1
|
Zhang MB, Meng ZL, Mao Y, Jiang X, Xu N, Xu QH, Tian J, Luo YK, Wang K. Cervical lymph node metastasis prediction from papillary thyroid carcinoma US videos: a prospective multicenter study. BMC Med 2024; 22:153. [PMID: 38609953 PMCID: PMC11015607 DOI: 10.1186/s12916-024-03367-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/07/2023] [Accepted: 03/22/2024] [Indexed: 04/14/2024] Open
Abstract
BACKGROUND Prediction of lymph node metastasis (LNM) is critical for individualized management of papillary thyroid carcinoma (PTC) patients to avoid unnecessary overtreatment as well as undesired under-treatment. Artificial intelligence (AI) trained by thyroid ultrasound (US) may improve prediction performance. METHODS From September 2017 to December 2018, patients with suspicious PTC from the first medical center of the Chinese PLA general hospital were retrospectively enrolled to pre-train the multi-scale, multi-frame, and dual-direction deep learning (MMD-DL) model. From January 2019 to July 2021, PTC patients from four different centers were prospectively enrolled to fine-tune and independently validate MMD-DL. Its diagnostic performance and auxiliary effect on radiologists were analyzed in terms of receiver operating characteristic (ROC) curves, areas under the ROC curve (AUC), accuracy, sensitivity, and specificity. RESULTS In total, 488 PTC patients were enrolled in the pre-training cohort, and 218 PTC patients were included for model fine-tuning (n = 109), internal test (n = 39), and external validation (n = 70). Diagnostic performances of MMD-DL achieved AUCs of 0.85 (95% CI: 0.73, 0.97) and 0.81 (95% CI: 0.73, 0.89) in the test and validation cohorts, respectively, and US radiologists significantly improved their average diagnostic accuracy (57% vs. 60%, P = 0.001) and sensitivity (62% vs. 65%, P < 0.001) by using the AI model for assistance. CONCLUSIONS The AI model using US videos can provide accurate and reproducible prediction of cervical lymph node metastasis in papillary thyroid carcinoma patients preoperatively, and it can be used as an effective assisting tool to improve diagnostic performance of US radiologists. TRIAL REGISTRATION We registered on the Chinese Clinical Trial Registry website with the number ChiCTR1900025592.
Collapse
Affiliation(s)
- Ming-Bo Zhang
- Department of Ultrasound, the First Medical Center, General Hospital of Chinese PLA, Beijing, China
| | - Zhe-Ling Meng
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Yi Mao
- Department of Ultrasound, the First Medical Center, General Hospital of Chinese PLA, Beijing, China
| | - Xue Jiang
- Department of Ultrasound, the Fourth Medical Center, General Hospital of Chinese PLA, Beijing, China
| | - Ning Xu
- Department of Ultrasound, Beijing Tong Ren Hospital, Beijing, China
| | - Qing-Hua Xu
- Department of Ultrasound, China-Japan Friendship Hospital, Beijing, China
| | - Jie Tian
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Yu-Kun Luo
- Department of Ultrasound, the First Medical Center, General Hospital of Chinese PLA, Beijing, China.
| | - Kun Wang
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China.
| |
Collapse
|
2
|
Yuan Y, Pan B, Mo H, Wu X, Long Z, Yang Z, Zhu J, Ming J, Qiu L, Sun Y, Yin S, Zhang F. Deep learning-based computer-aided diagnosis system for the automatic detection and classification of lateral cervical lymph nodes on original ultrasound images of papillary thyroid carcinoma: a prospective diagnostic study. Endocrine 2024:10.1007/s12020-024-03808-1. [PMID: 38570388 DOI: 10.1007/s12020-024-03808-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/25/2023] [Accepted: 03/26/2024] [Indexed: 04/05/2024]
Abstract
PURPOSE This study aims to develop a deep learning-based computer-aided diagnosis (CAD) system for the automatic detection and classification of lateral cervical lymph nodes (LNs) on original ultrasound images of papillary thyroid carcinoma (PTC) patients. METHODS A retrospective data set of 1801 cervical LN ultrasound images from 1675 patients with PTC and a prospective test set including 185 images from 160 patients were collected. Four different deep leaning models were trained and validated in the retrospective data set. The best model was selected for CAD system development and compared with three sonographers in the retrospective and prospective test sets. RESULTS The Deformable Detection Transformer (DETR) model showed the highest diagnostic efficacy, with a mean average precision score of 86.3% in the retrospective test set, and was therefore used in constructing the CAD system. The detection performance of the CAD system was superior to the junior sonographer and intermediate sonographer with accuracies of 86.3% and 92.4% in the retrospective and prospective test sets, respectively. The classification performance of the CAD system was better than all sonographers with the areas under the curve (AUCs) of 94.4% and 95.2% in the retrospective and prospective test sets, respectively. CONCLUSIONS This study developed a Deformable DETR model-based CAD system for automatically detecting and classifying lateral cervical LNs on original ultrasound images, which showed excellent diagnostic efficacy and clinical utility. It can be an important tool for assisting sonographers in the diagnosis process.
Collapse
Affiliation(s)
- Yuquan Yuan
- Department of Breast and Thyroid Surgery, Chongqing General Hospital, Chongqing, China
- Graduate School of Medicine, Chongqing Medical University, Chongqing, China
| | - Bin Pan
- Department of Breast and Thyroid Surgery, Chongqing General Hospital, Chongqing, China
- Graduate School of Medicine, Chongqing Medical University, Chongqing, China
| | - Hongbiao Mo
- Department of Breast and Thyroid Surgery, Chongqing General Hospital, Chongqing, China
| | - Xing Wu
- College of Computer Science, Chongqing University, Chongqing, China
| | - Zhaoxin Long
- College of Computer Science, Chongqing University, Chongqing, China
| | - Zeyu Yang
- Department of Breast and Thyroid Surgery, Chongqing General Hospital, Chongqing, China
- Graduate School of Medicine, Chongqing Medical University, Chongqing, China
| | - Junping Zhu
- Department of Breast and Thyroid Surgery, Chongqing General Hospital, Chongqing, China
| | - Jing Ming
- Department of Breast and Thyroid Surgery, Chongqing General Hospital, Chongqing, China
| | - Lin Qiu
- Department of Breast and Thyroid Surgery, Chongqing General Hospital, Chongqing, China
| | - Yiceng Sun
- Department of Breast and Thyroid Surgery, Chongqing General Hospital, Chongqing, China
| | - Supeng Yin
- Department of Breast and Thyroid Surgery, Chongqing General Hospital, Chongqing, China.
- Chongqing Hospital of Traditional Chinese Medicine, Chongqing, China.
| | - Fan Zhang
- Department of Breast and Thyroid Surgery, Chongqing General Hospital, Chongqing, China.
- Graduate School of Medicine, Chongqing Medical University, Chongqing, China.
- Chongqing Hospital of Traditional Chinese Medicine, Chongqing, China.
| |
Collapse
|
3
|
Zhang S, Liu R, Wang Y, Zhang Y, Li M, Wang Y, Wang S, Ma N, Ren J. Ultrasound-Base Radiomics for Discerning Lymph Node Metastasis in Thyroid Cancer: A Systematic Review and Meta-analysis. Acad Radiol 2024:S1076-6332(24)00154-5. [PMID: 38555183 DOI: 10.1016/j.acra.2024.03.012] [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: 11/14/2023] [Revised: 03/04/2024] [Accepted: 03/11/2024] [Indexed: 04/02/2024]
Abstract
PURPOSE Ultrasound is the imaging modality of choice for preoperative diagnosis of lymph node metastasis (LNM) in thyroid cancer (TC), yet its efficacy remains suboptimal. As radiomics gains traction in tumor diagnosis, its integration with ultrasound for LNM differentiation in TC has emerged, but its diagnostic merit is debated. This study assesses the accuracy of ultrasound-integrated radiomics in preoperatively diagnosing LNM in TC. METHODS Literatures were searched in PubMed, Embase, Cochrane, and Web of Science until July 11, 2023. Quality of the studies was assessed by the radiomics quality score (RQS). A meta-analysis was executed using a bivariate mixed effects model, with a subgroup analysis based on modeling variables (clinical features, radiomics features, or their combination). RESULTS Among 27 articles (16,410 TC patients, 6356 with LNM), the average RQS was 16.5 (SD:5.47). Sensitivity of the models based on clinical features, radiomics features, and radiomics features plus clinical features were 0.64, 0.76 and 0.69. Specificities were 0.77, 0.78 and 0.82. SROC values were 0.76, 0.84 and 0.81. CONCLUSION Ultrasound-based radiomics effectively evaluates LNM in TC preoperatively. Adding clinical features does not notably enhance the model's performance. Some radiomics studies showed high bias, possibly due to the absence of standard application guidelines.
Collapse
Affiliation(s)
- Sijie Zhang
- Department of Sonography, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100005, PR China
| | - Ruijuan Liu
- Department of Sonography, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100005, PR China
| | - Yiyang Wang
- Department of Sonography, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, PR China
| | - Yuewei Zhang
- Department of Sonography, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, PR China
| | - Mengpu Li
- Department of Sonography, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, PR China
| | - Yang Wang
- Department of Sonography, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, PR China
| | - Siyu Wang
- Department of Sonography, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, PR China
| | - Na Ma
- Department of Sonography, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, PR China
| | - Junhong Ren
- Department of Sonography, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100005, PR China; Department of Sonography, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, PR China.
| |
Collapse
|
4
|
Jayawickrama SM, Ranaweera PM, Pradeep RGGR, Jayasinghe YA, Senevirathna K, Hilmi AJ, Rajapakse RMG, Kanmodi KK, Jayasinghe RD. Developments and future prospects of personalized medicine in head and neck squamous cell carcinoma diagnoses and treatments. Cancer Rep (Hoboken) 2024; 7:e2045. [PMID: 38522008 PMCID: PMC10961052 DOI: 10.1002/cnr2.2045] [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: 08/28/2023] [Revised: 02/07/2024] [Accepted: 03/05/2024] [Indexed: 03/25/2024] Open
Abstract
BACKGROUND Precision healthcare has entered a new era because of the developments in personalized medicine, especially in the diagnosis and treatment of head and neck squamous cell carcinoma (HNSCC). This paper explores the dynamic landscape of personalized medicine as applied to HNSCC, encompassing both current developments and future prospects. RECENT FINDINGS The integration of personalized medicine strategies into HNSCC diagnosis is driven by the utilization of genetic data and biomarkers. Epigenetic biomarkers, which reflect modifications to DNA that can influence gene expression, have emerged as valuable indicators for early detection and risk assessment. Treatment approaches within the personalized medicine framework are equally promising. Immunotherapy, gene silencing, and editing techniques, including RNA interference and CRISPR/Cas9, offer innovative means to modulate gene expression and correct genetic aberrations driving HNSCC. The integration of stem cell research with personalized medicine presents opportunities for tailored regenerative approaches. The synergy between personalized medicine and technological advancements is exemplified by artificial intelligence (AI) and machine learning (ML) applications. These tools empower clinicians to analyze vast datasets, predict patient responses, and optimize treatment strategies with unprecedented accuracy. CONCLUSION The developments and prospects of personalized medicine in HNSCC diagnosis and treatment offer a transformative approach to managing this complex malignancy. By harnessing genetic insights, biomarkers, immunotherapy, gene editing, stem cell therapies, and advanced technologies like AI and ML, personalized medicine holds the key to enhancing patient outcomes and ushering in a new era of precision oncology.
Collapse
Affiliation(s)
| | | | | | | | - Kalpani Senevirathna
- Centre for Research in Oral Cancer, Faculty of Dental SciencesUniversity of PeradeniyaKandySri Lanka
| | | | | | - Kehinde Kazeem Kanmodi
- School of DentistryUniversity of RwandaKigaliRwanda
- Faculty of DentistryUniversity of PuthisastraPhnom PenhCambodia
- Cephas Health Research Initiative IncIbadanNigeria
- School of Health and Life SciencesTeesside UniversityMiddlesbroughUK
| | - Ruwan Duminda Jayasinghe
- Centre for Research in Oral Cancer, Faculty of Dental SciencesUniversity of PeradeniyaKandySri Lanka
- Faculty of DentistryUniversity of PuthisastraPhnom PenhCambodia
- School of Health and Life SciencesTeesside UniversityMiddlesbroughUK
- Department of Oral Medicine and Periodontology, Faculty of Dental SciencesUniversity of PeradeniyaKandySri Lanka
| |
Collapse
|
5
|
Chantadisai M, Wongwijitsook J, Ritlumlert N, Rakvongthai Y. Combined clinical variable and radiomics of post-treatment total body scan for prediction of successful I-131 ablation in low-risk papillary thyroid carcinoma patients. Sci Rep 2024; 14:5001. [PMID: 38424177 PMCID: PMC10904821 DOI: 10.1038/s41598-024-55755-6] [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: 10/12/2023] [Accepted: 02/27/2024] [Indexed: 03/02/2024] Open
Abstract
To explore the feasibility of combined radiomics of post-treatment I-131 total body scan (TBS) and clinical parameter to predict successful ablation in low-risk papillary thyroid carcinoma (PTC) patients. Data of low-risk PTC patients who underwent total/near total thyroidectomy and I-131 ablation 30 mCi between April 2015 and July 2021 were retrospectively reviewed. The clinical factors studied included age, sex, and pre-ablative serum thyroglobulin (Tg). Radiomic features were extracted via PyRadiomics, and radiomic feature selection was performed. The predictive performance for successful ablation of the clinical parameter, radiomic, and combined models (radiomics combined with clinical parameter) was calculated using the area under the receiver operating characteristic curve (AUC). One hundred and thirty patients were included. Successful ablation was achieved in 77 patients (59.2%). The mean pre-ablative Tg in the unsuccessful group (15.50 ± 18.04 ng/ml) was statistically significantly higher than those in the successful ablation group (7.12 ± 7.15 ng/ml). The clinical parameter, radiomic, and combined models produced AUCs of 0.66, 0.77, and 0.87 in the training sets, and 0.65, 0.69, and 0.78 in the validation sets, respectively. The combined model produced a significantly higher AUC than that of the clinical parameter (p < 0.05). Radiomic analysis of the post-treatment TBS combined with pre-ablative serum Tg showed a significant improvement in the predictive performance of successful ablation in low-risk PTC patients compared to the use of clinical parameter alone.Thai Clinical Trials Registry TCTR identification number is TCTR20230816004 ( https://www.thaiclinicaltrials.org/show/TCTR20230816004 ).
