1
|
Bourdillon AT. Computer Vision-Radiomics & Pathognomics. Otolaryngol Clin North Am 2024; 57:719-751. [PMID: 38910065 DOI: 10.1016/j.otc.2024.05.003] [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] [Indexed: 06/25/2024]
Abstract
The role of computer vision in extracting radiographic (radiomics) and histopathologic (pathognomics) features is an extension of molecular biomarkers that have been foundational to our understanding across the spectrum of head and neck disorders. Especially within head and neck cancers, machine learning and deep learning applications have yielded advances in the characterization of tumor features, nodal features, and various outcomes. This review aims to overview the landscape of radiomic and pathognomic applications, informing future work to address gaps. Novel methodologies will be needed to potentially engineer ways of integrating multidimensional data inputs to examine disease features to guide prognosis comprehensively and ultimately clinical management.
Collapse
Affiliation(s)
- Alexandra T Bourdillon
- Department of Otolaryngology-Head & Neck Surgery, University of California-San Francisco, San Francisco, CA 94115, USA.
| |
Collapse
|
2
|
Huang H, Shi Y, Hong Y, Zhu L, Li M, Zhang Y. A nomogram for predicting neonatal apnea: a retrospective analysis based on the MIMIC database. Front Pediatr 2024; 12:1357972. [PMID: 39301040 PMCID: PMC11410630 DOI: 10.3389/fped.2024.1357972] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Accepted: 08/20/2024] [Indexed: 09/22/2024] Open
Abstract
Introduction The objective of this study is to develop a model based on indicators in the routine examination of neonates to effectively predict neonatal apnea. Methods We retrospectively analysed 8024 newborns from the MIMIC IV database, building logistic regression models and decision tree models. The performance of the model is examined by decision curves, calibration curves and ROC curves. Variables were screened by stepwise logistic regression analysis and LASSO regression. Results A total of 7 indicators were ultimately included in the model: gestational age, birth weight, ethnicity, gender, monocytes, lymphocytes and acetaminophen. The mean AUC (the area under the ROC curve) of the 5-fold cross-validation of the logistic regression model in the training set and the AUC in the validation set are 0.879 and 0.865, respectively. The mean AUC (the area under the ROC curve) of the 5-fold cross-validation of the decision tree model in the training set and the AUC in the validation set are 0.861 and 0.850, respectively. The calibration and decision curves in the two cohorts also demonstrated satisfactory predictive performance of the model. However, the logistic regression model performs relatively well. Discussion Our results proved that blood indicators were valuable and effective predictors of neonatal apnea, which could provide effective predictive information for medical staff.
Collapse
Affiliation(s)
- Huisi Huang
- Department of Paediatrics, The Affiliated TCM Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Yanhong Shi
- Department of Paediatrics, The Affiliated TCM Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Yinghui Hong
- Department of Paediatrics, The Affiliated TCM Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Lizhen Zhu
- Department of Paediatrics, The Affiliated TCM Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Mengyao Li
- Department of Paediatrics, The Affiliated TCM Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Yue Zhang
- Department of Paediatrics, The Affiliated TCM Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
| |
Collapse
|
3
|
Wang Y, Han Q, Wen B, Yang B, Zhang C, Song Y, Zhang L, Xian J. Development and validation of a prediction model for malignant sinonasal tumors based on MR radiomics and machine learning. Eur Radiol 2024:10.1007/s00330-024-11033-7. [PMID: 39210161 DOI: 10.1007/s00330-024-11033-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Revised: 06/23/2024] [Accepted: 08/06/2024] [Indexed: 09/04/2024]
Abstract
OBJECTIVES This study aimed to utilize MR radiomics-based machine learning classifiers on a large-sample, multicenter dataset to develop an optimal model for predicting malignant sinonasal tumors and tumor-like lesions. METHODS This study included 1711 adult patients (875 benign and 836 malignant) with sinonasal tumors or tumor-like lesions from three institutions. Patients from institution 1 (n = 1367) constituted both the training and validation cohorts, while those from institution 2 and 3 (n = 158/186) made up the test cohorts. Manual segmentation of the region of interest of the tumor was performed on T1WI, T2WI, and contrast-enhanced T1WI (CE-T1WI). Data normalization, dimensional reductions, feature selection, and classifications were performed using ten machine-learning classifiers. Four fusion models, namely T1WI + T2WI, T1WI + CE-T1WI, T2WI + CE-T1WI, and T1WI + T2WI + CE-T1WI, were constructed using the top ten features with the highest contribution in feature selection in the optimal models of T1WI, T2WI, and CE-T1WI. The Delong test compared areas under the curve (AUC) between models. RESULTS The AUCs of training/validation/test1/test2 datasets for T1WI, T2WI, and CE-T1WI were 0.900/0.842/0.872/0.839, 0.876/0.789/0.842/0.863, and 0.899/0.824/0.831/0.707, respectively. The fusion model from T1WI + T2WI + CE-T1WI had the highest AUC. The AUCs of training/validation/test1/test2 datasets were 0.947/0.849/0.871/0.887. The T1WI + T2WI + CE-T1WI model demonstrated a significantly higher AUC than the T2WI + CE-T1WI model in both cohorts (p < 0.05) and outperformed the T2WI model in test 1 (p = 0.008) and the T1WI model in test 2 (p = 0.006). CONCLUSIONS This fusion model based on radiomics from T1WI + T2WI + CE-T1WI images and machine learning can improve the power in predicting malignant sinonasal tumors with high accuracy, resilience, and robustness. CLINICAL RELEVANCE STATEMENT Our study proposes a radiomics-based machine learning fusion model from T1- and T2-weighted images and contrast-enhanced T1-weighted images, which can non-invasively identify the nature of sinonasal tumors and improve the performance in predicting malignant sinonasal tumors. KEY POINTS Differentiating benign and malignant sinonasal tumors is difficult due to similar clinical presentations. A radiomics model from T1 + T2 + contrast-enhanced T1 images can identify the nature of sinonasal tumors. This model can help distinguish benign and malignant sinonasal tumors.
