1
|
Yang YF, Zhao E, Shi Y, Zhang H, Yang YY. Multicenter investigation of preoperative distinction between primary central nervous system lymphomas and glioblastomas through interpretable artificial intelligence models. Neuroradiology 2024:10.1007/s00234-024-03451-7. [PMID: 39225815 DOI: 10.1007/s00234-024-03451-7] [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: 03/25/2024] [Accepted: 08/09/2024] [Indexed: 09/04/2024]
Abstract
OBJECTIVE Research into the effectiveness and applicability of deep learning, radiomics, and their integrated models based on Magnetic Resonance Imaging (MRI) for preoperative differentiation between Primary Central Nervous System Lymphoma (PCNSL) and Glioblastoma (GBM), along with an exploration of the interpretability of these models. MATERIALS AND METHODS A retrospective analysis was performed on MRI images and clinical data from 261 patients across two medical centers. The data were split into a training set (n = 153, medical center 1) and an external test set (n = 108, medical center 2). Radiomic features were extracted using Pyradiomics to build the Radiomics Model. Deep learning networks, including the transformer-based MobileVIT Model and Convolutional Neural Networks (CNN) based ConvNeXt Model, were trained separately. By applying the "late fusion" theory, the radiomics model and deep learning model were fused to produce the optimal Max-Fusion Model. Additionally, Shapley Additive exPlanations (SHAP) and Grad-CAM were employed for interpretability analysis. RESULTS In the external test set, the Radiomics Model achieved an Area under the receiver operating characteristic curve (AUC) of 0.86, the MobileVIT Model had an AUC of 0.91, the ConvNeXt Model demonstrated an AUC of 0.89, and the Max-Fusion Model showed an AUC of 0.92. The Delong test revealed a significant difference in AUC between the Max-Fusion Model and the Radiomics Model (P = 0.02). CONCLUSION The Max-Fusion Model, combining different models, presents superior performance in distinguishing PCNSL and GBM, highlighting the effectiveness of model fusion for enhanced decision-making in medical applications. CLINICAL RELEVANCE STATEMENT The preoperative non-invasive differentiation between PCNSL and GBM assists clinicians in selecting appropriate treatment regimens and clinical management strategies.
Collapse
Affiliation(s)
- Yun-Feng Yang
- Laboratory for Medical Imaging Informatics, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai, 200083, China
- Laboratory for Medical Imaging Informatics, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Endong Zhao
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian, 116000, Liaoning, China
- Department of Radiology, The Second Affiliated Hospital of Dalian Medical University, Dalian, 116000, Liaoning, China
| | - Yutong Shi
- Department of Neurology, Dalian University Affiliated Xinhua Hospital, Dalian, Liaoning, China
| | - Hao Zhang
- Department of Interventional Radiology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China
| | - Yuan-Yuan Yang
- Laboratory for Medical Imaging Informatics, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai, 200083, China.
- Laboratory for Medical Imaging Informatics, University of Chinese Academy of Sciences, Beijing, 100049, China.
| |
Collapse
|
2
|
Zheng Y, Chen X, Zhang H, Ning X, Mao Y, Zheng H, Dai G, Liu B, Zhang G, Huang D. Multiparametric MRI-based radiomics nomogram for the preoperative prediction of lymph node metastasis in rectal cancer: A two-center study. Eur J Radiol 2024; 178:111591. [PMID: 39013271 DOI: 10.1016/j.ejrad.2024.111591] [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: 04/14/2024] [Revised: 06/06/2024] [Accepted: 06/24/2024] [Indexed: 07/18/2024]
Abstract
PURPOSE To develop a radiomic nomogram based on multiparametric magnetic resonance imaging for the preoperative prediction of lymph node metastasis (LNM) in rectal cancer. METHODS This retrospective study included 318 patients with pathologically proven rectal adenocarcinoma from two hospitals. Radiomic features were extracted from T2-weighted imaging, diffusion-weighted imaging, and contrast-enhanced T1-weighted imaging scans of the training cohort, and the radsore model was then constructed. The combined model was obtained by integrating the Radscore and clinical models. The area under the receiver operating characteristic curve (AUC) was used to assess the diagnostic effectiveness of each model, and the best-performing model was used to develop the nomogram. RESULTS The Radscore and clinical models exhibited similar diagnostic efficacy (DeLong's test, P > 0.05). The AUC of the combined model was significantly higher than those of the clinical and Radscore models in the training cohort (AUC: 0.837 vs. 0.763 and 0.787, P: 0.02120 and 0.02309) and the external validation cohort (AUC: 0.880 vs. 0.797 and 0.779, P: 0.02310 and 0.02471). However, the diagnostic performance of the three models was comparable in the internal validation cohort (P > 0.05). Thus, among the three models, the combined model exhibited the highest diagnostic efficiency. The calibration curve exhibited satisfactory consistency between the nomogram predictions and the actual results. DCA confirmed the considerable clinical usefulness of the nomogram. CONCLUSION The radiomics nomogram can accurately and noninvasively predict LNM in rectal cancer before surgery, serving as a convenient visualization tool for informing treatment decisions, including the choice of surgical approach and the need for neoadjuvant therapy.
Collapse
Affiliation(s)
- Yongfei Zheng
- Department of Radiology, Huangyan Hospital, Wenzhou Medical University, Taizhou First People's Hospital, Taizhou, Zhejiang Province, China
| | - Xu Chen
- Hangzhou Dianzi University Zhuoyue Honors College, Hangzhou, Zhejiang Province, China
| | - He Zhang
- Department of Radiology, Huangyan Hospital, Wenzhou Medical University, Taizhou First People's Hospital, Taizhou, Zhejiang Province, China
| | - Xiaoxiang Ning
- Department of Radiology, Tongde Hospital of Zhejiang Province, Hangzhou, Zhejiang Province, China
| | - Yichuan Mao
- Department of Radiology, Tongde Hospital of Zhejiang Province, Hangzhou, Zhejiang Province, China
| | - Hailan Zheng
- Department of Radiology, Huangyan Hospital, Wenzhou Medical University, Taizhou First People's Hospital, Taizhou, Zhejiang Province, China
| | - Guojiao Dai
- Department of Radiology, Huangyan Hospital, Wenzhou Medical University, Taizhou First People's Hospital, Taizhou, Zhejiang Province, China
| | - Binghui Liu
- Department of Pathology, Huangyan Hospital, Wenzhou Medical University, Taizhou First People's Hospital, Taizhou, Zhejiang Province, China
| | - Guohua Zhang
- Department of Radiology, Huangyan Hospital, Wenzhou Medical University, Taizhou First People's Hospital, Taizhou, Zhejiang Province, China.
| | - Danjiang Huang
- Department of Radiology, Huangyan Hospital, Wenzhou Medical University, Taizhou First People's Hospital, Taizhou, Zhejiang Province, China.
| |
Collapse
|
3
|
Al-Rahbi A, Al-Mahrouqi O, Al-Saadi T. Uses of artificial intelligence in glioma: A systematic review. MEDICINE INTERNATIONAL 2024; 4:40. [PMID: 38827949 PMCID: PMC11140312 DOI: 10.3892/mi.2024.164] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/21/2024] [Accepted: 04/26/2024] [Indexed: 06/05/2024]
Abstract
Glioma is the most prevalent type of primary brain tumor in adults. The use of artificial intelligence (AI) in glioma is increasing and has exhibited promising results. The present study performed a systematic review of the applications of AI in glioma as regards diagnosis, grading, prediction of genotype, progression and treatment response using different databases. The aim of the present study was to demonstrate the trends (main directions) of the recent applications of AI within the field of glioma, and to highlight emerging challenges in integrating AI within clinical practice. A search in four databases (Scopus, PubMed, Wiley and Google Scholar) yielded a total of 42 articles specifically using AI in glioma and glioblastoma. The articles were retrieved and reviewed, and the data were summarized and analyzed. The majority of the articles were from the USA (n=18) followed by China (n=11). The number of articles increased by year reaching the maximum number in 2022. The majority of the articles studied glioma as opposed to glioblastoma. In terms of grading, the majority of the articles were about both low-grade glioma (LGG) and high-grade glioma (HGG) (n=23), followed by HGG/glioblastoma (n=13). Additionally, three articles were about LGG only; two articles did not specify the grade. It was found that one article had the highest sample size among the other studies, reaching 897 samples. Despite the limitations and challenges that face AI, the use of AI in glioma has increased in recent years with promising results, with a variety of applications ranging from diagnosis, grading, prognosis prediction, and reaching to treatment and post-operative care.
Collapse
Affiliation(s)
- Adham Al-Rahbi
- College of Medicine and Health Sciences, Sultan Qaboos University, Muscat 123, Sultanate of Oman
| | - Omar Al-Mahrouqi
- College of Medicine and Health Sciences, Sultan Qaboos University, Muscat 123, Sultanate of Oman
| | - Tariq Al-Saadi
- Department of Neurosurgery, Khoula Hospital, Muscat 123, Sultanate of Oman
- Department of Neurology and Neurosurgery-Montreal Neurological Institute, Faculty of Medicine, McGill University, Montreal, QC H3A 2B4, Canada
| |
Collapse
|
4
|
Duan L, He Y, Guo W, Du Y, Yin S, Yang S, Dong G, Li W, Chen F. Machine learning-based pathomics signature of histology slides as a novel prognostic indicator in primary central nervous system lymphoma. J Neurooncol 2024; 168:283-298. [PMID: 38557926 PMCID: PMC11147825 DOI: 10.1007/s11060-024-04665-8] [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: 02/20/2024] [Accepted: 03/26/2024] [Indexed: 04/04/2024]
Abstract
PURPOSE To develop and validate a pathomics signature for predicting the outcomes of Primary Central Nervous System Lymphoma (PCNSL). METHODS In this study, 132 whole-slide images (WSIs) of 114 patients with PCNSL were enrolled. Quantitative features of hematoxylin and eosin (H&E) stained slides were extracted using CellProfiler. A pathomics signature was established and validated. Cox regression analysis, receiver operating characteristic (ROC) curves, Calibration, decision curve analysis (DCA), and net reclassification improvement (NRI) were performed to assess the significance and performance. RESULTS In total, 802 features were extracted using a fully automated pipeline. Six machine-learning classifiers demonstrated high accuracy in distinguishing malignant neoplasms. The pathomics signature remained a significant factor of overall survival (OS) and progression-free survival (PFS) in the training cohort (OS: HR 7.423, p < 0.001; PFS: HR 2.143, p = 0.022) and independent validation cohort (OS: HR 4.204, p = 0.017; PFS: HR 3.243, p = 0.005). A significantly lower response rate to initial treatment was found in high Path-score group (19/35, 54.29%) as compared to patients in the low Path-score group (16/70, 22.86%; p < 0.001). The DCA and NRI analyses confirmed that the nomogram showed incremental performance compared with existing models. The ROC curve demonstrated a relatively sensitive and specific profile for the nomogram (1-, 2-, and 3-year AUC = 0.862, 0.932, and 0.927, respectively). CONCLUSION As a novel, non-invasive, and convenient approach, the newly developed pathomics signature is a powerful predictor of OS and PFS in PCNSL and might be a potential predictive indicator for therapeutic response.
Collapse
Affiliation(s)
- Ling Duan
- Department of Neuro-Oncology, Cancer Center, Beijing Tiantan Hospital, Capital Medical University, No.119 West Nansihuan Road, Beijing, 100070, China
| | - Yongqi He
- Department of Neuro-Oncology, Cancer Center, Beijing Tiantan Hospital, Capital Medical University, No.119 West Nansihuan Road, Beijing, 100070, China
| | - Wenhui Guo
- Department of Neuro-Oncology, Cancer Center, Beijing Tiantan Hospital, Capital Medical University, No.119 West Nansihuan Road, Beijing, 100070, China
| | - Yanru Du
- Department of Pathology, Beijing Tiantan Hospital, Capital Medical University, No.119 West Nansihuan Road, Beijing, 100070, China
| | - Shuo Yin
- Department of Neuro-Oncology, Cancer Center, Beijing Tiantan Hospital, Capital Medical University, No.119 West Nansihuan Road, Beijing, 100070, China
| | - Shoubo Yang
- Department of Neuro-Oncology, Cancer Center, Beijing Tiantan Hospital, Capital Medical University, No.119 West Nansihuan Road, Beijing, 100070, China
| | - Gehong Dong
- Department of Pathology, Beijing Tiantan Hospital, Capital Medical University, No.119 West Nansihuan Road, Beijing, 100070, China.
| | - Wenbin Li
- Department of Neuro-Oncology, Cancer Center, Beijing Tiantan Hospital, Capital Medical University, No.119 West Nansihuan Road, Beijing, 100070, China.
| | - Feng Chen
- Department of Neuro-Oncology, Cancer Center, Beijing Tiantan Hospital, Capital Medical University, No.119 West Nansihuan Road, Beijing, 100070, China.
| |
Collapse
|
5
|
Guha A, Halder S, Shinde SH, Gawde J, Munnolli S, Talole S, Goda JS. How does deep learning/machine learning perform in comparison to radiologists in distinguishing glioblastomas (or grade IV astrocytomas) from primary CNS lymphomas?: a meta-analysis and systematic review. Clin Radiol 2024; 79:460-472. [PMID: 38614870 DOI: 10.1016/j.crad.2024.03.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 03/05/2024] [Accepted: 03/07/2024] [Indexed: 04/15/2024]
Abstract
BACKGROUND Several studies have been published comparing deep learning (DL)/machine learning (ML) to radiologists in differentiating PCNSLs from GBMs with equivocal results. We aimed to perform this meta-analysis to evaluate the diagnostic accuracy of ML/DL versus radiologists in classifying PCNSL versus GBM using MRI. METHODOLOGY The study was performed in accordance with PRISMA guidelines. Data was extracted and interpreted by two researchers with 12 and 23 years' experience, respectively, and QUADAS-2 tool was used for quality and risk-bias assessment. We constructed contingency tables to derive sensitivity, specificity accuracy, summary receiver operating characteristic (SROC) curve, and the area under the curve (AUC). RESULTS Our search identified 11 studies, of which 8 satisfied our inclusion criteria and restricted the analysis in each study to reporting the model showing highest accuracy, with a total sample size of 1159 patients. The random effects model showed a pooled sensitivity of 0.89 [95% CI:0.84-0.92] for ML and 0.82 [95% CI:0.76-0.87] for radiologists. Pooled specificity was 0.88 [95% CI: 0.84-0.91] for ML and 0.90 [95% CI: 0.81-0.95] for radiologists. Pooled accuracy was 0.88 [95% CI: 0.86-0.90] for ML and 0.86 [95% CI: 0.78-0.91] for radiologists. Pooled AUC of ML was 0.94 [95% CI:0.92-0.96]and for radiologists, it was 0.90 [95% CI: 0.84-0.93]. CONCLUSIONS MRI-based ML/DL techniques can complement radiologists to improve the accuracy of classifying GBMs from PCNSL, possibly reduce the need for a biopsy, and avoid any unwanted neurosurgical resection of a PCNSL.
Collapse
Affiliation(s)
- A Guha
- Department of Radio-diagnosis, Tata Memorial Hospital, Parel, Mumbai, 400012, India; Homi Bhabha National Institute, Anushakti Nagar, Trombay, 400094, India.
| | - S Halder
- Centre for Cancer Epidemiology, Advanced Centre for Treatment Research & Education in Cancer, Tata Memorial Centre, Kharghar, Navi Mumbai, 410210, India; Homi Bhabha National Institute, Anushakti Nagar, Trombay, 400094, India
| | - S H Shinde
- Department of Radio-diagnosis, Tata Memorial Hospital, Parel, Mumbai, 400012, India; Homi Bhabha National Institute, Anushakti Nagar, Trombay, 400094, India
| | - J Gawde
- Centre for Cancer Epidemiology, Advanced Centre for Treatment Research & Education in Cancer, Tata Memorial Centre, Kharghar, Navi Mumbai, 410210, India; Homi Bhabha National Institute, Anushakti Nagar, Trombay, 400094, India
| | - S Munnolli
- Librarian and Officer In-Charge, Advanced Centre for Treatment Research & Education in Cancer, Tata Memorial Centre, Kharghar, Navi Mumbai, 410210, India; Homi Bhabha National Institute, Anushakti Nagar, Trombay, 400094, India
| | - S Talole
- Biostatistician, Centre for Cancer Epidemiology, Advanced Centre for Treatment Research & Education in Cancer, Tata Memorial Centre, Kharghar, Navi Mumbai, 410210, India; Homi Bhabha National Institute, Anushakti Nagar, Trombay, 400094, India
| | - J S Goda
- Department of Radiation Oncology, Advanced Centre for Treatment Research & Education in Cancer, Tata Memorial Centre, Kharghar, Navi Mumbai, 410210, India; Homi Bhabha National Institute, Anushakti Nagar, Trombay, 400094, India.
| |
Collapse
|
6
|
Sim Y, Kim M, Kim J, Lee SK, Han K, Sohn B. Multiparametric MRI-based radiomics model for predicting human papillomavirus status in oropharyngeal squamous cell carcinoma: optimization using oversampling and machine learning techniques. Eur Radiol 2024; 34:3102-3112. [PMID: 37848774 DOI: 10.1007/s00330-023-10338-3] [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: 08/29/2022] [Revised: 07/08/2023] [Accepted: 08/20/2023] [Indexed: 10/19/2023]
Abstract
OBJECTIVES To develop and validate a multiparametric MRI-based radiomics model with optimal oversampling and machine learning techniques for predicting human papillomavirus (HPV) status in oropharyngeal squamous cell carcinoma (OPSCC). METHODS This retrospective, multicenter study included consecutive patients with newly diagnosed and pathologically confirmed OPSCC between January 2017 and December 2020 (110 patients in the training set, 44 patients in the external validation set). A total of 293 radiomics features were extracted from three sequences (T2-weighted images [T2WI], contrast-enhanced T1-weighted images [CE-T1WI], and ADC). Combinations of three feature selection, five oversampling, and 12 machine learning techniques were evaluated to optimize its diagnostic performance. The area under the receiver operating characteristic curve (AUC) of the top five models was validated in the external validation set. RESULTS A total of 154 patients (59.2 ± 9.1 years; 132 men [85.7%]) were included, and oversampling was employed to account for data imbalance between HPV-positive and HPV-negative OPSCC (86.4% [133/154] vs. 13.6% [21/154]). For the ADC radiomics model, the combination of random oversampling and ridge showed the highest diagnostic performance in the external validation set (AUC, 0.791; 95% CI, 0.775-0.808). The ADC radiomics model showed a higher trend in diagnostic performance compared to the radiomics model using CE-T1WI (AUC, 0.604; 95% CI, 0.590-0.618), T2WI (AUC, 0.695; 95% CI, 0.673-0.717), and a combination of both (AUC, 0.642; 95% CI, 0.626-0.657). CONCLUSIONS The ADC radiomics model using random oversampling and ridge showed the highest diagnostic performance in predicting the HPV status of OPSCC in the external validation set. CLINICAL RELEVANCE STATEMENT Among multiple sequences, the ADC radiomics model has a potential for generalizability and applicability in clinical practice. Exploring multiple oversampling and machine learning techniques was a valuable strategy for optimizing radiomics model performance. KEY POINTS • Previous radiomics studies using multiparametric MRI were conducted at single centers without external validation and had unresolved data imbalances. • Among the ADC, CE-T1WI, and T2WI radiomics models and the ADC histogram models, the ADC radiomics model was the best-performing model for predicting human papillomavirus status in oropharyngeal squamous cell carcinoma. • The ADC radiomics model with the combination of random oversampling and ridge showed the highest diagnostic performance.
