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Chen AM. Translational risk-adapted approaches to de-escalated radiation for human papillomavirus-positive oropharyngeal cancer: Past, present, and future. Oral Oncol 2024; 154:106850. [PMID: 38749113 DOI: 10.1016/j.oraloncology.2024.106850] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2024] [Revised: 05/05/2024] [Accepted: 05/11/2024] [Indexed: 06/11/2024]
Abstract
Interest in the use of de-escalated radiation to treat patients with newly diagnosed human papillomavirus (HPV)-positive oropharyngeal cancer has grown dramatically with the publication of prospective trials demonstrating the efficacy of such an approach. While the rationale for de-escalation--- namely to decrease treatment-related toxicity while maintaining the excellent rates of disease control historically observed in patients with this disease-is inherently obvious, uncertainty exists regarding how to best select patients for de-escalation. Consequently, risk-adapted strategies using a variety of translational and clinical platforms have been increasingly popularized to better refine treatment. These have integrated contemporary methods of mid-treatment response assessment using advanced technologies and molecular assays to customize the radiation dose. By monitoring the response as patients actively proceed through treatment, risk-adapted protocols have the potential to provide insight into the biological behavior of tumors and make individualized therapy possible. The purpose of this review is to summarize the evidence to date on risk-adapted approaches to de-escalated radiation-- highlighting the clinical, radiological, and biological data which may ultimately help usher the principles of precision medicine into practice for patients with HPV-positive oropharyngeal cancer.
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Affiliation(s)
- Allen M Chen
- Department of Radiation Oncology, Chao Family Comprehensive Cancer Center, University of California- Irvine, School of Medicine, Irvine, CA 92617, United states.
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Khongwirotphan S, Oonsiri S, Kitpanit S, Prayongrat A, Kannarunimit D, Chakkabat C, Lertbutsayanukul C, Sriswasdi S, Rakvongthai Y. Multimodality radiomics for tumor prognosis in nasopharyngeal carcinoma. PLoS One 2024; 19:e0298111. [PMID: 38346058 PMCID: PMC10861073 DOI: 10.1371/journal.pone.0298111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2023] [Accepted: 01/13/2024] [Indexed: 02/15/2024] Open
Abstract
BACKGROUND The prognosis of nasopharyngeal carcinoma (NPC) is challenging due to late-stage identification and frequently undetectable Epstein-Barr virus (EBV) DNA. Incorporating radiomic features, which quantify tumor characteristics from imaging, may enhance prognosis assessment. PURPOSE To investigate the predictive power of radiomic features on overall survival (OS), progression-free survival (PFS), and distant metastasis-free survival (DMFS) in NPC. MATERIALS AND METHODS A retrospective analysis of 183 NPC patients treated with chemoradiotherapy from 2010 to 2019 was conducted. All patients were followed for at least three years. The pretreatment CT images with contrast medium, MR images (T1W and T2W), as well as gross tumor volume (GTV) contours, were used to extract radiomic features using PyRadiomics v.2.0. Robust and efficient radiomic features were chosen using the intraclass correlation test and univariate Cox proportional hazard regression analysis. They were then combined with clinical data including age, gender, tumor stage, and EBV DNA level for prognostic evaluation using Cox proportional hazard regression models with recursive feature elimination (RFE) and were optimized using 20 repetitions of a five-fold cross-validation scheme. RESULTS Integrating radiomics with clinical data significantly enhanced the predictive power, yielding a C-index of 0.788 ± 0.066 to 0.848 ± 0.079 for the combined model versus 0.745 ± 0.082 to 0.766 ± 0.083 for clinical data alone (p<0.05). Multimodality radiomics combined with clinical data offered the highest performance. Despite the absence of EBV DNA, radiomics integration significantly improved survival predictions (C-index ranging from 0.770 ± 0.070 to 0.831 ± 0.083 in combined model versus 0.727 ± 0.084 to 0.734 ± 0.088 in clinical model, p<0.05). CONCLUSIONS The combination of multimodality radiomic features from CT and MR images could offer superior predictive performance for OS, PFS, and DMFS compared to relying on conventional clinical data alone.
