Deep learning in ovarian cancer diagnosis: a comprehensive review of various imaging modalities

Abstract

Ovarian cancer poses a major worldwide health issue, marked by high death rates and a deficiency in reliable diagnostic methods. The precise and prompt detection of ovarian cancer holds great importance in advancing patient outcomes and determining suitable treatment plans. Medical imaging techniques are vital in diagnosing ovarian cancer, but achieving accurate diagnoses remains challenging. Deep learning (DL), particularly convolutional neural networks (CNNs), has emerged as a promising solution to improve the accuracy of ovarian cancer detection. <br /> This systematic review explores the role of DL in improving the diagnostic accuracy for ovarian cancer. The methodology involved the establishment of research questions, inclusion and exclusion criteria, and a comprehensive search strategy across relevant databases. The selected studies focused on DL techniques applied to ovarian cancer diagnosis using medical imaging modalities, as well as tumour differentiation and radiomics. Data extraction, analysis, and synthesis were performed to summarize the characteristics and findings of the selected studies.<br /> The review emphasizes the potential of DL in enhancing the diagnosis of ovarian cancer by accelerating the diagnostic process and offering more precise and efficient solutions. DL models have demonstrated their effectiveness in categorizing ovarian tissues and achieving comparable diagnostic performance to that of experienced radiologists. The integration of DL into ovarian cancer diagnosis holds the promise of improving patient outcomes, refining treatment approaches, and supporting well-informed decision-making. Nevertheless, additional research and validation are necessary to ensure the dependability and applicability of DL models in everyday clinical settings.

Diagnosis of Medical Images Using Convolutional Neural Networks

“…Similarly, it demonstrated the viability of employing CNNs to detect pulmonary nodules in chest radiographs, with good sensitivity and specificity in identifying malignant nodules. MH Sadeghi et al [19]. researchers have investigated the use of CNNs in conjunction with other imaging modalities, such as computed tomography (CT) and positron emission tomography (PET), to increase the accuracy of lung nodule identification and classification.…”

Developing a deep learning model for predicting ovarian cancer in Ovarian-Adnexal Reporting and Data System Ultrasound (O-RADS US) Category 4 lesions: A multicenter study

“…Deep learning (DL) can quantitatively analyze of medical images and has been applied in the field of oncology (Taddese et al 2024 ). Several studies demonstrated that DL can improve the diagnosis, predict treatment responses, and progression-free survival of patients with ovarian tumors (Arezzo et al 2022 ; Boehm et al 2022 ; Na et al 2024 ; Sadeghi et al 2024 ; Yao et al 2021 ). Compared with traditional imaging diagnosis by radiologists, the DL method can improve the accuracy and reduce the bias of diagnosis results (Chen et al 2022 ).…”