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Srinivasan Y, Liu A, Rameau A. Machine learning in the evaluation of voice and swallowing in the head and neck cancer patient. Curr Opin Otolaryngol Head Neck Surg 2024; 32:105-112. [PMID: 38116798 DOI: 10.1097/moo.0000000000000948] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2023]
Abstract
PURPOSE OF REVIEW The purpose of this review is to present recent advances and limitations in machine learning applied to the evaluation of speech, voice, and swallowing in head and neck cancer. RECENT FINDINGS Novel machine learning models incorporating diverse data modalities with improved discriminatory capabilities have been developed for predicting toxicities following head and neck cancer therapy, including dysphagia, dysphonia, xerostomia, and weight loss as well as guiding treatment planning. Machine learning has been applied to the care of posttreatment voice and swallowing dysfunction by offering objective and standardized assessments and aiding innovative technologies for functional restoration. Voice and speech are also being utilized in machine learning algorithms to screen laryngeal cancer. SUMMARY Machine learning has the potential to help optimize, assess, predict, and rehabilitate voice and swallowing function in head and neck cancer patients as well as aid in cancer screening. However, existing studies are limited by the lack of sufficient external validation and generalizability, insufficient transparency and reproducibility, and no clear superior predictive modeling strategies. Algorithms and applications will need to be trained on large multiinstitutional data sets, incorporate sociodemographic data to reduce bias, and achieve validation through clinical trials for optimal performance and utility.
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Affiliation(s)
- Yashes Srinivasan
- Sean Parker Institute for the Voice, Department of Otolaryngology-Head and Neck Surgery, Weill Cornell Medicine, New York, New York
| | - Amy Liu
- University of California, San Diego, School of Medicine, San Diego, California, USA
| | - Anaïs Rameau
- Sean Parker Institute for the Voice, Department of Otolaryngology-Head and Neck Surgery, Weill Cornell Medicine, New York, New York
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Maragno D, Buti G, Birbil Şİ, Liao Z, Bortfeld T, den Hertog D, Ajdari A. Embedding machine learning based toxicity models within radiotherapy treatment plan optimization. Phys Med Biol 2024; 69:075003. [PMID: 38412530 DOI: 10.1088/1361-6560/ad2d7e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Accepted: 02/27/2024] [Indexed: 02/29/2024]
Abstract
Objective.This study addresses radiation-induced toxicity (RIT) challenges in radiotherapy (RT) by developing a personalized treatment planning framework. It leverages patient-specific data and dosimetric information to create an optimization model that limits adverse side effects using constraints learned from historical data.Approach.The study uses the optimization with constraint learning (OCL) framework, incorporating patient-specific factors into the optimization process. It consists of three steps: optimizing the baseline treatment plan using population-wide dosimetric constraints; training a machine learning (ML) model to estimate the patient's RIT for the baseline plan; and adapting the treatment plan to minimize RIT using ML-learned patient-specific constraints. Various predictive models, including classification trees, ensembles of trees, and neural networks, are applied to predict the probability of grade 2+ radiation pneumonitis (RP2+) for non-small cell lung (NSCLC) cancer patients three months post-RT. The methodology is assessed with four high RP2+ risk NSCLC patients, with the goal of optimizing the dose distribution to constrain the RP2+ outcome below a pre-specified threshold. Conventional and OCL-enhanced plans are compared based on dosimetric parameters and predicted RP2+ risk. Sensitivity analysis on risk thresholds and data uncertainty is performed using a toy NSCLC case.Main results.Experiments show the methodology's capacity to directly incorporate all predictive models into RT treatment planning. In the four patients studied, mean lung dose and V20 were reduced by an average of 1.78 Gy and 3.66%, resulting in an average RP2+ risk reduction from 95% to 42%. Notably, this reduction maintains tumor coverage, although in two cases, sparing the lung slightly increased spinal cord max-dose (0.23 and 0.79 Gy).Significance.By integrating patient-specific information into learned constraints, the study significantly reduces adverse side effects like RP2+ without compromising target coverage. This unified framework bridges the gap between predicting toxicities and optimizing treatment plans in personalized RT decision-making.
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Affiliation(s)
- Donato Maragno
- Amsterdam Business School, University of Amsterdam, Amsterdam, The Netherlands
| | - Gregory Buti
- Massachusetts General Hospital and Harvard Medical School, Department of Radiation Oncology, Division of Radiation BioPhysics, Boston, MA, United States of America
| | - Ş İlker Birbil
- Amsterdam Business School, University of Amsterdam, Amsterdam, The Netherlands
| | - Zhongxing Liao
- University of Texas' MD Anderson Cancer Center, Department of Radiation Oncology, Division of Radiation Oncology, Houston, TX, United States of America
| | - Thomas Bortfeld
- Massachusetts General Hospital and Harvard Medical School, Department of Radiation Oncology, Division of Radiation BioPhysics, Boston, MA, United States of America
| | - Dick den Hertog
- Amsterdam Business School, University of Amsterdam, Amsterdam, The Netherlands
| | - Ali Ajdari
- Massachusetts General Hospital and Harvard Medical School, Department of Radiation Oncology, Division of Radiation BioPhysics, Boston, MA, United States of America
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Jayawickrama SM, Ranaweera PM, Pradeep RGGR, Jayasinghe YA, Senevirathna K, Hilmi AJ, Rajapakse RMG, Kanmodi KK, Jayasinghe RD. Developments and future prospects of personalized medicine in head and neck squamous cell carcinoma diagnoses and treatments. Cancer Rep (Hoboken) 2024; 7:e2045. [PMID: 38522008 PMCID: PMC10961052 DOI: 10.1002/cnr2.2045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Revised: 02/07/2024] [Accepted: 03/05/2024] [Indexed: 03/25/2024] Open
Abstract
BACKGROUND Precision healthcare has entered a new era because of the developments in personalized medicine, especially in the diagnosis and treatment of head and neck squamous cell carcinoma (HNSCC). This paper explores the dynamic landscape of personalized medicine as applied to HNSCC, encompassing both current developments and future prospects. RECENT FINDINGS The integration of personalized medicine strategies into HNSCC diagnosis is driven by the utilization of genetic data and biomarkers. Epigenetic biomarkers, which reflect modifications to DNA that can influence gene expression, have emerged as valuable indicators for early detection and risk assessment. Treatment approaches within the personalized medicine framework are equally promising. Immunotherapy, gene silencing, and editing techniques, including RNA interference and CRISPR/Cas9, offer innovative means to modulate gene expression and correct genetic aberrations driving HNSCC. The integration of stem cell research with personalized medicine presents opportunities for tailored regenerative approaches. The synergy between personalized medicine and technological advancements is exemplified by artificial intelligence (AI) and machine learning (ML) applications. These tools empower clinicians to analyze vast datasets, predict patient responses, and optimize treatment strategies with unprecedented accuracy. CONCLUSION The developments and prospects of personalized medicine in HNSCC diagnosis and treatment offer a transformative approach to managing this complex malignancy. By harnessing genetic insights, biomarkers, immunotherapy, gene editing, stem cell therapies, and advanced technologies like AI and ML, personalized medicine holds the key to enhancing patient outcomes and ushering in a new era of precision oncology.
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Affiliation(s)
| | | | | | | | - Kalpani Senevirathna
- Centre for Research in Oral Cancer, Faculty of Dental SciencesUniversity of PeradeniyaKandySri Lanka
| | | | | | - Kehinde Kazeem Kanmodi
- School of DentistryUniversity of RwandaKigaliRwanda
- Faculty of DentistryUniversity of PuthisastraPhnom PenhCambodia
- Cephas Health Research Initiative IncIbadanNigeria
- School of Health and Life SciencesTeesside UniversityMiddlesbroughUK
| | - Ruwan Duminda Jayasinghe
- Centre for Research in Oral Cancer, Faculty of Dental SciencesUniversity of PeradeniyaKandySri Lanka
- Faculty of DentistryUniversity of PuthisastraPhnom PenhCambodia
- School of Health and Life SciencesTeesside UniversityMiddlesbroughUK
- Department of Oral Medicine and Periodontology, Faculty of Dental SciencesUniversity of PeradeniyaKandySri Lanka
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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: 9] [Impact Index Per Article: 9.0] [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: 1] [Impact Index Per Article: 1.0] [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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Tan D, Mohamad Salleh SA, Manan HA, Yahya N. Delta-radiomics-based models for toxicity prediction in radiotherapy: A systematic review and meta-analysis. J Med Imaging Radiat Oncol 2023; 67:564-579. [PMID: 37309680 DOI: 10.1111/1754-9485.13546] [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: 01/10/2023] [Accepted: 05/28/2023] [Indexed: 06/14/2023]
Abstract
INTRODUCTION Delta-radiomics models are potentially able to improve the treatment assessment than single-time point features. The purpose of this study is to systematically synthesize the performance of delta-radiomics-based models for radiotherapy (RT)-induced toxicity. METHODS A literature search was performed following the PRISMA guidelines. Systematic searches were performed in PubMed, Scopus, Cochrane and Embase databases in October 2022. Retrospective and prospective studies on the delta-radiomics model for RT-induced toxicity were included based on predefined PICOS criteria. A random-effect meta-analysis of AUC was performed on the performance of delta-radiomics models, and a comparison with non-delta radiomics models was included. RESULTS Of the 563 articles retrieved, 13 selected studies of RT-treated patients on different types of cancer (HNC = 571, NPC = 186, NSCLC = 165, oesophagus = 106, prostate = 33, OPC = 21) were eligible for inclusion in the systematic review. Included studies show that morphological and dosimetric features may improve the predictive model performance for the selected toxicity. Four studies that reported both delta and non-delta radiomics features with AUC were included in the meta-analysis. The AUC random effects estimate for delta and non-delta radiomics models were 0.80 and 0.78 with heterogeneity, I2 of 73% and 27% respectively. CONCLUSION Delta-radiomics-based models were found to be promising predictors of predefined end points. Future studies should consider using standardized methods and radiomics features and external validation to the reviewed delta-radiomics model.
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Affiliation(s)
- Daryl Tan
- Faculty of Health Sciences, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia
| | | | - Hanani Abdul Manan
- Functional Image Processing Laboratory, Department of Radiology, Universiti Kebangsaan Malaysia Medical Centre, Kuala Lumpur, Malaysia
| | - Noorazrul Yahya
- Faculty of Health Sciences, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia
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Mireștean CC, Iancu RI, Iancu DPT. Image Guided Radiotherapy (IGRT) and Delta (Δ) Radiomics-An Urgent Alliance for the Front Line of the War against Head and Neck Cancers. Diagnostics (Basel) 2023; 13:2045. [PMID: 37370940 DOI: 10.3390/diagnostics13122045] [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: 03/15/2023] [Revised: 05/24/2023] [Accepted: 06/09/2023] [Indexed: 06/29/2023] Open
Abstract
The identification of a biomarker that is response predictive could offer a solution for the stratification of the treatment of head and neck cancers (HNC) in the context of high recurrence rates, especially those associated with loco-regional failure. Delta (Δ) radiomics, a concept based on the variation of parameters extracted from medical imaging using artificial intelligence (AI) algorithms, demonstrates its potential as a predictive biomarker of treatment response in HNC. The concept of image-guided radiotherapy (IGRT), including computer tomography simulation (CT) and position control imaging with cone-beam-computed tomography (CBCT), now offers new perspectives for radiomics applied in radiotherapy. The use of Δ features of texture, shape, and size, both from the primary tumor and from the tumor-involved lymph nodes, demonstrates the best predictive accuracy. If, in the case of treatment response, promising Δ radiomics results could be obtained, even after 24 h from the start of treatment, for radiation-induced xerostomia, the evaluation of Δ radiomics in the middle of treatment could be recommended. The fused models (clinical and Δ radiomics) seem to offer benefits, both in comparison to the clinical model and to the radiomic model. The selection of patients who benefit from induction chemotherapy is underestimated in Δ radiomic studies and may be an unexplored territory with major potential. The advantage offered by "in house" simulation CT and CBCT favors the rapid implementation of Δ radiomics studies in radiotherapy departments. Positron emission tomography (PET)-CT Δ radiomics could guide the new concepts of dose escalation on radio-resistant sub-volumes based on radiobiological criteria, but also guide the "next level" of HNC adaptive radiotherapy (ART).
