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Zhao T, Meng X, Wang Z, Hu Y, Fan H, Han J, Zhu N, Niu F. Diagnostic evaluation of blunt chest trauma by imaging-based application of artificial intelligence: A review. Am J Emerg Med 2024; 85:35-43. [PMID: 39213808 DOI: 10.1016/j.ajem.2024.08.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2024] [Accepted: 08/12/2024] [Indexed: 09/04/2024] Open
Abstract
Artificial intelligence (AI) is becoming increasingly integral in clinical practice, such as during imaging tasks associated with the diagnosis and evaluation of blunt chest trauma (BCT). Due to significant advances in imaging-based deep learning, recent studies have demonstrated the efficacy of AI in the diagnosis of BCT, with a focus on rib fractures, pulmonary contusion, hemopneumothorax and others, demonstrating significant clinical progress. However, the complicated nature of BCT presents challenges in providing a comprehensive diagnosis and prognostic evaluation, and current deep learning research concentrates on specific clinical contexts, limiting its utility in addressing BCT intricacies. Here, we provide a review of the available evidence surrounding the potential utility of AI in BCT, and additionally identify the challenges impeding its development. This review offers insights on how to optimize the role of AI in the diagnostic evaluation of BCT, which can ultimately enhance patient care and outcomes in this critical clinical domain.
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Affiliation(s)
- Tingting Zhao
- The Department of Radiology, Tianjin University Tianjin Hospital, 406 Jiefang Southern Road, Tianjin, China; Graduate School, Tianjin University, Tianjin, China
| | - Xianghong Meng
- The Department of Radiology, Tianjin University Tianjin Hospital, 406 Jiefang Southern Road, Tianjin, China; Graduate School, Tianjin University, Tianjin, China.
| | - Zhi Wang
- The Department of Radiology, Tianjin University Tianjin Hospital, 406 Jiefang Southern Road, Tianjin, China; Graduate School, Tianjin University, Tianjin, China.
| | - Yongcheng Hu
- The Department of Radiology, Tianjin University Tianjin Hospital, 406 Jiefang Southern Road, Tianjin, China
| | - Hongxing Fan
- The Department of Radiology, Tianjin University Tianjin Hospital, 406 Jiefang Southern Road, Tianjin, China; Graduate School, Tianjin Medical University, Tianjin, China
| | - Jun Han
- The Department of Radiology, Tianjin University Tianjin Hospital, 406 Jiefang Southern Road, Tianjin, China; Graduate School, Tianjin University, Tianjin, China
| | - Nana Zhu
- The Department of Radiology, Tianjin University Tianjin Hospital, 406 Jiefang Southern Road, Tianjin, China; Graduate School, Tianjin Medical University, Tianjin, China
| | - Feige Niu
- The Department of Radiology, Tianjin University Tianjin Hospital, 406 Jiefang Southern Road, Tianjin, China; Graduate School, Tianjin Medical University, Tianjin, China
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Meng Y, Yang Y, Hu M, Zhang Z, Zhou X. Artificial intelligence-based radiomics in bone tumors: Technical advances and clinical application. Semin Cancer Biol 2023; 95:75-87. [PMID: 37499847 DOI: 10.1016/j.semcancer.2023.07.003] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Revised: 07/21/2023] [Accepted: 07/22/2023] [Indexed: 07/29/2023]
Abstract
Radiomics is the extraction of predefined mathematic features from medical images for predicting variables of clinical interest. Recent research has demonstrated that radiomics can be processed by artificial intelligence algorithms to reveal complex patterns and trends for diagnosis, and prediction of prognosis and response to treatment modalities in various types of cancer. Artificial intelligence tools can utilize radiological images to solve next-generation issues in clinical decision making. Bone tumors can be classified as primary and secondary (metastatic) tumors. Osteosarcoma, Ewing sarcoma, and chondrosarcoma are the dominating primary tumors of bone. The development of bone tumor model systems and relevant research, and the assessment of novel treatment methods are ongoing to improve clinical outcomes, notably for patients with metastases. Artificial intelligence and radiomics have been utilized in almost full spectrum of clinical care of bone tumors. Radiomics models have achieved excellent performance in the diagnosis and grading of bone tumors. Furthermore, the models enable to predict overall survival, metastases, and recurrence. Radiomics features have exhibited promise in assisting therapeutic planning and evaluation, especially neoadjuvant chemotherapy. This review provides an overview of the evolution and opportunities for artificial intelligence in imaging, with a focus on hand-crafted features and deep learning-based radiomics approaches. We summarize the current application of artificial intelligence-based radiomics both in primary and metastatic bone tumors, and discuss the limitations and future opportunities of artificial intelligence-based radiomics in this field. In the era of personalized medicine, our in-depth understanding of emerging artificial intelligence-based radiomics approaches will bring innovative solutions to bone tumors and achieve clinical application.