Collapse
Affiliation(s)
- Yongsik Sim
- Department of Radiology, Research Institute of Radiological Science, Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, South Korea
| | - Minjae Kim
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea
| | - Jinna Kim
- Department of Radiology, Research Institute of Radiological Science, Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, South Korea
| | - Seung-Koo Lee
- Department of Radiology, Research Institute of Radiological Science, Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, South Korea
| | - Kyunghwa Han
- Department of Radiology, Research Institute of Radiological Science, Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, South Korea
| | - Beomseok Sohn
- Department of Radiology, Research Institute of Radiological Science, Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, South Korea.
- Department of Radiology and Center for Imaging Sciences, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea.
| |
Collapse
|
7
|
Cheong EN, Park JE, Park SY, Jung SC, Kim HS. Achieving imaging and computational reproducibility on multiparametric MRI radiomics features in brain tumor diagnosis: phantom and clinical validation. Eur Radiol 2024; 34:2008-2023. [PMID: 37665391 DOI: 10.1007/s00330-023-10164-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Revised: 07/02/2023] [Accepted: 07/06/2023] [Indexed: 09/05/2023]
Abstract
OBJECTIVES The Image Biomarker Standardization Initiative has helped improve the computational reproducibility of MRI radiomics features. Nonetheless, the MRI sequences and features with high imaging reproducibility are yet to be established. To determine reproducible multiparametric MRI radiomics features across test-retest, multi-scanner, and computational reproducibility comparisons, and to evaluate their clinical value in brain tumor diagnosis. METHODS To assess reproducibility, T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), and diffusion-weighted imaging (DWI) were acquired from three 3-T MRI scanners using standardized phantom, and radiomics features were extracted using two computational algorithms. Reproducible radiomics features were selected when the concordance correlation coefficient value above 0.9 across multiple sessions, scanners, and computational algorithms. Random forest classifiers were trained with reproducible features (n = 117) and validated in a clinical cohort (n = 50) to evaluate whether features with high reproducibility improved the differentiation of glioblastoma from primary central nervous system lymphomas (PCNSLs). RESULTS Radiomics features from T2WI demonstrated higher repeatability (65-94%) than those from DWI (38-48%) or T1WI (2-92%). Across test-retest, multi-scanner, and computational comparisons, T2WI provided 41 reproducible features, DWI provided six, and T1WI provided two. The performance of the classification model with reproducible features was higher than that using non-reproducible features in both training set (AUC, 0.916 vs. 0.877) and validation set (AUC, 0.957 vs. 0.869). CONCLUSION Radiomics features with high reproducibility across multiple sessions, scanners, and computational algorithms were identified, and they showed higher diagnostic performance than non-reproducible radiomics features in the differentiation of glioblastoma from PCNSL. CLINICAL RELEVANCE STATEMENT By identifying the radiomics features showing higher multi-machine reproducibility, our results also demonstrated higher radiomics diagnostic performance in the differentiation of glioblastoma from PCNSL, paving the way for further research designs and clinical application in neuro-oncology. KEY POINTS • Highly reproducible radiomics features across multiple sessions, scanners, and computational algorithms were identified using phantom and applied to clinical diagnosis. • Radiomics features from T2-weighted imaging were more reproducible than those from T1-weighted and diffusion-weighted imaging. • Radiomics features with good reproducibility had better diagnostic performance for brain tumors than features with poor reproducibility.
Collapse
Affiliation(s)
- E-Nae Cheong
- Department of Medical Science and Asan Medical Institute of Convergence Science and Technology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea
| | - Ji Eun Park
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea.
| | - Seo Young Park
- Department of Statistics and Data Science, Korea National Open University, Seoul, Republic of Korea
| | - Seung Chai Jung
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea
| | - Ho Sung Kim
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea
| |
Collapse
|
8
|
Garaba A, Aslam N, Ponzio F, Panciani PP, Brinjikji W, Fontanella M, De Maria L. Radiomics for differentiation of gliomas from primary central nervous system lymphomas: a systematic review and meta-analysis. Front Oncol 2024; 14:1291861. [PMID: 38420015 PMCID: PMC10899458 DOI: 10.3389/fonc.2024.1291861] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2023] [Accepted: 01/29/2024] [Indexed: 03/02/2024] Open
Abstract
Background and objective Numerous radiomics-based models have been proposed to discriminate between central nervous system (CNS) gliomas and primary central nervous system lymphomas (PCNSLs). Given the heterogeneity of the existing models, we aimed to define their overall performance and identify the most critical variables to pilot future algorithms. Methods A systematic review of the literature and a meta-analysis were conducted, encompassing 12 studies and a total of 1779 patients, focusing on radiomics to differentiate gliomas from PCNSLs. A comprehensive literature search was performed through PubMed, Ovid MEDLINE, Ovid EMBASE, Web of Science, and Scopus databases. Overall sensitivity (SEN) and specificity (SPE) were estimated. Event rates were pooled using a random-effects meta-analysis, and the heterogeneity was assessed using the χ2 test. Results The overall SEN and SPE for differentiation between CNS gliomas and PCNSLs were 88% (95% CI = 0.83 - 0.91) and 87% (95% CI = 0.83 - 0.91), respectively. The best-performing features were the ones extracted from the Gray Level Run Length Matrix (GLRLM; ACC 97%), followed by those obtained from the Neighboring Gray Tone Difference Matrix (NGTDM; ACC 93%), and shape-based features (ACC 91%). The 18F-FDG-PET/CT was the best-performing imaging modality (ACC 97%), followed by the MRI CE-T1W (ACC 87% - 95%). Most studies applied a cross-validation analysis (92%). Conclusion The current SEN and SPE of radiomics to discriminate CNS gliomas from PCNSLs are high, making radiomics a helpful method to differentiate these tumor types. The best-performing features are the GLRLM, NGTDM, and shape-based features. The 18F-FDG-PET/CT imaging modality is the best-performing, while the MRI CE-T1W is the most used.
Collapse
Affiliation(s)
- Alexandru Garaba
- Department of Surgical Specialties, Radiological Sciences, and Public Health, University of Brescia, Brescia, Italy
| | - Nummra Aslam
- Department of Surgical Specialties, Radiological Sciences, and Public Health, University of Brescia, Brescia, Italy
| | - Francesco Ponzio
- Interuniversity Department of Regional and Urban Studies and Planning, Politecnico di Torino, Torino, Italy
| | - Pier Paolo Panciani
- Department of Surgical Specialties, Radiological Sciences, and Public Health, University of Brescia, Brescia, Italy
| | - Waleed Brinjikji
- Department of Neurosurgery and Interventional Neuroradiology, Mayo Clinic, Rochester, MN, United States
| | - Marco Fontanella
- Department of Surgical Specialties, Radiological Sciences, and Public Health, University of Brescia, Brescia, Italy
| | - Lucio De Maria
- Department of Surgical Specialties, Radiological Sciences, and Public Health, University of Brescia, Brescia, Italy
- Department of Clinical Neuroscience, Geneva University Hospitals (HUG), Geneva, Switzerland
| |
Collapse
|
9
|
Sabeghi P, Zarand P, Zargham S, Golestany B, Shariat A, Chang M, Yang E, Rajagopalan P, Phung DC, Gholamrezanezhad A. Advances in Neuro-Oncological Imaging: An Update on Diagnostic Approach to Brain Tumors. Cancers (Basel) 2024; 16:576. [PMID: 38339327 PMCID: PMC10854543 DOI: 10.3390/cancers16030576] [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: 12/27/2023] [Revised: 01/22/2024] [Accepted: 01/24/2024] [Indexed: 02/12/2024] Open
Abstract
This study delineates the pivotal role of imaging within the field of neurology, emphasizing its significance in the diagnosis, prognostication, and evaluation of treatment responses for central nervous system (CNS) tumors. A comprehensive understanding of both the capabilities and limitations inherent in emerging imaging technologies is imperative for delivering a heightened level of personalized care to individuals with neuro-oncological conditions. Ongoing research in neuro-oncological imaging endeavors to rectify some limitations of radiological modalities, aiming to augment accuracy and efficacy in the management of brain tumors. This review is dedicated to the comparison and critical examination of the latest advancements in diverse imaging modalities employed in neuro-oncology. The objective is to investigate their respective impacts on diagnosis, cancer staging, prognosis, and post-treatment monitoring. By providing a comprehensive analysis of these modalities, this review aims to contribute to the collective knowledge in the field, fostering an informed approach to neuro-oncological care. In conclusion, the outlook for neuro-oncological imaging appears promising, and sustained exploration in this domain is anticipated to yield further breakthroughs, ultimately enhancing outcomes for individuals grappling with CNS tumors.
Collapse
Affiliation(s)
- Paniz Sabeghi
- Department of Radiology, Keck School of Medicine, University of Southern California, 1500 San Pablo St., Los Angeles, CA 90033, USA; (P.S.); (E.Y.); (P.R.); (D.C.P.)
| | - Paniz Zarand
- School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran 1985717411, Iran;
| | - Sina Zargham
- Department of Basic Science, California Northstate University College of Medicine, 9700 West Taron Drive, Elk Grove, CA 95757, USA;
| | - Batis Golestany
- Division of Biomedical Sciences, Riverside School of Medicine, University of California, 900 University Ave., Riverside, CA 92521, USA;
| | - Arya Shariat
- Kaiser Permanente Los Angeles Medical Center, 4867 W Sunset Blvd, Los Angeles, CA 90027, USA;
| | - Myles Chang
- Keck School of Medicine, University of Southern California, 1975 Zonal Avenue, Los Angeles, CA 90089, USA;
| | - Evan Yang
- Department of Radiology, Keck School of Medicine, University of Southern California, 1500 San Pablo St., Los Angeles, CA 90033, USA; (P.S.); (E.Y.); (P.R.); (D.C.P.)
| | - Priya Rajagopalan
- Department of Radiology, Keck School of Medicine, University of Southern California, 1500 San Pablo St., Los Angeles, CA 90033, USA; (P.S.); (E.Y.); (P.R.); (D.C.P.)
| | - Daniel Chang Phung
- Department of Radiology, Keck School of Medicine, University of Southern California, 1500 San Pablo St., Los Angeles, CA 90033, USA; (P.S.); (E.Y.); (P.R.); (D.C.P.)
| | - Ali Gholamrezanezhad
- Department of Radiology, Keck School of Medicine, University of Southern California, 1500 San Pablo St., Los Angeles, CA 90033, USA; (P.S.); (E.Y.); (P.R.); (D.C.P.)
| |
Collapse
|
10
|
何 慧, 郭 二, 蒙 文, 王 彧, 王 雯, 何 文, 吴 元, 阳 维. [Predicting cerebral glioma enhancement pattern using a machine learning-based magnetic resonance imaging radiomics model]. NAN FANG YI KE DA XUE XUE BAO = JOURNAL OF SOUTHERN MEDICAL UNIVERSITY 2024; 44:194-200. [PMID: 38293992 PMCID: PMC10878898 DOI: 10.12122/j.issn.1673-4254.2024.01.23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Indexed: 02/01/2024]
Abstract
OBJECTIVE To establish a machine learning radiomics model that can accurately predict MRI enhancement patterns of glioma based on T2 fluid attenuated inversion recovery (T2-FLAIR) images for optimizing the workflow of magnetic resonance imaging (MRI) examinations of glioma patients. METHODS We retrospectively collected preoperative MR T2-FLAIR images from 385 patients with pathologically confirmed glioma, who were divided into enhancing and non-enhancing groups according to the enhancement pattern. Predictive radiomics models were established using Gaussian Process, Linear Regression, Linear Regression-Least absolute shrinkage and selection operator, Support Vector Machine, Linear Discriminant Analysis or Naive Bayes as the classifiers in the training cohort (n=201)and tested both in the internal (n=85) and external validation cohorts (n=99). The receiver-operating characteristic curve was used to assess the predictive performance of the models. RESULTS The predictive model constructed based on 15 radiomics features using Gaussian Process as the classifier had the best predictive performance in both the training cohort and the internal validation cohort, with areas under the curve (AUC) of 0.88 (95% CI: 0.81-0.94) and 0.80 (95% CI: 0.71-0.88), respectively. In the external validation cohort, the model showed an AUC of 0.81 (95% CI: 0.71-0.90) with sensitivity, specificity, positive predictive value and negative predictive value of 0.98, 0.61, 0.76 and 0.96, respectively. CONCLUSION The T2-FLAIR-based machine learning radiomics model can accurately predict the enhancement pattern of gliomas on MRI.
Collapse
Affiliation(s)
- 慧珊 何
- 南方医科大学南方医院(第一临床医学院),广东 广州 510515Nanfang Hospital/First School of Clinical Medicine, Southern Medical University, Guangzhou 510515, China
| | - 二嘉 郭
- 南方医科大学南方医院(第一临床医学院),广东 广州 510515Nanfang Hospital/First School of Clinical Medicine, Southern Medical University, Guangzhou 510515, China
| | - 文仪 蒙
- 南方医科大学南方医院(第一临床医学院),广东 广州 510515Nanfang Hospital/First School of Clinical Medicine, Southern Medical University, Guangzhou 510515, China
| | - 彧 王
- 南方医科大学南方医院(第一临床医学院),广东 广州 510515Nanfang Hospital/First School of Clinical Medicine, Southern Medical University, Guangzhou 510515, China
| | - 雯 王
- 南方医科大学南方医院(第一临床医学院),广东 广州 510515Nanfang Hospital/First School of Clinical Medicine, Southern Medical University, Guangzhou 510515, China
| | - 文乐 何
- 广东三九脑科医院影像中心,广东 广州 510515Medical Imaging Center, Guangdong 999 Brain Hospital, Guangzhou 510515, China
| | - 元魁 吴
- 南方医科大学南方医院(第一临床医学院),广东 广州 510515Nanfang Hospital/First School of Clinical Medicine, Southern Medical University, Guangzhou 510515, China
| | - 维 阳
- 南方医科大学生物医学工程学院,广东 广州 510515School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
| |
Collapse
|
11
|
Kolasa K, Admassu B, Hołownia-Voloskova M, Kędzior KJ, Poirrier JE, Perni S. Systematic reviews of machine learning in healthcare: a literature review. Expert Rev Pharmacoecon Outcomes Res 2024; 24:63-115. [PMID: 37955147 DOI: 10.1080/14737167.2023.2279107] [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: 07/17/2023] [Accepted: 10/31/2023] [Indexed: 11/14/2023]
Abstract
INTRODUCTION The increasing availability of data and computing power has made machine learning (ML) a viable approach to faster, more efficient healthcare delivery. METHODS A systematic literature review (SLR) of published SLRs evaluating ML applications in healthcare settings published between1 January 2010 and 27 March 2023 was conducted. RESULTS In total 220 SLRs covering 10,462 ML algorithms were reviewed. The main application of AI in medicine related to the clinical prediction and disease prognosis in oncology and neurology with the use of imaging data. Accuracy, specificity, and sensitivity were provided in 56%, 28%, and 25% SLRs respectively. Internal and external validation was reported in 53% and less than 1% of the cases respectively. The most common modeling approach was neural networks (2,454 ML algorithms), followed by support vector machine and random forest/decision trees (1,578 and 1,522 ML algorithms, respectively). EXPERT OPINION The review indicated considerable reporting gaps in terms of the ML's performance, both internal and external validation. Greater accessibility to healthcare data for developers can ensure the faster adoption of ML algorithms into clinical practice.
Collapse
Affiliation(s)
- Katarzyna Kolasa
- Division of Health Economics and Healthcare Management, Kozminski University, Warsaw, Poland
| | - Bisrat Admassu
- Division of Health Economics and Healthcare Management, Kozminski University, Warsaw, Poland
| | | | | | | | | |
Collapse
|
12
|
Li T, Chen X, Jing Y, Wang H, Zhang T, Zhang L, Ding H, Xie M, He L. Diagnostic Value of Multiparameter MRI-Based Radiomics in Pediatric Myelin Oligodendrocyte Glycoprotein Antibody-Associated Disorders. AJNR Am J Neuroradiol 2023; 44:1425-1431. [PMID: 37973182 PMCID: PMC10714848 DOI: 10.3174/ajnr.a8045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Accepted: 09/28/2023] [Indexed: 11/19/2023]
Abstract
BACKGROUND AND PURPOSE Myelin oligodendrocyte glycoprotein antibody-associated disorders (MOGAD) have a higher prevalence among children. For children undergoing the initial manifestation of MOGAD, prompt diagnosis has paramount importance. This study assessed the performance of multiparameter MRI-based radiomics in distinguishing patients with and without MOGAD with idiopathic inflammatory demyelinating diseases. MATERIALS AND METHODS We enrolled a cohort of 121 patients diagnosed with idiopathic inflammatory demyelinating diseases, including 68 children with MOGAD and 53 children without MOGAD. Radiomics models (T1WI, T2WI, FLAIR, and compound model) using features extracted from demyelinating lesions within the brain parenchyma were developed in the training set. The performance of these models underwent validation within the internal testing set. Additionally, we gathered clinical factors and MRI features of brain parenchymal lesions at their initial presentation. Subsequently, these variables were used in the construction of a clinical prediction model through multivariate logistic regression analysis. RESULTS The areas under the curve for the radiomics models (T1WI, T2WI, FLAIR, and the compound model) in the training set were 0.781 (95% CI, 0.689-0.864), 0.959 (95% CI, 0.924-0.987), 0.939 (95% CI, 0.898-0.979), and 0.989 (95% CI, 0.976-0.999), respectively. The areas under the curve for the radiomics models (T1WI, T2WI, FLAIR, and the compound model) in the testing set were 0.500 (95% CI, 0.304-0.652), 0.833 (95% CI, 0.697-0.944), 0.804 (95% CI, 0.664-0.918), and 0.905 (95% CI, 0.803-0.979), respectively. The areas under the curve of the clinical prediction model in the training set and testing set were 0.700 and 0.289, respectively. CONCLUSIONS Multiparameter MRI-based radiomics helps distinguish MOGAD from non-MOGAD in patients with idiopathic inflammatory demyelinating diseases.