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Affiliation(s)
- Camil Ciprian Mireștean
- Department of Oncology and Radiotherapy, University of Medicine and Pharmacy Craiova, 200349 Craiova, Romania
- Department of Surgery, Railways Clinical Hospital Iasi, 700506 Iași, Romania
| | - Roxana Irina Iancu
- Oral Pathology Department, "Gr. T. Popa" Faculty of Dental Medicine, University of Medicine and Pharmacy, 700115 Iași, Romania
- Department of Clinical Laboratory, "St. Spiridon" Emergency Universitary Hospital, 700111 Iași, Romania
| | - Dragoș Petru Teodor Iancu
- Oncology and Radiotherapy Department, Faculty of Medicine, "Gr. T. Popa" University of Medicine and Pharmacy, 700115 Iași, Romania
- Department of Radiation Oncology, Regional Institute of Oncology, 700483 Iași, Romania
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Smith DK, Clark H, Hovan A, Wu J. Neural network and spline-based regression for the prediction of salivary hypofunction in patients undergoing radiation therapy. Radiat Oncol 2023; 18:77. [PMID: 37158946 PMCID: PMC10165827 DOI: 10.1186/s13014-023-02274-9] [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: 12/13/2022] [Accepted: 04/28/2023] [Indexed: 05/10/2023] Open
Abstract
BACKGROUND This study leverages a large retrospective cohort of head and neck cancer patients in order to develop machine learning models to predict radiation induced hyposalivation from dose-volume histograms of the parotid glands. METHODS The pre and post-radiotherapy salivary flow rates of 510 head and neck cancer patients were used to fit three predictive models of salivary hypofunction, (1) the Lyman-Kutcher-Burman (LKB) model, (2) a spline-based model, (3) a neural network. A fourth LKB-type model using literature reported parameter values was included for reference. Predictive performance was evaluated using a cut-off dependent AUC analysis. RESULTS The neural network model dominated the LKB models demonstrating better predictive performance at every cutoff with AUCs ranging from 0.75 to 0.83 depending on the cutoff selected. The spline-based model nearly dominated the LKB models with the fitted LKB model only performing better at the 0.55 cutoff. The AUCs for the spline model ranged from 0.75 to 0.84 depending on the cutoff chosen. The LKB models had the lowest predictive ability with AUCs ranging from 0.70 to 0.80 (fitted) and 0.67 to 0.77 (literature reported). CONCLUSION Our neural network model showed improved performance over the LKB and alternative machine learning approaches and provided clinically useful predictions of salivary hypofunction without relying on summary measures.
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Affiliation(s)
- Derek K Smith
- American Dental Association Science and Research Institute, 211 E Chicago Ave., Chicago, IL, 60611, USA.
| | - Haley Clark
- Medical Physics; BC Cancer, Surrey, BC, Canada
| | | | - Jonn Wu
- Department of Radiation Oncology, BC Cancer, Vancouver, BC, Canada
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Zhang Z, Wei X. Artificial intelligence-assisted selection and efficacy prediction of antineoplastic strategies for precision cancer therapy. Semin Cancer Biol 2023; 90:57-72. [PMID: 36796530 DOI: 10.1016/j.semcancer.2023.02.005] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 01/12/2023] [Accepted: 02/13/2023] [Indexed: 02/16/2023]
Abstract
The rapid development of artificial intelligence (AI) technologies in the context of the vast amount of collectable data obtained from high-throughput sequencing has led to an unprecedented understanding of cancer and accelerated the advent of a new era of clinical oncology with a tone of precision treatment and personalized medicine. However, the gains achieved by a variety of AI models in clinical oncology practice are far from what one would expect, and in particular, there are still many uncertainties in the selection of clinical treatment options that pose significant challenges to the application of AI in clinical oncology. In this review, we summarize emerging approaches, relevant datasets and open-source software of AI and show how to integrate them to address problems from clinical oncology and cancer research. We focus on the principles and procedures for identifying different antitumor strategies with the assistance of AI, including targeted cancer therapy, conventional cancer therapy, and cancer immunotherapy. In addition, we also highlight the current challenges and directions of AI in clinical oncology translation. Overall, we hope this article will provide researchers and clinicians with a deeper understanding of the role and implications of AI in precision cancer therapy, and help AI move more quickly into accepted cancer guidelines.
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Affiliation(s)
- Zhe Zhang
- Laboratory of Aging Research and Cancer Drug Target, State Key Laboratory of Biotherapy and Cancer Center, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu 610041, PR China; State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, and Collaborative Innovation Center for Biotherapy, Chengdu 610041, PR China
| | - Xiawei Wei
- Laboratory of Aging Research and Cancer Drug Target, State Key Laboratory of Biotherapy and Cancer Center, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu 610041, PR China.
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Araújo ALD, Moraes MC, Pérez-de-Oliveira ME, Silva VMD, Saldivia-Siracusa C, Pedroso CM, Lopes MA, Vargas PA, Kochanny S, Pearson A, Khurram SA, Kowalski LP, Migliorati CA, Santos-Silva AR. Machine learning for the prediction of toxicities from head and neck cancer treatment: A systematic review with meta-analysis. Oral Oncol 2023; 140:106386. [PMID: 37023561 DOI: 10.1016/j.oraloncology.2023.106386] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Revised: 03/20/2023] [Accepted: 03/26/2023] [Indexed: 04/08/2023]
Abstract
INTRODUCTION The aim of the present systematic review (SR) is to summarize Machine Learning (ML) models currently used to predict head and neck cancer (HNC) treatment-related toxicities, and to understand the impact of image biomarkers (IBMs) in prediction models (PMs). The present SR was conducted following the guidelines of the PRISMA 2022 and registered in PROSPERO database (CRD42020219304). METHODS The acronym PICOS was used to develop the focused review question (Can PMs accurately predict HNC treatment toxicities?) and the eligibility criteria. The inclusion criteria enrolled Prediction Model Studies (PMSs) with patient cohorts that were treated for HNC and developed toxicities. Electronic database search encompassed PubMed, EMBASE, Scopus, Cochrane Library, Web of Science, LILACS, and Gray Literature (Google Scholar and ProQuest). Risk of Bias (RoB) was assessed through PROBAST and the results were synthesized based on the data format (with and without IBMs) to allow comparison. RESULTS A total of 28 studies and 4,713 patients were included. Xerostomia was the most frequently investigated toxicity (17; 60.71 %). Sixteen (57.14 %) studies reported using radiomics features in combination with clinical or dosimetrics/dosiomics for modelling. High RoB was identified in 23 studies. Meta-analysis (MA) showed an area under the receiver operating characteristics curve (AUROC) of 0.82 for models with IBMs and 0.81 for models without IBMs (p value < 0.001), demonstrating no difference among IBM- and non-IBM-based models. DISCUSSION The development of a PM based on sample-specific features represents patient selection bias and may affect a model's performance. Heterogeneity of the studies as well as non-standardized metrics prevent proper comparison of studies, and the absence of an independent/external test does not allow the evaluation of the model's generalization ability. CONCLUSION IBM-featured PMs are not superior to PMs based on non-IBM predictors. The evidence was appraised as of low certainty.
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Affiliation(s)
- Anna Luíza Damaceno Araújo
- Oral Diagnosis Department, Piracicaba Dental School, University of Campinas (UNICAMP), Piracicaba, São Paulo, Brazil; Head and Neck Surgery Department, University of São Paulo Medical School (UFMUSP), São Paulo, São Paulo, Brazil
| | - Matheus Cardoso Moraes
- Institute of Science and Technology, Federal University of São Paulo (ICT-Unifesp), São José dos Campos, São Paulo, Brazil
| | | | - Viviane Mariano da Silva
- Institute of Science and Technology, Federal University of São Paulo (ICT-Unifesp), São José dos Campos, São Paulo, Brazil
| | - Cristina Saldivia-Siracusa
- Oral Diagnosis Department, Piracicaba Dental School, University of Campinas (UNICAMP), Piracicaba, São Paulo, Brazil
| | - Caique Mariano Pedroso
- Oral Diagnosis Department, Piracicaba Dental School, University of Campinas (UNICAMP), Piracicaba, São Paulo, Brazil
| | - Marcio Ajudarte Lopes
- Oral Diagnosis Department, Piracicaba Dental School, University of Campinas (UNICAMP), Piracicaba, São Paulo, Brazil
| | - Pablo Agustin Vargas
- Oral Diagnosis Department, Piracicaba Dental School, University of Campinas (UNICAMP), Piracicaba, São Paulo, Brazil
| | - Sara Kochanny
- Section of Hemathology/Oncology, Department of Medicine, University of Chicago, Chicago, IL, United States; University of Chicago Comprehensive Cancer Center, Chicago, Chicago, IL, United States
| | - Alexander Pearson
- Section of Hemathology/Oncology, Department of Medicine, University of Chicago, Chicago, IL, United States; University of Chicago Comprehensive Cancer Center, Chicago, Chicago, IL, United States
| | - Syed Ali Khurram
- Unit of Oral and Maxillofacial Pathology, School of Clinical Dentistry, University of Sheffield, S10 2TA Sheffield, United Kingdom
| | - Luiz Paulo Kowalski
- Department of Head and Neck Surgery and Otorhinolaryngology, A.C. Camargo Cancer Center, São Paulo, Brazil; Head and Neck Surgery Department and LIM 28, University of São Paulo Medical School, São Paulo, Brazil
| | | | - Alan Roger Santos-Silva
- Oral Diagnosis Department, Piracicaba Dental School, University of Campinas (UNICAMP), Piracicaba, São Paulo, Brazil.
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Bang C, Bernard G, Le WT, Lalonde A, Kadoury S, Bahig H. Artificial intelligence to predict outcomes of head and neck radiotherapy. Clin Transl Radiat Oncol 2023; 39:100590. [PMID: 36935854 PMCID: PMC10014342 DOI: 10.1016/j.ctro.2023.100590] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Revised: 01/28/2023] [Accepted: 01/28/2023] [Indexed: 02/01/2023] Open
Abstract
Head and neck radiotherapy induces important toxicity, and its efficacy and tolerance vary widely across patients. Advancements in radiotherapy delivery techniques, along with the increased quality and frequency of image guidance, offer a unique opportunity to individualize radiotherapy based on imaging biomarkers, with the aim of improving radiation efficacy while reducing its toxicity. Various artificial intelligence models integrating clinical data and radiomics have shown encouraging results for toxicity and cancer control outcomes prediction in head and neck cancer radiotherapy. Clinical implementation of these models could lead to individualized risk-based therapeutic decision making, but the reliability of the current studies is limited. Understanding, validating and expanding these models to larger multi-institutional data sets and testing them in the context of clinical trials is needed to ensure safe clinical implementation. This review summarizes the current state of the art of machine learning models for prediction of head and neck cancer radiotherapy outcomes.
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Key Words
- ADASYN, adaptive synthetic sampling
- AI, artificial intelligence
- ANN, artificial neural network
- AUC, Area Under the ROC Curve
- Artificial intelligence
- BMI, body mass index
- C-Index, concordance index
- CART, Classification and Regression Tree
- CBCT, cone-beam computed tomography
- CIFE, conditional informax feature extraction
- CNN, convolutional neural network
- CRT, chemoradiation
- CT, computed tomography
- Cancer outcomes
- DL, deep learning
- DM, distant metastasis
- DSC, Dice Similarity Coefficient
- DSS, clinical decision support systems
- DT, Decision Tree
- DVH, Dose-volume histogram
- GANs, Generative Adversarial Networks
- GB, Gradient boosting
- GPU, graphical process units
- HNC, head and neck cancer
- HPV, human papillomavirus
- HR, hazard ratio
- Head and neck cancer
- IAMB, incremental association Markov blanket
- IBDM, image based data mining
- IBMs, image biomarkers
- IMRT, intensity-modulated RT
- KNN, k nearest neighbor
- LLR, Local linear forest
- LR, logistic regression
- LRR, loco-regional recurrence
- MIFS, mutual information based feature selection
- ML, machine learning
- MRI, Magnetic resonance imaging
- MRMR, Minimum redundancy feature selection
- Machine learning
- N-MLTR, Neural Multi-Task Logistic Regression
- NPC, nasopharynx
- NTCP, Normal Tissue Complication Probability
- OPC, oropharyngeal cancer
- ORN, osteoradionecrosis
- OS, overall survival
- PCA, Principal component analysis
- PET, Positron emission tomography
- PG, parotid glands
- PLR, Positive likelihood ratio
- PM, pharyngeal mucosa
- PTV, Planning target volumes
- PreSANet, deep preprocessor module and self-attention
- Predictive modeling
- QUANTEC, Quantitative Analyses of Normal Tissue Effects in the Clinic
- RF, random forest
- RFC, random forest classifier
- RFS, recurrence free survival
- RLR, Rigid logistic regression
- RRF, Regularized random forest
- RSF, random survival forest
- RT, radiotherapy
- RTLI, radiation-induced temporal lobe injury
- Radiomic
- SDM, shared decision making
- SMG, submandibular glands
- SMOTE, synthetic minority over-sampling technique
- STIC, sticky saliva
- SVC, support vector classifier
- SVM, support vector machine
- XGBoost, extreme gradient boosting
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Affiliation(s)
- Chulmin Bang
- Centre Hospitalier de l’Université de Montréal, Montreal, QC, Canada
- Corresponding author at: Centre Hospitalier de l'Université de Montréal, 3840 Rue Saint-Urbain, Montréal, QC H2W 1T8, Canada.
