A non-invasive diagnostic approach for neuroblastoma utilizing preoperative enhanced computed tomography and deep learning techniques
Abstract Neuroblastoma presents a wide variety of clinical phenotypes, demonstrating different levels of benignity and malignancy among its subtypes. Early diagnosis is essential for effective patient management. Computed tomography (CT) serves as a significant diagnostic tool for neuroblastoma, uti...
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Nature Portfolio
2025-04-01
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| Online Access: | https://doi.org/10.1038/s41598-025-99451-5 |
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| author | Yuanyuan Wang Fangfang Wang Zixin Qin Yongcheng Fu Jingyue Wang Shangkun Li Da Zhang |
| author_facet | Yuanyuan Wang Fangfang Wang Zixin Qin Yongcheng Fu Jingyue Wang Shangkun Li Da Zhang |
| author_sort | Yuanyuan Wang |
| collection | DOAJ |
| description | Abstract Neuroblastoma presents a wide variety of clinical phenotypes, demonstrating different levels of benignity and malignancy among its subtypes. Early diagnosis is essential for effective patient management. Computed tomography (CT) serves as a significant diagnostic tool for neuroblastoma, utilizing machine vision imaging, which offers advantages over traditional X-ray and ultrasound imaging modalities. However, the high degree of similarity among neuroblastoma subtypes complicates the diagnostic process. In response to these challenges, this study presents a modified version of the You Only Look Once (YOLO) algorithm, called YOLOv8-IE. This revised approach integrates feature fusion and inverse residual attention mechanisms. The aim of YOLO-IE is to improve the detection and classification of neuroblastoma tumors. In light of the image features, we have implemented the inverse residual-based attention structure (iRMB) within the detection network of YOLOv8, thereby enhancing the model’s ability to focus on significant features present in the images. Additionally, we have incorporated the centered feature pyramid EVC module. Experimental results show that the proposed detection network, named YOLO-IE, attains a mean Average Precision (mAP) 7.9% higher than the baseline model, YOLO. The individual contributions of iRMB and EVC to the performance improvement are 0.8% and 3.6% above the baseline model, respectively. This study represents a significant advancement in the field, as it not only facilitates the detection and classification of neuroblastoma but also demonstrates the considerable potential of machine learning and artificial intelligence in the realm of medical diagnosis. |
| format | Article |
| id | doaj-art-4e96ff07ada6470b9ccdd9c1f1811ae5 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-4e96ff07ada6470b9ccdd9c1f1811ae52025-08-20T03:13:55ZengNature PortfolioScientific Reports2045-23222025-04-0115111610.1038/s41598-025-99451-5A non-invasive diagnostic approach for neuroblastoma utilizing preoperative enhanced computed tomography and deep learning techniquesYuanyuan Wang0Fangfang Wang1Zixin Qin2Yongcheng Fu3Jingyue Wang4Shangkun Li5Da Zhang6Department of Pediatric Surgery, The First Affiliated Hospital of Zhengzhou UniversityDepartment of Electronic information, Zhengzhou University Cyberspace Security CollegeDepartment of Neurosurgery, The First Affiliated Hospital of Zhengzhou UniversityDepartment of Pediatric Surgery, The First Affiliated Hospital of Zhengzhou UniversityDepartment of Pediatric Surgery, The First Affiliated Hospital of Zhengzhou UniversityDepartment of Pediatric Surgery, The First Affiliated Hospital of Zhengzhou UniversityDepartment of Pediatric Surgery, The First Affiliated Hospital of Zhengzhou UniversityAbstract Neuroblastoma presents a wide variety of clinical phenotypes, demonstrating different levels of benignity and malignancy among its subtypes. Early diagnosis is essential for effective patient management. Computed tomography (CT) serves as a significant diagnostic tool for neuroblastoma, utilizing machine vision imaging, which offers advantages over traditional X-ray and ultrasound imaging modalities. However, the high degree of similarity among neuroblastoma subtypes complicates the diagnostic process. In response to these challenges, this study presents a modified version of the You Only Look Once (YOLO) algorithm, called YOLOv8-IE. This revised approach integrates feature fusion and inverse residual attention mechanisms. The aim of YOLO-IE is to improve the detection and classification of neuroblastoma tumors. In light of the image features, we have implemented the inverse residual-based attention structure (iRMB) within the detection network of YOLOv8, thereby enhancing the model’s ability to focus on significant features present in the images. Additionally, we have incorporated the centered feature pyramid EVC module. Experimental results show that the proposed detection network, named YOLO-IE, attains a mean Average Precision (mAP) 7.9% higher than the baseline model, YOLO. The individual contributions of iRMB and EVC to the performance improvement are 0.8% and 3.6% above the baseline model, respectively. This study represents a significant advancement in the field, as it not only facilitates the detection and classification of neuroblastoma but also demonstrates the considerable potential of machine learning and artificial intelligence in the realm of medical diagnosis.https://doi.org/10.1038/s41598-025-99451-5Deep learningArtificial intelligenceNeuroblastomaMachine learningDiagnosis |
| spellingShingle | Yuanyuan Wang Fangfang Wang Zixin Qin Yongcheng Fu Jingyue Wang Shangkun Li Da Zhang A non-invasive diagnostic approach for neuroblastoma utilizing preoperative enhanced computed tomography and deep learning techniques Scientific Reports Deep learning Artificial intelligence Neuroblastoma Machine learning Diagnosis |
| title | A non-invasive diagnostic approach for neuroblastoma utilizing preoperative enhanced computed tomography and deep learning techniques |
| title_full | A non-invasive diagnostic approach for neuroblastoma utilizing preoperative enhanced computed tomography and deep learning techniques |
| title_fullStr | A non-invasive diagnostic approach for neuroblastoma utilizing preoperative enhanced computed tomography and deep learning techniques |
| title_full_unstemmed | A non-invasive diagnostic approach for neuroblastoma utilizing preoperative enhanced computed tomography and deep learning techniques |
| title_short | A non-invasive diagnostic approach for neuroblastoma utilizing preoperative enhanced computed tomography and deep learning techniques |
| title_sort | non invasive diagnostic approach for neuroblastoma utilizing preoperative enhanced computed tomography and deep learning techniques |
| topic | Deep learning Artificial intelligence Neuroblastoma Machine learning Diagnosis |
| url | https://doi.org/10.1038/s41598-025-99451-5 |
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