Collapse
Affiliation(s)
- Ting Li
- From the Department of Radiology (T.L., X.C., H.W., T.Z., L.Z., H.D., M.X., L.H.), Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Chongqing, China
| | - Xin Chen
- From the Department of Radiology (T.L., X.C., H.W., T.Z., L.Z., H.D., M.X., L.H.), Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Chongqing, China
| | - Yang Jing
- Huiying Medical Technology Co (Y.J.), Dongsheng Science and Technology Park, Beijing, China
| | - Haoru Wang
- From the Department of Radiology (T.L., X.C., H.W., T.Z., L.Z., H.D., M.X., L.H.), Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Chongqing, China
| | - Ting Zhang
- From the Department of Radiology (T.L., X.C., H.W., T.Z., L.Z., H.D., M.X., L.H.), Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Chongqing, China
| | - Li Zhang
- From the Department of Radiology (T.L., X.C., H.W., T.Z., L.Z., H.D., M.X., L.H.), Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Chongqing, China
| | - Hao Ding
- From the Department of Radiology (T.L., X.C., H.W., T.Z., L.Z., H.D., M.X., L.H.), Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Chongqing, China
| | - Mingye Xie
- From the Department of Radiology (T.L., X.C., H.W., T.Z., L.Z., H.D., M.X., L.H.), Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Chongqing, China
| | - Ling He
- From the Department of Radiology (T.L., X.C., H.W., T.Z., L.Z., H.D., M.X., L.H.), Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Chongqing, China
| |
Collapse
|
13
|
Sufyan M, Shokat Z, Ashfaq UA. Artificial intelligence in cancer diagnosis and therapy: Current status and future perspective. Comput Biol Med 2023; 165:107356. [PMID: 37688994 DOI: 10.1016/j.compbiomed.2023.107356] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 07/21/2023] [Accepted: 08/12/2023] [Indexed: 09/11/2023]
Abstract
Artificial intelligence (AI) in healthcare plays a pivotal role in combating many fatal diseases, such as skin, breast, and lung cancer. AI is an advanced form of technology that uses mathematical-based algorithmic principles similar to those of the human mind for cognizing complex challenges of the healthcare unit. Cancer is a lethal disease with many etiologies, including numerous genetic and epigenetic mutations. Cancer being a multifactorial disease is difficult to be diagnosed at an early stage. Therefore, genetic variations and other leading factors could be identified in due time through AI and machine learning (ML). AI is the synergetic approach for mining the drug targets, their mechanism of action, and drug-organism interaction from massive raw data. This synergetic approach is also facing several challenges in data mining but computational algorithms from different scientific communities for multi-target drug discovery are highly helpful to overcome the bottlenecks in AI for drug-target discovery. AI and ML could be the epicenter in the medical world for the diagnosis, treatment, and evaluation of almost any disease in the near future. In this comprehensive review, we explore the immense potential of AI and ML when integrated with the biological sciences, specifically in the context of cancer research. Our goal is to illuminate the many ways in which AI and ML are being applied to the study of cancer, from diagnosis to individualized treatment. We highlight the prospective role of AI in supporting oncologists and other medical professionals in making informed decisions and improving patient outcomes by examining the intersection of AI and cancer control. Although AI-based medical therapies show great potential, many challenges must be overcome before they can be implemented in clinical practice. We critically assess the current hurdles and provide insights into the future directions of AI-driven approaches, aiming to pave the way for enhanced cancer interventions and improved patient care.
Collapse
Affiliation(s)
- Muhammad Sufyan
- Department of Bioinformatics and Biotechnology, Government College University Faisalabad, Pakistan.
| | - Zeeshan Shokat
- Department of Bioinformatics and Biotechnology, Government College University Faisalabad, Pakistan.
| | - Usman Ali Ashfaq
- Department of Bioinformatics and Biotechnology, Government College University Faisalabad, Pakistan.
| |
Collapse
|
14
|
Huang Z, Chen X, Jiang N, Hu S, Hu C. A clinical radiomics nomogram preoperatively to predict ductal carcinoma in situ with microinvasion in women with biopsy-confirmed ductal carcinoma in situ: a preliminary study. BMC Med Imaging 2023; 23:118. [PMID: 37679713 PMCID: PMC10483851 DOI: 10.1186/s12880-023-01092-5] [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: 01/02/2023] [Accepted: 08/30/2023] [Indexed: 09/09/2023] Open
Abstract
PURPOSE To predict ductal carcinoma in situ with microinvasion (DCISMI) based on clinicopathologic, conventional breast magnetic resonance imaging (MRI), and dynamic contrast enhanced MRI (DCE-MRI) radiomics signatures in women with biopsy-confirmed ductal carcinoma in situ (DCIS). METHODS Eighty-six women with eighty-seven biopsy-proven DCIS who underwent preoperative MRI and underwent surgery were retrospectively identified. Clinicopathologic, conventional MRI, DCE-MRI radiomics, combine (based on conventional MRI and DCE-MRI radiomics), traditional (based on clinicopathologic and conventional MRI) and mixed (based on clinicopathologic, conventional MRI and DCE-MRI radiomics) models were constructed by logistic regression (LR) with a 3-fold cross-validation, all evaluated using receiver operating characteristic (ROC) curve analysis. A clinical radiomics nomogram was then built by incorporating the Radiomics score, significant clinicopathologic and conventional MRI features of mixed model. RESULTS The area under the curves (AUCs) of clinicopathologic, conventional MRI, DCE-MRI radiomics, traditional, combine, and mixed model were 0.76 (95% confidence interval [CI] 0.59-0.94), 0.77 (95%CI 0.59-0.95), 0.74 (95%CI 0.55-0.93), 0.87 (95%CI 0.73-1), 0.8 (95%CI 0.63-0.96), and 0.93 (95%CI 0.84-1) in the validation cohort, respectively. The clinical radiomics nomogram based on mixed model showed higher AUCs than both clinicopathologic and DCE-MRI radiomics models in training/test (all P < 0.05) set and showed the greatest overall net benefit for upstaging according to decision curve analysis (DCA). CONCLUSION A nomogram constructed by combining clinicopathologic, conventional MRI features and DCE-MRI radiomics signatures may be useful in predicting DCISMI from DICS preoperatively.
Collapse
Affiliation(s)
- Zhou Huang
- Department of Radiology, the First Affiliated Hospital of Soochow University, No. 899 Pinghai Road, Gusu District, Suzhou City, Jiangsu Province, 215006, PR China
| | - Xue Chen
- Department of Radiology, the Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Suzhou City, Jiangsu Province, 215002, PR China
| | - Nan Jiang
- Department of Radiology, the First Affiliated Hospital of Soochow University, No. 899 Pinghai Road, Gusu District, Suzhou City, Jiangsu Province, 215006, PR China
| | - Su Hu
- Department of Radiology, the First Affiliated Hospital of Soochow University, No. 899 Pinghai Road, Gusu District, Suzhou City, Jiangsu Province, 215006, PR China
| | - Chunhong Hu
- Department of Radiology, the First Affiliated Hospital of Soochow University, No. 899 Pinghai Road, Gusu District, Suzhou City, Jiangsu Province, 215006, PR China.
| |
Collapse
|
15
|
Jacobs F, D'Amico S, Benvenuti C, Gaudio M, Saltalamacchia G, Miggiano C, De Sanctis R, Della Porta MG, Santoro A, Zambelli A. Opportunities and Challenges of Synthetic Data Generation in Oncology. JCO Clin Cancer Inform 2023; 7:e2300045. [PMID: 37535875 DOI: 10.1200/cci.23.00045] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 05/05/2023] [Accepted: 05/25/2023] [Indexed: 08/05/2023] Open
Abstract
Widespread interest in artificial intelligence (AI) in health care has focused mainly on deductive systems that analyze available real-world data to discover patterns not otherwise visible. Generative adversarial network, a new type of inductive AI, has recently evolved to generate high-fidelity virtual synthetic data (SD) trained on relatively limited real-world information. The AI system is fed with a collection of real data, and it learns to generate new augmented data while maintaining the general characteristics of the original data set. The use of SD to enhance clinical research and protect patient privacy has drawn a lot of interest in medicine and in the complex field of oncology. This article summarizes the main characteristics of this innovative technology and critically discusses how it can be used to accelerate data access for secondary purposes, providing an overview of the opportunities and challenges of SD generation for clinical cancer research and health care.
Collapse
Affiliation(s)
- Flavia Jacobs
- Department of Biomedical Sciences, Humanitas University, Milan, Italy
- IRCCS Istituto Clinico Humanitas, Milan, Italy
| | | | - Chiara Benvenuti
- Department of Biomedical Sciences, Humanitas University, Milan, Italy
- IRCCS Istituto Clinico Humanitas, Milan, Italy
| | - Mariangela Gaudio
- Department of Biomedical Sciences, Humanitas University, Milan, Italy
- IRCCS Istituto Clinico Humanitas, Milan, Italy
| | | | - Chiara Miggiano
- Department of Biomedical Sciences, Humanitas University, Milan, Italy
- IRCCS Istituto Clinico Humanitas, Milan, Italy
| | - Rita De Sanctis
- Department of Biomedical Sciences, Humanitas University, Milan, Italy
- IRCCS Istituto Clinico Humanitas, Milan, Italy
| | - Matteo Giovanni Della Porta
- Department of Biomedical Sciences, Humanitas University, Milan, Italy
- IRCCS Istituto Clinico Humanitas, Milan, Italy
| | - Armando Santoro
- Department of Biomedical Sciences, Humanitas University, Milan, Italy
- IRCCS Istituto Clinico Humanitas, Milan, Italy
| | - Alberto Zambelli
- Department of Biomedical Sciences, Humanitas University, Milan, Italy
- IRCCS Istituto Clinico Humanitas, Milan, Italy
| |
Collapse
|
16
|
Yu X, Hong W, Ye M, Lai M, Shi C, Li L, Ye K, Xu J, Ai R, Shan C, Cai L, Luo L. Atypical primary central nervous system lymphoma and glioblastoma: multiparametric differentiation based on non-enhancing volume, apparent diffusion coefficient, and arterial spin labeling. Eur Radiol 2023; 33:5357-5367. [PMID: 37171492 PMCID: PMC10326108 DOI: 10.1007/s00330-023-09681-2] [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: 06/11/2022] [Revised: 01/02/2023] [Accepted: 02/24/2023] [Indexed: 05/13/2023]
Abstract
OBJECTIVES To evaluate the multiparametric diagnostic performance with non-enhancing tumor volume, apparent diffusion coefficient (ADC), and arterial spin labeling (ASL) to differentiate between atypical primary central nervous system lymphoma (PCNSL) and glioblastoma (GBM). METHODS One hundred and fifty-eight patients with pathologically confirmed typical PCNSL (n = 59), atypical PCNSL (hemorrhage, necrosis, or heterogeneous contrast enhancement, n = 29), and GBM (n = 70) were selected. Relative minimum ADC (rADCmin), mean (rADCmean), maximum (rADCmax), and rADCmax-min (rADCdif) were obtained by standardization of the contralateral white matter. Maximum cerebral blood flow (CBFmax) was obtained according to the ASL-CBF map. The regions of interests (ROIs) were manually delineated on the inner side of the tumor to further generate a 3D-ROI and obtain the non-enhancing tumor (nET) volume. The area under the curve (AUC) was used to evaluate the diagnostic performance. RESULTS Atypical PCNSLs showed significantly lower rADCmax, rADCmean, and rADCdif than that of GBMs. GBMs showed significantly higher CBFmax and nET volume ratios than that of atypical PCNSLs. Combined three-variable models with rADCmean, CBFmax, and nET volume ratio were superior to one- and two-variable models. The AUC of the three-variable model was 0.96, and the sensitivity and specificity were 90% and 96.55%, respectively. CONCLUSION The combined evaluation of rADCmean, CBFmax, and nET volume allowed for reliable differentiation between atypical PCNSL and GBM. KEY POINTS • Atypical PCNSL is easily misdiagnosed as glioblastoma, which leads to unnecessary surgical resection. • The nET volume, ADC, and ASL-derived parameter (CBF) were lower for atypical PCNSL than that for glioblastoma. • The combination of multiple parameters performed well (AUC = 0.96) in the discrimination between atypical PCNSL and glioblastoma.
Collapse
Affiliation(s)
- Xiaojun Yu
- Medical Imaging Center, The First Affiliated Hospital of Jinan University, No. 613, Huangpu Road West, Tianhe District, Guangdong Province, Guangzhou, 510630, China
| | - Weiping Hong
- Department of Oncology, Guangdong Sanjiu Brain Hospital, Guangzhou, 510510, China
| | - Minting Ye
- Department of Oncology, Guangdong Sanjiu Brain Hospital, Guangzhou, 510510, China
| | - Mingyao Lai
- Department of Oncology, Guangdong Sanjiu Brain Hospital, Guangzhou, 510510, China
| | - Changzheng Shi
- Medical Imaging Center, The First Affiliated Hospital of Jinan University, No. 613, Huangpu Road West, Tianhe District, Guangdong Province, Guangzhou, 510630, China
| | - Linzhen Li
- Medical Imaging Center, The First Affiliated Hospital of Jinan University, No. 613, Huangpu Road West, Tianhe District, Guangdong Province, Guangzhou, 510630, China
| | - Kunlin Ye
- Medical Imaging Center, The First Affiliated Hospital of Jinan University, No. 613, Huangpu Road West, Tianhe District, Guangdong Province, Guangzhou, 510630, China
| | - Jiali Xu
- Medical Imaging Center, The First Affiliated Hospital of Jinan University, No. 613, Huangpu Road West, Tianhe District, Guangdong Province, Guangzhou, 510630, China
| | - Ruyu Ai
- Department of Oncology, Guangdong Sanjiu Brain Hospital, Guangzhou, 510510, China
| | - Changguo Shan
- Department of Oncology, Guangdong Sanjiu Brain Hospital, Guangzhou, 510510, China
| | - Linbo Cai
- Department of Oncology, Guangdong Sanjiu Brain Hospital, Guangzhou, 510510, China.
| | - Liangping Luo
- Medical Imaging Center, The First Affiliated Hospital of Jinan University, No. 613, Huangpu Road West, Tianhe District, Guangdong Province, Guangzhou, 510630, China.
| |
Collapse
|
17
|
Fushimi Y, Nakajima S. Can we distinguish atypical primary brain lymphoma from glioblastoma multiforme by looking at the non-enhancing tumor? Eur Radiol 2023; 33:5355-5356. [PMID: 37212848 DOI: 10.1007/s00330-023-09750-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2023] [Revised: 03/26/2023] [Accepted: 04/01/2023] [Indexed: 05/23/2023]
Affiliation(s)
- Yasutaka Fushimi
- Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
| | - Satoshi Nakajima
- Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| |
Collapse
|
18
|
Feng A, Li L, Huang T, Li S, He N, Huang L, Zeng M, Lyu J. Differentiating glioblastoma from primary central nervous system lymphoma of atypical manifestation using multiparametric magnetic resonance imaging: A comparative study. Heliyon 2023; 9:e15150. [PMID: 37095995 PMCID: PMC10121909 DOI: 10.1016/j.heliyon.2023.e15150] [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: 01/06/2023] [Revised: 03/24/2023] [Accepted: 03/28/2023] [Indexed: 04/26/2023] Open
Abstract
Background The aim of this study is to evaluate the diagnostic efficiency of magnetic resonance imaging (MRI) of single parameters, unimodality, and bimodality in distinguishing glioblastoma (GBM) from atypical primary central nervous system lymphoma (PCNSL) based on diffusion-weighted imaging (DWI), dynamic susceptibility contrast (DSC) enhancement, diffusion tensor imaging (DTI), and proton magnetic resonance spectroscopy (1H-MRS) findings. Methods The cohort included 108 patients pathologically diagnosed with GBM and 54 patients pathologically diagnosed with PCNSL. Pretreatment morphological MRI, DWI, DSC, DTI and MRS were all performed on each patient. The quantitative parameters of multimodal MRI were measured and compared between the patients in the GBM and atypical PCNSL groups, and those parameters showing a significant difference (p < 0.05) between patients in the GBM and atypical PCNSL groups were used to develop one-parameters, unimodality, and bimodality models. We evaluated the efficiency of different models in distinguishing GBM from atypical PCNSL by performing receiver operating characteristic analysis (ROC). Results Atypical PCNSL had lower minimum apparent diffusion coefficient (ADCmin), mean ADC (ADCmean), relative ADC (rADC), mean relative cerebral blood volume (rCBVmean), maximum rCBV (rCBVmax), fractional anisotropy (FA), axial diffusion coefficient (DA) and radial diffusion coefficient (DR) values and higher choline/creatine (Cho/Cr) and lipid/creatine (Lip/Cr) ratios than GBM (all p < 0.05). The rCBVmax, DTI and DSC + DTI data were optimal models of single-parameter, unimodality and bimodality for differentiation of GBM from atypical PCNSL, yielding areas under the curves (AUCs) of 0.905, 0.954, and 0.992, respectively. Conclusions Models of single-parameter, unimodality and bimodality based on muti multiparameter functional MRI may help to discriminate GBM from atypical PCNSL.
Collapse
Affiliation(s)
- Aozi Feng
- Department of Clinical Research, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong 510632, China
| | - Li Li
- Department of Clinical Research, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong 510632, China
| | - Tao Huang
- Department of Clinical Research, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong 510632, China
| | - Shuna Li
- Department of Clinical Research, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong 510632, China
| | - Ningxia He
- Department of Clinical Research, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong 510632, China
| | - Liying Huang
- Department of Clinical Research, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong 510632, China
| | - Mengnan Zeng
- College of Pharmacy, Henan University of Chinese Medicine, Zhengzhou, Henan 450046, China
- Corresponding author.
| | - Jun Lyu
- Department of Clinical Research, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong 510632, China
- Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Informatization, Guangzhou, Guangdong 510632, China
- Corresponding author. Department of Clinical Research, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong 510632, China.
| |
Collapse
|
19
|
Cui C, Yao X, Xu L, Chao Y, Hu Y, Zhao S, Hu Y, Zhang J. Improving the Classification of PCNSL and Brain Metastases by Developing a Machine Learning Model Based on 18F-FDG PET. J Pers Med 2023; 13:jpm13030539. [PMID: 36983721 PMCID: PMC10056979 DOI: 10.3390/jpm13030539] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Revised: 03/06/2023] [Accepted: 03/14/2023] [Indexed: 03/19/2023] Open
Abstract
Background: The characteristic magnetic resonance imaging (MRI) and the positron emission tomography (PET) findings of PCNSL often overlap with other intracranial tumors, making definitive diagnosis challenging. PCNSL typically shows iso-hypointense to grey matter on T2-weighted imaging. However, a particular part of PCNSL can demonstrate T2-weighted hyperintensity as other intracranial tumors. Moreover, normal high uptake of FDG in the basal ganglia, thalamus, and grey matter can mask underlying PCNSL in 18F-FDG PET. In order to promote the efficiency of diagnosis, the MRI-based or PET/CT-based radiomics models combining histograms with texture features in diagnosing glioma and brain metastases have been widely established. However, the diagnosing model for PCNSL has not been widely reported. The study was designed to investigate a machine-learning (ML) model based on multiple parameters of 2-deoxy-2-[18F]-floor-D-glucose (18F-FDG) PET for differential diagnosis of PCNSL and metastases in the brain. Methods: Patients who underwent an 18F-FDG PET scan with untreated PCNSL or metastases in the brain were included between May 2016 and May 2022. A total of 126 lesions from 51 patients (43 patients with untreated brain metastases and eight patients with untreated PCNSL), including 14 lesions of PCNSL, and 112 metastatic lesions in the brain, met the inclusion criteria. PCNSL or brain metastasis was confirmed after pathology or clinical history. Principal component analysis (PCA) was used to decompose the datasets. Logistic regression (LR), support vector machine (SVM), and random forest classification (RFC) models were trained by two different groups of datasets, the group of multi-class features and the group of density features, respectively. The model with the highest mean precision score was selected. The testing sets and original data were used to examine the efficacy of models separately by using the weighted average F1 score and area under the curve (AUC) of the receiver operating characteristic curve (ROC). Results: The multi-class features-based RFC and SVM models reached identical weighted-average F1 scores in the testing set, and the score was 0.98. The AUCs of RFC and SVM models calculated from the testing set were 1.00 equally. Evaluated by the original dataset, the RFC model based on multi-class features performs better than the SVM model, whose weighted-average F1 scores of the RFC model calculated from the original data were 0.85 with an AUC of 0.93. Conclusions: The ML based on multi-class features of 18F-FDG PET exhibited the potential to distinguish PCNSL from brain metastases. The RFC models based on multi-class features provided comparatively high efficiency in our study.