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Affiliation(s)
- Yichen Meng
- Department of Orthopedics, Second Affiliated Hospital of Naval Medical University, Shanghai 200003, PR China
| | - Yue Yang
- Department of Orthopedics, Second Affiliated Hospital of Naval Medical University, Shanghai 200003, PR China
| | - Miao Hu
- Department of Orthopedics, Second Affiliated Hospital of Naval Medical University, Shanghai 200003, PR China
| | - Zheng Zhang
- Department of Orthopedics, Second Affiliated Hospital of Naval Medical University, Shanghai 200003, PR China.
| | - Xuhui Zhou
- Department of Orthopedics, Second Affiliated Hospital of Naval Medical University, Shanghai 200003, PR China.
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Zhan F, He L, Yu Y, Chen Q, Guo Y, Wang L. A multimodal radiomic machine learning approach to predict the LCK expression and clinical prognosis in high-grade serous ovarian cancer. Sci Rep 2023; 13:16397. [PMID: 37773310 PMCID: PMC10541909 DOI: 10.1038/s41598-023-43543-7] [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: 05/25/2023] [Accepted: 09/25/2023] [Indexed: 10/01/2023] Open
Abstract
We developed and validated a multimodal radiomic machine learning approach to noninvasively predict the expression of lymphocyte cell-specific protein-tyrosine kinase (LCK) expression and clinical prognosis of patients with high-grade serous ovarian cancer (HGSOC). We analyzed gene enrichment using 343 HGSOC cases extracted from The Cancer Genome Atlas. The corresponding biomedical computed tomography images accessed from The Cancer Imaging Archive were used to construct the radiomic signature (Radscore). A radiomic nomogram was built by combining the Radscore and clinical and genetic information based on multimodal analysis. We compared the model performances and clinical practicability via area under the curve (AUC), Kaplan-Meier survival, and decision curve analyses. LCK mRNA expression was associated with the prognosis of HGSOC patients, serving as a significant prognostic marker of the immune response and immune cells infiltration. Six radiomic characteristics were chosen to predict the expression of LCK and overall survival (OS) in HGSOC patients. The logistic regression (LR) radiomic model exhibited slightly better predictive abilities than the support vector machine model, as assessed by comparing combined results. The performance of the LR radiomic model for predicting the level of LCK expression with five-fold cross-validation achieved AUCs of 0.879 and 0.834, respectively, in the training and validation sets. Decision curve analysis at 60 months demonstrated the high clinical utility of our model within thresholds of 0.25 and 0.7. The radiomic nomograms were robust and displayed effective calibration. Abnormally high expression of LCK in HGSOC patients is significantly correlated with the tumor immune microenvironment and can be used as an essential indicator for predicting the prognosis of HGSOC. The multimodal radiomic machine learning approach can capture the heterogeneity of HGSOC, noninvasively predict the expression of LCK, and replace LCK for predictive analysis, providing a new idea for predicting the clinical prognosis of HGSOC and formulating a personalized treatment plan.
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Affiliation(s)
- Feng Zhan
- School of Electronic Information Engineering, Taiyuan University of Science and Technology, Taiyuan, Shanxi, People's Republic of China
- College of Engineering, Fujian Jiangxia University, Fuzhou, Fujian, People's Republic of China
| | - Lidan He
- Department of Obstetrics and Gynecology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, People's Republic of China
| | - Yuanlin Yu
- Department of Medical Imaging, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, People's Republic of China
| | - Qian Chen
- School of Electronic Information Engineering, Taiyuan University of Science and Technology, Taiyuan, Shanxi, People's Republic of China
| | - Yina Guo
- School of Electronic Information Engineering, Taiyuan University of Science and Technology, Taiyuan, Shanxi, People's Republic of China.
