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Carsuzaa F, Chabrillac E, Marcy PY, Mehanna H, Thariat J. Advances and residual knowledge gaps in the neck management of head and neck squamous cell carcinoma patients with advanced nodal disease undergoing definitive (chemo)radiotherapy for their primary. Strahlenther Onkol 2024; 200:553-567. [PMID: 38600366 DOI: 10.1007/s00066-024-02228-4] [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: 11/29/2023] [Accepted: 03/03/2024] [Indexed: 04/12/2024]
Abstract
PURPOSE Substantial changes have been made in the neck management of patients with head and neck squamous cell carcinomas (HNSCC) in the past century. These have been fostered by changes in cancer epidemiology and technological progress in imaging, surgery, or radiotherapy, as well as disruptive concepts in oncology. We aimed to review changes in nodal management, with a focus on HNSCC patients with nodal involvement (cN+) undergoing (chemo)radiotherapy. METHODS A narrative review was conducted to review current advances and address knowledge gaps in the multidisciplinary management of the cN+ neck in the context of (chemo)radiotherapy. RESULTS Metastatic neck nodes are associated with poorer prognosis and poorer response to radiotherapy, and have therefore been systematically treated by surgery. Radical neck dissection (ND) has gradually evolved toward more personalized and less morbid approaches, i.e., from functional to selective ND. Omission of ND has been made feasible by use of positron-emission tomography/computed tomography to monitor the radiation response in cN+ patients. Human papillomavirus-driven oropharyngeal cancers and their cystic nodes have shown dramatically better prognosis than tobacco-related cancers, justifying a specific prognostic classification (AJCC) creation. Finally, considering the role of lymph nodes in anti-tumor immunity, de-escalation of ND and prophylactic nodal irradiation in combination are intense areas of investigation. However, the management of bulky cN3 disease remains an issue, as aggressive multidisciplinary strategies or innovative combined treatments have not yet significantly improved their prognosis. CONCLUSION Personalized neck management is an increasingly important aspect of the overall therapeutic strategies in cN+ HNSCC.
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Affiliation(s)
- Florent Carsuzaa
- Department of Oto-Rhino-Laryngology & Head and Neck Surgery, Poitiers University Hospital, Poitiers, France
| | - Emilien Chabrillac
- Department of Surgery, University Cancer Institute of Toulouse-Oncopole, Toulouse, France
| | - Pierre Yves Marcy
- Department of Radiology, Clinique du Cap d'Or, La Seyne-sur-mer, France
| | - Hisham Mehanna
- Institute for Head and Neck Studies and Education (InHANSE), University of Birmingham, Birmingham, UK
| | - Juliette Thariat
- Department of radiotherapy, Centre François Baclesse, Caen, France.
- Laboratoire de physique Corpusculaire, IN2P3/ENSICAEN/CNRS, UMR 6534, Normandie Université, Caen, France.
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Li Y, van Rijn-Dekker MI, de Vette SPM, van der Schaaf A, van den Bosch L, Langendijk JA, van Dijk LV, Sijtsema NM. Late-xerostomia prediction model based on 18F-FDG PET image biomarkers of the main salivary glands. Radiother Oncol 2024; 196:110319. [PMID: 38702014 DOI: 10.1016/j.radonc.2024.110319] [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: 01/24/2024] [Revised: 04/13/2024] [Accepted: 04/26/2024] [Indexed: 05/06/2024]
Abstract
BACKGROUND AND PURPOSE Recently, a comprehensive xerostomia prediction model was published, based on baseline xerostomia, mean dose to parotid glands (PG) and submandibular glands (SMG). Previously, PET imaging biomarkers (IBMs) of PG were shown to improve xerostomia prediction. Therefore, this study aimed to explore the potential improvement of the additional PET-IBMs from both PG and SMG to the recent comprehensive xerostomia prediction model (i.e., the reference model). MATERIALS AND METHODS Totally, 540 head and neck cancer patients were split into training and validation cohorts. PET-IBMs from the PG and SMG, were selected using bootstrapped forward selection based on the reference model. The IBMs from both the PG and SMG with the highest selection frequency were added to the reference model, resulting in a PG-IBM model and a SMG-IBM model which were combined into a composite model. Model performance was assessed using the area under the curve (AUC). Likelihood ratio test compared the predictive performance between the reference model and models including IBMs. RESULTS The final selected PET-IBMs were 90th percentile of the PG SUV and total energy of the SMG SUV. The additional two PET-IBMs in the composite model improved the predictive performance of the reference model significantly. The AUC of the reference model and the composite model were 0.67 and 0.69 in the training cohort, and 0.71 and 0.73 in the validation cohort, respectively. CONCLUSION The composite model including two additional PET-IBMs from PG and SMG improved the predictive performance of the reference xerostomia model significantly, facilitating a more personalized prediction approach.
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Affiliation(s)
- Yan Li
- Department of Radiation Oncology, University Medical Centre Groningen, University of Groningen, The Netherlands.
| | - Maria Irene van Rijn-Dekker
- Department of Radiation Oncology, University Medical Centre Groningen, University of Groningen, The Netherlands
| | | | - Arjen van der Schaaf
- Department of Radiation Oncology, University Medical Centre Groningen, University of Groningen, The Netherlands
| | - Lisa van den Bosch
- Department of Radiation Oncology, University Medical Centre Groningen, University of Groningen, The Netherlands
| | | | - Lisanne Vania van Dijk
- Department of Radiation Oncology, University Medical Centre Groningen, University of Groningen, The Netherlands
| | - Nanna Maria Sijtsema
- Department of Radiation Oncology, University Medical Centre Groningen, University of Groningen, The Netherlands
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Bourdillon AT. Computer Vision-Radiomics & Pathognomics. Otolaryngol Clin North Am 2024:S0030-6665(24)00072-0. [PMID: 38910065 DOI: 10.1016/j.otc.2024.05.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/25/2024]
Abstract
The role of computer vision in extracting radiographic (radiomics) and histopathologic (pathognomics) features is an extension of molecular biomarkers that have been foundational to our understanding across the spectrum of head and neck disorders. Especially within head and neck cancers, machine learning and deep learning applications have yielded advances in the characterization of tumor features, nodal features, and various outcomes. This review aims to overview the landscape of radiomic and pathognomic applications, informing future work to address gaps. Novel methodologies will be needed to potentially engineer ways of integrating multidimensional data inputs to examine disease features to guide prognosis comprehensively and ultimately clinical management.
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Affiliation(s)
- Alexandra T Bourdillon
- Department of Otolaryngology-Head & Neck Surgery, University of California-San Francisco, San Francisco, CA 94115, USA.