Collapse
Affiliation(s)
- Can Cui
- Department of PET/CT Center, Jiangsu Cancer Hospital and Jiangsu Institute of Cancer Research and the Affiliated Cancer Hospital of Nanjing Medical University, Nanjing 210009, China
| | - Xiaochen Yao
- Department of Nuclear Medicine, Nanjing First Hospital, Nanjing Medical University, Nanjing 210006, China
| | - Lei Xu
- Department of Nuclear Medicine, Nanjing First Hospital, Nanjing Medical University, Nanjing 210006, China
| | - Yuelin Chao
- Department of Cardiology, Nanjing First Hospital, Nanjing Medical University, Nanjing 210006, China
| | - Yao Hu
- Department of PET/CT Center, Jiangsu Cancer Hospital and Jiangsu Institute of Cancer Research and the Affiliated Cancer Hospital of Nanjing Medical University, Nanjing 210009, China
| | - Shuang Zhao
- Department of PET/CT Center, Jiangsu Cancer Hospital and Jiangsu Institute of Cancer Research and the Affiliated Cancer Hospital of Nanjing Medical University, Nanjing 210009, China
| | - Yuxiao Hu
- Department of PET/CT Center, Jiangsu Cancer Hospital and Jiangsu Institute of Cancer Research and the Affiliated Cancer Hospital of Nanjing Medical University, Nanjing 210009, China
- Correspondence: ; Tel.: +86-2583284736
| | - Jia Zhang
- Department of PET/CT Center, Jiangsu Cancer Hospital and Jiangsu Institute of Cancer Research and the Affiliated Cancer Hospital of Nanjing Medical University, Nanjing 210009, China
| |
Collapse
|
20
|
Wang J, Xiong X, Zou J, Fu J, Yin Y, Ye J. Combination of Hematoma Volume and Perihematoma Radiomics Analysis on Baseline CT Scan Predicts the Growth of Perihematomal Edema. Clin Neuroradiol 2023; 33:199-209. [PMID: 35943522 DOI: 10.1007/s00062-022-01201-x] [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: 02/23/2022] [Accepted: 07/11/2022] [Indexed: 11/28/2022]
Abstract
PURPOSE The aim is to explore the potential value of CT-based radiomics in predicting perihematomal edema (PHE) volumes after acute intracerebral hemorrhage (ICH) from admission to 24 h. METHODS A total of 231 patients newly diagnosed with acute ICH at two institutes were analyzed retrospectively. The patients were randomly divided into training (N = 117) and internal validation cohort (N = 45) from institute 1 with a ratio of 7:3. According to radiomics features extracted from baseline CT, the radiomics signatures were constructed. Multiple logistic regression analysis was used for clinical radiological factors and then the nomogram model was generated to predict the extent of PHE according to the optimal radiomics signature and the clinical radiological factors. The receiver operating characteristic (ROC) curve was used to evaluate the discrimination performance. The calibration curve and Hosmer-Lemeshow test were used to evaluate the consistency between the predicted and actual probability. The support vector regression (SVR) model was constructed to predict the overall value of follow-up PHE. The performance of the models was evaluated on the internal and independent validation cohorts. RESULTS The perihematoma 5 mm radiomics signature (AUC: 0.875) showed good ability to discriminate the small relative PHE(rPHE) from large rPHE volumes, comparing to intrahematoma radiomics signature (AUC: 0.711) or perihematoma 10 mm radiomics signature (AUC: 0.692) on the training cohort. The AUC of the combined nomogram model was 0.922 for the training cohort, 0.945 and 0.902 for the internal and independent validation cohorts, respectively. The calibration curves and Hosmer-Lemeshow test of the nomogram model suggested that the predictive performance and actual outcome were in favorable agreement. The SVR model also predicted the overall value of follow-up rPHE (root mean squared error, 0.60 and 0.45; Pearson correlation coefficient, 0.73 and 0.68; P < 0.001). CONCLUSION Among patients with acute ICH, the established nomogram and SVR model with favorable performance can offer a noninvasive tool for the prediction of PHE after ICH.
Collapse
Affiliation(s)
- Jia Wang
- Department of Radiology, Northern Jiangsu People's Hospital, 225001, Yangzhou, China
| | - Xing Xiong
- Department of Radiology, The First Affiliated Hospital of Soochow University, 215006, Suzhou, Jiangsu, China
| | - Jinzhao Zou
- Department of Radiology, Northern Jiangsu People's Hospital, 225001, Yangzhou, China
| | - Jianxiong Fu
- Department of Radiology, Northern Jiangsu People's Hospital, 225001, Yangzhou, China
| | - Yili Yin
- Department of Radiology, Northern Jiangsu People's Hospital, 225001, Yangzhou, China.
| | - Jing Ye
- Department of Radiology, Northern Jiangsu People's Hospital, 225001, Yangzhou, China.
| |
Collapse
|
21
|
Radiomics-Based Machine Learning Model for Predicting Overall and Progression-Free Survival in Rare Cancer: A Case Study for Primary CNS Lymphoma Patients. Bioengineering (Basel) 2023; 10:bioengineering10030285. [PMID: 36978676 PMCID: PMC10045100 DOI: 10.3390/bioengineering10030285] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 02/15/2023] [Accepted: 02/20/2023] [Indexed: 02/24/2023] Open
Abstract
Primary Central Nervous System Lymphoma (PCNSL) is an aggressive neoplasm with a poor prognosis. Although therapeutic progresses have significantly improved Overall Survival (OS), a number of patients do not respond to HD–MTX-based chemotherapy (15–25%) or experience relapse (25–50%) after an initial response. The reasons underlying this poor response to therapy are unknown. Thus, there is an urgent need to develop improved predictive models for PCNSL. In this study, we investigated whether radiomics features can improve outcome prediction in patients with PCNSL. A total of 80 patients diagnosed with PCNSL were enrolled. A patient sub-group, with complete Magnetic Resonance Imaging (MRI) series, were selected for the stratification analysis. Following radiomics feature extraction and selection, different Machine Learning (ML) models were tested for OS and Progression-free Survival (PFS) prediction. To assess the stability of the selected features, images from 23 patients scanned at three different time points were used to compute the Interclass Correlation Coefficient (ICC) and to evaluate the reproducibility of each feature for both original and normalized images. Features extracted from Z-score normalized images were significantly more stable than those extracted from non-normalized images with an improvement of about 38% on average (p-value < 10−12). The area under the ROC curve (AUC) showed that radiomics-based prediction overcame prediction based on current clinical prognostic factors with an improvement of 23% for OS and 50% for PFS, respectively. These results indicate that radiomics features extracted from normalized MR images can improve prognosis stratification of PCNSL patients and pave the way for further study on its potential role to drive treatment choice.
Collapse
|
22
|
Osuala R, Kushibar K, Garrucho L, Linardos A, Szafranowska Z, Klein S, Glocker B, Diaz O, Lekadir K. Data synthesis and adversarial networks: A review and meta-analysis in cancer imaging. Med Image Anal 2023; 84:102704. [PMID: 36473414 DOI: 10.1016/j.media.2022.102704] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Revised: 11/02/2022] [Accepted: 11/21/2022] [Indexed: 11/26/2022]
Abstract
Despite technological and medical advances, the detection, interpretation, and treatment of cancer based on imaging data continue to pose significant challenges. These include inter-observer variability, class imbalance, dataset shifts, inter- and intra-tumour heterogeneity, malignancy determination, and treatment effect uncertainty. Given the recent advancements in image synthesis, Generative Adversarial Networks (GANs), and adversarial training, we assess the potential of these technologies to address a number of key challenges of cancer imaging. We categorise these challenges into (a) data scarcity and imbalance, (b) data access and privacy, (c) data annotation and segmentation, (d) cancer detection and diagnosis, and (e) tumour profiling, treatment planning and monitoring. Based on our analysis of 164 publications that apply adversarial training techniques in the context of cancer imaging, we highlight multiple underexplored solutions with research potential. We further contribute the Synthesis Study Trustworthiness Test (SynTRUST), a meta-analysis framework for assessing the validation rigour of medical image synthesis studies. SynTRUST is based on 26 concrete measures of thoroughness, reproducibility, usefulness, scalability, and tenability. Based on SynTRUST, we analyse 16 of the most promising cancer imaging challenge solutions and observe a high validation rigour in general, but also several desirable improvements. With this work, we strive to bridge the gap between the needs of the clinical cancer imaging community and the current and prospective research on data synthesis and adversarial networks in the artificial intelligence community.
Collapse
Affiliation(s)
- Richard Osuala
- Artificial Intelligence in Medicine Lab (BCN-AIM), Facultat de Matemàtiques i Informàtica, Universitat de Barcelona, Spain.
| | - Kaisar Kushibar
- Artificial Intelligence in Medicine Lab (BCN-AIM), Facultat de Matemàtiques i Informàtica, Universitat de Barcelona, Spain
| | - Lidia Garrucho
- Artificial Intelligence in Medicine Lab (BCN-AIM), Facultat de Matemàtiques i Informàtica, Universitat de Barcelona, Spain
| | - Akis Linardos
- Artificial Intelligence in Medicine Lab (BCN-AIM), Facultat de Matemàtiques i Informàtica, Universitat de Barcelona, Spain
| | - Zuzanna Szafranowska
- Artificial Intelligence in Medicine Lab (BCN-AIM), Facultat de Matemàtiques i Informàtica, Universitat de Barcelona, Spain
| | - Stefan Klein
- Biomedical Imaging Group Rotterdam, Department of Radiology & Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands
| | - Ben Glocker
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, UK
| | - Oliver Diaz
- Artificial Intelligence in Medicine Lab (BCN-AIM), Facultat de Matemàtiques i Informàtica, Universitat de Barcelona, Spain
| | - Karim Lekadir
- Artificial Intelligence in Medicine Lab (BCN-AIM), Facultat de Matemàtiques i Informàtica, Universitat de Barcelona, Spain
| |
Collapse
|
23
|
Zhao LM, Hu R, Xie FF, Clay Kargilis D, Imami M, Yang S, Guo JQ, Jiao X, Chen RT, Wei-Hua L, Li L. Radiomic-Based MRI for Classification of Solitary Brain Metastases Subtypes From Primary Lymphoma of the Central Nervous System. J Magn Reson Imaging 2023; 57:227-235. [PMID: 35652509 DOI: 10.1002/jmri.28276] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 05/16/2022] [Accepted: 05/17/2022] [Indexed: 02/04/2023] Open
Abstract
BACKGROUND Differential diagnosis of brain metastases subtype and primary central nervous system lymphoma (PCNSL) is necessary for treatment decisions. The application of machine learning facilitates the classification of brain tumors, but prior investigations into primary lymphoma and brain metastases subtype classification have been limited. PURPOSE To develop a machine-learning model to classify PCNSL, brain metastases with primary lung and non-lung origin. STUDY TYPE Retrospective. POPULATION A total of 211 subjects with pathologically confirmed PCNSL or brain metastases (training cohort 168 and testing cohort 43). FIELD STRENGTH/SEQUENCE A 3.0 T axial contrast-enhanced T1-weighted spin-echo inversion recovery sequence (T1WI-CE), axial T2-weighted fluid-attenuation inversion recovery sequence (T2FLAIR) ASSESSMENT: Several machine-learning models (support vector machine, random forest, and K-nearest neighbors) were built with least absolute shrinkage and selection operator (LASSO) using features from T1WI-CE, T2FLAIR, and clinical. The model with the highest performance in the training cohort was selected to differentiate lesions in the testing cohort. Then, three radiologists conducted a two-round classification (with and without model reference) using images and clinical information from testing cohorts. STATISTICAL TESTS Five-fold cross-validation was used for model evaluation and calibration. Model performance was assessed based on sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC). RESULTS Twenty-five image features were selected by LASSO analysis. Random forest classifier was selected for its highest performance on the training set with an AUC of 0.73. After calibration, this model achieved an accuracy of 0.70 on the testing set. Accuracies of all three radiologists improved under model reference (0.49 vs. 0.70, 0.60 vs. 0.77, 0.58 vs. 0.72, respectively). DATA CONCLUSION The random forest model based on conventional MRI and clinical data can diagnose PCNSL and brain metastases subtypes (lung and non-lung origin). Model classification can help foster the diagnostic accuracy of specialists and streamline prognostication workflow. EVIDENCE LEVEL 4 TECHNICAL EFFICACY: Stage 2.
Collapse
Affiliation(s)
- Lin-Mei Zhao
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China.,National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Xiangya Hospital, Central South University, Changsha, China
| | - Rong Hu
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China.,National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Xiangya Hospital, Central South University, Changsha, China
| | - Fang-Fang Xie
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China.,National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Xiangya Hospital, Central South University, Changsha, China
| | - Daniel Clay Kargilis
- Department of Radiology and Radiological Sciences, Johns Hopkins Hospital, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Maliha Imami
- Department of Radiology and Radiological Sciences, Johns Hopkins Hospital, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Shuai Yang
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China.,National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Xiangya Hospital, Central South University, Changsha, China
| | - Jiu-Qing Guo
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China.,National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Xiangya Hospital, Central South University, Changsha, China
| | - Xiao Jiao
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China.,National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Xiangya Hospital, Central South University, Changsha, China
| | - Rui-Ting Chen
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China.,National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Xiangya Hospital, Central South University, Changsha, China
| | - Liao Wei-Hua
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China.,National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Xiangya Hospital, Central South University, Changsha, China
| | - Lang Li
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China.,National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Xiangya Hospital, Central South University, Changsha, China
| |
Collapse
|
24
|
Chen X, Zhu J, Zou Z, Du M, Xie J, Ye Y, Zhang L, Li Y. Nomogram based on MRI for preoperative prediction of Ki-67 expression in patients with intrahepatic mass cholangiocarcinoma. Abdom Radiol (NY) 2023; 48:567-578. [PMID: 36401626 PMCID: PMC9902416 DOI: 10.1007/s00261-022-03719-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 10/16/2022] [Accepted: 10/17/2022] [Indexed: 11/21/2022]
Abstract
OBJECTIVES To validate a new nomogram based on magnetic resonance imaging (MRI) for pre-operative prediction of Ki-67 expression in patients with intrahepatic mass cholangiocarcinoma (IMCC). METHODS A total of 78 patients with clinicopathologically confirmed IMCC who underwent pre-operative gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid enhanced MRI between 2016 and 2022 were enrolled in the training and validation group (53 patients and 25 patients, respectively). Images including qualitative, quantitative MRI features and clinical data were evaluated. Univariate analysis and multivariate logistic regression were used to select the independent predictors and establish different predictive models. The predictive performance was validated by operating characteristic curve (ROC) analysis, calibration curve, and decision curve analysis (DCA). The validation cohort was used to test the predictive performance of the optimal model. The nomogram was constructed with the optimal model. RESULTS In the training cohort, independent predictors obtained from the combined model were DWI (OR 1822.741; 95% CI 6.189, 536,781.805; P = 0.01) and HBP enhancement pattern (OR 14.270; 95% CI 1.044, 195.039; P = 0.046). The combined model showed the good performance (AUC 0.981; 95% CI 0.952, 1.000) for predicting Ki-67 expression. In the validation cohort, The combined model (AUC 0.909; 95% CI 0.787, 1.000)showed the best performance compared to the clinical model (AUC 0.448; 95% CI 0.196, 0.700) and MRI model (AUC 0.770; 95% CI 0.570, 0.970). CONCLUSION This new nomogram has a good performance in predicting Ki-67 expression in patients with IMCC, which could help the decision-making of the patients' therapy strategies.
Collapse
Affiliation(s)
- Xiang Chen
- Department of Radiology, The First Affiliated Hospital of Soochow University, Shizi Street 188#, Suzhou, 215000 Jiangsu People’s Republic of China
| | - Jingfen Zhu
- Department of Radiology, The First Affiliated Hospital of Soochow University, Shizi Street 188#, Suzhou, 215000 Jiangsu People’s Republic of China
| | - Zigui Zou
- Department of Pathology, The First Affiliated Hospital of Soochow University, Suzhou, 215000 Jiangsu People’s Republic of China
| | - Mingzhan Du
- Department of Pathology, The First Affiliated Hospital of Soochow University, Suzhou, 215000 Jiangsu People’s Republic of China
| | - Junjian Xie
- Department of Radiology, The First Affiliated Hospital of Soochow University, Shizi Street 188#, Suzhou, 215000 Jiangsu People’s Republic of China
| | - Yujie Ye
- Department of Radiology, The First Affiliated Hospital of Soochow University, Shizi Street 188#, Suzhou, 215000 Jiangsu People’s Republic of China
| | - Ling Zhang
- Department of Radiology, Sun Yat-Sen University Cancer Center, Dongfeng East Road 651#, Guangzhou, 510060, Guangdong, People's Republic of China. .,State Key Laboratory of Oncology in South China, Guangzhou, 510060, Guangdong, People's Republic of China. .,Collaborative Innovation Center for Cancer Medicine, Guangzhou, 510060, Guangdong, People's Republic of China.
| | - Yonggang Li
- Department of Radiology, The First Affiliated Hospital of Soochow University, Shizi Street 188#, Suzhou, 215000, Jiangsu, People's Republic of China. .,National Clinical Research Center for Hematologic Diseases, The First Affiliated Hospital of Soochow University, Suzhou, 215000, Jiangsu, People's Republic of China. .,Institute of Medical Imaging, Soochow University, Suzhou, 215000, Jiangsu, People's Republic of China.
| |
Collapse
|
25
|
Machine learning–based multiparametric magnetic resonance imaging radiomics model for distinguishing central neurocytoma from glioma of lateral ventricle. Eur Radiol 2022; 33:4259-4269. [PMID: 36547672 DOI: 10.1007/s00330-022-09319-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Revised: 10/16/2022] [Accepted: 11/25/2022] [Indexed: 12/24/2022]
Abstract
OBJECTIVES To develop a machine learning-based radiomics model based on multiparametric magnetic resonance imaging (MRI) for preoperative discrimination between central neurocytomas (CNs) and gliomas of lateral ventricles. METHODS A total of 132 patients from two medical centers were enrolled in this retrospective study. Patients from the first medical center were divided into a training cohort (n = 74) and an internal validation cohort (n = 30). Patients from the second medical center were used as the external validation cohort (n = 28). Features were extracted from contrast-enhanced T1-weighted and T2-weighted images. A support vector machine was used for radiomics model investigation. Performance was evaluated using the sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC). The model's performance was also compared with those of three radiologists. RESULTS The radiomics model achieved an AUC of 0.986 in the training cohort, 0.933 in the internal validation cohort, and 0.903 in the external validation cohort. In the three cohorts, the AUC values were 0.657, 0.786, and 0.708 for radiologist 1; 0.838, 0.799, and 0.790 for radiologist 2; and 0.827, 0.871, and 0.862 for radiologist 3. When assisted by the radiomics model, two radiologists improved their performance in the training cohort (p < 0.05) but not in the internal or external validation cohorts. CONCLUSIONS The machine learning radiomics model based on multiparametric MRI showed better performance for distinguishing CNs from lateral ventricular gliomas than did experienced radiologists, and it showed the potential to improve radiologist performance. KEY POINTS • The machine learning radiomics model shows excellent performance in distinguishing CNs from gliomas. • The radiomics model outweighs two experienced radiologists (area under the receiver operating characteristic curve, 0.90 vs 0.79 and 0.86, respectively). • The radiomics model has the potential to enhance radiologist performance.