| | - Galaad Bernard
- Centre Hospitalier de l’Université de Montréal, Montreal, QC, Canada
| | - William T. Le
- Centre de recherche du Centre Hospitalier de l’Université de Montréal, Montreal, QC, Canada
- Polytechnique Montréal, Montreal, QC, Canada
| | - Arthur Lalonde
- Centre Hospitalier de l’Université de Montréal, Montreal, QC, Canada
- Centre de recherche du Centre Hospitalier de l’Université de Montréal, Montreal, QC, Canada
- Université de Montréal, Montreal, QC, Canada
| | - Samuel Kadoury
- Centre de recherche du Centre Hospitalier de l’Université de Montréal, Montreal, QC, Canada
- Polytechnique Montréal, Montreal, QC, Canada
| | - Houda Bahig
- Centre Hospitalier de l’Université de Montréal, Montreal, QC, Canada
- Centre de recherche du Centre Hospitalier de l’Université de Montréal, Montreal, QC, Canada
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Zhou L, Zheng W, Huang S, Yang X. Integrated radiomics, dose-volume histogram criteria and clinical features for early prediction of saliva amount reduction after radiotherapy in nasopharyngeal cancer patients. Discov Oncol 2022; 13:145. [PMID: 36581739 PMCID: PMC9800672 DOI: 10.1007/s12672-022-00606-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Accepted: 12/15/2022] [Indexed: 12/31/2022] Open
Abstract
PURPOSE Previously, the evaluation of xerostomia depended on subjective grading systems, rather than the accurate saliva amount reduction. Our aim was to quantify acute xerostomia with reduced saliva amount, and apply radiomics, dose-volume histogram (DVH) criteria and clinical features to predict saliva amount reduction by machine learning techniques. MATERIAL AND METHODS Computed tomography (CT) of parotid glands, DVH, and clinical data of 52 patients were collected to extract radiomics, DVH criteria and clinical features, respectively. Firstly, radiomics, DVH criteria and clinical features were divided into 3 groups for feature selection, in order to alleviate the masking effect of the number of features in different groups. Secondly, the top features in the 3 groups composed integrated features, and features selection was performed again for integrated features. In this study, feature selection was used as a combination of eXtreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP) to alleviate multicollinearity. Finally, 6 machine learning techniques were used for predicting saliva amount reduction. Meanwhile, top radiomics features were modeled using the same machine learning techniques for comparison. RESULT 17 integrated features (10 radiomics, 4 clinical, 3 DVH criteria) were selected to predict saliva amount reduction, with a mean square error (MSE) of 0.6994 and a R2 score of 0.9815. Top 17 and 10 selected radiomics features predicted saliva amount reduction, with MSE of 0.7376, 0.7519, and R2 score of 0.9805, 0.9801, respectively. CONCLUSION With the same number of features, integrated features (radiomics + DVH criteria + clinical) performed better than radiomics features alone. The important DVH criteria and clinical features mainly included, white blood cells (WBC), parotid_glands_Dmax, Age, parotid_glands_V15, hemoglobin (Hb), BMI and parotid_glands_V45.
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Affiliation(s)
- Lang Zhou
- State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine; Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, Guangdong Province, China
- Department of Biomedical Engineering, South China University of Technology, Guangzhou, 510640, Guangdong Province, China
| | - Wanjia Zheng
- State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine; Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, Guangdong Province, China
- Department of Radiation Oncology, Southern Theater Air Force Hospital of the People's Liberation Army, Guangzhou, 510050, Guangdong Province, China
| | - Sijuan Huang
- State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine; Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, Guangdong Province, China.
| | - Xin Yang
- State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine; Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, Guangdong Province, China.
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13
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Bin X, Zhu C, Tang Y, Li R, Ding Q, Xia W, Tang Y, Tang X, Yao D, Tang A. Nomogram Based on Clinical and Radiomics Data for Predicting Radiation-induced Temporal Lobe Injury in Patients with Non-metastatic Stage T4 Nasopharyngeal Carcinoma. Clin Oncol (R Coll Radiol) 2022; 34:e482-e492. [PMID: 36008245 DOI: 10.1016/j.clon.2022.07.007] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Revised: 06/19/2022] [Accepted: 07/21/2022] [Indexed: 01/31/2023]
Abstract
AIMS To use pre-treatment magnetic resonance imaging-based radiomics data with clinical data to predict radiation-induced temporal lobe injury (RTLI) in nasopharyngeal carcinoma (NPC) patients with stage T4/N0-3/M0 within 5 years after radiotherapy. MATERIALS AND METHODS This study retrospectively examined 98 patients (198 temporal lobes) with stage T4/N0-3/M0 NPC. Participants were enrolled into a training cohort or a validation cohort in a ratio of 7:3. Radiomics features were extracted from pre-treatment magnetic resonance imaging that were T1-and T2-weighted. Spearman rank correlation, the t-test and the least absolute shrinkage and selection operator (LASSO) algorithm were used to select significant radiomics features; machine-learning models were used to generate radiomics signatures (Rad-Scores). Rad-Scores and clinical factors were integrated into a nomogram for prediction of RTLI. Nomogram discrimination was evaluated using receiver operating characteristic analysis and clinical benefits were evaluated using decision curve analysis. RESULTS Participants were enrolled into a training cohort (n = 139) or a validation cohort (n = 59). In total, 3568 radiomics features were initially extracted from T1-and T2-weighted images. Age, Dmax, D1cc and 16 stable radiomics features (six from T1-weighted and 10 from T2-weighted images) were identified as independent predictive factors. A greater Rad-Score was associated with a greater risk of RTLI. The nomogram showed good discrimination, with a C-index of 0.85 (95% confidence interval 0.79-0.92) in the training cohort and 0.82 (95% confidence interval 0.71-0.92) in the validation cohort. CONCLUSION We developed models for the prediction of RTLI in patients with stage T4/N0-3/M0 NPC using pre-treatment radiomics data and clinical data. Nomograms from these pre-treatment data improved the prediction of RTLI. These results may allow the selection of patients for earlier clinical interventions.
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Affiliation(s)
- X Bin
- Department of Otolaryngology-Head and Neck Surgery, First Affiliated Hospital, Guangxi Medical University, Nanning, Guangxi, China
| | - C Zhu
- Department of Radiation Oncology, People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China
| | - Y Tang
- Department of Neurology, First Affiliated Hospital, Guangxi Medical University, Nanning, Guangxi, China
| | - R Li
- Interdisciplinary Institute of Neuroscience and Technology, Zhejiang University Hangzhou, Zhejiang Province, China; Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Q Ding
- Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai, China
| | - W Xia
- Department of Otolaryngology-Head and Neck Surgery, First Affiliated Hospital, Guangxi Medical University, Nanning, Guangxi, China
| | - Y Tang
- Department of Radiology, First Affiliated Hospital, Guangxi Medical University, Nanning, Guangxi, China
| | - X Tang
- Department of Otolaryngology-Head and Neck Surgery, First Affiliated Hospital, Guangxi Medical University, Nanning, Guangxi, China
| | - D Yao
- Department of Otolaryngology-Head and Neck Surgery, First Affiliated Hospital, Guangxi Medical University, Nanning, Guangxi, China
| | - A Tang
- Department of Otolaryngology-Head and Neck Surgery, First Affiliated Hospital, Guangxi Medical University, Nanning, Guangxi, China.
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The Role of Radiomics in Salivary Gland Imaging: A Systematic Review and Radiomics Quality Assessment. Diagnostics (Basel) 2022; 12:diagnostics12123002. [PMID: 36553009 PMCID: PMC9777175 DOI: 10.3390/diagnostics12123002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Revised: 11/16/2022] [Accepted: 11/29/2022] [Indexed: 12/04/2022] Open
Abstract
Background: Radiomics of salivary gland imaging can support clinical decisions in different clinical scenarios, such as tumors, radiation-induced xerostomia and sialadenitis. This review aims to evaluate the methodological quality of radiomics studies on salivary gland imaging. Material and Methods: A systematic search was performed, and the methodological quality was evaluated using the radiomics quality score (RQS). Subgroup analyses according to the first author's professional role (medical or not medical), journal type (radiological journal or other) and the year of publication (2021 or before) were performed. The correlation of RQS with the number of patients was calculated. Results: Twenty-three articles were included (mean RQS 11.34 ± 3.68). Most studies well-documented the imaging protocol (87%), while neither prospective validations nor cost-effectiveness analyses were performed. None of the included studies provided open-source data. A statistically significant difference in RQS according to the year of publication was found (p = 0.009), with papers published in 2021 having slightly higher RQSs than older ones. No differences according to journal type or the first author's professional role were demonstrated. A moderate relationship between the overall RQS and the number of patients was found. Conclusions: Radiomics application in salivary gland imaging is increasing. Although its current clinical applicability can be affected by the somewhat inadequate quality of the papers, a significant improvement in radiomics methodologies has been demonstrated in the last year.
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Iliadou V, Kakkos I, Karaiskos P, Kouloulias V, Platoni K, Zygogianni A, Matsopoulos GK. Early Prediction of Planning Adaptation Requirement Indication Due to Volumetric Alterations in Head and Neck Cancer Radiotherapy: A Machine Learning Approach. Cancers (Basel) 2022; 14:cancers14153573. [PMID: 35892831 PMCID: PMC9331795 DOI: 10.3390/cancers14153573] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 07/14/2022] [Accepted: 07/20/2022] [Indexed: 11/16/2022] Open
Abstract
Background: During RT cycles, the tumor response pattern could affect tumor coverage and may lead to organs at risk of overdose. As such, early prediction of significant volumetric changes could therefore reduce potential radiation-related adverse effects. Nevertheless, effective machine learning approaches based on the radiomic features of the clinically used CBCT images to determine the tumor volume variations due to RT not having been implemented so far. Methods: CBCT images from 40 HN cancer patients were collected weekly during RT treatment. From the obtained images, the Clinical Target Volume (CTV) and Parotid Glands (PG) regions of interest were utilized to calculate 104 delta-radiomics features. These features were fed on a feature selection and classification procedure for the early prediction of significant volumetric alterations. Results: The proposed framework was able to achieve 0.90 classification performance accuracy while detecting a small subset of discriminative characteristics from the 1st week of RT. The selected features were further analyzed regarding their effects on temporal changes in anatomy and tumor response modeling. Conclusion: The use of machine learning algorithms offers promising perspectives for fast and reliable early prediction of large volumetric deviations as a result of RT treatment, exploiting hidden patterns in the overall anatomical characteristics.
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Affiliation(s)
- Vasiliki Iliadou
- School of Electrical and Computer Engineering, National Technical University of Athens, 157 73 Athens, Greece; (I.K.); (G.K.M.)
- Correspondence: ; Tel.: +30-21-0772-3577
| | - Ioannis Kakkos
- School of Electrical and Computer Engineering, National Technical University of Athens, 157 73 Athens, Greece; (I.K.); (G.K.M.)
- Department of Biomedical Engineering, University of West Attica, 122 43 Athens, Greece
| | - Pantelis Karaiskos
- Medical Physics Laboratory, Medical School, National and Kapodistrian University of Athens, 115 27 Athens, Greece;
| | - Vassilis Kouloulias
- 2nd Department of Radiology, Radiotherapy Unit, ATTIKON University Hospital, 124 62 Athens, Greece; (V.K.); (K.P.)
| | - Kalliopi Platoni
- 2nd Department of Radiology, Radiotherapy Unit, ATTIKON University Hospital, 124 62 Athens, Greece; (V.K.); (K.P.)
| | - Anna Zygogianni
- 1st Department of Radiology, Radiotherapy Unit, ARETAIEION University Hospital, 115 28 Athens, Greece;
| | - George K. Matsopoulos
- School of Electrical and Computer Engineering, National Technical University of Athens, 157 73 Athens, Greece; (I.K.); (G.K.M.)
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Abdollahi H, Chin E, Clark H, Hyde DE, Thomas S, Wu J, Uribe CF, Rahmim A. Radiomics-guided radiation therapy: opportunities and challenges. Phys Med Biol 2022; 67. [DOI: 10.1088/1361-6560/ac6fab] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Accepted: 05/13/2022] [Indexed: 11/11/2022]
Abstract
Abstract
Radiomics is an advanced image-processing framework, which extracts image features and considers them as biomarkers towards personalized medicine. Applications include disease detection, diagnosis, prognosis, and therapy response assessment/prediction. As radiation therapy aims for further individualized treatments, radiomics could play a critical role in various steps before, during and after treatment. Elucidation of the concept of radiomics-guided radiation therapy (RGRT) is the aim of this review, attempting to highlight opportunities and challenges underlying the use of radiomics to guide clinicians and physicists towards more effective radiation treatments. This work identifies the value of RGRT in various steps of radiotherapy from patient selection to follow-up, and subsequently provides recommendations to improve future radiotherapy using quantitative imaging features.
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Special Issue “Fuzzy Decision Making and Soft Computing Applications”. APPLIED SYSTEM INNOVATION 2022. [DOI: 10.3390/asi5030054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/10/2022]
Abstract
Research on fuzzy logic [...]
