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Alabi RO, Elmusrati M, Leivo I, Almangush A, Mäkitie AA. Artificial Intelligence-Driven Radiomics in Head and Neck Cancer: Current Status and Future Prospects. Int J Med Inform 2024; 188:105464. [PMID: 38728812 DOI: 10.1016/j.ijmedinf.2024.105464] [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: 10/15/2023] [Revised: 04/20/2024] [Accepted: 04/22/2024] [Indexed: 05/12/2024]
Abstract
BACKGROUND Radiomics is a rapidly growing field used to leverage medical radiological images by extracting quantitative features. These are supposed to characterize a patient's phenotype, and when combined with artificial intelligence techniques, to improve the accuracy of diagnostic models and clinical outcome prediction. OBJECTIVES This review aims at examining the application areas of artificial intelligence-based radiomics (AI-based radiomics) for the management of head and neck cancer (HNC). It further explores the workflow of AI-based radiomics for personalized and precision oncology in HNC. Finally, it examines the current challenges of AI-based radiomics in daily clinical oncology and offers possible solutions to these challenges. METHODS Comprehensive electronic databases (PubMed, Medline via Ovid, Scopus, Web of Science, CINAHL, and Cochrane Library) were searched following the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines. The quality of included studies and their risk of biases were evaluated using the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD)and Prediction Model Risk of Bias Assessment Tool (PROBAST). RESULTS Out of the 659 search hits retrieved, 45 fulfilled the inclusion criteria. Our review revealed that the application of AI-based radiomics model as an ancillary tool for improved decision-making in HNC management includes radiomics-based cancer diagnosis and radiomics-based cancer prognosis. The radiomics-based cancer diagnosis includes tumor staging, tumor grading, and classification of malignant and benign tumors. Similarly, radiomics-based cancer prognosis includes prediction for treatment response, recurrence, metastasis, and survival. In addition, the challenges in the implementation of these models for clinical evaluations include data imbalance, feature engineering (extraction and selection), model generalizability, multi-modal fusion, and model interpretability. CONCLUSION Considering the highly subjective and interobserver variability that is peculiar to the interpretation of medical images by expert clinicians, AI-based radiomics seeks to offer potentially useful quantitative information, which is not visible to the human eye or unintentionally often remain ignored during clinical imaging practice. By enabling the extraction of this type of information, AI-based radiomics has the potential to revolutionize HNC oncology, providing a platform for more personalized, higher quality, and cost-effective care for HNC patients.
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Affiliation(s)
- Rasheed Omobolaji Alabi
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland; Department of Industrial Digitalization, School of Technology and Innovations, University of Vaasa, Vaasa, Finland.
| | - Mohammed Elmusrati
- Department of Industrial Digitalization, School of Technology and Innovations, University of Vaasa, Vaasa, Finland
| | - Ilmo Leivo
- University of Turku, Institute of Biomedicine, Pathology, Turku, Finland
| | - Alhadi Almangush
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland; University of Turku, Institute of Biomedicine, Pathology, Turku, Finland; Department of Pathology, University of Helsinki, Helsinki, Finland; Faculty of Dentistry, Misurata University, Misurata, Libya
| | - Antti A Mäkitie
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland; Department of Otorhinolaryngology - Head and Neck Surgery, University of Helsinki and Helsinki University Hospital, Helsinki, Finland; Division of Ear, Nose and Throat Diseases, Department of Clinical Sciences, Intervention and Technology, Karolinska Institute and Karolinska University Hospital, Stockholm, Sweden
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2
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Lin CH, Yan JL, Yap WK, Kang CJ, Chang YC, Tsai TY, Chang KP, Liao CT, Hsu CL, Chou WC, Wang HM, Huang PW, Fan KH, Huang BS, Tung-Chieh Chang J, Tu SJ, Lin CY. Prognostic value of interim CT-based peritumoral and intratumoral radiomics in laryngeal and hypopharyngeal cancer patients undergoing definitive radiotherapy. Radiother Oncol 2023; 189:109938. [PMID: 37806562 DOI: 10.1016/j.radonc.2023.109938] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Revised: 09/20/2023] [Accepted: 10/02/2023] [Indexed: 10/10/2023]
Abstract
BACKGROUND AND PURPOSE We aimed to investigate the prognostic value of peritumoral and intratumoral computed tomography (CT)-based radiomics during the course of radiotherapy (RT) in patients with laryngeal and hypopharyngeal cancer (LHC). MATERIALS AND METHODS A total of 92 eligible patients were 1:1 randomly assigned into training and validation cohorts. Pre-RT and mid-RT radiomic features were extracted from pre-treatment and interim CT. LASSO-Cox regression was used for feature selection and model construction. Time-dependent area under the receiver operating curve (AUC) analysis was applied to evaluate the models' prognostic performances. Risk stratification ability on overall survival (OS) and progression-free survival (PFS) were assessed using the Kaplan-Meier method and Cox regression. The associations between radiomics and clinical parameters as well as circulating lymphocyte counts were also evaluated. RESULTS The mid-RT peritumoral (AUC: 0.77) and intratumoral (AUC: 0.79) radiomic models yielded better performance for predicting OS than the pre-RT intratumoral model (AUC: 0.62) in validation cohort. This was confirmed by Kaplan-Meier analysis, in which risk stratification depended on the mid-RT peritumoral (p = 0.009) and intratumoral (p = 0.003) radiomics could be improved for OS, in comparison to the pre-RT intratumoral radiomics (p = 0.199). Multivariate analysis identified mid-RT peritumoral and intratumoral radiomic models as independent prognostic factors for both OS and PFS. Mid-RT peritumoral and intratumoral radiomics were correlated with treatment-related lymphopenia. CONCLUSION Mid-RT peritumoral and intratumoral radiomic models are promising image biomarkers that could have clinical utility for predicting OS and PFS in patients with LHC treated with RT.
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Affiliation(s)
- Chia-Hsin Lin
- Proton and Radiation Therapy Center, Chang Gung Memorial Hospital-Linkou Medical Center, Department of Radiation Oncology, Chang Gung University, Taoyuan, Taiwan.
| | - Jiun-Lin Yan
- Department of Neurosurgery, Keelung Chang Gung Memorial Hospital, Keelung, Taiwan; School of Traditional Chinese Medicine, College of Medicine, Chang Gung University, Taoyuan, Taiwan.
| | - Wing-Keen Yap
- Proton and Radiation Therapy Center, Chang Gung Memorial Hospital-Linkou Medical Center, Department of Radiation Oncology, Chang Gung University, Taoyuan, Taiwan.
| | - Chung-Jan Kang
- Department of Otorhinolaryngology, Head and Neck Surgery, Chang Gung Memorial Hospital, Medical College of Chang Gung University, Taoyuan, Taiwan.
| | - Yun-Chen Chang
- Department of Otorhinolaryngology, Head and Neck Surgery, Chang Gung Memorial Hospital, Medical College of Chang Gung University, Taoyuan, Taiwan.
| | - Tsung-You Tsai
- Department of Otorhinolaryngology, Head and Neck Surgery, Chang Gung Memorial Hospital, Medical College of Chang Gung University, Taoyuan, Taiwan.
| | - Kai-Ping Chang
- Department of Otorhinolaryngology, Head and Neck Surgery, Chang Gung Memorial Hospital, Medical College of Chang Gung University, Taoyuan, Taiwan.
| | - Chun-Ta Liao
- Department of Otorhinolaryngology, Head and Neck Surgery, Chang Gung Memorial Hospital, Medical College of Chang Gung University, Taoyuan, Taiwan.
| | - Cheng-Lung Hsu
- Department of Hematology-Oncology, Chang Gung Memorial Hospital, Medical College of Chang Gung University, Taoyuan, Taiwan.
| | - Wen-Chi Chou
- Department of Hematology-Oncology, Chang Gung Memorial Hospital, Medical College of Chang Gung University, Taoyuan, Taiwan.
| | - Hung-Ming Wang
- Department of Hematology-Oncology, Chang Gung Memorial Hospital, Medical College of Chang Gung University, Taoyuan, Taiwan.
| | - Pei-Wei Huang
- Department of Hematology-Oncology, Chang Gung Memorial Hospital, Medical College of Chang Gung University, Taoyuan, Taiwan.
| | - Kang-Hsing Fan
- Department of Radiation Oncology, New Taipei Municipal Tucheng Hospital, New Taipei City, Taiwan.
| | - Bing-Shen Huang
- Proton and Radiation Therapy Center, Chang Gung Memorial Hospital-Linkou Medical Center, Department of Radiation Oncology, Chang Gung University, Taoyuan, Taiwan; Graduate Institute of Clinical Medical Science, Chang Gung University, Taoyuan, Taiwan.
| | - Joseph Tung-Chieh Chang
- Proton and Radiation Therapy Center, Chang Gung Memorial Hospital-Linkou Medical Center, Department of Radiation Oncology, Chang Gung University, Taoyuan, Taiwan; Department of Radiation Oncology, Xiamen Chang Gung Memorial Hospital, Xiamen, Fujian, China.
| | - Shu-Ju Tu
- Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan; Department of Medical Imaging and Intervention, Linkou Chang Gung Memorial Hospital, Taoyuan, Taiwan.
| | - Chien-Yu Lin
- Proton and Radiation Therapy Center, Chang Gung Memorial Hospital-Linkou Medical Center, Department of Radiation Oncology, Chang Gung University, Taoyuan, Taiwan.
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Chang YS, Nair JR, McDougall CC, Qiu W, Banerjee R, Joshi M, Lysack JT. Risk Stratification for Oropharyngeal Squamous Cell Carcinoma Using Texture Analysis on CT - A Step Beyond HPV Status. Can Assoc Radiol J 2023; 74:657-666. [PMID: 36856197 DOI: 10.1177/08465371231157592] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/02/2023] Open
Abstract
Background and Purpose: Human papillomavirus-associated oropharyngeal squamous cell carcinoma (OPSCC) is increasingly prevalent. Despite the overall more favorable outcome, the observed heterogeneous treatment response within this patient group highlights the need for additional means to prognosticate and guide clinical decision-making. Promising prediction models using radiomics from primary OPSCC have been derived. However, no model/s using metastatic lymphadenopathy exist to allow prognostication in those instances when the primary tumor is not seen. The aim of our study was to evaluate whether radiomics using metastatic lymphadenopathy allows for the development of a useful risk assessment model comparable to the primary tumor and whether additional knowledge of the HPV status further improves its prognostic efficacy. Materials and Methods: 80 consecutive patients diagnosed with stage III-IV OPSCC between February 2009 and October 2015, known human papillomavirus status, and pre-treatment CT images were retrospectively identified. Manual segmentation of primary tumor and metastatic lymphadenopathy was performed and the extracted texture features were used to develop multivariate assessment models to prognosticate treatment response. Results: Texture analysis of either the primary or metastatic lymphadenopathy from pre-treatment enhanced CT images can be used to develop models for the stratification of treatment outcomes in OPSCC patients. AUCs range from .78 to .85 for the various OPSCC groups tested, indicating high predictive capability of the models. Conclusions: This preliminary study can form the basis multi-centre trial that may help optimize treatment and improve quality of life in patients with OPSCC in the era of personalized medicine.
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Affiliation(s)
- Yuh-Shin Chang
- Division of Neuroradiology, University of Calgary, Calgary, AB, Canada
- Department of Radiology, Massachusetts Eye and Ear Infirmary, Harvard Medical School, Boston, MA, USA
| | - Jaykumar Raghavan Nair
- Division of Neuroradiology, University of Calgary, Calgary, AB, Canada
- Department of Radiology, QEII Health Science Centre, Halifax Infirmary Hospital, Dalhousie University, Halifax, NS, Canada
| | - Connor C McDougall
- Department of Mechanical Engineering, University of Calgary, Calgary, AB, Canada
| | - Wu Qiu
- Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada
| | - Robyn Banerjee
- Division of Radiation Oncology, University of Calgary, Calgary, AB, Canada
| | - Manish Joshi
- Division of Neuroradiology, University of Calgary, Calgary, AB, Canada
| | - John T Lysack
- Division of Neuroradiology, University of Calgary, Calgary, AB, Canada
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Bicci E, Calamandrei L, Mungai F, Granata V, Fusco R, De Muzio F, Bonasera L, Miele V. Imaging of human papilloma virus (HPV) related oropharynx tumour: what we know to date. Infect Agent Cancer 2023; 18:58. [PMID: 37814320 PMCID: PMC10563217 DOI: 10.1186/s13027-023-00530-x] [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: 05/23/2023] [Accepted: 09/11/2023] [Indexed: 10/11/2023] Open
Abstract
The tumours of head and neck district are around 3% of all malignancies and squamous cell carcinoma is the most frequent histotype, with rapid increase during the last two decades because of the increment of the infection due to human papilloma virus (HPV). Even if the gold standard for the diagnosis is histological examination, including the detection of viral DNA and transcription products, imaging plays a fundamental role in the detection and staging of HPV + tumours, in order to assess the primary tumour, to establish the extent of disease and for follow-up. The main diagnostic tools are Computed Tomography (CT), Positron Emission Tomography-Computed Tomography (PET-CT) and Magnetic Resonance Imaging (MRI), but also Ultrasound (US) and the use of innovative techniques such as Radiomics have an important role. Aim of our review is to illustrate the main imaging features of HPV + tumours of the oropharynx, in US, CT and MRI imaging. In particular, we will outline the main limitations and strengths of the various imaging techniques, the main uses in the diagnosis, staging and follow-up of disease and the fundamental differential diagnoses of this type of tumour. Finally, we will focus on the innovative technique of texture analysis, which is increasingly gaining importance as a diagnostic tool in aid of the radiologist.
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Affiliation(s)
- Eleonora Bicci
- Department of Radiology, University of Florence - Azienda Ospedaliero-Universitaria Careggi, Florence, 50134, Italy.
| | - Leonardo Calamandrei
- Department of Experimental and Clinical Biomedical Sciences, Radiodiagnostic Unit n. 2, University of Florence - Azienda Ospedaliero-Universitaria Careggi, Florence, 50134, Italy
| | - Francesco Mungai
- Department of Radiology, University of Florence - Azienda Ospedaliero-Universitaria Careggi, Florence, 50134, Italy
| | - Vincenza Granata
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS di Napoli, Naples, 80131, Italy
| | - Roberta Fusco
- Medical Oncology Division, Igea SpA, Naples, 80013, Italy
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Milan, 20122, Italy
| | - Federica De Muzio
- Department of Medicine and Health Sciences V. Tiberio, University of Molise, Campobasso, 86100, Italy
| | - Luigi Bonasera
- Department of Radiology, University of Florence - Azienda Ospedaliero-Universitaria Careggi, Florence, 50134, Italy
| | - Vittorio Miele
- Department of Radiology, University of Florence - Azienda Ospedaliero-Universitaria Careggi, Florence, 50134, Italy
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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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Canahuate G, Wentzel A, Mohamed ASR, van Dijk LV, Vock DM, Elgohari B, Elhalawani H, Fuller CD, Marai GE. Spatially-aware clustering improves AJCC-8 risk stratification performance in oropharyngeal carcinomas. Oral Oncol 2023; 144:106460. [PMID: 37390759 PMCID: PMC10561377 DOI: 10.1016/j.oraloncology.2023.106460] [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: 09/08/2021] [Revised: 05/26/2023] [Accepted: 06/05/2023] [Indexed: 07/02/2023]
Abstract
OBJECTIVE Evaluate the effectiveness of machine learning tools that incorporate spatial information such as disease location and lymph node metastatic patterns-of-spread, for prediction of survival and toxicity in HPV+ oropharyngeal cancer (OPC). MATERIALS & METHODS 675 HPV+ OPC patients that were treated at MD Anderson Cancer Center between 2005 and 2013 with curative intent IMRT were retrospectively collected under IRB approval. Risk stratifications incorporating patient radiometric data and lymph node metastasis patterns via an anatomically-adjacent representation with hierarchical clustering were identified. These clusterings were combined into a 3-level patient stratification and included along with other known clinical features in a Cox model for predicting survival outcomes, and logistic regression for toxicity, using independent subsets for training and validation. RESULTS Four groups were identified and combined into a 3-level stratification. The inclusion of patient stratifications in predictive models for 5-yr Overall survival (OS), 5-year recurrence free survival, (RFS) and Radiation-associated dysphagia (RAD) consistently improved model performance measured using the area under the curve (AUC). Test set AUC improvements over models with clinical covariates, was 9 % for predicting OS, and 18 % for predicting RFS, and 7 % for predicting RAD. For models with both clinical and AJCC covariates, AUC improvement was 7 %, 9 %, and 2 % for OS, RFS, and RAD, respectively. CONCLUSION Including data-driven patient stratifications considerably improve prognosis for survival and toxicity outcomes over the performance achieved by clinical staging and clinical covariates alone. These stratifications generalize well to across cohorts, and sufficient information for reproducing these clusters is included.