Collapse
|
26
|
Kotsyfakis S, Iliaki-Giannakoudaki E, Anagnostopoulos A, Papadokostaki E, Giannakoudakis K, Goumenakis M, Kotsyfakis M. The application of machine learning to imaging in hematological oncology: A scoping review. Front Oncol 2022; 12:1080988. [PMID: 36605438 PMCID: PMC9808781 DOI: 10.3389/fonc.2022.1080988] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Accepted: 12/05/2022] [Indexed: 12/24/2022] Open
Abstract
Background Here, we conducted a scoping review to (i) establish which machine learning (ML) methods have been applied to hematological malignancy imaging; (ii) establish how ML is being applied to hematological cancer radiology; and (iii) identify addressable research gaps. Methods The review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Extension for Scoping Reviews guidelines. The inclusion criteria were (i) pediatric and adult patients with suspected or confirmed hematological malignancy undergoing imaging (population); (ii) any study using ML techniques to derive models using radiological images to apply to the clinical management of these patients (concept); and (iii) original research articles conducted in any setting globally (context). Quality Assessment of Diagnostic Accuracy Studies 2 criteria were used to assess diagnostic and segmentation studies, while the Newcastle-Ottawa scale was used to assess the quality of observational studies. Results Of 53 eligible studies, 33 applied diverse ML techniques to diagnose hematological malignancies or to differentiate them from other diseases, especially discriminating gliomas from primary central nervous system lymphomas (n=18); 11 applied ML to segmentation tasks, while 9 applied ML to prognostication or predicting therapeutic responses, especially for diffuse large B-cell lymphoma. All studies reported discrimination statistics, but no study calculated calibration statistics. Every diagnostic/segmentation study had a high risk of bias due to their case-control design; many studies failed to provide adequate details of the reference standard; and only a few studies used independent validation. Conclusion To deliver validated ML-based models to radiologists managing hematological malignancies, future studies should (i) adhere to standardized, high-quality reporting guidelines such as the Checklist for Artificial Intelligence in Medical Imaging; (ii) validate models in independent cohorts; (ii) standardize volume segmentation methods for segmentation tasks; (iv) establish comprehensive prospective studies that include different tumor grades, comparisons with radiologists, optimal imaging modalities, sequences, and planes; (v) include side-by-side comparisons of different methods; and (vi) include low- and middle-income countries in multicentric studies to enhance generalizability and reduce inequity.
Collapse
Affiliation(s)
| | | | | | | | | | | | - Michail Kotsyfakis
- Biology Center of the Czech Academy of Sciences, Budweis (Ceske Budejovice), Czechia,*Correspondence: Michail Kotsyfakis,
| |
Collapse
|
27
|
Cao L, Zhang M, Zhang Y, Ji B, Wang X, Wang X. Progress of radiological‑pathological workflows in the differential diagnosis between primary central nervous system lymphoma and high‑grade glioma (Review). Oncol Rep 2022; 49:20. [PMID: 36484403 PMCID: PMC9773014 DOI: 10.3892/or.2022.8457] [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: 06/24/2022] [Accepted: 11/03/2022] [Indexed: 12/13/2022] Open
Abstract
Primary central nervous system lymphoma (PCNSL) and high‑grade glioma (HGG) are distinct entities of the CNS with completely distinct treatments. The treatment of PCNSL is chemotherapy‑based, while surgery is the first choice for HGG. However, the clinical features of the two entities often overlap, and a clear pathological diagnosis is important for subsequent management, especially for the management of PCNSL. Stereotactic biopsy is recognized as one of the minimally invasive alternatives for evaluating the involvement of the CNS. However, in the case of limited tissue materials, the differential diagnosis between the two entities is still difficult. In addition, some patients are too ill to tolerate a needle biopsy. Therefore, combining imaging, histopathology and laboratory examinations is essential in order to make a clear diagnosis as soon as possible. The present study reviews the progress of comparative research on both imaging and laboratory tests based on the pathophysiological changes of the two entities, and proposes an integrative and optimized diagnostic process, with the purpose of building a better understanding for neurologists, hematologists, radiologists and pathologists.
Collapse
Affiliation(s)
- Luming Cao
- Department of Pathology, China-Japan Union Hospital, Jilin University, Changchun, Jilin 130033, P.R. China
| | - Mengchao Zhang
- Department of Radiology, China-Japan Union Hospital, Jilin University, Changchun, Jilin 130033, P.R. China
| | - Ying Zhang
- Department of Pathology, China-Japan Union Hospital, Jilin University, Changchun, Jilin 130033, P.R. China
| | - Bin Ji
- Department of Nuclear Medicine, China-Japan Union Hospital, Jilin University, Changchun, Jilin 130033, P.R. China
| | - Xuemei Wang
- Department of Pathology, China-Japan Union Hospital, Jilin University, Changchun, Jilin 130033, P.R. China
| | - Xueju Wang
- Department of Pathology, China-Japan Union Hospital, Jilin University, Changchun, Jilin 130033, P.R. China,Correspondence to: Dr Xueju Wang, Department of Pathology, China-Japan Union Hospital, Jilin University, 126 Xiantai Street, Changchun, Jilin 130033, P.R. China, E-mail:
| |
Collapse
|
28
|
Joo B, Ahn SS, An C, Han K, Choi D, Kim H, Park JE, Kim HS, Lee SK. Fully automated radiomics-based machine learning models for multiclass classification of single brain tumors: Glioblastoma, lymphoma, and metastasis. J Neuroradiol 2022; 50:388-395. [PMID: 36370829 DOI: 10.1016/j.neurad.2022.11.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Revised: 11/08/2022] [Accepted: 11/08/2022] [Indexed: 11/11/2022]
Abstract
BACKGROUND AND PURPOSE To investigate the diagnostic performance of fully automated radiomics-based models for multiclass classification of a single enhancing brain tumor among glioblastoma, central nervous system lymphoma, and metastasis. MATERIALS AND METHODS The training and test sets were comprised of 538 cases (300 glioblastomas, 73 lymphomas, and 165 metastases) and 169 cases (101 glioblastomas, 29 lymphomas, and 39 metastases), respectively. After fully automated segmentation, radiomic features were extracted. Three conventional machine learning classifiers, including least absolute shrinkage and selection operator (LASSO), adaptive boosting (Adaboost), and support vector machine with the linear kernel (SVC), combined with one of four feature selection methods, including forward sequential feature selection, F score, mutual information, and LASSO, were trained. Additionally, one ensemble classifier based on the three classifiers was used. The diagnostic performance of the optimized models was tested in the test set using the accuracy, F1-macro score, and the area under the receiver operating characteristic curve (AUCROC). RESULTS The best performance was achieved when the LASSO was used as a feature selection method. In the test set, the best performance was achieved by the ensemble classifier, showing an accuracy of 76.3% (95% CI, 70.0-82.7), a F1-macro score of 0.704, and an AUCROC of 0.878. CONCLUSION Our fully automated radiomics-based models for multiclass classification might be useful for differential diagnosis of a single enhancing brain tumor with a good diagnostic performance and generalizability.
Collapse
Affiliation(s)
- Bio Joo
- Department of Radiology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Sung Soo Ahn
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Image Data Science, Yonsei University College of Medicine, Seoul, Korea.
| | - Chansik An
- Department of Radiology, CHA Ilsan Medical Center, CHA University, Goyang, Korea
| | - Kyunghwa Han
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Image Data Science, Yonsei University College of Medicine, Seoul, Korea
| | - Dongmin Choi
- Department of Computer Science, Yonsei University, Seoul, Korea
| | - Hwiyoung Kim
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Image Data Science, Yonsei University College of Medicine, Seoul, Korea
| | - Ji Eun Park
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Seoul, Korea
| | - Ho Sung Kim
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Seoul, Korea
| | - Seung-Koo Lee
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Image Data Science, Yonsei University College of Medicine, Seoul, Korea
| |
Collapse
|
29
|
Kim M, Lee JH, Joo L, Jeong B, Kim S, Ham S, Yun J, Kim N, Chung SR, Choi YJ, Baek JH, Lee JY, Kim JH. Development and Validation of a Model Using Radiomics Features from an Apparent Diffusion Coefficient Map to Diagnose Local Tumor Recurrence in Patients Treated for Head and Neck Squamous Cell Carcinoma. Korean J Radiol 2022; 23:1078-1088. [PMID: 36126954 PMCID: PMC9614290 DOI: 10.3348/kjr.2022.0299] [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: 05/10/2022] [Revised: 07/25/2022] [Accepted: 08/17/2022] [Indexed: 12/24/2022] Open
Abstract
OBJECTIVE To develop and validate a model using radiomics features from apparent diffusion coefficient (ADC) map to diagnose local tumor recurrence in head and neck squamous cell carcinoma (HNSCC). MATERIALS AND METHODS This retrospective study included 285 patients (mean age ± standard deviation, 62 ± 12 years; 220 male, 77.2%), including 215 for training (n = 161) and internal validation (n = 54) and 70 others for external validation, with newly developed contrast-enhancing lesions at the primary cancer site on the surveillance MRI following definitive treatment of HNSCC between January 2014 and October 2019. Of the 215 and 70 patients, 127 and 34, respectively, had local tumor recurrence. Radiomics models using radiomics scores were created separately for T2-weighted imaging (T2WI), contrast-enhanced T1-weighted imaging (CE-T1WI), and ADC maps using non-zero coefficients from the least absolute shrinkage and selection operator in the training set. Receiver operating characteristic (ROC) analysis was used to evaluate the diagnostic performance of each radiomics score and known clinical parameter (age, sex, and clinical stage) in the internal and external validation sets. RESULTS Five radiomics features from T2WI, six from CE-T1WI, and nine from ADC maps were selected and used to develop the respective radiomics models. The area under ROC curve (AUROC) of ADC radiomics score was 0.76 (95% confidence interval [CI], 0.62-0.89) and 0.77 (95% CI, 0.65-0.88) in the internal and external validation sets, respectively. These were significantly higher than the AUROC values of T2WI (0.53 [95% CI, 0.40-0.67], p = 0.006), CE-T1WI (0.53 [95% CI, 0.40-0.67], p = 0.012), and clinical parameters (0.53 [95% CI, 0.39-0.67], p = 0.021) in the external validation set. CONCLUSION The radiomics model using ADC maps exhibited higher diagnostic performance than those of the radiomics models using T2WI or CE-T1WI and clinical parameters in the diagnosis of local tumor recurrence in HNSCC following definitive treatment.
Collapse
Affiliation(s)
- Minjae Kim
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea.,Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea
| | - Jeong Hyun Lee
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Leehi Joo
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Boryeong Jeong
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Seonok Kim
- Department of Clinical Epidemiology and Biostatistics, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Sungwon Ham
- Department of Convergence Medicine, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Jihye Yun
- Department of Convergence Medicine, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - NamKug Kim
- Department of Convergence Medicine, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Sae Rom Chung
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Young Jun Choi
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Jung Hwan Baek
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Ji Ye Lee
- Department of Radiology, Seoul National University Hospital, Seoul, Korea.,Department of Radiology, Seoul National University College of Medicine, Seoul, Korea
| | - Ji-hoon Kim
- Department of Radiology, Seoul National University Hospital, Seoul, Korea.,Department of Radiology, Seoul National University College of Medicine, Seoul, Korea
| |
Collapse
|
30
|
Guha A, Goda JS, Dasgupta A, Mahajan A, Halder S, Gawde J, Talole S. Classifying primary central nervous system lymphoma from glioblastoma using deep learning and radiomics based machine learning approach - a systematic review and meta-analysis. Front Oncol 2022; 12:884173. [PMID: 36263203 PMCID: PMC9574102 DOI: 10.3389/fonc.2022.884173] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Accepted: 09/07/2022] [Indexed: 01/06/2023] Open
Abstract
BackgroundGlioblastoma (GBM) and primary central nervous system lymphoma (PCNSL) are common in elderly yet difficult to differentiate on MRI. Their management and prognosis are quite different. Recent surge of interest in predictive analytics, using machine learning (ML) from radiomic features and deep learning (DL) for diagnosing, predicting response and prognosticating disease has evinced interest among radiologists and clinicians. The objective of this systematic review and meta-analysis was to evaluate the deep learning & ML algorithms in classifying PCNSL from GBM.MethodsThe authors performed a systematic review of the literature from MEDLINE, EMBASE and the Cochrane central trials register for the search strategy in accordance with PRISMA guidelines to select and evaluate studies that included themes of ML, DL, AI, GBM, PCNSL. All studies reporting on ML algorithms or DL that for differentiating PCNSL from GBM on MR imaging were included. These studies were further narrowed down to focus on works published between 2018 and 2021. Two researchers independently conducted the literature screening, database extraction and risk bias assessment. The extracted data was synthesised and analysed by forest plots. Outcomes assessed were test characteristics such as accuracy, sensitivity, specificity and balanced accuracy.ResultsTen articles meeting the eligibility criteria were identified addressing use of ML and DL in training and validation classifiers to distinguish PCNSL from GBM on MR imaging. The total sample size was 1311 in the included studies. ML approach was used in 6 studies while DL in 4 studies. The lowest reported sensitivity was 80%, while the highest reported sensitivity was 99% in studies in which ML and DL was directly compared with the gold standard histopathology. The lowest reported specificity was 87% while the highest reported specificity was 100%. The highest reported balanced accuracy was 100% and the lowest was 84%.ConclusionsExtensive search of the database revealed a limited number of studies that have applied ML or DL to differentiate PCNSL from GBM. Of the currently published studies, Both DL & ML algorithms have demonstrated encouraging results and certainly have the potential to aid neurooncologists in taking preoperative decisions in the future leading to not only reduction in morbidities but also be cost effective.
Collapse
Affiliation(s)
- Amrita Guha
- Department of Radio Diagnosis, Tata Memorial Centre, Homi Bhaba National Institute, Mumbai, India
- *Correspondence: Amrita Guha, ; Jayant S. Goda,
| | - Jayant S. Goda
- Department of Radio Diagnosis, Tata Memorial Centre, Homi Bhaba National Institute, Mumbai, India
- *Correspondence: Amrita Guha, ; Jayant S. Goda,
| | - Archya Dasgupta
- Department of Radiation Oncology, Tata Memorial Centre, Homi Bhaba National Institute, Mumbai, India
| | - Abhishek Mahajan
- Department of Radio Diagnosis, Tata Memorial Centre, Homi Bhaba National Institute, Mumbai, India
| | - Soutik Halder
- Department of Biostatistics, Tata Memorial Centre, Homi Bhaba National Institute, Mumbai, India
| | - Jeetendra Gawde
- Department of Biostatistics, Tata Memorial Centre, Homi Bhaba National Institute, Mumbai, India
| | - Sanjay Talole
- Department of Biostatistics, Tata Memorial Centre, Homi Bhaba National Institute, Mumbai, India
| |
Collapse
|
31
|
Lu G, Zhang Y, Wang W, Miao L, Mou W. Machine Learning and Deep Learning CT-Based Models for Predicting the Primary Central Nervous System Lymphoma and Glioma Types: A Multicenter Retrospective Study. Front Neurol 2022; 13:905227. [PMID: 36110392 PMCID: PMC9469735 DOI: 10.3389/fneur.2022.905227] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2022] [Accepted: 06/23/2022] [Indexed: 11/13/2022] Open
Abstract
Purpose and BackgroundDistinguishing primary central nervous system lymphoma (PCNSL) and glioma on computed tomography (CT) is an important task since treatment options differ vastly from the two diseases. This study aims to explore various machine learning and deep learning methods based on radiomic features extracted from CT scans and end-to-end convolutional neural network (CNN) model to predict PCNSL and glioma types and compare the performance of different models.MethodsA total of 101 patients from five Chinese medical centers with pathologically confirmed PCNSL and glioma were analyzed retrospectively, including 50 PCNSL and 51 glioma. After manual segmentation of the region of interest (ROI) on CT scans, 293 radiomic features of each patient were extracted. The radiomic features were used as input, and then, we established six machine learning models and one deep learning model and three readers to identify the two types of tumors. We also established a 2D CNN model using raw CT scans as input. The area under the receiver operating characteristic curve (AUC) and accuracy (ACC) were used to evaluate different models.ResultsThe cohort was split into a training (70, 70% patients) and validation cohort (31,30% patients) according to the stratified sampling strategy. Among all models, the MLP performed best, with an accuracy of 0.886 and 0.903, sensitivity of 0.914 and 0.867, specificity of 0.857 and 0.937, and AUC of 0.957 and 0.908 in the training and validation cohorts, respectively, which was significantly higher than the three primary physician's diagnoses (ACCs ranged from 0.710 to 0.742, p < 0.001 for all) and comparable with the senior radiologist (ACC 0.839, p = 0.988). Among all the machine learning models, the AUC ranged from 0.605 to 0.821 in the validation cohort. The end-to-end CNN model achieved an AUC of 0.839 and an ACC of 0.840 in the validation cohort, which had no significant difference in accuracy compared to the MLP model (p = 0.472) and the senior radiologist (p = 0.470).ConclusionThe established PCNSL and glioma prediction model based on deep neural network methods from CT scans or radiomic features are feasible and provided high performance, which shows the potential to assist clinical decision-making.