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Viswanathan VS, Gupta A, Madabhushi A. Novel Imaging Biomarkers to Assess Oncologic Treatment-Related Changes. Am Soc Clin Oncol Educ Book 2022; 42:1-13. [PMID: 35671432 DOI: 10.1200/edbk_350931] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Cancer therapeutics cause various treatment-related changes that may impact patient follow-up and disease monitoring. Although atypical responses such as pseudoprogression may be misinterpreted as treatment nonresponse, other changes, such as hyperprogressive disease seen with immunotherapy, must be recognized early for timely management. Radiation necrosis in the brain is a known response to radiotherapy and must be distinguished from local tumor recurrence. Radiotherapy can also cause adverse effects such as pneumonitis and local tissue toxicity. Systemic therapies, like chemotherapy and targeted therapies, are known to cause long-term cardiovascular effects. Thus, there is a need for robust biomarkers to identify, distinguish, and predict cancer treatment-related changes. Radiomics, which refers to the high-throughput extraction of subvisual features from radiologic images, has been widely explored for disease classification, risk stratification, and treatment-response prediction. Lately, there has been much interest in investigating the role of radiomics to assess oncologic treatment-related changes. We review the utility and various applications of radiomics in identifying and distinguishing atypical responses to treatments, as well as in predicting adverse effects. Although artificial intelligence tools show promise, several challenges-including multi-institutional clinical validation, deployment in health care settings, and artificial-intelligence bias-must be addressed for seamless clinical translation of these tools.
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Affiliation(s)
| | - Amit Gupta
- Department of Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH
| | - Anant Madabhushi
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH.,Louis Stokes Cleveland VA Medical Center, Cleveland, OH
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Marcu LG, Marcu DC. Current Omics Trends in Personalised Head and Neck Cancer Chemoradiotherapy. J Pers Med 2021; 11:jpm11111094. [PMID: 34834445 PMCID: PMC8625829 DOI: 10.3390/jpm11111094] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Revised: 10/22/2021] [Accepted: 10/25/2021] [Indexed: 12/12/2022] Open
Abstract
Chemoradiotherapy remains the most common management of locally advanced head and neck cancer. While both treatment components have greatly developed over the years, the quality of life and long-term survival of patients undergoing treatment for head and neck malignancies are still poor. Research in head and neck oncology is equally focused on the improvement of tumour response to treatment and on the limitation of normal tissue toxicity. In this regard, personalised therapy through a multi-omics approach targeting patient management from diagnosis to treatment shows promising results. The aim of this paper is to discuss the latest results regarding the personalised approach to chemoradiotherapy of head and neck cancer by gathering the findings of the newest omics, involving radiotherapy (dosiomics), chemotherapy (pharmacomics), and medical imaging for treatment monitoring (radiomics). The incorporation of these omics into head and neck cancer management offers multiple viewpoints to treatment that represent the foundation of personalised therapy.
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Affiliation(s)
- Loredana G. Marcu
- Faculty of Informatics & Science, University of Oradea, 410087 Oradea, Romania
- Cancer Research Institute, University of South Australia, Adelaide, SA 5001, Australia
- Correspondence:
| | - David C. Marcu
- Faculty of Electrical Engineering & Information Technology, University of Oradea, 410087 Oradea, Romania;
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Cavallo A, Iacovelli NA, Facchinetti N, Rancati T, Alfieri S, Giandini T, Cicchetti A, Fallai C, Ingargiola R, Licitra L, Locati L, Cavalieri S, Pignoli E, Romanello DA, Valdagni R, Orlandi E. Modelling Radiation-Induced Salivary Dysfunction during IMRT and Chemotherapy for Nasopharyngeal Cancer Patients. Cancers (Basel) 2021; 13:cancers13163983. [PMID: 34439136 PMCID: PMC8392585 DOI: 10.3390/cancers13163983] [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: 05/16/2021] [Revised: 07/24/2021] [Accepted: 07/30/2021] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND Radiation-induced xerostomia is one of the most prevalent adverse effects of head and neck cancer treatment, and it could seriously affect patients' qualities of life. It results primarily from damage to the salivary glands, but its onset and severity may also be influenced by other patient-, tumour-, and treatment-related factors. We aimed to build and validate a predictive model for acute salivary dysfunction (aSD) for locally advanced nasopharyngeal carcinoma (NPC) patients by combining clinical and dosimetric factors. METHODS A cohort of consecutive NPC patients treated curatively with IMRT and chemotherapy at 70 Gy (2-2.12 Gy/fraction) were utilised. Parotid glands (cPG, considered as a single organ) and the oral cavity (OC) were selected as organs-at-risk. The aSD was assessed at baseline and weekly during RT, grade ≥ 2 aSD chosen as the endpoint. Dose-volume histograms were reduced to the Equivalent Uniform Dose (EUD). Dosimetric and clinical/treatment features selected via LASSO were inserted into a multivariable logistic model. Model validation was performed on two cohorts of patients with prospective aSD, and scored using the same schedule/scale: a cohort (NPC_V) of NPC patients (as in model training), and a cohort of mixed non-NPC head and neck cancer patients (HNC_V). RESULTS The model training cohort included 132 patients. Grade ≥ 2 aSD was reported in 90 patients (68.2%). Analyses resulted in a 4-variables model, including doses of up to 98% of cPG (cPG_D98%, OR = 1.04), EUD to OC with n = 0.05 (OR = 1.11), age (OR = 1.08, 5-year interval) and smoking history (OR = 1.37, yes vs. no). Calibration was good. The NPC_V cohort included 38 patients, with aSD scored in 34 patients (89.5%); the HNC_V cohort included 93 patients, 77 with aSD (92.8%). As a general observation, the incidence of aSD was significantly different in the training and validation populations (p = 0.01), thus impairing calibration-in-the-large. At the same time, the effect size for the two dosimetric factors was confirmed. Discrimination was also satisfactory in both cohorts: AUC was 0.73, and 0.68 in NPC_V and HNC_V cohorts, respectively. CONCLUSION cPG D98% and the high doses received by small OC volumes were found to have the most impact on grade ≥ 2 acute xerostomia, with age and smoking history acting as a dose-modifying factor. Findings on the development population were confirmed in two prospectively collected validation populations.
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Affiliation(s)
- Anna Cavallo
- Department of Medical Physics, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy; (A.C.); (T.G.); (E.P.)
| | - Nicola Alessandro Iacovelli
- Department of Radiation Oncology 2, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy; (N.A.I.); (N.F.); (C.F.); (R.I.); (D.A.R.); (E.O.)
| | - Nadia Facchinetti
- Department of Radiation Oncology 2, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy; (N.A.I.); (N.F.); (C.F.); (R.I.); (D.A.R.); (E.O.)
- National Center for Oncological Hadrontherapy (CNAO), Clinical Trial Center, 27100 Pavia, Italy
| | - Tiziana Rancati
- Prostate Cancer Program, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy; (A.C.); (R.V.)
- Correspondence:
| | - Salvatore Alfieri
- Department of Medical Oncology 3, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy; (S.A.); (L.L.); (L.L.); (S.C.)
- Centro di Riferimento Oncologico di Aviano (PN) CRO IRCCS, Department of Medical Oncology, 33018 Aviano, Italy
| | - Tommaso Giandini
- Department of Medical Physics, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy; (A.C.); (T.G.); (E.P.)
| | - Alessandro Cicchetti
- Prostate Cancer Program, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy; (A.C.); (R.V.)
| | - Carlo Fallai
- Department of Radiation Oncology 2, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy; (N.A.I.); (N.F.); (C.F.); (R.I.); (D.A.R.); (E.O.)
| | - Rossana Ingargiola
- Department of Radiation Oncology 2, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy; (N.A.I.); (N.F.); (C.F.); (R.I.); (D.A.R.); (E.O.)
- National Center for Oncological Hadrontherapy (CNAO), Radiation Oncology Clinical Department, 27100 Pavia, Italy
| | - Lisa Licitra
- Department of Medical Oncology 3, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy; (S.A.); (L.L.); (L.L.); (S.C.)
- Department of Oncolgy and Hemato-Oncology, Università Degli Studi di Milano, 20122 Milan, Italy
| | - Laura Locati
- Department of Medical Oncology 3, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy; (S.A.); (L.L.); (L.L.); (S.C.)
| | - Stefano Cavalieri
- Department of Medical Oncology 3, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy; (S.A.); (L.L.); (L.L.); (S.C.)
| | - Emanuele Pignoli
- Department of Medical Physics, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy; (A.C.); (T.G.); (E.P.)
| | - Domenico Attilio Romanello
- Department of Radiation Oncology 2, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy; (N.A.I.); (N.F.); (C.F.); (R.I.); (D.A.R.); (E.O.)
| | - Riccardo Valdagni
- Prostate Cancer Program, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy; (A.C.); (R.V.)
- Department of Oncolgy and Hemato-Oncology, Università Degli Studi di Milano, 20122 Milan, Italy
- Department of Radiation Oncology 1, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy
| | - Ester Orlandi
- Department of Radiation Oncology 2, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy; (N.A.I.); (N.F.); (C.F.); (R.I.); (D.A.R.); (E.O.)
- National Center for Oncological Hadrontherapy (CNAO), Radiation Oncology Clinical Department, 27100 Pavia, Italy
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21
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Bruixola G, Remacha E, Jiménez-Pastor A, Dualde D, Viala A, Montón JV, Ibarrola-Villava M, Alberich-Bayarri Á, Cervantes A. Radiomics and radiogenomics in head and neck squamous cell carcinoma: Potential contribution to patient management and challenges. Cancer Treat Rev 2021; 99:102263. [PMID: 34343892 DOI: 10.1016/j.ctrv.2021.102263] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Revised: 07/06/2021] [Accepted: 07/23/2021] [Indexed: 12/12/2022]
Abstract
The application of imaging biomarkers in oncology is still in its infancy, but with the expansion of radiomics and radiogenomics a revolution is expected in this field. This may be of special interest in head and neck cancer, since it can promote precision medicine and personalization of treatment by overcoming several intrinsic obstacles in this pathology. Our goal is to provide the medical oncologist with the basis to approach these disciplines and appreciate their main uses in clinical research and clinical practice in the medium term. Aligned with this objective we analyzed the most relevant studies in the field, also highlighting novel opportunities and current challenges.
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Affiliation(s)
- Gema Bruixola
- Department of Medical Oncology, INCLIVA Biomedical Research Institute, University of Valencia, Valencia, Spain
| | - Elena Remacha
- Quantitative Imaging Biomarkers in Medicine (QUIBIM SL), Valencia, Spain
| | - Ana Jiménez-Pastor
- Quantitative Imaging Biomarkers in Medicine (QUIBIM SL), Valencia, Spain
| | - Delfina Dualde
- Department of Radiology, INCLIVA Biomedical Research Institute, University of Valencia, Valencia, Spain
| | - Alba Viala
- Department of Medical Oncology, INCLIVA Biomedical Research Institute, University of Valencia, Valencia, Spain
| | - Jose Vicente Montón
- Department of Medical Oncology, INCLIVA Biomedical Research Institute, University of Valencia, Valencia, Spain
| | - Maider Ibarrola-Villava
- Department of Medical Oncology, INCLIVA Biomedical Research Institute, University of Valencia, Valencia, Spain; CIBERONC, Instituto de Salud Carlos III, Madrid, Spain
| | | | - Andrés Cervantes
- Department of Medical Oncology, INCLIVA Biomedical Research Institute, University of Valencia, Valencia, Spain; CIBERONC, Instituto de Salud Carlos III, Madrid, Spain.
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22
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Investigation of Radiation-Induced Toxicity in Head and Neck Cancer Patients through Radiomics and Machine Learning: A Systematic Review. JOURNAL OF ONCOLOGY 2021; 2021:5566508. [PMID: 34211551 PMCID: PMC8211491 DOI: 10.1155/2021/5566508] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/10/2021] [Revised: 05/18/2021] [Accepted: 05/25/2021] [Indexed: 12/24/2022]
Abstract
Background. Radiation-induced toxicity represents a crucial concern in oncological treatments of patients affected by head and neck neoplasms, due to its impact on survivors' quality of life. Published reports suggested the potential of radiomics combined with machine learning methods in the prediction and assessment of radiation-induced toxicities, supporting a tailored radiation treatment management. In this paper, we present an update of the current knowledge concerning these modern approaches. MATERIALS AND METHODS A systematic review according to PICO-PRISMA methodology was conducted in MEDLINE/PubMed and EMBASE databases until June 2019. Studies assessing the use of radiomics combined with machine learning in predicting radiation-induced toxicity in head and neck cancer patients were specifically included. Four authors (two independently and two in concordance) assessed the methodological quality of the included studies using the Radiomic Quality Score (RQS). The overall score for each analyzed study was obtained by the sum of the single RQS items; the average and standard deviation values of the authors' RQS were calculated and reported. RESULTS Eight included papers, presenting data on parotid glands, cochlea, masticatory muscles, and white brain matter, were specifically analyzed in this review. Only one study had an average RQS was ≤ 30% (50%), while 3 studies obtained a RQS almost ≤ 25%. Potential variability in the interpretations of specific RQS items could have influenced the inter-rater agreement in specific cases. CONCLUSIONS Published radiomic studies provide encouraging but still limited and preliminary data that require further validation to improve the decision-making processes in preventing and managing radiation-induced toxicities.
