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Decoding incidental ovarian lesions: use of texture analysis and machine learning for characterization and detection of malignancy

  • Special Section: ovarian cancer
  • Published:
Abdominal Radiology Aims and scope Submit manuscript

Abstract

Purpose

To compare CT texture features of benign and malignant ovarian lesions and to build a machine learning model to detect malignancy in incidental ovarian lesions.

Methods

In this IRB-approved, HIPAA-compliant, retrospective study, 427 consecutive patients with incidental ovarian lesions detected on contrast-enhanced CT (348, 81.5% benign and 79, 18.5% malignant) were included. The following CT texture features were analyzed using commercially available software (TexRAD, Feedback Plc, Cambridge, UK): total pixel, mean, standard deviation (SD), entropy, mean value of positive pixels (MPP), skewness, kurtosis and entropy. Three machine learning models were created by combining texture features and patients’ age, and performance of these models was assessed using tenfold cross-validation. Receiver operating characteristics (ROC) were constructed to assess sensitivity and specificity. The cutoff value was picked using a cost-weighted method.

Results

Total pixels, mean, SD, entropy, MPP, and skewness were significantly different between benign and malignant groups (p < 0.05). With a selected 10 as a cost factor to optimize cutoff value selection, sensitivity 92%, specificity 60% in the random forest (RF) model, sensitivity 91%, specificity 69% in SVM model, and sensitivity 92%, specificity 61% in the logistic regression, respectively.

Conclusion

CT texture analysis could provide objective imaging analysis of incidental ovarian lesions and ML models using CT texture features and age demonstrated high sensitivity and moderate specificity for detection of malignant lesions.

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Data availability

Yes.

Code availability

Yes.

Abbreviations

ML:

Machine learning

LR:

Logistic regression

SVM:

Support vector machine

RF:

Random forest

MPP:

Mean value of positive pixels

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Authors and Affiliations

  1. Department of Radiology, Brigham and Women’s Hospital and Dana-Farber Cancer Institute, 450 Brookline Ave., Boston, MA, 02215, USA

    Hyesun Park, Lei Qin, Pamela Guerra, Camden P. Bay & Atul B. Shinagare

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  1. Hyesun Park
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  2. Lei Qin
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Correspondence to Hyesun Park.

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Park, H., Qin, L., Guerra, P. et al. Decoding incidental ovarian lesions: use of texture analysis and machine learning for characterization and detection of malignancy. Abdom Radiol 46, 2376–2383 (2021). https://doi.org/10.1007/s00261-020-02668-3

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