Background: To develop an artificial intelligence (AI) model with radiomics and deep learning (DL) features extracted from CT images to distinguish benign from malignant ovarian tumors.
Methods: We enrolled 149 patients with pathologically confirmed ovarian tumors. A total of 185 tumors were included and divided into training and testing sets in a 7:3 ratio. All tumors were manually segmented from preoperative contrast-enhanced CT images. CT image features were extracted using radiomics and DL. Five models with different combinations of feature sets were built. Benign and malignant tumors were classified using machine learning (ML) classifiers. The model performance was compared with five radiologists on the testing set.
Results: Among the five models, the best performing model is the ensemble model with a combination of radiomics, DL, and clinical feature sets. The model achieved an accuracy of 82%, specificity of 89% and sensitivity of 68%. Compared with junior radiologists averaged results, the model had a higher accuracy (82% vs 66%) and specificity (89% vs 65%) with comparable sensitivity (68% vs 67%). With the assistance of the model, the junior radiologists achieved a higher average accuracy (81% vs 66%), specificity (80% vs 65%), and sensitivity (82% vs 67%), approaching to the performance of senior radiologists.
Conclusions: We developed a CT-based AI model that can differentiate benign and malignant ovarian tumors with high accuracy and specificity. This model significantly improved the performance of less-experienced radiologists in ovarian tumor assessment, and may potentially guide gynecologists to provide better therapeutic strategies for these patients.
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http://dx.doi.org/10.1186/s13244-023-01412-x | DOI Listing |
Abdom Radiol (NY)
December 2024
Weill Cornell Medical College, New York, USA.
Cholangiocarcinoma (CCA) is the second most common primary malignancy of the hepatobiliary system and presents as a heterogeneous disease with three distinct morphological subtypes: mass-forming, periductal-infiltrating, and intraductal-growing, each characterized by distinguishing imaging features. Accurate diagnosis of CCA is challenging due to the overlap of imaging findings with a broad range of benign and malignant conditions. Therefore, it is essential for radiologists to recognize these mimickers and offer a reasonable differential diagnosis, as this has a significant impact on patient management.
View Article and Find Full Text PDFAbdom Radiol (NY)
December 2024
Massachusetts General Hospital, Boston, USA.
Adnexal masses are frequently encountered in general practice. Whether employing CT, US, or MRI, imaging plays a pivotal role in guiding appropriate treatment for patients with adnexal masses, potentially minimizing the need for surgery in benign cases and expediting the management of those with suspected malignancy. Accurately distinguishing benign from malignant adnexal masses can be challenging due to the confined pelvic space and the proximity of organs, making it difficult to determine their organ of origin or to distinguish tissue characteristics and imaging features.
View Article and Find Full Text PDFAnn Chir Plast Esthet
December 2024
Service de chirurgie générale, pavillon militaire du CHU Sylvanus Olympio, Togo.
Introduction: In Africa, rare publications have focused on phyllodes tumors (PTs). The aim of our study is to describe the special feature of PTs surgery.
Patients And Method: Retrospective and descriptive study of 11 cases of PT operated from January 1, 2015 to March 31, 2023 at the medical-surgical clinic in Teaching Hospital Center of Sylvanus Olympio of Lome.
BMC Surg
December 2024
Department of Gynecology, Second Hospital of Hebei Medical University, Shijiazhuang, China.
Background: Transumbilical laparoendoscopic single-site surgery (TU-LESS) has gained increasing attention due to the potential to maximize the benefits of laparoscopic surgery. This study aimed to compare outcomes of TU-LESS and multiport laparoscopic surgery (MLS) for the treatment of benign ovarian cysts.
Methods: This retrospective cohort study included patients with benign ovarian cysts that were admitted to the Second Hospital of Hebei Medical University between September 2010 and September 2022.
Ultrasound Med Biol
December 2024
School of Instrumentation and Optoelectronics Engineering, Beihang University, Beijing, China. Electronic address:
Objective: Breast ultrasound (BUS) is used to classify benign and malignant breast tumors, and its automatic classification can reduce subjectivity. However, current convolutional neural networks (CNNs) face challenges in capturing global features, while vision transformer (ViT) networks have limitations in effectively extracting local features. Therefore, this study aimed to develop a deep learning method that enables the interaction and updating of intermediate features between CNN and ViT to achieve high-accuracy BUS image classification.
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