Collapse
Affiliation(s)
- Maythinee Chantadisai
- Division of Nuclear Medicine, Department of Radiology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand.
- Division of Nuclear Medicine, Department of Radiology, King Chulalongkorn Memorial Hospital, The Thai Red Cross Society, Bangkok, Thailand.
| | - Jirarot Wongwijitsook
- Division of Nuclear Medicine, Department of Radiology, King Chulalongkorn Memorial Hospital, The Thai Red Cross Society, Bangkok, Thailand
- Division of Nuclear Medicine, Department of Radiology, Surin Hospital, Surin, Thailand
| | - Napat Ritlumlert
- Chulalongkorn University Biomedical Imaging Group, Department of Radiology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
- Biomedical Engineering Program, Faculty of Engineering, Chulalongkorn University, Bangkok, Thailand
- School of Radiological Technology, Faculty of Health Science Technology, Chulabhorn Royal Academy, Bangkok, Thailand
| | - Yothin Rakvongthai
- Division of Nuclear Medicine, Department of Radiology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
- Chulalongkorn University Biomedical Imaging Group, Department of Radiology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| |
Collapse
|
6
|
Mu J, Cao Y, Zhong X, Diao W, Jia Z. Prediction of cervical lymph node metastasis in differentiated thyroid cancer based on radiomics models. Br J Radiol 2024; 97:526-534. [PMID: 38366237 PMCID: PMC11027254 DOI: 10.1093/bjr/tqae010] [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: 11/11/2022] [Revised: 07/06/2023] [Accepted: 01/11/2024] [Indexed: 02/18/2024] Open
Abstract
OBJECTIVE The accurate clinical diagnosis of cervical lymph node metastasis plays an important role in the treatment of differentiated thyroid cancer (DTC). This study aimed to explore and summarize a more objective approach to detect cervical malignant lymph node metastasis of DTC via radiomics models. METHODS PubMed, Web of Science, MEDLINE, EMBASE, and Cochrane databases were searched for all eligible studies. Articles using radiomics models based on ultrasound, computed tomography, or magnetic resonance imaging to assess cervical lymph node metastasis preoperatively were included. Characteristics and diagnostic accuracy measures were extracted. Bias and applicability judgments were evaluated by the revised QUADAS-2 tool. The estimates were pooled using a random-effects model. Additionally, the leave-one-out method was conducted to assess the heterogeneity. RESULTS Twenty-nine radiomics studies with 6160 validation set patients were included in the qualitative analysis, and 11 studies with 3863 validation set patients were included in the meta-analysis. Four of them had an external independent validation set. The studies were heterogeneous, and a significant risk of bias was found in 29 studies. Meta-analysis showed that the pooled sensitivity and specificity for preoperative prediction of lymph node metastasis via US-based radiomics were 0.81 (95% CI, 0.73-0.86) and 0.87 (95% CI, 0.83-0.91), respectively. CONCLUSIONS Although radiomics-based models for cervical lymphatic metastasis in DTC have been demonstrated to have moderate diagnostic capabilities, broader data, standardized radiomics features, robust feature selection, and model exploitation are still needed in the future. ADVANCES IN KNOWLEDGE The radiomics models showed great potential in detecting malignant lymph nodes in thyroid cancer.
Collapse
Affiliation(s)
- Jingshi Mu
- Department of Nuclear Medicine, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Yuan Cao
- Department of Nuclear Medicine, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Xiao Zhong
- Department of Nuclear Medicine, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Wei Diao
- Department of Nuclear Medicine, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Zhiyun Jia
- Department of Nuclear Medicine, West China Hospital, Sichuan University, Chengdu 610041, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu 610041, China
| |
Collapse
|
7
|
Demircioğlu A. The effect of data resampling methods in radiomics. Sci Rep 2024; 14:2858. [PMID: 38310165 PMCID: PMC10838284 DOI: 10.1038/s41598-024-53491-5] [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: 12/12/2023] [Accepted: 02/01/2024] [Indexed: 02/05/2024] Open
Abstract
Radiomic datasets can be class-imbalanced, for instance, when the prevalence of diseases varies notably, meaning that the number of positive samples is much smaller than that of negative samples. In these cases, the majority class may dominate the model's training and thus negatively affect the model's predictive performance, leading to bias. Therefore, resampling methods are often utilized to class-balance the data. However, several resampling methods exist, and neither their relative predictive performance nor their impact on feature selection has been systematically analyzed. In this study, we aimed to measure the impact of nine resampling methods on radiomic models utilizing a set of fifteen publicly available datasets regarding their predictive performance. Furthermore, we evaluated the agreement and similarity of the set of selected features. Our results show that applying resampling methods did not improve the predictive performance on average. On specific datasets, slight improvements in predictive performance (+ 0.015 in AUC) could be seen. A considerable disagreement on the set of selected features was seen (only 28.7% of features agreed), which strongly impedes feature interpretability. However, selected features are similar when considering their correlation (82.9% of features correlated on average).
Collapse
Affiliation(s)
- Aydin Demircioğlu
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstraße 55, 45147, Essen, Germany.
| |
Collapse
|
8
|
Li MH, Liu L, Feng L, Zheng LJ, Xu QM, Zhang YJ, Zhang FR, Feng LN. Prediction of cervical lymph node metastasis in solitary papillary thyroid carcinoma based on ultrasound radiomics analysis. Front Oncol 2024; 14:1291767. [PMID: 38333681 PMCID: PMC10850287 DOI: 10.3389/fonc.2024.1291767] [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: 01/08/2024] [Indexed: 02/10/2024] Open
Abstract
Objective To assess the utility of predictive models using ultrasound radiomic features to predict cervical lymph node metastasis (CLNM) in solitary papillary thyroid carcinoma (PTC) patients. Methods A total of 570 PTC patients were included (456 patients in the training set and 114 in the testing set). Pyradiomics was employed to extract radiomic features from preoperative ultrasound images. After dimensionality reduction and meticulous selection, we developed radiomics models using various machine learning algorithms. Univariate and multivariate logistic regressions were conducted to identify independent risk factors for CLNM. We established clinical models using these risk factors. Finally, we integrated radiomic and clinical models to create a combined nomogram. We plotted ROC curves to assess diagnostic performance and used calibration curves to evaluate alignment between predicted and observed probabilities. Results A total of 1561 radiomics features were extracted from preoperative ultrasound images. After dimensionality reduction and feature selection, 16 radiomics features were identified. Among radiomics models, the logistic regression (LR) model exhibited higher predictive efficiency. Univariate and multivariate logistic regression results revealed that patient age, tumor size, gender, suspicious cervical lymph node metastasis, and capsule contact were independent predictors of CLNM (all P < 0.05). By constructing a clinical model, the LR model demonstrated favorable diagnostic performance. The combined model showed superior diagnostic efficacy, with an AUC of 0.758 (95% CI: 0.712-0.803) in the training set and 0.759 (95% CI: 0.669-0.849) in the testing set. In the training dataset, the AUC value of the nomogram was higher than that of the clinical and radiomics models (P = 0.027 and 0.002, respectively). In the testing dataset, the AUC value of the nomogram model was also greater than that of the radiomics models (P = 0.012). However, there was no significant statistical difference between the nomogram and the clinical model (P = 0.928). The calibration curve indicated a good fit of the combined model. Conclusion Ultrasound radiomics technology offers a quantitative and objective method for predicting CLNM in PTC patients. Nonetheless, the clinical indicators persists as irreplaceable.
Collapse
Affiliation(s)
- Mei hua Li
- Department of Ultrasound, Sijing Hospital of Songjiang District, Shanghai, China
| | - Long Liu
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Lian Feng
- Department of Ultrasound, Sijing Hospital of Songjiang District, Shanghai, China
| | - Li jun Zheng
- Department of Ultrasound, Sijing Hospital of Songjiang District, Shanghai, China
| | - Qin mei Xu
- Department of Ultrasound, Sijing Hospital of Songjiang District, Shanghai, China
| | - Yin juan Zhang
- Department of Ultrasound, Sijing Hospital of Songjiang District, Shanghai, China
| | - Fu rong Zhang
- Department of Ultrasound, Sijing Hospital of Songjiang District, Shanghai, China
| | - Lin na Feng
- Department of Ultrasound, Sijing Hospital of Songjiang District, Shanghai, China
| |
Collapse
|
9
|
Zhang M, Lyu S, Yang L, Wei H, Liu R, Wang X, Liu Y, Zhang B, Kwok JKS, Zhang Y. A nomogram based on ultrasound radiomics for predicting the invasiveness of cN0 single papillary thyroid microcarcinoma. Gland Surg 2023; 12:1735-1745. [PMID: 38229850 PMCID: PMC10788574 DOI: 10.21037/gs-23-473] [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: 11/18/2023] [Accepted: 12/13/2023] [Indexed: 01/18/2024]
Abstract
Background Up to 15.3% of papillary thyroid microcarcinoma (PTMC) patients with negative clinical lymph node metastasis (cN0) were confirmed to have pathological lymph node metastasis in level VI. Conventional ultrasound (US) focuses on the characteristics of tumor capsule and the periphery to determine whether the tumor has invasive growth. However, due to its small size, the typical features of invasiveness shown by conventional 2-dimensional (2D) US are not well visualized. US-based radiomics makes use of artificial intelligence and big data to build a model that can help improving diagnostic accuracy and providing prognostic implication of the disease. We hope to establish and assess the value of a nomogram based on US radiomics combined with independent risk factors in predicting the invasiveness of a single PTMC without clinical lymph node metastasis (cN0). Methods A total of 317 patients with cN0 single PTMC who underwent US examination and operation were included in this retrospective cohort study. Patients were randomly divided into training and testing set in the ratio of 8:2. The US images of all patients were segmented, and the radiomics features were extracted. In the training dataset, the US with features of minimum redundancy maximum relevance (mRMR) and the least absolute shrinkage and selection operator (LASSO) were selected and radiomics signatures were then established according to their respective weighting coefficients. Univariate and multivariate logistic regression analyses were employed to generate the risk factors of possible invasive PTMC. The nomogram is then made by combining high risk factors and the radiomics signature. The efficiency of the nomogram was evaluated by the receiver operating characteristic (ROC) curve and calibration curve, and its clinical application value was assessed by decision curve analysis (DCA). The testing dataset was used to validate the model. Results In the model, seven radiomics features were selected to establish the radiomics signature. A nomogram was made by incorporating clinically independent risk factors and the radiomics signature. Both the ROC curve and calibration curve showed good prediction efficiency. The area under the curve (AUC), accuracy, sensitivity, and specificity of the nomogram in the training data were 0.76 [95% confidence interval (CI): 0.71-0.82], 0.811, 0.914, and 0.727, respectively whereas the results of the testing dataset were 0.71 (95% CI: 0.58-0.84), 0.841, 0.533, and 0.868. As such, the efficacy of the nomogram in predicting the invasiveness of PTMC was subsequently validated by the DCA. Conclusions Nomogram based on thyroid US radiomics has an excellent predictive value of the potential invasiveness of a single PTMC without clinical lymph node metastasis. With these promising results, it can potentially be the imaging marker used in daily clinical practice.
