Evaluating the risk of ovarian cancer before surgery using the ADNEX model: a multicentre external validation study

Sayasneh, Ahmad; Ferrara, Laura; Cock, B. De; Saso, Srdjan; Al‐Memar, M.; Johnson, Susanne; Kaijser, Jeroen; Carvalho, João; Husicka, Richard; Smith, Alison; Stalder, C.; Blanco, Montse; Ettore, Giuseppe; Calster, Ben Van; Timmerman, Dirk; Bourne, Tom

doi:10.1038/bjc.2016.227

Cited by 79 publications

(94 citation statements)

References 26 publications

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“…In general, the results confirm good polytomous discrimination from the first study by Van Calster et al, and were comparable to later ADNEX model evaluation studies (Table 6) (Van Calster et al, 2014, Araujo et al, 2016, Meys et al, 2016, Sayasneh et al, 2016). Both polytomous discrimination indexes (PDI) were > 0.2, which in this case is a threshold for a random performance.…”

Section: Resultssupporting

confidence: 87%

Response to letter to the editor concerning validation of IOTA ADNEX model

Szubert

Wójtowicz

Żywica

2016

Gynecologic Oncology Reports

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Section: Resultssupporting

confidence: 87%

Response to letter to the editor concerning validation of IOTA ADNEX model

Szubert

Wójtowicz

Żywica

2016

Gynecologic Oncology Reports

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“…As a multiclass prediction model, ADNEX not only calculates the likelihood of malignancy in adnexal masses, but also divides this into the likelihood that the mass is borderline malignant, stage I primary invasive ovarian cancer, stage II–IV primary invasive ovarian cancer, or a metastasis in the ovary from another primary tumor [25]. The performance of ADNEX is at least as good as the performance of previous IOTA methods, as confirmed by external validation studies [26,27,28,29,30]. The ADNEX model is available online and in mobile applications ().…”

Section: Introductionmentioning

confidence: 96%

Validation of the Performance of International Ovarian Tumor Analysis (IOTA) Methods in the Diagnosis of Early Stage Ovarian Cancer in a Non-Screening Population

et al. 2017

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Background: The aim of this study was to assess and compare the performance of different ultrasound-based International Ovarian Tumor Analysis (IOTA) strategies and subjective assessment for the diagnosis of early stage ovarian malignancy. Methods: This is a secondary analysis of a prospective multicenter cross-sectional diagnostic accuracy study that included 1653 patients recruited at 18 centers from 2009 to 2012. All patients underwent standardized transvaginal ultrasonography by experienced ultrasound investigators. We assessed test performance of the IOTA Simple Rules (SRs), Simple Rules Risk (SRR), the Assessment of Different NEoplasias in the adneXa (ADNEX) model and subjective assessment to discriminate between stage I-II ovarian cancer and benign disease. Reference standard was histology after surgery. Results: 230 (13.9%) patients proved to have stage I–II primary invasive ovarian malignancy, and 1423 (86.1%) had benign disease. Sensitivity and specificity with respect to malignancy (95% confidence intervals) of the original SRs (classifying all inconclusive cases as malignant) were 94.3% (90.6% to 96.7%) and 73.4% (71.0% to 75.6%). Subjective assessment had a sensitivity and specificity of 90.0% (85.4% to 93.2%) and 86.7% (84.9% to 88.4%), respectively. The areas under the receiver operator characteristic curves of SRR and ADNEX were 0.917 (0.902 to 0.933) and 0.905 (0.920 to 0.934), respectively. At a 1% risk cut-off, sensitivity and specificity for SRR were 100% (98.4% to 100%) and 38.0% (35.5% to 40.6%), and for ADNEX were 100% (98.4% to 100%) and 19.4% (17.4% to 21.5%). At a 30% risk cut-off, sensitivity and specificity for SRR were 88.3% (83.5% to 91.8%) and 81.1% (79% to 83%), and for ADNEX were 84.5% (80.5% to 89.6%) and 84.5% (82.6% to 86.3%). Conclusion: This study shows that all three IOTA strategies have good ability to discriminate between stage I-II ovarian malignancy and benign disease.

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“…The six ultrasound-image parameters used in the ADNEX model are: lesion diameter in mm, solid tissue proportion, number of papillary projections, number of cysts (10 or more, yes or no), the presence or absence of acoustic shadows, and the presence or absence of ascites. The three clinical parameters used are type of medical centre (oncology vs other hospital), age of patient in years, and serum CA125 level in µml -1 in the blood [3]. Validation of the model was achieved using parameters collected by experienced ultrasound clinicians with a special interest in gynaecological ultrasonography, who were equivalent in experience and knowledge to UK radiology consultants (Education and Practical Standards Committee, European Federation of Societies for Ultrasound in Medicine and Biology (EFSUMB).…”

Section: -Ovarian Tumour Diagnosis Based On Adnex Modelmentioning

confidence: 99%

“…This less aggressive treatment often allows younger women to retain their fertility. Identification of metastatic ovarian cancers should, nevertheless, be treated in the same manner as the primary cancer [3] [4].…”

Section: -Ovarian Tumour Diagnosis Based On Adnex Modelmentioning

confidence: 99%

“…The second beneficial aspect is the combined use of Doppler images to gain blood flow information. The results of these different aspects enable clinicians to determine the seriousness of the imaged mass [2] [3]. However, to gain a more accurate classification of tumour type, International Ovarian Tumour Analysis (IOTA) researchers created a model system that utilised sonography.…”

Section: -Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Multi-level Trainable Segmentation for Measuring Gestational and Yolk Sacs from Ultrasound Images

Ibrahim

Al-Assam

Jassim

et al. 2017

Communications in Computer and Information Science

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Ovarian masses are categorised into different types of malignant and benign. In order to optimize patient treatment, it is necessary to carry out pre-operational characterisation of the suspect ovarian mass to determine its category. Ultrasound imaging has been widely used in differentiating malignant from benign cases due to its safe and non-intrusive nature, and can be used for determining the number of cysts in the ovary. Presently, the gynaecologist is tasked with manually counting the number of cysts shown on the ultrasound image. This paper proposes, a new approach that automatically segments the ovarian masses and cysts from a static B-mode image. Initially, the method uses a trainable segmentation procedure and a trained neural network classifier to accurately identify the position of the masses and cysts. After that, the borders of the masses can be appraised using watershed transform. The effectiveness of the proposed method has been tested by comparing the number of cysts identified by the method against the manual examination by a gynaecologist. A total of 65 ultrasound images were used for the comparison, and the results showed that the proposed solution is a viable alternative to the manual counting method for accurately determining the number of cysts in a US ovarian image.

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Evaluating the risk of ovarian cancer before surgery using the ADNEX model: a multicentre external validation study

Cited by 79 publications

References 26 publications

Response to letter to the editor concerning validation of IOTA ADNEX model

Response to letter to the editor concerning validation of IOTA ADNEX model

Validation of the Performance of International Ovarian Tumor Analysis (IOTA) Methods in the Diagnosis of Early Stage Ovarian Cancer in a Non-Screening Population

Multi-level Trainable Segmentation for Measuring Gestational and Yolk Sacs from Ultrasound Images

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