Dataset Information

Computer-aided diagnosis for screening of lower extremity lymphedema in pelvic computed tomography images using deep learning.

ABSTRACT: Lower extremity lymphedema (LEL) is a common complication after gynecological cancer treatment, which significantly reduces the quality of life. While early diagnosis and intervention can prevent severe complications, there is currently no consensus on the optimal screening strategy for postoperative LEL. In this study, we developed a computer-aided diagnosis (CAD) software for LEL screening in pelvic computed tomography (CT) images using deep learning. A total of 431 pelvic CT scans from 154 gynecological cancer patients were used for this study. We employed ResNet-18, ResNet-34, and ResNet-50 models as the convolutional neural network (CNN) architecture. The input image for the CNN model used a single CT image at the greater trochanter level. Fat-enhanced images were created and used as input to improve classification performance. Receiver operating characteristic analysis was used to evaluate our method. The ResNet-34 model with fat-enhanced images achieved the highest area under the curve of 0.967 and an accuracy of 92.9%. Our CAD software enables LEL diagnosis from a single CT image, demonstrating the feasibility of LEL screening only on CT images after gynecologic cancer treatment. To increase the usefulness of our CAD software, we plan to validate it using external datasets.

SUBMITTER: Nomura Y

PROVIDER: S-EPMC10533488 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature

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Computer-aided diagnosis for screening of lower extremity lymphedema in pelvic computed tomography images using deep learning.

Nomura Yukihiro Y Hoshiyama Masato M Akita Shinsuke S Naganishi Hiroki H Zenbutsu Satoki S Matsuoka Ayumu A Ohnishi Takashi T Haneishi Hideaki H Mitsukawa Nobuyuki N

Scientific reports 20230927 1

Lower extremity lymphedema (LEL) is a common complication after gynecological cancer treatment, which significantly reduces the quality of life. While early diagnosis and intervention can prevent severe complications, there is currently no consensus on the optimal screening strategy for postoperative LEL. In this study, we developed a computer-aided diagnosis (CAD) software for LEL screening in pelvic computed tomography (CT) images using deep learning. A total of 431 pelvic CT scans from 154 gy ...[more]

PMID: 37758908

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Project description:BackgroundColposcopy is widely used to detect cervical cancer, but developing countries lack the experienced colposcopists necessary for accurate diagnosis. Artificial intelligence (AI) is being widely used in computer-aided diagnosis (CAD) systems. In this study, we developed and validated a CAD model based on deep learning to classify cervical lesions on colposcopy images.MethodsPatient data, including clinical information, colposcopy images, and pathological results, were collected from Qilu Hospital. The study included 15,276 images from 7,530 patients. We performed two tasks in this study: normal cervix (NC) vs. low grade squamous intraepithelial lesion or worse (LSIL+) and high-grade squamous intraepithelial lesion (HSIL)- vs. HSIL+. The residual neural network (ResNet) probability was calculated for each patient to reflect the probability of lesions through a ResNet model. Next, a combination model was constructed by incorporating the ResNet probability and clinical features. We divided the dataset into a training set, validation set, and testing set at a ratio of 7:1:2. Finally, we randomly selected 300 patients from the testing set and compared the results with the diagnosis of a senior colposcopist and a junior colposcopist.ResultsThe model that combines ResNet and clinical features performs better than ResNet alone. In the classification of NC and LSIL+, the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were 0.953, 0.886, 0.932, 0.846, 0.838, and 0.936, respectively. In the classification of HSIL- and HSIL+, the AUC, accuracy, sensitivity, specificity, PPV, and NPV were 0.900, 0.807, 0.823, 0.800, 0.618, and 0.920, respectively. In the two classification tasks, the diagnostic performance of the model was determined to be comparable to that of the senior colposcopist and exhibited a stronger diagnostic performance than the junior colposcopist.ConclusionsThe CAD system for cervical lesion diagnosis based on deep learning performs well in the classification of cervical lesions and can provide an objective diagnostic basis for colposcopists.

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Dataset Information

Computer-aided diagnosis for screening of lower extremity lymphedema in pelvic computed tomography images using deep learning.

Publications

Computer-aided diagnosis for screening of lower extremity lymphedema in pelvic computed tomography images using deep learning.

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