1
|
Zhong J. Deep learning-based diagnostic models for bone lesions: is current research ready for clinical translation? Eur Radiol 2024; 34:4284-4286. [PMID: 38189983 PMCID: PMC11213795 DOI: 10.1007/s00330-023-10555-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2023] [Revised: 11/05/2023] [Accepted: 11/08/2023] [Indexed: 01/09/2024]
Affiliation(s)
- Jingyu Zhong
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China.
| |
Collapse
|
2
|
Wu L, He C, Zhao T, Li T, Xu H, Wen J, Xu X, Gao L. Diagnosis and treatment status of inoperable locally advanced breast cancer and the application value of inorganic nanomaterials. J Nanobiotechnology 2024; 22:366. [PMID: 38918821 PMCID: PMC11197354 DOI: 10.1186/s12951-024-02644-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2024] [Accepted: 06/16/2024] [Indexed: 06/27/2024] Open
Abstract
Locally advanced breast cancer (LABC) is a heterogeneous group of breast cancer that accounts for 10-30% of breast cancer cases. Despite the ongoing development of current treatment methods, LABC remains a severe and complex public health concern around the world, thus prompting the urgent requirement for innovative diagnosis and treatment strategies. The primary treatment challenges are inoperable clinical status and ineffective local control methods. With the rapid advancement of nanotechnology, inorganic nanoparticles (INPs) exhibit a potential application prospect in diagnosing and treating breast cancer. Due to the unique inherent characteristics of INPs, different functions can be performed via appropriate modifications and constructions, thus making them suitable for different imaging technology strategies and treatment schemes. INPs can improve the efficacy of conventional local radiotherapy treatment. In the face of inoperable LABC, INPs have proposed new local therapeutic methods and fostered the evolution of novel strategies such as photothermal and photodynamic therapy, magnetothermal therapy, sonodynamic therapy, and multifunctional inorganic nanoplatform. This article reviews the advances of INPs in local accurate imaging and breast cancer treatment and offers insights to overcome the existing clinical difficulties in LABC management.
Collapse
Affiliation(s)
- Linxuan Wu
- School of Intelligent Medicine, China Medical University, Shenyang, 110122, China
| | - Chuan He
- Department of Laboratory Medicine, The First Hospital of China Medical University, Shenyang, 110001, China
| | - Tingting Zhao
- Department of Breast Surgery, The First Affiliated Hospital of China Medical University, Shenyang, 110001, China
| | - Tianqi Li
- School of Intelligent Medicine, China Medical University, Shenyang, 110122, China
| | - Hefeng Xu
- School of Intelligent Medicine, China Medical University, Shenyang, 110122, China
| | - Jian Wen
- Department of Breast Surgery, The Fourth Affiliated Hospital of China Medical University, Shenyang, 110032, China.
| | - Xiaoqian Xu
- School of Intelligent Medicine, China Medical University, Shenyang, 110122, China.
| | - Lin Gao
- Department of Ultrasound, Shengjing Hospital of China Medical University, Shenyang, 110022, China.
| |
Collapse
|
3
|
Rachmil O, Artzi M, Iluz M, Druckmann I, Yosibash Z, Sternheim A. Automatic segmentation of femoral tumors by nnU-net. Clin Biomech (Bristol, Avon) 2024; 116:106265. [PMID: 38810478 DOI: 10.1016/j.clinbiomech.2024.106265] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 04/05/2024] [Accepted: 05/09/2024] [Indexed: 05/31/2024]
Abstract
BACKGROUND Metastatic femoral tumors may lead to pathological fractures during daily activities. A CT-based finite element analysis of a patient's femurs was shown to assist orthopedic surgeons in making informed decisions about the risk of fracture and the need for a prophylactic fixation. Improving the accuracy of such analyses ruqires an automatic and accurate segmentation of the tumors and their automatic inclusion in the finite element model. We present herein a deep learning algorithm (nnU-Net) to automatically segment lytic tumors within the femur. METHOD A dataset consisting of fifty CT scans of patients with manually annotated femoral tumors was created. Forty of them, chosen randomly, were used for training the nnU-Net, while the remaining ten CT scans were used for testing. The deep learning model's performance was compared to two experienced radiologists. FINDINGS The proposed algorithm outperformed the current state-of-the-art solutions, achieving dice similarity scores of 0.67 and 0.68 on the test data when compared to two experienced radiologists, while the dice similarity score for inter-individual variability between the radiologists was 0.73. INTERPRETATION The automatic algorithm may segment lytic femoral tumors in CT scans as accurately as experienced radiologists with similar dice similarity scores. The influence of the realistic tumors inclusion in an autonomous finite element algorithm is presented in (Rachmil et al., "The Influence of Femoral Lytic Tumors Segmentation on Autonomous Finite Element Analyses", Clinical Biomechanics, 112, paper 106192, (2024)).