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Tsai YL, Kang YT, Chan HC, Chattopadhyay A, Chiang CJ, Lee WC, Cheng SHC, Lu TP. Population-Based Prognostic Models for Head and Neck Cancers Using National Cancer Registry Data from Taiwan. J Epidemiol Glob Health 2024; 14:433-443. [PMID: 38353918 PMCID: PMC11176144 DOI: 10.1007/s44197-024-00196-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Accepted: 01/17/2024] [Indexed: 06/15/2024] Open
Abstract
PURPOSE This study aims to raise awareness of the disparities in survival predictions among races in head and neck cancer (HNC) patients by developing and validating population-based prognostic models specifically tailored for Taiwanese and Asian populations. METHODS A total of 49,137 patients diagnosed with HNCs were included from the Taiwan Cancer Registry (TCR). Six prognostic models, divided into three categories based on surgical status, were developed to predict both overall survival (OS) and cancer-specific survival using the registered demographic and clinicopathological characteristics in the Cox proportional hazards model. The prognostic models underwent internal evaluation through a tenfold cross-validation among the TCR Taiwanese datasets and external validation across three primary racial populations using the Surveillance, Epidemiology, and End Results database. Predictive performance was assessed using discrimination analysis employing Harrell's c-index and calibration analysis with proportion tests. RESULTS The TCR training and testing datasets demonstrated stable and favorable predictive performance, with all Harrell's c-index values ≥ 0.7 and almost all differences in proportion between the predicted and observed mortality being < 5%. In external validation, Asians exhibited the best performance compared with white and black populations, particularly in predicting OS, with all Harrell's c-index values > 0.7. CONCLUSIONS Survival predictive disparities exist among different racial groups in HNCs. We have developed population-based prognostic models for Asians that can enhance clinical practice and treatment plans.
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Affiliation(s)
- Yu-Lun Tsai
- Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan
- Department of Radiation Oncology, Cathay General Hospital, Taipei, Taiwan
| | - Yi-Ting Kang
- Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan
| | - Han-Ching Chan
- Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan
| | - Amrita Chattopadhyay
- Bioinformatics and Biostatistics Core, Center of Genomic and Precision Medicine, National Taiwan University, Taipei, Taiwan
| | - Chun-Ju Chiang
- Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan
- Taiwan Cancer Registry, Taipei, Taiwan
| | - Wen-Chung Lee
- Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan
- Taiwan Cancer Registry, Taipei, Taiwan
- Institute of Health Data Analytics and Statistics, College of Public Health, National Taiwan University, Taipei, Taiwan
| | - Skye Hung-Chun Cheng
- Department of Radiation Oncology, Koo Foundation Sun Yat-Sen Cancer Center, Taipei, Taiwan
- Taitung Christian Hospital, Taitung, Taiwan
| | - Tzu-Pin Lu
- Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan.
- Bioinformatics and Biostatistics Core, Center of Genomic and Precision Medicine, National Taiwan University, Taipei, Taiwan.
- Institute of Health Data Analytics and Statistics, College of Public Health, National Taiwan University, Taipei, Taiwan.
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Luo Y, Huang Z, Gao Z, Wang B, Zhang Y, Bai Y, Wu Q, Wang M. Prognostic Value of 18F-FDG PET/CT Radiomics in Extranodal Nasal-Type NK/T Cell Lymphoma. Korean J Radiol 2024; 25:189-198. [PMID: 38288898 PMCID: PMC10831304 DOI: 10.3348/kjr.2023.0618] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 11/08/2023] [Accepted: 11/16/2023] [Indexed: 02/01/2024] Open
Abstract
OBJECTIVE To investigate the prognostic utility of radiomics features extracted from 18F-fluorodeoxyglucose (FDG) PET/CT combined with clinical factors and metabolic parameters in predicting progression-free survival (PFS) and overall survival (OS) in individuals diagnosed with extranodal nasal-type NK/T cell lymphoma (ENKTCL). MATERIALS AND METHODS A total of 126 adults with ENKTCL who underwent 18F-FDG PET/CT examination before treatment were retrospectively included and randomly divided into training (n = 88) and validation cohorts (n = 38) at a ratio of 7:3. Least absolute shrinkage and selection operation Cox regression analysis was used to select the best radiomics features and calculate each patient's radiomics scores (RadPFS and RadOS). Kaplan-Meier curve and Log-rank test were used to compare survival between patient groups risk-stratified by the radiomics scores. Various models to predict PFS and OS were constructed, including clinical, metabolic, clinical + metabolic, and clinical + metabolic + radiomics models. The discriminative ability of each model was evaluated using Harrell's C index. The performance of each model in predicting PFS and OS for 1-, 3-, and 5-years was evaluated using the time-dependent receiver operating characteristic (ROC) curve. RESULTS Kaplan-Meier curve analysis demonstrated that the radiomics scores effectively identified high- and low-risk patients (all P < 0.05). Multivariable Cox analysis showed that the Ann Arbor stage, maximum standardized uptake value (SUVmax), and RadPFS were independent risk factors associated with PFS. Further, β2-microglobulin, Eastern Cooperative Oncology Group performance status score, SUVmax, and RadOS were independent risk factors for OS. The clinical + metabolic + radiomics model exhibited the greatest discriminative ability for both PFS (Harrell's C-index: 0.805 in the validation cohort) and OS (Harrell's C-index: 0.833 in the validation cohort). The time-dependent ROC analysis indicated that the clinical + metabolic + radiomics model had the best predictive performance. CONCLUSION The PET/CT-based clinical + metabolic + radiomics model can enhance prognostication among patients with ENKTCL and may be a non-invasive and efficient risk stratification tool for clinical practice.
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Affiliation(s)
- Yu Luo
- Department of Medical Imaging, Henan Provincial People's Hospital, The People's Hospital of Zhengzhou University, Zhengzhou, China
- Academy of Medical Sciences, Zhengzhou University, Zhengzhou, China
| | - Zhun Huang
- Department of Medical Imaging, Henan Provincial People's Hospital, The People's Hospital of Zhengzhou University, Zhengzhou, China
| | - Zihan Gao
- Department of Medical Imaging, Henan Provincial People's Hospital, The People's Hospital of Zhengzhou University, Zhengzhou, China
| | - Bingbing Wang
- Department of Medical Imaging, Henan Provincial People's Hospital, The People's Hospital of Zhengzhou University, Zhengzhou, China
| | - Yanwei Zhang
- Department of Bethune International Peace Hospital, Department of Radiology, Shijiazhuang, China
| | - Yan Bai
- Department of Medical Imaging, Henan Provincial People's Hospital, The People's Hospital of Zhengzhou University, Zhengzhou, China
| | - Qingxia Wu
- Beijing United Imaging Research Institute of Intelligent Imaging, Beijing, China
| | - Meiyun Wang
- Department of Medical Imaging, Henan Provincial People's Hospital, The People's Hospital of Zhengzhou University, Zhengzhou, China
- Henan Key Laboratory for Medical Imaging of Neurological Diseases, Zhengzhou, China
- Laboratory of Brain Science and Brain-Like Intelligence Technology, Institute for Integrated Medical Science and Engineering, Henan Academy of Sciences, Zhengzhou, China.