Collapse
Affiliation(s)
- Yuchen Wang
- Department of Radiology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Qinghe Han
- Department of Radiology, The Second Hospital of Jilin University, Changchun, China
| | - Baohong Wen
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Bingbing Yang
- Department of Radiology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Chen Zhang
- MR Research Collaboration Team, Siemens Healthcare, Beijing, China
| | - Yang Song
- MR Research Collaboration Team, Siemens Healthcare, Beijing, China
| | - Luo Zhang
- Department of Otolaryngology-Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, China.
- Beijing Laboratory of Allergic Diseases and Beijing Key Laboratory of Nasal Diseases, Beijing Institute of Otorhinolaryngology, Beijing, China.
- Research Unit of Diagnosis and Treatment of Chronic Nasal Diseases, Chinese Academy of Medical Sciences, Beijing, China.
- Department of Allergy, Beijing Tongren Hospital, Capital Medical University, Beijing, China.
| | - Junfang Xian
- Department of Radiology, Beijing Tongren Hospital, Capital Medical University, Beijing, China.
| |
Collapse
|
4
|
Park CJ, Choi SH, Kim D, Kim SB, Han K, Ahn SS, Lee WH, Choi EC, Keum KC, Kim J. MRI radiomics may predict early tumor recurrence in patients with sinonasal squamous cell carcinoma. Eur Radiol 2024; 34:3151-3159. [PMID: 37926740 DOI: 10.1007/s00330-023-10389-6] [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: 11/15/2022] [Revised: 08/28/2023] [Accepted: 09/07/2023] [Indexed: 11/07/2023]
Abstract
OBJECTIVES Sinonasal squamous cell carcinoma (SCC) follows a poor prognosis with high tendency for local recurrence. We aimed to evaluate whether MRI radiomics can predict early local failure in sinonasal SCC. METHODS Sixty-eight consecutive patients with node-negative sinonasal SCC (January 2005-December 2020) were enrolled, allocated to the training (n = 47) and test sets (n = 21). Early local failure, which occurred within 12 months of completion of initial treatment, was the primary endpoint. For clinical features (age, location, treatment modality, and clinical T stage), binary logistic regression analysis was performed. For 186 extracted radiomic features, different feature selections and classifiers were combined to create two prediction models: (1) a pure radiomics model; and (2) a combined model with clinical features and radiomics. The areas under the receiver operating characteristic curves (AUCs) were calculated and compared using DeLong's method. RESULTS Early local failure occurred in 38.3% (18/47) and 23.8% (5/21) in the training and test sets, respectively. We identified several radiomic features which were strongly associated with early local failure. In the test set, both the best-performing radiomics model and the combined model (clinical + radiomic features) yielded higher AUCs compared to the clinical model (AUC, 0.838 vs. 0.438, p = 0.020; 0.850 vs. 0.438, p = 0.016, respectively). The performances of the best-performing radiomics model and the combined model did not differ significantly (AUC, 0.838 vs. 0.850, p = 0.904). CONCLUSION MRI radiomics integrated with a machine learning classifier may predict early local failure in patients with sinonasal SCC. CLINICAL RELEVANCE STATEMENT MRI radiomics intergrated with machine learning classifiers may predict early local failure in sinonasal squamous cell carcinomas more accurately than the clinical model. KEY POINTS • A subset of radiomic features which showed significant association with early local failure in patients with sinonasal squamous cell carcinomas was identified. • MRI radiomics integrated with machine learning classifiers can predict early local failure with high accuracy, which was validated in the test set (area under the curve = 0.838). • The combined clinical and radiomics model yielded superior performance for early local failure prediction compared to that of the radiomics (area under the curve 0.850 vs. 0.838 in the test set), without a statistically significant difference.
Collapse
Affiliation(s)
- Chae Jung Park
- Department of Radiology, Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yongin Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Seo Hee Choi
- Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Dain Kim
- Graduate School of Artificial Intelligence, Pohang University of Science and Technology, Pohang, Republic of Korea
| | - Si Been Kim
- Undergraduate School of Biomedical Engineering, Korea University College of Health Science, Seoul, Republic of Korea
| | - Kyunghwa Han
- Department of Radiology, Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Severance Hospital, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea
| | - Sung Soo Ahn
- Department of Radiology, Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Severance Hospital, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea
| | - Won Hee Lee
- Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Eun Chang Choi
- Department of Otorhinolaryngology, Yongin Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Ki Chang Keum
- Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Jinna Kim
- Department of Radiology, Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Severance Hospital, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea.
| |
Collapse
|
5
|
Wu Q, Wang X, Liang G, Luo X, Zhou M, Deng H, Zhang Y, Huang X, Yang Q. Advances in Image-Based Artificial Intelligence in Otorhinolaryngology-Head and Neck Surgery: A Systematic Review. Otolaryngol Head Neck Surg 2023; 169:1132-1142. [PMID: 37288505 DOI: 10.1002/ohn.391] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 04/27/2023] [Accepted: 05/13/2023] [Indexed: 06/09/2023]
Abstract
OBJECTIVE To update the literature and provide a systematic review of image-based artificial intelligence (AI) applications in otolaryngology, highlight its advances, and propose future challenges. DATA SOURCES Web of Science, Embase, PubMed, and Cochrane Library. REVIEW METHODS Studies written in English, published between January 2020 and December 2022. Two independent authors screened the search results, extracted data, and assessed studies. RESULTS Overall, 686 studies were identified. After screening titles and abstracts, 325 full-text studies were assessed for eligibility, and 78 studies were included in this systematic review. The studies originated from 16 countries. Among these countries, the top 3 were China (n = 29), Korea (n = 8), the United States, and Japan (n = 7 each). The most common area was otology (n = 35), followed by rhinology (n = 20), pharyngology (n = 18), and head and neck surgery (n = 5). Most applications of AI in otology, rhinology, pharyngology, and head and neck surgery mainly included chronic otitis media (n = 9), nasal polyps (n = 4), laryngeal cancer (n = 12), and head and neck squamous cell carcinoma (n = 3), respectively. The overall performance of AI in accuracy, the area under the curve, sensitivity, and specificity were 88.39 ± 9.78%, 91.91 ± 6.70%, 86.93 ± 11.59%, and 88.62 ± 14.03%, respectively. CONCLUSION This state-of-the-art review aimed to highlight the increasing applications of image-based AI in otorhinolaryngology head and neck surgery. The following steps will entail multicentre collaboration to ensure data reliability, ongoing optimization of AI algorithms, and integration into real-world clinical practice. Future studies should consider 3-dimensional (3D)-based AI, such as 3D surgical AI.