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Affiliation(s)
- Guadalupe Canahuate
- Department of Electrical and Computer Engineering, University of Iowa, Iowa City, IA 52242, USA.
| | - Andrew Wentzel
- Department of Computer Science, The University of Illinois at Chicago, Chicago, IL 60612, USA
| | - Abdallah S R Mohamed
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Lisanne V van Dijk
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - David M Vock
- Division of Biostatistics, University of Minnesota, Minneapolis, MN 55455, USA
| | - Baher Elgohari
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Hesham Elhalawani
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Clifton D Fuller
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - G Elisabeta Marai
- Department of Computer Science, The University of Illinois at Chicago, Chicago, IL 60612, USA
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Zhang YP, Zhang XY, Cheng YT, Li B, Teng XZ, Zhang J, Lam S, Zhou T, Ma ZR, Sheng JB, Tam VCW, Lee SWY, Ge H, Cai J. Artificial intelligence-driven radiomics study in cancer: the role of feature engineering and modeling. Mil Med Res 2023; 10:22. [PMID: 37189155 DOI: 10.1186/s40779-023-00458-8] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 05/04/2023] [Indexed: 05/17/2023] Open
Abstract
Modern medicine is reliant on various medical imaging technologies for non-invasively observing patients' anatomy. However, the interpretation of medical images can be highly subjective and dependent on the expertise of clinicians. Moreover, some potentially useful quantitative information in medical images, especially that which is not visible to the naked eye, is often ignored during clinical practice. In contrast, radiomics performs high-throughput feature extraction from medical images, which enables quantitative analysis of medical images and prediction of various clinical endpoints. Studies have reported that radiomics exhibits promising performance in diagnosis and predicting treatment responses and prognosis, demonstrating its potential to be a non-invasive auxiliary tool for personalized medicine. However, radiomics remains in a developmental phase as numerous technical challenges have yet to be solved, especially in feature engineering and statistical modeling. In this review, we introduce the current utility of radiomics by summarizing research on its application in the diagnosis, prognosis, and prediction of treatment responses in patients with cancer. We focus on machine learning approaches, for feature extraction and selection during feature engineering and for imbalanced datasets and multi-modality fusion during statistical modeling. Furthermore, we introduce the stability, reproducibility, and interpretability of features, and the generalizability and interpretability of models. Finally, we offer possible solutions to current challenges in radiomics research.
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Affiliation(s)
- Yuan-Peng Zhang
- Department of Medical Informatics, Nantong University, Nantong, 226001, Jiangsu, China
- Department of Health Technology and Informatics, the Hong Kong Polytechnic University, Hong Kong, 999077, China
- The Hong Kong Polytechnic University Shenzhen Research Institute, Shenzhen, 518000, Guangdong, China
| | - Xin-Yun Zhang
- Department of Medical Informatics, Nantong University, Nantong, 226001, Jiangsu, China
| | - Yu-Ting Cheng
- Department of Medical Informatics, Nantong University, Nantong, 226001, Jiangsu, China
| | - Bing Li
- Department of Radiation Oncology, the Affiliated Cancer Hospital of Zhengzhou University and Henan Cancer Hospital, Zhengzhou, 450008, Henan, China
| | - Xin-Zhi Teng
- Department of Health Technology and Informatics, the Hong Kong Polytechnic University, Hong Kong, 999077, China
| | - Jiang Zhang
- Department of Health Technology and Informatics, the Hong Kong Polytechnic University, Hong Kong, 999077, China
| | - Saikit Lam
- Department of Health Technology and Informatics, the Hong Kong Polytechnic University, Hong Kong, 999077, China
| | - Ta Zhou
- Department of Health Technology and Informatics, the Hong Kong Polytechnic University, Hong Kong, 999077, China
| | - Zong-Rui Ma
- Department of Health Technology and Informatics, the Hong Kong Polytechnic University, Hong Kong, 999077, China
| | - Jia-Bao Sheng
- Department of Health Technology and Informatics, the Hong Kong Polytechnic University, Hong Kong, 999077, China
| | - Victor C W Tam
- Department of Health Technology and Informatics, the Hong Kong Polytechnic University, Hong Kong, 999077, China
| | - Shara W Y Lee
- Department of Health Technology and Informatics, the Hong Kong Polytechnic University, Hong Kong, 999077, China
| | - Hong Ge
- Department of Radiation Oncology, the Affiliated Cancer Hospital of Zhengzhou University and Henan Cancer Hospital, Zhengzhou, 450008, Henan, China
| | - Jing Cai
- Department of Health Technology and Informatics, the Hong Kong Polytechnic University, Hong Kong, 999077, China.
- The Hong Kong Polytechnic University Shenzhen Research Institute, Shenzhen, 518000, Guangdong, China.
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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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9
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Kotevski DP, Smee RI, Vajdic CM, Field M. Machine Learning and Nomogram Prognostic Modeling for 2-Year Head and Neck Cancer-Specific Survival Using Electronic Health Record Data: A Multisite Study. JCO Clin Cancer Inform 2023; 7:e2200128. [PMID: 36596211 DOI: 10.1200/cci.22.00128] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023] Open
Abstract
PURPOSE There is limited knowledge of the prediction of 2-year cancer-specific survival (CSS) in the head and neck cancer (HNC) population. The aim of this study is to develop and validate machine learning models and a nomogram for the prediction of 2-year CSS in patients with HNC using real-world data collected by major teaching and tertiary referral hospitals in New South Wales (NSW), Australia. MATERIALS AND METHODS Data collected in oncology information systems at multiple NSW Cancer Centres were extracted for 2,953 eligible adults diagnosed between 2000 and 2017 with squamous cell carcinoma of the head and neck. Death data were sourced from the National Death Index using record linkage. Machine learning and Cox regression/nomogram models were developed and internally validated in Python and R, respectively. RESULTS Machine learning models demonstrated highest performance (C-index) in the larynx and nasopharynx cohorts (0.82), followed by the oropharynx (0.79) and the hypopharynx and oral cavity cohorts (0.73). In the whole HNC population, C-indexes of 0.79 and 0.70 and Brier scores of 0.10 and 0.27 were reported for the machine learning and nomogram model, respectively. Cox regression analysis identified age, T and N classification, and time-corrected biologic equivalent dose in two gray fractions as independent prognostic factors for 2-year CSS. N classification was the most important feature used for prediction in the machine learning model followed by age. CONCLUSION Machine learning and nomogram analysis predicted 2-year CSS with high performance using routinely collected and complete clinical information extracted from oncology information systems. These models function as visual decision-making tools to guide radiotherapy treatment decisions and provide insight into the prediction of survival outcomes in patients with HNC.
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Affiliation(s)
- Damian P Kotevski
- Department of Radiation Oncology, Prince of Wales Hospital and Community Health Services, Sydney, New South Wales, Australia.,Prince of Wales Clinical School, Faculty of Medicine, University of New South Wales, Sydney, New South Wales, Australia
| | - Robert I Smee
- Department of Radiation Oncology, Prince of Wales Hospital and Community Health Services, Sydney, New South Wales, Australia.,Prince of Wales Clinical School, Faculty of Medicine, University of New South Wales, Sydney, New South Wales, Australia.,Department of Radiation Oncology, Tamworth Base Hospital, Tamworth, New South Wales, Australia
| | - Claire M Vajdic
- Kirby Institute, Faculty of Medicine, University of New South Wales, Sydney, New South Wales, Australia
| | - Matthew Field
- South Western Sydney Clinical Campus, School of Clinical Medicine, University of New South Wales, Sydney, New South Wales, Australia.,South Western Sydney Cancer Services, NSW Health, Sydney, Sydney, New South Wales, Australia.,Ingham Institute for Applied Medical Research, Sydney, New South Wales, Australia
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10
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Artificial Intelligence for Outcome Modeling in Radiotherapy. Semin Radiat Oncol 2022; 32:351-364. [DOI: 10.1016/j.semradonc.2022.06.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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11
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Bourdillon AT, Shah HP, Cohen O, Hajek MA, Mehra S. Novel Machine Learning Model to Predict Interval of Oral Cancer Recurrence for Surveillance Stratification. Laryngoscope 2022. [DOI: 10.1002/lary.30351] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 07/29/2022] [Accepted: 08/01/2022] [Indexed: 12/24/2022]
Affiliation(s)
| | - Hemali P. Shah
- Yale University School of Medicine New Haven Connecticut U.S.A
| | - Oded Cohen
- Division of Otolaryngology–Head and Neck Surgery, Department of Surgery Yale University School of Medicine New Haven Connecticut U.S.A
| | - Michael A. Hajek
- Division of Otolaryngology–Head and Neck Surgery, Department of Surgery Yale University School of Medicine New Haven Connecticut U.S.A
| | - Saral Mehra
- Division of Otolaryngology–Head and Neck Surgery, Department of Surgery Yale University School of Medicine New Haven Connecticut U.S.A
- Yale Cancer Center New Haven Connecticut U.S.A
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12
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Bahig H, Huang SH, O’Sullivan B. Oligometastatic Head and Neck Cancer: Challenges and Perspectives. Cancers (Basel) 2022; 14:cancers14163894. [PMID: 36010888 PMCID: PMC9405984 DOI: 10.3390/cancers14163894] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Revised: 08/09/2022] [Accepted: 08/10/2022] [Indexed: 11/17/2022] Open
Abstract
Simple Summary Oligometastasis represents a disease state and an opportunity for cure when metastases emerge. Emerging evidence supports that most head and neck cancer patients with oligometastatic disease are likely to benefit from curative intent local ablative therapy if appropriate selection criteria are applied. Biomarkers to predict development of oligometastasis, as well as to identify which patients could benefit from a radical intent approach, are under investigation. This review summarizes recent knowledge about the characteristics, investigational efforts, and evidence for local ablation regarding oligometastasis in head and neck cancer. We also describe the challenges and opportunities in patient selection and discuss the role of radiotherapy and immunotherapy combinations to enhance anti-tumor immunity. Abstract A minority of patients with metastatic head and neck squamous cell carcinoma (HNSCC) present with oligometastatic disease. Oligometastasis not only reflects a disease state, but might also present an opportunity for cure in the metastatic setting. Radical ablation of all oligometastatic sites may confer prolonged survival and possibly achieve cure in some patients. However, substantial debate remains about whether patients with oligometastatic disease could benefit from curative intent therapy or whether aggressive treatments expose some patients to futile toxicity. Optimal selection of patients, carefully balancing the currently known prognostic factors against the risks of toxicity is critical. Emerging evidence suggests that patients with a limited burden of disease, viral-related pharyngeal cancer, metachronous metastasis and lung-only metastasis may benefit most from this approach. Efforts are underway to identify biomarkers that can detect oligometastasis and better select patients who would derive the maximum benefit from an aggressive radical approach. The combination of radiotherapy and immunotherapy promises to enhance the anti-tumoral immune response and help overcome resistance. However, optimization of management algorithms, including patient selection, radiation dose and sequencing, will be critical in upcoming clinical trials. This review summarizes recent knowledge about the characteristics and investigational efforts regarding oligometastasis in HNSCC.
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Affiliation(s)
- Houda Bahig
- Department of Radiation Oncology, University of Montreal, Montreal, QC H2X 3E4, Canada
| | - Shao Hui Huang
- Department of Radiation Oncology, Princess Margaret Cancer Centre, University of Toronto, Toronto, ON M5G 2M9, Canada
- Department of Otolaryngology-Head and Neck Surgery, Princess Margaret Cancer Centre, University of Toronto, Toronto, ON M5G 2M9, Canada
| | - Brian O’Sullivan
- Department of Radiation Oncology, University of Montreal, Montreal, QC H2X 3E4, Canada
- Department of Radiation Oncology, Princess Margaret Cancer Centre, University of Toronto, Toronto, ON M5G 2M9, Canada
- Department of Otolaryngology-Head and Neck Surgery, Princess Margaret Cancer Centre, University of Toronto, Toronto, ON M5G 2M9, Canada
- Correspondence:
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13
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An Investigation on Radiomics Feature Handling for HNSCC Staging Classification. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12157826] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
The incidence of Head and Neck Squamous Cell Carcinoma (HNSCC) has been growing in the last few decades. Its diagnosis is usually performed through clinical evaluation and analyzing radiological images, then confirmed by histopathological examination, an invasive and time-consuming operation. The recent advances in the artificial intelligence field are leading to interesting results in the early diagnosis, personalized treatment and monitoring of HNSCC only by analyzing radiological images, without performing a tissue biopsy. The large amount of radiological images and the increasing interest in radiomics approaches can help to develop machine learning (ML) methods to support diagnosis. In this work, we propose an ML method based on the use of radiomics features, extracted from CT and PET images, to classify the disease in terms of pN-Stage, pT-Stage and Overall Stage. After the extraction of radiomics features, a selection step is performed to remove dataset redundancy. Finally, ML methods are employed to complete the classification task. Our pipeline is applied on the “Head-Neck-PET-CT” TCIA open-source dataset, considering a cohort of 201 patients from four different institutions. An AUC of 97%, 83% and 93% in terms of pN-Stage, pT-Stage and Overall Stage classification, respectively, is achieved. The obtained results are promising, showing the potential efficiency of the use of radiomics approaches in staging classification.
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14
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Alberti A, Lorini L, Ravanelli M, Perri F, Vinches M, Rondi P, Romani C, Bossi P. New Challenges in Evaluating Outcomes after Immunotherapy in Recurrent and/or Metastatic Head and Neck Squamous Cell Carcinoma. Vaccines (Basel) 2022; 10:vaccines10060885. [PMID: 35746493 PMCID: PMC9228441 DOI: 10.3390/vaccines10060885] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 05/16/2022] [Accepted: 05/28/2022] [Indexed: 01/04/2023] Open
Abstract
In many recurrent and/or metastatic cancers, the advent of immunotherapy opens up new scenarios of treatment response, with new phenomena, such as pseudoprogression and hyperprogression. Because of this, different immune-related response criteria have been developed, and new therapeutic strategies adopted, such as treatment beyond progression. Moreover, the role of progression-free survival as a surrogate has been questioned, and new surrogate endpoint hypotheses have arisen. A proper understanding of radiological imaging, an assessment of the biological events triggered by therapy, and the clinical evolution of the lesions and of the patient performance status are all factors that should be considered to guide the oncologist’s treatment choice. The primary aim of this article is to discuss how all these concepts apply to recurrent/metastatic head and neck squamous cell carcinoma patients when treated with immunotherapy.
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Affiliation(s)
- Andrea Alberti
- Medical Oncology Unit, Department of Medical & Surgical Specialties, Radiological Sciences & Public Health, ASST Spedali Civili di Brescia, University of Brescia, 25123 Brescia, Italy; (A.A.); (L.L.)
| | - Luigi Lorini
- Medical Oncology Unit, Department of Medical & Surgical Specialties, Radiological Sciences & Public Health, ASST Spedali Civili di Brescia, University of Brescia, 25123 Brescia, Italy; (A.A.); (L.L.)
| | - Marco Ravanelli
- Radiology Unit, Department of Medical & Surgical Specialties, Radiological Sciences & Public Health, ASST Spedali Civili di Brescia, University of Brescia, 25123 Brescia, Italy; (M.R.); (P.R.)
| | - Francesco Perri
- Medical and Experimental Head and Neck Oncology Unit, INT IRCCS Foundation G Pascale, 80131 Naples, Italy;
| | - Marie Vinches
- Medical Oncology Department, Institut Régional du Cancer de Montpellier (ICM), 34090 Montpellier, France;
| | - Paolo Rondi
- Radiology Unit, Department of Medical & Surgical Specialties, Radiological Sciences & Public Health, ASST Spedali Civili di Brescia, University of Brescia, 25123 Brescia, Italy; (M.R.); (P.R.)
| | - Chiara Romani
- Angelo Nocivelli Institute of Molecular Medicine, ASST Spedali Civili di Brescia, University of Brescia, 25123 Brescia, Italy;
| | - Paolo Bossi
- Medical Oncology Unit, Department of Medical & Surgical Specialties, Radiological Sciences & Public Health, ASST Spedali Civili di Brescia, University of Brescia, 25123 Brescia, Italy; (A.A.); (L.L.)
- Correspondence:
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15
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Role of Texture Analysis in Oropharyngeal Carcinoma: A Systematic Review of the Literature. Cancers (Basel) 2022; 14:cancers14102445. [PMID: 35626048 PMCID: PMC9139172 DOI: 10.3390/cancers14102445] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 05/02/2022] [Accepted: 05/10/2022] [Indexed: 11/16/2022] Open
Abstract
Simple Summary The incidence of squamous cell carcinomas of the oropharynx has rapidly increased in the last two decades due to human papilloma virus infection (HPV). HPV-positive and HPV-negative squamous cell tumours differ in radiological imaging, treatment, and prognosis; therefore, differential diagnosis is mandatory. Radiomics with texture analysis is an innovative technique that has been used increasingly in recent years to characterise the tissue heterogeneity of certain structures such as neoplasms or organs by measuring the spatial distribution of pixel values on radiological imaging. This review delineates the application of texture analysis in oropharyngeal tumours and explores how radiomics may potentially improve clinical decision-making. Abstract Human papilloma virus infection (HPV) is associated with the development of lingual and palatine tonsil carcinomas. Diagnosing, differentiating HPV-positive from HPV-negative cancers, and assessing the presence of lymph node metastases or recurrences by the visual interpretation of images is not easy. Texture analysis can provide structural information not perceptible to human eyes. A systematic literature search was performed on 16 February 2022 for studies with a focus on texture analysis in oropharyngeal cancers. We conducted the research on PubMed, Scopus, and Web of Science platforms. Studies were screened for inclusion according to the preferred reporting items for systematic reviews. Twenty-six studies were included in our review. Nineteen articles related specifically to the oropharynx and seven articles analysed the head and neck area with sections dedicated to the oropharynx. Six, thirteen, and seven articles used MRI, CT, and PET, respectively, as the imaging techniques by which texture analysis was performed. Regarding oropharyngeal tumours, this review delineates the applications of texture analysis in (1) the diagnosis, prognosis, and assessment of disease recurrence or persistence after therapy, (2) early differentiation of HPV-positive versus HPV-negative cancers, (3) the detection of cancers not visualised by imaging alone, and (4) the assessment of lymph node metastases from unknown primary carcinomas.