Collapse
Affiliation(s)
- Meiwu Zhang
- Department of Interventional Therapy, Ningbo No. 2 Hospital, Ningbo, China
| | - Shuyi Lyu
- Department of Interventional Therapy, Ningbo No. 2 Hospital, Ningbo, China
| | - Liu Yang
- Department of Interventional Therapy, Ningbo No. 2 Hospital, Ningbo, China
| | - Huilin Wei
- Department of Interventional Therapy, Ningbo No. 2 Hospital, Ningbo, China
| | - Rui Liu
- Department of Interventional Therapy, Ningbo No. 2 Hospital, Ningbo, China
| | - Xin Wang
- Department of Interventional Therapy, Ningbo No. 2 Hospital, Ningbo, China
| | - Yi Liu
- Department of Interventional Therapy, Ningbo No. 2 Hospital, Ningbo, China
| | - Baisong Zhang
- Department of Interventional Therapy, Ningbo No. 2 Hospital, Ningbo, China
| | | | - Yan Zhang
- Department of Interventional Therapy, Ningbo No. 2 Hospital, Ningbo, China
| |
Collapse
|
10
|
Fan F, Li F, Wang Y, Dai Z, Lin Y, Liao L, Wang B, Sun H. Integration of ultrasound-based radiomics with clinical features for predicting cervical lymph node metastasis in postoperative patients with differentiated thyroid carcinoma. Endocrine 2023:10.1007/s12020-023-03644-9. [PMID: 38129723 DOI: 10.1007/s12020-023-03644-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Accepted: 12/04/2023] [Indexed: 12/23/2023]
Abstract
OBJECTIVE The primary objective was to establish a radiomics model utilizing longitudinal +cross-sectional ultrasound (US) images of lymph nodes (LNs) to predict cervical lymph node metastasis (CLNM) following differentiated thyroid carcinoma (DTC) surgery. METHODS A retrospective collection of 211 LNs from 211 postoperative DTC patients who underwent neck US with suspicious LN fine needle aspiration cytopathology findings at our institution was conducted between June 2021 and April 2023. Conventional US and clinicopathological information of patients were gathered. Based on the pathological results, patients were categorized into CLNM and non-CLNM groups. The database was randomly divided into a training cohort (n = 147) and a test cohort (n = 64) at a 7:3 ratio. The least absolute shrinkage and selection operator algorithm was applied to screen the most relevant radiomic features from the longitudinal + cross-sectional US images, and a radiomics model was constructed. Univariate and multivariate analyses were used to assess US and clinicopathological significance features. Subsequently, a combined model for predicting CLNM was constructed by integrating radiomics, conventional US, and clinicopathological features and presented as a nomogram. RESULTS The area under the curves (AUCs) of the longitudinal + cross-sectional radiomics models were 0.846 and 0.801 in the training and test sets, respectively, outperforming the single longitudinal and cross-sectional models (p < 0.05). In the testing cohort, the AUC of the combined model in predicting CLNM was 0.901, surpassing that of the single US model (AUC, 0.731) and radiomics model (AUC, 0.801). CONCLUSIONS The US-based radiomics model exhibits the potential to accurately predict CLNM following DTC surgery, thereby enhancing diagnostic accuracy.
Collapse
Affiliation(s)
- Fengjing Fan
- Department of Medical Ultrasound, Shandong Provincial Qianfoshan Hospital, Shandong University, Jinan, China
| | - Fei Li
- Department of Medical Ultrasound, Shandong Provincial Qianfoshan Hospital, Shandong University, Jinan, China
| | - Yixuan Wang
- Department of Medical Ultrasound, Shandong Provincial Qianfoshan Hospital, Shandong University, Jinan, China
| | - Zhengjun Dai
- Scientific Research Department, Huiying Medical Technology Co., Ltd, Beijing, China
| | - Yuyang Lin
- Department of Medical Ultrasound, Shandong Provincial Qianfoshan Hospital, Shandong University, Jinan, China
| | - Lin Liao
- Department of Endocrinology and Metabology, Shandong Provincial Qianfoshan Hospital, Shandong University, Jinan, China
| | - Bei Wang
- Department of Medical Ultrasound, Shandong Provincial Qianfoshan Hospital, Shandong University, Jinan, China.
| | - Hongjun Sun
- Department of Medical Ultrasound, Shandong Provincial Qianfoshan Hospital, Shandong University, Jinan, China
| |
Collapse
|
11
|
Dong L, Han X, Yu P, Zhang W, Wang C, Sun Q, Song F, Zhang H, Zheng G, Mao N, Song X. CT Radiomics-Based Nomogram for Predicting the Lateral Neck Lymph Node Metastasis in Papillary Thyroid Carcinoma: A Prospective Multicenter Study. Acad Radiol 2023; 30:3032-3046. [PMID: 37210266 DOI: 10.1016/j.acra.2023.03.039] [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: 01/16/2023] [Revised: 03/26/2023] [Accepted: 03/27/2023] [Indexed: 05/22/2023]
Abstract
RATIONALE AND OBJECTIVES This study is based on multicenter cohorts and aims to utilize computed tomography (CT) images to construct a radiomics nomogram for predicting the lateral neck lymph node (LNLN) metastasis in the papillary thyroid carcinoma (PTC) and further explore the biological basis under its prediction. MATERIALS AND METHODS In the multicenter study, 1213 lymph nodes from 409 patients with PTC who underwent CT examinations and received open surgery and lateral neck dissection were included. A prospective test cohort was used in validating the model. Radiomics features were extracted from the CT images of each patient's LNLNs. Selectkbest, maximum relevance and minimum redundancy and the least absolute shrinkage and selection operator (LASSO) algorithm were used in reducing the dimensionality of radiomics features in the training cohort. Then, a radiomics signature (Rad-score) was calculated as the sum of each feature multiplied by the nonzero coefficient from LASSO. A nomogram was generated using the clinical risk factors of the patients and Rad-score. The nomograms' performance was analyzed in terms of accuracy, sensitivity, specificity, confusion matrix, receiver operating characteristic curves, and areas under the receiver operating characteristic curve (AUCs). The clinical usefulness of the nomogram was evaluated by decision curve analysis. Moreover, three radiologists with different working experiences and nomogram were compared to one another. Whole transcriptomics sequencing was performed in 14 tumor samples; the correlation of biological functions and high and low LNLN samples predicted by the nomogram was further investigated. RESULTS A total of 29 radiomics features were used in constructing the Rad-score. Rad-score and clinical risk factors (age, tumor diameter, location and number of suspected tumors) compose the nomogram. The nomogram exhibited good discrimination performance of the nomogram for predicting LNLN metastasis in the training cohort (AUC, 0.866), internal test cohort (0.845), external test cohort (0.725), and prospective test cohort (0.808) and showed diagnostic capability comparable to senior radiologists, significantly outperforming junior radiologists (p < 0.05). Functional enrichment analysis suggested that the nomogram can reflect the ribosome-related structures of cytoplasmic translation in patients with PTC. CONCLUSION Our radiomics nomogram provides a noninvasive method that incorporates radiomics features and clinical risk factors for predicting LNLN metastasis in patients with PTC.
Collapse
Affiliation(s)
- Luchao Dong
- Second Clinical Medicine College, Binzhou Medical University, Yantai, Shandong 264003, People's Republic of China (L.D., F.S.); Department of Otorhinolaryngology, Head and Neck Surgery, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong 264000, People's Republic of China (L.D., X.H., P.Y., W.Z., C.W., Q.S., F.S., X.S.); Shandong Provincial Clinical Research Center for Otorhinolaryngologic Diseases, Yantai, Shandong 264000, People's Republic of China (L.D., X.H., P.Y., W.Z., C.W., Q.S., F.S., N.M., X.S.)
| | - Xiao Han
- Department of Otorhinolaryngology, Head and Neck Surgery, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong 264000, People's Republic of China (L.D., X.H., P.Y., W.Z., C.W., Q.S., F.S., X.S.); Shandong Provincial Clinical Research Center for Otorhinolaryngologic Diseases, Yantai, Shandong 264000, People's Republic of China (L.D., X.H., P.Y., W.Z., C.W., Q.S., F.S., N.M., X.S.)
| | - Pengyi Yu
- Department of Otorhinolaryngology, Head and Neck Surgery, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong 264000, People's Republic of China (L.D., X.H., P.Y., W.Z., C.W., Q.S., F.S., X.S.); Shandong Provincial Clinical Research Center for Otorhinolaryngologic Diseases, Yantai, Shandong 264000, People's Republic of China (L.D., X.H., P.Y., W.Z., C.W., Q.S., F.S., N.M., X.S.)
| | - Wenbin Zhang
- Department of Otorhinolaryngology, Head and Neck Surgery, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong 264000, People's Republic of China (L.D., X.H., P.Y., W.Z., C.W., Q.S., F.S., X.S.); Shandong Provincial Clinical Research Center for Otorhinolaryngologic Diseases, Yantai, Shandong 264000, People's Republic of China (L.D., X.H., P.Y., W.Z., C.W., Q.S., F.S., N.M., X.S.)
| | - Cai Wang
- Department of Otorhinolaryngology, Head and Neck Surgery, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong 264000, People's Republic of China (L.D., X.H., P.Y., W.Z., C.W., Q.S., F.S., X.S.); Shandong Provincial Clinical Research Center for Otorhinolaryngologic Diseases, Yantai, Shandong 264000, People's Republic of China (L.D., X.H., P.Y., W.Z., C.W., Q.S., F.S., N.M., X.S.); School of Clinical Medicine, Weifang Medical University, Weifang, Shandong 261042, People's Republic of China (C.W.)
| | - Qi Sun
- Department of Otorhinolaryngology, Head and Neck Surgery, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong 264000, People's Republic of China (L.D., X.H., P.Y., W.Z., C.W., Q.S., F.S., X.S.); Shandong Provincial Clinical Research Center for Otorhinolaryngologic Diseases, Yantai, Shandong 264000, People's Republic of China (L.D., X.H., P.Y., W.Z., C.W., Q.S., F.S., N.M., X.S.)
| | - Fei Song
- Second Clinical Medicine College, Binzhou Medical University, Yantai, Shandong 264003, People's Republic of China (L.D., F.S.); Department of Otorhinolaryngology, Head and Neck Surgery, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong 264000, People's Republic of China (L.D., X.H., P.Y., W.Z., C.W., Q.S., F.S., X.S.); Shandong Provincial Clinical Research Center for Otorhinolaryngologic Diseases, Yantai, Shandong 264000, People's Republic of China (L.D., X.H., P.Y., W.Z., C.W., Q.S., F.S., N.M., X.S.)
| | - Haicheng Zhang
- Big Data and Artificial Intelligence Laboratory, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong 264000, People's Republic of China (H.Z., N.M., X.S.); Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong 264000, People's Republic of China (H.Z., N.M.)
| | - Guibin Zheng
- Department of Thyroid Surgery, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong 264000, People's Republic of China (G.Z.)
| | - Ning Mao
- Shandong Provincial Clinical Research Center for Otorhinolaryngologic Diseases, Yantai, Shandong 264000, People's Republic of China (L.D., X.H., P.Y., W.Z., C.W., Q.S., F.S., N.M., X.S.); Big Data and Artificial Intelligence Laboratory, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong 264000, People's Republic of China (H.Z., N.M., X.S.); Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong 264000, People's Republic of China (H.Z., N.M.)
| | - Xicheng Song
- Department of Otorhinolaryngology, Head and Neck Surgery, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong 264000, People's Republic of China (L.D., X.H., P.Y., W.Z., C.W., Q.S., F.S., X.S.); Shandong Provincial Clinical Research Center for Otorhinolaryngologic Diseases, Yantai, Shandong 264000, People's Republic of China (L.D., X.H., P.Y., W.Z., C.W., Q.S., F.S., N.M., X.S.); Big Data and Artificial Intelligence Laboratory, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong 264000, People's Republic of China (H.Z., N.M., X.S.).
| |
Collapse
|
12
|
Lu S, Ren Y, Lu C, Qian X, Liu Y, Zhang J, Shan X, Sun E. Radiomics features from whole thyroid gland tissue for prediction of cervical lymph node metastasis in the patients with papillary thyroid carcinoma. J Cancer Res Clin Oncol 2023; 149:13005-13016. [PMID: 37466794 DOI: 10.1007/s00432-023-05184-1] [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: 06/23/2023] [Accepted: 07/13/2023] [Indexed: 07/20/2023]
Abstract
OBJECTIVE We aimed to develop a clinical-radiomics nomogram that could predict the cervical lymph node metastasis (CLNM) of patients with papillary thyroid carcinoma (PTC) using clinical characteristics as well as radiomics features of dual energy computed tomography (DECT). METHOD Patients from our hospital with suspected PTC who underwent DECT for preoperative assessment between January 2021 and February 2022 were retrospectively recruited. Clinical characteristics were obtained from the medical record system. Clinical characteristics and rad-scores were examined by univariate and multivariate logistic regression. All features were incorporated into the LASSO regression model, with penalty parameter tuning performed using tenfold cross-validation, to screen risk factors for CLNM. An easily accessible radiomics nomogram was constructed. Receiver Operating Characteristic (ROC) curve together with Area Under the Curve (AUC) analysis was conducted to evaluate the discrimination performance of the model. Calibration curves were employed to assess the calibration performance of the clinical-radiomics nomogram, followed by goodness-of-fit testing. Decision curve analysis (DCA) was performed to determine the clinical utility of the established models by estimating net benefits at varying threshold probabilities for training and testing groups. RESULTS A total of 461 patients were retrospectively recruited. The rates of CLNM were 49.3% (70 /142) in the training cohort and 53.3% (32/60) in the testing cohort. Out of the 960 extracted radiomics features, 192 were significantly different in positive and negative groups (p < 0.05). On the basis of the training cohort, 12 stable features with nonzero coefficients were selected using LASSO regression. LASSO regression identified 7 risk factors for CLNM, including male gender, maximum tumor size > 10 mm, multifocality, CT-reported central CLN status, US-reported central CLN status, rad-score, and TGAb. A nomogram was developed using these factors to predict the risk of CLNM. The AUC values in each cohort were 0.850 and 0.797, respectively. The calibration curve together with the Hosmer-Lemeshow test for the nomogram indicated good agreement between predicted and pathological CLN statuses in the training and testing cohorts. Results of DCA proved that the nomogram offers a superior net benefit for predicting CLNM compared to the "treat all or none" strategy across the majority of risk thresholds. CONCLUSION A nomogram comprising the clinical characteristics as well as radiomics features of DECT and US was constructed for the prediction of CLNM for patients with PTC, which in determining whether lateral compartment neck dissection is warranted.