| | - Lili Wang
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, Fujian, People's Republic of China
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Wennmann M, Neher P, Stanczyk N, Kahl KC, Kächele J, Weru V, Hielscher T, Grözinger M, Chmelik J, Zhang KS, Bauer F, Nonnenmacher T, Debic M, Sauer S, Rotkopf LT, Jauch A, Schlamp K, Mai EK, Weinhold N, Afat S, Horger M, Goldschmidt H, Schlemmer HP, Weber TF, Delorme S, Kurz FT, Maier-Hein K. Deep Learning for Automatic Bone Marrow Apparent Diffusion Coefficient Measurements From Whole-Body Magnetic Resonance Imaging in Patients With Multiple Myeloma: A Retrospective Multicenter Study. Invest Radiol 2023; 58:273-282. [PMID: 36256790 DOI: 10.1097/rli.0000000000000932] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/15/2023]
Abstract
OBJECTIVES Diffusion-weighted magnetic resonance imaging (MRI) is increasingly important in patients with multiple myeloma (MM). The objective of this study was to train and test an algorithm for automatic pelvic bone marrow analysis from whole-body apparent diffusion coefficient (ADC) maps in patients with MM, which automatically segments pelvic bones and subsequently extracts objective, representative ADC measurements from each bone. MATERIALS AND METHODS In this retrospective multicentric study, 180 MRIs from 54 patients were annotated (semi)manually and used to train an nnU-Net for automatic, individual segmentation of the right hip bone, the left hip bone, and the sacral bone. The quality of the automatic segmentation was evaluated on 15 manually segmented whole-body MRIs from 3 centers using the dice score. In 3 independent test sets from 3 centers, which comprised a total of 312 whole-body MRIs, agreement between automatically extracted mean ADC values from the nnU-Net segmentation and manual ADC measurements from 2 independent radiologists was evaluated. Bland-Altman plots were constructed, and absolute bias, relative bias to mean, limits of agreement, and coefficients of variation were calculated. In 56 patients with newly diagnosed MM who had undergone bone marrow biopsy, ADC measurements were correlated with biopsy results using Spearman correlation. RESULTS The ADC-nnU-Net achieved automatic segmentations with mean dice scores of 0.92, 0.93, and 0.85 for the right pelvis, the left pelvis, and the sacral bone, whereas the interrater experiment gave mean dice scores of 0.86, 0.86, and 0.77, respectively. The agreement between radiologists' manual ADC measurements and automatic ADC measurements was as follows: the bias between the first reader and the automatic approach was 49 × 10 -6 mm 2 /s, 7 × 10 -6 mm 2 /s, and -58 × 10 -6 mm 2 /s, and the bias between the second reader and the automatic approach was 12 × 10 -6 mm 2 /s, 2 × 10 -6 mm 2 /s, and -66 × 10 -6 mm 2 /s for the right pelvis, the left pelvis, and the sacral bone, respectively. The bias between reader 1 and reader 2 was 40 × 10 -6 mm 2 /s, 8 × 10 -6 mm 2 /s, and 7 × 10 -6 mm 2 /s, and the mean absolute difference between manual readers was 84 × 10 -6 mm 2 /s, 65 × 10 -6 mm 2 /s, and 75 × 10 -6 mm 2 /s. Automatically extracted ADC values significantly correlated with bone marrow plasma cell infiltration ( R = 0.36, P = 0.007). CONCLUSIONS In this study, a nnU-Net was trained that can automatically segment pelvic bone marrow from whole-body ADC maps in multicentric data sets with a quality comparable to manual segmentations. This approach allows automatic, objective bone marrow ADC measurements, which agree well with manual ADC measurements and can help to overcome interrater variability or nonrepresentative measurements. Automatically extracted ADC values significantly correlate with bone marrow plasma cell infiltration and might be of value for automatic staging, risk stratification, or therapy response assessment.
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Affiliation(s)
| | - Peter Neher
- Medical Image Computing, German Cancer Research Center (DKFZ)
| | | | - Kim-Celine Kahl
- Medical Image Computing, German Cancer Research Center (DKFZ)
| | - Jessica Kächele
- Medical Image Computing, German Cancer Research Center (DKFZ)
| | - Vivienn Weru
- Division of Biostatistics, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Thomas Hielscher
- Division of Biostatistics, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | | | | | | | | | | | | | - Sandra Sauer
- Department of Internal Medicine V, Section Multiple Myeloma
| | | | | | | | - Elias Karl Mai
- Department of Internal Medicine V, Section Multiple Myeloma
| | - Niels Weinhold
- Department of Internal Medicine V, Section Multiple Myeloma
| | - Saif Afat
- Department of Diagnostic and Interventional Radiology, Eberhard Karls University, Tuebingen University Hospital, Tuebingen
| | - Marius Horger
- Department of Diagnostic and Interventional Radiology, Eberhard Karls University, Tuebingen University Hospital, Tuebingen
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Zheng WQ, Hu ZD. Pleural fluid biochemical analysis: the past, present and future. Clin Chem Lab Med 2022; 61:921-934. [PMID: 36383033 DOI: 10.1515/cclm-2022-0844] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Accepted: 11/07/2022] [Indexed: 11/18/2022]
Abstract
Abstract
Identifying the cause of pleural effusion is challenging for pulmonologists. Imaging, biopsy, microbiology and biochemical analyses are routinely used for diagnosing pleural effusion. Among these diagnostic tools, biochemical analyses are promising because they have the advantages of low cost, minimal invasiveness, observer independence and short turn-around time. Here, we reviewed the past, present and future of pleural fluid biochemical analysis. We reviewed the history of Light’s criteria and its modifications and the current status of biomarkers for heart failure, malignant pleural effusion, tuberculosis pleural effusion and parapneumonic pleural effusion. In addition, we anticipate the future of pleural fluid biochemical analysis, including the utility of machine learning, molecular diagnosis and high-throughput technologies. Clinical Chemistry and Laboratory Medicine (CCLM) should address the topic of pleural fluid biochemical analysis in the future to promote specific knowledge in the laboratory professional community.
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Affiliation(s)
- Wen-Qi Zheng
- Department of Laboratory Medicine , The Affiliated Hospital of Inner Mongolia Medical University , Hohhot , P.R. China
| | - Zhi-De Hu
- Department of Laboratory Medicine , The Affiliated Hospital of Inner Mongolia Medical University , Hohhot , P.R. China
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