Collapse
Affiliation(s)
- Qingwu Wu
- Department of Otorhinolaryngology-Head and Neck Surgery, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
- Department of Allergy, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Xinyue Wang
- Department of Otorhinolaryngology-Head and Neck Surgery, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Guixian Liang
- Department of Otorhinolaryngology-Head and Neck Surgery, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Xin Luo
- Department of Otorhinolaryngology-Head and Neck Surgery, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Min Zhou
- Department of Otorhinolaryngology-Head and Neck Surgery, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
- Department of Allergy, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Huiyi Deng
- Department of Otorhinolaryngology-Head and Neck Surgery, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Yana Zhang
- Department of Otorhinolaryngology-Head and Neck Surgery, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Xuekun Huang
- Department of Otorhinolaryngology-Head and Neck Surgery, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Qintai Yang
- Department of Otorhinolaryngology-Head and Neck Surgery, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
- Department of Allergy, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| |
Collapse
|
6
|
Chen W, Gao C, Hu C, Zheng Y, Wang L, Chen H, Jiang H. Risk Stratification and Overall Survival Prediction in Advanced Gastric Cancer Patients Based on Whole-Volume MRI Radiomics. J Magn Reson Imaging 2023; 58:1161-1174. [PMID: 36722356 DOI: 10.1002/jmri.28621] [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: 12/13/2022] [Revised: 01/19/2023] [Accepted: 01/20/2023] [Indexed: 02/02/2023] Open
Abstract
BACKGROUND The prognosis of advanced gastric cancer (AGC) patients has attracted much attention, but there is a lack of evaluation method. MRI-based radiomics has the potential to evaluate AGC patients' prognosis. PURPOSE To identify and validate the risk stratification and overall survival (OS) in AGC patients using MRI-based radiomics. STUDY TYPE Retrospective. SUBJECTS A total of 233 patients (168 males, 63.6 ± 11.1 years; 65 females, 59.7 ± 11.8 years) confirmed AGC were collected. The data were randomly divided into a training (164) and validation set (69). SEQUENCE A 3.0 T, axial T2-weighted, diffusion-weighted imaging, and contrast-enhanced T1-weighted (CE-T1WI). ASSESSMENT Radiologist 1 segmented 233 patients and radiologist 2 segmented randomly 50 patients on CE-T1WI. The risk score (RS) was summed by each sample based on the radiomics features and correlation coefficients. Patients were followed up for 7-67 months (median 41; 138 dead and 95 alive). STATISTICAL TESTS The intraclass correlation coefficient (ICC) and Kappa value were calculated. Differences in survival analysis were assessed by Kaplan-Meier curves and log-rank test. Cox-regression analysis was performed to identify the radiomics features and clinical indicators associated with OS. The calibration curves were built to assess the model. A two-tailed P value < 0.05 was considered statistically significant. RESULTS Integrated with age, lymphovascular invasion (LVI) and RS, a survival combined model was built. The area under the curve (AUC) for predicting 3-year and 5-year OS was 0.765 and 0.788 in the training set, 0.757 and 0.729 in the validation set. There was no significant difference between the radiomics model and survival combined model for 3-year (0.690 vs. 0.757, P = 0.425) and 5-year OS (0.687 vs. 729, P = 0.412) in the validation set. The calibration curves showed a high degree of fit for the survival combined model. DATA CONCLUSION This study established a survival combined model that might help AGC patients in future clinical decision-making. EVIDENCE LEVEL 33 TECHNICAL EFFICACY: Stage 5.
