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Liu Q, Liu S, Mao Y, Kang X, Yu M, Chen G. Machine learning model to preoperatively predict T2/T3 staging of laryngeal and hypopharyngeal cancer based on the CT radiomic signature. Eur Radiol 2024; 34:5349-5359. [PMID: 38206403 DOI: 10.1007/s00330-023-10557-8] [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: 10/01/2023] [Revised: 11/28/2023] [Accepted: 12/06/2023] [Indexed: 01/12/2024]
Abstract
OBJECTIVES To develop and assess a radiomics-based prediction model for distinguishing T2/T3 staging of laryngeal and hypopharyngeal squamous cell carcinoma (LHSCC) METHODS: A total of 118 patients with pathologically proven LHSCC were enrolled in this retrospective study. We performed feature processing based on 851 radiomic features derived from contrast-enhanced CT images and established multiple radiomic models by combining three feature selection methods and seven machine learning classifiers. The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity were used to assess the performance of the models. The radiomic signature obtained from the optimal model and statistically significant morphological image characteristics were incorporated into the predictive nomogram. The performance of the nomogram was assessed by calibration curve and decision curve analysis. RESULTS Using analysis of variance (ANOVA) feature selection and logistic regression (LR) classifier produced the best model. The AUCs of the training, validation, and test sets were 0.919, 0.857, and 0.817, respectively. A nomogram based on the model integrating the radiomic signature and a morphological imaging characteristic (suspicious thyroid cartilage invasion) exhibited C-indexes of 0.899 (95% confidence interval (CI) 0.843-0.955), fitting well in calibration curves (p > 0.05). Decision curve analysis further confirmed the clinical usefulness of the nomogram. CONCLUSIONS The nomogram based on the radiomics model derived from contrast-enhanced CT images had good diagnostic performance for distinguishing T2/T3 staging of LHSCC. CLINICAL RELEVANCE STATEMENT Accurate T2/T3 staging assessment of LHSCC aids in determining whether laryngectomy or laryngeal preservation therapy should be performed. The nomogram based on the radiomics model derived from contrast-enhanced CT images has the potential to predict the T2/T3 staging of LHSCC, which can provide a non-invasive and robust approach for guiding the optimization of clinical decision-making. KEY POINTS • Combining analysis of variance with logistic regression yielded the optimal radiomic model. • A nomogram based on the CT-radiomic signature has good performance for differentiating T2 from T3 staging of laryngeal and hypopharyngeal squamous cell carcinoma. • It provides a non-invasive and robust approach for guiding the optimization of clinical decision-making.
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Affiliation(s)
- Qianhan Liu
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, No. 23 Tai Ping Street, Luzhou, 646000, Sichuan, China
| | - Shengdan Liu
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, No. 23 Tai Ping Street, Luzhou, 646000, Sichuan, China
| | - Yu Mao
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, No. 23 Tai Ping Street, Luzhou, 646000, Sichuan, China
| | - Xuefeng Kang
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, No. 23 Tai Ping Street, Luzhou, 646000, Sichuan, China
| | - Mingling Yu
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, No. 23 Tai Ping Street, Luzhou, 646000, Sichuan, China
| | - Guangxiang Chen
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, No. 23 Tai Ping Street, Luzhou, 646000, Sichuan, China.