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16
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Tardini E, Zhang X, Canahuate G, Wentzel A, Mohamed ASR, Van Dijk L, Fuller CD, Marai GE. Optimal Treatment Selection in Sequential Systemic and Locoregional Therapy of Oropharyngeal Squamous Carcinomas: Deep Q-Learning With a Patient-Physician Digital Twin Dyad. J Med Internet Res 2022; 24:e29455. [PMID: 35442211 PMCID: PMC9069283 DOI: 10.2196/29455] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Revised: 09/03/2021] [Accepted: 02/09/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Currently, selection of patients for sequential versus concurrent chemotherapy and radiation regimens lacks evidentiary support and it is based on locally optimal decisions for each step. OBJECTIVE We aim to optimize the multistep treatment of patients with head and neck cancer and predict multiple patient survival and toxicity outcomes, and we develop, apply, and evaluate a first application of deep Q-learning (DQL) and simulation to this problem. METHODS The treatment decision DQL digital twin and the patient's digital twin were created, trained, and evaluated on a data set of 536 patients with oropharyngeal squamous cell carcinoma with the goal of, respectively, determining the optimal treatment decisions with respect to survival and toxicity metrics and predicting the outcomes of the optimal treatment on the patient. Of the data set of 536 patients, the models were trained on a subset of 402 (75%) patients (split randomly) and evaluated on a separate set of 134 (25%) patients. Training and evaluation of the digital twin dyad was completed in August 2020. The data set includes 3-step sequential treatment decisions and complete relevant history of the patient cohort treated at MD Anderson Cancer Center between 2005 and 2013, with radiomics analysis performed for the segmented primary tumor volumes. RESULTS On the test set, we found mean 87.35% (SD 11.15%) and median 90.85% (IQR 13.56%) accuracies in treatment outcome prediction, matching the clinicians' outcomes and improving the (predicted) survival rate by +3.73% (95% CI -0.75% to 8.96%) and the dysphagia rate by +0.75% (95% CI -4.48% to 6.72%) when following DQL treatment decisions. CONCLUSIONS Given the prediction accuracy and predicted improvement regarding the medically relevant outcomes yielded by this approach, this digital twin dyad of the patient-physician dynamic treatment problem has the potential of aiding physicians in determining the optimal course of treatment and in assessing its outcomes.
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Affiliation(s)
- Elisa Tardini
- Department of Computer Science, University of Illinois at Chicago, Chicago, IL, United States
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, Italy
| | - Xinhua Zhang
- Department of Computer Science, University of Illinois at Chicago, Chicago, IL, United States
| | - Guadalupe Canahuate
- Department of Electrical and Computer Engineering, University Of Iowa, Iowa City, IA, United States
| | - Andrew Wentzel
- Department of Computer Science, University of Illinois at Chicago, Chicago, IL, United States
| | - Abdallah S R Mohamed
- MD Anderson Cancer Center, Houston, TX, United States
- Department of Radiation Oncology, The University of Texas, Austin, TX, United States
| | | | - Clifton D Fuller
- MD Anderson Cancer Center, Houston, TX, United States
- Department of Radiation Oncology, The University of Texas, Austin, TX, United States
| | - G Elisabeta Marai
- Department of Computer Science, University of Illinois at Chicago, Chicago, IL, United States
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17
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Sellami S, Bourbonne V, Hatt M, Tixier F, Bouzid D, Lucia F, Pradier O, Goasduff G, Visvikis D, Schick U. Predicting response to radiotherapy of head and neck squamous cell carcinoma using radiomics from cone-beam CT images. Acta Oncol 2022; 61:73-80. [PMID: 34632924 DOI: 10.1080/0284186x.2021.1983207] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
INTRODUCTION Radiotherapy (RT) for head and neck cancer is now guided by cone-beam computed tomography (CBCT). We aim to identify a CBCT radiomic signature predictive of progression to RT. MATERIAL AND METHODS A cohort of 93 patients was split into training (n = 60) and testing (n = 33) sets. A total of 88 features were extracted from the gross tumor volume (GTV) on each CBCT. Receiver operating characteristic (ROC) curves were used to determine the power of each feature at each week of treatment to predict progression to radio(chemo)therapy. Only features with AUC > 0.65 at each week were pre-selected. Absolute differences were calculated between features from each weekly CBCT and baseline CBCT1 images. The smallest detectable change (C = 1.96 × SD, SD being the standard deviation of differences between feature values calculated on CBCT1 and CBCTn) with its confidence interval (95% confidence interval [CI]) was determined for each feature. The features for which the change was larger than C for at least 5% of patients were then selected. A radiomics-based model was built at the time-point that showed the highest AUC and compared with models relying on clinical variables. RESULTS Seven features had an AUC > 0.65 at each week, and six exhibited a change larger than the predefined CI 95%. After exclusion of inter-correlated features, only one parameter remains, Coarseness. Among clinical variable, only hemoglobin value was significant. AUC for predicting the treatment response were 0.78 (p = .006), 0.85 (p < .001), and 0.99 (p < .001) for clinical, CBCT4-radiomics (Coarseness) and clinical + radiomics based models respectively. The mean AUC of this last model on a 5-fold cross-validation was 0.80 (±0.09). On the testing cohort, the best prediction was given by the combined model (balanced accuracy [BAcc] 0.67 , p < .001). CONCLUSIONS We described a feature selection methodology for delta-radiomics that is able to select reproducible features which are informative due to their change during treatment. A selected delta radiomics feature may improve clinical-based prediction models.
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Affiliation(s)
- S. Sellami
- Radiation Oncology Department, University Hospital, Brest, France
| | - V. Bourbonne
- Radiation Oncology Department, University Hospital, Brest, France
| | - M. Hatt
- INSERM, UMR 1101, LaTIM, University of Brest, Brest, France
| | - F. Tixier
- INSERM, UMR 1101, LaTIM, University of Brest, Brest, France
| | - D. Bouzid
- INSERM, UMR 1101, LaTIM, University of Brest, Brest, France
| | - F. Lucia
- Radiation Oncology Department, University Hospital, Brest, France
- INSERM, UMR 1101, LaTIM, University of Brest, Brest, France
| | - O. Pradier
- Radiation Oncology Department, University Hospital, Brest, France
- INSERM, UMR 1101, LaTIM, University of Brest, Brest, France
- Faculté de Médecine et des Sciences de la Santé, Université de Bretagne Occidentale, Brest, France
| | - G. Goasduff
- Radiation Oncology Department, University Hospital, Brest, France
| | - D. Visvikis
- INSERM, UMR 1101, LaTIM, University of Brest, Brest, France
| | - U. Schick
- Radiation Oncology Department, University Hospital, Brest, France
- INSERM, UMR 1101, LaTIM, University of Brest, Brest, France
- Faculté de Médecine et des Sciences de la Santé, Université de Bretagne Occidentale, Brest, France
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18
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Corrias G, Micheletti G, Barberini L, Suri JS, Saba L. Texture analysis imaging "what a clinical radiologist needs to know". Eur J Radiol 2021; 146:110055. [PMID: 34902669 DOI: 10.1016/j.ejrad.2021.110055] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Revised: 04/09/2021] [Accepted: 11/15/2021] [Indexed: 02/07/2023]
Abstract
Texture analysis has arisen as a tool to explore the amount of data contained in images that cannot be explored by humans visually. Radiomics is a method that extracts a large number of features from radiographic medical images using data-characterisation algorithms. These features, termed radiomic features, have the potential to uncover disease characteristics. The goal of both radiomics and texture analysis is to go beyond size or human-eye based semantic descriptors, to enable the non-invasive extraction of quantitative radiological data to correlate them with clinical outcomes or pathological characteristics. In the latest years there has been a flourishing sub-field of radiology where texture analysis and radiomics have been used in many settings. It is difficult for the clinical radiologist to cope with such amount of data in all the different radiological sub-fields and to identify the most significant papers. The aim of this review is to provide a tool to better understand the basic principles underlining texture analysis and radiological data mining and a summary of the most significant papers of the latest years.
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Affiliation(s)
| | | | | | - Jasjit S Suri
- Stroke Diagnosis and Monitoring Division, AtheroPoint™, Roseville, CA, USA and Knowledge Engineering Center, Global Biomedical Technologies, Inc, Roseville, CA, USA
| | - Luca Saba
- Department of Radiology, University of Cagliari, Italy.
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19
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Bagher Ebadian H, Siddiqui F, Ghanem A, Zhu S, Lu M, Movsas B, Chetty IJ. Radiomics outperforms clinical factors in characterizing human papilloma virus (HPV) for patients with oropharyngeal squamous cell carcinomas. Biomed Phys Eng Express 2021; 8. [PMID: 34781281 DOI: 10.1088/2057-1976/ac39ab] [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: 08/05/2021] [Accepted: 11/15/2021] [Indexed: 11/11/2022]
Abstract
Purpose:To utilize radiomic features extracted from CT images to characterize Human Papilloma Virus (HPV) for patients with oropharyngeal cancer squamous cell carcinoma (OPSCC).Methods:One hundred twenty-eight OPSCC patients with known HPV-status (60-HPV+ and 68-HPV-, confirmed by immunohistochemistry-P16-protein testing) were retrospectively studied. Radiomic features (11 feature-categories) were extracted in 3D from contrast-enhanced (CE)-CT images of gross-tumor-volumes using 'in-house' software ('ROdiomiX') developed and validated following the image-biomarker-standardization-initiative (IBSI) guidelines. Six clinical factors were investigated: Age-at-Diagnosis, Gender, Total-Charlson, Alcohol-Use, Smoking-History, and T-Stage. A Least-Absolute-Shrinkage-and-Selection-Operation (Lasso) technique combined with a Generalized-Linear-Model (Lasso-GLM) were applied to perform regularization in the radiomic and clinical feature spaces to identify the ranking of optimal feature subsets with most representative information for prediction of HPV. Lasso-GLM models/classifiers based on clinical factors only, radiomics only, and combined clinical and radiomics (ensemble/integrated) were constructed using random-permutation-sampling. Tests of significance (One-way ANOVA), average Area-Under-Receiver-Operating-Characteristic (AUC), and Positive and Negative Predictive values (PPV and NPV) were computed to estimate the generalization-error and prediction performance of the classifiers.Results:Five clinical factors, including T-stage, smoking status, and age, and 14 radiomic features, including tumor morphology, and intensity contrast were found to be statistically significant discriminators between HPV positive and negative cohorts. Performances for prediction of HPV for the 3 classifiers were: Radiomics-Lasso-GLM: AUC/PPV/NPV=0.789/0.755/0.805; Clinical-Lasso-GLM: 0.676/0.747/0.672, and Integrated/Ensemble-Lasso-GLM: 0.895/0.874/0.844. Results imply that the radiomics-based classifier enabled better characterization and performance prediction of HPV relative to clinical factors, and that the combination of both radiomics and clinical factors yields even higher accuracy characterization and predictive performance.Conclusion:Albeit subject to confirmation in a larger cohort, this pilot study presents encouraging results in support of the role of radiomic features towards characterization of HPV in patients with OPSCC.
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Affiliation(s)
- Hassan Bagher Ebadian
- Department of Radiation Oncology , Henry Ford Hospital, 2799 West Grand Blvd., Detroit, Detroit, Michigan, 48202, UNITED STATES
| | - Farzan Siddiqui
- Radiation Oncology, Henry Ford Hospital, 2799 West Grand Blvd., Detroit, Michigan, 48202, UNITED STATES
| | - Ahmed Ghanem
- Radiation Oncology, Henry Ford Hospital, 2799 West Grand Blvd., Detroit, Michigan, 48202, UNITED STATES
| | - Simeng Zhu
- Radiation Oncology, Henry Ford Hospital, 2799 West Grand Blvd., Detroit, Michigan, 48202, UNITED STATES
| | - Mei Lu
- Henry Ford Hospital, 2799 West Grand Blvd., Detroit, Michigan, 48202, UNITED STATES
| | - Benjamin Movsas
- Dept of Radiation Oncology, Henry Ford Hospital, 2799 West Grand Blvd., Detroit, 48202, UNITED STATES
| | - Indrin J Chetty
- Dept of Radiation Oncology, Henry Ford Hospital, 2799 West Grand Blvd, Detroit, MI 48202-2689, USA, Detroit, Michigan, 48202, UNITED STATES
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20
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Rich B, Huang J, Yang Y, Jin W, Johnson P, Wang L, Yang F. Radiomics Predicts for Distant Metastasis in Locally Advanced Human Papillomavirus-Positive Oropharyngeal Squamous Cell Carcinoma. Cancers (Basel) 2021; 13:cancers13225689. [PMID: 34830844 PMCID: PMC8616361 DOI: 10.3390/cancers13225689] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Revised: 11/07/2021] [Accepted: 11/11/2021] [Indexed: 01/04/2023] Open
Abstract
Simple Summary There is strong evidence that locally advanced human papillomavirus positive (HPV+) oropharyngeal squamous cell carcinoma (OPSCC) carries a significantly better prognosis than HPV negative OPSCC, suggesting the possibility of treatment de-escalation and, therefore, toxicity reduction in this patient population. The lack of success in clinical trials towards this end presses the need to risk stratify locally advanced HPV+ OPSCC patients who can safely have treatment de-escalated. The present study had recourse to radiomics for this purpose and showed that radiomics has the ability to discriminate patients with locally advanced HPV+ OPSCC who went on to develop distant metastasis after completion of definitive chemoradiation or radiation alone. The implications of this study aid in demonstrating the potential pivotal role of radiomics in predictive risk assessment and personalizing therapy for this patient population. Abstract (1) Background and purpose: clinical trials have unsuccessfully tried to de-escalate treatment in locally advanced human papillomavirus positive (HPV+) oropharyngeal squamous cell carcinoma (OPSCC) with the goal of reducing treatment toxicity. The aim of this study was to explore the role of radiomics for risk stratification in this patient population to guide treatment. (2) Methods: the study population consisted of 225 patients with locally advanced HPV+ OPSCC treated with curative-intent radiation or chemoradiation therapy. Appearance of distant metastasis was used as the endpoint event. Radiomics data were extracted from the gross tumor volumes (GTVs) identified on the planning CT, with gray level being discretized using three different bin widths (8, 16, and 32). The data extracted for the groups with and without distant metastasis were subsequently balanced using three different algorithms including synthetic minority over-sampling technique (SMOTE), adaptive synthetic sampling (ADASYN), and borderline SMOTE. From these different combinations, a total of nine radiomics datasets were derived. Top features that minimized redundancy while maximizing relevance to the endpoint were selected individually and collectively for the nine radiomics datasets to build support vector machine (SVM) based predictive classifiers. Performance of the developed classifiers was evaluated by receiver operating characteristic (ROC) curve analysis. (3) Results: of the 225 locally advanced HPV+ OPSCC patients being studied, 9.3% had developed distant metastases at last follow-up. SVM classifiers built for the nine radiomics dataset using either their own respective top features or the top consensus ones were all able to differentiate the two cohorts at a level of excellence or beyond, with ROC area under curve (AUC) ranging from 0.84 to 0.95 (median = 0.90). ROC comparisons further revealed that the majority of the built classifiers did not distinguish the two cohorts significantly better than each other. (4) Conclusions: radiomics demonstrated discriminative ability in distinguishing patients with locally advanced HPV+ OPSCC who went on to develop distant metastasis after completion of definitive chemoradiation or radiation alone and may serve to risk stratify this patient population with the purpose of guiding the appropriate therapy.
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Affiliation(s)
- Benjamin Rich
- Department of Radiation Oncology, University of Miami, Miami, FL 33136, USA; (B.R.); (W.J.); (L.W.)
| | - Jianfeng Huang
- Department of Radiation Oncology, Affiliated Hospital of Jiangnan University, Wuxi 214125, China;
| | - Yidong Yang
- Department of Radiation Oncology, The First Affiliated Hospital of University of Science and Technology of China, Hefei 230026, China;
| | - William Jin
- Department of Radiation Oncology, University of Miami, Miami, FL 33136, USA; (B.R.); (W.J.); (L.W.)
| | - Perry Johnson
- Department of Radiation Oncology, University of Florida, Jacksonville, FL 32209, USA;
| | - Lora Wang
- Department of Radiation Oncology, University of Miami, Miami, FL 33136, USA; (B.R.); (W.J.); (L.W.)
| | - Fei Yang
- Department of Radiation Oncology, University of Miami, Miami, FL 33136, USA; (B.R.); (W.J.); (L.W.)
