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Li W, Li Y, Gao S, Huang N, Kojima I, Kusama T, Ou Y, Iikubo M, Niu X. Integrating lipid metabolite analysis with MRI-based transformer and radiomics for early and late stage prediction of oral squamous cell carcinoma. BMC Cancer 2024; 24:795. [PMID: 38961418 PMCID: PMC11221018 DOI: 10.1186/s12885-024-12533-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Accepted: 06/18/2024] [Indexed: 07/05/2024] Open
Abstract
BACKGROUND Oral Squamous Cell Carcinoma (OSCC) presents significant diagnostic challenges in its early and late stages. This study aims to utilize preoperative MRI and biochemical indicators of OSCC patients to predict the stage of tumors. METHODS This study involved 198 patients from two medical centers. A detailed analysis of contrast-enhanced T1-weighted (ceT1W) and T2-weighted (T2W) MRI were conducted, integrating these with biochemical indicators for a comprehensive evaluation. Initially, 42 clinical biochemical indicators were selected for consideration. Through univariate analysis and multivariate analysis, only those indicators with p-values less than 0.05 were retained for model development. To extract imaging features, machine learning algorithms in conjunction with Vision Transformer (ViT) techniques were utilized. These features were integrated with biochemical indicators for predictive modeling. The performance of model was evaluated using the Receiver Operating Characteristic (ROC) curve. RESULTS After rigorously screening biochemical indicators, four key markers were selected for the model: cholesterol, triglyceride, very low-density lipoprotein cholesterol and chloride. The model, developed using radiomics and deep learning for feature extraction from ceT1W and T2W images, showed a lower Area Under the Curve (AUC) of 0.85 in the validation cohort when using these imaging modalities alone. However, integrating these biochemical indicators improved the model's performance, increasing the validation cohort AUC to 0.87. CONCLUSION In this study, the performance of the model significantly improved following multimodal fusion, outperforming the single-modality approach. CLINICAL RELEVANCE STATEMENT This integration of radiomics, ViT models, and lipid metabolite analysis, presents a promising non-invasive technique for predicting the staging of OSCC.
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Affiliation(s)
- Wen Li
- Department of Oral and Maxillofacial Surgery, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Yang Li
- School and Hospital of Stomatology, Fujian Medical University, Fuzhou, China
- Department of Oral Diagnosis, Tohoku University Graduate School of Dentistry, Sendai, Japan
| | - Shiyu Gao
- School of Mathematics and Statistics, Huazhong University of Science and Technology, Wuhan, China
| | - Nengwen Huang
- School and Hospital of Stomatology, Fujian Medical University, Fuzhou, China
| | - Ikuho Kojima
- Department of Oral Diagnosis, Tohoku University Graduate School of Dentistry, Sendai, Japan
| | - Taro Kusama
- Department of Oral Diagnosis, Tohoku University Graduate School of Dentistry, Sendai, Japan
| | - Yanjing Ou
- School and Hospital of Stomatology, Fujian Medical University, Fuzhou, China
| | - Masahiro Iikubo
- Department of Oral Diagnosis, Tohoku University Graduate School of Dentistry, Sendai, Japan.
| | - Xuegang Niu
- Department of Neurosurgey, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, China.
- Department of Neurosurgey, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China.
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Corti A, Cavalieri S, Calareso G, Mattavelli D, Ravanelli M, Poli T, Licitra L, Corino VDA, Mainardi L. MRI radiomics in head and neck cancer from reproducibility to combined approaches. Sci Rep 2024; 14:9451. [PMID: 38658630 PMCID: PMC11043398 DOI: 10.1038/s41598-024-60009-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2023] [Accepted: 04/17/2024] [Indexed: 04/26/2024] Open
Abstract
The clinical applicability of radiomics in oncology depends on its transferability to real-world settings. However, the absence of standardized radiomics pipelines combined with methodological variability and insufficient reporting may hamper the reproducibility of radiomic analyses, impeding its translation to clinics. This study aimed to identify and replicate published, reproducible radiomic signatures based on magnetic resonance imaging (MRI), for prognosis of overall survival in head and neck squamous cell carcinoma (HNSCC) patients. Seven signatures were identified and reproduced on 58 HNSCC patients from the DB2Decide Project. The analysis focused on: assessing the signatures' reproducibility and replicating them by addressing the insufficient reporting; evaluating their relationship and performances; and proposing a cluster-based approach to combine radiomic signatures, enhancing the prognostic performance. The analysis revealed key insights: (1) despite the signatures were based on different features, high correlations among signatures and features suggested consistency in the description of lesion properties; (2) although the uncertainties in reproducing the signatures, they exhibited a moderate prognostic capability on an external dataset; (3) clustering approaches improved prognostic performance compared to individual signatures. Thus, transparent methodology not only facilitates replication on external datasets but also advances the field, refining prognostic models for potential personalized medicine applications.
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Affiliation(s)
- Anna Corti
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Via Ponzio 34/5, 20133, Milan, Italy.
