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Zhang S, Zhuang Y, Luo Y, Zhu F, Zhao W, Zeng H. Deep learning-based automated lesion segmentation on pediatric focal cortical dysplasia II preoperative MRI: a reliable approach. Insights Imaging 2024; 15:71. [PMID: 38472513 DOI: 10.1186/s13244-024-01635-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Accepted: 01/27/2024] [Indexed: 03/14/2024] Open
Abstract
OBJECTIVES Focal cortical dysplasia (FCD) represents one of the most common causes of refractory epilepsy in children. Deep learning demonstrates great power in tissue discrimination by analyzing MRI data. A prediction model was built and verified using 3D full-resolution nnU-Net for automatic lesion detection and segmentation of children with FCD II. METHODS High-resolution brain MRI structure data from 65 patients, confirmed with FCD II by pathology, were retrospectively studied. Experienced neuroradiologists segmented and labeled the lesions as the ground truth. Also, we used 3D full-resolution nnU-Net to segment lesions automatically, generating detection maps. The algorithm was trained using fivefold cross-validation, with data partitioned into training (N = 200) and testing (N = 15). To evaluate performance, detection maps were compared to expert manual labels. The Dice-Sørensen coefficient (DSC) and sensitivity were used to assess the algorithm performance. RESULTS The 3D nnU-Net showed a good performance for FCD lesion detection at the voxel level, with a sensitivity of 0.73. The best segmentation model achieved a mean DSC score of 0.57 on the testing dataset. CONCLUSION This pilot study confirmed that 3D full-resolution nnU-Net can automatically segment FCD lesions with reliable outcomes. This provides a novel approach to FCD lesion detection. CRITICAL RELEVANCE STATEMENT Our fully automatic models could process the 3D T1-MPRAGE data and segment FCD II lesions with reliable outcomes. KEY POINTS • Simplified image processing promotes the DL model implemented in clinical practice. • The histopathological confirmed lesion masks enhance the clinical credibility of the AI model. • The voxel-level evaluation metrics benefit lesion detection and clinical decisions.
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Affiliation(s)
- Siqi Zhang
- Shantou University Medical College, Shantou University, 22 Xinling Road, Jinping District, Shantou, 515041, China
- Department of Radiology, Shenzhen Children's Hospital, District, 7019 Yitian Road, Futian, Shenzhen, 518038, China
| | - Yijiang Zhuang
- Department of Radiology, Shenzhen Children's Hospital, District, 7019 Yitian Road, Futian, Shenzhen, 518038, China
| | - Yi Luo
- Department of Radiology, Shenzhen Children's Hospital, District, 7019 Yitian Road, Futian, Shenzhen, 518038, China
| | - Fengjun Zhu
- Department of Epilepsy Surgical Department, Shenzhen Children's Hospital, 7019 Yitian Road, Futian District, Shenzhen, 518038, China
| | - Wen Zhao
- Shantou University Medical College, Shantou University, 22 Xinling Road, Jinping District, Shantou, 515041, China
- Department of Radiology, Shenzhen Children's Hospital, District, 7019 Yitian Road, Futian, Shenzhen, 518038, China
| | - Hongwu Zeng
- Department of Radiology, Shenzhen Children's Hospital, District, 7019 Yitian Road, Futian, Shenzhen, 518038, China.
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Xu N, Guo X, Ouyang Z, Ran F, Li Q, Duan X, Zhu Y, Niu X, Liao C, Yang J. Multiparametric MRI-based radiomics combined with pathomics features for prediction of the efficacy of neoadjuvant chemotherapy in breast cancer. Heliyon 2024; 10:e24371. [PMID: 38298695 PMCID: PMC10827766 DOI: 10.1016/j.heliyon.2024.e24371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 12/25/2023] [Accepted: 01/08/2024] [Indexed: 02/02/2024] Open
Abstract
Purpose The aim of this study is to investigate a new method that combines radiological and pathological breast cancer information to predict discrepancies in pathological responses for individualized treatment planning. We used baseline multiparametric magnetic resonance imaging and hematoxylin and eosin-stained biopsy slides to extract quantitative feature information and predict the pathological response to neoadjuvant chemotherapy in breast cancer patients. Methods We retrospectively collected data from breast cancer patients who received neoadjuvant chemotherapy in our hospital from August 2016 to January 2018; multiparametric magnetic resonance imaging (contrast-enhanced T1-weighted imaging and diffusion-weighted imaging) and whole slide image of hematoxylin and eosin-stained biopsy sections were collected. Quantitative imaging features were extracted from the multiparametric magnetic resonance imaging and the whole slide image were used to construct a radiopathomics signature model powered by machine learning methods. Models based on multiparametric magnetic resonance imaging or whole slide image alone were also constructed for comparison and referred to as the radiomics signature and pathomics signature models, respectively. Four modeling methods were used to establish prediction models. Model performances were evaluated using receiver operating characteristic curve analysis and the area under the curve, accuracy, sensitivity, specificity, positive predictive value, and negative predictive value. Results The radiopathomics signature model had favourable performance for the prediction of pathological complete response in the training set (the best value: area under the curve 0.83, accuracy 0.84, and sensitivity 0.87), and in the test set (the best value: area under the curve 0.91, accuracy 0.90, and sensitivity 0.88). In the test set, the radiopathomics signature model also significantly outperformed the radiomics signature (the best value: area under the curve 0.83, accuracy 0.64, and sensitivity 0.62), pathomics signature (the best value: area under the curve 0.60, accuracy 0.74, and sensitivity 0.62) (p > 0.05). Decision curve analysis and calibration curves confirmed the excellent performance of these prediction models in discrimination, calibration, and clinical usefulness. Conclusions The results of this study suggest that radiopathomics, the combination of both radiological information regarding the whole tumor and pathological information at the cellular level, could potentially predict discrepancies in pathological response and provide evidence for rational treatment plans.