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23
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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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24
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van Dijk LV, Fuller CD. Artificial Intelligence and Radiomics in Head and Neck Cancer Care: Opportunities, Mechanics, and Challenges. Am Soc Clin Oncol Educ Book 2021; 41:1-11. [PMID: 33929877 DOI: 10.1200/edbk_320951] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
The advent of large-scale high-performance computing has allowed the development of machine-learning techniques in oncologic applications. Among these, there has been substantial growth in radiomics (machine-learning texture analysis of images) and artificial intelligence (which uses deep-learning techniques for "learning algorithms"); however, clinical implementation has yet to be realized at scale. To improve implementation, opportunities, mechanics, and challenges, models of imaging-enabled artificial intelligence approaches need to be understood by clinicians who make the treatment decisions. This article aims to convey the basic conceptual premises of radiomics and artificial intelligence using head and neck cancer as a use case. This educational overview focuses on approaches for head and neck oncology imaging, detailing current research efforts and challenges to implementation.
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Affiliation(s)
- Lisanne V van Dijk
- Department of Radiation Oncology, MD Anderson Cancer Center, Houston, TX.,Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
| | - Clifton D Fuller
- Department of Radiation Oncology, MD Anderson Cancer Center, Houston, TX
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25
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Parimbelli E, Wilk S, Cornet R, Sniatala P, Sniatala K, Glaser SLC, Fraterman I, Boekhout AH, Ottaviano M, Peleg M. A review of AI and Data Science support for cancer management. Artif Intell Med 2021; 117:102111. [PMID: 34127240 DOI: 10.1016/j.artmed.2021.102111] [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: 08/07/2020] [Revised: 12/23/2020] [Accepted: 05/11/2021] [Indexed: 02/09/2023]
Abstract
INTRODUCTION Thanks to improvement of care, cancer has become a chronic condition. But due to the toxicity of treatment, the importance of supporting the quality of life (QoL) of cancer patients increases. Monitoring and managing QoL relies on data collected by the patient in his/her home environment, its integration, and its analysis, which supports personalization of cancer management recommendations. We review the state-of-the-art of computerized systems that employ AI and Data Science methods to monitor the health status and provide support to cancer patients managed at home. OBJECTIVE Our main objective is to analyze the literature to identify open research challenges that a novel decision support system for cancer patients and clinicians will need to address, point to potential solutions, and provide a list of established best-practices to adopt. METHODS We designed a review study, in compliance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, analyzing studies retrieved from PubMed related to monitoring cancer patients in their home environments via sensors and self-reporting: what data is collected, what are the techniques used to collect data, semantically integrate it, infer the patient's state from it and deliver coaching/behavior change interventions. RESULTS Starting from an initial corpus of 819 unique articles, a total of 180 papers were considered in the full-text analysis and 109 were finally included in the review. Our findings are organized and presented in four main sub-topics consisting of data collection, data integration, predictive modeling and patient coaching. CONCLUSION Development of modern decision support systems for cancer needs to utilize best practices like the use of validated electronic questionnaires for quality-of-life assessment, adoption of appropriate information modeling standards supplemented by terminologies/ontologies, adherence to FAIR data principles, external validation, stratification of patients in subgroups for better predictive modeling, and adoption of formal behavior change theories. Open research challenges include supporting emotional and social dimensions of well-being, including PROs in predictive modeling, and providing better customization of behavioral interventions for the specific population of cancer patients.
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Affiliation(s)
| | - S Wilk
- Poznan University of Technology, Poland
| | - R Cornet
- Amsterdam University Medical Centre, the Netherlands
| | | | | | - S L C Glaser
- Amsterdam University Medical Centre, the Netherlands
| | - I Fraterman
- Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - A H Boekhout
- Netherlands Cancer Institute, Amsterdam, the Netherlands
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26
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Zheng YM, Xu WJ, Hao DP, Liu XJ, Gao CP, Tang GZ, Li J, Wang HX, Dong C. A CT-based radiomics nomogram for differentiation of lympho-associated benign and malignant lesions of the parotid gland. Eur Radiol 2021; 31:2886-2895. [PMID: 33123791 DOI: 10.1007/s00330-020-07421-4] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2020] [Revised: 09/25/2020] [Accepted: 10/13/2020] [Indexed: 02/07/2023]
Abstract
OBJECTIVES Preoperative differentiation between benign lymphoepithelial lesion (BLEL) and mucosa-associated lymphoid tissue lymphoma (MALToma) in the parotid gland is important for treatment decisions. The purpose of this study was to develop and validate a CT-based radiomics nomogram combining radiomics signature and clinical factors for the preoperative differentiation of BLEL from MALToma in the parotid gland. METHODS A total of 101 patients with BLEL (n = 46) or MALToma (n = 55) were divided into a training set (n = 70) and validation set (n = 31). Radiomics features were extracted from non-contrast CT images, a radiomics signature was constructed, and a radiomics score (Rad-score) was calculated. Demographics and CT findings were assessed to build a clinical factor model. A radiomics nomogram combining the Rad-score and independent clinical factors was constructed using multivariate logistic regression analysis. The performance levels of the nomogram, radiomics signature, and clinical model were evaluated and validated on the training and validation datasets, and then compared among the three models. RESULTS Seven features were used to build the radiomics signature. The radiomics nomogram incorporating the clinical factors and radiomics signature showed favorable predictive value for differentiating parotid BLEL from MALToma, with AUCs of 0.983 and 0.950 for the training set and validation set, respectively. Decision curve analysis showed that the nomogram outperformed the clinical factor model in terms of clinical usefulness. CONCLUSIONS The CT-based radiomics nomogram incorporating the Rad-score and clinical factors showed favorable predictive efficacy for differentiating BLEL from MALToma in the parotid gland, and may help in the clinical decision-making process. KEY POINTS • Differential diagnosis between BLEL and MALToma in parotid gland is rather difficult by conventional imaging modalities. • A radiomics nomogram integrated with the radiomics signature, demographics, and CT findings facilitates differentiation of BLEL from MALToma with improved diagnostic efficacy.
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Affiliation(s)
- Ying-Mei Zheng
- Health Management Center, The Affiliated Hospital of Qingdao University, No.16, Jiangsu Road, Qingdao, 266000, China
| | - Wen-Jian Xu
- Department of Radiology, The Affiliated Hospital of Qingdao University, No.16, Jiangsu Road, Qingdao, 266000, China
| | - Da-Peng Hao
- Department of Radiology, The Affiliated Hospital of Qingdao University, No.16, Jiangsu Road, Qingdao, 266000, China
| | - Xue-Jun Liu
- Department of Radiology, The Affiliated Hospital of Qingdao University, No.16, Jiangsu Road, Qingdao, 266000, China
| | - Chuan-Ping Gao
- Department of Radiology, The Affiliated Hospital of Qingdao University, No.16, Jiangsu Road, Qingdao, 266000, China
| | - Guo-Zhang Tang
- Department of Cardiac Ultrasound, The Affiliated Hospital of Qingdao University, No.16, Jiangsu Road, Qingdao, 266000, China
| | - Jie Li
- Department of Radiology, The Affiliated Hospital of Qingdao University, No.16, Jiangsu Road, Qingdao, 266000, China
| | - He-Xiang Wang
- Department of Radiology, The Affiliated Hospital of Qingdao University, No.16, Jiangsu Road, Qingdao, 266000, China
| | - Cheng Dong
- Department of Radiology, The Affiliated Hospital of Qingdao University, No.16, Jiangsu Road, Qingdao, 266000, China.
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27
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Feng M, Yin Q, Ren J, Wu F, Lan M, Wang H, Wang M, Li L, Chen X, Lang J. Dynamic Three-Dimensional ADC Changes of Parotid Glands During Radiotherapy Predict the Salivary Secretary Function in Patients With Head and Neck Squamous Carcinoma. Front Oncol 2021; 11:651537. [PMID: 33928037 PMCID: PMC8076545 DOI: 10.3389/fonc.2021.651537] [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: 01/10/2021] [Accepted: 03/24/2021] [Indexed: 11/13/2022] Open
Abstract
Objective To investigate the changes of three-dimensional apparent diffusion coefficient (3D-ADC) of bilateral parotid glands during radiotherapy for head and neck squamous cell carcinoma (HNSCC) patients and explore the correlations with the radiation dose, volume reduction of parotid gland and the salivary secretary function. Materials and Methods 60 HNSCC were retrospectively collected in Sichuan cancer hospital. The patients were all received diffusion-weighted imaging (DWI) scan at pre-radiation, the 15th radiation, the 25th radiation and completion of radiation. Dynamic 3D-ADC were measured in different lobes of parotid glands (P1: deep lobe of ipsilateral; P2: superficial lobe of ipsilateral; P3: deep lobe of contralateral; P4: superficial lobe of contralateral), and the 3D-ADC of spinal cord were also recorded. Chewing stimulates test, radionuclide scan and RTOG criteria were recorded to evaluate the salivary secretary function. Pearson analysis was used to assess the correlation between 3D-ADC value, radiation dose, volume change, and salivary secretary function. Results The mean 3D-ADC of parotid glands increased. It began to change at the 15th radiation and the mostly increased in P1. However, there was no change for the maximum and minimum 3D-ADC. The 3D-ADC values of spinal cord changes were almost invisible (ratio ≤ 0.03 ± 0.01). The mean 3D-ADC was negatively correlated with the salivary secretary function (r=-0.72) and volume reduction of different lobes of parotid glands (r1=-0.64; r2=-0.61; r3=-0.57; r4=-0.49), but it was positively correlated with the delivered dose (r1 = 0.73; r2 = 0.69; r3 = 0.65; r4 = 0.78). Conclusion Dynamic 3D-ADC changes might be a new and early indicator to predict and evaluate the secretary function of parotid glands during radiotherapy.
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Affiliation(s)
- Mei Feng
- Department of Radiation Oncology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.,Department of Medical Oncology, Sichuan The Third People's Hospital, Chengdu, China
| | - Qingping Yin
- Department of Radiation Oncology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.,Department of Radiation Oncology, School of Clinical Medicine, North Sichuan Medical College, Nanchong, China
| | - Jing Ren
- Department of Radiology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Fei Wu
- Department of Oncology, People's Hospital of Deyang City, Deyang, China
| | - Mei Lan
- Department of Radiation Oncology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - He Wang
- Department of Radiation Oncology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Min Wang
- Department of Radiology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Lu Li
- Department of Radiation Oncology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Xiaojian Chen
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Jinyi Lang
- Department of Radiation Oncology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
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Forde E, Leech M, Robert C, Herron E, Marignol L. Influence of inter-observer delineation variability on radiomic features of the parotid gland. Phys Med 2021; 82:240-248. [PMID: 33677385 DOI: 10.1016/j.ejmp.2021.01.084] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Revised: 01/06/2021] [Accepted: 01/29/2021] [Indexed: 12/14/2022] Open
Abstract
PURPOSE This study aimed to quantify the variability in the values of radiomic features extracted from a right parotid gland (RPG) delineated by a series of independent observers. METHODS This was a secondary analysis of anonymous data from a delineation workshop. Inter-observer variability of the RPG from 40 participants was quantified using DICE similarity coefficient (DSC) and Hausdorff distance (HD). An additional contour was generated using Varian SmartSegmentation. Radiomic features extracted include four shape features, six histogram features, and 32 texture features. The absolute mean paired percentage difference (PPD) in feature values from the expert and participants were ranked . Feature robustness was classified using pre- determined thresholds. RESULTS 63% of participants achieved a DSC > 0.7, the auto- segmentation DSC was 0.76. The average HD for the participants was 16.16 mm ± 0.66 mm, and 15.16 mm for the auto-segmentation. 48% (n = 20) and 33% (n = 14) of features were deemed to be robust with a mean absolute PPD < 5%, for the auto-segmentation and manual delineations respectively; the majority of which were from the grey-run length matrix family. 7% (n = 3) of features from the auto- segmentation and 10% (n = 4) from the manual contours were deemed to be unstable with a mean absolute PPD > 50%. The value of the most robust feature was not related to DSC and HD. CONCLUSION Inter-observer delineation variability affects the value of the radiomic features extracted from the RPG. This study identifies the radiomic features least sensitive to these uncertainties. Further investigation of the clinical relevance of these features in prediction of xerostomia is warranted.
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Affiliation(s)
- E Forde
- Applied Radiation Therapy Trinity, Discipline of Radiation Therapy, Trinity St James' Cancer Institute, Trinity College Dublin, Dublin, Ireland.
| | - M Leech
- Applied Radiation Therapy Trinity, Discipline of Radiation Therapy, Trinity St James' Cancer Institute, Trinity College Dublin, Dublin, Ireland
| | - C Robert
- Molecular Radiotherapy and Innovative Therapeutics, INSERM UMR1030, Gustave Roussy Cancer Campus, Université Paris Salcay, Villejuif, France
| | - E Herron
- Department of Psychiatry School of Medicine, Trinity College Dublin, Dublin, Ireland
| | - L Marignol
- Applied Radiation Therapy Trinity, Discipline of Radiation Therapy, Trinity St James' Cancer Institute, Trinity College Dublin, Dublin, Ireland
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29
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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: 49] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [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)
| | | | | | | | | | | | - Weihong Jiang
- Department of Otolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha 410078, Hunan, China
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30
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Magnetic Resonance and CT Imaging Biomarkers for Prediction of Acute and Chronic Radiation-Induced Xerostomia. MAGNETOCHEMISTRY 2020. [DOI: 10.3390/magnetochemistry7010005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Xerostomia is a common adverse effect of radiation therapy at the head and neck area. Radiation-induced xerostomia can be severe and detrimental for the quality of life. Clinicians and radiologists have focused on the prevention of xerostomia as feasible, which has been significantly improved in the recent decades with the use of the contemporary radiation technology. However, radiation-induced xerostomia still remains one of the most devastating side effects of radiation therapy. Clinical risk factors have been identified, but the variation of its incidence and presentation has turned the focus on the investigation of parameters that would be able to predict the onset of acute or chronic xerostomia for each individual patient. Recently, potential imaging parameters and biomarkers are investigated in order for early prediction of the incidence and severity of xerostomia. Here, we compile the resulting imaging biomarkers as have been identified in the recent literature based on MRI and CT performed in correlation with radiation therapy. The identification of such biomarkers is very promising for the prevention and control of xerostomia in the head and neck radiation setting.