- Correspondence: ; Tel.: +1-(305)-243-4255
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21
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Blomain ES, Moding EJ. Liquid Biopsies for Molecular Biology-Based Radiotherapy. Int J Mol Sci 2021; 22:11267. [PMID: 34681925 PMCID: PMC8538046 DOI: 10.3390/ijms222011267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Revised: 10/13/2021] [Accepted: 10/15/2021] [Indexed: 11/29/2022] Open
Abstract
Molecular alterations drive cancer initiation and evolution during development and in response to therapy. Radiotherapy is one of the most commonly employed cancer treatment modalities, but radiobiologic approaches for personalizing therapy based on tumor biology and individual risks remain to be defined. In recent years, analysis of circulating nucleic acids has emerged as a non-invasive approach to leverage tumor molecular abnormalities as biomarkers of prognosis and treatment response. Here, we evaluate the roles of circulating tumor DNA and related analyses as powerful tools for precision radiotherapy. We highlight emerging work advancing liquid biopsies beyond biomarker studies into translational research investigating tumor clonal evolution and acquired resistance.
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Affiliation(s)
- Erik S. Blomain
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA 94305, USA;
| | - Everett J. Moding
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA 94305, USA;
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA 94305, USA
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Salzillo TC, Taku N, Wahid KA, McDonald BA, Wang J, van Dijk LV, Rigert JM, Mohamed ASR, Wang J, Lai SY, Fuller CD. Advances in Imaging for HPV-Related Oropharyngeal Cancer: Applications to Radiation Oncology. Semin Radiat Oncol 2021; 31:371-388. [PMID: 34455992 DOI: 10.1016/j.semradonc.2021.05.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
While there has been an overall decline of tobacco and alcohol-related head and neck cancer in recent decades, there has been an increased incidence of HPV-associated oropharyngeal cancer (OPC). Recent research studies and clinical trials have revealed that the cancer biology and clinical progression of HPV-positive OPC is unique relative to its HPV-negative counterparts. HPV-positive OPC is associated with higher rates of disease control following definitive treatment when compared to HPV-negative OPC. Thus, these conditions should be considered unique diseases with regards to treatment strategies and survival. In order to sufficiently characterize HPV-positive OPC and guide treatment strategies, there has been a considerable effort to diagnose, prognose, and track the treatment response of HPV-associated OPC through advanced imaging research. Furthermore, HPV-positive OPC patients are prime candidates for radiation de-escalation protocols, which will ideally reduce toxicities associated with radiation therapy and has prompted additional imaging research to detect radiation-induced changes in organs at risk. This manuscript reviews the various imaging modalities and current strategies for tackling these challenges as well as provides commentary on the potential successes and suggested improvements for the optimal treatment of these tumors.
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Affiliation(s)
- Travis C Salzillo
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX.
| | - Nicolette Taku
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX.
| | - Kareem A Wahid
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX.
| | - Brigid A McDonald
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX; Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX.
| | - Jarey Wang
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX.
| | - Lisanne V van Dijk
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX.
| | - Jillian M Rigert
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX; Department of Head and Neck Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX.
| | - Abdallah S R Mohamed
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX.
| | - Jihong Wang
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX.
| | - Stephen Y Lai
- Department of Head and Neck Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX.
| | - Clifton D Fuller
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX.
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Bagher-Ebadian H, Zhu S, Siddiqui F, Lu M, Movsas B, Chetty IJ. Technical Note: On the development of an outcome-driven frequency filter for improving Radiomics-based modeling of Human Papilloma Virus (HPV) in patients with oropharyngeal squamous cell carcinomas. Med Phys 2021; 48:7552-7562. [PMID: 34390003 DOI: 10.1002/mp.15159] [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: 05/12/2021] [Revised: 07/08/2021] [Accepted: 08/03/2021] [Indexed: 11/11/2022] Open
Abstract
PURPOSE To implement an outcome-driven frequency filter for improving radiomics-based modeling of human papilloma virus (HPV) for patients with oropharyngeal squamous cell carcinoma (OPSCC). METHODS AND MATERIALS One hundred twenty-eight OPSCC patients with known HPV status (60-HPV+ and 68-HPV-, confirmed by immunohistochemistry-P16 protein testing) were retrospectively studied. A 3D Discrete Fourier Transform was applied on contrast-enhanced CT images of patient gross tumor volumes (GTV's) to transform intensity distributions to the frequency domain and estimate frequency power spectrums of HPV- and HPV+ patient cohorts. Statistical analyses were performed to rank frequency bands contributing towards prediction of HPV status. An outcome-driven frequency filter was designed accordingly and applied to GTV frequency information. 3D Inverse-Discrete-Fourier-Transform was applied to reconstruct HPV-related frequency-filtered images. Radiomics features (11 feature-categories) were extracted from pre- and post- frequency filtered images using our previously published 'ROdiomiX' software. Least-Absolute-Shrinkage-and-Selection-Operation (Lasso) combined with a Generalized-Linear-Model (Lasso-GLM) was developed to identify and rank feature subsets with optimal information for prediction of HPV+/-. Radiomics-based Lasso-GLM classifiers (pre- and post-frequency filtered) were constructed and validated using a random permutation sampling and nested cross-validation techniques. Average Area Under Receiver Operating Characteristic (AUC), and Positive and Negative Predictive values (PPV, NPV) were computed to estimate generalization error and prediction performance. RESULTS Among 192 radiomic features, 15 features were found to be statistically significant discriminators between HPV+/- cohorts on post-frequency filtered CE-CT images; 14 such radiomic features were observed on pre-frequency filtered datasets. Discriminant features included tumor morphology and intensity contrast. Performances for prediction of HPV for the pre- and post-frequency filtered Lasso-GLM classifiers were: AUC/PPV/NPV = 0.789/0.755/0.805 and 0.850/0.808/0.877 respectively. Nested-CV performances for prediction of HPV for the pre- and post-frequency filtered Lasso-GLM classifiers were: AUC/PPV/NPV = 0.814/0.725/0.877 and 0.890/0.820/0.911 respectively. CONCLUSION Albeit subject to confirmation in a larger cohort, this pilot study presents encouraging results on the importance of frequency analysis prior to radiomic feature extraction toward enhancement of model performance for characterizing HPV in patients with OPSCC. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Hassan Bagher-Ebadian
- Department of Radiation Oncology, Henry Ford Hospital, 2799 West Grand Blvd, Detroit, MI, 48202, USA
| | - Simeng Zhu
- Department of Radiation Oncology, Henry Ford Hospital, 2799 West Grand Blvd, Detroit, MI, 48202, USA
| | - Farzan Siddiqui
- Department of Radiation Oncology, Henry Ford Hospital, 2799 West Grand Blvd, Detroit, MI, 48202, USA
| | - Mei Lu
- Department of Public Health, Henry Ford Health System, Michigan, MI, 48202, USA
| | - Benjamin Movsas
- Department of Radiation Oncology, Henry Ford Hospital, 2799 West Grand Blvd, Detroit, MI, 48202, USA
| | - Indrin J Chetty
- Department of Radiation Oncology, Henry Ford Hospital, 2799 West Grand Blvd, Detroit, MI, 48202, USA
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Wong J, Baine M, Wisnoskie S, Bennion N, Zheng D, Yu L, Dalal V, Hollingsworth MA, Lin C, Zheng D. Effects of interobserver and interdisciplinary segmentation variabilities on CT-based radiomics for pancreatic cancer. Sci Rep 2021; 11:16328. [PMID: 34381070 PMCID: PMC8357939 DOI: 10.1038/s41598-021-95152-x] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Accepted: 07/16/2021] [Indexed: 02/06/2023] Open
Abstract
Radiomics is a method to mine large numbers of quantitative imaging features and develop predictive models. It has shown exciting promise for improved cancer decision support from early detection to personalized precision treatment, and therefore offers a desirable new direction for pancreatic cancer where the mortality remains high despite the current care and intense research. For radiomics, interobserver segmentation variability and its effect on radiomic feature stability is a crucial consideration. While investigations have been reported for high-contrast cancer sites such as lung cancer, no studies to date have investigated it on CT-based radiomics for pancreatic cancer. With three radiation oncology observers and three radiology observers independently contouring on the contrast CT of 21 pancreatic cancer patients, we conducted the first interobserver segmentation variability study on CT-based radiomics for pancreatic cancer. Moreover, our novel investigation assessed whether there exists an interdisciplinary difference between the two disciplines. For each patient, a consensus tumor volume was generated using the simultaneous truth and performance level expectation algorithm, using the dice similarity coefficient (DSC) to assess each observer's delineation against the consensus volume. Radiation oncology observers showed a higher average DSC of 0.81 ± 0.06 than the radiology observers at 0.69 ± 0.16 (p = 0.002). On a panel of 1277 radiomic features, the intraclass correlation coefficients (ICC) was calculated for all observers and those of each discipline. Large variations of ICCs were observed for different radiomic features, but ICCs were generally higher for the radiation oncology group than for the radiology group. Applying a threshold of ICC > 0.75 for considering a feature as stable, 448 features (35%) were found stable for the radiation oncology group and 214 features (16%) were stable from the radiology group. Among them, 205 features were found stable for both groups. Our results provide information for interobserver segmentation variability and its effect on CT-based radiomics for pancreatic cancer. An interesting interdisciplinary variability found in this study also introduces new considerations for the deployment of radiomics models.
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Affiliation(s)
- Jeffrey Wong
- Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE, USA
| | - Michael Baine
- Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE, USA
| | - Sarah Wisnoskie
- Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE, USA
| | - Nathan Bennion
- Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE, USA
| | - Dechun Zheng
- Department of Radiology, Fujian Medical University Cancer Hospital, Fuzhou, Fujian, China
| | - Lei Yu
- Department of Radiology, University of Nebraska Medical Center, Omaha, NE, USA
| | - Vipin Dalal
- Department of Biochemistry and Molecular Biology, University of Nebraska Medical Center, Omaha, NE, USA
| | - Michael A Hollingsworth
- Eppley Institute for Research in Cancer, University of Nebraska Medical Center, Omaha, NE, USA
| | - Chi Lin
- Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE, USA.
| | - Dandan Zheng
- Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE, USA.
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Fujima N, Andreu-Arasa VC, Meibom SK, Mercier GA, Truong MT, Hirata K, Yasuda K, Kano S, Homma A, Kudo K, Sakai O. Prediction of the local treatment outcome in patients with oropharyngeal squamous cell carcinoma using deep learning analysis of pretreatment FDG-PET images. BMC Cancer 2021; 21:900. [PMID: 34362317 PMCID: PMC8344209 DOI: 10.1186/s12885-021-08599-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Accepted: 07/09/2021] [Indexed: 11/21/2022] Open
Abstract
Background This study aimed to assess the utility of deep learning analysis using pretreatment FDG-PET images to predict local treatment outcome in oropharyngeal squamous cell carcinoma (OPSCC) patients. Methods One hundred fifty-four OPSCC patients who received pretreatment FDG-PET were included and divided into training (n = 102) and test (n = 52) sets. The diagnosis of local failure and local progression-free survival (PFS) rates were obtained from patient medical records. In deep learning analyses, axial and coronal images were assessed by three different architectures (AlexNet, GoogLeNET, and ResNet). In the training set, FDG-PET images were analyzed after the data augmentation process for the diagnostic model creation. A multivariate clinical model was also created using a binomial logistic regression model from a patient’s clinical characteristics. The test data set was subsequently analyzed for confirmation of diagnostic accuracy. Assessment of local PFS rates was also performed. Results Training sessions were successfully performed with an accuracy of 74–89%. ROC curve analyses revealed an AUC of 0.61–0.85 by the deep learning model in the test set, whereas it was 0.62 by T-stage, 0.59 by clinical stage, and 0.74 by a multivariate clinical model. The highest AUC (0.85) was obtained with deep learning analysis of ResNet architecture. Cox proportional hazards regression analysis revealed deep learning-based classification by a multivariate clinical model (P < .05), and ResNet (P < .001) was a significant predictor of the treatment outcome. In the Kaplan-Meier analysis, the deep learning-based classification divided the patient’s local PFS rate better than the T-stage, clinical stage, and a multivariate clinical model. Conclusions Deep learning-based diagnostic model with FDG-PET images indicated its possibility to predict local treatment outcomes in OPSCCs. Supplementary Information The online version contains supplementary material available at 10.1186/s12885-021-08599-6.
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Affiliation(s)
- Noriyuki Fujima
- Departments of Radiology, Boston University School of Medicine, One Boston Medical Center Place, 3rd Floor, 820 Harrison Avenue, Boston, MA, 02118, USA.,Research Center for Cooperative Projects, Hokkaido University Graduate School of Medicine, kita 15, nishi 7, kita-ku, Sapporo, Hokkaido, 060-8638, Japan
| | - V Carlota Andreu-Arasa
- Departments of Radiology, Boston University School of Medicine, One Boston Medical Center Place, 3rd Floor, 820 Harrison Avenue, Boston, MA, 02118, USA
| | - Sara K Meibom
- Departments of Radiology, Boston University School of Medicine, One Boston Medical Center Place, 3rd Floor, 820 Harrison Avenue, Boston, MA, 02118, USA
| | - Gustavo A Mercier
- Departments of Radiology, Boston University School of Medicine, One Boston Medical Center Place, 3rd Floor, 820 Harrison Avenue, Boston, MA, 02118, USA
| | - Minh Tam Truong
- Departments of Radiation Oncology, Boston Medical Center, Boston University School of Medicine, One Boston Medical Center Place, Boston, MA, 02118, USA
| | - Kenji Hirata
- Departments of Diagnostic Imaging, Hokkaido University Graduate School of Medicine, kita 15, nishi 7, kita-ku, Sapporo, Hokkaido, 060-8638, Japan
| | - Koichi Yasuda
- Departments of Radiation Medicine, Hokkaido University Graduate School of Medicine, kita 15, nishi 7, kita-ku, Sapporo, Hokkaido, 060-8638, Japan
| | - Satoshi Kano
- Otolaryngology-Head and Neck Surgery, Hokkaido University Graduate School of Medicine, kita 15, nishi 7, kita-ku, Sapporo, Hokkaido, 060-8638, Japan
| | - Akihiro Homma
- Otolaryngology-Head and Neck Surgery, Hokkaido University Graduate School of Medicine, kita 15, nishi 7, kita-ku, Sapporo, Hokkaido, 060-8638, Japan
| | - Kohsuke Kudo
- Departments of Diagnostic Imaging, Hokkaido University Graduate School of Medicine, kita 15, nishi 7, kita-ku, Sapporo, Hokkaido, 060-8638, Japan.,The Global Station for Quantum Medical Science and Engineering, Global Institution for collaborative research and education, Sapporo, Hokkaido, 060-0808, Japan
| | - Osamu Sakai
- Departments of Radiology, Boston University School of Medicine, One Boston Medical Center Place, 3rd Floor, 820 Harrison Avenue, Boston, MA, 02118, USA. .,Departments of Radiation Oncology, Boston Medical Center, Boston University School of Medicine, One Boston Medical Center Place, Boston, MA, 02118, USA.
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Liu X, Maleki F, Muthukrishnan N, Ovens K, Huang SH, Pérez-Lara A, Romero-Sanchez G, Bhatnagar SR, Chatterjee A, Pusztaszeri MP, Spatz A, Batist G, Payabvash S, Haider SP, Mahajan A, Reinhold C, Forghani B, O’Sullivan B, Yu E, Forghani R. Site-Specific Variation in Radiomic Features of Head and Neck Squamous Cell Carcinoma and Its Impact on Machine Learning Models. Cancers (Basel) 2021; 13:cancers13153723. [PMID: 34359623 PMCID: PMC8345201 DOI: 10.3390/cancers13153723] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2021] [Revised: 07/07/2021] [Accepted: 07/20/2021] [Indexed: 12/17/2022] Open
Abstract
Simple Summary Head and neck squamous cell carcinoma (HNSCC) is the most common mucosal malignancy of the head and neck and a leading cause of cancer death. HNSCC arises from different primary anatomical locations that are typically combined during radiomic analyses assuming that the radiomic features, i.e., quantitative image-based features, are similar based on histopathologic characteristics. However, whether these quantitative features are comparable across tumor sites remains unknown. The aim of our retrospective study was to assess if systematic differences exist between radiomic features based on different tumor sites in HNSCC and how they might affect machine learning model performance in endpoint prediction. Using a population of 605 HNSCC patients, we observed significant differences in radiomic features of tumors from different locations and showed that these differences can impact machine learning model performance. This suggests that tumor site should be considered when developing and evaluating radiomics-based models. Abstract Current radiomic studies of head and neck squamous cell carcinomas (HNSCC) are typically based on datasets combining tumors from different locations, assuming that the radiomic features are similar based on histopathologic characteristics. However, molecular pathogenesis and treatment in HNSCC substantially vary across different tumor sites. It is not known if a statistical difference exists between radiomic features from different tumor sites and how they affect machine learning model performance in endpoint prediction. To answer these questions, we extracted radiomic features from contrast-enhanced neck computed tomography scans (CTs) of 605 patients with HNSCC originating from the oral cavity, oropharynx, and hypopharynx/larynx. The difference in radiomic features of tumors from these sites was assessed using statistical analyses and Random Forest classifiers on the radiomic features with 10-fold cross-validation to predict tumor sites, nodal metastasis, and HPV status. We found statistically significant differences (p-value ≤ 0.05) between the radiomic features of HNSCC depending on tumor location. We also observed that differences in quantitative features among HNSCC from different locations impact the performance of machine learning models. This suggests that radiomic features may reveal biologic heterogeneity complementary to current gold standard histopathologic evaluation. We recommend considering tumor site in radiomic studies of HNSCC.