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Affiliation(s)
- Nan Xu
- Department of Radiology, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital/Center. No. 519 Kunzhou Road, Xishan District, Kunming 650118, Yunnan, PR China
| | - Xiaobin Guo
- Department of Radiology, Fuwai Central China Cardiovascular Hospital, Fuwai Road, Zhengzhou, Henan, 461464, PR China
| | - Zhiqiang Ouyang
- Department of Radiology, Kunming Yan’an Hospital (Yan’an Hospital Affiliated to Kunming Medical University), Kunming, PR China
| | - Fengming Ran
- Department of Pathology, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital/Center. No. 519 Kunzhou Road, Xishan District, Kunming 650118, Yunnan, PR China
| | - Qinqing Li
- Department of Radiology, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital/Center. No. 519 Kunzhou Road, Xishan District, Kunming 650118, Yunnan, PR China
| | - Xirui Duan
- Department of Radiology, Kunming Yan’an Hospital (Yan’an Hospital Affiliated to Kunming Medical University), Kunming, PR China
| | - Yu Zhu
- Department of Radiology, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital/Center. No. 519 Kunzhou Road, Xishan District, Kunming 650118, Yunnan, PR China
| | - Xiaofeng Niu
- Department of Radiology, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital/Center. No. 519 Kunzhou Road, Xishan District, Kunming 650118, Yunnan, PR China
| | - Chengde Liao
- Department of Radiology, Kunming Yan’an Hospital (Yan’an Hospital Affiliated to Kunming Medical University), Kunming, PR China
| | - Jun Yang
- Department of Radiology, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital/Center. No. 519 Kunzhou Road, Xishan District, Kunming 650118, Yunnan, PR China
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Jiménez-Murillo D, Castro-Ospina AE, Duque-Muñoz L, Martínez-Vargas JD, Suárez-Revelo JX, Vélez-Arango JM, de la Iglesia-Vayá M. Automatic Detection of Focal Cortical Dysplasia Using MRI: A Systematic Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:7072. [PMID: 37631608 PMCID: PMC10458261 DOI: 10.3390/s23167072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/08/2023] [Revised: 08/02/2023] [Accepted: 08/07/2023] [Indexed: 08/27/2023]
Abstract
Focal cortical dysplasia (FCD) is a congenital brain malformation that is closely associated with epilepsy. Early and accurate diagnosis is essential for effectively treating and managing FCD. Magnetic resonance imaging (MRI)-one of the most commonly used non-invasive neuroimaging methods for evaluating the structure of the brain-is often implemented along with automatic methods to diagnose FCD. In this review, we define three categories for FCD identification based on MRI: visual, semi-automatic, and fully automatic methods. By conducting a systematic review following the PRISMA statement, we identified 65 relevant papers that have contributed to our understanding of automatic FCD identification techniques. The results of this review present a comprehensive overview of the current state-of-the-art in the field of automatic FCD identification and highlight the progress made and challenges ahead in developing reliable, efficient methods for automatic FCD diagnosis using MRI images. Future developments in this area will most likely lead to the integration of these automatic identification tools into medical image-viewing software, providing neurologists and radiologists with enhanced diagnostic capabilities. Moreover, new MRI sequences and higher-field-strength scanners will offer improved resolution and anatomical detail for precise FCD characterization. This review summarizes the current state of automatic FCD identification, thereby contributing to a deeper understanding and the advancement of FCD diagnosis and management.