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Huang BT, Wang Y, Lin PX. Developing a clinical-radiomic prediction model for 3-year cancer-specific survival in lung cancer patients treated with stereotactic body radiation therapy. J Cancer Res Clin Oncol 2024; 150:34. [PMID: 38277078 PMCID: PMC10817845 DOI: 10.1007/s00432-023-05536-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: 10/07/2023] [Accepted: 11/03/2023] [Indexed: 01/27/2024]
Abstract
PURPOSE The study aims to develop and validate a combined model for predicting 3-year cancer-specific survival (CSS) in lung cancer patients treated with stereotactic body radiation therapy (SBRT) by integrating clinical and radiomic parameters. METHODS Clinical data and pre-treatment CT images were collected from 102 patients treated with lung SBRT. Multivariate logistic regression and the least absolute shrinkage and selection operator were used to determine the clinical and radiomic factors associated with 3-year CSS. Three prediction models were developed using clinical factors, radiomic factors, and a combination of both. The performance of the models was assessed using receiver operating characteristic curve and calibration curve. A nomogram was also created to visualize the 3-year CSS prediction. RESULTS With a 36-month follow-up, 40 patients (39.2%) died of lung cancer and 62 patients (60.8%) survived. Three clinical factors, including gender, clinical stage, and lymphocyte ratio, along with three radiomic features, were found to be independent factors correlated with 3-year CSS. The area under the curve values for the clinical, radiomic, and combined model were 0.839 (95% CI 0.735-0.914), 0.886 (95% CI 0.790-0.948), and 0.914 (95% CI 0.825-0.966) in the training cohort, and 0.757 (95% CI 0.580-0.887), 0.818 (95% CI 0.648-0.929), and 0.843 (95% CI 0.677-0.944) in the validation cohort, respectively. Additionally, the calibration curve demonstrated good calibration performance and the nomogram created from the combined model showed potential for clinical utility. CONCLUSION A clinical-radiomic model was developed to predict the 3-year CSS for lung cancer patients treated with SBRT.
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Affiliation(s)
- Bao-Tian Huang
- Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, Shantou, 515000, China.
| | - Ying Wang
- Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, Shantou, 515000, China
| | - Pei-Xian Lin
- Department of Nosocomial Infection Management, The Second Affiliated Hospital of Shantou University Medical College, Shantou, 515000, China
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Zhong NN, Wang HQ, Huang XY, Li ZZ, Cao LM, Huo FY, Liu B, Bu LL. Enhancing head and neck tumor management with artificial intelligence: Integration and perspectives. Semin Cancer Biol 2023; 95:52-74. [PMID: 37473825 DOI: 10.1016/j.semcancer.2023.07.002] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2023] [Revised: 07/11/2023] [Accepted: 07/15/2023] [Indexed: 07/22/2023]
Abstract
Head and neck tumors (HNTs) constitute a multifaceted ensemble of pathologies that primarily involve regions such as the oral cavity, pharynx, and nasal cavity. The intricate anatomical structure of these regions poses considerable challenges to efficacious treatment strategies. Despite the availability of myriad treatment modalities, the overall therapeutic efficacy for HNTs continues to remain subdued. In recent years, the deployment of artificial intelligence (AI) in healthcare practices has garnered noteworthy attention. AI modalities, inclusive of machine learning (ML), neural networks (NNs), and deep learning (DL), when amalgamated into the holistic management of HNTs, promise to augment the precision, safety, and efficacy of treatment regimens. The integration of AI within HNT management is intricately intertwined with domains such as medical imaging, bioinformatics, and medical robotics. This article intends to scrutinize the cutting-edge advancements and prospective applications of AI in the realm of HNTs, elucidating AI's indispensable role in prevention, diagnosis, treatment, prognostication, research, and inter-sectoral integration. The overarching objective is to stimulate scholarly discourse and invigorate insights among medical practitioners and researchers to propel further exploration, thereby facilitating superior therapeutic alternatives for patients.
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Affiliation(s)
- Nian-Nian Zhong
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Han-Qi Wang
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Xin-Yue Huang
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Zi-Zhan Li
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Lei-Ming Cao
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Fang-Yi Huo
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Bing Liu
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China; Department of Oral & Maxillofacial - Head Neck Oncology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China.
| | - Lin-Lin Bu
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China; Department of Oral & Maxillofacial - Head Neck Oncology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China.
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Huynh BN, Groendahl AR, Tomic O, Liland KH, Knudtsen IS, Hoebers F, van Elmpt W, Malinen E, Dale E, Futsaether CM. Head and neck cancer treatment outcome prediction: a comparison between machine learning with conventional radiomics features and deep learning radiomics. Front Med (Lausanne) 2023; 10:1217037. [PMID: 37711738 PMCID: PMC10498924 DOI: 10.3389/fmed.2023.1217037] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Accepted: 07/07/2023] [Indexed: 09/16/2023] Open
Abstract
Background Radiomics can provide in-depth characterization of cancers for treatment outcome prediction. Conventional radiomics rely on extraction of image features within a pre-defined image region of interest (ROI) which are typically fed to a classification algorithm for prediction of a clinical endpoint. Deep learning radiomics allows for a simpler workflow where images can be used directly as input to a convolutional neural network (CNN) with or without a pre-defined ROI. Purpose The purpose of this study was to evaluate (i) conventional radiomics and (ii) deep learning radiomics for predicting overall survival (OS) and disease-free survival (DFS) for patients with head and neck squamous cell carcinoma (HNSCC) using pre-treatment 18F-fluorodeoxuglucose positron emission tomography (FDG PET) and computed tomography (CT) images. Materials and methods FDG PET/CT images and clinical data of patients with HNSCC treated with radio(chemo)therapy at Oslo University Hospital (OUS; n = 139) and Maastricht University Medical Center (MAASTRO; n = 99) were collected retrospectively. OUS data was used for model training and initial evaluation. MAASTRO data was used for external testing to assess cross-institutional generalizability. Models trained on clinical and/or conventional radiomics features, with or without feature selection, were compared to CNNs trained on PET/CT images without or with the gross tumor volume (GTV) included. Model performance was measured using accuracy, area under the receiver operating characteristic curve (AUC), Matthew's correlation coefficient (MCC), and the F1 score calculated for both classes separately. Results CNNs trained directly on images achieved the highest performance on external data for both endpoints. Adding both clinical and radiomics features to these image-based models increased performance further. Conventional radiomics including clinical data could achieve competitive performance. However, feature selection on clinical and radiomics data lead to overfitting and poor cross-institutional generalizability. CNNs without tumor and node contours achieved close to on-par performance with CNNs including contours. Conclusion High performance and cross-institutional generalizability can be achieved by combining clinical data, radiomics features and medical images together with deep learning models. However, deep learning models trained on images without contours can achieve competitive performance and could see potential use as an initial screening tool for high-risk patients.