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Affiliation(s)
- Sararas Khongwirotphan
- Department of Radiological Technology and Medical Physics, Faculty of Allied Health Sciences, Chulalongkorn University, Bangkok, Thailand
- Chulalongkorn University Biomedical Imaging Group, Department of Radiology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Sornjarod Oonsiri
- Division of Radiation Oncology, Department of Radiology, King Chulalongkorn Memorial Hospital, Bangkok, Thailand
| | - Sarin Kitpanit
- Division of Radiation Oncology, Department of Radiology, King Chulalongkorn Memorial Hospital, Bangkok, Thailand
| | - Anussara Prayongrat
- Division of Radiation Oncology, Department of Radiology, King Chulalongkorn Memorial Hospital, Bangkok, Thailand
| | - Danita Kannarunimit
- Division of Radiation Oncology, Department of Radiology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Chakkapong Chakkabat
- Division of Radiation Oncology, Department of Radiology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Chawalit Lertbutsayanukul
- Division of Radiation Oncology, Department of Radiology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Sira Sriswasdi
- Center for Artificial Intelligence in Medicine, Research Affairs, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
- Center of Excellence in Computational Molecular Biology, Chulalongkorn University, Bangkok, Thailand
| | - Yothin Rakvongthai
- Chulalongkorn University Biomedical Imaging Group, Department of Radiology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
- Division of Nuclear Medicine, Department of Radiology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
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Lee S, Park S, Rim CH, Lee YH, Kwon SY, Oh KH, Yoon WS. A Potential Radiomics-Clinical Model for Predicting Failure of Lymph Node Control after Definite Radiotherapy in Locally Advanced Head and Neck Cancer. MEDICINA (KAUNAS, LITHUANIA) 2024; 60:92. [PMID: 38256353 PMCID: PMC10821036 DOI: 10.3390/medicina60010092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Revised: 12/21/2023] [Accepted: 12/30/2023] [Indexed: 01/24/2024]
Abstract
Background and Objectives: To optimally predict lymph node (LN) failure after definite radiotherapy (RT) in head and neck cancer (HNC) with LN metastases, this study examined radiomics models extracted from CT images of different periods during RT. Materials and Methods: This study retrospectively collected radiologic and clinical information from patients undergoing definite RT over 60 Gy for HNC with LN metastases from January 2010 to August 2021. The same largest LNs in each patient from the initial simulation CT (CTpre) and the following simulation CT (CTmid) at approximately 40 Gy were indicated as regions of interest. LN failure was defined as residual or recurrent LN within 3 years after the end of RT. After the radiomics features were extracted, the radiomics alone model and the radiomics plus clinical parameters model from the set of CTpre and CTmid were compared. The LASSO method was applied to select features associated with LN failure. Results: Among 66 patients, 17 LN failures were observed. In the radiomics alone model, CTpre and CTmid had similar mean accuracies (0.681 and 0.697, respectively) and mean areas under the curve (AUC) (0.521 and 0.568, respectively). Radiomics features of spherical disproportion, size zone variance, and log minimum 2 were selected for CTpre plus clinical parameters. Volume, energy, homogeneity, and log minimum 1 were selected for CTmid plus clinical parameters. Clinical parameters including smoking, T-stage, ECE, and regression rate of LN were important for both CTpre and CTmid. In the radiomics plus clinical parameters models, the mean accuracy and mean AUC of CTmid (0.790 and 0.662, respectively) were more improved than those of CTpre (0.731 and 0.582, respectively). Conclusions: Both models using CTpre and CTmid were improved by adding clinical parameters. The radiomics model using CTmid plus clinical parameters was the best in predicting LN failure in our preliminary analyses.