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31
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Duffton A, Li W, Forde E. The Pivotal Role of the Therapeutic Radiographer/Radiation Therapist in Image-guided Radiotherapy Research and Development. Clin Oncol (R Coll Radiol) 2020; 32:852-860. [PMID: 33087296 DOI: 10.1016/j.clon.2020.09.009] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2020] [Revised: 08/21/2020] [Accepted: 09/22/2020] [Indexed: 12/24/2022]
Abstract
The ability to personalise radiotherapy to fit the individual patient and their diagnosis has been realised through technological advancements. There is now more opportunity to utilise these technologies and deliver precision radiotherapy for more patients. Image-guided radiotherapy (IGRT) has enabled users to safely and accurately plan, treat and verify complex cases; and deliver a high dose to the target volume, while minimising dose to normal tissue. Rapid changes in IGRT have required a multidisciplinary team (MDT) approach, carefully deciding optimum protocols to achieve clinical benefit. Therapeutic radiographer/radiation therapists (RTTs) play a pivotal role in this MDT. There is already a great deal of evidence that illustrates the contribution of RTTs in IGRT development; implementation; quality assurance; and maintaining training and competency programmes. Often this has required the RTT to undertake additional roles and responsibilities. These publications show how the profession has evolved, expanding the scope of practice. There are now more opportunities for RTT-led IGRT research. This is not only undertaken in the more traditional aspects of practice, but in recent times, more RTTs are becoming involved in imaging biomarkers research and radiomic analysis. The aim of this overview is to describe the RTT contribution to the ongoing development of IGRT and to showcase some of the profession's involvement in IGRT research.
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Affiliation(s)
- A Duffton
- Department of Radiotherapy, Beatson West of Scotland Cancer Centre, Glasgow, UK.
| | - W Li
- University of Toronto, Toronto, Ontario, Canada; Radiation Medicine Program, Princess Margaret Cancer Centre, Toronto, Ontario, Canada
| | - E Forde
- Applied Radiation Therapy Trinity, Discipline of Radiation Therapy, Trinity College Dublin, The University of Dublin, Dublin, Ireland
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Desideri I, Loi M, Francolini G, Becherini C, Livi L, Bonomo P. Application of Radiomics for the Prediction of Radiation-Induced Toxicity in the IMRT Era: Current State-of-the-Art. Front Oncol 2020; 10:1708. [PMID: 33117669 PMCID: PMC7574641 DOI: 10.3389/fonc.2020.01708] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2020] [Accepted: 07/30/2020] [Indexed: 12/14/2022] Open
Abstract
Normal tissue complication probability (NTCP) models that were formulated in the Quantitative Analyses of Normal Tissue Effects in the Clinic (QUANTEC) are one of the pillars in support of everyday’s clinical radiation oncology. Because of steady therapeutic refinements and the availability of cutting-edge technical solutions, the ceiling of organs-at-risk-sparing has been reached for photon-based intensity modulated radiotherapy (IMRT). The possibility to capture heterogeneity of patients and tissues in the prediction of toxicity is still an unmet need in modern radiation therapy. Potentially, a major step towards a wider therapeutic index could be obtained from refined assessment of radiation-induced morbidity at an individual level. The rising integration of quantitative imaging and machine learning applications into radiation oncology workflow offers an unprecedented opportunity to further explore the biologic interplay underlying the normal tissue response to radiation. Based on these premises, in this review we focused on the current-state-of-the-art on the use of radiomics for the prediction of toxicity in the field of head and neck, lung, breast and prostate radiotherapy.
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Affiliation(s)
- Isacco Desideri
- Radiation Oncology, Azienda Ospedaliero-Universitaria Careggi, University of Florence, Florence, Italy
| | - Mauro Loi
- Radiation Oncology, Azienda Ospedaliero-Universitaria Careggi, University of Florence, Florence, Italy
| | - Giulio Francolini
- Radiation Oncology, Azienda Ospedaliero-Universitaria Careggi, University of Florence, Florence, Italy
| | - Carlotta Becherini
- Radiation Oncology, Azienda Ospedaliero-Universitaria Careggi, University of Florence, Florence, Italy
| | - Lorenzo Livi
- Radiation Oncology, Azienda Ospedaliero-Universitaria Careggi, University of Florence, Florence, Italy
| | - Pierluigi Bonomo
- Radiation Oncology, Azienda Ospedaliero-Universitaria Careggi, University of Florence, Florence, Italy
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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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Isaksson LJ, Pepa M, Zaffaroni M, Marvaso G, Alterio D, Volpe S, Corrao G, Augugliaro M, Starzyńska A, Leonardi MC, Orecchia R, Jereczek-Fossa BA. Machine Learning-Based Models for Prediction of Toxicity Outcomes in Radiotherapy. Front Oncol 2020; 10:790. [PMID: 32582539 PMCID: PMC7289968 DOI: 10.3389/fonc.2020.00790] [Citation(s) in RCA: 51] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2020] [Accepted: 04/22/2020] [Indexed: 12/20/2022] Open
Abstract
In order to limit radiotherapy (RT)-related side effects, effective toxicity prediction and assessment schemes are essential. In recent years, the growing interest toward artificial intelligence and machine learning (ML) within the science community has led to the implementation of innovative tools in RT. Several researchers have demonstrated the high performance of ML-based models in predicting toxicity, but the application of these approaches in clinics is still lagging, partly due to their low interpretability. Therefore, an overview of contemporary research is needed in order to familiarize practitioners with common methods and strategies. Here, we present a review of ML-based models for predicting and classifying RT-induced complications from both a methodological and a clinical standpoint, focusing on the type of features considered, the ML methods used, and the main results achieved. Our work overviews published research in multiple cancer sites, including brain, breast, esophagus, gynecological, head and neck, liver, lung, and prostate cancers. The aim is to define the current state of the art and main achievements within the field for both researchers and clinicians.
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Affiliation(s)
- Lars J Isaksson
- Division of Radiotherapy, IEO European Institute of Oncology IRCCS, Milan, Italy
| | - Matteo Pepa
- Division of Radiotherapy, IEO European Institute of Oncology IRCCS, Milan, Italy
| | - Mattia Zaffaroni
- Division of Radiotherapy, IEO European Institute of Oncology IRCCS, Milan, Italy
| | - Giulia Marvaso
- Division of Radiotherapy, IEO European Institute of Oncology IRCCS, Milan, Italy.,Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
| | - Daniela Alterio
- Division of Radiotherapy, IEO European Institute of Oncology IRCCS, Milan, Italy
| | - Stefania Volpe
- Division of Radiotherapy, IEO European Institute of Oncology IRCCS, Milan, Italy
| | - Giulia Corrao
- Division of Radiotherapy, IEO European Institute of Oncology IRCCS, Milan, Italy.,Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
| | - Matteo Augugliaro
- Division of Radiotherapy, IEO European Institute of Oncology IRCCS, Milan, Italy
| | - Anna Starzyńska
- Department of Oral Surgery, Medical University of Gdańsk, Gdańsk, Poland
| | - Maria C Leonardi
- Division of Radiotherapy, IEO European Institute of Oncology IRCCS, Milan, Italy
| | - Roberto Orecchia
- Scientific Directorate, IEO European Institute of Oncology IRCCS, Milan, Italy
| | - Barbara A Jereczek-Fossa
- Division of Radiotherapy, IEO European Institute of Oncology IRCCS, Milan, Italy.,Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
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Radiomic biomarkers for head and neck squamous cell carcinoma. Strahlenther Onkol 2020; 196:868-878. [PMID: 32495038 DOI: 10.1007/s00066-020-01638-4] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2020] [Accepted: 05/13/2020] [Indexed: 12/22/2022]
Abstract
Tumor heterogeneity is a well-known prognostic factor in head and neck squamous cell carcinoma (HNSCC). A major limitation of tissue- and blood-derived tumor markers is the lack of spatial resolution to image tumor heterogeneity. Tissue markers derived from tumor biopsies usually represent only a small tumor subregion at a single timepoint and are therefore often not representative of the tumors' biology or the biological alterations during and after treatment. Similarly, liquid biopsies give an overall picture of the tumors' secreted factors but completely lack any spatial resolution. Radiomics has the potential to give complete three-dimensional information about the tumor. We conducted a comprehensive literature search to assess the correlation of radiomics to tumor biology and treatment outcome in HNSCC and to assess current limitations of the radiomic biomarkers. In total, 25 studies that explored the ability of radiomics to predict tumor biology and phenotype in HNSCC and 28 studies that explored radiomics to predict post-treatment events were identified. Out of these 53 studies, only three failed to show a significant correlation. The major technical challenges are currently artifacts due to metal implants, non-standardized contrast injection, and delineation uncertainties. All studies to date were retrospective and none of the above-mentioned radiomics signatures have been validated in an independent cohort using an independent software implementation, which shows that transferability due to the numerous technical challenges is currently a major limitation. However, radiomics is a very young field and these studies hopefully pave the way for clinical implementation of radiomics for HNSCC in the future.
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Vaugier L, Ferrer L, Mengue L, Jouglar E. Radiomics for radiation oncologists: are we ready to go? BJR Open 2020; 2:20190046. [PMID: 33178967 PMCID: PMC7594896 DOI: 10.1259/bjro.20190046] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Revised: 03/06/2020] [Accepted: 03/09/2020] [Indexed: 12/19/2022] Open
Abstract
Radiomics have emerged as an exciting field of research over the past few years, with very wide potential applications in personalised and precision medicine of the future. Radiomics-based approaches are still however limited in daily clinical practice in oncology. This review focus on how radiomics could be incorporated into the radiation therapy pipeline, and globally help the radiation oncologist, from the tumour diagnosis to follow-up after treatment. Radiomics could impact on all steps of the treatment pipeline, once the limitations in terms of robustness and reproducibility are overcome. Major ongoing efforts should be made to collect and share data in the most standardised manner possible.
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Affiliation(s)
- Loïg Vaugier
- Department of Radiation Oncology, Institut de Cancérologie de l'Ouest, Nantes - Saint Herblain, France
| | - Ludovic Ferrer
- Department of Medical Physics, Institut de Cancérologie de l'Ouest, Nantes - Saint Herblain, France
| | - Laurence Mengue
- Department of Radiation Oncology, Institut de Cancérologie de l'Ouest, Nantes - Saint Herblain, France
| | - Emmanuel Jouglar
- Department of Radiation Oncology, Institut de Cancérologie de l'Ouest, Nantes - Saint Herblain, France
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Sheikh K, Lee SH, Cheng Z, Lakshminarayanan P, Peng L, Han P, McNutt TR, Quon H, Lee J. Predicting acute radiation induced xerostomia in head and neck Cancer using MR and CT Radiomics of parotid and submandibular glands. Radiat Oncol 2019; 14:131. [PMID: 31358029 PMCID: PMC6664784 DOI: 10.1186/s13014-019-1339-4] [Citation(s) in RCA: 60] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2018] [Accepted: 07/17/2019] [Indexed: 12/24/2022] Open
Abstract
PURPOSE To analyze baseline CT/MR-based image features of salivary glands to predict radiation-induced xerostomia 3-months after head-and-neck cancer (HNC) radiotherapy. METHODS A retrospective analysis was performed on 266 HNC patients who were treated using radiotherapy at our institution between 2009 and 2018. CT and T1 post-contrast MR images along with NCI-CTCAE xerostomia grade (3-month follow-up) were prospectively collected at our institution. CT and MR images were registered on which parotid/submandibular glands were contoured. Image features were extracted for ipsilateral/contralateral parotid and submandibular glands relative to the location of the primary tumor. Dose-volume-histogram (DVH) parameters were also acquired. Features were pre-selected based on Spearman correlation before modelling by examining the correlation with xerostomia (p < 0.05). A shrinkage regression analysis of the pre-selected features was performed using LASSO. The internal validity of the variable selection was estimated by repeating the entire variable selection procedure using a leave-one-out-cross-validation. The most frequently selected variables were considered in the final model. A generalized linear regression with repeated ten-fold cross-validation was developed to predict radiation-induced xerostomia at 3-months after radiotherapy. This model was tested in an independent dataset (n = 50) of patients who were treated at the same institution in 2017-2018. We compared the prediction performances under eight conditions (DVH-only, CT-only, MR-only, CT + MR, DVH + CT, DVH + CT + MR, Clinical+CT + MR, and Clinical+DVH + CT + MR) using the area under the receiver operating characteristic curve (ROC-AUC). RESULTS Among extracted features, 7 CT, 5 MR, and 2 DVH features were selected. The internal cohort (n = 216) ROC-AUC values for DVH, CT, MR, and Clinical+DVH + CT + MR features were 0.73 ± 0.01, 0.69 ± 0.01, 0.70 ± 0.01, and 0.79 ± 0.01, respectively. The validation cohort (n = 50) ROC-AUC values for DVH, CT, MR, and Clinical+DVH + CT + MR features were 0.63, 0.57, 0.66, and 0.68, respectively. The DVH-ROC was not significantly different than the CT-ROC (p = 0.8) or MR-ROC (p = 0.4). However, the CT + MR-ROC was significantly different than the CT-ROC (p = 0.03), but not the Clinical+DVH + CT + MR model (p = 0.5). CONCLUSION Our results suggest that baseline CT and MR image features may reflect baseline salivary gland function and potential risk for radiation injury. The integration of baseline image features into prediction models has the potential to improve xerostomia risk stratification with the ultimate goal of truly personalized HNC radiotherapy.