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Affiliation(s)
- Bao Ngoc Huynh
- Faculty of Science and Technology, Norwegian University of Life Sciences, Ås, Norway
| | | | - Oliver Tomic
- Faculty of Science and Technology, Norwegian University of Life Sciences, Ås, Norway
| | - Kristian Hovde Liland
- Faculty of Science and Technology, Norwegian University of Life Sciences, Ås, Norway
| | - Ingerid Skjei Knudtsen
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Medical Physics, Oslo University Hospital, Oslo, Norway
| | - Frank Hoebers
- Department of Radiation Oncology (MAASTRO), Maastricht University Medical Center, Maastricht, Netherlands
- GROW School for Oncology and Reproduction, Maastricht University Medical Center, Maastricht, Netherlands
| | - Wouter van Elmpt
- Department of Radiation Oncology (MAASTRO), Maastricht University Medical Center, Maastricht, Netherlands
- GROW School for Oncology and Reproduction, Maastricht University Medical Center, Maastricht, Netherlands
| | - Eirik Malinen
- Department of Medical Physics, Oslo University Hospital, Oslo, Norway
- Department of Physics, University of Oslo, Oslo, Norway
| | - Einar Dale
- Department of Oncology, Oslo University Hospital, Oslo, Norway
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Kawashima Y, Miyakoshi M, Kawabata Y, Indo H. Efficacy of texture analysis of ultrasonographic images in the differentiation of metastatic and non-metastatic cervical lymph nodes in patients with squamous cell carcinoma of the tongue. Oral Surg Oral Med Oral Pathol Oral Radiol 2023:S2212-4403(23)00439-X. [PMID: 37353468 DOI: 10.1016/j.oooo.2023.04.012] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 04/13/2023] [Accepted: 04/23/2023] [Indexed: 06/25/2023]
Abstract
OBJECTIVE We investigated the efficacy of using texture analysis of ultrasonographic images of the cervical lymph nodes of patients with squamous cell carcinoma of the tongue to differentiate between metastatic and non-metastatic lymph nodes. STUDY DESIGN We analyzed 32 metastatic and 28 non-metastatic lymph nodes diagnosed by histopathologic examination on presurgical US images. Using the LIFEx texture analysis program, we extracted 36 texture features from the images and calculated the statistical significance of differences in texture features between metastatic and non-metastatic lymph nodes using the t test. To assess the diagnostic ability of the significantly different texture features to discriminate between metastatic and non-metastatic nodes, we performed receiver operating characteristic curve analysis and calculated the area under the curve. We set the cutoff points that maximized the sensitivity and specificity for each curve according to the Youden J statistic. RESULTS We found that 20 texture features significantly differed between metastatic and non-metastatic lymph nodes. Among them, only the gray-level run length matrix feature of run length non-uniformity and the gray-level zone length matrix features of gray-level non-uniformity and zone length non-uniformity showed an excellent ability to discriminate between metastatic and non-metastatic lymph nodes as indicated by the area under the curve and the sum of sensitivity and specificity. CONCLUSIONS Analysis of the texture features of run length non-uniformity, gray-level non-uniformity, and zone length non-uniformity values allows for differentiation between metastatic and non-metastatic lymph nodes, with the use of gray-level non-uniformity appearing to be the best means of predicting metastatic lymph nodes.
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Affiliation(s)
- Yusuke Kawashima
- Department of Maxillofacial Radiology, Kagoshima University Graduate School of Medical and Dental Sciences Field of Oncology, Kagoshima, Japan.
| | - Masaaki Miyakoshi
- Department of Maxillofacial Radiology, Kagoshima University Graduate School of Medical and Dental Sciences Field of Oncology, Kagoshima, Japan
| | - Yoshihiro Kawabata
- Department of Maxillofacial Radiology, Kagoshima University Graduate School of Medical and Dental Sciences Field of Oncology, Kagoshima, Japan
| | - Hiroko Indo
- Department of Maxillofacial Radiology, Kagoshima University Graduate School of Medical and Dental Sciences Field of Oncology, Kagoshima, Japan
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Mori M, Deantoni C, Olivieri M, Spezi E, Chiara A, Baroni S, Picchio M, Del Vecchio A, Di Muzio NG, Fiorino C, Dell'Oca I. External validation of an 18F-FDG-PET radiomic model predicting survival after radiotherapy for oropharyngeal cancer. Eur J Nucl Med Mol Imaging 2023; 50:1329-1336. [PMID: 36604325 DOI: 10.1007/s00259-022-06098-9] [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: 08/12/2022] [Accepted: 12/24/2022] [Indexed: 01/06/2023]
Abstract
PURPOSE/OBJECTIVE The purpose of the study is to externally validate published 18F-FDG-PET radiomic models for outcome prediction in patients with oropharyngeal cancer treated with chemoradiotherapy. MATERIAL/METHODS Outcome data and pre-radiotherapy PET images of 100 oropharyngeal cancer patients (stage IV:78) treated with concomitant chemotherapy to 66-69 Gy/30 fr were available. Tumors were segmented using a previously validated semi-automatic method; 450 radiomic features (RF) were extracted according to IBSI (Image Biomarker Standardization Initiative) guidelines. Only one model for cancer-specific survival (CSS) prediction was suitable to be independently tested, according to our criteria. This model, in addition to HPV status, SUVmean and SUVmax, included two independent meta-factors (Fi), resulting from combining selected RF clusters. In a subgroup of 66 patients with complete HPV information, the global risk score R was computed considering the original coefficients and was tested by Cox regression as predictive of CSS. Independently, only the radiomic risk score RF derived from Fi was tested on the same subgroup to learn about the radiomics contribution to the model. The metabolic tumor volume (MTV) was also tested as a single predictor and its prediction performances were compared to the global and radiomic models. Finally, the validation of MTV and the radiomic score RF were also tested on the entire dataset. RESULTS Regarding the analysis of the subgroup with HPV information, with a median follow-up of 41.6 months, seven patients died due to cancer. R was confirmed to be associated to CSS (p value = 0.05) with a C-index equal 0.75 (95% CI=0.62-0.85). The best cut-off value (equal to 0.15) showed high ability in patient stratification (p=0.01, HR=7.4, 95% CI=1.6-11.4). The 5-year CSS for R were 97% (95% CI: 93-100%) vs 74% (56-92%) for low- and high-risk groups, respectively. RF and MTV alone were also significantly associated to CSS for the subgroup with an almost identical C-index. According to best cut-off value (RF>0.12 and MTV>15.5cc), the 5-year CSS were 96% (95% CI: 89-100%) vs 65% (36-94%) and 97% (95% CI: 88-100%) vs 77% (58-93%) for RF and MTV, respectively. Results regarding RF and MTV were confirmed in the overall group. CONCLUSION A previously published PET radiomic model for CSS prediction was independently validated. Performances of the model were similar to the ones of using only the MTV, without improvement of prediction accuracy.