Collapse
Affiliation(s)
- Wujie Chen
- Department of Radiology, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, Zhejiang, China
- Key Laboratory of Prevention Diagnosis and Therapy of Upper Gastrointestinal Cancer of Zhejiang Province, Hangzhou, China
| | - Chen Gao
- Key Laboratory of Prevention Diagnosis and Therapy of Upper Gastrointestinal Cancer of Zhejiang Province, Hangzhou, China
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, China
| | - Can Hu
- Department of Radiology, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, Zhejiang, China
- Key Laboratory of Prevention Diagnosis and Therapy of Upper Gastrointestinal Cancer of Zhejiang Province, Hangzhou, China
| | - Yao Zheng
- Department of Radiology, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, Zhejiang, China
- Key Laboratory of Prevention Diagnosis and Therapy of Upper Gastrointestinal Cancer of Zhejiang Province, Hangzhou, China
| | - Lijing Wang
- Department of Ultrasound, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, Zhejiang, China
| | - Haibo Chen
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, China
| | - Haitao Jiang
- Department of Radiology, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, Zhejiang, China
| |
Collapse
|
7
|
Lin M, Lin N, Yu S, Sha Y, Zeng Y, Liu A, Niu Y. Automated Prediction of Early Recurrence in Advanced Sinonasal Squamous Cell Carcinoma With Deep Learning and Multi-parametric MRI-based Radiomics Nomogram. Acad Radiol 2023; 30:2201-2211. [PMID: 36925335 DOI: 10.1016/j.acra.2022.11.013] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Revised: 11/12/2022] [Accepted: 11/13/2022] [Indexed: 03/16/2023]
Abstract
RATIONALE AND OBJECTIVES Preoperative prediction of the recurrence risk in patients with advanced sinonasal squamous cell carcinoma (SNSCC) is critical for individualized treatment. To evaluate the predictive ability of radiomics signature (RS) based on deep learning and multiparametric MRI for the risk of 2-year recurrence in advanced SNSCC. MATERIALS AND METHODS Preoperative MRI datasets were retrospectively collected from 265 SNSCC patients (145 recurrences) who underwent preoperative MRI, including T2-weighted (T2W), contrast-enhanced T1-weighted (T1c) sequences and diffusion-weighted (DW). All patients were divided into 165 training cohort and 70 test cohort. A deep learning segmentation model based on VB-Net was used to segment regions of interest (ROIs) for preoperative MRI and radiomics features were extracted from automatically segmented ROIs. Least absolute shrinkage and selection operator (LASSO) and logistic regression (LR) were applied for feature selection and radiomics score construction. Combined with meaningful clinicopathological predictors, a nomogram was developed and its performance was evaluated. In addition, X-title software was used to divide patients into high-risk or low-risk early relapse (ER) subgroups. Recurrence-free survival probability (RFS) was assessed for each subgroup. RESULTS The radiomics score, T stage, histological grade and Ki-67 predictors were independent predictors. The segmentation models of T2WI, T1c, and apparent diffusion coefficient (ADC) sequences achieved Dice coefficients of 0.720, 0.727, and 0.756, respectively, in the test cohort. RS-T2, RS-T1c and RS-ADC were derived from single-parameter MRI. RS-Combined (combined with T2WI, T1c, and ADC features) was derived from multiparametric MRI and reached area under curve (AUC) and accuracy of 0.854 (0.749-0.927) and 74.3% (0.624-0.840), respectively, in the test cohort. The calibration curve and decision curve analysis (DCA) illustrate its value in clinical practice. Kaplan-Meier analysis showed that the 2-year RFS rate for low-risk patients was significantly greater than that for high-risk patients in both the training and testing cohorts (p < 0.001). CONCLUSION Automated nomograms based on multi-sequence MRI help to predict ER in SNSCC patients preoperatively.
Collapse
Affiliation(s)
- Mengyan Lin
- Shanghai Institute of Medical Imaging, Shanghai, China
| | - Naier Lin
- Department of Radiology, Eye & ENT Hospital, Fudan University, Shanghai, China
| | - Sihui Yu
- Department of Radiology, Eye & ENT Hospital, Fudan University, Shanghai, China
| | - Yan Sha
- Department of Radiology, Eye & ENT Hospital, Fudan University, Shanghai, China.
| | - Yan Zeng
- Department of Research Center, Shanghai United Imaging Intelligence Inc., Shanghai, China
| | - Aie Liu
- Department of Research Center, Shanghai United Imaging Intelligence Inc., Shanghai, China
| | - Yue Niu
- Department of Radiology, Eye & ENT Hospital, Fudan University, Shanghai, China
| |
Collapse
|
8
|
Wang T, Hao J, Gao A, Zhang P, Wang H, Nie P, Jiang Y, Bi S, Liu S, Hao D. An MRI-Based Radiomics Nomogram to Assess Recurrence Risk in Sinonasal Malignant Tumors. J Magn Reson Imaging 2023; 58:520-531. [PMID: 36448476 DOI: 10.1002/jmri.28548] [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: 09/16/2022] [Revised: 11/19/2022] [Accepted: 11/21/2022] [Indexed: 12/05/2022] Open
Abstract
BACKGROUND Sinonasal malignant tumors (SNMTs) have a high recurrence risk, which is responsible for the poor prognosis of patients. Assessing recurrence risk in SNMT patients is a current problem. PURPOSE To establish an MRI-based radiomics nomogram for assessing relapse risk in patients with SNMT. STUDY TYPE Retrospective. POPULATION A total of 143 patients with 68.5% females (development/validation set, 98/45 patients). FIELD STRENGTH/SEQUENCE A 1.5-T and 3-T, fat-suppressed fast spin echo (FSE) T2-weighted imaging (FS-T2WI), FSE T1-weighted imaging (T1WI), and FSE contrast-enhanced T1WI (T1WI + C). ASSESSMENT Three MRI sequences were used to manually delineate the region of interest. Three radiomics signatures (T1WI and FS-T2WI sequences, T1WI + C sequence, and three sequences combined) were built through dimensional reduction of high-dimensional features. The clinical model was built based on clinical and MRI features. The Ki-67-based and tumor-node-metastasis (TNM) model were established for comparison. The radiomics nomogram was built by combining the clinical model and best radiomics signature. The relapse-free survival analysis was used among 143 patients. STATISTICAL TESTS The intraclass/interclass correlation coefficients, univariate/multivariate Cox regression analysis, least absolute shrinkage and selection operator Cox regression algorithm, concordance index (C index), area under the curve (AUC), integrated Brier score (IBS), DeLong test, Kaplan-Meier curve, log-rank test, optimal cutoff values. A P value < 0.05 was considered statistically significant. RESULTS The T1 + C-based radiomics signature had best prognostic ability than the other two signatures (T1WI and FS-T2WI sequences, and three sequences combined). The radiomics nomogram had better prognostic ability and less error than the clinical model, Ki-67-based model, and TNM model (C index, 0.732; AUC, 0.765; IBS, 0.185 in the validation set). The cutoff values were 0.2 and 0.7 and then the cumulative risk rates were calculated. DATA CONCLUSION A radiomics nomogram for assessing relapse risk in patients with SNMT may provide better prognostic ability than the clinical model, Ki-67-based model, and TNM model. EVIDENCE LEVEL 3. TECHNICAL EFFICACY Stage 5.