Collapse
Affiliation(s)
- Siyuan Lu
- Department of Radiology, Affiliated People's Hospital of Jiangsu University, Zhenjiang, 212002, Jiangsu Province, China
| | - Yongzhen Ren
- Department of Ultrasonography, Affiliated People's Hospital of Jiangsu University, Zhenjiang, 212002, Jiangsu Province, China
| | - Chao Lu
- Department of Radiology, Affiliated People's Hospital of Jiangsu University, Zhenjiang, 212002, Jiangsu Province, China
| | - Xiaoqin Qian
- Department of Ultrasonography, Affiliated People's Hospital of Jiangsu University, Zhenjiang, 212002, Jiangsu Province, China
| | - Yingzhao Liu
- Department of Endocrinology, Affiliated People's Hospital of Jiangsu University, Zhenjiang, 212002, Jiangsu Province, China
| | - Jiulou Zhang
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Xiuhong Shan
- Department of Radiology, Affiliated People's Hospital of Jiangsu University, Zhenjiang, 212002, Jiangsu Province, China.
| | - Eryi Sun
- Department of Neurosurgery, Affiliated People's Hospital of Jiangsu University, Zhenjiang, 212002, Jiangsu Province, China.
| |
Collapse
|
13
|
HajiEsmailPoor Z, Kargar Z, Tabnak P. Radiomics diagnostic performance in predicting lymph node metastasis of papillary thyroid carcinoma: A systematic review and meta-analysis. Eur J Radiol 2023; 168:111129. [PMID: 37820522 DOI: 10.1016/j.ejrad.2023.111129] [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: 03/25/2023] [Revised: 09/03/2023] [Accepted: 09/28/2023] [Indexed: 10/13/2023]
Abstract
PURPOSE To evaluate the diagnostic performance of radiomics in lymph node metastasis (LNM) prediction in patients with papillary thyroid carcinoma (PTC) through a systematic review and meta-analysis. METHOD A literature search of PubMed, EMBASE, and Web of Science was conducted to find relevant studies published until February 18th, 2023. Studies that reported the accuracy of radiomics in different imaging modalities for LNM prediction in PTC patients were selected. The methodological quality of included studies was evaluated by radiomics quality score (RQS) and quality assessment of diagnostic accuracy studies (QUADAS-2) tools. General characteristics and radiomics accuracy were extracted. Overall sensitivity, specificity, and area under the curve (AUC) were calculated for diagnostic accuracy evaluation. Spearman correlation coefficient and subgroup analysis were performed for heterogeneity exploration. RESULTS In total, 25 studies were included, of which 22 studies provided adequate data for meta-analysis. We conducted two types of meta-analysis: one focused solely on radiomics features models and the other combined radiomics and non-radiomics features models in the analysis. The pooled sensitivity, specificity, and AUC of radiomics and combined models were 0.75 [0.68, 0.80] vs. 0.77 [0.74, 0.80], 0.77 [0.74, 0.81] vs. 0.83 [0.78, 0.87] and 0.80 [0.73, 0.85] vs 0.82 [0.75, 0.88], respectively. The analysis showed a high heterogeneity level among the included studies. There was no threshold effect. The subgroup analysis demonstrated that utilizing ultrasonography, 2D segmentation, central and lateral LNM detection, automatic segmentation, and PyRadiomics software could slightly improve diagnostic accuracy. CONCLUSIONS Our meta-analysis shows that the radiomics has the potential for pre-operative LNM prediction in PTC patients. Although methodological quality is sufficient but we still need more prospective studies with larger sample sizes from different centers.
Collapse
Affiliation(s)
| | - Zana Kargar
- Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Peyman Tabnak
- Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran
| |
Collapse
|
14
|
Ding X, Liu Y, Zhao J, Wang R, Li C, Luo Q, Shen C. A novel wavelet-transform-based convolution classification network for cervical lymph node metastasis of papillary thyroid carcinoma in ultrasound images. Comput Med Imaging Graph 2023; 109:102298. [PMID: 37769402 DOI: 10.1016/j.compmedimag.2023.102298] [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: 05/05/2023] [Revised: 08/29/2023] [Accepted: 08/29/2023] [Indexed: 09/30/2023]
Abstract
Preoperative assessment of cervical lymph nodes metastasis (CLNM) for accurate qualitative and locating diagnosis is important for choosing the best treatment option for patients with papillary thyroid cancer. Non-destructive, non-invasive ultrasound is currently the imaging method of choice for lymph node metastatic assessment. For lymph node characteristics and ultrasound images, this paper proposes a multitasking network framework for diagnosing metastatic lymph nodes in ultrasound images, in which localization module not only provides information on the location of lymph nodes to focus on the peripheral and self regions of lymph nodes, but also provides structural features of lymph nodes for subsequent classification module. In the classification module, we design a novel wavelet-transform-based convolution network. Wavelet transform is introduced into the deep learning convolution module to analyze ultrasound images in both spatial and frequency domains, which effectively enriches the feature information and improves the classification performance of the model without increasing the model parameters. We collected 510 patient data (N = 1376) from Shanghai Sixth People's Hospital regarding ultrasound lymph nodes in the neck, as well as used three publicly available ultrasound datasets, including SCUI2020 (N = 2914), DDTI (N = 480), and BUSI (N = 780). Compared to the optimal two-stage model, our model has improved its accuracy and AUC indexes by 5.83% and 4%, which outperforms the two-stage architectures and also surpasses the latest classification networks.
Collapse
Affiliation(s)
- Xuehai Ding
- School of Computer Engineering and Science, Shanghai University, Shangda Rd, Shanghai, 200444, China
| | - Yanting Liu
- School of Computer Engineering and Science, Shanghai University, Shangda Rd, Shanghai, 200444, China
| | - Junjuan Zhao
- School of Computer Engineering and Science, Shanghai University, Shangda Rd, Shanghai, 200444, China.
| | - Ren Wang
- Department of Ultrasound Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Yishan Rd, Shanghai, 200233, China
| | - Chengfan Li
- School of Computer Engineering and Science, Shanghai University, Shangda Rd, Shanghai, 200444, China
| | - Quanyong Luo
- Department of Nuclear Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Yishan Rd, Shanghai, 200233, China
| | - Chentian Shen
- Department of Nuclear Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Yishan Rd, Shanghai, 200233, China.
| |
Collapse
|
15
|
Zhang M, Zhang Y, Wei H, Yang L, Liu R, Zhang B, Lyu S. Ultrasound radiomics nomogram for predicting large-number cervical lymph node metastasis in papillary thyroid carcinoma. Front Oncol 2023; 13:1159114. [PMID: 37361586 PMCID: PMC10285658 DOI: 10.3389/fonc.2023.1159114] [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: 02/05/2023] [Accepted: 05/25/2023] [Indexed: 06/28/2023] Open
Abstract
Purpose To evaluate the value of preoperative ultrasound (US) radiomics nomogram of primary papillary thyroid carcinoma (PTC) for predicting large-number cervical lymph node metastasis (CLNM). Materials and methods A retrospective study was conducted to collect the clinical and ultrasonic data of primary PTC. 645 patients were randomly divided into training and testing datasets according to the proportion of 7:3. Minimum redundancy-maximum relevance (mRMR) and least absolution shrinkage and selection operator (LASSO) were used to select features and establish radiomics signature. Multivariate logistic regression was used to establish a US radiomics nomogram containing radiomics signature and selected clinical characteristics. The efficiency of the nomogram was evaluated by the receiver operating characteristic (ROC) curve and calibration curve, and the clinical application value was assessed by decision curve analysis (DCA). Testing dataset was used to validate the model. Results TG level, tumor size, aspect ratio, and radiomics signature were significantly correlated with large-number CLNM (all P< 0.05). The ROC curve and calibration curve of the US radiomics nomogram showed good predictive efficiency. In the training dataset, the AUC, accuracy, sensitivity, and specificity were 0.935, 0.897, 0.956, and 0.837, respectively, and in the testing dataset, the AUC, accuracy, sensitivity, and specificity were 0.782, 0.910, 0.533 and 0.943 respectively. DCA showed that the nomogram had some clinical benefits in predicting large-number CLNM. Conclusion We have developed an easy-to-use and non-invasive US radiomics nomogram for predicting large-number CLNM with PTC, which combines radiomics signature and clinical risk factors. The nomogram has good predictive efficiency and potential clinical application value.
Collapse
|
16
|
Zhao F, Wang P, Yu C, Song X, Wang H, Fang J, Zhu C, Li Y. A LASSO-based model to predict central lymph node metastasis in preoperative patients with cN0 papillary thyroid cancer. Front Oncol 2023; 13:1034047. [PMID: 36761950 PMCID: PMC9905414 DOI: 10.3389/fonc.2023.1034047] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Accepted: 01/09/2023] [Indexed: 01/26/2023] Open
Abstract
Introduction Central lymph node metastasis (CLNM) is common in papillary thyroid carcinoma (PTC). Prophylactic central lymph node dissection (PCLND) in clinically negative central compartment lymph node (cN0) PTC patients is still controversial. How to predict CLNM before the operation is very important for surgical decision making. Methods In this article, we retrospectively enrolled 243 cN0 PTC patients and gathered data including clinical characteristics, ultrasound (US) characteristics, pathological results of fine-needle aspiration (FNA), thyroid function, eight gene mutations, and immunoenzymatic results. Least absolute shrinkage and selection operator (LASSO) analysis was used for data dimensionality reduction and feature analysis. Results According to the results, the important predictors of CLNM were identified. Multivariable logistic regression analysis was used to establish a new nomogram prediction model. The receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) curve were used to evaluate the performance of the new prediction model. Discussion The new nomogram prediction model was a reasonable and reliable model for predicting CLNM in cN0 PTC patients, but further validation is warranted.
Collapse
Affiliation(s)
- Feng Zhao
- Department of General Surgery, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ping Wang
- Department of General Surgery, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Chaoran Yu
- Department of General Surgery, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xuefei Song
- Department of Ophthalmology, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Hui Wang
- Department of Ophthalmology, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jun Fang
- Department of General Surgery, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Chenfang Zhu
- Department of General Surgery, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China,*Correspondence: Yousheng Li, ; Chenfang Zhu,
| | - Yousheng Li
- Department of General Surgery, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China,*Correspondence: Yousheng Li, ; Chenfang Zhu,
| |
Collapse
|
17
|
Zhu H, Yu B, Li Y, Zhang Y, Jin J, Ai Y, Jin X, Yang Y. Models of ultrasonic radiomics and clinical characters for lymph node metastasis assessment in thyroid cancer: a retrospective study. PeerJ 2023; 11:e14546. [PMID: 36650830 PMCID: PMC9840861 DOI: 10.7717/peerj.14546] [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: 09/22/2022] [Accepted: 11/18/2022] [Indexed: 01/14/2023] Open
Abstract
Background Preoperative prediction of cervical lymph node metastasis in papillary thyroid carcinoma provided a basis for tumor staging and treatment decision. This study aimed to investigate the utility of machine learning and develop different models to preoperatively predict cervical lymph node metastasis based on ultrasonic radiomic features and clinical characteristics in papillary thyroid carcinoma nodules. Methods Data from 400 papillary thyroid carcinoma nodules were included and divided into training and validation group. With the help of machine learning, clinical characteristics and ultrasonic radiomic features were extracted and selected using randomforest and least absolute shrinkage and selection operator regression before classified by five classifiers. Finally, 10 models were built and their area under the receiver operating characteristic curve, accuracy, sensitivity, specificity, positive predictive value and negative predictive value were measured. Results Among the 10 models, RF-RF model revealed the highest area under curve (0.812) and accuracy (0.7542) in validation group. The top 10 variables of it included age, seven textural features, one shape feature and one first-order feature, in which eight were high-dimensional features. Conclusions RF-RF model showed the best predictive performance for cervical lymph node metastasis. And the importance features selected by it highlighted the unique role of higher-dimensional statistical methods for radiomics analysis.