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Affiliation(s)
- Khadija Sheikh
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, 401 North Broadway, Suite 1440, Baltimore, MD 21287-5678 USA
| | - Sang Ho Lee
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, 401 North Broadway, Suite 1440, Baltimore, MD 21287-5678 USA
| | - Zhi Cheng
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, 401 North Broadway, Suite 1440, Baltimore, MD 21287-5678 USA
| | - Pranav Lakshminarayanan
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, 401 North Broadway, Suite 1440, Baltimore, MD 21287-5678 USA
| | - Luke Peng
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, 401 North Broadway, Suite 1440, Baltimore, MD 21287-5678 USA
| | - Peijin Han
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, 401 North Broadway, Suite 1440, Baltimore, MD 21287-5678 USA
| | - Todd R. McNutt
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, 401 North Broadway, Suite 1440, Baltimore, MD 21287-5678 USA
| | - Harry Quon
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, 401 North Broadway, Suite 1440, Baltimore, MD 21287-5678 USA
| | - Junghoon Lee
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, 401 North Broadway, Suite 1440, Baltimore, MD 21287-5678 USA
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Harms J, Lei Y, Wang T, Zhang R, Zhou J, Tang X, Curran WJ, Liu T, Yang X. Paired cycle-GAN-based image correction for quantitative cone-beam computed tomography. Med Phys 2019; 46:3998-4009. [PMID: 31206709 DOI: 10.1002/mp.13656] [Citation(s) in RCA: 139] [Impact Index Per Article: 27.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2019] [Revised: 06/07/2019] [Accepted: 06/07/2019] [Indexed: 12/15/2022] Open
Abstract
PURPOSE The incorporation of cone-beam computed tomography (CBCT) has allowed for enhanced image-guided radiation therapy. While CBCT allows for daily 3D imaging, images suffer from severe artifacts, limiting the clinical potential of CBCT. In this work, a deep learning-based method for generating high quality corrected CBCT (CCBCT) images is proposed. METHODS The proposed method integrates a residual block concept into a cycle-consistent adversarial network (cycle-GAN) framework, called res-cycle GAN, to learn a mapping between CBCT images and paired planning CT images. Compared with a GAN, a cycle-GAN includes an inverse transformation from CBCT to CT images, which constrains the model by forcing calculation of both a CCBCT and a synthetic CBCT. A fully convolution neural network with residual blocks is used in the generator to enable end-to-end CBCT-to-CT transformations. The proposed algorithm was evaluated using 24 sets of patient data in the brain and 20 sets of patient data in the pelvis. The mean absolute error (MAE), peak signal-to-noise ratio (PSNR), normalized cross-correlation (NCC) indices, and spatial non-uniformity (SNU) were used to quantify the correction accuracy of the proposed algorithm. The proposed method is compared to both a conventional scatter correction and another machine learning-based CBCT correction method. RESULTS Overall, the MAE, PSNR, NCC, and SNU were 13.0 HU, 37.5 dB, 0.99, and 0.05 in the brain, 16.1 HU, 30.7 dB, 0.98, and 0.09 in the pelvis for the proposed method, improvements of 45%, 16%, 1%, and 93% in the brain, and 71%, 38%, 2%, and 65% in the pelvis, over the CBCT image. The proposed method showed superior image quality as compared to the scatter correction method, reducing noise and artifact severity. The proposed method produced images with less noise and artifacts than the comparison machine learning-based method. CONCLUSIONS The authors have developed a novel deep learning-based method to generate high-quality corrected CBCT images. The proposed method increases onboard CBCT image quality, making it comparable to that of the planning CT. With further evaluation and clinical implementation, this method could lead to quantitative adaptive radiation therapy.
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Affiliation(s)
- Joseph Harms
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 30322, USA
| | - Yang Lei
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 30322, USA
| | - Tonghe Wang
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 30322, USA
| | - Rongxiao Zhang
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 30322, USA
| | - Jun Zhou
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 30322, USA
| | - Xiangyang Tang
- Department of Radiology and Imaging Sciences and Winship Cancer Institute, Emory University, Atlanta, GA, 30322, USA
| | - Walter J Curran
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 30322, USA
| | - Tian Liu
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 30322, USA
| | - Xiaofeng Yang
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 30322, USA
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Liu Y, Shi H, Huang S, Chen X, Zhou H, Chang H, Xia Y, Wang G, Yang X. Early prediction of acute xerostomia during radiation therapy for nasopharyngeal cancer based on delta radiomics from CT images. Quant Imaging Med Surg 2019; 9:1288-1302. [PMID: 31448214 DOI: 10.21037/qims.2019.07.08] [Citation(s) in RCA: 48] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Background Acute xerostomia is the most common side effect of radiation therapy (RT) for head and neck (H&N) malignancies. Investigating radiation-induced changes of computed tomography (CT) radiomics in parotid glands (PGs) and saliva amount (SA) can predict acute xerostomia during the RT for nasopharyngeal cancer (NPC). Methods CT and SA data from 35 patients with stages I-IVB were randomly collected from an NPC clinical trial registered on the clinicaltrials.gov (ID: NCT01762514). All patients received radical treatment based on intensity-modulated RT (IMRT) with a prescription dose of 68.1 Gy in 30 fractions. The patients' ages ranged 24-72 years, and each patient had five CT sets acquired at treatment position: at the 0th, 10th, 20th, 30th fractions during the RT, and at 3-month later after the RT. The PGs for each CT set were delineated by a radiation oncologist and verified independently by another. Patients' saliva was collected every other 10 days during the RT. Acute xerostomia was evaluated based on the RTOG acute toxicity scoring and the SA. In total, 1,703 radiomics features were calculated for PGs from each CT set, including feature value at 0th fraction (FV0F), FV10F, and delta FV (ΔFV10F-0F), respectively. Extensive experiments were conducted to achieve the optimal results. RidgeCV and Recursive Feature Elimination (RFE) were used for feature selection, while linear regression was used for predicting SA30F. Four more patients were added for independent testing. Results Substantial changes in various radiomics metrics of PGs were observed during the RT. Eight normalized feature value (NFV), selected from NFV0F, predicted SA10F with a mean square error (MSE) of 0.9042 and a R2 score of 0.7406. Fourteen NFV, selected from ΔNFV10F-0F, NFV0F, and NFV10F to predict SA30F, showed the best predictive ability with an MSE of 0.0569. The model predicted the level of acute xerostomia with a precision of 0.9220 and a sensitivity of 100%, compared to the clinical observed SA. For the independent test, the MSE of PSA30F was 0.0233. Conclusions This study demonstrated that radiation-induced acute xerostomia level could be early predicted based on the SA and radiomics changes of the PGs during IMRT delivery. SA, NFV0F, NFV10F, and especially ΔNFV10F-0F provided the best performance on acute xerostomia prediction for individual patient based on RidgeCV_RFE_LinearRegression method of delta radiomics.
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Affiliation(s)
- Yanxia Liu
- School of Software Engineering, South China University of Technology, Guangzhou 510006, China
| | - Hongyu Shi
- School of Software Engineering, South China University of Technology, Guangzhou 510006, China
| | - Sijuan Huang
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou 510060, China
| | - Xiaochuan Chen
- School of Software Engineering, South China University of Technology, Guangzhou 510006, China
| | - Huimin Zhou
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou 510060, China.,Department of Oncology, the Seventy-fourth Group Army Hospital of the Chinese People's Liberation Army, Guangzhou 510318, China
| | - Hui Chang
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou 510060, China
| | - Yunfei Xia
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou 510060, China
| | - Guohua Wang
- School of Software Engineering, South China University of Technology, Guangzhou 510006, China
| | - Xin Yang
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou 510060, China
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Men K, Geng H, Zhong H, Fan Y, Lin A, Xiao Y. A Deep Learning Model for Predicting Xerostomia Due to Radiation Therapy for Head and Neck Squamous Cell Carcinoma in the RTOG 0522 Clinical Trial. Int J Radiat Oncol Biol Phys 2019; 105:440-447. [PMID: 31201897 DOI: 10.1016/j.ijrobp.2019.06.009] [Citation(s) in RCA: 47] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2019] [Revised: 05/14/2019] [Accepted: 06/04/2019] [Indexed: 12/15/2022]
Abstract
PURPOSE Xerostomia commonly occurs in patients who undergo head and neck radiation therapy and can seriously affect patients' quality of life. In this study, we developed a xerostomia prediction model with radiation treatment data using a 3-dimensional (3D) residual convolutional neural network (rCNN). The model can be used to guide radiation therapy to reduce toxicity. METHODS AND MATERIALS A total of 784 patients with head and neck squamous cell carcinoma enrolled in the Radiation Therapy Oncology Group 0522 clinical trial were included in this study. Late xerostomia is defined as xerostomia of grade ≥2 occurring in the 12th month of radiation therapy. The computed tomography (CT) planning images, 3D dose distributions, and contours of the parotid and submandibular glands were included as 3D rCNN inputs. Comparative experiments were performed for the 3D rCNN model without 1 of the 3 inputs and for the logistic regression model. Accuracy, sensitivity, specificity, F-score, and area under the receiver operator characteristic curve were evaluated. RESULTS The proposed model achieved promising prediction results. The performance metrics for 3D rCNN model with contour, CT images, and radiation therapy dose; 3D rCNN without contour; 3D rCNN without CT images; 3D rCNN without the dose; logistic regression with the dose and clinical parameters; and logistic regression without clinical parameters were as follows: accuracy: 0.76, 0.74, 0.73, 0.65, 0.64, and 0.56; sensitivity: 0.76, 0.72, 0.77, 0.59, 0.72, and 0.75; specificity: 0.76, 0.76, 0.71, 0.69, 0.59, and 0.43; F-score: 0.70, 0.68, 0.69, 0.56, 0.60, and 0.57; and area under the receiver operator characteristic curve: 0.84, 0.82, 0.78, 0.70, 0.74, and 0.68, respectively. CONCLUSIONS The proposed model uses 3D rCNN filters to extract low- and high-level spatial features and to achieve promising performance. This is a potentially effective model for predicting objective toxicity for supporting clinical decision making.
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Affiliation(s)
- Kuo Men
- University of Pennsylvania, Philadelphia, Pennsylvania; National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
| | - Huaizhi Geng
- University of Pennsylvania, Philadelphia, Pennsylvania
| | - Haoyu Zhong
- University of Pennsylvania, Philadelphia, Pennsylvania
| | - Yong Fan
- University of Pennsylvania, Philadelphia, Pennsylvania
| | - Alexander Lin
- University of Pennsylvania, Philadelphia, Pennsylvania
| | - Ying Xiao
- University of Pennsylvania, Philadelphia, Pennsylvania
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Wu ZY, Wu WJ, Zheng L, Huang MW, Shi Y, Lv XM, Liu SM, Zhang JG, Zhang J. Efficacy of combined surgery and 125 I seed brachytherapy for treatment of primary mucoepidermoid carcinoma of the parotid gland. Head Neck 2019; 41:3219-3225. [PMID: 31116488 DOI: 10.1002/hed.25813] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2018] [Revised: 04/18/2019] [Accepted: 05/13/2019] [Indexed: 12/30/2022] Open
Abstract
BACKGROUND This study aimed to determine the effectiveness and safety of surgery combined with postoperative 125 I seed brachytherapy for treatment of primary mucoepidermoid carcinoma (MEC) of the parotid gland. METHODS Retrospective analysis of data of patients with MEC (n = 108) treated with surgery plus postoperative 125 I seed brachytherapy between 2004 and 2016. Overall survival (OS), disease-free survival (DFS), local control rate (LCR), distant metastasis, and radiation-associated toxicities were analyzed, and factors affecting outcomes were evaluated. RESULTS The 5- and 10-year OS were 98.8% and 95.8%, respectively. The DFS and LCR at 5 and 10 years were all 91.4%. Age ≥ 60 years (hazard ratio [HR] = 6.86, 95% confidence interval [CI]: 1.54-30.55) and T4 disease (HR = 7.15, 95% CI: 1.40-36.52) were poor prognostic factors. Acute radiation-associated toxicities were minor. CONCLUSION Surgery plus 125 I seed brachytherapy appears to be an effective treatment for parotid gland MEC, capable of providing satisfactory outcomes with few complications.
