Clincoradiological deep learning model reaches high prediction accuracy in the diagnosis of ovarian cancer (Preprint)
Authors: Wang Zimo , Luo Shuyu , Chen Jing , Jiao Yang , Cui Chen , Shi Siyuan , Yang Yang , Zhao Junyi , Jiang Yitao , Zhang Yujuan , Xu Fanhua , Xu Jinfeng , Lin Qi , Dong Fajin 
Published: July 2023

BACKGROUND

Ovarian cancer is the most lethal gynecological malignancy, with a low five-year survival rate of 46%[1]. Owning to the lack of effective screening, 70% of ovarian cancer patients are already at an advanced stage when the disease is detected, with only 28% five-year survival rate. Meanwhile, if ovarian cancer is found at an early stage, the survival rate can be improved to 90%[2, 3]. The treatment strategies and prognoses for benign and malignant ovarian tumors vary greatly.
Ultrasound (US) is a safe, cost-effective method for identifying ovarian tumors. It is the preferred choice for identifying adnexal masses[9]. But its accuracy can be affected by the complexity of ovarian tissue and doctors' subjectivity, leading to misdiagnosis[10, 11]. According to the existing research, effectively diagnosing ovarian cancer is still an issue[2]. Artificial Intelligence (AI) has shown great potential in the medical field, assisting in diagnosing various cancers, including ovarian cancer[12]. Deeping learning (DL) is a branch of AI that can automatically learn mid and high-level abstract features from raw data such as the US, mammograms, and MRI, thereby replacing the traditional time-consuming manual extraction method[13].
Although prior work has made significant progress, it has been limited to single-modality US images. In clinical practice, the diagnosis of ovarian cancer should include imaging methods, serum tumor markers, and patient age. For example, the FDA recommends using CA125 to assess treatment response and to monitor residual disease or recurrent hazards after first-line treatment. The link between CA125 clinical stage and survival rate is also reported by serval studies[18-20]. HE4 is a promising ovarian cancer biomarker with a higher specificity for diagnosing ovarian cancer than CA125[21-23]. Thus, these markers can be combined to screen for ovarian cancer[3].
Therefore, to improve the US diagnostic accuracy of ovarian masses. Our work aimed to build a Clincoradiological model to improve AI diagnostics of benign and malignant ovarian tumors.

OBJECTIVE

To evaluate the diagnostic performance of a Clincoradiological deep learning (DL) model for differential diagnosis of malignant and benign ovarian masses.

METHODS

This retrospective study included 1054 patients with ultrasound-detected ovarian tumors in Shenzhen People’s Hospital from January 2015 to March 2022. Among them, 699 patients were benign, and 355 patients were malignant. All patients were randomly divided into training (n=675), validation (n=169), and testing (n=210) sets. The model was developed using ResNet-50. Three deep learning-based models were proposed to perform benign-malignant classification tasks on these lesions, including a single-modality model which only utilized US images, a dual-modality model that used both US images and menopausal status as inputs, and a Clincoradiological model that integrated US images, menopausal status and serum indicators (Carbohydrate antigen 125（CA125）and serum Human Epididymis Protein 4 (HE4)). After 5-fold cross-validation, the test was performed on 210 lesions. The area under the curve (AUC), accuracy, sensitivity, and specificity were used as primary evaluation metrics to evaluate the performance of three models.

RESULTS

In the test set, the diagnostic accuracy and AUC of the single-modality model were 90.95% and 0.957. After combing menopausal status, the accuracy and AUC of the dual-modality model reached 92.38% and 0.968. The diagnostic performance of the Clincoradiological model was significantly improved, with 93.80% accuracy and 0.983 AUC, achieving the best performance.

CONCLUSIONS

Clincoradiological DL model has excellent performance in distinguishing benign and malignant ovarian tumors, it outperforms the single- and dual-modality models.