Original Investigation| Volume 30, ISSUE 5, P814-822, May 2023

Download started.


Discriminating Between Benign and Malignant Solid Ovarian Tumors Based on Clinical and Radiomic Features of MRI

      Rationale and Objectives

      To develop and validate a combined model integrating clinical and radiomic features to non-invasive discriminate between the benign and malignant solid ovarian tumors.

      Materials and Methods

      A total of 148 patients with 156 solid ovarian tumors (86 benign and 70 malignant tumors) were included in this study. The dataset was split into the training and the test set with a ratio of 8:2 using stratified random sampling. 12 clinical features and 1612 radiomic features were extracted from each tumor. These features were selected by least absolute shrinkage and selection operator (Lasso). Three classification models were built using extreme gradient boosting (XGB) algorithm: clinical model, radiomic model, combined model. The area under the receiver operating characteristic curve (AUC), accuracy, precision and sensitivity were analyzed to evaluate the performance of these models.


      All of the three models obtained good performances in differentiating benign with malignant solid ovarian tumors in both training and test sets. The AUC, accuracy, precision, sensitivity of clinical model and radiomic model in test set were 0.847 (95% confidence interval (CI), 0.707-0.986, p <0.01), 0.774, 0.769, 0.714, and 0.807 (95%CI, 0.652-0.961, p <0.05), 0.677, 0.643, 0.643, respectively. Combined model had the best prediction results, the AUC, accuracy, precision and sensitivity were 0.954 (95%CI, 0.862-1.0, p <0.01), 0.839, 0.909 and 0.714 in test set.


      Radiomics based on machine learning can be helpful for radiologists in differentiating the benign and malignant solid ovarian tumors.

      Key Words

      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'


      Subscribe to Academic Radiology
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect


        • Imaoka I
        • Wada A
        • Kaji Y
        • et al.
        Developing an MR imaging strategy for diagnosis of ovarian masses.
        Radiographics. 2006; 26: 1431-1448
        • Cho YJ
        • Lee HS
        • Kim JM
        • et al.
        Clinical characteristics and surgical management options for ovarian fibroma/fibrothecoma: a study of 97 cases.
        Gynecol Obstet Invest. 2013; 76: 182-187
        • Son CE
        • Choi JS
        • Lee JH
        • et al.
        Laparoscopic surgical management and clinical characteristics of ovarian fibromas.
        Jsls. 2011; 15: 16-20
        • Chung BM
        • Park SB
        • Lee JB
        • et al.
        Magnetic resonance imaging features of ovarian fibroma, fibrothecoma, and thecoma.
        Abdom Imaging. 2015; 40: 1263-1272
        • Kalampokas E
        • Kalampokas T
        • Tourountous I.
        Primary fallopian tube carcinoma.
        Eur J Obstet Gynecol Reprod Biol. 2013; 169: 155-161
        • Stasenko M
        • Fillipova O
        • Tew WP
        Fallopian tube carcinoma.
        J Oncol Pract. 2019; 15: 375-382
        • Basha MAA
        • Abdelrahman HM
        • Metwally MI
        • et al.
        Validity and reproducibility of the ADNEX MR scoring system in the diagnosis of sonographically indeterminate adnexal masses.
        J Magn Reson Imaging. 2021; 53: 292-304
        • Thomassin-Naggara I
        • Poncelet E
        • Jalaguier-Coudray A
        • et al.
        Ovarian-Adnexal Reporting Data System Magnetic Resonance Imaging (O-RADS MRI) score for risk stratification of sonographically indeterminate adnexal masses.
        JAMA Netw Open. 2020; 3e1919896
        • Foti PV
        • Attinà G
        • Spadola S
        • et al.
        MR imaging of ovarian masses: classification and differential diagnosis.
        Insights Imaging. 2016; 7: 21-41
        • Hosny A
        • Parmar C
        • Quackenbush J
        • et al.
        Artificial intelligence in radiology.
        Nat Rev Cancer. 2018; 18: 500-510
        • Miller DD
        • Brown EW
        Artificial intelligence in medical practice: the question to the answer?.
        Am J Med. 2018; 131: 129-133
        • Topol EJ
        High-performance medicine: the convergence of human and artificial intelligence.
        Nat Med. 2019; 25: 44-56
        • Peek N
        • Combi C
        • Marin R
        • et al.
        Thirty years of artificial intelligence in medicine (AIME) conferences: a review of research themes.
        Artif Intell Med. 2015; 65: 61-73
        • Cuocolo R
        • Caruso M
        • Perillo T
        • et al.
        Machine learning in oncology: a clinical appraisal.
        Cancer Lett. 2020; 481: 55-62
        • Deo RC
        Machine learning in medicine.
        Circulation. 2015; 132: 1920-1930
        • Erickson BJ
        • Korfiatis P
        • Akkus Z
        • et al.
        Machine learning for medical imaging.
        Radiographics. 2017; 37: 505-515
        • Mayerhoefer ME
        • Materka A
        • Langs G
        • et al.
        Introduction to radiomics.
        J Nucl Med. 2020; 61: 488-495
        • Gillies R
        • Kinahan P
        • Hricak HJR
        Radiomics: images are more than pictures, they are data.
        Radiology. 2016; 278: 563-577
        • Zhang G
        • Xu L
        • Zhao L
        • et al.
        CT-based radiomics to predict the pathological grade of bladder cancer.
        Eur Radiol. 2020; 30: 6749-6756
        • Liu Z
        • Zhang XY
        • Shi YJ
        • et al.
        Radiomics analysis for evaluation of pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer.
        Clin Cancer Res. 2017; 23: 7253-7262
        • Wei J
        • Cheng J
        • Gu D
        • et al.
        Deep learning-based radiomics predicts response to chemotherapy in colorectal liver metastases.
        Med Phys. 2021; 48: 513-522
        • Song XL
        • Ren JL
        • Zhao D
        • et al.
        Radiomics derived from dynamic contrast-enhanced MRI pharmacokinetic protocol features: the value of precision diagnosis ovarian neoplasms.
        Eur Radiol. 2021; 31: 368-378
        • Yu XP
        • Wang L
        • Yu HY
        • et al.
        MDCT-based radiomics features for the differentiation of serous borderline ovarian tumors and serous malignant ovarian tumors.
        Cancer Manag Res. 2021; 13: 329-336
        • Zhang H
        • Mao Y
        • Chen X
        • et al.
        Magnetic resonance imaging radiomics in categorizing ovarian masses and predicting clinical outcome: a preliminary study.
        Eur Radiol. 2019; 29: 3358-3371
        • Reinhold C
        • Rockall A
        • Sadowski EA
        • et al.
        Ovarian-adnexal reporting lexicon for mri: a white paper of the acr ovarian-adnexal reporting and data systems mri committee.
        J Am Coll Radiol. 2021; 18: 713-729
        • Ahmed SA
        • Abou-Taleb H
        • Yehia A
        • et al.
        The accuracy of multi-detector computed tomography and laparoscopy in the prediction of peritoneal carcinomatosis index score in primary ovarian cancer.
        Acad Radiol. 2019; 26: 1650-1658
      1. Cui L, Xu H, Zhang YJAR Diagnostic accuracies of the ultrasound and magnetic resonance imaging ADNEX scoring systems for ovarian adnexal mass: systematic review and meta-analysis. Acad Radiol 2022; 29:897–908