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Affiliation(s)
- Seunghak Lee
- Core Research and Development Center, Korea University Ansan Hospital, Ansan 15355, Republic of Korea;
| | - Sunmin Park
- Department of Radiation Oncology, College of Medicine, Korea University Ansan Hospital, 123 Jeokgeum-ro, Danwon-gu, Ansan 15355, Republic of Korea; (S.P.); (C.H.R.)
| | - Chai Hong Rim
- Department of Radiation Oncology, College of Medicine, Korea University Ansan Hospital, 123 Jeokgeum-ro, Danwon-gu, Ansan 15355, Republic of Korea; (S.P.); (C.H.R.)
| | - Young Hen Lee
- Department of Radiology, Korea University Ansan Hospital, Ansan 15355, Republic of Korea;
| | - Soon Young Kwon
- Department of Otolaryngology, Korea University Ansan Hospital, Ansan 15355, Republic of Korea; (S.Y.K.); (K.H.O.)
| | - Kyung Ho Oh
- Department of Otolaryngology, Korea University Ansan Hospital, Ansan 15355, Republic of Korea; (S.Y.K.); (K.H.O.)
| | - Won Sup Yoon
- Department of Radiation Oncology, College of Medicine, Korea University Ansan Hospital, 123 Jeokgeum-ro, Danwon-gu, Ansan 15355, Republic of Korea; (S.P.); (C.H.R.)
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Nakamoto T, Yamashita H, Jinnouchi H, Nawa K, Imae T, Takenaka S, Aoki A, Ohta T, Ozaki S, Nozawa Y, Nakagawa K. Cone-beam computed-tomography-based delta-radiomic analysis for investigating prognostic power for esophageal squamous cell cancer patients undergoing concurrent chemoradiotherapy. Phys Med 2024; 117:103182. [PMID: 38086310 DOI: 10.1016/j.ejmp.2023.103182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Revised: 10/22/2023] [Accepted: 11/19/2023] [Indexed: 01/15/2024] Open
Abstract
PURPOSE To investigate the prognostic power of cone-beam computed-tomography (CBCT)-based delta-radiomics in esophageal squamous cell cancer (ESCC) patients treated with concurrent chemoradiotherapy (CCRT). METHODS We collected data from 26 ESCC patients treated with CCRT. CBCT images acquired at five time points (1st-5th week) per patient during CCRT were used in this study. Radiomic features were extracted from the five CBCT images on the gross tumor volumes. Then, 17 delta-radiomic feature sets derived from five types of calculations were obtained for all the cases. Leave-one-out cross-validation was applied to investigate the prognostic power of CBCT-based delta-radiomic features. Feature selection and construction of a prediction model using Coxnet were performed using training samples. Then, the test sample was classified into high or low risk in each cross-validation fold. Survival analysis for the two groups were performed to evaluate the prognostic power of the extracted CBCT-based delta-radiomic features. RESULTS Four delta-radiomic feature sets indicated significant differences between the high- and low-risk groups (p < 0.05). The highest C-index in the 17 delta-radiomic feature sets was 0.821 (95 % confidence interval, 0.735-0.907). That feature set had p-value of the log-rank test and hazard ratio of 0.003 and 4.940 (95 % confidence interval, 1.391-17.544), respectively. CONCLUSIONS We investigated the potential of using CBCT-based delta-radiomics for prognosis of ESCC patients treated with CCRT. It was demonstrated that delta-radiomic feature sets based on the absolute value of relative difference obtained from the early to the middle treatment stages have high prognostic power for ESCC.
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Affiliation(s)
- Takahiro Nakamoto
- Department of Biological Science and Engineering, Faculty of Health Sciences, Hokkaido University, N12-W5, Kita-ku, Sapporo, Hokkaido 060-0812, Japan; Department of Radiology, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan.