Collapse
Affiliation(s)
- Guang Lu
- Department of Hematology, Shengli Oilfield Central Hospital, Dongying, China
| | - Yuxin Zhang
- Department of Neurosurgery, Guangrao County People's Hospital, Dongying, China
| | | | - Lixin Miao
- Department of Medical Imaging Center, Shengli Oilfield Central Hospital, Dongying, China
- *Correspondence: Lixin Miao
| | - Weiwei Mou
- Department of Pediatrics, Shengli Oilfield Central Hospital, Dongying, China
- Weiwei Mou
| |
Collapse
|
32
|
Zhu M, Li S, Kuang Y, Hill VB, Heimberger AB, Zhai L, Zhai S. Artificial intelligence in the radiomic analysis of glioblastomas: A review, taxonomy, and perspective. Front Oncol 2022; 12:924245. [PMID: 35982952 PMCID: PMC9379255 DOI: 10.3389/fonc.2022.924245] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Accepted: 07/04/2022] [Indexed: 11/17/2022] Open
Abstract
Radiological imaging techniques, including magnetic resonance imaging (MRI) and positron emission tomography (PET), are the standard-of-care non-invasive diagnostic approaches widely applied in neuro-oncology. Unfortunately, accurate interpretation of radiological imaging data is constantly challenged by the indistinguishable radiological image features shared by different pathological changes associated with tumor progression and/or various therapeutic interventions. In recent years, machine learning (ML)-based artificial intelligence (AI) technology has been widely applied in medical image processing and bioinformatics due to its advantages in implicit image feature extraction and integrative data analysis. Despite its recent rapid development, ML technology still faces many hurdles for its broader applications in neuro-oncological radiomic analysis, such as lack of large accessible standardized real patient radiomic brain tumor data of all kinds and reliable predictions on tumor response upon various treatments. Therefore, understanding ML-based AI technologies is critically important to help us address the skyrocketing demands of neuro-oncology clinical deployments. Here, we provide an overview on the latest advancements in ML techniques for brain tumor radiomic analysis, emphasizing proprietary and public dataset preparation and state-of-the-art ML models for brain tumor diagnosis, classifications (e.g., primary and secondary tumors), discriminations between treatment effects (pseudoprogression, radiation necrosis) and true progression, survival prediction, inflammation, and identification of brain tumor biomarkers. We also compare the key features of ML models in the realm of neuroradiology with ML models employed in other medical imaging fields and discuss open research challenges and directions for future work in this nascent precision medicine area.
Collapse
Affiliation(s)
- Ming Zhu
- Department of Electrical and Computer Engineering, University of Nevada Las Vegas, Las Vegas, NV, United States
| | - Sijia Li
- Kirk Kerkorian School of Medicine, University of Nevada Las Vegas, Las Vegas, NV, United States
| | - Yu Kuang
- Medical Physics Program, Department of Health Physics, University of Nevada Las Vegas, Las Vegas, NV, United States
| | - Virginia B. Hill
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | - Amy B. Heimberger
- Department of Neurological Surgery, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
- Malnati Brain Tumor Institute of the Lurie Comprehensive Cancer Center, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | - Lijie Zhai
- Department of Neurological Surgery, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
- Malnati Brain Tumor Institute of the Lurie Comprehensive Cancer Center, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
- *Correspondence: Lijie Zhai, ; Shengjie Zhai,
| | - Shengjie Zhai
- Department of Electrical and Computer Engineering, University of Nevada Las Vegas, Las Vegas, NV, United States
- *Correspondence: Lijie Zhai, ; Shengjie Zhai,
| |
Collapse
|
33
|
Gao P, Shan W, Guo Y, Wang Y, Sun R, Cai J, Li H, Chan WS, Liu P, Yi L, Zhang S, Li W, Jiang T, He K, Wu Z. Development and Validation of a Deep Learning Model for Brain Tumor Diagnosis and Classification Using Magnetic Resonance Imaging. JAMA Netw Open 2022; 5:e2225608. [PMID: 35939301 PMCID: PMC9361083 DOI: 10.1001/jamanetworkopen.2022.25608] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
IMPORTANCE Deep learning may be able to use patient magnetic resonance imaging (MRI) data to aid in brain tumor classification and diagnosis. OBJECTIVE To develop and clinically validate a deep learning system for automated identification and classification of 18 types of brain tumors from patient MRI data. DESIGN, SETTING, AND PARTICIPANTS This diagnostic study was conducted using MRI data collected between 2000 and 2019 from 37 871 patients. A deep learning system for segmentation and classification of 18 types of intracranial tumors based on T1- and T2-weighted images and T2 contrast MRI sequences was developed and tested. The diagnostic accuracy of the system was tested using 1 internal and 3 external independent data sets. The clinical value of the system was assessed by comparing the tumor diagnostic accuracy of neuroradiologists with vs without assistance of the proposed system using a separate internal test data set. Data were analyzed from March 2019 through February 2020. MAIN OUTCOMES AND MEASURES Changes in neuroradiologist clinical diagnostic accuracy in brain MRI scans with vs without the deep learning system were evaluated. RESULTS A deep learning system was trained among 37 871 patients (mean [SD] age, 41.6 [11.4] years; 18 519 women [48.9%]). It achieved a mean area under the receiver operating characteristic curve of 0.92 (95% CI, 0.84-0.99) on 1339 patients from 4 centers' data sets in diagnosis and classification of 18 types of tumors. Higher outcomes were found compared with neuroradiologists for accuracy and sensitivity and similar outcomes for specificity (for 300 patients in the Tiantan Hospital test data set: accuracy, 73.3% [95% CI, 67.7%-77.7%] vs 60.9% [95% CI, 46.8%-75.1%]; sensitivity, 88.9% [95% CI, 85.3%-92.4%] vs 53.4% [95% CI, 41.8%-64.9%]; and specificity, 96.3% [95% CI, 94.2%-98.4%] vs 97.9%; [95% CI, 97.3%-98.5%]). With the assistance of the deep learning system, the mean accuracy of neuroradiologists among 1166 patients increased by 12.0 percentage points, from 63.5% (95% CI, 60.7%-66.2%) without assistance to 75.5% (95% CI, 73.0%-77.9%) with assistance. CONCLUSIONS AND RELEVANCE These findings suggest that deep learning system-based automated diagnosis may be associated with improved classification and diagnosis of intracranial tumors from MRI data among neuroradiologists.
Collapse
Affiliation(s)
- Peiyi Gao
- National Center for Clinical Medicine of Neurological Diseases, Beijing, People’s Republic of China
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, People’s Republic of China
| | - Wei Shan
- National Center for Clinical Medicine of Neurological Diseases, Beijing, People’s Republic of China
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, People’s Republic of China
| | - Yue Guo
- National Center for Clinical Medicine of Neurological Diseases, Beijing, People’s Republic of China
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, People’s Republic of China
| | - Yinyan Wang
- National Center for Clinical Medicine of Neurological Diseases, Beijing, People’s Republic of China
- China National Clinical Research Center for Neurological Diseases, Beijing Hanalytics Artificial Intelligence Research Center for Neurological Disorders Beijing, PR China
| | - Rujing Sun
- National Center for Clinical Medicine of Neurological Diseases, Beijing, People’s Republic of China
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, People’s Republic of China
| | - Jinxiu Cai
- National Center for Clinical Medicine of Neurological Diseases, Beijing, People’s Republic of China
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, People’s Republic of China
| | - Hao Li
- National Center for Clinical Medicine of Neurological Diseases, Beijing, People’s Republic of China
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, People’s Republic of China
| | - Wei Sheng Chan
- China National Clinical Research Center for Neurological Diseases, Beijing Hanalytics Artificial Intelligence Research Center for Neurological Disorders Beijing, PR China
| | - Pan Liu
- China National Clinical Research Center for Neurological Diseases, Beijing Hanalytics Artificial Intelligence Research Center for Neurological Disorders Beijing, PR China
| | - Lei Yi
- Medical Imaging Department, Shenzhen Second People’s Hospital/the First Affiliated Hospital of Shenzhen University Health Science Center, Shenzhen, People’s Republic of China
| | - Shaosen Zhang
- National Center for Clinical Medicine of Neurological Diseases, Beijing, People’s Republic of China
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, People’s Republic of China
| | - Weihua Li
- Medical Imaging Department, Shenzhen Second People’s Hospital/the First Affiliated Hospital of Shenzhen University Health Science Center, Shenzhen, People’s Republic of China
| | - Tao Jiang
- National Center for Clinical Medicine of Neurological Diseases, Beijing, People’s Republic of China
- China National Clinical Research Center for Neurological Diseases, Beijing Hanalytics Artificial Intelligence Research Center for Neurological Disorders Beijing, PR China
| | - Kunlun He
- Translational Medicine Laboratory, Chinese People's Liberation Army General Hospital, Beijing, People’s Republic of China
- Key Laboratory of Ministry of Industry and Information Technology of Biomedical Engineering and Translational Medicine, Chinese People's Liberation Army General Hospital, Beijing, People’s Republic of China
| | - Zhenzhou Wu
- National Center for Clinical Medicine of Neurological Diseases, Beijing, People’s Republic of China
- China National Clinical Research Center for Neurological Diseases, Beijing Hanalytics Artificial Intelligence Research Center for Neurological Disorders Beijing, PR China
| |
Collapse
|
34
|
Eckel-Passow JE, Lachance DH, Decker PA, Kollmeyer TM, Kosel ML, Drucker KL, Slager S, Wrensch M, Tobin WO, Jenkins RB. Inherited genetics of adult diffuse glioma and polygenic risk scores-a review. Neurooncol Pract 2022; 9:259-270. [PMID: 35859544 PMCID: PMC9290891 DOI: 10.1093/nop/npac017] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Knowledge about inherited and acquired genetics of adult diffuse glioma has expanded significantly over the past decade. Genomewide association studies (GWAS) stratified by histologic subtype identified six germline variants that were associated specifically with glioblastoma (GBM) and 12 that were associated with lower grade glioma. A GWAS performed using the 2016 WHO criteria, stratifying patients by IDH mutation and 1p/19q codeletion (as well as TERT promoter mutation), discovered that many of the known variants are associated with specific WHO glioma subtypes. In addition, the GWAS stratified by molecular group identified two additional novel regions: variants in D2HGDH that were associated with tumors that had an IDH mutation and a variant near FAM20C that was associated with tumors that had both IDH mutation and 1p/19q codeletion. The results of these germline associations have been used to calculate polygenic risk scores, from which to estimate relative and absolute risk of overall glioma and risk of specific glioma subtypes. We will review the concept of polygenic risk models and their potential clinical utility, as well as discuss the published adult diffuse glioma polygenic risk models. To date, these prior genetic studies have been done on European populations. Using the published glioma polygenic risk model, we show that the genetic associations published to date do not generalize across genetic ancestries, demonstrating that genetic studies need to be done on more diverse populations.
Collapse
Affiliation(s)
- Jeanette E Eckel-Passow
- Corresponding Author: Jeanette E. Eckel-Passow, PhD, Department of Quantitative Health Sciences, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA ()
| | - Daniel H Lachance
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Paul A Decker
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA
| | - Thomas M Kollmeyer
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA
| | - Matthew L Kosel
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA
| | - Kristen L Drucker
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA
| | - Susan Slager
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA
| | - Margaret Wrensch
- Department of Neurological Surgery, Institute of Human Genetics, University of California, San Francisco, San Francisco, California, USA
| | | | | |
Collapse
|
35
|
Yang Q, Sun J, Guo Y, Zeng P, Jin K, Huang C, Xu J, Hou L, Li C, Feng J. Radiomics Features on Computed Tomography Combined With Clinical-Radiological Factors Predicting Progressive Hemorrhage of Cerebral Contusion. Front Neurol 2022; 13:839784. [PMID: 35775053 PMCID: PMC9237337 DOI: 10.3389/fneur.2022.839784] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Accepted: 04/29/2022] [Indexed: 01/02/2023] Open
Abstract
Background Traumatic brain injury (TBI) is the main cause of death and severe disability in young adults worldwide. Progressive hemorrhage (PH) worsens the disease and can cause a poor neurological prognosis. Radiomics analysis has been used for hematoma expansion of hypertensive intracerebral hemorrhage. This study attempts to develop an optimal radiomics model based on non-contrast CT to predict PH by machine learning (ML) methods and compare its prediction performance with clinical-radiological models. Methods We retrospectively analyzed 165 TBI patients, including 89 patients with PH and 76 patients without PH, whose data were randomized into a training set and a testing set at a ratio of 7:3. A total of 10 different machine learning methods were used to predict PH. Univariate and multivariable logistic regression analyses were implemented to screen clinical-radiological factors and to establish a clinical-radiological model. Then, a combined model combining clinical-radiological factors with the radiomics score was constructed. The area under the receiver operating characteristic curve (AUC), accuracy and F1 score, sensitivity, and specificity were used to evaluate the models. Results Among the 10 various ML algorithms, the support vector machine (SVM) had the best prediction performance based on 12 radiomics features, including the AUC (training set: 0.918; testing set: 0.879) and accuracy (training set: 0.872; test set: 0.834). Among the clinical and radiological factors, the onset-to-baseline CT time, the scalp hematoma, and fibrinogen were associated with PH. The radiomics model's prediction performance was better than the clinical-radiological model, while the predictive nomogram combining the radiomics features with clinical-radiological characteristics performed best. Conclusions The radiomics model outperformed the traditional clinical-radiological model in predicting PH. The nomogram model of the combined radiomics features and clinical-radiological factors is a helpful tool for PH.
Collapse
Affiliation(s)
- Qingning Yang
- Department of Radiology, Chongqing University Central Hospital, Chongqing, China
| | - Jun Sun
- Department of Radiology, Chongqing University Central Hospital, Chongqing, China
| | - Yi Guo
- Department of Radiology, Chongqing University Central Hospital, Chongqing, China
- *Correspondence: Yi Guo
| | - Ping Zeng
- Department of Radiology, Chongqing University Central Hospital, Chongqing, China
- Ping Zeng
| | - Ke Jin
- Department of Research Collaboration, R&D Center, Beijing Deepwise & League of PHD Technology Co., Beijing, China
| | - Chencui Huang
- Department of Research Collaboration, R&D Center, Beijing Deepwise & League of PHD Technology Co., Beijing, China
| | - Jingxu Xu
- Department of Research Collaboration, R&D Center, Beijing Deepwise & League of PHD Technology Co., Beijing, China
| | - Liran Hou
- Department of Radiology, Panjiang Central Hospital, Guizhou, China
| | - Chuanming Li
- Department of Radiology, Chongqing University Central Hospital, Chongqing, China
| | - Junbang Feng
- Department of Radiology, Chongqing University Central Hospital, Chongqing, China
| |
Collapse
|
36
|
Liu Y, Chen J, Zhang C, Li Q, Zhou H, Zeng Y, Zhang Y, Li J, Xv W, Li W, Zhu J, Zhao Y, Chen Q, Huang Y, Li H, Huang Y, Yang G, Huang P. Ultrasound-Based Radiomics Can Classify the Etiology of Cervical Lymphadenopathy: A Multi-Center Retrospective Study. Front Oncol 2022; 12:856605. [PMID: 35656511 PMCID: PMC9152112 DOI: 10.3389/fonc.2022.856605] [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: 01/17/2022] [Accepted: 04/14/2022] [Indexed: 11/13/2022] Open
Abstract
Medical diagnostic imaging is essential for the differential diagnosis of cervical lymphadenopathy. Here we develop an ultrasound radiomics method for accurately differentiating cervical lymph node tuberculosis (LNTB), cervical lymphoma, reactive lymph node hyperplasia, and metastatic lymph nodes especially in the multi-operator, cross-machine, multicenter context. The inter-observer and intra-observer consistency of radiomics parameters from the region of interest were 0.8245 and 0.9228, respectively. The radiomics model showed good and repeatable diagnostic performance for multiple classification diagnosis of cervical lymphadenopathy, especially in LNTB (area under the curve, AUC: 0.673, 0.662, and 0.626) and cervical lymphoma (AUC: 0.623, 0.644, and 0.602) in the whole set, training set, and test set, respectively. However, the diagnostic performance of lymphadenopathy among skilled radiologists was varied (Kappa coefficient: 0.108, *p < 0.001). The diagnostic performance of radiomics is comparable and more reproducible compared with those of skilled radiologists. Our study offers a more comprehensive method for differentiating LNTB, cervical lymphoma, reactive lymph node hyperplasia, and metastatic LN.
Collapse
Affiliation(s)
- Yajing Liu
- Department of Ultrasound in Medicine, The Second Affiliated Hospital of Zhejiang University School of Medicine, Zhejiang University, Hangzhou, China
| | - Jifan Chen
- Department of Ultrasound in Medicine, The Second Affiliated Hospital of Zhejiang University School of Medicine, Zhejiang University, Hangzhou, China
| | - Chao Zhang
- Department of Ultrasound in Medicine, The Second Affiliated Hospital of Zhejiang University School of Medicine, Zhejiang University, Hangzhou, China
| | - Qunying Li
- Department of Ultrasound in Medicine, The Second Affiliated Hospital of Zhejiang University School of Medicine, Zhejiang University, Hangzhou, China
| | - Hang Zhou
- Department of Ultrasound in Medicine, The Second Affiliated Hospital of Zhejiang University School of Medicine, Zhejiang University, Hangzhou, China
| | - Yiqing Zeng
- Department of Ultrasound in Medicine, The Second Affiliated Hospital of Zhejiang University School of Medicine, Zhejiang University, Hangzhou, China
| | - Ying Zhang
- Department of Ultrasound, Hangzhou Red Cross Hospital, Hangzhou, China
| | - Jia Li
- Department of Ultrasound in Medicine, The Second Affiliated Hospital of Zhejiang University School of Medicine, Zhejiang University, Hangzhou, China
| | - Wen Xv
- Department of Ultrasound in Medicine, The Second Affiliated Hospital of Zhejiang University School of Medicine, Zhejiang University, Hangzhou, China
| | - Wencun Li
- Department of Ultrasound in Medicine, The Second Affiliated Hospital of Zhejiang University School of Medicine, Zhejiang University, Hangzhou, China
| | - Jianing Zhu
- Department of Ultrasound in Medicine, The Second Affiliated Hospital of Zhejiang University School of Medicine, Zhejiang University, Hangzhou, China
| | - Yanan Zhao
- Department of Ultrasound in Medicine, The Second Affiliated Hospital of Zhejiang University School of Medicine, Zhejiang University, Hangzhou, China
| | - Qin Chen
- Department of Ultrasound, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, School of Medicine, Chengdu, China
| | - Yi Huang
- Department of Ultrasound Diagnosis, Xi'an Chest Hospital, Xi'an, China
| | - Hongming Li
- Physical Diagnosis Department, Infectious Disease Hospital of Heilongjiang Province, Harbin, China
| | - Ying Huang
- Department of Ultrasound, Shengjing Hospital of China Medical University, Liaoning Province, Shenyang, China
| | - Gaoyi Yang
- Department of Ultrasound, Hangzhou Red Cross Hospital, Hangzhou, China
| | - Pintong Huang
- Department of Ultrasound in Medicine, The Second Affiliated Hospital of Zhejiang University School of Medicine, Zhejiang University, Hangzhou, China.,Research Center of Ultrasound in Medicine and Biomedical Engineering, The Second Affiliated Hospital of Zhejiang University School of Medicine, Zhejiang University, Hangzhou, China
| |
Collapse
|
37
|
A Systematic Review of the Current Status and Quality of Radiomics for Glioma Differential Diagnosis. Cancers (Basel) 2022; 14:cancers14112731. [PMID: 35681711 PMCID: PMC9179305 DOI: 10.3390/cancers14112731] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 05/26/2022] [Accepted: 05/30/2022] [Indexed: 02/01/2023] Open
Abstract
Simple Summary Gliomas can be difficult to discern clinically and radiologically from other brain lesions (either neoplastic or non-neoplastic) since their clinical manifestations as well as preoperative imaging features often overlap and appear misleading. Radiomics could be extremely helpful for non-invasive glioma differential diagnosis (DDx). However, implementation in clinical practice is still distant and concerns have been raised regarding the methodological quality of radiomic studies. In this context, we aimed to summarize the current status and quality of radiomic studies concerning glioma DDx in a systematic review. In total, 42 studies were selected and examined in our work. Our study revealed that, despite promising and encouraging results, current studies on radiomics for glioma DDx still lack the quality required to allow its introduction into clinical practice. This work could provide new insights and help to reach a consensus on the use of the radiomic approach for glioma DDx. Abstract Radiomics is a promising tool that may increase the value of imaging in differential diagnosis (DDx) of glioma. However, implementation in clinical practice is still distant and concerns have been raised regarding the methodological quality of radiomic studies. Therefore, we aimed to systematically review the current status of radiomic studies concerning glioma DDx, also using the radiomics quality score (RQS) to assess the quality of the methodology used in each study. A systematic literature search was performed to identify original articles focused on the use of radiomics for glioma DDx from 2015. Methodological quality was assessed using the RQS tool. Spearman’s correlation (ρ) analysis was performed to explore whether RQS was correlated with journal metrics and the characteristics of the studies. Finally, 42 articles were selected for the systematic qualitative analysis. Selected articles were grouped and summarized in terms of those on DDx between glioma and primary central nervous system lymphoma, those aiming at differentiating glioma from brain metastases, and those based on DDx of glioma and other brain diseases. Median RQS was 8.71 out 36, with a mean RQS of all studies of 24.21%. Our study revealed that, despite promising and encouraging results, current studies on radiomics for glioma DDx still lack the quality required to allow its introduction into clinical practice. This work could provide new insights and help to reach a consensus on the use of the radiomic approach for glioma DDx.
