problem (benign tumor, invasive cancer, or ovarian metastasis), we proposed a waterfall classification model: a first model was trained and validated to discriminate benign versus malignant, a second model was trained to distinguish nonmetastatic versus metastatic malignant lesion which occurs when a patient is predicted to be malignant by the first model. Firstly, a stepwise feature selection procedure was implemented. The classification performances were validated on Leave One Out scheme.ResultsThe accuracy of the three‐class model reaches an overall accuracy of 86.36%, and the precision per‐class of the benign, nonmetastatic malignant, and metastatic malignant classes were 86.96%, 87.27%, and 77.78%, respectively. Discussion: SHapley Additive exPlanations were performed to visually show how the machine learning model made a specific decision. For each patient, the SHAP values expressed how each characteristic contributed to the classification result. Such information represents an added value for the clinical usability of a diagnostic system.ConclusionsThis is the first work that attempts to design an explainable machine‐learning tool for the histological diagnosis of solid masses of the ovary.

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IOTA simple rules for discriminating between benign and malignant adnexal masses: prospective external validation

Abstract: Objective (95% CI, specificity 97.5% (95% CI, LR+ 34.7 (95% CI,

Cited by 67 publications

References 20 publications

Diagnostic performance of IOTA simple rules for adnexal masses classification: a comparison between two centers with different ovarian cancer prevalence

Diagnostic performance of IOTA simple rules for adnexal masses classification: a comparison between two centers with different ovarian cancer prevalence

Should Cut-Off Values of the Risk of Malignancy Index be Changed for Evaluation of Adnexal Masses in Asian and Pacific Populations?

An explainable machine learning model to solid adnexal masses diagnosis based on clinical data and qualitative ultrasound indicators

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