Collapse
Affiliation(s)
- Tongyu Wang
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Jingwei Hao
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Aixin Gao
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Peng Zhang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Hexiang Wang
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Pei Nie
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Yan Jiang
- Department of Otolaryngology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Shucheng Bi
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Shunli Liu
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Dapeng Hao
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| |
Collapse
|
9
|
Pan H, Sun J, Luo X, Ai H, Zeng J, Shi R, Zhang A. A risk prediction model for type 2 diabetes mellitus complicated with retinopathy based on machine learning and its application in health management. Front Med (Lausanne) 2023; 10:1136653. [PMID: 37181375 PMCID: PMC10172657 DOI: 10.3389/fmed.2023.1136653] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Accepted: 03/31/2023] [Indexed: 05/16/2023] Open
Abstract
Objective This study aimed to establish a risk prediction model for diabetic retinopathy (DR) in the Chinese type 2 diabetes mellitus (T2DM) population using few inspection indicators and to propose suggestions for chronic disease management. Methods This multi-centered retrospective cross-sectional study was conducted among 2,385 patients with T2DM. The predictors of the training set were, respectively, screened by extreme gradient boosting (XGBoost), a random forest recursive feature elimination (RF-RFE) algorithm, a backpropagation neural network (BPNN), and a least absolute shrinkage selection operator (LASSO) model. Model I, a prediction model, was established through multivariable logistic regression analysis based on the predictors repeated ≥3 times in the four screening methods. Logistic regression Model II built on the predictive factors in the previously released DR risk study was introduced into our current study to evaluate the model's effectiveness. Nine evaluation indicators were used to compare the performance of the two prediction models, including the area under the receiver operating characteristic curve (AUROC), accuracy, precision, recall, F1 score, balanced accuracy, calibration curve, Hosmer-Lemeshow test, and Net Reclassification Index (NRI). Results When including predictors, such as glycosylated hemoglobin A1c, disease course, postprandial blood glucose, age, systolic blood pressure, and albumin/urine creatinine ratio, multivariable logistic regression Model I demonstrated a better prediction ability than Model II. Model I revealed the highest AUROC (0.703), accuracy (0.796), precision (0.571), recall (0.035), F1 score (0.066), Hosmer-Lemeshow test (0.887), NRI (0.004), and balanced accuracy (0.514). Conclusion We have built an accurate DR risk prediction model with fewer indicators for patients with T2DM. It can be used to predict the individualized risk of DR in China effectively. In addition, the model can provide powerful auxiliary technical support for the clinical and health management of patients with diabetes comorbidities.
Collapse
Affiliation(s)
- Hong Pan
- Department of Health Management, School of Public Health, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Jijia Sun
- Department of Mathematics and Physics, School of Pharmacy, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Xin Luo
- Department of Health Management, School of Public Health, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Heling Ai
- Department of Public Utilities Management, School of Public Health, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Jing Zeng
- Department of Public Utilities Management, School of Public Health, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Rong Shi
- Department of Public Utilities Management, School of Public Health, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- *Correspondence: Rong Shi,
| | - An Zhang
- Department of Health Management, School of Public Health, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- An Zhang,
| |
Collapse
|
10
|
Hu HH. Editorial for “An
MRI
‐Based Radiomics Nomogram to Predict Recurrence in Sinonasal Malignant Tumors”. J Magn Reson Imaging 2022. [DOI: 10.1002/jmri.28551] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Accepted: 11/08/2022] [Indexed: 12/05/2022] Open
Affiliation(s)
- Houchun Harry Hu
- Department of Radiology, Section of Radiological Science University of Colorado Denver, Anschutz Medical Campus Aurora Colorado USA
| |
Collapse
|
11
|
Gu J, Yu Q, Li Q, Peng J, Lv F, Gong B, Zhang X. MRI radiomics-based machine learning model integrated with clinic-radiological features for preoperative differentiation of sinonasal inverted papilloma and malignant sinonasal tumors. Front Oncol 2022; 12:1003639. [PMID: 36212455 PMCID: PMC9538572 DOI: 10.3389/fonc.2022.1003639] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Accepted: 09/07/2022] [Indexed: 12/04/2022] Open
Abstract
Objective To explore the best MRI radiomics-based machine learning model for differentiation of sinonasal inverted papilloma (SNIP) and malignant sinonasal tumor (MST), and investigate whether the combination of radiomics features and clinic–radiological features can produce a superior diagnostic performance. Methods The database of 247 patients with SNIP (n=106) or MST (n=141) were analyzed. Dataset from scanner A were randomly divided into training set (n=135) and test set 1 (n=58) in a ratio of 7:3, and dataset from scanner B and C were used as an additional independent test set 2 (n=54). Fourteen clinic-radiological features were analyzed by using univariate analysis, and those with significant differences were applied to construct clinical model. Based on the radiomics features extracted from single sequence (T2WI or CE-T1WI) and combined sequence, four commonly used classifiers (logistic regression (LR), support vector machine (SVM), decision tree (DT) and k-nearest neighbor (KNN)) were employed to constitute twelve different machine learning models, and the best-performing one was confirmed as the optimal radiomics model. Furthermore, a combined model incorporated best radiomics feature subsets and clinic-radiological features was developed. The diagnostic performances of these models were assessed by the area under the receiver operating characteristic (ROC) curve (AUC) and the calibration curves. Results Five clinic-radiological features (age, convoluted cerebriform pattern sign, heterogeneity, adjacent bone involvement and infiltration of surrounding tissue) were considered to be significantly different between the tumor groups (P < 0.05). Among the twelve machine learning models, the T2WI-SVM model exhibited optimal predictive efficacy for classification tasks on the two test sets, with the AUC of 0.878 and 0.914, respectively. For three types of diagnostic models, the combined model achieved highest AUC of 0.912 (95%CI: 0.807-0.970) and 0.927 (95%CI: 0.823-0.980) for differentiation of SNIP and MST in test 1 and test 2 sets, which performed prominently better than clinical model (P=0.011, 0.005), but not significantly different from the optimal radiomics model (P=0.100, 0.452). Conclusion The machine learning model based on T2WI sequence and SVM classifier achieved best performance in differentiation of SNIP and MST, and the combination of radiomics features and clinic-radiological features significantly improved the diagnostic capability of the model.