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Affiliation(s)
- Xiaoyang Liu
- Princess Margaret Hospital, University of Toronto, University Health Network, Toronto, ON M5G 2C1, Canada; (X.L.); (S.H.H.); (B.O.)
- Department of Radiology, Brigham and Women’s Hospital, Harvard University, Cambridge, MA 02115, USA
- Department of Medical Imaging, University of Toronto, Toronto, ON M5S 1A1, Canada
| | - Farhad Maleki
- Augmented Intelligence & Precision Health Laboratory (AIPHL), Department of Radiology and the Research Institute of the McGill University Health Centre, McGill University, Montreal, QC H4A 3J1, Canada; (F.M.); (N.M.); (K.O.); (S.R.B.); (C.R.); (B.F.)
| | - Nikesh Muthukrishnan
- Augmented Intelligence & Precision Health Laboratory (AIPHL), Department of Radiology and the Research Institute of the McGill University Health Centre, McGill University, Montreal, QC H4A 3J1, Canada; (F.M.); (N.M.); (K.O.); (S.R.B.); (C.R.); (B.F.)
| | - Katie Ovens
- Augmented Intelligence & Precision Health Laboratory (AIPHL), Department of Radiology and the Research Institute of the McGill University Health Centre, McGill University, Montreal, QC H4A 3J1, Canada; (F.M.); (N.M.); (K.O.); (S.R.B.); (C.R.); (B.F.)
| | - Shao Hui Huang
- Princess Margaret Hospital, University of Toronto, University Health Network, Toronto, ON M5G 2C1, Canada; (X.L.); (S.H.H.); (B.O.)
- Princess Margaret Cancer Centre, Department of Radiation Oncology, University of Toronto, Toronto, ON M5S 1A1, Canada
| | - Almudena Pérez-Lara
- Segal Cancer Centre & Lady Davis Institute for Medical Research, Jewish General Hospital, McGill University, Montreal, QC H3T 1E2, Canada; (A.P.-L.); (G.R.-S.); (G.B.)
| | - Griselda Romero-Sanchez
- Segal Cancer Centre & Lady Davis Institute for Medical Research, Jewish General Hospital, McGill University, Montreal, QC H3T 1E2, Canada; (A.P.-L.); (G.R.-S.); (G.B.)
| | - Sahir Rai Bhatnagar
- Augmented Intelligence & Precision Health Laboratory (AIPHL), Department of Radiology and the Research Institute of the McGill University Health Centre, McGill University, Montreal, QC H4A 3J1, Canada; (F.M.); (N.M.); (K.O.); (S.R.B.); (C.R.); (B.F.)
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC H3A 1A2, Canada
| | | | | | - Alan Spatz
- Division of Pathology, Jewish General Hospital, Montreal, QC H3Y 1E2, Canada; (M.P.P.); (A.S.)
| | - Gerald Batist
- Segal Cancer Centre & Lady Davis Institute for Medical Research, Jewish General Hospital, McGill University, Montreal, QC H3T 1E2, Canada; (A.P.-L.); (G.R.-S.); (G.B.)
| | - Seyedmehdi Payabvash
- Section of Neuroradiology, Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06520, USA; (S.P.); (S.P.H.); (A.M.)
| | - Stefan P. Haider
- Section of Neuroradiology, Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06520, USA; (S.P.); (S.P.H.); (A.M.)
| | - Amit Mahajan
- Section of Neuroradiology, Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06520, USA; (S.P.); (S.P.H.); (A.M.)
| | - Caroline Reinhold
- Augmented Intelligence & Precision Health Laboratory (AIPHL), Department of Radiology and the Research Institute of the McGill University Health Centre, McGill University, Montreal, QC H4A 3J1, Canada; (F.M.); (N.M.); (K.O.); (S.R.B.); (C.R.); (B.F.)
| | - Behzad Forghani
- Augmented Intelligence & Precision Health Laboratory (AIPHL), Department of Radiology and the Research Institute of the McGill University Health Centre, McGill University, Montreal, QC H4A 3J1, Canada; (F.M.); (N.M.); (K.O.); (S.R.B.); (C.R.); (B.F.)
- Segal Cancer Centre & Lady Davis Institute for Medical Research, Jewish General Hospital, McGill University, Montreal, QC H3T 1E2, Canada; (A.P.-L.); (G.R.-S.); (G.B.)
| | - Brian O’Sullivan
- Princess Margaret Hospital, University of Toronto, University Health Network, Toronto, ON M5G 2C1, Canada; (X.L.); (S.H.H.); (B.O.)
- Princess Margaret Cancer Centre, Department of Radiation Oncology, University of Toronto, Toronto, ON M5S 1A1, Canada
| | - Eugene Yu
- Princess Margaret Hospital, University of Toronto, University Health Network, Toronto, ON M5G 2C1, Canada; (X.L.); (S.H.H.); (B.O.)
- Department of Medical Imaging, University of Toronto, Toronto, ON M5S 1A1, Canada
- Correspondence: (E.Y.); (R.F.)
| | - Reza Forghani
- Augmented Intelligence & Precision Health Laboratory (AIPHL), Department of Radiology and the Research Institute of the McGill University Health Centre, McGill University, Montreal, QC H4A 3J1, Canada; (F.M.); (N.M.); (K.O.); (S.R.B.); (C.R.); (B.F.)
- Segal Cancer Centre & Lady Davis Institute for Medical Research, Jewish General Hospital, McGill University, Montreal, QC H3T 1E2, Canada; (A.P.-L.); (G.R.-S.); (G.B.)
- Correspondence: (E.Y.); (R.F.)
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Patel H, Vock DM, Marai GE, Fuller CD, Mohamed ASR, Canahuate G. Oropharyngeal cancer patient stratification using random forest based-learning over high-dimensional radiomic features. Sci Rep 2021; 11:14057. [PMID: 34234160 PMCID: PMC8263609 DOI: 10.1038/s41598-021-92072-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Accepted: 05/10/2021] [Indexed: 11/27/2022] Open
Abstract
To improve risk prediction for oropharyngeal cancer (OPC) patients using cluster analysis on the radiomic features extracted from pre-treatment Computed Tomography (CT) scans. 553 OPC Patients randomly split into training (80%) and validation (20%), were classified into 2 or 3 risk groups by applying hierarchical clustering over the co-occurrence matrix obtained from a random survival forest (RSF) trained over 301 radiomic features. The cluster label was included together with other clinical data to train an ensemble model using five predictive models (Cox, random forest, RSF, logistic regression, and logistic-elastic net). Ensemble performance was evaluated over the independent test set for both recurrence free survival (RFS) and overall survival (OS). The Kaplan-Meier curves for OS stratified by cluster label show significant differences for both training and testing (p val < 0.0001). When compared to the models trained using clinical data only, the inclusion of the cluster label improves AUC test performance from .62 to .79 and from .66 to .80 for OS and RFS, respectively. The extraction of a single feature, namely a cluster label, to represent the high-dimensional radiomic feature space reduces the dimensionality and sparsity of the data. Moreover, inclusion of the cluster label improves model performance compared to clinical data only and offers comparable performance to the models including raw radiomic features.
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Affiliation(s)
- Harsh Patel
- Department of Electrical and Computer Engineering, University of Iowa, Iowa City, 52242, USA
| | - David M Vock
- Division of Biostatistics, University of Minnesota, Minneapolis, 55455, USA
| | - G Elisabeta Marai
- Department of Department of Computer Science, University of Illinois at Chicago, Chicago, 60607, USA
| | - Clifton D Fuller
- Department of Radiation Oncology, MD Anderson Cancer Center, Houston, 77030, USA
| | - Abdallah S R Mohamed
- Department of Radiation Oncology, MD Anderson Cancer Center, Houston, 77030, USA
| | - Guadalupe Canahuate
- Department of Electrical and Computer Engineering, University of Iowa, Iowa City, 52242, USA.
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28
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Gul M, Bonjoc KJC, Gorlin D, Wong CW, Salem A, La V, Filippov A, Chaudhry A, Imam MH, Chaudhry AA. Diagnostic Utility of Radiomics in Thyroid and Head and Neck Cancers. Front Oncol 2021; 11:639326. [PMID: 34307123 PMCID: PMC8293690 DOI: 10.3389/fonc.2021.639326] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Accepted: 06/08/2021] [Indexed: 11/21/2022] Open
Abstract
Radiomics is an emerging field in radiology that utilizes advanced statistical data characterizing algorithms to evaluate medical imaging and objectively quantify characteristics of a given disease. Due to morphologic heterogeneity and genetic variation intrinsic to neoplasms, radiomics have the potential to provide a unique insight into the underlying tumor and tumor microenvironment. Radiomics has been gaining popularity due to potential applications in disease quantification, predictive modeling, treatment planning, and response assessment - paving way for the advancement of personalized medicine. However, producing a reliable radiomic model requires careful evaluation and construction to be translated into clinical practices that have varying software and/or medical equipment. We aim to review the diagnostic utility of radiomics in otorhinolaryngology, including both cancers of the head and neck as well as the thyroid.
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Affiliation(s)
- Maryam Gul
- Amaze Research Foundation, Department of Biomarker Discovery, Anaheim, CA, United States
| | - Kimberley-Jane C. Bonjoc
- Department of Diagnostic and Interventional Radiology, City of Hope National Medical Center, Duarte, CA, United States
| | - David Gorlin
- Department of Diagnostic and Interventional Radiology, City of Hope National Medical Center, Duarte, CA, United States
| | - Chi Wah Wong
- Department of Diagnostic and Interventional Radiology, City of Hope National Medical Center, Duarte, CA, United States
| | - Amirah Salem
- Department of Diagnostic and Interventional Radiology, City of Hope National Medical Center, Duarte, CA, United States
| | - Vincent La
- Department of Diagnostic and Interventional Radiology, City of Hope National Medical Center, Duarte, CA, United States
| | - Aleksandr Filippov
- Department of Diagnostic and Interventional Radiology, City of Hope National Medical Center, Duarte, CA, United States
| | - Abbas Chaudhry
- Amaze Research Foundation, Department of Biomarker Discovery, Anaheim, CA, United States
| | - Muhammad H. Imam
- Florida Cancer Specialists, Department of Oncology, Orlando, FL, United States
| | - Ammar A. Chaudhry
- Department of Diagnostic and Interventional Radiology, City of Hope National Medical Center, Duarte, CA, United States
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29
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Keek SA, Wesseling FWR, Woodruff HC, van Timmeren JE, Nauta IH, Hoffmann TK, Cavalieri S, Calareso G, Primakov S, Leijenaar RTH, Licitra L, Ravanelli M, Scheckenbach K, Poli T, Lanfranco D, Vergeer MR, Leemans CR, Brakenhoff RH, Hoebers FJP, Lambin P. A Prospectively Validated Prognostic Model for Patients with Locally Advanced Squamous Cell Carcinoma of the Head and Neck Based on Radiomics of Computed Tomography Images. Cancers (Basel) 2021; 13:3271. [PMID: 34210048 PMCID: PMC8269129 DOI: 10.3390/cancers13133271] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Revised: 06/15/2021] [Accepted: 06/23/2021] [Indexed: 12/28/2022] Open
Abstract
BACKGROUND Locoregionally advanced head and neck squamous cell carcinoma (HNSCC) patients have high relapse and mortality rates. Imaging-based decision support may improve outcomes by optimising personalised treatment, and support patient risk stratification. We propose a multifactorial prognostic model including radiomics features to improve risk stratification for advanced HNSCC, compared to TNM eighth edition, the gold standard. PATIENT AND METHODS Data of 666 retrospective- and 143 prospective-stage III-IVA/B HNSCC patients were collected. A multivariable Cox proportional-hazards model was trained to predict overall survival (OS) using diagnostic CT-based radiomics features extracted from the primary tumour. Separate analyses were performed using TNM8, tumour volume, clinical and biological variables, and combinations thereof with radiomics features. Patient risk stratification in three groups was assessed through Kaplan-Meier (KM) curves. A log-rank test was performed for significance (p-value < 0.05). The prognostic accuracy was reported through the concordance index (CI). RESULTS A model combining an 11-feature radiomics signature, clinical and biological variables, TNM8, and volume could significantly stratify the validation cohort into three risk groups (p < 0∙01, CI of 0.79 as validation). CONCLUSION A combination of radiomics features with other predictors can predict OS very accurately for advanced HNSCC patients and improves on the current gold standard of TNM8.
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Affiliation(s)
- Simon A. Keek
- The D-Lab, Department of Precision Medicine, GROW-School for Oncology, Maastricht University, Maastricht, Universiteitssingel 40, 6229 ER Maastricht, The Netherlands; (S.A.K.); (H.C.W.); (S.P.)
| | - Frederik W. R. Wesseling
- Department of Radiation Oncology (MAASTRO), GROW-School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Postbus 3035, 6202 NA Maastricht, The Netherlands; (F.W.R.W.); (F.J.P.H.)
| | - Henry C. Woodruff
- The D-Lab, Department of Precision Medicine, GROW-School for Oncology, Maastricht University, Maastricht, Universiteitssingel 40, 6229 ER Maastricht, The Netherlands; (S.A.K.); (H.C.W.); (S.P.)
- Department of Radiology and Nuclear Medicine, GROW-School for Oncology, Maastricht University Medical Centre+, P.O. Box 5800, 6202 AZ Maastricht, The Netherlands
| | - Janita E. van Timmeren
- Department of Radiation Oncology, University Hospital Zürich, University of Zürich, Rämistrasse 100, 8091 Zürich, Switzerland;
| | - Irene H. Nauta
- Amsterdam UMC, Otolaryngology/Head and Neck Surgery, Cancer Center Amsterdam, Vrije Universiteit Amsterdam, Postbus 7057, 1007 MB Amsterdam, The Netherlands; (I.H.N.); (C.R.L.); (R.H.B.)
| | - Thomas K. Hoffmann
- Department of Otorhinolaryngology, Head Neck Surgery, i2SOUL Consortium, University of Ulm, Frauensteige 14a (Haus 18), 89075 Ulm, Germany;
| | - Stefano Cavalieri
- Head and Neck Medical Oncology Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, via Giacomo Venezian, University of Milan, 1 20133 Milano, Italy; (S.C.); (L.L.)
| | - Giuseppina Calareso
- Radiology Unit, Fondazione IRCCS Istituto Nazionale dei Tumori via Giacomo Venezian, 1 20133 Milano, Italy;
| | - Sergey Primakov
- The D-Lab, Department of Precision Medicine, GROW-School for Oncology, Maastricht University, Maastricht, Universiteitssingel 40, 6229 ER Maastricht, The Netherlands; (S.A.K.); (H.C.W.); (S.P.)
| | | | - Lisa Licitra
- Head and Neck Medical Oncology Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, via Giacomo Venezian, University of Milan, 1 20133 Milano, Italy; (S.C.); (L.L.)
- Department of Oncology and Hemato-Oncology, University of Milan, via S. Sofia 9/1, 20122 Milano, Italy
| | - Marco Ravanelli
- Department of Medicine and Surgery, University of Brescia, Viale Europa, 11-25123 Brescia, Italy;
| | - Kathrin Scheckenbach
- Department. of Otorhinolaryngology-Head and Neck Surgery, University Hospital Düsseldorf, Moorenstr. 5, 40225 Düsseldorf, Germany;
| | - Tito Poli
- Maxillofacial Surgery Unit, Department of Medicine and Surgery, University of Parma-University Hospital of Parma, via Università, 12-I, 43121 Parma, Italy; (T.P.); (D.L.)
| | - Davide Lanfranco
- Maxillofacial Surgery Unit, Department of Medicine and Surgery, University of Parma-University Hospital of Parma, via Università, 12-I, 43121 Parma, Italy; (T.P.); (D.L.)
| | - Marije R. Vergeer
- Amsterdam UMC, Cancer Center Amsterdam, Department of Radiation Oncology, Vrije Universiteit Amsterdam, Postbus 7057, 1007 MB Amsterdam, The Netherlands;
| | - C. René Leemans
- Amsterdam UMC, Otolaryngology/Head and Neck Surgery, Cancer Center Amsterdam, Vrije Universiteit Amsterdam, Postbus 7057, 1007 MB Amsterdam, The Netherlands; (I.H.N.); (C.R.L.); (R.H.B.)
| | - Ruud H. Brakenhoff
- Amsterdam UMC, Otolaryngology/Head and Neck Surgery, Cancer Center Amsterdam, Vrije Universiteit Amsterdam, Postbus 7057, 1007 MB Amsterdam, The Netherlands; (I.H.N.); (C.R.L.); (R.H.B.)
| | - Frank J. P. Hoebers
- Department of Radiation Oncology (MAASTRO), GROW-School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Postbus 3035, 6202 NA Maastricht, The Netherlands; (F.W.R.W.); (F.J.P.H.)
| | - Philippe Lambin
- The D-Lab, Department of Precision Medicine, GROW-School for Oncology, Maastricht University, Maastricht, Universiteitssingel 40, 6229 ER Maastricht, The Netherlands; (S.A.K.); (H.C.W.); (S.P.)