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Affiliation(s)
- Zhi-Yuan Wu
- Department of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology, Beijing, China
| | - Wen-Jie Wu
- Department of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology, Beijing, China
| | - Lei Zheng
- Department of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology, Beijing, China
| | - Ming-Wei Huang
- Department of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology, Beijing, China
| | - Yan Shi
- Department of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology, Beijing, China
| | - Xiao-Ming Lv
- Department of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology, Beijing, China
| | - Shu-Ming Liu
- Department of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology, Beijing, China
| | - Jian-Guo Zhang
- Department of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology, Beijing, China
| | - Jie Zhang
- Department of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology, Beijing, China
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Astaraki M, Wang C, Buizza G, Toma-Dasu I, Lazzeroni M, Smedby Ö. Early survival prediction in non-small cell lung cancer from PET/CT images using an intra-tumor partitioning method. Phys Med 2019; 60:58-65. [DOI: 10.1016/j.ejmp.2019.03.024] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/27/2018] [Revised: 02/12/2019] [Accepted: 03/21/2019] [Indexed: 12/26/2022] Open
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Gardin I, Grégoire V, Gibon D, Kirisli H, Pasquier D, Thariat J, Vera P. Radiomics: Principles and radiotherapy applications. Crit Rev Oncol Hematol 2019; 138:44-50. [PMID: 31092384 DOI: 10.1016/j.critrevonc.2019.03.015] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2018] [Revised: 12/26/2018] [Accepted: 03/26/2019] [Indexed: 12/14/2022] Open
Abstract
Radiomics is defined as the extraction of a large quantity of quantitative image features. The different radiomic indexes that have been proposed in the literature are described as well as the various factors that have an impact on the robustness of these indexes. We will see that several hundred quantitative features can be extracted per lesion and imaging modality. The ever-growing number of features studied raises the question of the statistical method of analysis used. This review addresses the research supporting the clinical use of radiomics in oncology in the staging of disease, discrimination between healthy and pathological tissues, the identification of genetic features, the prediction of patient survival, the response to treatment, the recurrence after radiotherapy and chemoradiotherapy and the side effects. Based on the existing literature, it remains difficult to identify features that should be used for current clinical practice.
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Affiliation(s)
- I Gardin
- Department of Nuclear Medicine, Centre Henri-Becquerel, France; LITIS EA4108, Normandie University, Rouen, France.
| | - V Grégoire
- Department of Radiation Oncology, Centre Léon Bérard, France
| | - D Gibon
- Research and Innovation Department, AQUILAB, Loos Les Lille, France
| | - H Kirisli
- Research and Innovation Department, AQUILAB, Loos Les Lille, France
| | - D Pasquier
- Department of Radiation Oncology, Centre Oscar Lambret, CRIStAL UMR CNRS 9189, Lille University, Lille, France
| | - J Thariat
- Radiotherapy Department, Centre François Baclesse, Caen, France
| | - P Vera
- Department of Nuclear Medicine, Centre Henri-Becquerel, France; LITIS EA4108, Normandie University, Rouen, France
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Du Y, Zhang R, Zargari A, Thai TC, Gunderson CC, Moxley KM, Liu H, Zheng B, Qiu Y. Classification of Tumor Epithelium and Stroma by Exploiting Image Features Learned by Deep Convolutional Neural Networks. Ann Biomed Eng 2018; 46:1988-1999. [PMID: 30051247 PMCID: PMC6286645 DOI: 10.1007/s10439-018-2095-6] [Citation(s) in RCA: 42] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2018] [Accepted: 07/13/2018] [Indexed: 01/17/2023]
Abstract
The tumor-stroma ratio (TSR) reflected on hematoxylin and eosin (H&E)-stained histological images is a potential prognostic factor for survival. Automatic image processing techniques that allow for high-throughput and precise discrimination of tumor epithelium and stroma are required to elevate the prognostic significance of the TSR. As a variant of deep learning techniques, transfer learning leverages nature-images features learned by deep convolutional neural networks (CNNs) to relieve the requirement of deep CNNs for immense sample size when handling biomedical classification problems. Herein we studied different transfer learning strategies for accurately distinguishing epithelial and stromal regions of H&E-stained histological images acquired from either breast or ovarian cancer tissue. We compared the performance of important deep CNNs as either a feature extractor or as an architecture for fine-tuning with target images. Moreover, we addressed the current contradictory issue about whether the higher-level features would generalize worse than lower-level ones because they are more specific to the source-image domain. Under our experimental setting, the transfer learning approach achieved an accuracy of 90.2 (vs. 91.1 for fine tuning) with GoogLeNet, suggesting the feasibility of using it in assisting pathology-based binary classification problems. Our results also show that the superiority of the lower-level or the higher-level features over the other ones was determined by the architecture of deep CNNs.
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Affiliation(s)
- Yue Du
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, 73019, USA
| | - Roy Zhang
- Department of Pathology, University of Oklahoma Health Sciences Center, Oklahoma City, OK, 73104, USA
| | - Abolfazl Zargari
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, 73019, USA
| | - Theresa C Thai
- Department of Radiology, University of Oklahoma Health Sciences Center, Oklahoma City, OK, 73104, USA
| | - Camille C Gunderson
- Department of Obstetrics and Gynecology, University of Oklahoma Health Sciences Center, Oklahoma City, OK, 73104, USA
| | - Katherine M Moxley
- Department of Obstetrics and Gynecology, University of Oklahoma Health Sciences Center, Oklahoma City, OK, 73104, USA
| | - Hong Liu
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, 73019, USA
| | - Bin Zheng
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, 73019, USA
| | - Yuchen Qiu
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, 73019, USA.
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Resteghini C, Trama A, Borgonovi E, Hosni H, Corrao G, Orlandi E, Calareso G, De Cecco L, Piazza C, Mainardi L, Licitra L. Big Data in Head and Neck Cancer. Curr Treat Options Oncol 2018; 19:62. [DOI: 10.1007/s11864-018-0585-2] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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Comparison of radiomics machine-learning classifiers and feature selection for differentiation of sacral chordoma and sacral giant cell tumour based on 3D computed tomography features. Eur Radiol 2018; 29:1841-1847. [PMID: 30280245 DOI: 10.1007/s00330-018-5730-6] [Citation(s) in RCA: 81] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2018] [Revised: 08/01/2018] [Accepted: 08/28/2018] [Indexed: 12/26/2022]
Abstract
OBJECTIVE We aimed to identify optimal machine-learning methods for preoperative differentiation of sacral chordoma (SC) and sacral giant cell tumour (SGCT) based on 3D non-enhanced computed tomography (CT) and CT-enhanced (CTE) features. METHODS A total of 95 patients were divided into a training set and a validation set. Three best feature selection methods (Relief, least absolute shrinkage and selection operator (LASSO) and Random Forest (RF)) and three classification methods, including generalised linear models (GLM), support vector machines (SVM) and RF, were compared for their performance in distinguishing SC and SGCT. The performance of the radiomics model was investigated via area under the receiver-operating characteristic curve (AUC) and accuracy (ACC) analysis. RESULTS The selection method LASSO + classifier GLM had the highest AUC of 0.984 and ACC of 0.897 in the validating set, followed by Relief + GLM (AUC = 0.909, ACC = 0.862) and LASSO + SVM (AUC = 0.900, ACC = 0.862) based on CTE features. For CT features, RF + GLM had the highest AUC of 0.889, while LASSO + GLM achieved a high ACC of 0.793 in the validating set. Regardless of the methods, CTE features significantly outperformed those from CT for the differentiation of SC and SGCT (ZAUC = -3.029, ZACC = -4.553; p < 0.05). CONCLUSIONS Our study demonstrated CTE features performed better than CT features. The selection method LASSO + classifier GLM had the best performance in differentiation of SC and SGCT, which could enhance the application of radiomics methods in sacral tumours. KEY POINTS • Sacral chordoma and sacral giant cell tumour are the two most common primary tumours of the sacrum with many common clinical and imaging characteristics. • A radiomics model helps clinicians to identify the histology of a sacral tumour. • CTE features should be preferred.
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Abdollahi H, Mahdavi SR, Mofid B, Bakhshandeh M, Razzaghdoust A, Saadipoor A, Tanha K. Rectal wall MRI radiomics in prostate cancer patients: prediction of and correlation with early rectal toxicity. Int J Radiat Biol 2018; 94:829-837. [PMID: 29969358 DOI: 10.1080/09553002.2018.1492756] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
PURPOSE To investigate MRI radiomic analysis to assess IMRT associated rectal wall changes and also for predicting radiotherapy induced rectal toxicity. MATERIAL AND METHODS At first, a machine learning radiomic analysis was applied on T2-weighted (T2W) and apparent diffusion coefficient (ADC) rectal wall MR images of prostate cancer patients' pre- and post-IMRT to predict rectal toxicity. Next, Wilcoxon singed ranked test was performed to find radiomic features with significant changes pre- and post-IMRT. A logistic regression classifier was used to find correlation between features with significant changes and radiation toxicity. Area under the curve (AUC) of receiver operating characteristic (ROC) curve was used in two levels of study for finding performances. RESULTS AUCmean, 0.68 ± 0.086 and 0.61 ± 0.065 were obtained for pre- and post-IMRT T2 radiomic models, respectively. For ADC radiomic models, AUCmean was 0.58 ± 0.034 for pre-IMRT and was 0.56 ± 0.038 for post-IMRT. Wilcoxon-signed rank test revealed that 9 T2 radiomic features vary significantly post-IMRT. The AUC of logistic-regression was in the range of 0.46-0.58 for single significant features and was 0.81 when all significant features were combined. CONCLUSIONS Pre-IMRT MR image radiomic features could predict rectal toxicity in prostate cancer patients. Radiotherapy associated complications may be assessed by studying the changes in the MR radiomic features.
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Affiliation(s)
- Hamid Abdollahi
- a Department of Medical Physics, School of Medicine , Iran University of Medical Sciences , Tehran , Iran
| | - Seied Rabi Mahdavi
- a Department of Medical Physics, School of Medicine , Iran University of Medical Sciences , Tehran , Iran.,b Radiation Biology Research Center, Iran University of Medical Sciences , Tehran , Iran
| | - Bahram Mofid
- c Department of Radiation Oncology , Shohada Tajrish Medical Center, Shahid Beheshti University of Medical Sciences , Tehran , Iran
| | - Mohsen Bakhshandeh
- d Radiology Technology Department, Allied Medical Faculty , Shahid Beheshti University of Medical Sciences , Tehran , Iran
| | - Abolfazl Razzaghdoust
- e Urology and Nephrology Research Center, Student Research Committee, Shahid Beheshti University of Medical Sciences , Tehran , Iran
| | - Afshin Saadipoor
- c Department of Radiation Oncology , Shohada Tajrish Medical Center, Shahid Beheshti University of Medical Sciences , Tehran , Iran
| | - Kiarash Tanha
- f Department of Biostatistics, School of Public Health , Iran University of Medical Sciences , Tehran , Iran
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Abstract
Precision medicine is increasingly pushed forward, also with respect to upcoming new targeted therapies. Individual characterization of diseases on the basis of biomarkers is a prerequisite for this development. So far, biomarkers are characterized clinically, histologically or on a molecular level. The implementation of broad screening methods (“Omics”) and the analysis of big data – in addition to single markers – allow to define biomarker signatures. Next to “Genomics”, “Proteomics”, and “Metabolicis”, “Radiomics” gained increasing interest during the last years. Based on radiologic imaging, multiple radiomic markers are extracted with the help of specific algorithms. These are correlated with clinical, (immuno-) histopathological, or genomic data. Underlying structural differences are based on the imaging metadata and are often not visible and therefore not detectable without specific software. Radiomics are depicted numerically or by graphs. The fact that radiomic information can be extracted from routinely performed imaging adds a specific appeal to this method. Radiomics could potentially replace biopsies and additional investigations. Alternatively, radiomics could complement other biomarkers and thus lead to a more precise, multimodal prediction. Until now, radiomics are primarily used to investigate solid tumors. Some promising studies in head and neck cancer have already been published.
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On the Fuzziness of Machine Learning, Neural Networks, and Artificial Intelligence in Radiation Oncology. Int J Radiat Oncol Biol Phys 2018; 100:1-4. [DOI: 10.1016/j.ijrobp.2017.06.011] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2017] [Accepted: 06/08/2017] [Indexed: 12/25/2022]
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Artificial Intelligence in Medicine AIME 2015. Artif Intell Med 2017; 81:1-2. [PMID: 28733119 DOI: 10.1016/j.artmed.2017.06.011] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2017] [Accepted: 06/29/2017] [Indexed: 11/23/2022]
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