Collapse
Affiliation(s)
- Oren Rachmil
- Computational Mechanics & Experimental Biomechanics Lab, School of Mechanical Engineering, The Iby and Aladar Fleischman Faculty of Engineering, Tel-Aviv University, Ramat Aviv 69978, Israel
| | - Moran Artzi
- Sagol Brain Institute, Tel-Aviv Sourasky Medical Center, Tel-Aviv, Israel; Faculty of Medicine, Tel Aviv University, Ramat Aviv 69978, Israel
| | - Moshe Iluz
- Department of Radiology, Tel-Aviv Sourasky Medical Center, Tel-Aviv, Israel
| | - Ido Druckmann
- Department of Radiology, Tel-Aviv Sourasky Medical Center, Tel-Aviv, Israel
| | - Zohar Yosibash
- Computational Mechanics & Experimental Biomechanics Lab, School of Mechanical Engineering, The Iby and Aladar Fleischman Faculty of Engineering, Tel-Aviv University, Ramat Aviv 69978, Israel.
| | - Amir Sternheim
- Faculty of Medicine, Tel Aviv University, Ramat Aviv 69978, Israel; Department of Orthopedic Oncology, Tel-Aviv Sourasky Medical Center, Tel-Aviv, Israel
| |
Collapse
|
4
|
Wang S, Sun M, Sun J, Wang Q, Wang G, Wang X, Meng X, Wang Z, Yu H. Advancing musculoskeletal tumor diagnosis: Automated segmentation and predictive classification using deep learning and radiomics. Comput Biol Med 2024; 175:108502. [PMID: 38678943 DOI: 10.1016/j.compbiomed.2024.108502] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Revised: 03/18/2024] [Accepted: 04/21/2024] [Indexed: 05/01/2024]
Abstract
OBJECTIVES Musculoskeletal (MSK) tumors, given their high mortality rate and heterogeneity, necessitate precise examination and diagnosis to guide clinical treatment effectively. Magnetic resonance imaging (MRI) is pivotal in detecting MSK tumors, as it offers exceptional image contrast between bone and soft tissue. This study aims to enhance the speed of detection and the diagnostic accuracy of MSK tumors through automated segmentation and grading utilizing MRI. MATERIALS AND METHODS The research included 170 patients (mean age, 58 years ±12 (standard deviation), 84 men) with MSK lesions, who underwent MRI scans from April 2021 to May 2023. We proposed a deep learning (DL) segmentation model MSAPN based on multi-scale attention and pixel-level reconstruction, and compared it with existing algorithms. Using MSAPN-segmented lesions to extract their radiomic features for the benign and malignant classification of tumors. RESULTS Compared to the most advanced segmentation algorithms, MSAPN demonstrates better performance. The Dice similarity coefficients (DSC) are 0.871 and 0.815 in the testing set and independent validation set, respectively. The radiomics model for classifying benign and malignant lesions achieves an accuracy of 0.890. Moreover, there is no statistically significant difference between the radiomics model based on manual segmentation and MSAPN segmentation. CONCLUSION This research contributes to the advancement of MSK tumor diagnosis through automated segmentation and predictive classification. The integration of DL algorithms and radiomics shows promising results, and the visualization analysis of feature maps enhances clinical interpretability.