- Department of Radiology and Nuclear Medicine, GROW-School for Oncology, Maastricht University Medical Centre+, P.O. Box 5800, 6202 AZ Maastricht, The Netherlands
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30
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Lafata KJ, Chang Y, Wang C, Mowery YM, Vergalasova I, Niedzwiecki D, Yoo DS, Liu JG, Brizel DM, Yin FF. Intrinsic radiomic expression patterns after 20 Gy demonstrate early metabolic response of oropharyngeal cancers. Med Phys 2021; 48:3767-3777. [PMID: 33959972 DOI: 10.1002/mp.14926] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Revised: 03/26/2021] [Accepted: 04/25/2021] [Indexed: 01/01/2023] Open
Abstract
PURPOSE This study investigated the prognostic potential of intra-treatment PET radiomics data in patients undergoing definitive (chemo) radiation therapy for oropharyngeal cancer (OPC) on a prospective clinical trial. We hypothesized that the radiomic expression of OPC tumors after 20 Gy is associated with recurrence-free survival (RFS). MATERIALS AND METHODS Sixty-four patients undergoing definitive (chemo)radiation for OPC were prospectively enrolled on an IRB-approved study. Investigational 18 F-FDG-PET/CT images were acquired prior to treatment and 2 weeks (20 Gy) into a seven-week course of therapy. Fifty-five quantitative radiomic features were extracted from the primary tumor as potential biomarkers of early metabolic response. An unsupervised data clustering algorithm was used to partition patients into clusters based only on their radiomic expression. Clustering results were naïvely compared to residual disease and/or subsequent recurrence and used to derive Kaplan-Meier estimators of RFS. To test whether radiomic expression provides prognostic value beyond conventional clinical features associated with head and neck cancer, multivariable Cox proportional hazards modeling was used to adjust radiomic clusters for T and N stage, HPV status, and change in tumor volume. RESULTS While pre-treatment radiomics were not prognostic, intra-treatment radiomic expression was intrinsically associated with both residual/recurrent disease (P = 0.0256, χ 2 test) and RFS (HR = 7.53, 95% CI = 2.54-22.3; P = 0.0201). On univariate Cox analysis, radiomic cluster was associated with RFS (unadjusted HR = 2.70; 95% CI = 1.26-5.76; P = 0.0104) and maintained significance after adjustment for T, N staging, HPV status, and change in tumor volume after 20 Gy (adjusted HR = 2.69; 95% CI = 1.03-7.04; P = 0.0442). The particular radiomic characteristics associated with outcomes suggest that metabolic spatial heterogeneity after 20 Gy portends complete and durable therapeutic response. This finding is independent of baseline metabolic imaging characteristics and clinical features of head and neck cancer, thus providing prognostic advantages over existing approaches. CONCLUSIONS Our data illustrate the prognostic value of intra-treatment metabolic image interrogation, which may potentially guide adaptive therapy strategies for OPC patients and serve as a blueprint for other disease sites. The quality of our study was strengthened by its prospective image acquisition protocol, homogenous patient cohort, relatively long patient follow-up times, and unsupervised clustering formalism that is less prone to hyper-parameter tuning and over-fitting compared to supervised learning.
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Affiliation(s)
- Kyle J Lafata
- Department of Radiation Oncology, Duke University School of Medicine, Durham, NC, USA.,Department of Radiology, Duke University School of Medicine, Durham, NC, USA.,Department of Electrical & Computer Engineering, Duke University Pratt School of Engineering, Durham, NC, USA.,Medical Physics Graduate Program, Duke University, Durham, NC, USA
| | - Yushi Chang
- Department of Radiation Oncology, Duke University School of Medicine, Durham, NC, USA.,Medical Physics Graduate Program, Duke University, Durham, NC, USA
| | - Chunhao Wang
- Department of Radiation Oncology, Duke University School of Medicine, Durham, NC, USA.,Medical Physics Graduate Program, Duke University, Durham, NC, USA
| | - Yvonne M Mowery
- Department of Radiation Oncology, Duke University School of Medicine, Durham, NC, USA.,Department of Head and Neck Surgery & Communication Sciences, Duke University School of Medicine, Durham, NC, USA
| | - Irina Vergalasova
- Department of Radiation Oncology, Rutgers Cancer Institute of New Jersey, Robert Wood Johnson Medical School, New Brunswick, NJ, USA
| | - Donna Niedzwiecki
- Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, USA
| | - David S Yoo
- Department of Radiation Oncology, Duke University School of Medicine, Durham, NC, USA
| | - Jian-Guo Liu
- Department of Mathematics, Duke University, Durham, NC, USA.,Department of Physics, Duke University, Durham, NC, USA
| | - David M Brizel
- Department of Radiation Oncology, Duke University School of Medicine, Durham, NC, USA.,Department of Head and Neck Surgery & Communication Sciences, Duke University School of Medicine, Durham, NC, USA
| | - Fang-Fang Yin
- Department of Radiation Oncology, Duke University School of Medicine, Durham, NC, USA.,Medical Physics Graduate Program, Duke University, Durham, NC, USA
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31
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Bogowicz M, Pavic M, Riesterer O, Finazzi T, Garcia Schüler H, Holz-Sapra E, Rudofsky L, Basler L, Spaniol M, Ambrusch A, Hüllner M, Guckenberger M, Tanadini-Lang S. Targeting Treatment Resistance in Head and Neck Squamous Cell Carcinoma - Proof of Concept for CT Radiomics-Based Identification of Resistant Sub-Volumes. Front Oncol 2021; 11:664304. [PMID: 34123824 PMCID: PMC8191457 DOI: 10.3389/fonc.2021.664304] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Accepted: 04/06/2021] [Indexed: 12/18/2022] Open
Abstract
Purpose Radiomics has already been proposed as a prognostic biomarker in head and neck cancer (HNSCC). However, its predictive power in radiotherapy has not yet been studied. Here, we investigated a local radiomics approach to distinguish between tumor sub-volumes with different levels of radiosensitivity as a possible target for radiation dose intensification. Materials and Methods Of 40 patients (n=28 training and n=12 validation) with biopsy confirmed locally recurrent HNSCC, pretreatment contrast-enhanced CT images were registered with follow-up PET/CT imaging allowing identification of controlled (GTVcontrol) vs non-controlled (GTVrec) tumor sub-volumes on pretreatment imaging. A bi-regional model was built using radiomic features extracted from pretreatment CT in the GTVrec and GTVcontrol to differentiate between those regions. Additionally, concept of local radiomics was implemented to perform detection task. The original tumor volume was divided into sub-volumes with no prior information on the location of recurrence. Radiomic features from those sub-volumes were then used to detect recurrent sub-volumes using multivariable logistic regression. Results Radiomic features extracted from non-controlled regions differed significantly from those in controlled regions (training AUC = 0.79 CI 95% 0.66 - 0.91 and validation AUC = 0.88 CI 95% 0.72 – 1.00). Local radiomics analysis allowed efficient detection of non-controlled sub-volumes both in the training AUC = 0.66 (CI 95% 0.56 – 0.75) and validation cohort 0.70 (CI 95% 0.53 – 0.86), however performance of this model was inferior to bi-regional model. Both models indicated that sub-volumes characterized by higher heterogeneity were linked to tumor recurrence. Conclusion Local radiomics is able to detect sub-volumes with decreased radiosensitivity, associated with location of tumor recurrence in HNSCC in the pre-treatment CT imaging. This proof of concept study, indicates that local CT radiomics can be used as predictive biomarker in radiotherapy and potential target for dose intensification.
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Affiliation(s)
- Marta Bogowicz
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Matea Pavic
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Oliver Riesterer
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland.,Centre for Radiation Oncology KSA-KSB, Cantonal Hospital Aarau, Aarau, Switzerland
| | - Tobias Finazzi
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Helena Garcia Schüler
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Edna Holz-Sapra
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Leonie Rudofsky
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Lucas Basler
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Manon Spaniol
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Andreas Ambrusch
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Martin Hüllner
- Department of Nuclear Medicine, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Matthias Guckenberger
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Stephanie Tanadini-Lang
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
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32
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Zhang MH, Hasse A, Carroll T, Pearson AT, Cipriani NA, Ginat DT. Differentiating low and high grade mucoepidermoid carcinoma of the salivary glands using CT radiomics. Gland Surg 2021; 10:1646-1654. [PMID: 34164309 DOI: 10.21037/gs-20-830] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Background The purpose of this study is to determine if Haralick texture analysis on CT imaging of mucoepidermoid carcinomas (MEC) can differentiate low-grade and high-grade tumors. Methods A retrospective review of 18 patients with MEC of the salivary glands, corresponding CT imaging and pathology report was performed. Tumors were manually segmented and image analysis was performed to calculate radiomic features. Radiomic features were compared between low-grade and high-grade MEC. A multivariable logistic regression model and receiver operating characteristic analysis was performed. Results A total of 18 patients (mean age, 51, range 9-83 years, 8 men and 10 women) were included. Nine patients had low-grade pathology and nine patients had high-grade pathology. Of the 18 cases, 7 (39%) occurred in the parotid gland and 11 (61%) occurred in minor salivary glands. No individual feature was significantly different between low-grade and high-grade MEC. A logistic regression model including surface regularity, energy and information measure II of correlation was performed and was able to predict high-grade MEC accurately (sensitivity 89%, specificity 68%). The area under the receiver operating characteristic curve was 0.802. Conclusions High-grade MEC tend to have a low energy, high correlation texture as well as surface irregularity. Together, these three features may comprise a tumor phenotype that is able to predict high-grade pathology in MECs.
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Affiliation(s)
- Michael H Zhang
- Pritzker School of Medicine, The University of Chicago, Chicago IL, USA
| | - Adam Hasse
- Graduate Program in Medical Physics, The University of Chicago, Chicago, IL, USA
| | - Timothy Carroll
- Graduate Program in Medical Physics, The University of Chicago, Chicago, IL, USA
| | | | | | - Daniel T Ginat
- Department of Radiology, The University of Chicago, Chicago IL, USA
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Barua S, Elhalawani H, Volpe S, Al Feghali KA, Yang P, Ng SP, Elgohari B, Granberry RC, Mackin DS, Gunn GB, Hutcheson KA, Chambers MS, Court LE, Mohamed ASR, Fuller CD, Lai SY, Rao A. Computed Tomography Radiomics Kinetics as Early Imaging Correlates of Osteoradionecrosis in Oropharyngeal Cancer Patients. Front Artif Intell 2021; 4:618469. [PMID: 33898983 PMCID: PMC8063205 DOI: 10.3389/frai.2021.618469] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2020] [Accepted: 03/04/2021] [Indexed: 01/08/2023] Open
Abstract
Osteoradionecrosis (ORN) is a major side-effect of radiation therapy in oropharyngeal cancer (OPC) patients. In this study, we demonstrate that early prediction of ORN is possible by analyzing the temporal evolution of mandibular subvolumes receiving radiation. For our analysis, we use computed tomography (CT) scans from 21 OPC patients treated with Intensity Modulated Radiation Therapy (IMRT) with subsequent radiographically-proven ≥ grade II ORN, at three different time points: pre-IMRT, 2-months, and 6-months post-IMRT. For each patient, radiomic features were extracted from a mandibular subvolume that developed ORN and a control subvolume that received the same dose but did not develop ORN. We used a Multivariate Functional Principal Component Analysis (MFPCA) approach to characterize the temporal trajectories of these features. The proposed MFPCA model performs the best at classifying ORN vs. Control subvolumes with an area under curve (AUC) = 0.74 [95% confidence interval (C.I.): 0.61–0.90], significantly outperforming existing approaches such as a pre-IMRT features model or a delta model based on changes at intermediate time points, i.e., at 2- and 6-month follow-up. This suggests that temporal trajectories of radiomics features derived from sequential pre- and post-RT CT scans can provide markers that are correlates of RT-induced mandibular injury, and consequently aid in earlier management of ORN.
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Affiliation(s)
- Souptik Barua
- Department of Electrical and Computer Engineering, Rice University, Houston, TX, United States.,Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, United States
| | - Hesham Elhalawani
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Stefania Volpe
- Department of Radiation Oncology, European Institute of Oncology IRCSS, Milan, Italy.,Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
| | - Karine A Al Feghali
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Pei Yang
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Sweet Ping Ng
- Department of Radiation Oncology, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Baher Elgohari
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Robin C Granberry
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Dennis S Mackin
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - G Brandon Gunn
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Katherine A Hutcheson
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Mark S Chambers
- Department of Oncologic Dentistry and Prosthodontics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Laurence E Court
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Abdallah S R Mohamed
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Clifton D Fuller
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States.,Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Stephen Y Lai
- Department of Head and Neck Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Arvind Rao
- Department of Electrical and Computer Engineering, Rice University, Houston, TX, United States.,Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, United States.,Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, United States
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34
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Utility of CT texture analysis to differentiate olfactory neuroblastoma from sinonasal squamous cell carcinoma. Sci Rep 2021; 11:4679. [PMID: 33633160 PMCID: PMC7907098 DOI: 10.1038/s41598-021-84048-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Accepted: 02/11/2021] [Indexed: 01/06/2023] Open
Abstract
The purpose of this study was to examine differences in texture features between olfactory neuroblastoma (ONB) and sinonasal squamous cell carcinoma (SCC) on contrast-enhanced CT (CECT) images, and to evaluate the predictive accuracy of texture analysis compared to radiologists’ interpretations. Forty-three patients with pathologically-diagnosed primary nasal and paranasal tumor (17 ONB and 26 SCC) were included. We extracted 42 texture features from tumor regions on CECT images obtained before treatment. In univariate analysis, each texture features were compared, with adjustment for multiple comparisons. In multivariate analysis, the elastic net was used to select useful texture features and to construct a texture-based prediction model with leave-one-out cross-validation. The prediction accuracy was compared with two radiologists’ visual interpretations. In univariate analysis, significant differences were observed for 28 of 42 texture features between ONB and SCC, with areas under the receiver operating characteristic curve between 0.68 and 0.91 (median: 0.80). In multivariate analysis, the elastic net model selected 18 texture features that contributed to differentiation. It tended to show slightly higher predictive accuracy than radiologists’ interpretations (86% and 74%, respectively; P = 0.096). In conclusion, several texture features contributed to differentiation of ONB from SCC, and the texture-based prediction model was considered useful.
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Peng L, Hong X, Yuan Q, Lu L, Wang Q, Chen W. Prediction of local recurrence and distant metastasis using radiomics analysis of pretreatment nasopharyngeal [18F]FDG PET/CT images. Ann Nucl Med 2021; 35:458-468. [PMID: 33543393 DOI: 10.1007/s12149-021-01585-9] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2020] [Accepted: 01/11/2021] [Indexed: 12/13/2022]
Abstract
OBJECTIVES To develop a radiomics signature to predict locoregional recurrence (LR) and distant metastasis (DM), as extracted from pretreatment 2-deoxy-2-[18F]fluoro-D-glucose ([18F]FDG) positron emission tomography/X-ray computed tomography (PET/CT) images in locally advanced nasopharyngeal carcinoma (NPC). MATERIALS AND METHODS Eighty-five patients with Stage III-IVB NPC underwent pretreatment [18F]FDG PET/CT scans and received radiotherapy or chemoradiotherapy. 53 of them achieved disease control, and 32 of them failed after treatment (15: LR, 17: DM). A total of 114 radiomic features were extracted from PET/CT images. For univariate analysis, Wilcoxon test and Chi-square test were used to compare median values of features between different treatment outcomes and predict the risk of treatment failure, respectively. For multivariate analysis, all features were grouped into clusters based on Pearson correlation using hierarchical clustering, and the representative feature of each cluster was chosen by the Relief algorithm. Then sequential floating forward selection (SFFS) coupled with a support vector machine (SVM) classifier were used to derive the optimized feature set in terms of the area under receiver operating characteristic (ROC) curve (AUC). The performance of the model was evaluated by leave-one-out-cross-validation, fivefold cross-validation, tenfold cross-validation. RESULTS Twenty features had significant differences between disease control and treatment failure. NPC patients with values of Compactness1, Compactness2, Coarseness_NGTDM or SGE_GLGLM above the median as well as patients with values of Irregularity, RLN_GLRLM or GLV_GLSZM below the median, showed a significant (p < 0.05) higher risk of treatment failure (about 50% vs. 25%). The derived radiomics signature consisted of 5 features with the highest AUC value of 0.8290 (sensitivity: 0.8438, specificity: 0.7736) using leave-one-out-cross-validation. CONCLUSION Locoregional recurrence (LR) and DM of locally advanced NPC can be predicted using radiomics analysis of pretreatment [18F]FDG PET/CT. The SFFS feature selection coupled with SVM classifier can derive the optimized feature set with correspondingly highest AUC value for pretreatment prediction of LR and/or DM of NPC.
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Affiliation(s)
- Lihong Peng
- School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, 510515, Guangdong, China
| | - Xiaotong Hong
- School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, 510515, Guangdong, China
| | - Qingyu Yuan
- Nanfang PET Center, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, Guangdong, China
| | - Lijun Lu
- School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, 510515, Guangdong, China.
| | - Quanshi Wang
- Nanfang PET Center, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, Guangdong, China.
| | - Wufan Chen
- School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, 510515, Guangdong, China.