| | - Stefano Cavalieri
- Head and Neck Medical Oncology Department, Fondazione IRCCS, Istituto Nazionale dei Tumori, Milan, Italy
- Department of Oncology and Hemato-Oncology, Università degli studi di Milano, Milan, Italy
| | - Giuseppina Calareso
- Radiology Department, Fondazione IRCCS, Istituto Nazionale dei Tumori, Milan, Italy
| | - Davide Mattavelli
- Unit of Otorhinolaryngology-Head and Neck Surgery, Department of Medical and Surgical Specialties, Radiological Sciences, and Public Health, ASST Spedali Civili of Brescia, University of Brescia, Brescia, Italy
| | - Marco Ravanelli
- Unit of Radiology, Department of Medical and Surgical Specialties, Radiological Sciences, and Public Health, ASST Spedali Civili of Brescia, University of Brescia, Brescia, Italy
| | - Tito Poli
- Maxillo-Facial Surgery Division, Head and Neck Department, University Hospital of Parma, Parma, Italy
| | - Lisa Licitra
- Head and Neck Medical Oncology Department, Fondazione IRCCS, Istituto Nazionale dei Tumori, Milan, Italy
- Department of Oncology and Hemato-Oncology, Università degli studi di Milano, Milan, Italy
| | - Valentina D A Corino
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Via Ponzio 34/5, 20133, Milan, Italy
- Cardiotech Lab, Centro Cardiologico Monzino IRCCS, Milan, Italy
| | - Luca Mainardi
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Via Ponzio 34/5, 20133, Milan, Italy
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3
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Chen W, Lin G, Chen Y, Cheng F, Li X, Ding J, Zhong Y, Kong C, Chen M, Xia S, Lu C, Ji J. Prediction of the Ki-67 expression level in head and neck squamous cell carcinoma with machine learning-based multiparametric MRI radiomics: a multicenter study. BMC Cancer 2024; 24:418. [PMID: 38580939 PMCID: PMC10996101 DOI: 10.1186/s12885-024-12026-x] [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: 12/03/2023] [Accepted: 02/20/2024] [Indexed: 04/07/2024] Open
Abstract
BACKGROUND This study aimed to develop and validate a machine learning (ML)-based fusion model to preoperatively predict Ki-67 expression levels in patients with head and neck squamous cell carcinoma (HNSCC) using multiparametric magnetic resonance imaging (MRI). METHODS A total of 351 patients with pathologically proven HNSCC from two medical centers were retrospectively enrolled in the study and divided into training (n = 196), internal validation (n = 84), and external validation (n = 71) cohorts. Radiomics features were extracted from T2-weighted images and contrast-enhanced T1-weighted images and screened. Seven ML classifiers, including k-nearest neighbors (KNN), support vector machine (SVM), logistic regression (LR), random forest (RF), linear discriminant analysis (LDA), naive Bayes (NB), and eXtreme Gradient Boosting (XGBoost) were trained. The best classifier was used to calculate radiomics (Rad)-scores and combine clinical factors to construct a fusion model. Performance was evaluated based on calibration, discrimination, reclassification, and clinical utility. RESULTS Thirteen features combining multiparametric MRI were finally selected. The SVM classifier showed the best performance, with the highest average area under the curve (AUC) of 0.851 in the validation cohorts. The fusion model incorporating SVM-based Rad-scores with clinical T stage and MR-reported lymph node status achieved encouraging predictive performance in the training (AUC = 0.916), internal validation (AUC = 0.903), and external validation (AUC = 0.885) cohorts. Furthermore, the fusion model showed better clinical benefit and higher classification accuracy than the clinical model. CONCLUSIONS The ML-based fusion model based on multiparametric MRI exhibited promise for predicting Ki-67 expression levels in HNSCC patients, which might be helpful for prognosis evaluation and clinical decision-making.
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Affiliation(s)
- Weiyue Chen
- Zhejiang Key Laboratory of Imaging and Interventional Medicine, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, 323000, China
- Clinical College of The Affiliated Central Hospital, School of Medicine, Lishui University, Lishui, 323000, China
| | - Guihan Lin
- Zhejiang Key Laboratory of Imaging and Interventional Medicine, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, 323000, China
- Clinical College of The Affiliated Central Hospital, School of Medicine, Lishui University, Lishui, 323000, China
| | - Yongjun Chen
- Department of Radiology, The Sixth Affiliated Hospital of Wenzhou Medical University, Lishui, 323000, China
| | - Feng Cheng
- Zhejiang Key Laboratory of Imaging and Interventional Medicine, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, 323000, China
- Department of Head and Neck Surgery, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, 323000, China
| | - Xia Li
- Zhejiang Key Laboratory of Imaging and Interventional Medicine, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, 323000, China
- Clinical College of The Affiliated Central Hospital, School of Medicine, Lishui University, Lishui, 323000, China
| | - Jiayi Ding
- Zhejiang Key Laboratory of Imaging and Interventional Medicine, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, 323000, China
- Clinical College of The Affiliated Central Hospital, School of Medicine, Lishui University, Lishui, 323000, China
| | - Yi Zhong
- Zhejiang Key Laboratory of Imaging and Interventional Medicine, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, 323000, China
- Clinical College of The Affiliated Central Hospital, School of Medicine, Lishui University, Lishui, 323000, China
| | - Chunli Kong
- Zhejiang Key Laboratory of Imaging and Interventional Medicine, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, 323000, China
- Clinical College of The Affiliated Central Hospital, School of Medicine, Lishui University, Lishui, 323000, China
| | - Minjiang Chen
- Zhejiang Key Laboratory of Imaging and Interventional Medicine, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, 323000, China
- Clinical College of The Affiliated Central Hospital, School of Medicine, Lishui University, Lishui, 323000, China
| | - Shuiwei Xia
- Zhejiang Key Laboratory of Imaging and Interventional Medicine, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, 323000, China
- Clinical College of The Affiliated Central Hospital, School of Medicine, Lishui University, Lishui, 323000, China
| | - Chenying Lu
- Zhejiang Key Laboratory of Imaging and Interventional Medicine, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, 323000, China.
- Clinical College of The Affiliated Central Hospital, School of Medicine, Lishui University, Lishui, 323000, China.
| | - Jiansong Ji
- Zhejiang Key Laboratory of Imaging and Interventional Medicine, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, 323000, China.
- Clinical College of The Affiliated Central Hospital, School of Medicine, Lishui University, Lishui, 323000, China.
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Qi M, Sha Y, Zhang D, Ren J. An MRI-based radiomics nomogram for detecting cervical esophagus invasion in hypopharyngeal squamous cell carcinoma. Cancer Imaging 2023; 23:120. [PMID: 38102719 PMCID: PMC10724942 DOI: 10.1186/s40644-023-00642-y] [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: 07/03/2023] [Accepted: 12/04/2023] [Indexed: 12/17/2023] Open
Abstract
BACKGROUND Accurate detection of cervical esophagus invasion (CEI) in HPSCC is challenging but crucial. We aimed to investigate the value of magnetic resonance imaging (MRI)-based radiomics for detecting CEI in patients with HPSCC. METHODS This retrospective study included 151 HPSCC patients with or without CEI, which were randomly assigned into a training (n = 101) or validation (n = 50) cohort. A total of 750 radiomics features were extracted from T2-weighted imaging (T2WI) and contrast-enhanced T1-weighted imaging (ceT1WI), respectively. A radiomics signature was constructed using the least absolute shrinkage and selection operator method. Multivariable logistic regression analyses were adopted to establish a clinical model and a radiomics nomogram. Two experienced radiologists evaluated the CEI status based on morphological findings. Areas under the curve (AUCs) of the models and readers were compared using the DeLong method. The performance of the nomogram was also assessed by its calibration and clinical usefulness. RESULTS The radiomics signature, consisting of five T2WI and six ceT1WI radiomics features, was significantly associated with CEI in both cohorts (all p < 0.001). The radiomics nomogram combining the radiomics signature and clinical T stage achieved significantly higher predictive value than the clinical model and pooled readers in the training (AUC 0.923 vs. 0.723 and 0.621, all p < 0.001) and validation (AUC 0.888 vs. 0.754 and 0.647, all p < 0.05) cohorts. The radiomics nomogram showed favorable calibration in both cohorts and provided better net benefit than the clinical model. CONCLUSIONS The MRI-based radiomics nomogram is a promising method for detecting CEI in HPSCC.
