Deep learning-based artificial intelligence for assisting diagnosis, assessment and treatment in soft tissue sarcomas
Soft tissue sarcomas (STSs) represent a group of heterogeneous mesenchymal tumors of which are generally classified as per the histopathology. Despite being rare in incidence and prevalence, STSs are usually correlated with unfavorable prognosis and high mortality rate. Early and accurate diagnosis...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2024-06-01
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| Series: | Meta-Radiology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2950162824000225 |
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| author | Ruiling Xu Jinxin Tang Chenbei Li Hua Wang Lan Li Yu He Chao Tu Zhihong Li |
| author_facet | Ruiling Xu Jinxin Tang Chenbei Li Hua Wang Lan Li Yu He Chao Tu Zhihong Li |
| author_sort | Ruiling Xu |
| collection | DOAJ |
| description | Soft tissue sarcomas (STSs) represent a group of heterogeneous mesenchymal tumors of which are generally classified as per the histopathology. Despite being rare in incidence and prevalence, STSs are usually correlated with unfavorable prognosis and high mortality rate. Early and accurate diagnosis of STSs are critical in clinical management of STSs. Deep learning (DL) refers to a subtype of artificial intelligence that has been adopted to assist healthcare professionals to optimize personalized treatment for a given situation, particularly in image analysis. Recently, emerging studies have demonstrated that application of DL based on medical images could substantially improve the accuracy and efficiency of clinicians to the identification, diagnosis, treatment, and prognosis prediction of STSs, and thereby facilitating the clinical decision-making. Herein, we aimed to extensively summarize the recent applications of DL-based artificial intelligence in STSs from the aspects of data acquisition, algorithm, and model establishment. Besides, the reinforcement of the model by transfer learning and generative adversarial network (GAN) for data augmentation has also been elaborated. It is worth noting that high-quality data with accurate annotations, as well as optimized algorithmic performance are pivotal in the clinical application of DL in STSs. |
| format | Article |
| id | doaj-art-fed5e09e198846eb8f032458a31b5bbc |
| institution | Kabale University |
| issn | 2950-1628 |
| language | English |
| publishDate | 2024-06-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Meta-Radiology |
| spelling | doaj-art-fed5e09e198846eb8f032458a31b5bbc2024-11-12T05:22:42ZengKeAi Communications Co., Ltd.Meta-Radiology2950-16282024-06-0122100069Deep learning-based artificial intelligence for assisting diagnosis, assessment and treatment in soft tissue sarcomasRuiling Xu0Jinxin Tang1Chenbei Li2Hua Wang3Lan Li4Yu He5Chao Tu6Zhihong Li7Department of Orthopaedics, The Second Xiangya Hospital, Central South University, Changsha, China; Hunan Key Laboratory of Tumor Models and Individualized Medicine, The Second Xiangya Hospital, Changsha, ChinaDepartment of Orthopaedics, The Second Xiangya Hospital, Central South University, Changsha, China; Hunan Key Laboratory of Tumor Models and Individualized Medicine, The Second Xiangya Hospital, Changsha, ChinaDepartment of Orthopaedics, The Second Xiangya Hospital, Central South University, Changsha, China; Hunan Key Laboratory of Tumor Models and Individualized Medicine, The Second Xiangya Hospital, Changsha, ChinaDepartment of Orthopaedics, The Second Xiangya Hospital, Central South University, Changsha, China; Hunan Key Laboratory of Tumor Models and Individualized Medicine, The Second Xiangya Hospital, Changsha, ChinaDepartment of Pathology, The Second Xiangya Hospital, Central South University, Changsha, ChinaDepartment of Radiology, The Second Xiangya Hospital, Central South University, Changsha, ChinaDepartment of Orthopaedics, The Second Xiangya Hospital, Central South University, Changsha, China; Hunan Key Laboratory of Tumor Models and Individualized Medicine, The Second Xiangya Hospital, Changsha, China; Shenzhen Research Institute of Central South University, Guangdong 518063, China; Corresponding author. Department of Orthopaedics, The Second Xiangya Hospital, Central South University, No. 139 Renmin Road, Changsha, Hunan 410010, China.Department of Orthopaedics, The Second Xiangya Hospital, Central South University, Changsha, China; Hunan Key Laboratory of Tumor Models and Individualized Medicine, The Second Xiangya Hospital, Changsha, China; Shenzhen Research Institute of Central South University, Guangdong 518063, China; Corresponding author. Department of Orthopaedics, The Second Xiangya Hospital, Central South University, No. 139 Renmin Road, Changsha, Hunan 410010, China.Soft tissue sarcomas (STSs) represent a group of heterogeneous mesenchymal tumors of which are generally classified as per the histopathology. Despite being rare in incidence and prevalence, STSs are usually correlated with unfavorable prognosis and high mortality rate. Early and accurate diagnosis of STSs are critical in clinical management of STSs. Deep learning (DL) refers to a subtype of artificial intelligence that has been adopted to assist healthcare professionals to optimize personalized treatment for a given situation, particularly in image analysis. Recently, emerging studies have demonstrated that application of DL based on medical images could substantially improve the accuracy and efficiency of clinicians to the identification, diagnosis, treatment, and prognosis prediction of STSs, and thereby facilitating the clinical decision-making. Herein, we aimed to extensively summarize the recent applications of DL-based artificial intelligence in STSs from the aspects of data acquisition, algorithm, and model establishment. Besides, the reinforcement of the model by transfer learning and generative adversarial network (GAN) for data augmentation has also been elaborated. It is worth noting that high-quality data with accurate annotations, as well as optimized algorithmic performance are pivotal in the clinical application of DL in STSs.http://www.sciencedirect.com/science/article/pii/S2950162824000225Deep learningSoft tissue sarcomasTransfer learningGenerative adversarial networkAlgorithm |
| spellingShingle | Ruiling Xu Jinxin Tang Chenbei Li Hua Wang Lan Li Yu He Chao Tu Zhihong Li Deep learning-based artificial intelligence for assisting diagnosis, assessment and treatment in soft tissue sarcomas Meta-Radiology Deep learning Soft tissue sarcomas Transfer learning Generative adversarial network Algorithm |
| title | Deep learning-based artificial intelligence for assisting diagnosis, assessment and treatment in soft tissue sarcomas |
| title_full | Deep learning-based artificial intelligence for assisting diagnosis, assessment and treatment in soft tissue sarcomas |
| title_fullStr | Deep learning-based artificial intelligence for assisting diagnosis, assessment and treatment in soft tissue sarcomas |
| title_full_unstemmed | Deep learning-based artificial intelligence for assisting diagnosis, assessment and treatment in soft tissue sarcomas |
| title_short | Deep learning-based artificial intelligence for assisting diagnosis, assessment and treatment in soft tissue sarcomas |
| title_sort | deep learning based artificial intelligence for assisting diagnosis assessment and treatment in soft tissue sarcomas |
| topic | Deep learning Soft tissue sarcomas Transfer learning Generative adversarial network Algorithm |
| url | http://www.sciencedirect.com/science/article/pii/S2950162824000225 |
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