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Affiliation(s)
- Martina Mori
- Department of Medical Physics, San Raffaele Scientific Institute, Milano, Italy
| | - Chiara Deantoni
- Department of Radiotherapy, San Raffaele Scientific Institute, Milano, Italy
| | - Michela Olivieri
- Department of Medical Physics, San Raffaele Scientific Institute, Milano, Italy
| | - Emiliano Spezi
- School of Engineering, Cardiff University, Cardiff, UK
- Department of Medical Physics, Velindre Cancer Centre, Cardiff, UK
| | - Anna Chiara
- Department of Radiotherapy, San Raffaele Scientific Institute, Milano, Italy
| | - Simone Baroni
- Department of Radiotherapy, San Raffaele Scientific Institute, Milano, Italy
| | - Maria Picchio
- Department of Nuclear Medicine, San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
| | | | - Nadia Gisella Di Muzio
- Department of Radiotherapy, San Raffaele Scientific Institute, Milano, Italy
- Vita-Salute San Raffaele University, Milan, Italy
| | - Claudio Fiorino
- Department of Medical Physics, San Raffaele Scientific Institute, Milano, Italy.
| | - Italo Dell'Oca
- Department of Radiotherapy, San Raffaele Scientific Institute, Milano, Italy
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11
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Páez-Carpio A, Medrano-Martorell S, Berenguer J, Muxí A, Vilaseca I, Valduvieco I, Castillo P, Baste N, Avilés-Jurado FX, Grau JJ, Oleaga L. Persistent lymph nodes after curative chemoradiotherapy for head and neck cancer: imaging predictors of response for decision-making. Eur Arch Otorhinolaryngol 2023; 280:1369-1379. [PMID: 36181529 DOI: 10.1007/s00405-022-07658-3] [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: 05/21/2022] [Accepted: 09/12/2022] [Indexed: 02/07/2023]
Abstract
PURPOSE To identify response predictors in patients with head and neck squamous cell carcinoma (N + HNSCC) and persistent lymph nodes after curative chemoradiotherapy treatment (CCRT). MATERIALS AND METHODS Consecutive patients with N + HNSCC treated with CCRT and persistent lymph nodes at first follow-up between 2015 and 2021 were identified and analyzed. Complete response was defined as the absence of lymph node metastatic involvement in patients with salvage lymphadenectomy or the absence of progression after 1 year of successive follow-ups. Tumour type and location, staging, and human papillomavirus (HPV) status were considered for analysis. The number and size of lymph nodes, type, shape, enhancement and margins on diagnostic and follow-up CT were also analyzed. RESULTS The cohort included 46 patients with 134 pathological lymph nodes. Logistic regression models showed the following variables to be significant: performance of salvage lymphadenectomy (OR 0.094, [CI 95% 0.004-0.61], p = 0.037); the type of lymphadenopathy on diagnostic CE-CT (solid vs. cystic) (N1: OR = 4.11, [CI 95% 1.11-17.93], p = 0.042 and N3: OR 6.42, [CI 95% 1.2-42.56], p = 0.036); the change of shape (round to oval) on the follow-up CE-CT (OR 9.76, [CI 95% 1.79-8.57], p = 0.016) and the time in days between CCRT and the first follow-up CE-CT (OR 1.06, [CI 95% 1.004-1.13], p = 0.048). CONCLUSIONS In our experience, the presence of solid lymph nodes on pre-treatment CT and the change in shape from round to oval on post-treatment CT are predictors of response to treatment in patients with N + HNSCC persistent lymph nodes after CCRT. Increasing the temporal interval between treatment and follow-up CT should be considered to avoid unnecessary nodal dissections.
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Affiliation(s)
| | | | - Joan Berenguer
- Department of Radiology, CDI, Hospital Clínic Barcelona, Barcelona, Spain
| | - Africa Muxí
- Department of Nuclear Medicine, CDI, Hospital Clínic Barcelona, Barcelona, Spain
| | - Isabel Vilaseca
- Otorhinolaryngology Service, Hospital Clínic Barcelona, Barcelona, Spain
| | - Izaskun Valduvieco
- Radiotherapy Oncology Service, ICMHO, Hospital Clínic Barcelona, Barcelona, Spain
| | - Paola Castillo
- Pathology Service, CDB, Hospital Clínic Barcelona, Barcelona, Spain
| | - Neus Baste
- Medical Oncology Service, ICMHO, Hospital Clínic Barcelona, Barcelona, Spain
| | | | - Juan José Grau
- Medical Oncology Service, ICMHO, Hospital Clínic Barcelona, Barcelona, Spain
| | - Laura Oleaga
- Department of Radiology, CDI, Hospital Clínic Barcelona, Barcelona, Spain
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12
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Franzese C, Lillo S, Cozzi L, Teriaca MA, Badalamenti M, Di Cristina L, Vernier V, Stefanini S, Dei D, Pergolizzi S, De Virgilio A, Mercante G, Spriano G, Mancosu P, Tomatis S, Scorsetti M. Predictive value of clinical and radiomic features for radiation therapy response in patients with lymph node-positive head and neck cancer. Head Neck 2023; 45:1184-1193. [PMID: 36815619 DOI: 10.1002/hed.27332] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 02/02/2023] [Accepted: 02/14/2023] [Indexed: 02/24/2023] Open
Abstract
BACKGROUND Prediction of survival and radiation therapy response is challenging in head and neck cancer with metastatic lymph nodes (LNs). Here we developed novel radiomics- and clinical-based predictive models. METHODS Volumes of interest of LNs were employed for radiomic features extraction. Radiomic and clinical features were investigated for their predictive value relatively to locoregional failure (LRF), progression-free survival (PFS), and overall survival (OS) and used to build multivariate models. RESULTS Hundred and six subjects were suitable for final analysis. Univariate analysis identified two radiomic features significantly predictive for LRF, and five radiomic features plus two clinical features significantly predictive for both PFS and OS. The area under the curve of receiver operating characteristic curve combining clinical and radiomic predictors for PFS and OS resulted 0.71 (95%CI: 0.60-0.83) and 0.77 (95%CI: 0.64-0.89). CONCLUSIONS Radiomic and clinical features resulted to be independent predictive factors, but external independent validation is mandatory to support these findings.