Collapse
Affiliation(s)
- Jinming Gu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Qiang Yu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Quanjiang Li
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Juan Peng
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
- *Correspondence: Juan Peng,
| | - Fajin Lv
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Beibei Gong
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xiaodi Zhang
- Department of Clinical Science, Philips Healthcare, Chengdu, China
| |
Collapse
|
12
|
Apparent Diffusion Coefficient-Based Radiomic Nomogram in Sinonasal Squamous Cell Carcinoma: A Preliminary Study on Histological Grade Evaluation. J Comput Assist Tomogr 2022; 46:823-829. [PMID: 35675693 DOI: 10.1097/rct.0000000000001329] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
PURPOSE The aim of the study was to develop and validate a nomogram model combining radiomic features and clinical characteristics to preoperatively differentiate between low- and high-grade sinonasal squamous cell carcinomas. MATERIAL AND METHODS A total of 174 patients who underwent diffusion-weighted imaging were included in this study. The patients were allocated to the training and testing cohorts randomly at a ratio of 6:4. The least absolute shrinkage and selection operator regression was applied for feature selection and radiomic signature (radscore) construction. Multivariable logistic regression analysis was applied to identify independent predictors. The performance of the model was evaluated using the area under the receiver operating characteristic curve (AUC), the calibration curve, decision curve analysis, and the clinical impact curve. RESULTS The radscore included 9 selected radiomic features. The radscore and clinical stage were independent predictors. The nomogram showed better performance (training cohort: AUC, 0.92; 95% confidence interval, 0.85-0.96; testing cohort: AUC, 0.91; 95% CI, 0.82-0.97) than either the radscore or the clinical stage in both the training and test cohorts (P < 0.050). The nomogram demonstrated good calibration and clinical usefulness. CONCLUSIONS The apparent diffusion coefficient-based radiomic nomogram model could be useful in differentiating between low- and high-grade sinonasal squamous cell carcinomas.
Collapse
|
13
|
Lin N, Yu S, Lin M, Shi Y, Chen W, Xia Z, Cheng Y, Sha Y. A Clinical-Radiomics Nomogram Based on the Apparent Diffusion Coefficient (ADC) for Individualized Prediction of the Risk of Early Relapse in Advanced Sinonasal Squamous Cell Carcinoma: A 2-Year Follow-Up Study. Front Oncol 2022; 12:870935. [PMID: 35651794 PMCID: PMC9149576 DOI: 10.3389/fonc.2022.870935] [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: 02/07/2022] [Accepted: 04/19/2022] [Indexed: 11/30/2022] Open
Abstract
Purpose To develop and validate a nomogram model combining radiomic features and clinical characteristics to preoperatively predict the risk of early relapse (ER) in advanced sinonasal squamous cell carcinomas (SNSCCs). Methods A total of 152 SNSCC patients (clinical stage III-IV) who underwent diffusion-weighted imaging (DWI) were included in this study. The training cohort included 106 patients assessed at the headquarters of our hospital using MR scanner 1. The testing cohort included 46 patients assessed at the branch of our hospital using MR scanner 2. Least absolute shrinkage and selection operator (LASSO) regression was applied for feature selection and radiomic signature (radscore) construction. Multivariable logistic regression analysis was applied to identify independent predictors. The performance of the model was evaluated using the area under the receiver operating characteristic curve (AUC), calibration curve and decision curve analysis (DCA). Furthermore, the patients were classified into high- or low-risk ER subgroups according to the optimal cutoff value of the nomogram using X-tile. The recurrence-free survival probability (RFS) of each subgroup was assessed. Results ER was noted in 69 patients. The radscore included 8 selected radiomic features. The radscore, T stage and surgical margin were independent predictors. The nomogram showed better performance (AUC = 0.92) than either the radscore or the clinical factors in the training cohort (P < 0.050). In the testing cohort, the nomogram showed better performance (AUC = 0.92) than the clinical factors (P = 0.016) and tended to show better performance than the radscore (P = 0.177). The nomogram demonstrated good calibration and clinical utility. Kaplan-Meier analysis showed that the 2-year RFS rate for low-risk patients was significantly greater than that for high-risk patients in both the training and testing cohorts (P < 0.001). Conclusions The ADC-based radiomic nomogram model is potentially useful in predicting the risk of ER in advanced SNSCCs.
Collapse
Affiliation(s)
- Naier Lin
- Department of Radiology, Eye & ENT Hospital, Shanghai Medical College, Fudan University, Shanghai, China
| | - Sihui Yu
- Department of Radiology, Eye & ENT Hospital, Shanghai Medical College, Fudan University, Shanghai, China
| | - Mengyan Lin
- Department of Radiology, Eye & ENT Hospital, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yiqian Shi
- Department of Radiology, Eye & ENT Hospital, Shanghai Medical College, Fudan University, Shanghai, China
| | - Wei Chen
- Department of Radiology, Eye & ENT Hospital, Shanghai Medical College, Fudan University, Shanghai, China
| | - Zhipeng Xia
- Department of Radiology, Eye & ENT Hospital, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yushu Cheng
- Department of Radiology, Eye & ENT Hospital, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yan Sha
- Department of Radiology, Eye & ENT Hospital, Shanghai Medical College, Fudan University, Shanghai, China
| |
Collapse
|
14
|
Wang J, Chen J, Zhou R, Gao Y, Li J. Machine learning-based multiparametric MRI radiomics for predicting poor responders after neoadjuvant chemoradiotherapy in rectal Cancer patients. BMC Cancer 2022; 22:420. [PMID: 35439946 PMCID: PMC9017030 DOI: 10.1186/s12885-022-09518-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Accepted: 04/08/2022] [Indexed: 01/30/2023] Open
Abstract
BACKGROUND The purpose of this study was to investigate and validate multiparametric magnetic resonance imaging (MRI)-based machine learning classifiers for early identification of poor responders after neoadjuvant chemoradiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC). METHODS Patients with LARC who underwent nCRT were included in this retrospective study (207 patients). After preprocessing of multiparametric MRI, radiomics features were extracted and four feature selection methods were used to select robust features. The selected features were used to build five machine learning classifiers, and 20 (four feature selection methods × five machine learning classifiers) predictive models for the screening of poor responders were constructed. The predictive models were evaluated according to the area under the curve (AUC), F1 score, accuracy, sensitivity, and specificity. RESULTS Eighty percent of all predictive models constructed achieved an AUC of more than 0.70. A predictive model using a support vector machine classifier with the minimum redundancy maximum relevance (mRMR) selection method followed by the least absolute shrinkage and selection operator (LASSO) selection method showed superior prediction performance, with an AUC of 0.923, an F1 score of 88.14%, and accuracy of 91.03%. The predictive performance of the constructed models was not improved by ComBat compensation. CONCLUSIONS In rectal cancer patients who underwent neoadjuvant chemoradiotherapy, machine learning classifiers with radiomics features extracted from multiparametric MRI were able to accurately discriminate poor responders from good responders. The techniques should provide additional information to guide patient-tailored treatment.