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Liu H, Zhu C, Wang X, Chen X, Li Z, Xian J. Prediction of pathological complete response in locally advanced head and neck squamous cell carcinoma treated with neoadjuvant chemo-immunotherapy using volumetric multisequence MRI histogram analysis. Neuroradiology 2024; 66:919-929. [PMID: 38503986 DOI: 10.1007/s00234-024-03339-6] [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: 01/26/2024] [Accepted: 03/07/2024] [Indexed: 03/21/2024]
Abstract
PURPOSE This study aimed to develop a multisequence MRI-based volumetric histogram metrics model for predicting pathological complete response (pCR) in advanced head and neck squamous cell carcinoma (HNSCC) patients undergoing neoadjuvant chemo-immunotherapy (NCIT) and compare its predictive performance with AJCC staging and RECIST 1.1 criteria. METHODS Twenty-four patients with locally advanced HNSCC from a prospective phase II trial were enrolled for analysis. All patients underwent pre- and post-NCIT MRI examinations from which whole-tumor histogram features were extracted, including T1WI, T2WI, enhanced T1WI (T1Gd), diffusion-weighted imaging (DWI) sequences, and their corresponding apparent diffusion coefficient (ADC) maps. The pathological results divided the patients into pathological complete response (pCR) and non-pCR (N-pCR) groups. Delta features were calculated as the percentage change in histogram features from pre- to post-treatment. After data reduction and feature selection, logistic regression was used to build prediction models. ROC analysis was performed to assess the diagnostic performance. RESULTS Eleven of 24 patients achieved pCR. Pre_T2_original_firstorder_Minimum, Post_ADC_original_firstorder_MeanAbsoluteDeviation, and Delta_T1Gd_original_firstorder_Skewness were associated with achieving pCR after NCIT. The Combined_Model demonstrated the best predictive performance (AUC 0.95), outperforming AJCC staging (AUC 0.52) and RECIST 1.1 (AUC 0.72). The Pre_Model (AUC 0.83) or Post-Model (AUC 0.83) had a better predictive ability than AJCC staging. CONCLUSION Multisequence MRI-based volumetric histogram analysis can non-invasively predict the pCR status of HNSCC patients undergoing NCIT. The use of histogram features extracted from pre- and post-treatment MRI exhibits promising predictive performance and offers a novel quantitative assessment method for evaluating pCR in HNSCC patients receiving NCIT.
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Affiliation(s)
- Hangzhi Liu
- Department of Radiology, Beijing Tongren Hospital, Capital Medical University, NO.1 of Dongjiaominxiang Street, Dongcheng District, Beijing, 100730, China
| | - Changyu Zhu
- Cancer Center, Beijing Tongren Hospital, Capital Medical University, NO.1 of Dongjiaominxiang Street, Dongcheng District, Beijing, 100730, China
| | - Xinyan Wang
- Department of Radiology, Beijing Tongren Hospital, Capital Medical University, NO.1 of Dongjiaominxiang Street, Dongcheng District, Beijing, 100730, China
| | - Xiaohong Chen
- Department of Otolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, No.1 of Dongjiaominxiang Street, Dongcheng District, Beijing, 100730, China
| | - Zhixin Li
- Cancer Center, Beijing Tongren Hospital, Capital Medical University, NO.1 of Dongjiaominxiang Street, Dongcheng District, Beijing, 100730, China
| | - Junfang Xian
- Department of Radiology, Beijing Tongren Hospital, Capital Medical University, NO.1 of Dongjiaominxiang Street, Dongcheng District, Beijing, 100730, China.
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Li Z, Wang R, Wang L, Tan C, Xu J, Fang J, Xian J. Machine Learning-Based MRI Radiogenomics for Evaluation of Response to Induction Chemotherapy in Head and Neck Squamous Cell Carcinoma. Acad Radiol 2024; 31:2464-2475. [PMID: 37985290 DOI: 10.1016/j.acra.2023.10.054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 10/16/2023] [Accepted: 10/28/2023] [Indexed: 11/22/2023]
Abstract
RATIONALE AND OBJECTIVES To develop and validate a radiogenomics model integrating clinical data, radiomics-based machine learning (RBML) classifiers, and transcriptomics data for predicting the response to induction chemotherapy (IC) in patients with head and neck squamous cell carcinoma (HNSCC). MATERIALS AND METHODS Radiomics features derived from T2-weighted, pre- and post-contrast-enhanced T1-weighted MRI sequences, clinical data, and RNA sequencing data of 150 patients with HNSCC were included in the study. Analysis of variance or recursive feature elimination was used to reduce radiomics features. Three RBML classifiers were developed to distinguish non-responders from responders. Weighted correlation network analysis (WGCNA) was performed to identify the correlation between clinical data or radiomics features and molecular features; subsequently, protein interaction and functional enrichment analyses were performed. The predictive performance of the radiogenomics model integrating significant clinical variables, RBML classifiers, and molecular features was evaluated using receiver operating characteristic curve analysis. RESULTS Five radiomics features and two conventional MRI findings significantly stratified HNSCC patients into responders and non-responders. On WGCNA analysis, 809 genes showed a significant correlation with two radiomics features. Functional enrichment analysis suggested that our proposed radiomics features could reflect the T cell-mediated immune response and immune infiltration of HNSCC. The radiogenomics model showed the highest area under the curve (0.88[95%CI 0.75-0.96]) for predicting IC response, which was better than MRI findings(p = 0.0407) or molecular features(p = 0.004) alone, but showed no significant difference with that of RBML model (p = 0.2254) in test cohort. CONCLUSION Merging imaging phenotypes with transcriptomic data improved the prediction of IC response in HNSCC.