| | - Hideomi Yamashita
- Department of Radiology, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
| | - Haruka Jinnouchi
- Department of Radiology, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
| | - Kanabu Nawa
- Department of Radiology, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
| | - Toshikazu Imae
- Department of Radiology, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
| | - Shigeharu Takenaka
- Department of Radiology, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
| | - Atsushi Aoki
- Department of Radiology, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
| | - Takeshi Ohta
- Department of Radiology, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
| | - Sho Ozaki
- Department of Radiology, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan; Graduate School of Science and Technology, Hirosaki University, 3 Bunkyo, Hirosaki, Aomori 036-8561, Japan
| | - Yuki Nozawa
- Department of Radiology, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
| | - Keiichi Nakagawa
- Department of Radiology, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
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Boldrini L, D'Aviero A, De Felice F, Desideri I, Grassi R, Greco C, Iorio GC, Nardone V, Piras A, Salvestrini V. Artificial intelligence applied to image-guided radiation therapy (IGRT): a systematic review by the Young Group of the Italian Association of Radiotherapy and Clinical Oncology (yAIRO). LA RADIOLOGIA MEDICA 2024; 129:133-151. [PMID: 37740838 DOI: 10.1007/s11547-023-01708-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Accepted: 08/16/2023] [Indexed: 09/25/2023]
Abstract
INTRODUCTION The advent of image-guided radiation therapy (IGRT) has recently changed the workflow of radiation treatments by ensuring highly collimated treatments. Artificial intelligence (AI) and radiomics are tools that have shown promising results for diagnosis, treatment optimization and outcome prediction. This review aims to assess the impact of AI and radiomics on modern IGRT modalities in RT. METHODS A PubMed/MEDLINE and Embase systematic review was conducted to investigate the impact of radiomics and AI to modern IGRT modalities. The search strategy was "Radiomics" AND "Cone Beam Computed Tomography"; "Radiomics" AND "Magnetic Resonance guided Radiotherapy"; "Radiomics" AND "on board Magnetic Resonance Radiotherapy"; "Artificial Intelligence" AND "Cone Beam Computed Tomography"; "Artificial Intelligence" AND "Magnetic Resonance guided Radiotherapy"; "Artificial Intelligence" AND "on board Magnetic Resonance Radiotherapy" and only original articles up to 01.11.2022 were considered. RESULTS A total of 402 studies were obtained using the previously mentioned search strategy on PubMed and Embase. The analysis was performed on a total of 84 papers obtained following the complete selection process. Radiomics application to IGRT was analyzed in 23 papers, while a total 61 papers were focused on the impact of AI on IGRT techniques. DISCUSSION AI and radiomics seem to significantly impact IGRT in all the phases of RT workflow, even if the evidence in the literature is based on retrospective data. Further studies are needed to confirm these tools' potential and provide a stronger correlation with clinical outcomes and gold-standard treatment strategies.
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Affiliation(s)
- Luca Boldrini
- UOC Radioterapia Oncologica, Fondazione Policlinico Universitario IRCCS "A. Gemelli", Rome, Italy
- Università Cattolica del Sacro Cuore, Rome, Italy
| | - Andrea D'Aviero
- Radiation Oncology, Mater Olbia Hospital, Olbia, Sassari, Italy
| | - Francesca De Felice
- Radiation Oncology, Department of Radiological, Policlinico Umberto I, Rome, Italy
- Oncological and Pathological Sciences, "Sapienza" University of Rome, Rome, Italy
| | - Isacco Desideri
- Radiation Oncology Unit, Azienda Ospedaliero-Universitaria Careggi, Department of Experimental and Clinical Biomedical Sciences, University of Florence, Florence, Italy
| | - Roberta Grassi
- Department of Precision Medicine, University of Campania "L. Vanvitelli", Naples, Italy
| | - Carlo Greco
- Department of Radiation Oncology, Università Campus Bio-Medico di Roma, Fondazione Policlinico Universitario Campus Bio-Medico, Rome, Italy
| | | | - Valerio Nardone
- Department of Precision Medicine, University of Campania "L. Vanvitelli", Naples, Italy
| | - Antonio Piras
- UO Radioterapia Oncologica, Villa Santa Teresa, Bagheria, Palermo, Italy.