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Affiliation(s)
- Meng Qi
- Department of Radiology, Eye & ENT Hospital, Fudan University, No.83 Fenyang Road, Shanghai, 200030, China
| | - Yan Sha
- Department of Radiology, Eye & ENT Hospital, Fudan University, No.83 Fenyang Road, Shanghai, 200030, China
| | - Duo Zhang
- Department of Otolaryngology-HNS, Eye & ENT Hospital, Fudan University, No.83 Fenyang Road, Shanghai, 200030, China.
| | - Jiliang Ren
- Department of Radiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, No.639 Zhizaoju Road, Shanghai, 200010, China.
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5
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Fujima N, Kamagata K, Ueda D, Fujita S, Fushimi Y, Yanagawa M, Ito R, Tsuboyama T, Kawamura M, Nakaura T, Yamada A, Nozaki T, Fujioka T, Matsui Y, Hirata K, Tatsugami F, Naganawa S. Current State of Artificial Intelligence in Clinical Applications for Head and Neck MR Imaging. Magn Reson Med Sci 2023; 22:401-414. [PMID: 37532584 PMCID: PMC10552661 DOI: 10.2463/mrms.rev.2023-0047] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Accepted: 07/09/2023] [Indexed: 08/04/2023] Open
Abstract
Due primarily to the excellent soft tissue contrast depictions provided by MRI, the widespread application of head and neck MRI in clinical practice serves to assess various diseases. Artificial intelligence (AI)-based methodologies, particularly deep learning analyses using convolutional neural networks, have recently gained global recognition and have been extensively investigated in clinical research for their applicability across a range of categories within medical imaging, including head and neck MRI. Analytical approaches using AI have shown potential for addressing the clinical limitations associated with head and neck MRI. In this review, we focus primarily on the technical advancements in deep-learning-based methodologies and their clinical utility within the field of head and neck MRI, encompassing aspects such as image acquisition and reconstruction, lesion segmentation, disease classification and diagnosis, and prognostic prediction for patients presenting with head and neck diseases. We then discuss the limitations of current deep-learning-based approaches and offer insights regarding future challenges in this field.
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Affiliation(s)
- Noriyuki Fujima
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, Sapporo, Hokkaido, Japan
| | - Koji Kamagata
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Daiju Ueda
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University, Osaka, Osaka, Japan
| | - Shohei Fujita
- Department of Radiology, University of Tokyo, Tokyo, Japan
| | - Yasutaka Fushimi
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Kyoto, Japan
| | - Masahiro Yanagawa
- Department of Radiology, Osaka University Graduate School of Medicine, Suita, Osaka, Japan
| | - Rintaro Ito
- Department of Radiology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Takahiro Tsuboyama
- Department of Radiology, Osaka University Graduate School of Medicine, Suita, Osaka, Japan
| | - Mariko Kawamura
- Department of Radiology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Takeshi Nakaura
- Department of Diagnostic Radiology, Kumamoto University Graduate School of Medicine, Kumamoto, Kumamoto, Japan
| | - Akira Yamada
- Department of Radiology, Shinshu University School of Medicine, Matsumoto, Nagano, Japan
| | - Taiki Nozaki
- Department of Radiology, Keio University School of Medicine, Tokyo, Japan
| | - Tomoyuki Fujioka
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, Tokyo, Japan
| | - Yusuke Matsui
- Department of Radiology, Faculty of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama, Okayama, Japan
| | - Kenji Hirata
- Department of Diagnostic Imaging, Graduate School of Medicine, Hokkaido University, Sapporo, Hokkaido, Japan
| | - Fuminari Tatsugami
- Department of Diagnostic Radiology, Hiroshima University, Hiroshima, Hiroshima, Japan
| | - Shinji Naganawa
- Department of Radiology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
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Vijayalakshmi KR, Jain V. Accuracy of magnetic resonance imaging in the assessment of depth of invasion in tongue carcinoma: A systematic review and meta-analysis. Natl J Maxillofac Surg 2023; 14:341-353. [PMID: 38273911 PMCID: PMC10806321 DOI: 10.4103/njms.njms_174_22] [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: 10/02/2022] [Revised: 03/19/2023] [Accepted: 03/27/2023] [Indexed: 01/27/2024] Open
Abstract
Tongue carcinoma constitutes 10.4-46.9% of all oral squamous cell carcinomas (OSCCs) and is notoriously known for invading tissues deeper than the evident gross margins. The deeper the tumor invades, the higher are its chances of future morbidity and mortality due to extensive neck dissection and risk of recurrence. Magnetic resonance imaging (MRI) is a noninvasive diagnostic aid used for measuring a preoperative tumor's depth of invasion (DOI) as it can efficiently outline soft tissue tumors from adjacent normal tissue. To assess various MRI modalities used in measuring DOI in tongue carcinoma and their reliability compared with other DOI measuring modalities. The protocol was registered in the International Prospective Register of Systematic Reviews (PROSPERO) database (CRD42022330866), and the following Preferred Reporting Items for a Systematic Review and Meta-Analysis (PRISMA) Diagnostic Test Accuracy guidelines were performed. PubMed electronic database was searched using a combination of keywords for relevant articles in the English language since 2016. Critical appraisal was carried out using the Quality Assessment of Diagnostic Accuracy Studies-Comparative (QUADAS-C) risk-of-bias (RoB) assessment tool. A weighted mean difference (WMD) was calculated between MRI and histopathological DOI along with pooled correlation and subgroup analysis, where possible. A total of 795 records were retrieved of which 17 were included in the final review with 13 included for meta-analysis. A high RoB was found for most studies for all parameters except flow and timing. WMD showed a statistically significant MRI overestimation of 1.90 mm compared with histopathology. Subgroup analysis showed the 1.5 Tesla machine to be superior to the 3.0 Tesla machine, while imaging sequence subgroup analysis could not be performed. MRI is a viable preoperative DOI measurement modality that can help in efficient treatment planning to decrease surgical morbidity and mortality.