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Affiliation(s)
- Ciro Franzese
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy.,Department of Radiotherapy and Radiosurgery, IRCCS Humanitas Research Hospital, Rozzano, Italy
| | - Sara Lillo
- Department of Biomedical, Dental Science and Morphological and Functional Images, University of Messina, Messina, Italy
| | - Luca Cozzi
- Department of Radiotherapy and Radiosurgery, IRCCS Humanitas Research Hospital, Rozzano, Italy
| | - Maria Ausilia Teriaca
- Department of Radiotherapy and Radiosurgery, IRCCS Humanitas Research Hospital, Rozzano, Italy
| | - Marco Badalamenti
- Department of Radiotherapy and Radiosurgery, IRCCS Humanitas Research Hospital, Rozzano, Italy
| | - Luciana Di Cristina
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy.,Department of Radiotherapy and Radiosurgery, IRCCS Humanitas Research Hospital, Rozzano, Italy
| | - Veronica Vernier
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy.,Department of Radiotherapy and Radiosurgery, IRCCS Humanitas Research Hospital, Rozzano, Italy
| | - Sara Stefanini
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy.,Department of Radiotherapy and Radiosurgery, IRCCS Humanitas Research Hospital, Rozzano, Italy
| | - Damiano Dei
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy.,Department of Radiotherapy and Radiosurgery, IRCCS Humanitas Research Hospital, Rozzano, Italy
| | - Stefano Pergolizzi
- Department of Biomedical, Dental Science and Morphological and Functional Images, University of Messina, Messina, Italy
| | - Armando De Virgilio
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy.,Otorhinolaryngology Unit, IRCCS Humanitas Research Hospital, Rozzano, Italy
| | - Giuseppe Mercante
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy.,Otorhinolaryngology Unit, IRCCS Humanitas Research Hospital, Rozzano, Italy
| | - Giuseppe Spriano
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy.,Otorhinolaryngology Unit, IRCCS Humanitas Research Hospital, Rozzano, Italy
| | - Pietro Mancosu
- Department of Radiotherapy and Radiosurgery, IRCCS Humanitas Research Hospital, Rozzano, Italy
| | - Stefano Tomatis
- Department of Radiotherapy and Radiosurgery, IRCCS Humanitas Research Hospital, Rozzano, Italy
| | - Marta Scorsetti
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy.,Department of Radiotherapy and Radiosurgery, IRCCS Humanitas Research Hospital, Rozzano, Italy
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13
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Prediction of Incomplete Response of Primary Tumour Based on Clinical and Radiomics Features in Inoperable Head and Neck Cancers after Definitive Treatment. J Pers Med 2022; 12:jpm12071092. [PMID: 35887587 PMCID: PMC9317569 DOI: 10.3390/jpm12071092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Revised: 06/27/2022] [Accepted: 06/28/2022] [Indexed: 11/16/2022] Open
Abstract
Radical treatment of patients diagnosed with inoperable and locally advanced head and neck cancers (LAHNC) is still a challenge for clinicians. Prediction of incomplete response (IR) of primary tumour would be of value to the treatment optimization for patients with LAHNC. Aim of this study was to develop and evaluate models based on clinical and radiomics features for prediction of IR in patients diagnosed with LAHNC and treated with definitive chemoradiation or radiotherapy. Clinical and imaging data of 290 patients were included into this retrospective study. Clinical model was built based on tumour and patient related features. Radiomics features were extracted based on imaging data, consisting of contrast- and non-contrast-enhanced pre-treatment CT images, obtained in process of diagnosis and radiotherapy planning. Performance of clinical and combined models were evaluated with area under the ROC curve (AUROC). Classification performance was evaluated using 5-fold cross validation. Model based on selected clinical features including ECOG performance, tumour stage T3/4, primary site: oral cavity and tumour volume were significantly predictive for IR, with AUROC of 0.78. Combining clinical and radiomics features did not improve model’s performance, achieving AUROC 0.77 and 0.68 for non-contrast enhanced and contrast-enhanced images respectively. The model based on clinical features showed good performance in IR prediction. Combined model performance suggests that real-world imaging data might not yet be ready for use in predictive models.
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14
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George MM, Tolley NS. AIM in Otolaryngology and Head and Neck Surgery. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/09/2022]
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15
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Kang CY, Duarte SE, Kim HS, Kim E, Park J, Lee AD, Kim Y, Kim L, Cho S, Oh Y, Gim G, Park I, Lee D, Abazeed M, Velichko YS, Chae YK. OUP accepted manuscript. Oncologist 2022; 27:e471-e483. [PMID: 35348765 PMCID: PMC9177100 DOI: 10.1093/oncolo/oyac036] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Accepted: 01/14/2022] [Indexed: 11/17/2022] Open
Abstract
The recent, rapid advances in immuno-oncology have revolutionized cancer treatment and spurred further research into tumor biology. Yet, cancer patients respond variably to immunotherapy despite mounting evidence to support its efficacy. Current methods for predicting immunotherapy response are unreliable, as these tests cannot fully account for tumor heterogeneity and microenvironment. An improved method for predicting response to immunotherapy is needed. Recent studies have proposed radiomics—the process of converting medical images into quantitative data (features) that can be processed using machine learning algorithms to identify complex patterns and trends—for predicting response to immunotherapy. Because patients undergo numerous imaging procedures throughout the course of the disease, there exists a wealth of radiological imaging data available for training radiomics models. And because radiomic features reflect cancer biology, such as tumor heterogeneity and microenvironment, these models have enormous potential to predict immunotherapy response more accurately than current methods. Models trained on preexisting biomarkers and/or clinical outcomes have demonstrated potential to improve patient stratification and treatment outcomes. In this review, we discuss current applications of radiomics in oncology, followed by a discussion on recent studies that use radiomics to predict immunotherapy response and toxicity.
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Affiliation(s)
| | | | - Hye Sung Kim
- Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Eugene Kim
- Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | | | - Alice Daeun Lee
- Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Yeseul Kim
- Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Leeseul Kim
- Department of Internal Medicine, AMITA Health Saint Francis Hospital, Evanston, IL, USA
| | - Sukjoo Cho
- Department of Pediatrics, University of South Florida Morsani College of Medicine, Tampa, FL, USA
| | - Yoojin Oh
- Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Gahyun Gim
- Department of Hematology and Oncology, Department of Medicine, University of Rochester Medical Center, Rochester, NY, USA
| | - Inae Park
- Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Dongyup Lee
- Department of Physical Medicine and Rehabilitation, Geisinger Health System, Danville, PA, USA
| | - Mohamed Abazeed
- Department of Radiation Oncology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Yury S Velichko
- Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Young Kwang Chae
- Corresponding author: Young Kwang Chae, Department of Hematology and Oncology, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
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16
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Wu L, Lin P, Zhao Y, Li X, Yang H, He Y. Prediction of Genetic Alterations in Oncogenic Signaling Pathways in Squamous Cell Carcinoma of the Head and Neck: Radiogenomic Analysis Based on Computed Tomography Images. J Comput Assist Tomogr 2021; 45:932-940. [PMID: 34469904 PMCID: PMC8608003 DOI: 10.1097/rct.0000000000001213] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
OBJECTIVE This study investigated the role of radiomics in evaluating the alterations of oncogenic signaling pathways in head and neck cancer. METHODS Radiomics features were extracted from 106 enhanced computed tomography images with head and neck squamous cell carcinoma. Support vector machine-recursive feature elimination was used for feature selection. Support vector machine algorithm was used to develop radiomics scores to predict genetic alterations in oncogenic signaling pathways. The performance was evaluated by the area under the curve (AUC) of the receiver operating characteristic curve. RESULTS The alterations of the Cell Cycle, HIPPO, NOTCH, PI3K, RTK RAS, and TP53 signaling pathways were predicted by radiomics scores. The AUC values of the training cohort were 0.94, 0.91, 0.94, 0.93, 0.87, and 0.93, respectively. The AUC values of the validation cohort were all greater than 0.7. CONCLUSIONS Radiogenomics is a new method for noninvasive acquisition of tumor molecular information at the genetic level.