Collapse
|
38
|
Deep Neural Networks and Machine Learning Radiomics Modelling for Prediction of Relapse in Mantle Cell Lymphoma. Cancers (Basel) 2022; 14:cancers14082008. [PMID: 35454914 PMCID: PMC9028737 DOI: 10.3390/cancers14082008] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Revised: 04/05/2022] [Accepted: 04/12/2022] [Indexed: 12/12/2022] Open
Abstract
Simple Summary Mantle cell lymphoma (MCL) is an aggressive lymphoid tumour with a poor prognosis. There exist no routine biomarkers for the early prediction of relapse. Our study compared the potential of radiomics-based machine learning and 3D deep learning models as non-invasive biomarkers to risk-stratify MCL patients, thus promoting precision imaging in clinical oncology. Abstract Mantle cell lymphoma (MCL) is a rare lymphoid malignancy with a poor prognosis characterised by frequent relapse and short durations of treatment response. Most patients present with aggressive disease, but there exist indolent subtypes without the need for immediate intervention. The very heterogeneous behaviour of MCL is genetically characterised by the translocation t(11;14)(q13;q32), leading to Cyclin D1 overexpression with distinct clinical and biological characteristics and outcomes. There is still an unfulfilled need for precise MCL prognostication in real-time. Machine learning and deep learning neural networks are rapidly advancing technologies with promising results in numerous fields of application. This study develops and compares the performance of deep learning (DL) algorithms and radiomics-based machine learning (ML) models to predict MCL relapse on baseline CT scans. Five classification algorithms were used, including three deep learning models (3D SEResNet50, 3D DenseNet, and an optimised 3D CNN) and two machine learning models based on K-nearest Neighbor (KNN) and Random Forest (RF). The best performing method, our optimised 3D CNN, predicted MCL relapse with a 70% accuracy, better than the 3D SEResNet50 (62%) and the 3D DenseNet (59%). The second-best performing method was the KNN-based machine learning model (64%) after principal component analysis for improved accuracy. Our optimised CNN developed by ourselves correctly predicted MCL relapse in 70% of the patients on baseline CT imaging. Once prospectively tested in clinical trials with a larger sample size, our proposed 3D deep learning model could facilitate clinical management by precision imaging in MCL.
Collapse
|
39
|
Cassinelli Petersen GI, Shatalov J, Verma T, Brim WR, Subramanian H, Brackett A, Bahar RC, Merkaj S, Zeevi T, Staib LH, Cui J, Omuro A, Bronen RA, Malhotra A, Aboian MS. Machine Learning in Differentiating Gliomas from Primary CNS Lymphomas: A Systematic Review, Reporting Quality, and Risk of Bias Assessment. AJNR Am J Neuroradiol 2022; 43:526-533. [PMID: 35361577 PMCID: PMC8993193 DOI: 10.3174/ajnr.a7473] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Accepted: 01/31/2022] [Indexed: 12/12/2022]
Abstract
BACKGROUND Differentiating gliomas and primary CNS lymphoma represents a diagnostic challenge with important therapeutic ramifications. Biopsy is the preferred method of diagnosis, while MR imaging in conjunction with machine learning has shown promising results in differentiating these tumors. PURPOSE Our aim was to evaluate the quality of reporting and risk of bias, assess data bases with which the machine learning classification algorithms were developed, the algorithms themselves, and their performance. DATA SOURCES Ovid EMBASE, Ovid MEDLINE, Cochrane Central Register of Controlled Trials, and the Web of Science Core Collection were searched according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. STUDY SELECTION From 11,727 studies, 23 peer-reviewed studies used machine learning to differentiate primary CNS lymphoma from gliomas in 2276 patients. DATA ANALYSIS Characteristics of data sets and machine learning algorithms were extracted. A meta-analysis on a subset of studies was performed. Reporting quality and risk of bias were assessed using the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) and Prediction Model Study Risk Of Bias Assessment Tool. DATA SYNTHESIS The highest area under the receiver operating characteristic curve (0.961) and accuracy (91.2%) in external validation were achieved by logistic regression and support vector machines models using conventional radiomic features. Meta-analysis of machine learning classifiers using these features yielded a mean area under the receiver operating characteristic curve of 0.944 (95% CI, 0.898-0.99). The median TRIPOD score was 51.7%. The risk of bias was high for 16 studies. LIMITATIONS Exclusion of abstracts decreased the sensitivity in evaluating all published studies. Meta-analysis had high heterogeneity. CONCLUSIONS Machine learning-based methods of differentiating primary CNS lymphoma from gliomas have shown great potential, but most studies lack large, balanced data sets and external validation. Assessment of the studies identified multiple deficiencies in reporting quality and risk of bias. These factors reduce the generalizability and reproducibility of the findings.
Collapse
Affiliation(s)
- G I Cassinelli Petersen
- From the Department of Radiology and Biomedical Imaging (G.I.C.P., T.V., H.S., R.C.B., S.M., T.Z., L.H.S., J.C., R.A.B., A.M., M.S.A.)
- Universitätsmedizin Göttingen (G.I.C.P.), Göttingen, Germany
| | - J Shatalov
- University of Richmond (J.S.), Richmond, Virginia
| | - T Verma
- From the Department of Radiology and Biomedical Imaging (G.I.C.P., T.V., H.S., R.C.B., S.M., T.Z., L.H.S., J.C., R.A.B., A.M., M.S.A.)
- New York University (T.V.), New York, New York
| | - W R Brim
- Whiting School of Engineering (W.R.B.), Johns Hopkins University, Baltimore, Maryland
| | - H Subramanian
- From the Department of Radiology and Biomedical Imaging (G.I.C.P., T.V., H.S., R.C.B., S.M., T.Z., L.H.S., J.C., R.A.B., A.M., M.S.A.)
| | | | - R C Bahar
- From the Department of Radiology and Biomedical Imaging (G.I.C.P., T.V., H.S., R.C.B., S.M., T.Z., L.H.S., J.C., R.A.B., A.M., M.S.A.)
| | - S Merkaj
- From the Department of Radiology and Biomedical Imaging (G.I.C.P., T.V., H.S., R.C.B., S.M., T.Z., L.H.S., J.C., R.A.B., A.M., M.S.A.)
| | - T Zeevi
- From the Department of Radiology and Biomedical Imaging (G.I.C.P., T.V., H.S., R.C.B., S.M., T.Z., L.H.S., J.C., R.A.B., A.M., M.S.A.)
| | - L H Staib
- From the Department of Radiology and Biomedical Imaging (G.I.C.P., T.V., H.S., R.C.B., S.M., T.Z., L.H.S., J.C., R.A.B., A.M., M.S.A.)
| | - J Cui
- From the Department of Radiology and Biomedical Imaging (G.I.C.P., T.V., H.S., R.C.B., S.M., T.Z., L.H.S., J.C., R.A.B., A.M., M.S.A.)
| | - A Omuro
- Department of Neurology (A.O.), Yale School of Medicine, New Haven, Connecticut
| | - R A Bronen
- From the Department of Radiology and Biomedical Imaging (G.I.C.P., T.V., H.S., R.C.B., S.M., T.Z., L.H.S., J.C., R.A.B., A.M., M.S.A.)
| | - A Malhotra
- From the Department of Radiology and Biomedical Imaging (G.I.C.P., T.V., H.S., R.C.B., S.M., T.Z., L.H.S., J.C., R.A.B., A.M., M.S.A.)
| | - M S Aboian
- From the Department of Radiology and Biomedical Imaging (G.I.C.P., T.V., H.S., R.C.B., S.M., T.Z., L.H.S., J.C., R.A.B., A.M., M.S.A.)
| |
Collapse
|
40
|
Li Y, Xie Y, Xu Y, Zhang N, Li G, Ju S. A new scheme of global feature management improved the performance and stability of radiomics model: a study based on CT images of acute brainstem infarction. Eur Radiol 2022; 32:5508-5516. [PMID: 35267092 DOI: 10.1007/s00330-022-08659-w] [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: 10/11/2021] [Revised: 01/28/2022] [Accepted: 02/12/2022] [Indexed: 11/29/2022]
Abstract
OBJECTIVE The performance and stability of radiomics model caused by dimension reduction remain being confronted with major challenges. In this study, we aimed to propose a new scheme of global feature management independent of dimension reduction to improve it. METHODS The non-contrast computed tomography (NCCT) images of acute brainstem infarction (ABI) from two medical centers were used as test and validation sets. A new scheme was constructed based on global feature management, and the traditional scheme dependent on dimension reduction was used as control. The radiomic features of NCCT images were extracted in Matlab R2013a. The performance of prediction model was evaluated by the generalized linear model (GLM) and multivariate logistic regression. And, the stability of radiomics model was evaluated with the difference of area under curve (AUC) between the test and validation sets. RESULTS Compared with the traditional scheme, the new scheme presented a similar detection performance (AUC: 0.875 vs. 0.883), yet a better performance in predicting prognosis (AUC: 0.864, OR = 0.917, p = 0.021 vs. AUC:0.806, OR = 0.972, p = 0.007). All these results were well verified in an independent validation set. Moreover, the new scheme showed stronger stability in both the detection model (ΔAUC: 0.013 vs. 0.039) and prediction model (ΔAUC = 0.004 vs. 0.044). CONCLUSION Although there might be several limitations, this study proved that the scheme of global feature management independent of dimension reduction could be a powerful supplement to the radiomics methodology. KEY POINTS • The new scheme (Swavelet) presented similar detection performances for ABI with the traditional scheme. • A better predictive performance for END was found in the new scheme (Swavelet) compared with the traditional scheme. • Stronger model stability was found in both the detection and prediction models based on the new scheme.
Collapse
Affiliation(s)
- Yuefeng Li
- Department of Radiology, Affiliated Hospital of Jiangsu University, Zhenjiang, China.,Department of Psychiatry, Zhenjiang Mental Health Center, Zhenjiang, China
| | - Yuhang Xie
- Neuroimaging Laboratory, Medical College of Jiangsu University, Zhenjiang, China
| | - Yuhao Xu
- Neuroimaging Laboratory, Medical College of Jiangsu University, Zhenjiang, China
| | - Ningning Zhang
- Neuroimaging Laboratory, Medical College of Jiangsu University, Zhenjiang, China
| | - Guohai Li
- Department of Psychiatry, Zhenjiang Mental Health Center, Zhenjiang, China.
| | - Shenghong Ju
- Department of Radiology, Zhongda Affiliated Hospital of Southeast University, Nanjing, China.
| |
Collapse
|
41
|
Meng X, Gao D, He H, Sun S, Liu A, Jin H, Li Y. A Machine Learning Model Predicts the Outcome of SRS for Residual Arteriovenous malformations after partial embolization- A Real-World Clinical Obstacle. World Neurosurg 2022; 163:e73-e82. [PMID: 35276397 DOI: 10.1016/j.wneu.2022.03.007] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Revised: 02/28/2022] [Accepted: 03/01/2022] [Indexed: 11/30/2022]
Abstract
PURPOSE To propose a machine learning (ML) model predicting the favorable outcome of stereotactic radiosurgery (SRS) for residual brain arteriovenous malformation (bAVM) after partial embolization. MATERIALS AND METHODS One hundred and thirty bAVM patients who underwent partial embolization followed by SRS were retrospectively reviewed. Patients were randomly split into training datasets (n=100) and testing datasets (n=30). Radiomics and dosimetric features were extracted from pre-SRS treatment images. Feature selection was performed to select appropriate radiomics and dosimetric features. Three ML algorithms were applied to construct models using selected features respectively. A total of 9 models were trained to predict favorable outcomes (obliteration without complication) of bAVMs. The efficacy of these models was evaluated on the testing dataset using mean accuracy (ACC) and area under the receiver operating characteristic curve (AUROC). RESULTS The obliteration rate of this cohort was 70.77% (92/130) with a mean follow-up period of 43.8 (Range 12-108 months) months. Favorable outcomes were achieved in 89 (68.46%) patients. Four radiomics features and 7 dosimetric features were selected for ML model construction. The dosimetric SVM showed the best performance on the training dataset, with an ACC and AUC of 0.74 and 0.78 respectively. The dosimetric SVM model also showed the best performance on the testing dataset where the ACC and AUC were 0.83 and 0.77 respectively. CONCLUSION Dosimetric features are good predictors of prognosis for patients with partially embolized bAVM followed by SRS therapy. The use of ML models is an innovative method for predicting favorable outcomes of partially embolized bAVM followed by SRS therapy.
Collapse
Affiliation(s)
- Xiangyu Meng
- Department of Interventional Neuroradiology, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China; Department of Interventional Neuroradiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Dezhi Gao
- Department of Interventional Neuroradiology, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China; Department of Gamma-Knife Center, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Hongwei He
- Department of Interventional Neuroradiology, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China; Department of Interventional Neuroradiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Shibin Sun
- Department of Interventional Neuroradiology, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China; Department of Gamma-Knife Center, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Ali Liu
- Department of Interventional Neuroradiology, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China; Department of Gamma-Knife Center, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Hengwei Jin
- Department of Interventional Neuroradiology, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China; Department of Interventional Neuroradiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
| | - Youxiang Li
- Department of Interventional Neuroradiology, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China; Department of Interventional Neuroradiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; Beijing Engineering Research Center, Beijing, China.
| |
Collapse
|
42
|
Kalasauskas D, Kosterhon M, Keric N, Korczynski O, Kronfeld A, Ringel F, Othman A, Brockmann MA. Beyond Glioma: The Utility of Radiomic Analysis for Non-Glial Intracranial Tumors. Cancers (Basel) 2022; 14:cancers14030836. [PMID: 35159103 PMCID: PMC8834271 DOI: 10.3390/cancers14030836] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 01/30/2022] [Accepted: 02/04/2022] [Indexed: 02/05/2023] Open
Abstract
Simple Summary Tumor qualities, such as growth rate, firmness, and intrusion into healthy tissue, can be very important for operation planning and further treatment. Radiomics is a promising new method that allows the determination of some of these qualities on images performed before surgery. In this article, we provide a review of the use of radiomics in various tumors of the central nervous system, such as metastases, lymphoma, meningioma, medulloblastoma, and pituitary tumors. Abstract The field of radiomics is rapidly expanding and gaining a valuable role in neuro-oncology. The possibilities related to the use of radiomic analysis, such as distinguishing types of malignancies, predicting tumor grade, determining the presence of particular molecular markers, consistency, therapy response, and prognosis, can considerably influence decision-making in medicine in the near future. Even though the main focus of radiomic analyses has been on glial CNS tumors, studies on other intracranial tumors have shown encouraging results. Therefore, as the main focus of this review, we performed an analysis of publications on PubMed and Web of Science databases, focusing on radiomics in CNS metastases, lymphoma, meningioma, medulloblastoma, and pituitary tumors.
Collapse
Affiliation(s)
- Darius Kalasauskas
- Department of Neurosurgery, University Medical Centre, Johannes Gutenberg University Mainz, 55131 Mainz, Germany; (D.K.); (M.K.); (N.K.); (F.R.)
| | - Michael Kosterhon
- Department of Neurosurgery, University Medical Centre, Johannes Gutenberg University Mainz, 55131 Mainz, Germany; (D.K.); (M.K.); (N.K.); (F.R.)
| | - Naureen Keric
- Department of Neurosurgery, University Medical Centre, Johannes Gutenberg University Mainz, 55131 Mainz, Germany; (D.K.); (M.K.); (N.K.); (F.R.)
| | - Oliver Korczynski
- Department of Neuroradiology, University Medical Centre, Johannes Gutenberg University Mainz, 55131 Mainz, Germany; (O.K.); (A.K.); (A.O.)
| | - Andrea Kronfeld
- Department of Neuroradiology, University Medical Centre, Johannes Gutenberg University Mainz, 55131 Mainz, Germany; (O.K.); (A.K.); (A.O.)
| | - Florian Ringel
- Department of Neurosurgery, University Medical Centre, Johannes Gutenberg University Mainz, 55131 Mainz, Germany; (D.K.); (M.K.); (N.K.); (F.R.)
| | - Ahmed Othman
- Department of Neuroradiology, University Medical Centre, Johannes Gutenberg University Mainz, 55131 Mainz, Germany; (O.K.); (A.K.); (A.O.)
| | - Marc A. Brockmann
- Department of Neuroradiology, University Medical Centre, Johannes Gutenberg University Mainz, 55131 Mainz, Germany; (O.K.); (A.K.); (A.O.)