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Affiliation(s)
| | - Vanshika Jain
- Department of Oral Medicine and Radiology, Government Dental College and Research Institute, Bangalore, Karnataka, India
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Lin YC, Lin G, Pandey S, Yeh CH, Wang JJ, Lin CY, Ho TY, Ko SF, Ng SH. Fully automated segmentation and radiomics feature extraction of hypopharyngeal cancer on MRI using deep learning. Eur Radiol 2023; 33:6548-6556. [PMID: 37338554 PMCID: PMC10415433 DOI: 10.1007/s00330-023-09827-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2022] [Revised: 03/29/2023] [Accepted: 04/14/2023] [Indexed: 06/21/2023]
Abstract
OBJECTIVES To use convolutional neural network for fully automated segmentation and radiomics features extraction of hypopharyngeal cancer (HPC) tumor in MRI. METHODS MR images were collected from 222 HPC patients, among them 178 patients were used for training, and another 44 patients were recruited for testing. U-Net and DeepLab V3 + architectures were used for training the models. The model performance was evaluated using the dice similarity coefficient (DSC), Jaccard index, and average surface distance. The reliability of radiomics parameters of the tumor extracted by the models was assessed using intraclass correlation coefficient (ICC). RESULTS The predicted tumor volumes by DeepLab V3 + model and U-Net model were highly correlated with those delineated manually (p < 0.001). The DSC of DeepLab V3 + model was significantly higher than that of U-Net model (0.77 vs 0.75, p < 0.05), particularly in those small tumor volumes of < 10 cm3 (0.74 vs 0.70, p < 0.001). For radiomics extraction of the first-order features, both models exhibited high agreement (ICC: 0.71-0.91) with manual delineation. The radiomics extracted by DeepLab V3 + model had significantly higher ICCs than those extracted by U-Net model for 7 of 19 first-order features and for 8 of 17 shape-based features (p < 0.05). CONCLUSION Both DeepLab V3 + and U-Net models produced reasonable results in automated segmentation and radiomic features extraction of HPC on MR images, whereas DeepLab V3 + had a better performance than U-Net. CLINICAL RELEVANCE STATEMENT The deep learning model, DeepLab V3 + , exhibited promising performance in automated tumor segmentation and radiomics extraction for hypopharyngeal cancer on MRI. This approach holds great potential for enhancing the radiotherapy workflow and facilitating prediction of treatment outcomes. KEY POINTS • DeepLab V3 + and U-Net models produced reasonable results in automated segmentation and radiomic features extraction of HPC on MR images. • DeepLab V3 + model was more accurate than U-Net in automated segmentation, especially on small tumors. • DeepLab V3 + exhibited higher agreement for about half of the first-order and shape-based radiomics features than U-Net.
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Affiliation(s)
- Yu-Chun Lin
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, 5 Fuhsing St., Guishan, Taoyuan, 33382, Taiwan
- Department of Medical Imaging and Radiological Sciences, Chang Gung University, Taoyuan, Taiwan
- Clinical Metabolomics Core Laboratory, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan
| | - Gigin Lin
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, 5 Fuhsing St., Guishan, Taoyuan, 33382, Taiwan
- Clinical Metabolomics Core Laboratory, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan
| | - Sumit Pandey
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, 5 Fuhsing St., Guishan, Taoyuan, 33382, Taiwan
| | - Chih-Hua Yeh
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, 5 Fuhsing St., Guishan, Taoyuan, 33382, Taiwan
| | - Jiun-Jie Wang
- Department of Medical Imaging and Radiological Sciences, Chang Gung University, Taoyuan, Taiwan
| | - Chien-Yu Lin
- Department of Radiation Oncology, Chang Gung Memorial Hospital at Linkou and Chang Gung University, Taoyuan, Taiwan
| | - Tsung-Ying Ho
- Department of Nuclear Medicine and Molecular Imaging Center, Chang Gung Memorial Hospital and Chang Gung University, Taoyuan, Taiwan
| | - Sheung-Fat Ko
- Department of Radiology, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung, Taiwan
| | - Shu-Hang Ng
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, 5 Fuhsing St., Guishan, Taoyuan, 33382, Taiwan.
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Zheng YM, Che JY, Yuan MG, Wu ZJ, Pang J, Zhou RZ, Li XL, Dong C. A CT-Based Deep Learning Radiomics Nomogram to Predict Histological Grades of Head and Neck Squamous Cell Carcinoma. Acad Radiol 2023; 30:1591-1599. [PMID: 36460582 DOI: 10.1016/j.acra.2022.11.007] [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: 10/03/2022] [Revised: 11/01/2022] [Accepted: 11/10/2022] [Indexed: 12/03/2022]
Abstract
RATIONALE AND OBJECTIVES Accurate pretreatment assessment of histological differentiation grade of head and neck squamous cell carcinoma (HNSCC) is crucial for prognosis evaluation. This study aimed to construct and validate a contrast-enhanced computed tomography (CECT)-based deep learning radiomics nomogram (DLRN) to predict histological differentiation grades of HNSCC. MATERIALS AND METHODS A total of 204 patients with HNSCC who underwent CECT scans were enrolled in this study. The participants recruited from two hospitals were split into a training set (n=124, 74 well/moderately differentiated and 50 poorly differentiated) of patients from one hospital and an external test set of patients from the other hospital (n=80, 49 well/moderately differentiated and 31 poorly differentiated). CECT-based manually-extracted radiomics (MER) features and deep learning (DL) features were extracted and selected. The selected MER features and DL features were then combined to construct a DLRN via multivariate logistic regression. The predictive performance of the DLRN was assessed using ROCs and decision curve analysis (DCA). RESULTS Three MER features and seven DL features were finally selected. The DLRN incorporating the selected MER and DL features showed good predictive value for the histological differentiation grades of HNSCC (well/moderately differentiated vs. poorly differentiated) in both the training (AUC, 0.878) and test (AUC, 0.822) sets. DCA demonstrated that the DLRN was clinically useful for predicting histological differentiation grades of HNSCC. CONCLUSION A CECT-based DLRN was constructed to predict histological differentiation grades of HNSCC. The DLRN showed good predictive efficacy and might be useful for prognostic evaluation of patients with HNSCC.
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Affiliation(s)
- Ying-Mei Zheng
- Health Management Center, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Jun-Yi Che
- Department of Radiology, Qingdao Municipal Hospital, Qingdao, China
| | - Ming-Gang Yuan
- Department of Nuclear Medicine, Affiliated Qingdao Central Hospital, Qingdao University, Qingdao, China
| | - Zeng-Jie Wu
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Jing Pang
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Rui-Zhi Zhou
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Xiao-Li Li
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Cheng Dong
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China.