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Affiliation(s)
- Linyong Wu
- From the Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning
| | - Peng Lin
- From the Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning
| | - Yujia Zhao
- From the Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning
| | - Xin Li
- GE Healthcare, Shanghai, China
| | - Hong Yang
- From the Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning
| | - Yun He
- From the Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning
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17
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Bruixola G, Remacha E, Jiménez-Pastor A, Dualde D, Viala A, Montón JV, Ibarrola-Villava M, Alberich-Bayarri Á, Cervantes A. Radiomics and radiogenomics in head and neck squamous cell carcinoma: Potential contribution to patient management and challenges. Cancer Treat Rev 2021; 99:102263. [PMID: 34343892 DOI: 10.1016/j.ctrv.2021.102263] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Revised: 07/06/2021] [Accepted: 07/23/2021] [Indexed: 12/12/2022]
Abstract
The application of imaging biomarkers in oncology is still in its infancy, but with the expansion of radiomics and radiogenomics a revolution is expected in this field. This may be of special interest in head and neck cancer, since it can promote precision medicine and personalization of treatment by overcoming several intrinsic obstacles in this pathology. Our goal is to provide the medical oncologist with the basis to approach these disciplines and appreciate their main uses in clinical research and clinical practice in the medium term. Aligned with this objective we analyzed the most relevant studies in the field, also highlighting novel opportunities and current challenges.
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Affiliation(s)
- Gema Bruixola
- Department of Medical Oncology, INCLIVA Biomedical Research Institute, University of Valencia, Valencia, Spain
| | - Elena Remacha
- Quantitative Imaging Biomarkers in Medicine (QUIBIM SL), Valencia, Spain
| | - Ana Jiménez-Pastor
- Quantitative Imaging Biomarkers in Medicine (QUIBIM SL), Valencia, Spain
| | - Delfina Dualde
- Department of Radiology, INCLIVA Biomedical Research Institute, University of Valencia, Valencia, Spain
| | - Alba Viala
- Department of Medical Oncology, INCLIVA Biomedical Research Institute, University of Valencia, Valencia, Spain
| | - Jose Vicente Montón
- Department of Medical Oncology, INCLIVA Biomedical Research Institute, University of Valencia, Valencia, Spain
| | - Maider Ibarrola-Villava
- Department of Medical Oncology, INCLIVA Biomedical Research Institute, University of Valencia, Valencia, Spain; CIBERONC, Instituto de Salud Carlos III, Madrid, Spain
| | | | - Andrés Cervantes
- Department of Medical Oncology, INCLIVA Biomedical Research Institute, University of Valencia, Valencia, Spain; CIBERONC, Instituto de Salud Carlos III, Madrid, Spain.
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18
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Radiomic Model Predicts Lymph Node Response to Induction Chemotherapy in Locally Advanced Head and Neck Cancer. Diagnostics (Basel) 2021; 11:diagnostics11040588. [PMID: 33806029 PMCID: PMC8064478 DOI: 10.3390/diagnostics11040588] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Revised: 03/22/2021] [Accepted: 03/22/2021] [Indexed: 12/24/2022] Open
Abstract
This study developed a pretreatment CT-based radiomic model of lymph node response to induction chemotherapy in locally advanced head and neck squamous cell carcinoma (HNSCC) patients. This was a single-center retrospective study of patients with locally advanced HPV+ HNSCC. Forty-one enlarged lymph nodes were found from 27 patients on pretreatment CT and were split into 3:1 training and testing cohorts. Ninety-three radiomic features were extracted. A radiomic model and a combined radiomic-clinical model predicting lymph node response to induction chemotherapy were developed using multivariable logistic regression. Median age was 57 years old, and 93% of patients were male. Post-treatment evaluation was 32 days after treatment, with a median reduction in lymph node volume of 66%. A three-feature radiomic model (minimum, skewness, and low gray level run emphasis) and a combined radiomic-clinical model were developed. The combined model performed the best, with AUC = 0.85 on the training cohort and AUC = 0.75 on the testing cohort. A pretreatment CT-based lymph node radiomic signature combined with clinical parameters was able to predict nodal response to induction chemotherapy for patients with locally advanced HNSCC.
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19
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Meng L, Dong D, Chen X, Fang M, Wang R, Li J, Liu Z, Tian J. 2D and 3D CT Radiomic Features Performance Comparison in Characterization of Gastric Cancer: A Multi-Center Study. IEEE J Biomed Health Inform 2021; 25:755-763. [PMID: 32750940 DOI: 10.1109/jbhi.2020.3002805] [Citation(s) in RCA: 61] [Impact Index Per Article: 20.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
OBJECTIVE Radiomics, an emerging tool for medical image analysis, is potential towards precisely characterizing gastric cancer (GC). Whether using one-slice 2D annotation or whole-volume 3D annotation remains a long-time debate, especially for heterogeneous GC. We comprehensively compared 2D and 3D radiomic features' representation and discrimination capacity regarding GC, via three tasks ( TLNM, lymph node metastasis' prediction; TLVI, lymphovascular invasion's prediction; TpT, pT4 or other pT stages' classification). METHODS Four-center 539 GC patients were retrospectively enrolled and divided into the training and validation cohorts. From 2D or 3D regions of interest (ROIs) annotated by radiologists, radiomic features were extracted respectively. Feature selection and model construction procedures were customed for each combination of two modalities (2D or 3D) and three tasks. Subsequently, six machine learning models ( Model2DLNM, Model3DLNM; Model2DLVI, Model3DLVI; Model2DpT, Model3DpT) were derived and evaluated to reflect modalities' performances in characterizing GC. Furthermore, we performed an auxiliary experiment to assess modalities' performances when resampling spacing different. RESULTS Regarding three tasks, the yielded areas under the curve (AUCs) were: Model2DLNM's 0.712 (95% confidence interval, 0.613-0.811), Model3DLNM's 0.680 (0.584-0.775); Model2DLVI's 0.677 (0.595-0.761), Model3DLVI's 0.615 (0.528-0.703); Model2DpT's 0.840 (0.779-0.901), Model3DpT's 0.813 (0.747-0.879). Moreover, the auxiliary experiment indicated that Models2D are statistically advantageous than Models3D with different resampling spacings. CONCLUSION Models constructed with 2D radiomic features revealed comparable performances with those constructed with 3D features in characterizing GC. SIGNIFICANCE Our work indicated that time-saving 2D annotation would be the better choice in GC, and provided a related reference to further radiomics-based researches.