Collapse
Affiliation(s)
- Hui Zhu
- Department of Ultrasound, the Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Bing Yu
- Department of Radiotherapy Center, The 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Yanyan Li
- Department of Ultrasound, the Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Yuhua Zhang
- Department of Ultrasound, the Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Juebin Jin
- Department of Medical Engineering, The 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Yao Ai
- Department of Radiotherapy Center, The 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Xiance Jin
- Department of Radiotherapy Center, The 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Yan Yang
- Department of Ultrasound, the Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
| |
Collapse
|
18
|
Ren Y, Lu S, Zhang D, Wang X, Agyekum EA, Zhang J, Zhang Q, Xu F, Zhang G, Chen Y, Shen X, Zhang X, Wu T, Hu H, Shan X, Wang J, Qian X. Dual-modal radiomics for predicting cervical lymph node metastasis in papillary thyroid carcinoma. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2023; 31:1263-1280. [PMID: 37599557 DOI: 10.3233/xst-230091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/22/2023]
Abstract
BACKGROUND Preoperative prediction of cervical lymph node metastasis (CLNM) in patients with papillary thyroid carcinoma (PTC) is significant for surgical decision-making. OBJECTIVE This study aims to develop a dual-modal radiomics (DMR) model based on grayscale ultrasound (GSUS) and dual-energy computed tomography (DECT) for non-invasive CLNM in PTC. METHODS In this study, 348 patients with pathologically confirmed PTC at Jiangsu University Affiliated People's Hospital who completed preoperative ultrasound (US) and DECT examinations were enrolled and randomly assigned to training (n = 261) and test (n = 87) cohorts. The enrolled patients were divided into two groups based on pathology findings namely, CLNM (n = 179) and CLNM-Free (n = 169). Radiomics features were extracted from GSUS images (464 features) and DECT images (960 features), respectively. Pearson correlation coefficient (PCC) and the least absolute shrinkage and selection operator (LASSO) regression with 10-fold cross-validation were then used to select CLNM-related features. Based on the selected features, GSUS, DECT, and GSUS combined DECT radiomics models were constructed by using a Support Vector Machine (SVM) classifier. RESULTS Three predictive models based on GSUS, DECT, and a combination of GSUS and DECT, yielded performance of areas under the curve (AUC) = 0.700 [95% confidence interval (CI), 0.662-0.706], 0.721 [95% CI, 0.683-0.727], and 0.760 [95% CI, 0.728-0.762] in the training dataset, and AUC = 0.643 [95% CI, 0.582-0.734], 0.680 [95% CI, 0.623-0.772], and 0.744 [95% CI, 0.686-0.784] in the test dataset, respectively. It shows that the predictive model combined GSUS and DECT outperforms both models using GSUS and DECT only. CONCLUSIONS The newly developed combined radiomics model could more accurately predict CLNM in PTC patients and aid in better surgical planning.
Collapse
Affiliation(s)
- Yongzhen Ren
- Department of Medical Ultrasound, Jiangsu University Affiliated People's Hospital, Zhenjiang, Jiangsu Province, China
- School of Medicine, Jiangsu University, Zhenjiang, Jiangsu Province, China
| | - Siyuan Lu
- School of Medicine, Jiangsu University, Zhenjiang, Jiangsu Province, China
- Department of Radiology, Jiangsu University Affiliated People's Hospital, Zhenjiang, Jiangsu Province, China
| | - Dongmei Zhang
- Department of Medical Ultrasound, Jiangsu University Affiliated People's Hospital, Zhenjiang, Jiangsu Province, China
| | - Xian Wang
- Department of Medical Ultrasound, Jiangsu University Affiliated People's Hospital, Zhenjiang, Jiangsu Province, China
| | - Enock Adjei Agyekum
- Department of Medical Ultrasound, Jiangsu University Affiliated People's Hospital, Zhenjiang, Jiangsu Province, China
- School of Medicine, Jiangsu University, Zhenjiang, Jiangsu Province, China
| | - Jin Zhang
- Department of Medical Ultrasound, Jiangsu University Affiliated People's Hospital, Zhenjiang, Jiangsu Province, China
| | - Qing Zhang
- Department of Medical Ultrasound, Jiangsu University Affiliated People's Hospital, Zhenjiang, Jiangsu Province, China
| | - Feiju Xu
- Department of Medical Ultrasound, Jiangsu University Affiliated People's Hospital, Zhenjiang, Jiangsu Province, China
| | - Guoliang Zhang
- Department of General Surgery, Jiangsu University Affiliated People's Hospital, Zhenjiang, Jiangsu Province, China
| | - Yu Chen
- Materdicine Lab, School of Life Sciences, Shanghai University, Shanghai, China
| | - Xiangjun Shen
- School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang, Jiangsu Province, China
| | - Xuelin Zhang
- Department of Medical Ultrasound, Jiangsu University Affiliated People's Hospital, Zhenjiang, Jiangsu Province, China
| | - Ting Wu
- Department of Pathology, Jiangsu University Affiliated People's Hospital, Zhenjiang, Jiangsu Province, China
| | - Hui Hu
- Department of Radiology, Jiangsu University Affiliated People's Hospital, Zhenjiang, Jiangsu Province, China
| | - Xiuhong Shan
- Department of Radiology, Jiangsu University Affiliated People's Hospital, Zhenjiang, Jiangsu Province, China
| | - Jun Wang
- School of Communication and Information Engineering, Shanghai University, Shanghai, China
| | - Xiaoqin Qian
- Department of Medical Ultrasound, Jiangsu University Affiliated People's Hospital, Zhenjiang, Jiangsu Province, China
| |
Collapse
|
19
|
Chung HJ, Han K, Lee E, Yoon JH, Park VY, Lee M, Cho E, Kwak JY. Radiomics Analysis of Gray-Scale Ultrasonographic Images of Papillary Thyroid Carcinoma > 1 cm: Potential Biomarker for the Prediction of Lymph Node Metastasis. JOURNAL OF THE KOREAN SOCIETY OF RADIOLOGY 2023; 84:185-196. [PMID: 36818698 PMCID: PMC9935950 DOI: 10.3348/jksr.2021.0155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Revised: 03/09/2022] [Accepted: 04/19/2022] [Indexed: 02/10/2023]
Abstract
Purpose This study aimed to investigate radiomics analysis of ultrasonographic images to develop a potential biomarker for predicting lymph node metastasis in papillary thyroid carcinoma (PTC) patients. Materials and Methods This study included 431 PTC patients from August 2013 to May 2014 and classified them into the training and validation sets. A total of 730 radiomics features, including texture matrices of gray-level co-occurrence matrix and gray-level run-length matrix and single-level discrete two-dimensional wavelet transform and other functions, were obtained. The least absolute shrinkage and selection operator method was used for selecting the most predictive features in the training data set. Results Lymph node metastasis was associated with the radiomics score (p < 0.001). It was also associated with other clinical variables such as young age (p = 0.007) and large tumor size (p = 0.007). The area under the receiver operating characteristic curve was 0.687 (95% confidence interval: 0.616-0.759) for the training set and 0.650 (95% confidence interval: 0.575-0.726) for the validation set. Conclusion This study showed the potential of ultrasonography-based radiomics to predict cervical lymph node metastasis in patients with PTC; thus, ultrasonography-based radiomics can act as a biomarker for PTC.
Collapse
Affiliation(s)
- Hyun Jung Chung
- Department of Radiology, Severance Hospital, Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul, Korea
| | - Kyunghwa Han
- Department of Radiology, Severance Hospital, Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul, Korea
| | - Eunjung Lee
- Department of Computational Science and Engineering, Yonsei University, Seoul, Korea
| | - Jung Hyun Yoon
- Department of Radiology, Severance Hospital, Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul, Korea
| | - Vivian Youngjean Park
- Department of Radiology, Severance Hospital, Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul, Korea
| | - Mina Lee
- Department of Radiology, Severance Hospital, Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul, Korea
| | - Eun Cho
- Department of Radiology, Gyeongsang National University Changwon Hospital, Gyeongsang National University School of Medicine, Changwon, Korea
| | - Jin Young Kwak
- Department of Radiology, Severance Hospital, Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul, Korea
| |
Collapse
|
20
|
Agyekum EA, Ren YZ, Wang X, Cranston SS, Wang YG, Wang J, Akortia D, Xu FJ, Gomashie L, Zhang Q, Zhang D, Qian X. Evaluation of Cervical Lymph Node Metastasis in Papillary Thyroid Carcinoma Using Clinical-Ultrasound Radiomic Machine Learning-Based Model. Cancers (Basel) 2022; 14:5266. [PMID: 36358685 PMCID: PMC9655605 DOI: 10.3390/cancers14215266] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 10/10/2022] [Accepted: 10/21/2022] [Indexed: 08/30/2023] Open
Abstract
We aim to develop a clinical-ultrasound radiomic (USR) model based on USR features and clinical factors for the evaluation of cervical lymph node metastasis (CLNM) in patients with papillary thyroid carcinoma (PTC). This retrospective study used routine clinical and US data from 205 PTC patients. According to the pathology results, the enrolled patients were divided into a non-CLNM group and a CLNM group. All patients were randomly divided into a training cohort (n = 143) and a validation cohort (n = 62). A total of 1046 USR features of lesion areas were extracted. The features were reduced using Pearson's Correlation Coefficient (PCC) and Recursive Feature Elimination (RFE) with stratified 15-fold cross-validation. Several machine learning classifiers were employed to build a Clinical model based on clinical variables, a USR model based solely on extracted USR features, and a Clinical-USR model based on the combination of clinical variables and USR features. The Clinical-USR model could discriminate between PTC patients with CLNM and PTC patients without CLNM in the training (AUC, 0.78) and validation cohorts (AUC, 0.71). When compared to the Clinical model, the USR model had higher AUCs in the validation (0.74 vs. 0.63) cohorts. The Clinical-USR model demonstrated higher AUC values in the validation cohort (0.71 vs. 0.63) compared to the Clinical model. The newly developed Clinical-USR model is feasible for predicting CLNM in patients with PTC.
Collapse
Affiliation(s)
- Enock Adjei Agyekum
- Department of Ultrasound, Jiangsu University Affiliated People’s Hospital, Zhenjiang 212002, China
- School of Medicine, Jiangsu University, Zhenjiang 212002, China
| | - Yong-Zhen Ren
- Department of Ultrasound, Jiangsu University Affiliated People’s Hospital, Zhenjiang 212002, China
- School of Medicine, Jiangsu University, Zhenjiang 212002, China
| | - Xian Wang
- Department of Ultrasound, Jiangsu University Affiliated People’s Hospital, Zhenjiang 212002, China
| | | | - Yu-Guo Wang
- Department of Ultrasound, Nanjing Lishui District Hospital of Traditional Chinese Medicine, Nanjing 211200, China
| | - Jun Wang
- Department of Biomedical Engineering, School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China
| | - Debora Akortia
- Department of Clinical Microbiology, School of Medicine and Dentistry, Kwame Nkrumah University of Science and Technology, Kumasi 00233, Ghana
| | - Fei-Ju Xu
- Department of Ultrasound, Jiangsu University Affiliated People’s Hospital, Zhenjiang 212002, China
| | - Leticia Gomashie
- Department of Imaging, Klintaps University College, Accra 00233, Ghana
| | - Qing Zhang
- Department of Ultrasound, Jiangsu University Affiliated People’s Hospital, Zhenjiang 212002, China
| | - Dongmei Zhang
- Department of Ultrasound, Jiangsu University Affiliated People’s Hospital, Zhenjiang 212002, China
| | - Xiaoqin Qian
- Department of Ultrasound, Jiangsu University Affiliated People’s Hospital, Zhenjiang 212002, China
| |
Collapse
|
21
|
Sorrenti S, Dolcetti V, Radzina M, Bellini MI, Frezza F, Munir K, Grani G, Durante C, D’Andrea V, David E, Calò PG, Lori E, Cantisani V. Artificial Intelligence for Thyroid Nodule Characterization: Where Are We Standing? Cancers (Basel) 2022; 14:cancers14143357. [PMID: 35884418 PMCID: PMC9315681 DOI: 10.3390/cancers14143357] [Citation(s) in RCA: 37] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 06/24/2022] [Accepted: 07/08/2022] [Indexed: 12/12/2022] Open
Abstract
Simple Summary In the present review, an up-to-date summary of the state of the art of artificial intelligence (AI) implementation for thyroid nodule characterization and cancer is provided. The opinion on the real effectiveness of AI systems remains controversial. Taking into consideration the largest and most scientifically valid studies, it is possible to state that AI provides results that are comparable or inferior to expert ultrasound specialists and radiologists. Promising data approve AI as a support tool and simultaneously highlight the need for a radiologist supervisory framework for AI provided results. Therefore, current solutions might be more suitable for educational purposes. Abstract Machine learning (ML) is an interdisciplinary sector in the subset of artificial intelligence (AI) that creates systems to set up logical connections using algorithms, and thus offers predictions for complex data analysis. In the present review, an up-to-date summary of the current state of the art regarding ML and AI implementation for thyroid nodule ultrasound characterization and cancer is provided, highlighting controversies over AI application as well as possible benefits of ML, such as, for example, training purposes. There is evidence that AI increases diagnostic accuracy and significantly limits inter-observer variability by using standardized mathematical algorithms. It could also be of aid in practice settings with limited sub-specialty expertise, offering a second opinion by means of radiomics and computer-assisted diagnosis. The introduction of AI represents a revolutionary event in thyroid nodule evaluation, but key issues for further implementation include integration with radiologist expertise, impact on workflow and efficiency, and performance monitoring.