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Affiliation(s)
- Zheng Li
- Department of Radiology, Beijing Tongren Hospital, Capital Medical University, Beijing, China (Z.L., J.X.).
| | - Ru Wang
- Department of Otolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, China (R.W., L.W., C.T., J.X., J.F.).
| | - Lingwa Wang
- Department of Otolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, China (R.W., L.W., C.T., J.X., J.F.).
| | - Chen Tan
- Department of Otolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, China (R.W., L.W., C.T., J.X., J.F.).
| | - Jiaqi Xu
- Department of Otolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, China (R.W., L.W., C.T., J.X., J.F.).
| | - Jugao Fang
- Department of Otolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, China (R.W., L.W., C.T., J.X., J.F.).
| | - Junfang Xian
- Department of Radiology, Beijing Tongren Hospital, Capital Medical University, Beijing, China (Z.L., J.X.).
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Ravanelli M, Rondi P, Di Meo N, Farina D. The added value of radiomics in determining patient responsiveness to laryngeal preservation strategies. Curr Opin Otolaryngol Head Neck Surg 2024; 32:134-137. [PMID: 38259164 DOI: 10.1097/moo.0000000000000963] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
PURPOSE OF REVIEW Laryngeal cancer (LC) is a highly aggressive malignancy of the head and neck and represents about 1-2% of cancer worldwide.Treatment strategies for LC aim both to complete cancer removal and to preserve laryngeal function or maximize larynx retention.Predicting with high precision response to induction chemotherapy (IC) is one of the main fields of research when considering LC, since this could guide treatment strategies in locally advanced LC. RECENT FINDINGS Radiomics is a noninvasive method to extract quantitative data from the whole tumor using medical imaging. This signature could represent the underlying tumor heterogeneity and phenotype.During the last five years, some studies have highlighted the potential of radiomics in the pretreatment assessment of LC, in the prediction of response to IC, and in the early assessment of response to radiation therapy. Although these represent promising results, larger multicentric studies are demanded to validate the value of radiomics in this field. SUMMARY The role of radiomics in laryngeal preservation strategies is still to be defined. There are some early promising studies, but the lack of validation and larger multicentric studies limit the value of the papers published in the literature and its application in clinical practice.
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Affiliation(s)
- Marco Ravanelli
- Department of Radiology, University of Brescia, ASST Spedali Civili Brescia, Italy
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Nguyen TM, Bertolus C, Giraud P, Burgun A, Saintigny P, Bibault JE, Foy JP. A Radiomics Approach to Identify Immunologically Active Tumor in Patients with Head and Neck Squamous Cell Carcinomas. Cancers (Basel) 2023; 15:5369. [PMID: 38001629 PMCID: PMC10670096 DOI: 10.3390/cancers15225369] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 11/05/2023] [Accepted: 11/06/2023] [Indexed: 11/26/2023] Open
Abstract
BACKGROUND We recently developed a gene-expression-based HOT score to identify the hot/cold phenotype of head and neck squamous cell carcinomas (HNSCCs), which is associated with the response to immunotherapy. Our goal was to determine whether radiomic profiling from computed tomography (CT) scans can distinguish hot and cold HNSCC. METHOD We included 113 patients from The Cancer Genome Atlas (TCGA) and 20 patients from the Groupe Hospitalier Pitié-Salpêtrière (GHPS) with HNSCC, all with available pre-treatment CT scans. The hot/cold phenotype was computed for all patients using the HOT score. The IBEX software (version 4.11.9, accessed on 30 march 2020) was used to extract radiomic features from the delineated tumor region in both datasets, and the intraclass correlation coefficient (ICC) was computed to select robust features. Machine learning classifier models were trained and tested in the TCGA dataset and validated using the area under the receiver operator characteristic curve (AUC) in the GHPS cohort. RESULTS A total of 144 radiomic features with an ICC >0.9 was selected. An XGBoost model including these selected features showed the best performance prediction of the hot/cold phenotype with AUC = 0.86 in the GHPS validation dataset. CONCLUSIONS AND RELEVANCE We identified a relevant radiomic model to capture the overall hot/cold phenotype of HNSCC. This non-invasive approach could help with the identification of patients with HNSCC who may benefit from immunotherapy.