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9
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Corti A, De Cecco L, Cavalieri S, Lenoci D, Pistore F, Calareso G, Mattavelli D, de Graaf P, Leemans CR, Brakenhoff RH, Ravanelli M, Poli T, Licitra L, Corino V, Mainardi L. MRI-based radiomic prognostic signature for locally advanced oral cavity squamous cell carcinoma: development, testing and comparison with genomic prognostic signatures. Biomark Res 2023; 11:69. [PMID: 37455307 DOI: 10.1186/s40364-023-00494-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Accepted: 05/03/2023] [Indexed: 07/18/2023] Open
Abstract
BACKGROUND . At present, the prognostic prediction in advanced oral cavity squamous cell carcinoma (OCSCC) is based on the tumor-node-metastasis (TNM) staging system, and the most used imaging modality in these patients is magnetic resonance image (MRI). With the aim to improve the prediction, we developed an MRI-based radiomic signature as a prognostic marker for overall survival (OS) in OCSCC patients and compared it with published gene expression signatures for prognosis of OS in head and neck cancer patients, replicated herein on our OCSCC dataset. METHODS For each patient, 1072 radiomic features were extracted from T1 and T2-weighted MRI (T1w and T2w). Features selection was performed, and an optimal set of five of them was used to fit a Cox proportional hazard regression model for OS. The radiomic signature was developed on a multi-centric locally advanced OCSCC retrospective dataset (n = 123) and validated on a prospective cohort (n = 108). RESULTS The performance of the signature was evaluated in terms of C-index (0.68 (IQR 0.66-0.70)), hazard ratio (HR 2.64 (95% CI 1.62-4.31)), and high/low risk group stratification (log-rank p < 0.001, Kaplan-Meier curves). When tested on a multi-centric prospective cohort (n = 108), the signature had a C-index of 0.62 (IQR 0.58-0.64) and outperformed the clinical and pathologic TNM stage and six out of seven gene expression prognostic signatures. In addition, the significant difference of the radiomic signature between stages III and IVa/b in patients receiving surgery suggests a potential association of MRI features with the pathologic stage. CONCLUSIONS Overall, the present study suggests that MRI signatures, containing non-invasive and cost-effective remarkable information, could be exploited as prognostic tools.
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Affiliation(s)
- Anna Corti
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy.
| | - Loris De Cecco
- Integrated Biology of Rare Tumors, Department of Research, Fondazione IRCCS, Istituto Nazionale dei Tumori, Milan, Italy
| | - Stefano Cavalieri
- Head and Neck Medical Oncology Department, Fondazione IRCCS, Istituto Nazionale dei Tumori, Milan, Italy
- Department of Oncology and Hemato-Oncology, Università degli studi di Milano, Milan, Italy
| | - Deborah Lenoci
- Integrated Biology of Rare Tumors, Department of Research, Fondazione IRCCS, Istituto Nazionale dei Tumori, Milan, Italy
| | - Federico Pistore
- Head and Neck Medical Oncology Department, Fondazione IRCCS, Istituto Nazionale dei Tumori, Milan, Italy
| | - Giuseppina Calareso
- Radiology Department, Fondazione IRCCS, Istituto Nazionale dei Tumori, Milan, Italy
| | - Davide Mattavelli
- Unit of Otorhinolaryngology-Head and Neck Surgery, Department of Medical and Surgical Specialties, Radiological Sciences, and Public Health, ASST Spedali Civili of Brescia, University of Brescia, Brescia, Italy
| | - Pim de Graaf
- Amsterdam UMC location Vrije Universiteit, Radiology and Nuclear Medicine, de Boelelaan 1117, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands
| | - C René Leemans
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands
- Amsterdam UMC location Vrije Universiteit, Otolaryngology-Head and Neck Surgery, de Boelelaan 1117, Amsterdam, The Netherlands
| | - Ruud H Brakenhoff
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands
- Amsterdam UMC location Vrije Universiteit, Otolaryngology-Head and Neck Surgery, de Boelelaan 1117, Amsterdam, The Netherlands
| | - Marco Ravanelli
- Unit of Radiology, Department of Medical and Surgical Specialties, Radiological Sciences, and Public Health, ASST Spedali Civili of Brescia, University of Brescia, Brescia, Italy
| | - Tito Poli
- Maxillo-Facial Surgery Division, Head and Neck Department, University Hospital of Parma, Parma, Italy
| | - Lisa Licitra
- Head and Neck Medical Oncology Department, Fondazione IRCCS, Istituto Nazionale dei Tumori, Milan, Italy
- Department of Oncology and Hemato-Oncology, Università degli studi di Milano, Milan, Italy
| | - Valentina Corino
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
- Cardiotech Lab, Centro Cardiologico Monzino IRCCS, Milan, Italy
| | - Luca Mainardi
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
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Chiesa-Estomba CM, Mayo-Yanez M, Guntinas-Lichius O, Vander-Poorten V, Takes RP, de Bree R, Halmos GB, Saba NF, Nuyts S, Ferlito A. Radiomics in Hypopharyngeal Cancer Management: A State-of-the-Art Review. Biomedicines 2023; 11:biomedicines11030805. [PMID: 36979783 PMCID: PMC10045560 DOI: 10.3390/biomedicines11030805] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2023] [Revised: 02/25/2023] [Accepted: 03/04/2023] [Indexed: 03/09/2023] Open
Abstract
(1) Background: Hypopharyngeal squamous cell carcinomas usually present with locally advanced disease and a correspondingly poor prognosis. Currently, efforts are being made to improve tumor characterization and provide insightful information for outcome prediction. Radiomics is an emerging area of study that involves the conversion of medical images into mineable data; these data are then used to extract quantitative features based on shape, intensity, texture, and other parameters; (2) Methods: A systematic review of the peer-reviewed literature was conducted; (3) Results: A total of 437 manuscripts were identified. Fifteen manuscripts met the inclusion criteria. The main targets described were the evaluation of textural features to determine tumor-programmed death-ligand 1 expression; a surrogate for microvessel density and heterogeneity of perfusion; patient stratification into groups at high and low risk of progression; prediction of early recurrence, 1-year locoregional failure and survival outcome, including progression-free survival and overall survival, in patients with locally advanced HPSCC; thyroid cartilage invasion, early disease progression, recurrence, induction chemotherapy response, treatment response, and prognosis; and (4) Conclusions: our findings suggest that radiomics represents a potentially useful tool in the diagnostic workup as well as during the treatment and follow-up of patients with HPSCC. Large prospective studies are essential to validate this technology in these patients.