Collapse
Affiliation(s)
- Salvatore Sorrenti
- Department of Surgical Sciences, “Sapienza” University of Rome, 00161 Rome, Italy; (S.S.); (V.D.); (E.L.)
| | - Vincenzo Dolcetti
- Department of Radiological, Anatomo-Pathological Sciences, “Sapienza” University of Rome, 00161 Rome, Italy; (V.D.); (V.C.)
| | - Maija Radzina
- Radiology Research Laboratory, Riga Stradins University, LV-1007 Riga, Latvia;
- Medical Faculty, University of Latvia, Diagnostic Radiology Institute, Paula Stradina Clinical University Hospital, LV-1007 Riga, Latvia
| | - Maria Irene Bellini
- Department of Surgical Sciences, “Sapienza” University of Rome, 00161 Rome, Italy; (S.S.); (V.D.); (E.L.)
- Correspondence:
| | - Fabrizio Frezza
- Department of Information Engineering, Electronics and Telecommunications, “Sapienza” University of Rome, 00184 Rome, Italy; (F.F.); (K.M.)
- Consorzio Nazionale Interuniversitario per le Telecomunicazioni (CNIT), Viale G.P. Usberti 181/A Sede Scientifica di Ingegneria-Palazzina 3, 43124 Parma, Italy
| | - Khushboo Munir
- Department of Information Engineering, Electronics and Telecommunications, “Sapienza” University of Rome, 00184 Rome, Italy; (F.F.); (K.M.)
| | - Giorgio Grani
- Department of Translational and Precision Medicine, “Sapienza” University of Rome, 00161 Rome, Italy; (G.G.); (C.D.); (E.D.)
| | - Cosimo Durante
- Department of Translational and Precision Medicine, “Sapienza” University of Rome, 00161 Rome, Italy; (G.G.); (C.D.); (E.D.)
| | - Vito D’Andrea
- Department of Surgical Sciences, “Sapienza” University of Rome, 00161 Rome, Italy; (S.S.); (V.D.); (E.L.)
| | - Emanuele David
- Department of Translational and Precision Medicine, “Sapienza” University of Rome, 00161 Rome, Italy; (G.G.); (C.D.); (E.D.)
| | - Pietro Giorgio Calò
- Department of Surgical Sciences, “Policlinico Universitario Duilio Casula”, University of Cagliari, 09042 Monserrato, Italy;
| | - Eleonora Lori
- Department of Surgical Sciences, “Sapienza” University of Rome, 00161 Rome, Italy; (S.S.); (V.D.); (E.L.)
| | - Vito Cantisani
- Department of Radiological, Anatomo-Pathological Sciences, “Sapienza” University of Rome, 00161 Rome, Italy; (V.D.); (V.C.)
| |
Collapse
|
22
|
Zhang W, Peng J, Zhao S, Wu W, Yang J, Ye J, Xu S. Deep learning combined with radiomics for the classification of enlarged cervical lymph nodes. J Cancer Res Clin Oncol 2022; 148:2773-2780. [PMID: 35562596 DOI: 10.1007/s00432-022-04047-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2021] [Accepted: 04/27/2022] [Indexed: 12/17/2022]
Abstract
PURPOSE To investigate the application of deep learning combined with traditional radiomics methods for classifying enlarged cervical lymph nodes. METHODS The clinical and computed tomography (CT) imaging data of 276 patients with enlarged cervical lymph nodes (150 with lymph-node metastasis, 65 with lymphoma, and 61 with benign lymphadenopathy) who were treated at the hospital from January 2015 to January 2021 were retrospectively analysed. The patients were randomly divided into a training group and a test group at a ratio of 8:2. The radiomics features were extracted using one-by-one convolution and neural network activation, filtered with the least absolute shrinkage and selection operator (LASSO) model, and used to construct a discrimination model with PyTorch. Then, the performance of the model was compared with the radiologists' diagnostic performance. The neural network model was evaluated using the area under the receiver-operator characteristic curve (AUC), and the accuracy, sensitivity, and specificity were analysed. RESULTS A total of 102 features, comprising five traditional radiomic features and 97 deep learning features, were selected with LASSO and used to construct a discrimination model, which achieved a total accuracy of 87.50%. The AUC value, specificity, and sensitivity were, respectively, 0.92, 92.30%, and 90.00% for metastatic lymph nodes, 0.87, 95.45%, and 83.33% for benign lymphadenopathy, and 0.88, 90.47%, and 85.71% for lymphoma. The accuracies of the radiologists' diagnoses were 62.68% and 62.68%. The diagnostic performance of the model was significantly different from that of the radiologists (p < 0.05). CONCLUSION CT-based deep learning combined with the traditional radiomics methods has a high diagnostic value for the classification of cervical enlarged lymph nodes.
Collapse
Affiliation(s)
- Wentao Zhang
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - Jian Peng
- The Center for Clinical Molecular Medical Detection, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - Shan Zhao
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - Wenli Wu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - Junjun Yang
- Key Laboratory of Optoelectronic Technology, The Ministry of Education, Chongqing University, Chongqing, 400044, China
| | - Junyong Ye
- Key Laboratory of Optoelectronic Technology, The Ministry of Education, Chongqing University, Chongqing, 400044, China
| | - Shengsheng Xu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China.
| |
Collapse
|
23
|
Tong Y, Zhang J, Wei Y, Yu J, Zhan W, Xia H, Zhou S, Wang Y, Chang C. Ultrasound-based radiomics analysis for preoperative prediction of central and lateral cervical lymph node metastasis in papillary thyroid carcinoma: a multi-institutional study. BMC Med Imaging 2022; 22:82. [PMID: 35501717 PMCID: PMC9059387 DOI: 10.1186/s12880-022-00809-2] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Accepted: 04/20/2022] [Indexed: 12/12/2022] Open
Abstract
Background An accurate preoperative assessment of cervical lymph node metastasis (LNM) is important for choosing an optimal therapeutic strategy for papillary thyroid carcinoma (PTC) patients. This study aimed to develop and validate two ultrasound (US) nomograms for the individual prediction of central and lateral compartment LNM in patients with PTC. Methods A total of 720 PTC patients from 3 institutions were enrolled in this study. They were categorized into a primary cohort, an internal validation, and two external validation cohorts. Radiomics features were extracted from conventional US images. LASSO regression was used to select optimized features to construct the radiomics signature. Two nomograms integrating independent clinical variables and radiomics signature were established with multivariate logistic regression. The performance of the nomograms was assessed with regard to discrimination, calibration, and clinical usefulness. Results The radiomics scores were significantly higher in patients with central/lateral LNM. A radiomics nomogram indicated good discrimination for central compartment LNM, with an area under the curve (AUC) of 0.875 in the training set, the corresponding value in the validation sets were 0.856, 0.870 and 0.870, respectively. Another nomogram for predicting lateral LNM also demonstrated good performance with an AUC of 0.938 and 0.905 in the training and internal validation cohorts, respectively. The AUC for the two external validation cohorts were 0.881 and 0.903, respectively. The clinical utility of the nomograms was confirmed by the decision curve analysis. Conclusion The nomograms proposed here have favorable performance for preoperatively predicting cervical LNM, hold promise for optimizing the personalized treatment, and might greatly facilitate the decision-making in clinical practice. Supplementary Information The online version contains supplementary material available at 10.1186/s12880-022-00809-2.
Collapse
Affiliation(s)
- Yuyang Tong
- Department of Ultrasound, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, 270 Dong'an Road, Shanghai, 200032, China
| | - Jingwen Zhang
- Department of Ultrasound, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200025, China
| | - Yi Wei
- Department of Ultrasound, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, 270 Dong'an Road, Shanghai, 200032, China
| | - Jinhua Yu
- Department of Electronic Engineering, Fudan University and Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai, Shanghai, 200433, China
| | - Weiwei Zhan
- Department of Ultrasound, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200025, China
| | - Hansheng Xia
- Department of Ultrasound, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China.
| | - Shichong Zhou
- Department of Ultrasound, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, 270 Dong'an Road, Shanghai, 200032, China.
| | - Yuanyuan Wang
- Department of Electronic Engineering, Fudan University and Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai, Shanghai, 200433, China
| | - Cai Chang
- Department of Ultrasound, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, 270 Dong'an Road, Shanghai, 200032, China
| |
Collapse
|
24
|
Demircioğlu A. Evaluation of the dependence of radiomic features on the machine learning model. Insights Imaging 2022; 13:28. [PMID: 35201534 PMCID: PMC8873309 DOI: 10.1186/s13244-022-01170-2] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2021] [Accepted: 02/03/2022] [Indexed: 12/03/2022] Open
Abstract
Background In radiomic studies, several models are often trained with different combinations of feature selection methods and classifiers. The features of the best model are usually considered relevant to the problem, and they represent potential biomarkers. Features selected from statistically similarly performing models are generally not studied. To understand the degree to which the selected features of these statistically similar models differ, 14 publicly available datasets, 8 feature selection methods, and 8 classifiers were used in this retrospective study. For each combination of feature selection and classifier, a model was trained, and its performance was measured with AUC-ROC. The best-performing model was compared to other models using a DeLong test. Models that were statistically similar were compared in terms of their selected features. Results Approximately 57% of all models analyzed were statistically similar to the best-performing model. Feature selection methods were, in general, relatively unstable (0.58; range 0.35–0.84). The features selected by different models varied largely (0.19; range 0.02–0.42), although the selected features themselves were highly correlated (0.71; range 0.4–0.92). Conclusions Feature relevance in radiomics strongly depends on the model used, and statistically similar models will generally identify different features as relevant. Considering features selected by a single model is misleading, and it is often not possible to directly determine whether such features are candidate biomarkers. Supplementary Information The online version contains supplementary material available at 10.1186/s13244-022-01170-2.
Collapse
Affiliation(s)
- Aydin Demircioğlu
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstraße 55, 45157, Essen, Germany.
| |
Collapse
|
25
|
Application of Machine Learning Methods to Improve the Performance of Ultrasound in Head and Neck Oncology: A Literature Review. Cancers (Basel) 2022; 14:cancers14030665. [PMID: 35158932 PMCID: PMC8833587 DOI: 10.3390/cancers14030665] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Revised: 01/19/2022] [Accepted: 01/26/2022] [Indexed: 01/06/2023] Open
Abstract
Simple Summary Ultrasound (US) is a non-invasive imaging method that is routinely utilized in head and neck cancer patients to assess the anatomic extent of tumors, nodal and non-nodal neck masses and for imaging the salivary glands. In this review, we summarize the present evidence on whether the application of machine learning (ML) methods can potentially improve the performance of US in head and neck cancer patients. We found that published clinical literature on ML methods applied to US datasets was limited but showed evidence of improved diagnostic and prognostic performance. However, a majority of these studies were based on retrospective evaluation and conducted at a single center with a limited number of datasets. The conduct of multi-center studies could help better validate the performance of ML-based US radiomics and facilitate the integration of these approaches into routine clinical practice. Abstract Radiomics is a rapidly growing area of research within radiology that involves the extraction and modeling of high-dimensional quantitative imaging features using machine learning/artificial intelligence (ML/AI) methods. In this review, we describe the published clinical evidence on the application of ML methods to improve the performance of ultrasound (US) in head and neck oncology. A systematic search of electronic databases (MEDLINE, PubMed, clinicaltrials.gov) was conducted according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Of 15,080 initial articles identified, 34 studies were selected for in-depth analysis. Twenty-five out of 34 studies (74%) focused on the diagnostic application of US radiomics while 6 (18%) studies focused on response assessment and 3 (8%) studies utilized US radiomics for modeling normal tissue toxicity. Support vector machine (SVM) was the most commonly employed ML method (47%) followed by multivariate logistic regression (24%) and k-nearest neighbor analysis (21%). Only 11/34 (~32%) of the studies included an independent validation set. A majority of studies were retrospective in nature (76%) and based on single-center evaluation (85%) with variable numbers of patients (12–1609) and imaging datasets (32–1624). Despite these limitations, the application of ML methods resulted in improved diagnostic and prognostic performance of US highlighting the potential clinical utility of this approach.
Collapse
|
26
|
Abstract
OBJECTIVES A critical problem in radiomic studies is the high dimensionality of the datasets, which stems from small sample sizes and many generic features extracted from the volume of interest. Therefore, feature selection methods are used, which aim to remove redundant as well as irrelevant features. Because there are many feature selection algorithms, it is key to understand their performance in the context of radiomics. MATERIALS AND METHODS A total of 29 feature selection algorithms and 10 classifiers were evaluated on 10 publicly available radiomic datasets. Feature selection methods were compared for training times, for the stability of the selected features, and for ranking, which measures the pairwise similarity of the methods. In addition, the predictive performance of the algorithms was measured by utilizing the area under the receiver operating characteristic curve of the best-performing classifier. RESULTS Feature selections differed largely in training times as well as stability and similarity. No single method was able to outperform another one consistently in predictive performance. CONCLUSION Our results indicated that simpler methods are more stable than complex ones and do not perform worse in terms of area under the receiver operating characteristic curve. Analysis of variance, least absolute shrinkage and selection operator, and minimum redundancy, maximum relevance ensemble appear to be good choices for radiomic studies in terms of predictive performance, as they outperformed most other feature selection methods.