- Correspondence:
| |
Collapse
|
43
|
Fan Y, Liu P, Li Y, Liu F, He Y, Wang L, Zhang J, Wu Z. Non-Invasive Preoperative Imaging Differential Diagnosis of Intracranial Hemangiopericytoma and Angiomatous Meningioma: A Novel Developed and Validated Multiparametric MRI-Based Clini-Radiomic Model. Front Oncol 2022; 11:792521. [PMID: 35059316 PMCID: PMC8763962 DOI: 10.3389/fonc.2021.792521] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2021] [Accepted: 11/29/2021] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND Accurate preoperative differentiation of intracranial hemangiopericytoma and angiomatous meningioma can greatly assist operation plan making and prognosis prediction. In this study, a clini-radiomic model combining radiomic and clinical features was used to distinguish intracranial hemangiopericytoma and hemangioma meningioma preoperatively. METHODS A total of 147 patients with intracranial hemangiopericytoma and 73 patients with angiomatous meningioma from the Tiantan Hospital were retrospectively reviewed and randomly assigned to training and validation sets. Radiomic features were extracted from MR images, the elastic net and recursive feature elimination algorithms were applied to select radiomic features for constructing a fusion radiomic model. Subsequently, multivariable logistic regression analysis was used to construct a clinical model, then a clini-radiomic model incorporating the fusion radiomic model and clinical features was constructed for individual predictions. The calibration, discriminating capacity, and clinical usefulness were also evaluated. RESULTS Six significant radiomic features were selected to construct a fusion radiomic model that achieved an area under the curve (AUC) value of 0.900 and 0.900 in the training and validation sets, respectively. A clini-radiomic model that incorporated the radiomic model and clinical features was constructed and showed good discrimination and calibration, with an AUC of 0.920 in the training set and 0.910 in the validation set. The analysis of the decision curve showed that the fusion radiomic model and clini-radiomic model were clinically useful. CONCLUSIONS Our clini-radiomic model showed great performance and high sensitivity in the differential diagnosis of intracranial hemangiopericytoma and angiomatous meningioma, and could contribute to non-invasive development of individualized diagnosis and treatment for these patients.
Collapse
Affiliation(s)
- Yanghua Fan
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Department of Neurosurgery, Beijing Neurosurgical Institute, Beijing, China
| | - Panpan Liu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Department of Neurosurgery, Weihai Municipal Hospital, Cheeloo College of Medicine, Shandong University, Weihai, China
| | - Yiping Li
- Department of Gastroenterology, Weihai Municipal Hospital, Cheeloo College of Medicine, Shandong University, Weihai, China
| | - Feng Liu
- Department of Neurosurgery, Jiangxi Provincial Children's Hospital, The Affiliated Children's Hospital of Nanchang University, Nanchang, China
| | - Yu He
- Department of Craniomaxillofacial Surgery, Plastic Surgery Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Liang Wang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Junting Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Zhen Wu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| |
Collapse
|
44
|
Lisson CS, Lisson CG, Achilles S, Mezger MF, Wolf D, Schmidt SA, Thaiss WM, Bloehdorn J, Beer AJ, Stilgenbauer S, Beer M, Götz M. Longitudinal CT Imaging to Explore the Predictive Power of 3D Radiomic Tumour Heterogeneity in Precise Imaging of Mantle Cell Lymphoma (MCL). Cancers (Basel) 2022; 14:393. [PMID: 35053554 PMCID: PMC8773890 DOI: 10.3390/cancers14020393] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Revised: 12/29/2021] [Accepted: 01/06/2022] [Indexed: 02/06/2023] Open
Abstract
The study's primary aim is to evaluate the predictive performance of CT-derived 3D radiomics for MCL risk stratification. The secondary objective is to search for radiomic features associated with sustained remission. Included were 70 patients: 31 MCL patients and 39 control subjects with normal axillary lymph nodes followed over five years. Radiomic analysis of all targets (n = 745) was performed and features selected using the Mann Whitney U test; the discriminative power of identifying "high-risk MCL" was evaluated by receiver operating characteristics (ROC). The four radiomic features, "Uniformity", "Entropy", "Skewness" and "Difference Entropy" showed predictive significance for relapse (p < 0.05)-in contrast to the routine size measurements, which showed no relevant difference. The best prognostication for relapse achieved the feature "Uniformity" (AUC-ROC-curve 0.87; optimal cut-off ≤0.0159 to predict relapse with 87% sensitivity, 65% specificity, 69% accuracy). Several radiomic features, including the parameter "Short Axis," were associated with sustained remission. CT-derived 3D radiomics improves the predictive estimation of MCL patients; in combination with the ability to identify potential radiomic features that are characteristic for sustained remission, it may assist physicians in the clinical management of MCL.
Collapse
Affiliation(s)
- Catharina Silvia Lisson
- Department of Diagnostic and Interventional Radiology, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- Center for Personalized Medicine (ZPM), University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- Artificial Intelligence in Experimental Radiology (XAIRAD), Department of Diagnostic and Interventional Radiology, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
| | - Christoph Gerhard Lisson
- Department of Diagnostic and Interventional Radiology, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
| | - Sherin Achilles
- Department of Diagnostic and Interventional Radiology, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
| | - Marc Fabian Mezger
- Department of Diagnostic and Interventional Radiology, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- Artificial Intelligence in Experimental Radiology (XAIRAD), Department of Diagnostic and Interventional Radiology, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- Visual Computing Group, Institute of Media Informatics, Ulm University, James-Franck-Ring, 89081 Ulm, Germany
| | - Daniel Wolf
- Department of Diagnostic and Interventional Radiology, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- Artificial Intelligence in Experimental Radiology (XAIRAD), Department of Diagnostic and Interventional Radiology, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- Visual Computing Group, Institute of Media Informatics, Ulm University, James-Franck-Ring, 89081 Ulm, Germany
| | - Stefan Andreas Schmidt
- Department of Diagnostic and Interventional Radiology, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- Center for Personalized Medicine (ZPM), University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
| | - Wolfgang M Thaiss
- Department of Diagnostic and Interventional Radiology, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- Artificial Intelligence in Experimental Radiology (XAIRAD), Department of Diagnostic and Interventional Radiology, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- Department of Nuclear Medicine, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
| | - Johannes Bloehdorn
- Department of Internal Medicine III, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
| | - Ambros J Beer
- Center for Personalized Medicine (ZPM), University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- Artificial Intelligence in Experimental Radiology (XAIRAD), Department of Diagnostic and Interventional Radiology, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- Department of Nuclear Medicine, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- Center for Translational Imaging "From Molecule to Man" (MoMan), Department of Internal Medicine II, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- i2SouI-Innovative Imaging in Surgical Oncology Ulm, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
| | - Stephan Stilgenbauer
- Department of Internal Medicine III, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- Comprehensive Cancer Center Ulm (CCCU), University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
| | - Meinrad Beer
- Department of Diagnostic and Interventional Radiology, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- Center for Personalized Medicine (ZPM), University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- Artificial Intelligence in Experimental Radiology (XAIRAD), Department of Diagnostic and Interventional Radiology, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- Center for Translational Imaging "From Molecule to Man" (MoMan), Department of Internal Medicine II, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- i2SouI-Innovative Imaging in Surgical Oncology Ulm, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
| | - Michael Götz
- Department of Diagnostic and Interventional Radiology, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- Artificial Intelligence in Experimental Radiology (XAIRAD), Department of Diagnostic and Interventional Radiology, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
- German Cancer Research Center (DKFZ), Division Medical Image Computing, 69120 Heidelberg, Germany
| |
Collapse
|
45
|
Fan Y, Huo X, Li X, Wang L, Wu Z. Non-invasive preoperative imaging differential diagnosis of pineal region tumor: A novel developed and validated multiparametric MRI-based clinicoradiomic model. Radiother Oncol 2022; 167:277-284. [PMID: 35033600 DOI: 10.1016/j.radonc.2022.01.005] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Revised: 12/26/2021] [Accepted: 01/05/2022] [Indexed: 01/07/2023]
Abstract
BACKGROUND Preoperative differential diagnosis of pineal region tumor can greatly assist clinical decision-making and avoid economic costs and complications caused by unnecessary radiotherapy or invasive procedures. The present study was performed to pre-operatively distinguish pineal region germinoma and pinealoblastoma using a clinicoradiomic model by incorporating radiomic and clinical features. METHODS 134 pineal region tumor patients (germinoma, 69; pinealoblastoma, 65) with complete clinic-radiological and histopathological data from Tiantan hospital were retrospectively reviewed and randomly assigned to training and validation sets. Radiomic features were extracted from MR images, then the elastic net and recursive feature elimination algorithms were applied to select radiomic features for constructing a fusion radiomic model. Subsequently, multivariable logistic regression analysis was used to select the clinical features, and a clinicoradiomic model incorporating the fusion radiomic model and selected clinical features was constructed for individual predictions. The calibration, discriminating capacity, and clinical usefulness were also evaluated. RESULTS Seven significant radiomic features were selected to construct a fusion radiomic model that achieved an area under the curve (AUC) value of 0.920 and 0.880 in the training and validation sets, respectively. A clinicoradiomic model that incorporated the radiomic model and four selected clinical features was constructed and showed good discrimination and calibration, with an AUC of 0.950 in the training set and 0.940 in the validation set. The analysis of the decision curve showed that the radiomic model and clinicoradiomic model were clinically useful for patients with pineal region tumor. CONCLUSIONS Our clinicoradiomic model showed great performance and high sensitivity in the differential diagnosis of germinoma and pinealoblastoma, and could contribute to non-invasive development of individualized diagnosis and treatment of patients with pineal region tumor.
Collapse
Affiliation(s)
- Yanghua Fan
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China
| | - Xulei Huo
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China
| | - Xiaojie Li
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China
| | - Liang Wang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China.
| | - Zhen Wu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China.
| |
Collapse
|
46
|
Differentiation between Germinoma and Craniopharyngioma Using Radiomics-Based Machine Learning. J Pers Med 2022; 12:jpm12010045. [PMID: 35055362 PMCID: PMC8778008 DOI: 10.3390/jpm12010045] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 12/27/2021] [Accepted: 12/30/2021] [Indexed: 02/05/2023] Open
Abstract
For the tumors located in the anterior skull base, germinoma and craniopharyngioma (CP) are unusual types with similar clinical manifestations and imaging features. The difference in treatment strategies and outcomes of patients highlights the importance of making an accurate preoperative diagnosis. This retrospective study enrolled 107 patients diagnosed with germinoma (n = 44) and CP (n = 63). The region of interest (ROI) was drawn independently by two researchers. Radiomic features were extracted from contrast-enhanced T1WI and T2WI sequences. Here, we established the diagnosis models with a combination of three selection methods, as well as three classifiers. After training the models, their performances were evaluated on the independent validation cohort and compared based on the index of the area under the receiver operating characteristic curve (AUC) in the validation cohort. Nine models were established and compared to find the optimal one defined with the highest AUC in the validation cohort. For the models applied in the contrast-enhanced T1WI images, RFS + RFC and LASSO + LDA were observed to be the optimal models with AUCs of 0.91. For the models applied in the T2WI images, DC + LDA and LASSO + LDA were observed to be the optimal models with AUCs of 0.88. The evidence of this study indicated that radiomics-based machine learning could be potentially considered as the radiological method in the presurgical differential diagnosis of germinoma and CP with a reliable diagnostic performance.
Collapse
|
47
|
Nenning KH, Langs G. Machine learning in neuroimaging: from research to clinical practice. RADIOLOGIE (HEIDELBERG, GERMANY) 2022; 62:1-10. [PMID: 36044070 PMCID: PMC9732070 DOI: 10.1007/s00117-022-01051-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 07/07/2022] [Indexed: 12/14/2022]
Abstract
Neuroimaging is critical in clinical care and research, enabling us to investigate the brain in health and disease. There is a complex link between the brain's morphological structure, physiological architecture, and the corresponding imaging characteristics. The shape, function, and relationships between various brain areas change during development and throughout life, disease, and recovery. Like few other areas, neuroimaging benefits from advanced analysis techniques to fully exploit imaging data for studying the brain and its function. Recently, machine learning has started to contribute (a) to anatomical measurements, detection, segmentation, and quantification of lesions and disease patterns, (b) to the rapid identification of acute conditions such as stroke, or (c) to the tracking of imaging changes over time. As our ability to image and analyze the brain advances, so does our understanding of its intricate relationships and their role in therapeutic decision-making. Here, we review the current state of the art in using machine learning techniques to exploit neuroimaging data for clinical care and research, providing an overview of clinical applications and their contribution to fundamental computational neuroscience.
Collapse
Affiliation(s)
- Karl-Heinz Nenning
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute, Orangeburg, NY, USA
- Department of Biomedical Imaging and Image-guided Therapy, Computational Imaging Research Lab, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
| | - Georg Langs
- Department of Biomedical Imaging and Image-guided Therapy, Computational Imaging Research Lab, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria.
| |
Collapse
|
48
|
Wang J, Yi X, Fu Y, Pang P, Deng H, Tang H, Han Z, Li H, Nie J, Gong G, Hu Z, Tan Z, Chen BT. Preoperative Magnetic Resonance Imaging Radiomics for Predicting Early Recurrence of Glioblastoma. Front Oncol 2021; 11:769188. [PMID: 34778086 PMCID: PMC8579096 DOI: 10.3389/fonc.2021.769188] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Accepted: 10/11/2021] [Indexed: 01/03/2023] Open
Abstract
Purpose Early recurrence of glioblastoma after standard treatment makes patient care challenging. This study aimed to assess preoperative magnetic resonance imaging (MRI) radiomics for predicting early recurrence of glioblastoma. Patients and Methods A total of 122 patients (training cohort: n = 86; validation cohort: n = 36) with pathologically confirmed glioblastoma were included in this retrospective study. Preoperative brain MRI images were analyzed for both radiomics and the Visually Accessible Rembrandt Image (VASARI) features of glioblastoma. Models incorporating MRI radiomics, the VASARI parameters, and clinical variables were developed and presented in a nomogram. Performance was assessed based on calibration, discrimination, and clinical usefulness. Results The nomogram consisting of the radiomic signatures, the VASARI parameters, and blood urea nitrogen (BUN) values showed good discrimination between the patients with early recurrence and those with later recurrence, with an area under the curve of 0.85 (95% CI, 0.77-0.94) in the training cohort and 0.84 [95% CI, 0.71-0.97] in the validation cohort. Decision curve analysis demonstrated favorable clinical application of the nomogram. Conclusion This study showed the potential usefulness of preoperative brain MRI radiomics in predicting the early recurrence of glioblastoma, which should be helpful in personalized management of glioblastoma.
Collapse
Affiliation(s)
- Jing Wang
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
| | - Xiaoping Yi
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China.,National Clinical Research Center for Geriatric Disorders, Changsha, China.,Hunan Engineering Research Center of Skin Health and Disease, Changsha, China.,Hunan Key Laboratory of Skin Cancer and Psoriasis, Changsha, China
| | - Yan Fu
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China.,National Clinical Research Center for Geriatric Disorders (Xiangya Hospital), Central South University, Changsha, China
| | - Peipei Pang
- Department of Pharmaceuticals Diagnosis, GE Healthcare, Hangzhou, China
| | - Huihuang Deng
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
| | - Haiyun Tang
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
| | - Zaide Han
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
| | - Haiping Li
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
| | - Jilin Nie
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
| | - Guanghui Gong
- Department of Pathology, Xiangya Hospital, Central South University, Changsha, China
| | - Zhongliang Hu
- Department of Pathology, Xiangya Hospital, Central South University, Changsha, China
| | - Zeming Tan
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China
| | - Bihong T Chen
- Department of Diagnostic Radiology, City of Hope National Medical Center, Duarte, CA, United States
| |
Collapse
|
49
|
Ak M, Toll SA, Hein KZ, Colen RR, Khatua S. Evolving Role and Translation of Radiomics and Radiogenomics in Adult and Pediatric Neuro-Oncology. AJNR Am J Neuroradiol 2021; 43:792-801. [PMID: 34649914 DOI: 10.3174/ajnr.a7297] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Accepted: 07/19/2021] [Indexed: 12/24/2022]
Abstract
Exponential technologic advancements in imaging, high-performance computing, and artificial intelligence, in addition to increasing access to vast amounts of diverse data, have revolutionized the role of imaging in medicine. Radiomics is defined as a high-throughput feature-extraction method that unlocks microscale quantitative data hidden within standard-of-care medical imaging. Radiogenomics is defined as the linkage between imaging and genomics information. Multiple radiomics and radiogenomics studies performed on conventional and advanced neuro-oncology image modalities show that they have the potential to differentiate pseudoprogression from true progression, classify tumor subgroups, and predict recurrence, survival, and mutation status with high accuracy. In this article, we outline the technical steps involved in radiomics and radiogenomics analyses with the use of artificial intelligence methods and review current applications in adult and pediatric neuro-oncology.
Collapse
Affiliation(s)
- M Ak
- From the Department of Radiology (M.A., R.R.C.), University of Pittsburgh, Pittsburgh, Pennsylvania.,Hillman Cancer Center (M.A., R.R.C.), University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania
| | - S A Toll
- Department of Hematology-Oncology (S.A.T.), Children's Hospital of Michigan, Detroit, Michigan
| | - K Z Hein
- Department of Leukemia (K.Z.H.), The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - R R Colen
- From the Department of Radiology (M.A., R.R.C.), University of Pittsburgh, Pittsburgh, Pennsylvania.,Hillman Cancer Center (M.A., R.R.C.), University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania
| | - S Khatua
- Department of Pediatric Hematology-Oncology (S.K.), Mayo Clinic, Rochester, Minnesota.
| |
Collapse
|
50
|
Yi Z, Long L, Zeng Y, Liu Z. Current Advances and Challenges in Radiomics of Brain Tumors. Front Oncol 2021; 11:732196. [PMID: 34722274 PMCID: PMC8551958 DOI: 10.3389/fonc.2021.732196] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Accepted: 09/23/2021] [Indexed: 12/12/2022] Open
Abstract
Imaging diagnosis is crucial for early detection and monitoring of brain tumors. Radiomics enable the extraction of a large mass of quantitative features from complex clinical imaging arrays, and then transform them into high-dimensional data which can subsequently be mined to find their relevance with the tumor's histological features, which reflect underlying genetic mutations and malignancy, along with grade, progression, therapeutic effect, or even overall survival (OS). Compared to traditional brain imaging, radiomics provides quantitative information linked to meaningful biologic characteristics and application of deep learning which sheds light on the full automation of imaging diagnosis. Recent studies have shown that radiomics' application is broad in identifying primary tumor, differential diagnosis, grading, evaluation of mutation status and aggression, prediction of treatment response and recurrence in pituitary tumors, gliomas, and brain metastases. In this descriptive review, besides establishing a general understanding among protocols, results, and clinical significance of these studies, we further discuss the current limitations along with future development of radiomics.
Collapse
Affiliation(s)
- Zhenjie Yi
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China
- XiangYa School of Medicine, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Lifu Long
- XiangYa School of Medicine, Central South University, Changsha, China
| | - Yu Zeng
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Zhixiong Liu
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| |
Collapse
|