Collapse
Affiliation(s)
- Jia Wang
- Department of Ultrasound, Qingdao Women and Children Hospital, Shandong, Qingdao, China
| | - Jingjing Chen
- Department of Radiology, The Affiliated Hospital of Qingdao University, 16 Jiangsu Road, Shandong, Qingdao, China
| | - Ruizhi Zhou
- Department of Radiology, The Affiliated Hospital of Qingdao University, 16 Jiangsu Road, Shandong, Qingdao, China
| | - Yuanxiang Gao
- Department of Radiology, The Affiliated Hospital of Qingdao University, 16 Jiangsu Road, Shandong, Qingdao, China
| | - Jie Li
- Department of Radiology, The Affiliated Hospital of Qingdao University, 16 Jiangsu Road, Shandong, Qingdao, China.
| |
Collapse
|
15
|
Dong C, Zheng YM, Li J, Wu ZJ, Yang ZT, Li XL, Xu WJ, Hao DP. A CT-based radiomics nomogram for differentiation of squamous cell carcinoma and non-Hodgkin's lymphoma of the palatine tonsil. Eur Radiol 2022; 32:243-253. [PMID: 34236464 DOI: 10.1007/s00330-021-08153-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Revised: 06/07/2021] [Accepted: 06/14/2021] [Indexed: 02/07/2023]
Abstract
OBJECTIVES Accurate preoperative differentiation between squamous cell carcinoma (SCC) and non-Hodgkin's lymphoma (NHL) in the palatine tonsil is crucial because of their different treatment. This study aimed to construct and validate a contrast-enhanced CT (CECT)-based radiomics nomogram for preoperative differentiation of SCC and NHL in the palatine tonsil. METHODS This study enrolled 135 patients with a pathological diagnosis of SCC or NHL from two clinical centers, who were divided into training (n = 94; SCC = 50, NHL = 44) and external validation sets (n = 41; SCC = 22, NHL = 19). A radiomics signature was constructed from radiomics features extracted from routine CECT images and a radiomics score (Rad-score) was calculated. A clinical model was established using demographic features and CT findings. The independent clinical factors and Rad-score were combined to construct a radiomics nomogram. Performance of the clinical model, radiomics signature, and nomogram was assessed using receiver operating characteristics analysis and decision curve analysis. RESULTS Eleven features were finally selected to construct the radiomics signature. The radiomics nomogram incorporating gender, mean CECT value, and radiomics signature showed better predictive value for differentiating SCC from NHL than the clinical model for training (AUC, 0.919 vs. 0.801, p = 0.004) and validation (AUC, 0.876 vs. 0.703, p = 0.029) sets. Decision curve analysis demonstrated that the radiomics nomogram was more clinically useful than the clinical model. CONCLUSIONS A CECT-based radiomics nomogram was constructed incorporating gender, mean CECT value, and radiomics signature. This nomogram showed favorable predictive efficacy for differentiating SCC from NHL in the palatine tonsil, and might be useful for clinical decision-making. KEY POINTS • Differential diagnosis between SCC and NHL in the palatine tonsil is difficult by conventional imaging modalities. • A radiomics nomogram integrated with the radiomics signature, gender, and mean contrast-enhanced CT value facilitates differentiation of SCC from NHL with improved diagnostic efficacy.
Collapse
Affiliation(s)
- Cheng Dong
- Department of Radiology, The Affiliated Hospital of Qingdao University, NO. 16, Jiangsu Road, Qingdao, 266000, China
| | - Ying-Mei Zheng
- Health Management Center, The Affiliated Hospital of Qingdao University, NO. 16, Jiangsu Road, Qingdao, 266000, China
| | - Jian Li
- Department of Radiology, The University of Hong Kong - Shenzhen Hospital, 1, Haiyuan Road, Futian District, Shenzhen, 518000, NO, China
| | - Zeng-Jie Wu
- Department of Radiology, The Affiliated Hospital of Qingdao University, NO. 16, Jiangsu Road, Qingdao, 266000, China
| | - Zhi-Tao Yang
- Department of Radiology, The Affiliated Hospital of Qingdao University, NO. 16, Jiangsu Road, Qingdao, 266000, China
| | - Xiao-Li Li
- Department of Radiology, The Affiliated Hospital of Qingdao University, NO. 16, Jiangsu Road, Qingdao, 266000, China
| | - Wen-Jian Xu
- Department of Radiology, The Affiliated Hospital of Qingdao University, NO. 16, Jiangsu Road, Qingdao, 266000, China
| | - Da-Peng Hao
- Department of Radiology, The Affiliated Hospital of Qingdao University, NO. 16, Jiangsu Road, Qingdao, 266000, China.
| |
Collapse
|
16
|
Abstract
Artificial intelligence (AI) algorithms, particularly deep learning, have developed to the point that they can be applied in image recognition tasks. The use of AI in medical imaging can guide radiologists to more accurate image interpretation and diagnosis in radiology. The software will provide data that we cannot extract from the images. The rapid development in computational capabilities supports the wide applications of AI in a range of cancers. Among those are its widespread applications in head and neck cancer.