        • Mukuda N
        • Fujii S
        • Inoue C
        • et al.
        Apparent diffusion coefficient (ADC) measurement in ovarian tumor: effect of region-of-interest methods on ADC values and diagnostic ability.
        J Magn Reson Imaging. 2016; 43: 720-725
        • Zhang P
        • Cui Y
        • Li W
        • et al.
        Diagnostic accuracy of diffusion-weighted imaging with conventional MR imaging for differentiating complex solid and cystic ovarian tumors at 1.5T.
        World J Surg Oncol. 2012; 10: 237
        • Lu DS
        • Siripongsakun S
        • Kyong Lee J
        • et al.
        Complete tumor encapsulation on magnetic resonance imaging: a potentially useful imaging biomarker for better survival in solitary large hepatocellular carcinoma.
        Liver Transpl. 2013; 19: 283-291
        • Taşkın O
        • Armutlu A
        • Ağcaoğlu O
        • et al.
        Tumor border pattern and size help predict lymph node status in papillary microcarcinoma: a clinicopathologic study.
        Ann Diagn Pathol. 2020; 48151592
        • Türkoğlu S
        • Kayan M
        Differentiation between benign and malignant ovarian masses using multiparametric MRI.
        Diagn Interv Imaging. 2020; 101: 147-155
        • Surov A
        • Meyer HJ
        • Wienke A
        Correlation between apparent diffusion coefficient (ADC) and cellularity is different in several tumors: a meta-analysis.
        Oncotarget. 2017; 8: 59492-59499
        • Alcázar JL
        • Errasti T
        • Zornoza A
        • et al.
        Transvaginal color doppler ultrasonography and CA-125 in suspicious adnexal masses.
        Int J Gynaecol Obstet. 1999; 66: 255-261
        • Patel AG
        • Pizzitola VJ
        • Johnson CD
        • et al.
        Radiologists make more errors interpreting off-hours body ct studies during overnight assignments as compared with daytime assignments.
        Radiology. 2020; 297: 374-379
        • Qian L
        • Ren J
        • Liu A
        • et al.
        MR imaging of epithelial ovarian cancer: a combined model to predict histologic subtypes.
        Eur Radiol. 2020; 30: 5815-5825
        • Jian J
        • Li Y
        • Pickhardt PJ
        • et al.
        MR image-based radiomics to differentiate type Ι and type ΙΙ epithelial ovarian cancers.
        Eur Radiol. 2021; 31: 403-410
        • Gillingham N
        • Chandarana H
        • Kamath A
        • et al.
        Bosniak IIF and III renal cysts: can apparent diffusion coefficient-derived texture features discriminate between malignant and benign IIF and III cysts?.
        J Comput Assist Tomogr. 2019; 43: 485-492
        • Chen HZ
        • Wang XR
        • Zhao FM
        • et al.
        The development and validation of a ct-based radiomics nomogram to preoperatively predict lymph node metastasis in high-grade serous ovarian cancer.
        Front Oncol. 2021; 11711648
        • Li Q
        • Liu YJ
        • Dong D
        • et al.
        Multiparametric MRI radiomic model for preoperative predicting who/isup nuclear grade of clear cell renal cell carcinoma.
        J Magn Reson Imaging. 2020; 52: 1557-1566
        • Rizzo S
        • Botta F
        • Raimondi S
        • et al.
        Radiomics of high-grade serous ovarian cancer: association between quantitative CT features, residual tumour and disease progression within 12 months.
        Eur Radiol. 2018; 28: 4849-4859
        • Wang T
        • Gong J
        • Li Q
        • et al.
        A combined radiomics and clinical variables model for prediction of malignancy in T2 hyperintense uterine mesenchymal tumors on MRI.
        Eur Radiol. 2021; 31: 6125-6135
        • Qi L
        • Chen D
        • Li C
        • et al.
        Diagnosis of ovarian neoplasms using nomogram in combination with ultrasound image-based radiomics signature and clinical factors.
        Front Genet. 2021; 12753948