| | - Viola Salvestrini
- Radiation Oncology Unit, Azienda Ospedaliero-Universitaria Careggi, Department of Experimental and Clinical Biomedical Sciences, University of Florence, Florence, Italy
- Cyberknife Center, Istituto Fiorentino di Cura e Assistenza (IFCA), 50139, Florence, Italy
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Volpe S, Mastroleo F, Krengli M, Jereczek-Fossa BA. Quo vadis Radiomics? Bibliometric analysis of 10-year Radiomics journey. Eur Radiol 2023; 33:6736-6745. [PMID: 37071161 PMCID: PMC10110486 DOI: 10.1007/s00330-023-09645-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 03/12/2023] [Accepted: 03/26/2023] [Indexed: 04/19/2023]
Abstract
OBJECTIVES Radiomics is the high-throughput extraction of mineable and-possibly-reproducible quantitative imaging features from medical imaging. The aim of this work is to perform an unbiased bibliometric analysis on Radiomics 10 years after the first work became available, to highlight its status, pitfalls, and growing interest. METHODS Scopus database was used to investigate all the available English manuscripts about Radiomics. R Bibliometrix package was used for data analysis: a cumulative analysis of document categories, authors affiliations, country scientific collaborations, institution collaboration networks, keyword analysis, comprehensive of co-occurrence network, thematic map analysis, and 2021 sub-analysis of trend topics was performed. RESULTS A total of 5623 articles and 16,833 authors from 908 different sources have been identified. The first available document was published in March 2012, while the most recent included was released on the 31st of December 2021. China and USA were the most productive countries. Co-occurrence network analysis identified five words clusters based on top 50 authors' keywords: Radiomics, computed tomography, radiogenomics, deep learning, tomography. Trend topics analysis for 2021 showed an increased interest in artificial intelligence (n = 286), nomogram (n = 166), hepatocellular carcinoma (n = 125), COVID-19 (n = 63), and X-ray computed (n = 60). CONCLUSIONS Our work demonstrates the importance of bibliometrics in aggregating information that otherwise would not be available in a granular analysis, detecting unknown patterns in Radiomics publications, while highlighting potential developments to ensure knowledge dissemination in the field and its future real-life applications in the clinical practice. CLINICAL RELEVANCE STATEMENT This work aims to shed light on the state of the art in radiomics, which offers numerous tangible and intangible benefits, and to encourage its integration in the contemporary clinical practice for more precise imaging analysis. KEY POINTS • ML-based bibliometric analysis is fundamental to detect unknown pattern of data in Radiomics publications. • A raising interest in the field, the most relevant collaborations, keywords co-occurrence network, and trending topics have been investigated. • Some pitfalls still exist, including the scarce standardization and the relative lack of homogeneity across studies.
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Affiliation(s)
- Stefania Volpe
- Division of Radiation Oncology, IEO European Institute of Oncology, IRCCS, Via Ripamonti 435, 20141, Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, Via Festa del Perdono 7, 20122, Milan, Italy
| | - Federico Mastroleo
- Division of Radiation Oncology, IEO European Institute of Oncology, IRCCS, Via Ripamonti 435, 20141, Milan, Italy.
- Department of Translational Medicine, University of Piemonte Orientale, Via Solaroli 17, 28100, Novara, Italy.
| | - Marco Krengli
- Department of Translational Medicine, University of Piemonte Orientale, Via Solaroli 17, 28100, Novara, Italy
- Division of Radiation Oncology, University Hospital "Maggiore Della Carità", Corso Mazzini 18, 28100, Novara, Italy
| | - Barbara Alicja Jereczek-Fossa
- Division of Radiation Oncology, IEO European Institute of Oncology, IRCCS, Via Ripamonti 435, 20141, Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, Via Festa del Perdono 7, 20122, Milan, Italy
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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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Mireștean CC, Iancu RI, Iancu DPT. Simultaneous Integrated Boost (SIB) vs. Sequential Boost in Head and Neck Cancer (HNC) Radiotherapy: A Radiomics-Based Decision Proof of Concept. J Clin Med 2023; 12:jcm12062413. [PMID: 36983413 PMCID: PMC10057404 DOI: 10.3390/jcm12062413] [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: 01/12/2023] [Revised: 03/07/2023] [Accepted: 03/14/2023] [Indexed: 03/30/2023] Open
Abstract
Artificial intelligence (AI) and in particular radiomics has opened new horizons by extracting data from medical imaging that could be used not only to improve diagnostic accuracy, but also to be included in predictive models contributing to treatment stratification of cancer. Head and neck cancers (HNC) are associated with higher recurrence rates, especially in advanced stages of disease. It is considered that approximately 50% of cases will evolve with loco-regional recurrence, even if they will benefit from a current standard treatment consisting of definitive chemo-radiotherapy. Radiotherapy, the cornerstone treatment in locally advanced HNC, could be delivered either by the simultaneous integrated boost (SIB) technique or by the sequential boost technique, the decision often being a subjective one. The principles of radiobiology could be the basis of an optimal decision between the two methods of radiation dose delivery, but the heterogeneity of HNC radio-sensitivity makes this approach difficult. Radiomics has demonstrated the ability to non-invasively predict radio-sensitivity and the risk of relapse in HNC. Tumor heterogeneity evaluated with radiomics, the inclusion of coarseness, entropy and other first order features extracted from gross tumor volume (GTV) in multivariate models could identify pre-treatment cases that will benefit from one of the approaches (SIB or sequential boost radio-chemotherapy) considered the current standard of care for locally advanced HNC. Computer tomography (CT) simulation and daily cone beam CT (CBCT) could be chosen as imaging source for radiomic analysis.