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Affiliation(s)
- Tan Mai Nguyen
- Sorbonne Université, Department of Maxillo-Facial Surgery, Hôpital Pitié-Salpêtrière, Assistance Publique des Hôpitaux de Paris, 75013 Paris, France; (T.M.N.); (C.B.)
- Univ Lyon, Université Claude Bernard Lyon 1, INSERM 1052, CNRS 5286, Centre Léon Bérard, Centre de Recherche en Cancérologie de Lyon, 69008 Lyon, France;
- INSERM, UMR S1138, Cordeliers Research Center, Université Paris Cité, 75005 Paris, France; (P.G.); (A.B.); (J.-E.B.)
| | - Chloé Bertolus
- Sorbonne Université, Department of Maxillo-Facial Surgery, Hôpital Pitié-Salpêtrière, Assistance Publique des Hôpitaux de Paris, 75013 Paris, France; (T.M.N.); (C.B.)
- Univ Lyon, Université Claude Bernard Lyon 1, INSERM 1052, CNRS 5286, Centre Léon Bérard, Centre de Recherche en Cancérologie de Lyon, 69008 Lyon, France;
| | - Paul Giraud
- INSERM, UMR S1138, Cordeliers Research Center, Université Paris Cité, 75005 Paris, France; (P.G.); (A.B.); (J.-E.B.)
- Sorbonne Université, Department of Radiation Oncology, Hôpital Pitié-Salpêtrière, Assistance Publique des Hôpitaux de Paris, 75013 Paris, France
| | - Anita Burgun
- INSERM, UMR S1138, Cordeliers Research Center, Université Paris Cité, 75005 Paris, France; (P.G.); (A.B.); (J.-E.B.)
| | - Pierre Saintigny
- Univ Lyon, Université Claude Bernard Lyon 1, INSERM 1052, CNRS 5286, Centre Léon Bérard, Centre de Recherche en Cancérologie de Lyon, 69008 Lyon, France;
- Department of Medical Oncology, Centre Léon Bérard, 69008 Lyon, France
| | - Jean-Emmanuel Bibault
- INSERM, UMR S1138, Cordeliers Research Center, Université Paris Cité, 75005 Paris, France; (P.G.); (A.B.); (J.-E.B.)
- Department of Radiation Oncology, Hôpital Européen Georges-Pompidou, Université Paris Cité, 75015 Paris, France
| | - Jean-Philippe Foy
- Sorbonne Université, Department of Maxillo-Facial Surgery, Hôpital Pitié-Salpêtrière, Assistance Publique des Hôpitaux de Paris, 75013 Paris, France; (T.M.N.); (C.B.)
- Sorbonne Université, INSERM UMRS 938, Centre de Recherche de Saint Antoine, Team Cancer Biology and Therapeutics, 75011 Paris, France
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Konishi M, Kakimoto N. Radiomics analysis of intraoral ultrasound images for prediction of late cervical lymph node metastasis in patients with tongue cancer. Head Neck 2023; 45:2619-2626. [PMID: 37584449 DOI: 10.1002/hed.27487] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2023] [Revised: 07/25/2023] [Accepted: 08/07/2023] [Indexed: 08/17/2023] Open
Abstract
BACKGROUND We investigated the predictability of late cervical lymph node metastasis using radiomics analysis of ultrasonographic images of tongue cancer. METHODS We selected 120 patients with tongue cancer who underwent intraoral ultrasonography, 30 of which had late cervical lymph node metastasis. Radiomics analysis was used to extract and quantify the image features. Bootstrap forest (BF), support vector machine (SVM), and neural tanh boost (NTB) were used as the machine learning models, and receiver operating characteristic curve analysis was conducted to determine diagnostic performance. RESULTS The sensitivity, specificity, accuracy, and AUC in the validation group were, respectively, 0.600, 0.967, 0.875, and 0.923 for the BF model; 0.700, 0.967, 0.900, and 0.950 for the SVM model; and 0.900, 0.967, 0.950, and 0.967 for NTB model. CONCLUSIONS Radiomics analysis and machine learning models using ultrasonographic images of pretreated tongue cancer could predict late cervical lymph node metastasis with high accuracy.
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Affiliation(s)
- Masaru Konishi
- Department of Oral and Maxillofacial Radiology, Hiroshima University Hospital, Hiroshima, Japan
| | - Naoya Kakimoto
- Department of Oral and Maxillofacial Radiology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
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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; 11:805. [PMID: 36979783 PMCID: PMC10045560 DOI: 10.3390/biomedicines11030805] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2023] [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
| | - 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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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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