Multitask deep learning models for ultrasound image analysis: identification of high-grade serous ovarian cancer and segmentation of tumor regions and intratumoral solid components.

backgroundHigh-grade serous ovarian cancer (HGSOC) is characterized by high incidence and mortality rates, yet effective detection methods remain limited. In the present study, deep learning models capable of identifying HGSOC while concurrently providing segmentation results of tumor regions and intratumoral solid components were developed and validated.

methodsThis retrospective single-center diagnostic study included 395 patients (and their combined 1745 ultrasound images) with pathologically confirmed primary malignant ovarian tumors who underwent preoperative ultrasound examination between January 2018 and December 2022. The dataset was split into training (n = 237; 148 HGSOC, 89 Non-HGSOC), validation (n = 79; 49 HGSOC, 30 Non-HGSOC), and test (n = 79; 50 HGSOC, 29 Non-HGSOC) sets at a 6:2:2 ratio. Four models were constructed, all based on Residual Network-18 (ResNet-18) for feature extraction from grayscale and color Doppler ultrasound (US) images. Model A was built solely on image features, while Models B, C, and D further integrated additional features via a multilayer perceptron (MLP) as follows: Model B incorporated cancer antigen 125 (CA125); Model C combined CA125 and human epididymis protein 4 (HE4); and Model D integrated CA125, HE4, and age. All the models employed a dual-branch decoder for classification and segmentation to generate the corresponding outputs. The discriminative ability of the models was assessed using the area under the receiver operating characteristic curve (AUC), while classification performance was evaluated with the sensitivity and specificity. The Dice surface coefficient (DSC) was determined to evaluate segmentation performance.

resultsAmong the 395 patients, 62.5% had HGSOC, while 37.5% were diagnosed with other pathological types. In the identification of HGSOC, the four models yielded AUC values of 0.72 (Model A), 0.81 (Model B), 0.80 (Model C), and 0.84 (Model D). Compared with Model A, which solely used image features, Model D resulted in the most significant increase in the AUC value (p = 0.03). The sensitivity across the models ranged from 0.66 to 0.86, with Model B yielding the highest value (0.86); the specificity values ranged from 0.63 to 0.93, with Model D yielding the highest value (0.93). The DSC values ranged from 0.70 to 0.86.

conclusionsDeep learning models can be used to distinguish HGSOC and concurrently generate real-time segmentation results for relevant regions.