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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, Faculty of Dental Medicine, "Gr. T. Popa" 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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Berger T, Noble DJ, Yang Z, Shelley LE, McMullan T, Bates A, Thomas S, Carruthers LJ, Beckett G, Duffton A, Paterson C, Jena R, McLaren DB, Burnet NG, Nailon WH. Sub-regional analysis of the parotid glands: model development for predicting late xerostomia with radiomics features in head and neck cancer patients. Acta Oncol 2023; 62:166-173. [PMID: 36802351 DOI: 10.1080/0284186x.2023.2179895] [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: 12/21/2022] [Accepted: 02/08/2023] [Indexed: 02/23/2023]
Abstract
BACKGROUND The irradiation of sub-regions of the parotid has been linked to xerostomia development in patients with head and neck cancer (HNC). In this study, we compared the xerostomia classification performance of radiomics features calculated on clinically relevant and de novo sub-regions of the parotid glands of HNC patients. MATERIAL AND METHODS All patients (N = 117) were treated with TomoTherapy in 30-35 fractions of 2-2.167 Gy per fraction with daily mega-voltage-CT (MVCT) acquisition for image-guidance purposes. Radiomics features (N = 123) were extracted from daily MVCTs for the whole parotid gland and nine sub-regions. The changes in feature values after each complete week of treatment were considered as predictors of xerostomia (CTCAEv4.03, grade ≥ 2) at 6 and 12 months. Combinations of predictors were generated following the removal of statistically redundant information and stepwise selection. The classification performance of the logistic regression models was evaluated on train and test sets of patients using the Area Under the Curve (AUC) associated with the different sub-regions at each week of treatment and benchmarked with the performance of models solely using dose and toxicity at baseline. RESULTS In this study, radiomics-based models predicted xerostomia better than standard clinical predictors. Models combining dose to the parotid and xerostomia scores at baseline yielded an AUCtest of 0.63 and 0.61 for xerostomia prediction at 6 and 12 months after radiotherapy while models based on radiomics features extracted from the whole parotid yielded a maximum AUCtest of 0.67 and 0.75, respectively. Overall, across sub-regions, maximum AUCtest was 0.76 and 0.80 for xerostomia prediction at 6 and 12 months. Within the first two weeks of treatment, the cranial part of the parotid systematically yielded the highest AUCtest. CONCLUSION Our results indicate that variations of radiomics features calculated on sub-regions of the parotid glands can lead to earlier and improved prediction of xerostomia in HNC patients.