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Affiliation(s)
- Carlos M. Chiesa-Estomba
- Otorhinolaryngology-Head & Neck Surgery Department, Hospital Universitario Donostia, Biodonostia Research Institute, Faculty of Medicine, Deusto University, 20014 San Sebastian, Spain
- Correspondence:
| | - Miguel Mayo-Yanez
- Otorhinolaryngology-Head and Neck Surgery Department, Complexo Hospitalario Universitario A Coruña (CHUAC), 15006 A Coruña, Spain
| | | | - Vincent Vander-Poorten
- Section Head and Neck Oncology, Department of Oncology, KU Leuven—University of Leuven, 3000 Leuven, Belgium
| | - Robert P. Takes
- Department of Otolaryngology/Head and Neck Surgery, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands
| | - Remco de Bree
- Department of Head and Neck Surgical Oncology, University Medical Center Utrecht, 3584 CX Utrecht, The Netherlands
| | - Gyorgy B. Halmos
- Department of Otorhinolaryngology/Head and Neck Surgery, University Medical Center Groningen, University of Groningen, 9712 CP Groningen, The Netherlands
| | - Nabil F. Saba
- Department of Hematology and Medical Oncology, The Winship Cancer Institute, Emory University, Atlanta, GA 30322, USA
| | - Sandra Nuyts
- Department of Radiation Oncology, University Hospitals Leuven, KU Leuven—University of Leuven, 3000 Leuven, Belgium
| | - Alfio Ferlito
- Coordinator of the International Head and Neck Scientific Group, 35125 Padua, Italy
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11
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Siow TY, Yeh CH, Lin G, Lin CY, Wang HM, Liao CT, Toh CH, Chan SC, Lin CP, Ng SH. MRI Radiomics for Predicting Survival in Patients with Locally Advanced Hypopharyngeal Cancer Treated with Concurrent Chemoradiotherapy. Cancers (Basel) 2022; 14:cancers14246119. [PMID: 36551604 PMCID: PMC9775984 DOI: 10.3390/cancers14246119] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 12/06/2022] [Accepted: 12/09/2022] [Indexed: 12/14/2022] Open
Abstract
A reliable prognostic stratification of patients with locally advanced hypopharyngeal cancer who had been treated with concurrent chemoradiotherapy (CCRT) is crucial for informing tailored management strategies. The purpose of this retrospective study was to develop robust and objective magnetic resonance imaging (MRI) radiomics-based models for predicting overall survival (OS) and progression-free survival (PFS) in this patient population. The study participants included 198 patients (median age: 52.25 years (interquartile range = 46.88-59.53 years); 95.96% men) who were randomly divided into a training cohort (n = 132) and a testing cohort (n = 66). Radiomic parameters were extracted from post-contrast T1-weighted MR images. Radiomic features for model construction were selected from the training cohort using least absolute shrinkage and selection operator-Cox regression models. Prognostic performances were assessed by calculating the integrated area under the receiver operating characteristic curve (iAUC). The ability of radiomic models to predict OS (iAUC = 0.580, 95% confidence interval (CI): 0.558-0.591) and PFS (iAUC = 0.625, 95% CI = 0.600-0.633) was validated in the testing cohort. The combination of radiomic signatures with traditional clinical parameters outperformed clinical variables alone in the prediction of survival outcomes (observed iAUC increments = 0.279 [95% CI = 0.225-0.334] and 0.293 [95% CI = 0.232-0.351] for OS and PFS, respectively). In summary, MRI radiomics has value for predicting survival outcomes in patients with hypopharyngeal cancer treated with CCRT, especially when combined with clinical prognostic variables.
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Affiliation(s)
- Tiing Yee Siow
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, Chang Gung University College of Medicine, Taoyuan 333423, Taiwan
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei 112304, Taiwan
| | - Chih-Hua Yeh
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, Chang Gung University College of Medicine, Taoyuan 333423, Taiwan
| | - Gigin Lin
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, Chang Gung University College of Medicine, Taoyuan 333423, Taiwan
| | - Chien-Yu Lin
- Department of Radiation Oncology and Proton Therapy Center, Chang Gung Memorial Hospital at Linkou, Chang Gung University College of Medicine, Taoyuan 333423, Taiwan
| | - Hung-Ming Wang
- Division of Hematology-Oncology, Department of Internal Medicine, Chang Gung Memorial Hospital at Linkou, Chang Gung University College of Medicine, Taoyuan 333423, Taiwan
| | - Chun-Ta Liao
- Department of Otorhinolaryngology, Head and Neck Surgery, Chang Gung Memorial Hospital and Chang Gung University, Taoyuan 333423, Taiwan
| | - Cheng-Hong Toh
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, Chang Gung University College of Medicine, Taoyuan 333423, Taiwan
| | - Sheng-Chieh Chan
- Department of Nuclear Medicine, Hualien Tzu Chi Hospital, Tzu Chi University School of Medicine, Buddhist Tzu Chi Medical Foundation, Hualien 970473, Taiwan
| | - Ching-Po Lin
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei 112304, Taiwan
- Correspondence: (C.-P.L.); (S.-H.N.)
| | - Shu-Hang Ng
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, Chang Gung University College of Medicine, Taoyuan 333423, Taiwan
- Correspondence: (C.-P.L.); (S.-H.N.)
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12
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Yu Y, Bai Y, Zheng P, Wang N, Deng X, Ma H, Yu R, Ma C, Liu P, Xie Y, Wang C, Chen H. Radiomics-based prediction of response to immune checkpoint inhibitor treatment for solid cancers using computed tomography: a real-world study of two centers. BMC Cancer 2022; 22:1241. [PMID: 36451109 PMCID: PMC9710011 DOI: 10.1186/s12885-022-10344-6] [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: 04/30/2022] [Accepted: 11/21/2022] [Indexed: 12/05/2022] Open
Abstract
BACKGROUND Immune checkpoint inhibitors (ICIs) represent an approved treatment for various cancers; however, only a small proportion of the population is responsive to such treatment. We aimed to develop and validate a plain CT-based tool for predicting the response to ICI treatment among cancer patients. METHODS Data for patients with solid cancers treated with ICIs at two centers from October 2019 to October 2021 were randomly divided into training and validation sets. Radiomic features were extracted from pretreatment CT images of the tumor of interest. After feature selection, a radiomics signature was constructed based on the least absolute shrinkage and selection operator regression model, and the signature and clinical factors were incorporated into a radiomics nomogram. Model performance was evaluated using the training and validation sets. The Kaplan-Meier method was used to visualize associations with survival. RESULTS Data for 122 and 30 patients were included in the training and validation sets, respectively. Both the radiomics signature (radscore) and nomogram exhibited good discrimination of response status, with areas under the curve (AUC) of 0.790 and 0.814 for the training set and 0.831 and 0.847 for the validation set, respectively. The calibration evaluation indicated goodness-of-fit for both models, while the decision curves indicated that clinical application was favorable. Both models were associated with the overall survival of patients in the validation set. CONCLUSIONS We developed a radiomics model for early prediction of the response to ICI treatment. This model may aid in identifying the patients most likely to benefit from immunotherapy.
