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ADC‐derived spatial features can accurately classify adnexal lesions
Author(s) -
Fathi Kazerooni Anahita,
Nabil Mahnaz,
Haghighat Khah Hamidreza,
Alviri Mohammadreza,
HeidariSooreshjaani Maryam,
Gity Masoumeh,
Malek Mahrooz,
Saligheh Rad Hamidreza
Publication year - 2018
Publication title -
journal of magnetic resonance imaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.563
H-Index - 160
eISSN - 1522-2586
pISSN - 1053-1807
DOI - 10.1002/jmri.25854
Subject(s) - benignity , adnexal diseases , radiology , malignancy , medicine , magnetic resonance imaging , feature (linguistics) , effective diffusion coefficient , pathology , linguistics , philosophy , laparoscopy
Background The role of quantitative apparent diffusion coefficient (ADC) maps in differentiating adnexal masses is unresolved. Purpose/Hypothesis To propose an objective diagnostic method devised based on spatial features for predicting benignity/malignancy of adnexal masses in ADC maps. Study Type Prospective. Population In all, 70 women with sonographically indeterminate and histopathologically confirmed adnexal masses (38 benign, 3 borderline, and 29 malignant) were considered for this study. Field Strength/Sequence Conventional and diffusion‐weighted magnetic resonance (MR) images (b‐values = 50, 400, 1000 s/mm 2 ) were acquired on a 3T scanner. Assessment For each patient, two radiologists in consensus manually delineated lesion borders in whole ADC map volumes, which were consequently analyzed using spatial models (first‐order histogram [FOH], gray‐level co‐occurrence matrix [GLCM], run‐length matrix [RLM], and Gabor filters). Two independent radiologists were asked to identify the attributed (benign/malignant) classes of adnexal masses based on morphological features on conventional MRI. Statistical Tests Leave‐one‐out cross‐validated feature selection followed by cross‐validated classification were applied to the feature space to choose the spatial models that best discriminate benign from malignant adnexal lesions. Two schemes of feature selection/classification were evaluated: 1) including all benign and malignant masses, and 2) scheme 1 after excluding endometrioma, hemorrhagic cysts, and teratoma (14 benign, 29 malignant masses). The constructed feature subspaces for benign/malignant lesion differentiation were tested for classification of benign/borderline/malignant and also borderline/malignant adnexal lesions. Results The selected feature subspace consisting of RLM features differentiated benign from malignant adnexal masses with a classification accuracy of ∼92%. The same model discriminated benign, borderline, and malignant lesions with 87% and borderline from malignant with 100% accuracy. Qualitative assessment of the radiologists based on conventional MRI features reached an accuracy of 80%. Data Conclusion The spatial quantification methodology proposed in this study, which works based on cellular distributions within ADC maps of adnexal masses, may provide a helpful computer‐aided strategy for objective characterization of adnexal masses. Level of Evidence: 1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:1061–1071.