Collapse
|
17
|
Lin L, Liu J, Deng Q, Li N, Pan J, Sun H, Quan S. Radiomics Is Effective for Distinguishing Coronavirus Disease 2019 Pneumonia From Influenza Virus Pneumonia. Front Public Health 2021; 9:663965. [PMID: 34211951 PMCID: PMC8239147 DOI: 10.3389/fpubh.2021.663965] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Accepted: 05/06/2021] [Indexed: 01/06/2023] Open
Abstract
Objectives: To develop and validate a radiomics model for distinguishing coronavirus disease 2019 (COVID-19) pneumonia from influenza virus pneumonia. Materials and Methods: A radiomics model was developed on the basis of 56 patients with COVID-19 pneumonia and 90 patients with influenza virus pneumonia in this retrospective study. Radiomics features were extracted from CT images. The radiomics features were reduced by the Max-Relevance and Min-Redundancy algorithm and the least absolute shrinkage and selection operator method. The radiomics model was built using the multivariate backward stepwise logistic regression. A nomogram of the radiomics model was established, and the decision curve showed the clinical usefulness of the radiomics nomogram. Results: The radiomics features, consisting of nine selected features, were significantly different between COVID-19 pneumonia and influenza virus pneumonia in both training and validation data sets. The receiver operator characteristic curve of the radiomics model showed good discrimination in the training sample [area under the receiver operating characteristic curve (AUC), 0.909; 95% confidence interval (CI), 0.859–0.958] and in the validation sample (AUC, 0.911; 95% CI, 0.753–1.000). The nomogram was established and had good calibration. Decision curve analysis showed that the radiomics nomogram was clinically useful. Conclusions: The radiomics model has good performance for distinguishing COVID-19 pneumonia from influenza virus pneumonia and may aid in the diagnosis of COVID-19 pneumonia.
Collapse
Affiliation(s)
- Liaoyi Lin
- Department of Radiology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Jinjin Liu
- Department of Radiology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Qingshan Deng
- Department of Radiology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Na Li
- Department of Radiology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Jingye Pan
- Department of Intensive Care Unit, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Houzhang Sun
- Department of Radiology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Shichao Quan
- Department of General Medicine, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| |
Collapse
|
18
|
Bi SC, Zhang H, Wang HX, Ge YQ, Zhang P, Wang ZC, Hao DP. Radiomics Nomograms Based on Multi-Parametric MRI for Preoperative Differential Diagnosis of Malignant and Benign Sinonasal Tumors: A Two-Centre Study. Front Oncol 2021; 11:659905. [PMID: 34012922 PMCID: PMC8127839 DOI: 10.3389/fonc.2021.659905] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Accepted: 04/08/2021] [Indexed: 12/28/2022] Open
Abstract
Objectives To investigate the efficacy of multi-parametric MRI-based radiomics nomograms for preoperative distinction between benign and malignant sinonasal tumors. Methods Data of 244 patients with sinonasal tumor (training set, n=192; test set, n=52) who had undergone pre-contrast MRI, and 101 patients who underwent post-contrast MRI (training set, n=74; test set, n=27) were retrospectively analyzed. Independent predictors of malignancy were identified and their performance were evaluated. Seven radiomics signatures (RSs) using maximum relevance minimum redundancy (mRMR), and the least absolute shrinkage selection operator (LASSO) algorithm were established. The radiomics nomograms, comprising the clinical model and the RS algorithms were built: one based on pre-contrast MRI (RNWOC); the other based on pre-contrast and post-contrast MRI (RNWC). The performances of the models were evaluated with area under the curve (AUC), calibration, and decision curve analysis (DCA) respectively. Results The efficacy of the clinical model (AUC=0.81) of RNWC was higher than that of the model (AUC=0.76) of RNWOC in the test set. There was no significant difference in the AUC of radiomic algorithms in the test set. The RS-T1T2 (AUC=0.74) and RS-T1T2T1C (RSWC, AUC=0.81) achieved a good distinction efficacy in the test set. The RNWC and the RNWOC showed excellent distinction (AUC=0.89 and 0.82 respectively) in the test set. The DCA of the nomograms showed better clinical usefulness than the clinical models and radiomics signatures. Conclusions The radiomics nomograms combining the clinical model and RS can be accurately, safely and efficiently used to distinguish between benign and malignant sinonasal tumors.
Collapse
Affiliation(s)
- Shu-Cheng Bi
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Han Zhang
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - He-Xiang Wang
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | | | - Peng Zhang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Zhen-Chang Wang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Da-Peng Hao
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| |
Collapse
|
19
|
The Role of Elective Neck Treatment in the Management of Sinonasal Carcinomas: A Systematic Review of the Literature and a Meta-Analysis. Cancers (Basel) 2021; 13:cancers13081842. [PMID: 33924359 PMCID: PMC8069890 DOI: 10.3390/cancers13081842] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Revised: 04/02/2021] [Accepted: 04/08/2021] [Indexed: 12/28/2022] Open
Abstract
The impact of elective neck treatment (ENT), whether by irradiation or dissection, on the prognosis of patients with cN0 sinonasal carcinomas (SNCs) remains an understudied issue. METHODS: A systematic review and meta-analysis of the literature were performed according to PRISMA guidelines in order to assess regional nodal relapse rate after ENT compared to observation in cN0 SNCs patients. Twenty-six articles for a total of 1178 clinically N0 patients were analyzed. Globally, the 5-year overall survival was 52%; 34.6% of patients underwent ENT and 140 regional recurrences were registered (5.9% in the ENT cohort and 15% in the observation group). ENT appears to confer a lower risk of regional recurrence compared to observation alone, with a cumulative OR of 0.38 (95% CI 0.25-0.58). Our meta-analysis supports the efficacy of ENT for reducing the risk of regional recurrence, but its overall impact on survival remains uncertain.
Collapse
|