Collapse
Affiliation(s)
- Shuo Wang
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, 300072, China; State Key Laboratory of Advanced Medical Materials and Devices, Tianjin University, Tianjin, 300072, China.
| | - Man Sun
- Radiology Department, Tianjin University Tianjin Hospital, Tianjin, 300299, China.
| | - Jinglai Sun
- The School of Precision Instrument and Opto-Electronics Engineering, Tianjin University, Tianjin, 300072, China.
| | - Qingsong Wang
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, 300072, China.
| | - Guangpu Wang
- The School of Precision Instrument and Opto-Electronics Engineering, Tianjin University, Tianjin, 300072, China.
| | - Xiaolin Wang
- The School of Precision Instrument and Opto-Electronics Engineering, Tianjin University, Tianjin, 300072, China.
| | - Xianghong Meng
- Radiology Department, Tianjin University Tianjin Hospital, Tianjin, 300299, China.
| | - Zhi Wang
- Radiology Department, Tianjin University Tianjin Hospital, Tianjin, 300299, China.
| | - Hui Yu
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, 300072, China; State Key Laboratory of Advanced Medical Materials and Devices, Tianjin University, Tianjin, 300072, China; The School of Precision Instrument and Opto-Electronics Engineering, Tianjin University, Tianjin, 300072, China.
| |
Collapse
|
5
|
Rich JM, Bhardwaj LN, Shah A, Gangal K, Rapaka MS, Oberai AA, Fields BKK, Matcuk GR, Duddalwar VA. Deep learning image segmentation approaches for malignant bone lesions: a systematic review and meta-analysis. FRONTIERS IN RADIOLOGY 2023; 3:1241651. [PMID: 37614529 PMCID: PMC10442705 DOI: 10.3389/fradi.2023.1241651] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/17/2023] [Accepted: 07/28/2023] [Indexed: 08/25/2023]
Abstract
Introduction Image segmentation is an important process for quantifying characteristics of malignant bone lesions, but this task is challenging and laborious for radiologists. Deep learning has shown promise in automating image segmentation in radiology, including for malignant bone lesions. The purpose of this review is to investigate deep learning-based image segmentation methods for malignant bone lesions on Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and Positron-Emission Tomography/CT (PET/CT). Method The literature search of deep learning-based image segmentation of malignant bony lesions on CT and MRI was conducted in PubMed, Embase, Web of Science, and Scopus electronic databases following the guidelines of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). A total of 41 original articles published between February 2017 and March 2023 were included in the review. Results The majority of papers studied MRI, followed by CT, PET/CT, and PET/MRI. There was relatively even distribution of papers studying primary vs. secondary malignancies, as well as utilizing 3-dimensional vs. 2-dimensional data. Many papers utilize custom built models as a modification or variation of U-Net. The most common metric for evaluation was the dice similarity coefficient (DSC). Most models achieved a DSC above 0.6, with medians for all imaging modalities between 0.85-0.9. Discussion Deep learning methods show promising ability to segment malignant osseous lesions on CT, MRI, and PET/CT. Some strategies which are commonly applied to help improve performance include data augmentation, utilization of large public datasets, preprocessing including denoising and cropping, and U-Net architecture modification. Future directions include overcoming dataset and annotation homogeneity and generalizing for clinical applicability.
Collapse
Affiliation(s)
- Joseph M. Rich
- Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Lokesh N. Bhardwaj
- Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Aman Shah
- Department of Applied Biostatistics and Epidemiology, University of Southern California, Los Angeles, CA, United States
| | - Krish Gangal
- Bridge UnderGrad Science Summer Research Program, Irvington High School, Fremont, CA, United States
| | - Mohitha S. Rapaka
- Department of Biology, University of Texas at Austin, Austin, TX, United States
| | - Assad A. Oberai
- Department of Aerospace and Mechanical Engineering Department, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States
| | - Brandon K. K. Fields
- Department of Radiology & Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States
| | - George R. Matcuk
- Department of Radiology, Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | - Vinay A. Duddalwar
- Department of Radiology, Keck School of Medicine of the University of Southern California, Los Angeles, CA, United States
- Department of Radiology, USC Radiomics Laboratory, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| |
Collapse
|