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Affiliation(s)
- Yang Yu
- grid.411294.b0000 0004 1798 9345The Department of Tumor Surgery, Lanzhou University Second Hospital, Lanzhou, 730030 Gansu China ,grid.32566.340000 0000 8571 0482The Second Clinical Medical College, Lanzhou University, Lanzhou, 730000 Gansu China
| | - Yuping Bai
- grid.32566.340000 0000 8571 0482The Second Clinical Medical College, Lanzhou University, Lanzhou, 730000 Gansu China ,grid.411294.b0000 0004 1798 9345Department of MR, Lanzhou University Second Hospital, Lanzhou, 730030 Gansu China
| | - Peng Zheng
- grid.411294.b0000 0004 1798 9345The Department of Tumor Surgery, Lanzhou University Second Hospital, Lanzhou, 730030 Gansu China ,grid.32566.340000 0000 8571 0482The Second Clinical Medical College, Lanzhou University, Lanzhou, 730000 Gansu China
| | - Na Wang
- grid.411294.b0000 0004 1798 9345The Department of Tumor Surgery, Lanzhou University Second Hospital, Lanzhou, 730030 Gansu China ,grid.32566.340000 0000 8571 0482The Second Clinical Medical College, Lanzhou University, Lanzhou, 730000 Gansu China
| | - Xiaobo Deng
- grid.411294.b0000 0004 1798 9345The Department of Tumor Surgery, Lanzhou University Second Hospital, Lanzhou, 730030 Gansu China ,grid.32566.340000 0000 8571 0482The Second Clinical Medical College, Lanzhou University, Lanzhou, 730000 Gansu China
| | - Huanhuan Ma
- grid.411294.b0000 0004 1798 9345The Department of Tumor Surgery, Lanzhou University Second Hospital, Lanzhou, 730030 Gansu China ,grid.32566.340000 0000 8571 0482The Second Clinical Medical College, Lanzhou University, Lanzhou, 730000 Gansu China
| | - Rong Yu
- grid.411294.b0000 0004 1798 9345The Department of Tumor Surgery, Lanzhou University Second Hospital, Lanzhou, 730030 Gansu China ,grid.32566.340000 0000 8571 0482The Second Clinical Medical College, Lanzhou University, Lanzhou, 730000 Gansu China
| | - Chenhui Ma
- grid.411294.b0000 0004 1798 9345The Department of Tumor Surgery, Lanzhou University Second Hospital, Lanzhou, 730030 Gansu China ,grid.32566.340000 0000 8571 0482The Second Clinical Medical College, Lanzhou University, Lanzhou, 730000 Gansu China
| | - Peng Liu
- grid.461867.a0000 0004 1765 2646Department of Radiology, Gansu Provincial Cancer Hospital, Lanzhou, 730050 Gansu China
| | - Yijing Xie
- grid.411294.b0000 0004 1798 9345Department of Radiology, Lanzhou University Second Hospital, Lanzhou, 730030 Gansu China
| | - Chen Wang
- grid.411294.b0000 0004 1798 9345Department of General Surgery, Lanzhou University Second Hospital, Lanzhou, 730030 Gansu China
| | - Hao Chen
- grid.411294.b0000 0004 1798 9345The Department of Tumor Surgery, Lanzhou University Second Hospital, Lanzhou, 730030 Gansu China
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13
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Lu S, Ling H, Chen J, Tan L, Gao Y, Li H, Tan P, Huang D, Zhang X, Liu Y, Mao Y, Qiu Y. MRI-based radiomics analysis for preoperative evaluation of lymph node metastasis in hypopharyngeal squamous cell carcinoma. Front Oncol 2022; 12:936040. [PMID: 36212477 PMCID: PMC9539826 DOI: 10.3389/fonc.2022.936040] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Accepted: 09/06/2022] [Indexed: 12/24/2022] Open
Abstract
ObjectiveTo investigate the role of pre-treatment magnetic resonance imaging (MRI) radiomics for the preoperative prediction of lymph node (LN) metastasis in patients with hypopharyngeal squamous cell carcinoma (HPSCC).MethodsA total of 155 patients with HPSCC were eligibly enrolled from single institution. Radiomics features were extracted from contrast-enhanced axial T-1 weighted (CE-T1WI) sequence. The most relevant features of LN metastasis were selected by the least absolute shrinkage and selection operator (LASSO) method. Univariate and multivariate logistic regression analysis was adopted to determine the independent clinical risk factors. Three models were constructed to predict the LN metastasis status: one using radiomics only, one using clinical factors only, and the other one combined radiomics and clinical factors. Receiver operating characteristic (ROC) curves and calibration curve were used to evaluate the discrimination and the accuracy of the models, respectively. The performances were tested by an internal validation cohort (n=47). The clinical utility of the models was assessed by decision curve analysis.ResultsThe nomogram consisted of radiomics scores and the MRI-reported LN status showed satisfactory discrimination in the training and validation cohorts with AUCs of 0.906 (95% CI, 0.840 to 0.972) and 0.853 (95% CI, 0.739 to 0.966), respectively. The nomogram, i.e., the combined model, outperformed the radiomics and MRI-reported LN status in both discrimination and clinical usefulness.ConclusionsThe MRI-based radiomics nomogram holds promise for individual and non-invasive prediction of LN metastasis in patients with HPSCC.