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Affiliation(s)
- David Jiménez-Murillo
- Grupo de investigación Máquinas Inteligentes y Reconocimiento de Patrones, Instituto Tecnológico Metropolitano, Medellín 050013, Colombia; (D.J.-M.); (L.D.-M.)
| | - Andrés Eduardo Castro-Ospina
- Grupo de investigación Máquinas Inteligentes y Reconocimiento de Patrones, Instituto Tecnológico Metropolitano, Medellín 050013, Colombia; (D.J.-M.); (L.D.-M.)
| | - Leonardo Duque-Muñoz
- Grupo de investigación Máquinas Inteligentes y Reconocimiento de Patrones, Instituto Tecnológico Metropolitano, Medellín 050013, Colombia; (D.J.-M.); (L.D.-M.)
| | | | - Jazmín Ximena Suárez-Revelo
- Grupo de Investigación en Imágenes Médicas SURA, Ayudas Diagnósticas SURA, Carrera 48 # 26-50, Piso 2, Medellín 050021, Colombia; (J.X.S.-R.); (J.M.V.-A.)
| | - Jorge Mario Vélez-Arango
- Grupo de Investigación en Imágenes Médicas SURA, Ayudas Diagnósticas SURA, Carrera 48 # 26-50, Piso 2, Medellín 050021, Colombia; (J.X.S.-R.); (J.M.V.-A.)
| | - Maria de la Iglesia-Vayá
- Biomedical Imaging Unit FISABIO-CIPF, Foundation for the Promotion of the Research in Healthcare and Biomedicine (FISABIO), Avda. de Catalunya, 21, 46020 Valencia, Spain;
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM-G23), 28029 Madrid, Spain
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Zhao Q, He Y, Wu Y, Huang D, Wang Y, Sun C, Ju J, Wang J, Mahr JJL. Vocal cord lesions classification based on deep convolutional neural network and transfer learning. Med Phys 2021; 49:432-442. [PMID: 34813114 DOI: 10.1002/mp.15371] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2021] [Revised: 09/12/2021] [Accepted: 09/29/2021] [Indexed: 01/01/2023] Open
Abstract
PURPOSE Laryngoscopy, the most common diagnostic method for vocal cord lesions (VCLs), is based mainly on the visual subjective inspection of otolaryngologists. This study aimed to establish a highly objective computer-aided VCLs diagnosis system based on deep convolutional neural network (DCNN) and transfer learning. METHODS To classify VCLs, our method combined the DCNN backbone with transfer learning on a system specifically finetuned for a laryngoscopy image dataset. Laryngoscopy image database was collected to train the proposed system. The diagnostic performance was compared with other DCNN-based models. Analysis of F1 score and receiver operating characteristic curves were conducted to evaluate the performance of the system. RESULTS Beyond the existing VCLs diagnosis method, the proposed system achieved an overall accuracy of 80.23%, an F1 score of 0.7836, and an area under the curve (AUC) of 0.9557 for four fine-grained classes of VCLs, namely, normal, polyp, keratinization, and carcinoma. It also demonstrated robust classification capacity for detecting urgent (keratinization, carcinoma) and non-urgent (normal, polyp), with an overall accuracy of 0.939, a sensitivity of 0.887, a specificity of 0.993, and an AUC of 0.9828. The proposed method also outperformed clinicians in the classification of normal, polyps, and carcinoma at an extremely low time cost. CONCLUSION The VCLs diagnosis system succeeded in using DCNN to distinguish the most common VCLs and normal cases, holding a practical potential for improving the overall diagnostic efficacy in VCLs examinations. The proposed VCLs diagnosis system could be appropriately integrated into the conventional workflow of VCLs laryngoscopy as a highly objective auxiliary method.
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Affiliation(s)
- Qian Zhao
- Key Laboratory of Photoelectronic Imaging Technology and System, Ministry of Education, School of Optics and Photonics, Beijing Institute of Technology, Beijing, China
| | - Yuqing He
- Key Laboratory of Photoelectronic Imaging Technology and System, Ministry of Education, School of Optics and Photonics, Beijing Institute of Technology, Beijing, China
| | - Yanda Wu
- Key Laboratory of Photoelectronic Imaging Technology and System, Ministry of Education, School of Optics and Photonics, Beijing Institute of Technology, Beijing, China
| | - Dongyan Huang
- National Clinical Research Center for Otolaryngologic Diseases, College of Otolaryngology-Head and Neck Surgery, Chinese PLA General Hospital, Beijing, China
| | - Yang Wang
- National Clinical Research Center for Otolaryngologic Diseases, College of Otolaryngology-Head and Neck Surgery, Chinese PLA General Hospital, Beijing, China
| | - Cai Sun
- National Clinical Research Center for Otolaryngologic Diseases, College of Otolaryngology-Head and Neck Surgery, Chinese PLA General Hospital, Beijing, China
| | - Jun Ju
- National Clinical Research Center for Otolaryngologic Diseases, College of Otolaryngology-Head and Neck Surgery, Chinese PLA General Hospital, Beijing, China
| | - Jiasen Wang
- National Clinical Research Center for Otolaryngologic Diseases, College of Otolaryngology-Head and Neck Surgery, Chinese PLA General Hospital, Beijing, China
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