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36
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Binczyk F, Prazuch W, Bozek P, Polanska J. Radiomics and artificial intelligence in lung cancer screening. Transl Lung Cancer Res 2021; 10:1186-1199. [PMID: 33718055 PMCID: PMC7947422 DOI: 10.21037/tlcr-20-708] [Citation(s) in RCA: 67] [Impact Index Per Article: 22.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Lung cancer is responsible for more fatalities than any other cancer worldwide, with 1.76 million associated deaths reported in 2018. The key issue in the fight against this disease is the detection and diagnosis of all pulmonary nodules at an early stage. Artificial intelligence (AI) algorithms play a vital role in the automated detection, segmentation, and computer-aided diagnosis of malignant lesions. Among the existing algorithms, radiomics and deep-learning-based types appear to show the most promise. Radiomics is a growing field related to the extraction of a set of features from an image, which allows for automated classification of medical images into a predefined group. The process comprises a series of consecutive steps including image acquisition and pre-processing, segmentation of the desired region of interest, calculation of defined features, feature engineering, and construction of the classification model. The features calculated in this process are mainly shape features, as well as first- and higher-order texture features. To date, more than 100 features have been defined, although this number varies depending on the application. The greatest challenge in radiomics is building a cross-validated model based on a selected set of calculated features known as the radiomic signature. Numerous radiomic signatures have successfully been developed; however, reproducibility and clinical validity of the results obtained constitutes a considerable challenge of modern radiomics. Deep learning algorithms are another rapidly evolving technique and are recognized as a valuable tool in the field of medical image analysis for the detection, characterization, and assessment of lesions. Such an approach involves the design of artificial neural network architecture while upholding the goal of high classification accuracy. This paper illuminates the evolution and current state of artificial intelligence methods in lung imaging and the detection and diagnosis of pulmonary nodules, with a particular emphasis on radiomics and deep learning methods.
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Affiliation(s)
- Franciszek Binczyk
- Department of Data Science and Engineering, Silesian University of Technology, Gliwice, Poland
| | - Wojciech Prazuch
- Department of Data Science and Engineering, Silesian University of Technology, Gliwice, Poland
| | - Paweł Bozek
- Department of Radiology and Radiodiagnostics, Medical University of Silesia, Katowice, Poland
| | - Joanna Polanska
- Department of Data Science and Engineering, Silesian University of Technology, Gliwice, Poland
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Technical feasibility of radiomics signature analyses for improving detection of occult tonsillar cancer. Sci Rep 2021; 11:192. [PMID: 33420249 PMCID: PMC7794329 DOI: 10.1038/s41598-020-80597-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Accepted: 12/21/2020] [Indexed: 02/02/2023] Open
Abstract
Diagnosis of occult palatine tonsil squamous cell carcinoma (SCC) using conventional magnetic resonance imaging (MRI) is difficult in patients with cervical nodal metastasis from an unknown primary site at presentation. We aimed to establish a radiomics approach based on MRI features extracted from the volume of interest in these patients. An Elastic Net model was developed to differentiate between normal palatine tonsils and occult palatine tonsil SCC. The diagnostic performances of the model with radiomics features extracted from T1-weighted image (WI), T2WI, contrast-enhanced T1WI, and an apparent diffusion coefficient (ADC) map had area under the receiver operating characteristic (AUROC) curve values of 0.831, 0.840, 0.781, and 0.807, respectively, for differential diagnosis. The model with features from the ADC alone showed the highest sensitivity of 90.0%, while the model with features from T1WI + T2WI + contrast-enhanced T1WI showed the highest AUROC of 0.853. The added sensitivity of the radiomics feature analysis were 34.6% over that of conventional MRI to detect occult palatine tonsil SCC. Therefore, we concluded that adding radiomics feature analysis to MRI may improve the detection sensitivity for occult palatine tonsil SCC in patients with a cervical nodal metastasis from cancer of an unknown primary site.
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38
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Peng Z, Wang Y, Wang Y, Jiang S, Fan R, Zhang H, Jiang W. Application of radiomics and machine learning in head and neck cancers. Int J Biol Sci 2021; 17:475-486. [PMID: 33613106 PMCID: PMC7893590 DOI: 10.7150/ijbs.55716] [Citation(s) in RCA: 46] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Accepted: 12/10/2020] [Indexed: 02/07/2023] Open
Abstract
With the continuous development of medical image informatics technology, more and more high-throughput quantitative data could be extracted from digital medical images, which has resulted in a new kind of omics-Radiomics. In recent years, in addition to genomics, proteomics and metabolomics, radiomic has attracted the interest of more and more researchers. Compared to other omics, radiomics can be perfectly integrated with clinical data, even with the pathology and molecular biomarker, so that the study can be closer to the clinical reality and more revealing of the tumor development. Mass data will also be generated in this process. Machine learning, due to its own characteristics, has a unique advantage in processing massive radiomic data. By analyzing mass amounts of data with strong clinical relevance, people can construct models that more accurately reflect tumor development and progression, thereby providing the possibility of personalized and sequential treatment of patients. As one of the cancer types whose treatment and diagnosis rely on imaging examination, radiomics has a very broad application prospect in head and neck cancers (HNC). Until now, there have been some notable results in HNC. In this review, we will introduce the concepts and workflow of radiomics and machine learning and their current applications in head and neck cancers, as well as the directions and applications of artificial intelligence in the treatment and diagnosis of HNC.
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Affiliation(s)
- Zhouying Peng
- Department of Otolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha 410078, Hunan, China
| | - Yumin Wang
- Department of Otolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha 410078, Hunan, China
| | - Yaxuan Wang
- Department of Otolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha 410078, Hunan, China
| | - Sijie Jiang
- Department of Otolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha 410078, Hunan, China
| | - Ruohao Fan
- Department of Otolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha 410078, Hunan, China
| | - Hua Zhang
- Department of Otolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha 410078, Hunan, China
| | - Weihong Jiang
- Department of Otolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha 410078, Hunan, China
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39
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Artificial intelligence in oncology. Artif Intell Med 2021. [DOI: 10.1016/b978-0-12-821259-2.00018-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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40
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Dasgupta A, Fatima K, DiCenzo D, Bhardwaj D, Quiaoit K, Saifuddin M, Karam I, Poon I, Husain Z, Tran WT, Sannachi L, Czarnota GJ. Quantitative ultrasound radiomics in predicting recurrence for patients with node-positive head-neck squamous cell carcinoma treated with radical radiotherapy. Cancer Med 2020; 10:2579-2589. [PMID: 33314716 PMCID: PMC8026932 DOI: 10.1002/cam4.3634] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2020] [Revised: 11/05/2020] [Accepted: 11/05/2020] [Indexed: 12/24/2022] Open
Abstract
This prospective study was conducted to investigate the role of quantitative ultrasound (QUS) radiomics in predicting recurrence for patients with node‐positive head‐neck squamous cell carcinoma (HNSCC) treated with radical radiotherapy (RT). The most prominent cervical lymph node (LN) was scanned with a clinical ultrasound device having central frequency of 6.5 MHz. Ultrasound radiofrequency data were processed to obtain 7 QUS parameters. Color‐coded parametric maps were generated based on individual QUS spectral features corresponding to each of the smaller units. A total of 31 (7 primary QUS and 24 texture) features were obtained before treatment. All patients were treated with radical RT and followed according to standard institutional practice. Recurrence (local, regional, or distant) served as an endpoint. Three different machine learning classifiers with a set of maximally three features were used for model development and tested with leave‐one‐out cross‐validation for nonrecurrence and recurrence groups. Fifty‐one patients were included, with a median follow up of 38 months (range 7–64 months). Recurrence was observed in 17 patients. The best results were obtained using a k‐nearest neighbor (KNN) classifier with a sensitivity, specificity, accuracy, and an area under curve of 76%, 71%, 75%, and 0.74, respectively. All the three features selected for the KNN model were texture features. The KNN‐model‐predicted 3‐year recurrence‐free survival was 81% and 40% in the predicted no‐recurrence and predicted‐recurrence groups, respectively. (p = 0.001). The pilot study demonstrates pretreatment QUS‐radiomics can predict the recurrence group with an accuracy of 75% in patients with node‐positive HNSCC. Clinical trial registration: clinicaltrials.gov.in identifier NCT03908684.
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Affiliation(s)
- Archya Dasgupta
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada.,Department of Radiation Oncology, University of Toronto, Toronto, Canada.,Physical Sciences, Sunnybrook Research Institute, Toronto, Canada
| | - Kashuf Fatima
- Physical Sciences, Sunnybrook Research Institute, Toronto, Canada
| | - Daniel DiCenzo
- Physical Sciences, Sunnybrook Research Institute, Toronto, Canada
| | - Divya Bhardwaj
- Physical Sciences, Sunnybrook Research Institute, Toronto, Canada
| | - Karina Quiaoit
- Physical Sciences, Sunnybrook Research Institute, Toronto, Canada
| | | | - Irene Karam
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada.,Department of Radiation Oncology, University of Toronto, Toronto, Canada
| | - Ian Poon
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada.,Department of Radiation Oncology, University of Toronto, Toronto, Canada
| | - Zain Husain
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada.,Department of Radiation Oncology, University of Toronto, Toronto, Canada
| | - William T Tran
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada.,Department of Radiation Oncology, University of Toronto, Toronto, Canada.,Evaluative Clinical Sciences, Sunnybrook Research Institute, Toronto, Canada
| | | | - Gregory J Czarnota
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada.,Physical Sciences, Sunnybrook Research Institute, Toronto, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Canada
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41
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Le QC, Arimura H, Ninomiya K, Kabata Y. Radiomic features based on Hessian index for prediction of prognosis in head-and-neck cancer patients. Sci Rep 2020; 10:21301. [PMID: 33277570 PMCID: PMC7718925 DOI: 10.1038/s41598-020-78338-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Accepted: 11/23/2020] [Indexed: 12/23/2022] Open
Abstract
This study demonstrated the usefulness of radiomic features based on the Hessian index of differential topology for the prediction of prognosis prior to treatment in head-and-neck (HN) cancer patients. The Hessian index, which can indicate tumor heterogeneity with convex, concave, and other points (saddle points), was calculated as the number of negative eigenvalues of the Hessian matrix at each voxel on computed tomography (CT) images. Three types of signatures were constructed in a training cohort (n = 126), one type each from CT conventional features, Hessian index features, and combined features from the conventional and index feature sets. The prognostic value of the signatures were evaluated using statistically significant difference (p value, log-rank test) to compare the survival curves of low- and high-risk groups. In a test cohort (n = 68), the p values of the models built with conventional, index, combined features, and clinical variables were 2.95 [Formula: see text] 10-2, 1.85 [Formula: see text] 10-2, 3.17 [Formula: see text] 10-2, and 1.87 [Formula: see text] 10-3, respectively. When the features were integrated with clinical variables, the p values of conventional, index, and combined features were 3.53 [Formula: see text] 10-3, 1.28 [Formula: see text] 10-3, and 1.45 [Formula: see text] 10-3, respectively. This result indicates that index features could provide more prognostic information than conventional features and further increase the prognostic value of clinical variables in HN cancer patients.
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Affiliation(s)
- Quoc Cuong Le
- Department of Health Sciences, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Hidetaka Arimura
- Department of Health Sciences, Faculty of Medical Sciences, Kyushu University, Fukuoka, Japan.
| | - Kenta Ninomiya
- Department of Health Sciences, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Yutaro Kabata
- Institute of Mathematics for Industry, Kyushu University, Fukuoka, Japan
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42
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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: 14] [Impact Index Per Article: 3.5] [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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43
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Ravanelli M, Grammatica A, Maddalo M, Ramanzin M, Agazzi GM, Tononcelli E, Battocchio S, Bossi P, Vezzoli M, Maroldi R, Farina D. Pretreatment DWI with Histogram Analysis of the ADC in Predicting the Outcome of Advanced Oropharyngeal Cancer with Known Human Papillomavirus Status Treated with Chemoradiation. AJNR Am J Neuroradiol 2020; 41:1473-1479. [PMID: 32732272 DOI: 10.3174/ajnr.a6695] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2020] [Accepted: 05/23/2020] [Indexed: 12/16/2022]
Abstract
BACKGROUND AND PURPOSE The incidence of oropharyngeal squamous cell carcinoma (OPSCC) has increased in the period from the 1970s to 2004, due to increase of infection with human papilloma virus (HPV). This study aimed to examine the role of histogram analysis of the ADC in treatment response and survival prediction of patients with oropharyngeal squamous cell carcinoma and known human papillomavirus status. MATERIALS AND METHODS This was a retrospective single-center study. Following inclusion and exclusion criteria, data for 59 patients affected by T2-T4 (according to the 8th edition of the AJCC Cancer Staging Manual) oropharyngeal squamous cell carcinoma were retrieved. Twenty-eight had human papillomavirus-positive oropharyngeal squamous cell carcinoma, while 31 had human papillomavirus-negative oropharyngeal squamous cell carcinoma. All patients underwent a pretreatment MR imaging. Histogram analysis of ADC maps obtained by DWI (b = 0-1000 mm/s2) was performed on the central section of all of tumors. The minimum follow-up period was 2 years. Histogram ADC parameters were associated with progression-free survival and overall survival. Univariable and multivariable Cox models were applied to the data; P values were corrected using the Benjamini-Hochberg method. RESULTS At univariable analysis, both human papillomavirus status and mean ADC were associated with progression-free survival (hazard ratio = 0.267, P < .05, and hazard ratio = 1.0028, P ≤ .05, respectively), while only human papillomavirus status was associated with overall survival (hazard ratio = 0.213, P ≤ .05) before correction. At multivariable analysis, no parameter was included (in fact, human papillomavirus status lost significance after correction). If we separated the patients into 2 subgroups according to human papillomavirus status, ADC entropy was associated with overall survival in the human papillomavirus-negative group (hazard ratio = 4.846, P = .01). CONCLUSIONS ADC and human papillomavirus status are related to progression-free survival in patients treated with chemoradiation for advanced oropharyngeal squamous cell carcinoma; however, this association seems to result from the strong association between ADC and human papillomavirus status.
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Affiliation(s)
- M Ravanelli
- From the Departments of Radiology (M. Ravanelli, M. Ramanzin, G.M.A., E.T., R.M., D.F.)
| | | | | | - M Ramanzin
- From the Departments of Radiology (M. Ravanelli, M. Ramanzin, G.M.A., E.T., R.M., D.F.)
| | - G M Agazzi
- From the Departments of Radiology (M. Ravanelli, M. Ramanzin, G.M.A., E.T., R.M., D.F.)
| | - E Tononcelli
- From the Departments of Radiology (M. Ravanelli, M. Ramanzin, G.M.A., E.T., R.M., D.F.)
| | | | | | - M Vezzoli
- Molecular and Translational Medicine (M.V.), University of Brescia, Brescia, Italy
| | - R Maroldi
- From the Departments of Radiology (M. Ravanelli, M. Ramanzin, G.M.A., E.T., R.M., D.F.)
| | - D Farina
- From the Departments of Radiology (M. Ravanelli, M. Ramanzin, G.M.A., E.T., R.M., D.F.)
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44
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Sheng K. Artificial intelligence in radiotherapy: a technological review. Front Med 2020; 14:431-449. [PMID: 32728877 DOI: 10.1007/s11684-020-0761-1] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2019] [Accepted: 02/14/2020] [Indexed: 12/19/2022]
Abstract
Radiation therapy (RT) is widely used to treat cancer. Technological advances in RT have occurred in the past 30 years. These advances, such as three-dimensional image guidance, intensity modulation, and robotics, created challenges and opportunities for the next breakthrough, in which artificial intelligence (AI) will possibly play important roles. AI will replace certain repetitive and labor-intensive tasks and improve the accuracy and consistency of others, particularly those with increased complexity because of technological advances. The improvement in efficiency and consistency is important to manage the increasing cancer patient burden to the society. Furthermore, AI may provide new functionalities that facilitate satisfactory RT. The functionalities include superior images for real-time intervention and adaptive and personalized RT. AI may effectively synthesize and analyze big data for such purposes. This review describes the RT workflow and identifies areas, including imaging, treatment planning, quality assurance, and outcome prediction, that benefit from AI. This review primarily focuses on deep-learning techniques, although conventional machine-learning techniques are also mentioned.
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Affiliation(s)
- Ke Sheng
- Department of Radiation Oncology, University of California, Los Angeles, CA, 90095, USA.
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45
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Dercle L, Henry T, Carré A, Paragios N, Deutsch E, Robert C. Reinventing radiation therapy with machine learning and imaging bio-markers (radiomics): State-of-the-art, challenges and perspectives. Methods 2020; 188:44-60. [PMID: 32697964 DOI: 10.1016/j.ymeth.2020.07.003] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2020] [Revised: 07/02/2020] [Accepted: 07/06/2020] [Indexed: 12/14/2022] Open
Abstract
Radiation therapy is a pivotal cancer treatment that has significantly progressed over the last decade due to numerous technological breakthroughs. Imaging is now playing a critical role on deployment of the clinical workflow, both for treatment planning and treatment delivery. Machine-learning analysis of predefined features extracted from medical images, i.e. radiomics, has emerged as a promising clinical tool for a wide range of clinical problems addressing drug development, clinical diagnosis, treatment selection and implementation as well as prognosis. Radiomics denotes a paradigm shift redefining medical images as a quantitative asset for data-driven precision medicine. The adoption of machine-learning in a clinical setting and in particular of radiomics features requires the selection of robust, representative and clinically interpretable biomarkers that are properly evaluated on a representative clinical data set. To be clinically relevant, radiomics must not only improve patients' management with great accuracy but also be reproducible and generalizable. Hence, this review explores the existing literature and exposes its potential technical caveats, such as the lack of quality control, standardization, sufficient sample size, type of data collection, and external validation. Based upon the analysis of 165 original research studies based on PET, CT-scan, and MRI, this review provides an overview of new concepts, and hypotheses generating findings that should be validated. In particular, it describes evolving research trends to enhance several clinical tasks such as prognostication, treatment planning, response assessment, prediction of recurrence/relapse, and prediction of toxicity. Perspectives regarding the implementation of an AI-based radiotherapy workflow are presented.