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Affiliation(s)
- Shanhong Lu
- Department of Otolaryngology-Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha, China
- Otolaryngology Major Disease Research Key Laboratory of Hunan Province, Xiangya Hospital, Central South University, Changsha, China
- Clinical Research Center for Pharyngolaryngeal Diseases and Voice Disorders in Hunan Province, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Hang Ling
- Department of Otolaryngology-Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha, China
- Otolaryngology Major Disease Research Key Laboratory of Hunan Province, Xiangya Hospital, Central South University, Changsha, China
- Clinical Research Center for Pharyngolaryngeal Diseases and Voice Disorders in Hunan Province, Xiangya Hospital, Central South University, Changsha, China
| | - Juan Chen
- Department of Otolaryngology-Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha, China
| | - Lei Tan
- College of Computer and Information Engineering, Hunan University of Technology and Business, Changsha, China
| | - Yan Gao
- Department of Otolaryngology-Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha, China
| | - Huayu Li
- Department of Otolaryngology-Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha, China
| | - Pingqing Tan
- Department of Head and Neck Surgery, Hunan Cancer Hospital, Xiangya Medical School, Central South University, Changsha, China
| | - Donghai Huang
- Department of Otolaryngology-Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha, China
- Otolaryngology Major Disease Research Key Laboratory of Hunan Province, Xiangya Hospital, Central South University, Changsha, China
- Clinical Research Center for Pharyngolaryngeal Diseases and Voice Disorders in Hunan Province, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Xin Zhang
- Department of Otolaryngology-Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha, China
- Otolaryngology Major Disease Research Key Laboratory of Hunan Province, Xiangya Hospital, Central South University, Changsha, China
- Clinical Research Center for Pharyngolaryngeal Diseases and Voice Disorders in Hunan Province, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Yong Liu
- Department of Otolaryngology-Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha, China
- Otolaryngology Major Disease Research Key Laboratory of Hunan Province, Xiangya Hospital, Central South University, Changsha, China
- Clinical Research Center for Pharyngolaryngeal Diseases and Voice Disorders in Hunan Province, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Yitao Mao
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
- *Correspondence: Yuanzheng Qiu, ; Yitao Mao,
| | - Yuanzheng Qiu
- Department of Otolaryngology-Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha, China
- Otolaryngology Major Disease Research Key Laboratory of Hunan Province, Xiangya Hospital, Central South University, Changsha, China
- Clinical Research Center for Pharyngolaryngeal Diseases and Voice Disorders in Hunan Province, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
- *Correspondence: Yuanzheng Qiu, ; Yitao Mao,
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14
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Zheng YM, Yuan MG, Zhou RQ, Hou F, Zhan JF, Liu ND, Hao DP, Dong C. A computed tomography-based radiomics signature for predicting expression of programmed death ligand 1 in head and neck squamous cell carcinoma. Eur Radiol 2022; 32:5362-5370. [PMID: 35298679 DOI: 10.1007/s00330-022-08651-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Revised: 02/02/2022] [Accepted: 02/13/2022] [Indexed: 02/07/2023]
Abstract
OBJECTIVES Accurate prediction of the expression of programmed death ligand 1 (PD-L1) in head and neck squamous cell carcinoma (HNSCC) before immunotherapy is crucial. This study was performed to construct and validate a contrast-enhanced computed tomography (CECT)-based radiomics signature to predict the expression of PD-L1 in HNSCC. METHODS In total, 157 patients with confirmed HNSCC who underwent CECT scans and immunohistochemical examination of tumor PD-L1 expression were enrolled in this study. The patients were divided into a training set (n = 104; 62 PD-L1-positive and 42 PD-L1-negative) and an external validation set (n = 53; 34 PD-L1-positive and 19 PD-L1-negative). A radiomics signature was constructed from radiomics features extracted from the CECT images, and a radiomics score was calculated. Performance of the radiomics signature was assessed using receiver operating characteristics analysis. RESULTS Nine features were finally selected to construct the radiomics signature. The performance of the radiomics signature to distinguish between a PD-L1-positive and PD-L1-negative status in both the training and validation sets was good, with an area under the receiver operating characteristics curve of 0.852 and 0.802 for the training and validation sets, respectively. CONCLUSIONS A CECT-based radiomics signature was constructed to predict the expression of PD-L1 in HNSCC. This model showed favorable predictive efficacy and might be useful for identifying patients with HNSCC who can benefit from anti-PD-L1 immunotherapy. KEY POINTS • Accurate prediction of the expression of PD-L1 in HNSCC before immunotherapy is crucial. • A CECT-based radiomics signature showed favorable predictive efficacy in estimation of the PD-L1 expression status in patients with HNSCC.
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Affiliation(s)
- Ying-Mei Zheng
- Health Management Center, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Ming-Gang Yuan
- Department of Nuclear Medicine, Affiliated Qingdao Central Hospital, Qingdao University, Qingdao, China
| | - Rui-Qing Zhou
- Department of Radiology, Jiaozhou Hospital of Traditional Chinese Medicine, Jiaozhou, China
| | - Feng Hou
- Department of Pathology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Jin-Feng Zhan
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Nai-Dong Liu
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Da-Peng Hao
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Cheng Dong
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China.
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15
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Zheng YM, Zhan JF, Yuan MG, Hou F, Jiang G, Wu ZJ, Dong C. A CT-based radiomics signature for preoperative discrimination between high and low expression of programmed death ligand 1 in head and neck squamous cell carcinoma. Eur J Radiol 2022; 146:110093. [PMID: 34890937 DOI: 10.1016/j.ejrad.2021.110093] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Revised: 11/19/2021] [Accepted: 11/30/2021] [Indexed: 02/07/2023]
Abstract
PURPOSE Accurate prediction of the expression level of programmed death ligand 1 (PD-L1) in head and neck squamous cell carcinoma (HNSCC) is crucial before immunotherapy. The purpose of this study was to construct and validate a contrast-enhanced computed tomography (CECT)-based radiomics signature to discriminate between high and low expression status of PD-L1. METHODS A total of 179 HNSCC patients who underwent immunohistochemical examination of tumor PD-L1 expression at one of two centers were enrolled in this study and divided into a training set (n = 122; 55 high PD-L1 expression and 67 low PD-L1 expression) and an external validation set (n = 57; 26 high PD-L1 expression and 31 low PD-L1 expression). The least absolute shrinkage and selection operator method was used to select the key features for a CECT-image-based radiomics signature. The performance of the radiomics signature was assessed using receiver operating characteristics analysis. RESULTS Six features were finally selected to construct the radiomics signature. The performance of the radiomics signature in the discrimination between high and low PD-L1 expression status was good in both the training and validation sets, with areas under the receiver operating characteristics curve of 0.889 and 0.834 for the training and validation sets, respectively. CONCLUSIONS The constructed CECT-based radiomics signature model showed favorable performance for discriminating between high and low PD-L1 expression status in HNSCC patients. It may be useful for screening out those patients with HNSCC who can best benefit from anti-PD-L1 immunotherapy.
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Affiliation(s)
- Ying-Mei Zheng
- Health Management Center, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Jin-Feng Zhan
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Ming-Gang Yuan
- Department of Nuclear Medicine, Affiliated Qingdao Central Hospital, Qingdao University, Qingdao, China
| | - Feng Hou
- Department of Pathology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Gang Jiang
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Zeng-Jie Wu
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Cheng Dong
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China.
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