Collapse
|
27
|
Lai L, Guan Q, Liang Y, Chen J, Liao Y, Xu H, Wei X. A computed tomography-based radiomic nomogram for predicting lymph node metastasis in patients with early-stage papillary thyroid carcinoma. Acta Radiol 2021; 63:1187-1195. [PMID: 34859689 DOI: 10.1177/02841851211054194] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
BACKGROUND Accurate assessment of lymph node metastasis (LNM) is important for the selection of the optimal therapeutic strategy in patients with papillary thyroid carcinoma (PTC). PURPOSE To develop and validate a radiomics nomogram based on computed tomography (CT) for predicting LNM in patients with early-stage PTC. MATERIAL AND METHODS A total of 92 patients with pathologically confirmed PTC were divided into a training cohort (n = 64) and validation cohort (n = 28). Radiomic features of the tumor and peritumoral interstitium were extracted from contrast-enhanced CT images. The radiomic signature was constructed and the radiomic score (Rad-score) was calculated. Combined with the Rad-score and independent clinical factors, a radiomic nomogram was constructed and its performance was assessed by receiver operating characteristic (ROC) curves and calibration plots. The comparison of ROC curves was performed with DeLong's test. RESULTS A combined nomogram model of the thyroid tumor and peritumoral interstitium was constructed based on the Rad-score, tumor location, maximum diameter, and T stage, and it had areas under the ROC curve of 0.956 (95% confidence interval [CI] = 0.913-1.000) and 0.876 (95% CI = 0.741-1.000) in the training and validation cohorts, respectively. Decision curve analysis suggested that the combined nomogram model had better clinical usefulness than the other models. CONCLUSION A CT-based radiomics nomogram incorporating the radiomic signature and the selected clinical predictors can be a reliable approach to preoperatively predict the LNM status in patients with early-stage PTC, which is helpful for treatment decisions and prognosis.
Collapse
Affiliation(s)
- Lisha Lai
- Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong Province, PR China
| | - Qianwen Guan
- Department of Radiology, Huizhou Municipal Central Hospital, Huizhou, PR China
| | - Yingying Liang
- Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong Province, PR China
| | - Junwei Chen
- Department of Radiology, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong Province, PR China
| | | | - Honggang Xu
- Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong Province, PR China
| | - Xinhua Wei
- Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong Province, PR China
| |
Collapse
|
28
|
Demircioğlu A. Measuring the bias of incorrect application of feature selection when using cross-validation in radiomics. Insights Imaging 2021; 12:172. [PMID: 34817740 PMCID: PMC8613324 DOI: 10.1186/s13244-021-01115-1] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Accepted: 10/25/2021] [Indexed: 12/27/2022] Open
Abstract
BACKGROUND Many studies in radiomics are using feature selection methods to identify the most predictive features. At the same time, they employ cross-validation to estimate the performance of the developed models. However, if the feature selection is performed before the cross-validation, data leakage can occur, and the results can be biased. To measure the extent of this bias, we collected ten publicly available radiomics datasets and conducted two experiments. First, the models were developed by incorrectly applying the feature selection prior to cross-validation. Then, the same experiment was conducted by applying feature selection correctly within cross-validation to each fold. The resulting models were then evaluated against each other in terms of AUC-ROC, AUC-F1, and Accuracy. RESULTS Applying the feature selection incorrectly prior to the cross-validation showed a bias of up to 0.15 in AUC-ROC, 0.29 in AUC-F1, and 0.17 in Accuracy. CONCLUSIONS Incorrect application of feature selection and cross-validation can lead to highly biased results for radiomic datasets.
Collapse
Affiliation(s)
- Aydin Demircioğlu
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstr. 55, 45147, Essen, Germany.
| |
Collapse
|
29
|
Machine Learning: Applications and Advanced Progresses of Radiomics in Endocrine Neoplasms. JOURNAL OF ONCOLOGY 2021; 2021:8615450. [PMID: 34671399 PMCID: PMC8523238 DOI: 10.1155/2021/8615450] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Revised: 07/13/2021] [Accepted: 09/20/2021] [Indexed: 12/24/2022]
Abstract
Endocrine neoplasms remain a great threat to human health. It is extremely important to make a clear diagnosis and timely treatment of endocrine tumors. Machine learning includes radiomics, which has long been utilized in clinical cancer research. Radiomics refers to the extraction of valuable information by analyzing a large amount of standard data with high-throughput medical images mainly including computed tomography, positron emission tomography, magnetic resonance imaging, and ultrasound. With the quantitative imaging analysis and model building, radiomics can reflect specific underlying characteristics of a disease that otherwise could not be evaluated visually. More and more promising results of radiomics in oncological practice have been seen in recent years. Radiomics may have the potential to supplement traditional imaging analysis and assist in providing precision medicine for patients. Radiomics had developed rapidly in endocrine neoplasms practice in the past decade. In this review, we would introduce the general workflow of radiomics and summarize the applications and developments of radiomics in endocrine neoplasms in recent years. The limitations of current radiomic research studies and future development directions would also be discussed.
Collapse
|
30
|
Cao Y, Zhong X, Diao W, Mu J, Cheng Y, Jia Z. Radiomics in Differentiated Thyroid Cancer and Nodules: Explorations, Application, and Limitations. Cancers (Basel) 2021; 13:2436. [PMID: 34069887 PMCID: PMC8157383 DOI: 10.3390/cancers13102436] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Revised: 05/13/2021] [Accepted: 05/16/2021] [Indexed: 02/05/2023] Open
Abstract
Radiomics is an emerging technique that allows the quantitative extraction of high-throughput features from single or multiple medical images, which cannot be observed directly with the naked eye, and then applies to machine learning approaches to construct classification or prediction models. This method makes it possible to evaluate tumor status and to differentiate malignant from benign tumors or nodules in a more objective manner. To date, the classification and prediction value of radiomics in DTC patients have been inconsistent. Herein, we summarize the available literature on the classification and prediction performance of radiomics-based DTC in various imaging techniques. More specifically, we reviewed the recent literature to discuss the capacity of radiomics to predict lymph node (LN) metastasis, distant metastasis, tumor extrathyroidal extension, disease-free survival, and B-Raf proto-oncogene serine/threonine kinase (BRAF) mutation and differentiate malignant from benign nodules. This review discusses the application and limitations of the radiomics process, and explores its ability to improve clinical decision-making with the hope of emphasizing its utility for DTC patients.
Collapse
Affiliation(s)
- Yuan Cao
- Department of Nuclear Medicine, West China Hospital of Sichuan University, Chengdu 610040, China; (Y.C.); (X.Z.); (W.D.); (J.M.)
| | - Xiao Zhong
- Department of Nuclear Medicine, West China Hospital of Sichuan University, Chengdu 610040, China; (Y.C.); (X.Z.); (W.D.); (J.M.)
| | - Wei Diao
- Department of Nuclear Medicine, West China Hospital of Sichuan University, Chengdu 610040, China; (Y.C.); (X.Z.); (W.D.); (J.M.)
| | - Jingshi Mu
- Department of Nuclear Medicine, West China Hospital of Sichuan University, Chengdu 610040, China; (Y.C.); (X.Z.); (W.D.); (J.M.)
| | - Yue Cheng
- Department of Radiology, West China Hospital of Sichuan University, Chengdu 610040, China;
| | - Zhiyun Jia
- Department of Nuclear Medicine, West China Hospital of Sichuan University, Chengdu 610040, China; (Y.C.); (X.Z.); (W.D.); (J.M.)
| |
Collapse
|
31
|
Preoperative Prediction of Cervical Nodal Metastasis in Papillary Thyroid Carcinoma: Value of Quantitative Dual-Energy CT Parameters and Qualitative Morphologic Features. AJR Am J Roentgenol 2021; 216:1335-1343. [PMID: 33760651 DOI: 10.2214/ajr.20.23516] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
OBJECTIVE. The purpose of our study was to assess the value of combining quantitative dual-energy CT (DECT) parameters with qualitative morphologic parameters for the preoperative prediction of cervical nodal metastasis from papillary thyroid carcinoma (PTC). MATERIALS AND METHODS. Thirty-five patients with pathologically proven PTC underwent single-phase contrast-enhanced DECT before thyroidectomy and cervical lymphadenectomy. Analyses of quantitative DECT parameters and qualitative morphologic features of metastatic and benign lymph nodes (LNs) were independently performed. The diagnostic performances of using only quantitative parameters, only morphologic features, and their combination for predicting cervical nodal metastasis were statistically calculated with ROC curves and logistic regression models. RESULTS. A total of 206 LNs, 80 metastatic and 126 benign, were included. The best single performer in DECT was the normalized iodine concentration in the venous phase, which had low sensitivity (62.5%) but high specificity (85.7%), for diagnosing metastatic cervical LNs. On the other hand, the best single performer in qualitative morphologic parameters was using the criterion of shortest diameter of greater than 5 mm, which had low specificity (69.8%) but high sensitivity (86.3%). Combining these two parameters improved the AUC, sensitivity, and specificity to 0.846, 86.3%, and 72.2%, respectively. The combination of multiple quantitative DECT parameters and all morphologic data further improved AUC, sensitivity, and specificity to 0.878, 87.5%, and 73.8%, respectively, which was significant compared with the use of any single parameter. CONCLUSION. The combination of quantitative DECT parameters with morphologic data improves performance in the preoperative diagnosis of metastatic cervical LNs in patients with PTC.
Collapse
|
32
|
Masuda T, Nakaura T, Funama Y, Sugino K, Sato T, Yoshiura T, Baba Y, Awai K. Machine learning to identify lymph node metastasis from thyroid cancer in patients undergoing contrast-enhanced CT studies. Radiography (Lond) 2021; 27:920-926. [PMID: 33762147 DOI: 10.1016/j.radi.2021.03.001] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2020] [Revised: 02/14/2021] [Accepted: 03/02/2021] [Indexed: 02/07/2023]
Abstract
INTRODUCTION We compared the diagnostic performance of morphological methods such as the major axis, the minor axis, the volume and sphericity and of machine learning with texture analysis in the identification of lymph node metastasis in patients with thyroid cancer who had undergone contrast-enhanced CT studies. METHODS We sampled 772 lymph nodes with histology defined tissue types (84 metastatic and 688 benign lymph nodes) that were visualised on CT images of 117 patients. A support vector machine (SVM), free programming software (Python), and the scikit-learn machine learning library were used to discriminate metastatic-from benign lymph nodes. We assessed 96 texture and 4 morphological features (major axis, minor axis, volume, sphericity) that were reported useful for the differentiation between metastatic and benign lymph nodes on CT images. The area under the curve (AUC) obtained by receiver operating characteristic analysis of univariate logistic regression and SVM classifiers were calculated for the training and testing datasets. RESULTS The AUC for all classifiers in training and testing datasets was 0.96 and 0.86, at the SVM for machine learning. When we applied conventional methods to the training and testing datasets, the AUCs were 0.63 and 0.48 for the major axis, 0.70 and 0.44 for the minor axis, 0.66 and 0.43 for the volume, and 0.69 and 0.54 for sphericity, respectively. The SVM using texture features yielded significantly higher AUCs than univariate logistic regression models using morphological features (p = 0.001). CONCLUSION For the identification of metastatic lymph nodes from thyroid cancer on contrast-enhanced CT images, machine learning combined with texture analysis was superior to conventional diagnostic methods with the morphological parameters. IMPLICATIONS FOR PRACTICE Our findings suggest that in patients with thyroid cancer and suspected lymph node metastasis who undergo contrast-enhanced CT studies, machine learning using texture analysis is high diagnostic value for the identification of metastatic lymph nodes.