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Affiliation(s)
- Laurent Dercle
- Department of Radiology, New York Presbyterian Hospital, Columbia University Medical Center, New York, USA
| | - Theophraste Henry
- Molecular Radiotherapy and Innovative Therapeutics, INSERM UMR1030, Gustave Roussy Cancer Campus, Université Paris Saclay, Villejuif, France; Department of Nuclear Medicine and Endocrine Oncology, Gustave Roussy Cancer Campus, Villejuif, France
| | - Alexandre Carré
- Molecular Radiotherapy and Innovative Therapeutics, INSERM UMR1030, Gustave Roussy Cancer Campus, Université Paris Saclay, Villejuif, France; Department of Radiation Oncology, Gustave Roussy Cancer Campus, Villejuif, France
| | | | - Eric Deutsch
- Molecular Radiotherapy and Innovative Therapeutics, INSERM UMR1030, Gustave Roussy Cancer Campus, Université Paris Saclay, Villejuif, France; Department of Radiation Oncology, Gustave Roussy Cancer Campus, Villejuif, France
| | - Charlotte Robert
- Molecular Radiotherapy and Innovative Therapeutics, INSERM UMR1030, Gustave Roussy Cancer Campus, Université Paris Saclay, Villejuif, France; Department of Radiation Oncology, Gustave Roussy Cancer Campus, Villejuif, France.
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46
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Haider SP, Zeevi T, Baumeister P, Reichel C, Sharaf K, Forghani R, Kann BH, Judson BL, Prasad ML, Burtness B, Mahajan A, Payabvash S. Potential Added Value of PET/CT Radiomics for Survival Prognostication beyond AJCC 8th Edition Staging in Oropharyngeal Squamous Cell Carcinoma. Cancers (Basel) 2020; 12:cancers12071778. [PMID: 32635216 PMCID: PMC7407414 DOI: 10.3390/cancers12071778] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Revised: 06/29/2020] [Accepted: 06/30/2020] [Indexed: 12/18/2022] Open
Abstract
Accurate risk-stratification can facilitate precision therapy in oropharyngeal squamous cell carcinoma (OPSCC). We explored the potential added value of baseline positron emission tomography (PET)/computed tomography (CT) radiomic features for prognostication and risk stratification of OPSCC beyond the American Joint Committee on Cancer (AJCC) 8th edition staging scheme. Using institutional and publicly available datasets, we included OPSCC patients with known human papillomavirus (HPV) status, without baseline distant metastasis and treated with curative intent. We extracted 1037 PET and 1037 CT radiomic features quantifying lesion shape, imaging intensity, and texture patterns from primary tumors and metastatic cervical lymph nodes. Utilizing random forest algorithms, we devised novel machine-learning models for OPSCC progression-free survival (PFS) and overall survival (OS) using “radiomics” features, “AJCC” variables, and the “combined” set as input. We designed both single- (PET or CT) and combined-modality (PET/CT) models. Harrell’s C-index quantified survival model performance; risk stratification was evaluated in Kaplan–Meier analysis. A total of 311 patients were included. In HPV-associated OPSCC, the best “radiomics” model achieved an average C-index ± standard deviation of 0.62 ± 0.05 (p = 0.02) for PFS prediction, compared to 0.54 ± 0.06 (p = 0.32) utilizing “AJCC” variables. Radiomics-based risk-stratification of HPV-associated OPSCC was significant for PFS and OS. Similar trends were observed in HPV-negative OPSCC. In conclusion, radiomics imaging features extracted from pre-treatment PET/CT may provide complimentary information to the current AJCC staging scheme for survival prognostication and risk-stratification of HPV-associated OPSCC.
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Affiliation(s)
- Stefan P. Haider
- Section of Neuroradiology, Department of Radiology and Biomedical Imaging, Yale School of Medicine, 789 Howard Ave, New Haven, CT 06519, USA; (S.P.H.); (A.M.)
- Department of Otorhinolaryngology, University Hospital of Ludwig Maximilians Universität München, Marchioninistrasse 15, 81377 Munich, Germany; (P.B.); (C.R.); (K.S.)
| | - Tal Zeevi
- Center for Translational Imaging Analysis and Machine Learning, Department of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, New Haven, CT 06510, USA;
| | - Philipp Baumeister
- Department of Otorhinolaryngology, University Hospital of Ludwig Maximilians Universität München, Marchioninistrasse 15, 81377 Munich, Germany; (P.B.); (C.R.); (K.S.)
| | - Christoph Reichel
- Department of Otorhinolaryngology, University Hospital of Ludwig Maximilians Universität München, Marchioninistrasse 15, 81377 Munich, Germany; (P.B.); (C.R.); (K.S.)
| | - Kariem Sharaf
- Department of Otorhinolaryngology, University Hospital of Ludwig Maximilians Universität München, Marchioninistrasse 15, 81377 Munich, Germany; (P.B.); (C.R.); (K.S.)
| | - Reza Forghani
- Department of Diagnostic Radiology and Augmented Intelligence & Precision Health Laboratory, McGill University Health Centre & Research Institute, 1650 Cedar Avenue, Montreal, QC H3G 1A4, Canada;
| | - Benjamin H. Kann
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Harvard Medical School, 450 Brookline Avenue, Boston, MA 02215, USA;
| | - Benjamin L. Judson
- Division of Otolaryngology, Department of Surgery, Yale School of Medicine, 330 Cedar Street, New Haven, CT 06520, USA;
| | - Manju L. Prasad
- Department of Pathology, Yale School of Medicine, 310 Cedar Street, New Haven, CT 06520, USA;
| | - Barbara Burtness
- Section of Medical Oncology, Department of Internal Medicine, Yale School of Medicine, 25 York Street, New Haven, CT 06520, USA;
| | - Amit Mahajan
- Section of Neuroradiology, Department of Radiology and Biomedical Imaging, Yale School of Medicine, 789 Howard Ave, New Haven, CT 06519, USA; (S.P.H.); (A.M.)
| | - Seyedmehdi Payabvash
- Section of Neuroradiology, Department of Radiology and Biomedical Imaging, Yale School of Medicine, 789 Howard Ave, New Haven, CT 06519, USA; (S.P.H.); (A.M.)
- Correspondence: ; Tel.: +1-(203)-214-4650
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47
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Socarrás Fernández JA, Mönnich D, Leibfarth S, Welz S, Zwanenburg A, Leger S, Löck S, Pfannenberg C, La Fougère C, Reischl G, Baumann M, Zips D, Thorwarth D. Comparison of patient stratification by computed tomography radiomics and hypoxia positron emission tomography in head-and-neck cancer radiotherapy. Phys Imaging Radiat Oncol 2020; 15:52-59. [PMID: 33043157 PMCID: PMC7536307 DOI: 10.1016/j.phro.2020.07.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2020] [Revised: 07/21/2020] [Accepted: 07/21/2020] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND AND PURPOSE Hypoxia Positron-Emission-Tomography (PET) as well as Computed Tomography (CT) radiomics have been shown to be prognostic for radiotherapy outcome. Here, we investigate the stratification potential of CT-radiomics in head and neck cancer (HNC) patients and test if CT-radiomics is a surrogate predictor for hypoxia as identified by PET. MATERIALS AND METHODS Two independent cohorts of HNC patients were used for model development and validation, HN1 (n = 149) and HN2 (n = 47). The training set HN1 consisted of native planning CT data whereas for the validation cohort HN2 also hypoxia PET/CT data was acquired using [18F]-Fluoromisonidazole (FMISO). Machine learning algorithms including feature engineering and classifier selection were trained for two-year loco-regional control (LRC) to create optimal CT-radiomics signatures.Secondly, a pre-defined [18F]FMISO-PET tumour-to-muscle-ratio (TMRpeak ≥ 1.6) was used for LRC prediction. Comparison between risk groups identified by CT-radiomics or [18F]FMISO-PET was performed using area-under-the-curve (AUC) and Kaplan-Meier analysis including log-rank test. RESULTS The best performing CT-radiomics signature included two features with nearest-neighbour classification (AUC = 0.76 ± 0.09), whereas AUC was 0.59 for external validation. In contrast, [18F]FMISO TMRpeak reached an AUC of 0.66 in HN2. Kaplan-Meier analysis of the independent validation cohort HN2 did not confirm the prognostic value of CT-radiomics (p = 0.18), whereas for [18F]FMISO-PET significant differences were observed (p = 0.02). CONCLUSIONS No direct correlation of patient stratification using [18F]FMISO-PET or CT-radiomics was found in this study. Risk groups identified by CT-radiomics or hypoxia PET showed only poor overlap. Direct assessment of tumour hypoxia using PET seems to be more powerful to stratify HNC patients.
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Affiliation(s)
| | - David Mönnich
- Section for Biomedical Physics, Department of Radiation Oncology, University of Tübingen, Germany
| | - Sara Leibfarth
- Section for Biomedical Physics, Department of Radiation Oncology, University of Tübingen, Germany
| | - Stefan Welz
- Department of Radiation Oncology, University of Tübingen, Germany
| | - Alex Zwanenburg
- OncoRay National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden - Rossendorf, Dresden, Germany
- National Center for Tumor Diseases (NCT), Partner Site Dresden, Germany: German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Stefan Leger
- OncoRay National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden - Rossendorf, Dresden, Germany
- National Center for Tumor Diseases (NCT), Partner Site Dresden, Germany: German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Steffen Löck
- OncoRay National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden - Rossendorf, Dresden, Germany
| | - Christina Pfannenberg
- Department of Diagnostic and Interventional Radiology, University of Tübingen, Germany
| | | | - Gerald Reischl
- Department of Preclinical Imaging and Radiopharmacy, University of Tübingen, Germany
| | - Michael Baumann
- OncoRay National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden - Rossendorf, Dresden, Germany
- German Cancer Research Center DKFZ, Heidelberg, Germany
| | - Daniel Zips
- Department of Radiation Oncology, University of Tübingen, Germany
- German Cancer Consortium (DKTK), partner Site Tübingen, Tübingen, Germany
| | - Daniela Thorwarth
- Section for Biomedical Physics, Department of Radiation Oncology, University of Tübingen, Germany
- German Cancer Consortium (DKTK), partner Site Tübingen, Tübingen, Germany
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48
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Werth K, Ledbetter L. Artificial Intelligence in Head and Neck Imaging: A Glimpse into the Future. Neuroimaging Clin N Am 2020; 30:359-368. [PMID: 32600636 DOI: 10.1016/j.nic.2020.04.004] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Artificial intelligence, specifically machine learning and deep learning, is a rapidly developing field in imaging sciences with the potential to improve the efficiency and effectiveness of radiologists. This review covers common technical terms and basic concepts in imaging artificial intelligence and briefly reviews the application of these techniques to general imaging as well as head and neck imaging. Artificial intelligence has the potential to contribute improvements to all areas of patient care, including image acquisition, processing, segmentation, automated detection of findings, integration of clinical information, quality improvement, and research. Numerous challenges remain, however, before widespread imaging clinical adoption and integration occur.
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Affiliation(s)
- Kyle Werth
- Department of Radiology, University of Kansas Medical Center, 3901 Rainbow Boulevard, Mailstop 4032, Kansas City, KS 66160, USA
| | - Luke Ledbetter
- Department of Radiology, David Geffen School of Medicine at UCLA, 757 Westwood Plaza, Suite 1621D, Los Angeles, CA 90095, USA.
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Katsoulakis E, Yu Y, Apte AP, Leeman JE, Katabi N, Morris L, Deasy JO, Chan TA, Lee NY, Riaz N, Hatzoglou V, Oh JH. Radiomic analysis identifies tumor subtypes associated with distinct molecular and microenvironmental factors in head and neck squamous cell carcinoma. Oral Oncol 2020; 110:104877. [PMID: 32619927 DOI: 10.1016/j.oraloncology.2020.104877] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2019] [Revised: 06/17/2020] [Accepted: 06/22/2020] [Indexed: 01/03/2023]
Abstract
PURPOSE To identify whether radiomic features from pre-treatment computed tomography (CT) scans can predict molecular differences between head and neck squamous cell carcinoma (HNSCC) using The Cancer Imaging Archive (TCIA) and The Cancer Genome Atlas (TCGA). METHODS 77 patients from the TCIA with HNSCC had imaging suitable for analysis. Radiomic features were extracted and unsupervised consensus clustering was performed to identify subtypes. Genomic data was extracted from the matched patients in the TCGA database. We explored relationships between radiomic features and molecular profiles of tumors, including the tumor immune microenvironment. A machine learning method was used to build a model predictive of CD8 + T-cells. An independent cohort of 83 HNSCC patients was used to validate the radiomic clusters. RESULTS We initially extracted 104 two-dimensional radiomic features, and after feature stability tests and removal of volume dependent features, reduced this to 67 features for subsequent analysis. Consensus clustering based on these features resulted in two distinct clusters. The radiomic clusters differed by primary tumor subsite (p = 0.0096), HPV status (p = 0.0127), methylation-based clustering results (p = 0.0025), and tumor immune microenvironment. A random forest model using radiomic features predicted CD8 + T-cells independent of HPV status with R2 = 0.30 (p < 0.0001) on cross validation. Consensus clustering on the validation cohort resulted in two distinct clusters that differ in tumor subsite (p = 1.3 × 10-7) and HPV status (p = 4.0 × 10-7). CONCLUSION Radiomic analysis can identify biologic features of tumors such as HPV status and T-cell infiltration and may be able to provide other information in the near future to help with patient stratification.
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Affiliation(s)
- Evangelia Katsoulakis
- Department of Radiation Oncology, Veterans Affairs, James A Haley, Tampa, FL 33612, USA
| | - Yao Yu
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Aditya P Apte
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Jonathan E Leeman
- Department of Radiation Oncology, Dana Farber Cancer Institute/Brigham and Women's Hospital, Boston, MA 02189, USA
| | - Nora Katabi
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Luc Morris
- Immunogenomics and Precision Oncology Platform, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Joseph O Deasy
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Timothy A Chan
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; Immunogenomics and Precision Oncology Platform, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Nancy Y Lee
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Nadeem Riaz
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; Immunogenomics and Precision Oncology Platform, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Vaios Hatzoglou
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA.
| | - Jung Hun Oh
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA.
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50
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Konings H, Stappers S, Geens M, De Winter BY, Lamote K, van Meerbeeck JP, Specenier P, Vanderveken OM, Ledeganck KJ. A Literature Review of the Potential Diagnostic Biomarkers of Head and Neck Neoplasms. Front Oncol 2020; 10:1020. [PMID: 32670885 PMCID: PMC7332560 DOI: 10.3389/fonc.2020.01020] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2020] [Accepted: 05/22/2020] [Indexed: 12/19/2022] Open
Abstract
Head and neck neoplasms have a poor prognosis because of their late diagnosis. Finding a biomarker to detect these tumors in an early phase could improve the prognosis and survival rate. This literature review provides an overview of biomarkers, covering the different -omics fields to diagnose head and neck neoplasms in the early phase. To date, not a single biomarker, nor a panel of biomarkers for the detection of head and neck tumors has been detected with clinical applicability. Limitations for the clinical implementation of the investigated biomarkers are mainly the heterogeneity of the study groups (e.g., small population in which the biomarker was tested, and/or only including high-risk populations) and a low sensitivity and/or specificity of the biomarkers under study. Further research on biomarkers to diagnose head and neck neoplasms in an early stage, is therefore needed.
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Affiliation(s)
- Heleen Konings
- Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium
| | - Sofie Stappers
- Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium
| | - Margot Geens
- Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium
| | - Benedicte Y De Winter
- Laboratorium of Experimental Medicine and Pediatrics and Member of the Infla-Med Centre of Excellence, University of Antwerp, Antwerp, Belgium
| | - Kevin Lamote
- Laboratorium of Experimental Medicine and Pediatrics and Member of the Infla-Med Centre of Excellence, University of Antwerp, Antwerp, Belgium.,Department of Pneumology, Antwerp University Hospital, Edegem, Belgium.,Department of Internal Medicine and Pediatrics, Ghent University, Ghent, Belgium
| | - Jan P van Meerbeeck
- Laboratorium of Experimental Medicine and Pediatrics and Member of the Infla-Med Centre of Excellence, University of Antwerp, Antwerp, Belgium.,Department of Pneumology, Antwerp University Hospital, Edegem, Belgium.,Department of Internal Medicine and Pediatrics, Ghent University, Ghent, Belgium
| | - Pol Specenier
- Department of Oncology, Antwerp University Hospital, Edegem, Belgium.,Center for Oncological Research (CORE), University of Antwerp, Antwerp, Belgium
| | - Olivier M Vanderveken
- Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium.,Department of Otorhinolaryngology-Head and Neck Surgery, Antwerp University Hospital, Edegem, Belgium.,Department of Translational Neurosciences, Antwerp University, Antwerp, Belgium
| | - Kristien J Ledeganck
- Laboratorium of Experimental Medicine and Pediatrics and Member of the Infla-Med Centre of Excellence, University of Antwerp, Antwerp, Belgium
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