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20
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Wang X, Li BB. Deep Learning in Head and Neck Tumor Multiomics Diagnosis and Analysis: Review of the Literature. Front Genet 2021; 12:624820. [PMID: 33643386 PMCID: PMC7902873 DOI: 10.3389/fgene.2021.624820] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2020] [Accepted: 01/07/2021] [Indexed: 12/24/2022] Open
Abstract
Head and neck tumors are the sixth most common neoplasms. Multiomics integrates multiple dimensions of clinical, pathologic, radiological, and biological data and has the potential for tumor diagnosis and analysis. Deep learning (DL), a type of artificial intelligence (AI), is applied in medical image analysis. Among the DL techniques, the convolution neural network (CNN) is used for image segmentation, detection, and classification and in computer-aided diagnosis. Here, we reviewed multiomics image analysis of head and neck tumors using CNN and other DL neural networks. We also evaluated its application in early tumor detection, classification, prognosis/metastasis prediction, and the signing out of the reports. Finally, we highlighted the challenges and potential of these techniques.
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Affiliation(s)
- Xi Wang
- Department of Oral Pathology, Peking University School and Hospital of Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Laboratory for Digital and Material Technology of Stomatology & Beijing Key Laboratory of Digital Stomatology, Beijing, China
- Research Unit of Precision Pathologic Diagnosis in Tumors of the Oral and Maxillofacial Regions, Chinese Academy of Medical Sciences, Beijing, China
| | - Bin-bin Li
- Department of Oral Pathology, Peking University School and Hospital of Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Laboratory for Digital and Material Technology of Stomatology & Beijing Key Laboratory of Digital Stomatology, Beijing, China
- Research Unit of Precision Pathologic Diagnosis in Tumors of the Oral and Maxillofacial Regions, Chinese Academy of Medical Sciences, Beijing, China
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21
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Zhai TT, Wesseling F, Langendijk JA, Shi Z, Kalendralis P, van Dijk LV, Hoebers F, Steenbakkers RJHM, Dekker A, Wee L, Sijtsema NM. External validation of nodal failure prediction models including radiomics in head and neck cancer. Oral Oncol 2021; 112:105083. [PMID: 33189001 DOI: 10.1016/j.oraloncology.2020.105083] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Revised: 10/11/2020] [Accepted: 10/27/2020] [Indexed: 02/05/2023]
Abstract
PURPOSE To externally validate the previously published pre-treatment prediction models for lymph nodes failure after definitive radiotherapy in head and neck squamous cell carcinoma (HNSCC) patients. MATERIALS AND METHODS This external validation cohort consisted of 143 node positive HNSCC patients treated between July 2007 and June 2016 by curative radiotherapy with or without either cisplatin or cetuximab. Imaging and pathology reports during follow-up were analyzed to indicate persisting or recurring nodes. The previously established clinical, radiomic and combined models were validated on this cohort by assessing the concordance index (c-index) and model calibration. RESULTS Overall 113 patients with 374 pLNs were suitable for final analysis. There were 20 (5.3%) nodal failures from 15 patients after a median follow-up of 36.1 months. Baseline characteristics and radiomic features were comparable to the training cohort. Both the radiomic model (Least-axis-length of lymph node (LALLN) and correlation of gray level co-occurrence matrix (Corre-GLCM)) and the combined model (T stage, gender, WHO performance score, LALLN and Corre-GLCM) showed good agreement between predicted and observed nodal control probabilities. The radiomic (c-index: 0.71; 95% confidence interval (CI): 0.59-0.84) and combined (c-index: 0.71; 95% CI: 0.59-0.82) models performed better than the clinical model (c-index: 0.57; 95% CI: 0.47-0.68) on this cohort, with a significant difference between the combined and clinical models (z-score test: p = 0.005). CONCLUSION The combined model including clinical and radiomic features was externally validated and proved useful to predict nodal failures and could be helpful to guide treatment choices before and after curative radiation treatment for node positive HNSCC patients.
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Affiliation(s)
- Tian-Tian Zhai
- Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands; Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, Shantou, China.
| | - Frederik Wesseling
- Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Development Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Johannes A Langendijk
- Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Zhenwei Shi
- Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Development Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Petros Kalendralis
- Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Development Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Lisanne V van Dijk
- Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Frank Hoebers
- Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Development Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Roel J H M Steenbakkers
- Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Andre Dekker
- Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Development Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Leonard Wee
- Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Development Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Nanna M Sijtsema
- Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
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22
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George MM, Tolley NS. AIM in Otolaryngology and Head & Neck Surgery. Artif Intell Med 2021. [DOI: 10.1007/978-3-030-58080-3_198-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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23
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Chinnery T, Arifin A, Tay KY, Leung A, Nichols AC, Palma DA, Mattonen SA, Lang P. Utilizing Artificial Intelligence for Head and Neck Cancer Outcomes Prediction From Imaging. Can Assoc Radiol J 2020; 72:73-85. [PMID: 32735452 DOI: 10.1177/0846537120942134] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Artificial intelligence (AI)-based models have become a growing area of interest in predictive medicine and have the potential to aid physician decision-making to improve patient outcomes. Imaging and radiomics play an increasingly important role in these models. This review summarizes recent developments in the field of radiomics for AI in head and neck cancer. Prediction models for oncologic outcomes, treatment toxicity, and pathological findings have all been created. Exploratory studies are promising; however, validation studies that demonstrate consistency, reproducibility, and prognostic impact remain uncommon. Prospective clinical trials with standardized procedures are required for clinical translation.
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Affiliation(s)
- Tricia Chinnery
- Department of Medical Biophysics, 6221Western University, London, Ontario, Canada
| | - Andrew Arifin
- Department of Oncology, 6221Western University, London, Ontario, Canada
| | - Keng Yeow Tay
- Department of Medical Imaging, 6221Western University, London, Ontario, Canada
| | - Andrew Leung
- Department of Medical Imaging, 6221Western University, London, Ontario, Canada
| | - Anthony C Nichols
- Department of Otolaryngology-Head and Neck Surgery, 6221Western University, London, Ontario, Canada
| | - David A Palma
- Department of Oncology, 6221Western University, London, Ontario, Canada
| | - Sarah A Mattonen
- Department of Medical Biophysics, 6221Western University, London, Ontario, Canada.,Department of Oncology, 6221Western University, London, Ontario, Canada
| | - Pencilla Lang
- Department of Oncology, 6221Western University, London, Ontario, Canada
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