Collapse
Affiliation(s)
- T Masuda
- Department of Radiological Technology, Tsuchiya General Hospital, Nakajima-cho 3-30, Naka-ku, Hiroshima 730-8655, Japan; Department of Diagnostic Radiology, Graduate School of Biomedical Sciences, Hiroshima University, Hiroshima, Japan.
| | - T Nakaura
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1 Honjo, Kumamoto 860-8556, Japan
| | - Y Funama
- Department of Medical Physics, Faculty of Life Sciences, Kumamoto University, Kumamoto, Japan
| | - K Sugino
- Department of Surgery, Tsuchiya General Hospital, Nakajima-cho 3-30, Naka-ku, Hiroshima 730-8655, Japan
| | - T Sato
- Department of Diagnostic Radiology, Tsuchiya General Hospital, Nakajima-cho 3-30, Naka-ku, Hiroshima 730-8655, Japan
| | - T Yoshiura
- Department of Radiological Technology, Tsuchiya General Hospital, Nakajima-cho 3-30, Naka-ku, Hiroshima 730-8655, Japan
| | - Y Baba
- Saitama Medical University International Medical Center, 1397-1, Yamane, Hidaka-City, Saitama-Pref 350-1298, Japan
| | - K Awai
- Department of Diagnostic Radiology, Graduate School of Biomedical Sciences, Hiroshima University, Hiroshima, Japan
| |
Collapse
|
33
|
Yoon J, Lee E, Kang SW, Han K, Park VY, Kwak JY. Implications of US radiomics signature for predicting malignancy in thyroid nodules with indeterminate cytology. Eur Radiol 2021; 31:5059-5067. [PMID: 33459858 DOI: 10.1007/s00330-020-07670-3] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2020] [Revised: 11/16/2020] [Accepted: 12/23/2020] [Indexed: 12/13/2022]
Abstract
OBJECTIVES The purpose of this study was to evaluate the role of the radiomics score using US images to predict malignancy in AUS/FLUS and FN/SFN nodules. METHODS One hundred fifty-five indeterminate thyroid nodules in 154 patients who received initial US-guided FNA for diagnostic purposes were included in this retrospective study. A representative US image of each tumor was acquired, and square ROIs covering the whole nodule were drawn using the Paint program of Windows 7. Texture features were extracted by in-house texture analysis algorithms implemented in MATLAB 2019b. The LASSO logistic regression model was used to choose the most useful predictive features, and ten-fold cross-validation was performed. Two prediction models were constructed using multivariable logistic regression analysis: one based on clinical variables, and the other based on clinical variables with the radiomics score. Predictability of the two models was assessed with the AUC of the ROC curves. RESULTS Clinical characteristics did not significantly differ between malignant and benign nodules, except for mean nodule size. Among 730 candidate texture features generated from a single US image, 15 features were selected. Radiomics signatures were constructed with a radiomics score, using selected features. In multivariable logistic regression analysis, higher radiomics score was associated with malignancy (OR = 10.923; p < 0.001). The AUC of the malignancy prediction model composed of clinical variables with the radiomics score was significantly higher than the model composed of clinical variables alone (0.839 vs 0.583). CONCLUSIONS Quantitative US radiomics features can help predict malignancy in thyroid nodules with indeterminate cytology.
Collapse
Affiliation(s)
- Jiyoung Yoon
- Department of Radiology, Research Institute of Radiological Science, and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, South Korea
| | - Eunjung Lee
- Department of Computational Science and Engineering, Yonsei University, Seoul, South Korea
| | - Sang-Wook Kang
- Department of Surgery, Yonsei University, College of Medicine, Seoul, South Korea
| | - Kyunghwa Han
- Department of Radiology, Research Institute of Radiological Science, and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, South Korea
| | - Vivian Youngjean Park
- Department of Radiology, Research Institute of Radiological Science, and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, South Korea
| | - Jin Young Kwak
- Department of Radiology, Research Institute of Radiological Science, and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, South Korea.
| |
Collapse
|
34
|
Czarniecka A, Zeman M, Wozniak G, Maciejewski A, Stobiecka E, Chmielik E, Oczko-Wojciechowska M, Krajewska J, Handkiewicz-Junak D, Jarzab B. Therapeutic Strategy in Low-Risk Papillary Thyroid Carcinoma - Long-Term Results of the First Single-Center Prospective Non-Randomized Trial Between 2011 and 2015. Front Endocrinol (Lausanne) 2021; 12:718833. [PMID: 34552559 PMCID: PMC8450606 DOI: 10.3389/fendo.2021.718833] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Accepted: 08/18/2021] [Indexed: 11/13/2022] Open
Abstract
UNLABELLED Optimal therapeutic strategy in low advanced papillary thyroid carcinoma (PTC) is still a matter of debate. The management differs depending on the country. A prospective non-randomized study was performed to evaluate whether less extensive surgery could be a safe, acceptable, and sufficient therapeutic option in PTC cT1N0M0 patients. The present paper summarizes the results of over a 5-year follow-up. MATERIAL Our prospective group (PG) treated between 2011 and 2015 consisted of 139 patients with cT1aN0M0 PTC who underwent lobectomy (LT) as initial surgical treatment (PGcT1aN0M0 group) and 102 cT1bN0M0 patients in whom total thyroidectomy (TT) with unilateral central neck dissection (CND) was performed (PGcT1bN0M0). PG was compared with the retrospective group (RG) of patients who underwent TT with bilateral CND between 2004 and 2006: 103 cT1aN0M0 patients (RGcT1aN0M0) and 91cT1bN0M0 (RGcT1bN0M0). The risks of reoperation, cancer relapse and postoperative complications were analyzed. RESULTS Only 12 cT1aN0M0 patients (7.6%) withdrew from the trial and underwent TT with bilateral CND. Over 90% of patients accepted less extensive surgery. In 4 cT1aN0M0 cases, TT with CND was performed due to lymph node metastases found intraoperatively. The initial clinical stage according to the TNM/AJCC 7th edition was confirmed histologically in 77% of cases in PGT1aN0M0 and in 72% in PGT1bN0M0, respectively. 24 PGcT1aN0M0 patients were reoperated on. In this group, cancer lesions in the postoperative histological specimens were found in 8 cases (32%). Five-year disease-free survival (DFS) was excellent. However, no statistically significant differences were found between PG and RG groups (99.3% in PGcT1aN0M0 and 99.0%, in RGcT1aN0M0; p = 0.41 and 98%, in PGcT1bN0M0 and 94.4% in RGcT1bN0M0; p=0.19). No significant differences were observed in the incidence of early paresis of the recurrent laryngeal nerves between PG and RG. However, as predicted, LT completely eliminated the risk of postoperative hypoparathyroidism. SUMMARY The results of the prospective clinical trial confirm that less extensive surgery in adequately selected low-advanced PTC patients is both safe and sufficient.
Collapse
Affiliation(s)
- Agnieszka Czarniecka
- The Oncologic and Reconstructive Surgery Clinic, M. Sklodowska-Curie National Research, Institute of Oncology Gliwice Branch, Gliwice, Poland
- *Correspondence: Agnieszka Czarniecka,
| | - Marcin Zeman
- The Oncologic and Reconstructive Surgery Clinic, M. Sklodowska-Curie National Research, Institute of Oncology Gliwice Branch, Gliwice, Poland
| | - Grzegorz Wozniak
- The Oncologic and Reconstructive Surgery Clinic, M. Sklodowska-Curie National Research, Institute of Oncology Gliwice Branch, Gliwice, Poland
| | - Adam Maciejewski
- The Oncologic and Reconstructive Surgery Clinic, M. Sklodowska-Curie National Research, Institute of Oncology Gliwice Branch, Gliwice, Poland
| | - Ewa Stobiecka
- Tumor Pathology Department, M. Sklodowska-Curie National Research Institute of Oncology Gliwice Branch, Gliwice, Poland
| | - Ewa Chmielik
- Tumor Pathology Department, M. Sklodowska-Curie National Research Institute of Oncology Gliwice Branch, Gliwice, Poland
| | - Malgorzata Oczko-Wojciechowska
- Genetic and Molecular Diagnostics of Cancer Department, M. Sklodowska-Curie National Research Institute of Oncology Gliwice Branch, Gliwice, Poland
| | - Jolanta Krajewska
- Department of Nuclear Medicine and Endocrine Oncology, M. Sklodowska-Curie National Research Institute of Oncology Gliwice Branch, Gliwice, Poland
| | - Daria Handkiewicz-Junak
- Department of Nuclear Medicine and Endocrine Oncology, M. Sklodowska-Curie National Research Institute of Oncology Gliwice Branch, Gliwice, Poland
| | - Barbara Jarzab
- Department of Nuclear Medicine and Endocrine Oncology, M. Sklodowska-Curie National Research Institute of Oncology Gliwice Branch, Gliwice, Poland
| |
Collapse
|
35
|
Peng Z, Wang Y, Wang Y, Jiang S, Fan R, Zhang H, Jiang W. Application of radiomics and machine learning in head and neck cancers. Int J Biol Sci 2021; 17:475-486. [PMID: 33613106 PMCID: PMC7893590 DOI: 10.7150/ijbs.55716] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Accepted: 12/10/2020] [Indexed: 02/07/2023] Open
Abstract
With the continuous development of medical image informatics technology, more and more high-throughput quantitative data could be extracted from digital medical images, which has resulted in a new kind of omics-Radiomics. In recent years, in addition to genomics, proteomics and metabolomics, radiomic has attracted the interest of more and more researchers. Compared to other omics, radiomics can be perfectly integrated with clinical data, even with the pathology and molecular biomarker, so that the study can be closer to the clinical reality and more revealing of the tumor development. Mass data will also be generated in this process. Machine learning, due to its own characteristics, has a unique advantage in processing massive radiomic data. By analyzing mass amounts of data with strong clinical relevance, people can construct models that more accurately reflect tumor development and progression, thereby providing the possibility of personalized and sequential treatment of patients. As one of the cancer types whose treatment and diagnosis rely on imaging examination, radiomics has a very broad application prospect in head and neck cancers (HNC). Until now, there have been some notable results in HNC. In this review, we will introduce the concepts and workflow of radiomics and machine learning and their current applications in head and neck cancers, as well as the directions and applications of artificial intelligence in the treatment and diagnosis of HNC.
Collapse
Affiliation(s)
- Zhouying Peng
- Department of Otolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha 410078, Hunan, China
| | - Yumin Wang
- Department of Otolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha 410078, Hunan, China
| | - Yaxuan Wang
- Department of Otolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha 410078, Hunan, China
| | - Sijie Jiang
- Department of Otolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha 410078, Hunan, China
| | - Ruohao Fan
- Department of Otolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha 410078, Hunan, China
| | - Hua Zhang
- Department of Otolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha 410078, Hunan, China
| | - Weihong Jiang
- Department of Otolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha 410078, Hunan, China
| |
Collapse
|
36
|
Zhou SC, Liu TT, Zhou J, Huang YX, Guo Y, Yu JH, Wang YY, Chang C. An Ultrasound Radiomics Nomogram for Preoperative Prediction of Central Neck Lymph Node Metastasis in Papillary Thyroid Carcinoma. Front Oncol 2020; 10:1591. [PMID: 33014810 PMCID: PMC7498535 DOI: 10.3389/fonc.2020.01591] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2019] [Accepted: 07/23/2020] [Indexed: 12/24/2022] Open
Abstract
Purpose: This study aimed to establish and validate an ultrasound radiomics nomogram for the preoperative prediction of central lymph node (LN) metastasis in patients with papillary thyroid carcinoma (PTC). Patients and Methods: The prediction model was developed in 609 patients with clinicopathologically confirmed unifocal PTC who received ultrasonography between Jan 2018 and June 2018. Radiomic features were extracted after the ultrasonography of PTC. Lasso regression model was used for data dimensionality reduction, feature selection, and radiomics signature building. The predicting model was established based on the multivariable logistic regression analysis in which the radiomics signature, ultrasonography-reported LN status, and independent clinicopathologic risk factors were incorporated, and finally a radiomics nomogram was established. The performance of the nomogram was assessed with respect to the discrimination and consistence. An independent validation was performed in 326 consecutive patients from July 2018 to Sep 2018. Results: The radiomics signature consisted of 23 selected features and was significantly associated with LN status in both primary and validation cohorts. The independent predictors in the radiomics nomogram included the radiomics signature, age, TG level, TPOAB level, and ultrasonography-reported LN status. The model showed good discrimination and consistence in both cohorts: C-index of 0.816 (95% CI, 0.808–0.824) in the primary cohort and 0.858 (95% CI, 0.849–0.867) in the validation cohort. The area under receiver operating curve was 0.858. In the validation cohort, the accuracy, sensitivity, specificity and AUC of this model were 0.812, 0.816, 0.810, and 0.858 (95% CI, 0.785–0.930), respectively. Decision curve analysis indicated the radiomics nomogram was clinically useful. Conclusion: This study presents a convenient, clinically useful ultrasound radiomics nomogram that can be used for the pre-operative individualized prediction of central LN metastasis in patients with PTC.
Collapse
Affiliation(s)
- Shi-Chong Zhou
- Department of Ultrasonography, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Tong-Tong Liu
- Department of Electronic Engineering, Fudan University, Shanghai, China.,Key Laboratory of Medical Imaging, Computing and Computer-Assisted Intervention, Shanghai, China
| | - Jin Zhou
- Department of Ultrasonography, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yun-Xia Huang
- Department of Ultrasonography, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yi Guo
- Department of Electronic Engineering, Fudan University, Shanghai, China.,Key Laboratory of Medical Imaging, Computing and Computer-Assisted Intervention, Shanghai, China
| | - Jin-Hua Yu
- Department of Electronic Engineering, Fudan University, Shanghai, China.,Key Laboratory of Medical Imaging, Computing and Computer-Assisted Intervention, Shanghai, China
| | - Yuan-Yuan Wang
- Department of Electronic Engineering, Fudan University, Shanghai, China.,Key Laboratory of Medical Imaging, Computing and Computer-Assisted Intervention, Shanghai, China
| | - Cai Chang
- Department of Ultrasonography, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| |
Collapse
|