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Affiliation(s)
- Thomas Berger
- Department of Oncology Physics, Edinburgh Cancer Centre, Western General Hospital, Edinburgh, UK
- Edinburgh Cancer Research Centre, Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
| | - David J Noble
- Edinburgh Cancer Research Centre, Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
- Department of Oncology, The University of Cambridge, Cambridge, UK
- Department of Clinical Oncology, Edinburgh Cancer Centre, Western General Hospital, Edinburgh, UK
| | - Zhuolin Yang
- Department of Oncology Physics, Edinburgh Cancer Centre, Western General Hospital, Edinburgh, UK
- School of Engineering, the University of Edinburgh, the King's Buildings, Edinburgh, UK
| | - Leila Ea Shelley
- Department of Oncology Physics, Edinburgh Cancer Centre, Western General Hospital, Edinburgh, UK
| | - Thomas McMullan
- Department of Oncology Physics, Edinburgh Cancer Centre, Western General Hospital, Edinburgh, UK
| | - Amy Bates
- Department of Oncology, The University of Cambridge, Cambridge, UK
| | - Simon Thomas
- Department of Medical Physics and Clinical Engineering, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Linda J Carruthers
- Department of Oncology Physics, Edinburgh Cancer Centre, Western General Hospital, Edinburgh, UK
| | - George Beckett
- Edinburgh Parallel Computing Centre, Bayes Centre, Edinburgh, UK
| | | | | | - Raj Jena
- Department of Oncology, The University of Cambridge, Cambridge, UK
| | - Duncan B McLaren
- Department of Clinical Oncology, Edinburgh Cancer Centre, Western General Hospital, Edinburgh, UK
| | | | - William H Nailon
- Department of Oncology Physics, Edinburgh Cancer Centre, Western General Hospital, Edinburgh, UK
- School of Engineering, the University of Edinburgh, the King's Buildings, Edinburgh, UK
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10
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Current Role of Delta Radiomics in Head and Neck Oncology. Int J Mol Sci 2023; 24:ijms24032214. [PMID: 36768535 PMCID: PMC9916410 DOI: 10.3390/ijms24032214] [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: 11/27/2022] [Accepted: 01/19/2023] [Indexed: 01/26/2023] Open
Abstract
The latest developments in the management of head and neck cancer show an increasing trend in the implementation of novel approaches using artificial intelligence for better patient stratification and treatment-related risk evaluation. Radiomics, or the extraction of data from various imaging modalities, is a tool often used to evaluate specific features related to the tumour or normal tissue that are not identifiable by the naked eye and which can add value to existing clinical data. Furthermore, the assessment of feature variations from one time point to another based on subsequent images, known as delta radiomics, was shown to have even higher value for treatment-outcome prediction or patient stratification into risk categories. The information gathered from delta radiomics can, further, be used for decision making regarding treatment adaptation or other interventions found to be beneficial to the patient. The aim of this work is to collate the existing studies on delta radiomics in head and neck cancer and evaluate its role in tumour response and normal-tissue toxicity predictions alike. Moreover, this work also highlights the role of holomics, which brings under the same umbrella clinical and radiomic features, for a more complex patient characterization and treatment optimisation.
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Longitudinal and Multimodal Radiomics Models for Head and Neck Cancer Outcome Prediction. Cancers (Basel) 2023; 15:cancers15030673. [PMID: 36765628 PMCID: PMC9913206 DOI: 10.3390/cancers15030673] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 01/10/2023] [Accepted: 01/16/2023] [Indexed: 01/25/2023] Open
Abstract
Radiomics analysis provides a promising avenue towards the enabling of personalized radiotherapy. Most frequently, prognostic radiomics models are based on features extracted from medical images that are acquired before treatment. Here, we investigate whether combining data from multiple timepoints during treatment and from multiple imaging modalities can improve the predictive ability of radiomics models. We extracted radiomics features from computed tomography (CT) images acquired before treatment as well as two and three weeks after the start of radiochemotherapy for 55 patients with locally advanced head and neck squamous cell carcinoma (HNSCC). Additionally, we obtained features from FDG-PET images taken before treatment and three weeks after the start of therapy. Cox proportional hazards models were then built based on features of the different image modalities, treatment timepoints, and combinations thereof using two different feature selection methods in a five-fold cross-validation approach. Based on the cross-validation results, feature signatures were derived and their performance was independently validated. Discrimination regarding loco-regional control was assessed by the concordance index (C-index) and log-rank tests were performed to assess risk stratification. The best prognostic performance was obtained for timepoints during treatment for all modalities. Overall, CT was the best discriminating modality with an independent validation C-index of 0.78 for week two and weeks two and three combined. However, none of these models achieved statistically significant patient stratification. Models based on FDG-PET features from week three provided both satisfactory discrimination (C-index = 0.61 and 0.64) and statistically significant stratification (p=0.044 and p<0.001), but produced highly imbalanced risk groups. After independent validation on larger datasets, the value of (multimodal) radiomics models combining several imaging timepoints should be prospectively assessed for personalized treatment strategies.
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Hatt M, Krizsan AK, Rahmim A, Bradshaw TJ, Costa PF, Forgacs A, Seifert R, Zwanenburg A, El Naqa I, Kinahan PE, Tixier F, Jha AK, Visvikis D. Joint EANM/SNMMI guideline on radiomics in nuclear medicine : Jointly supported by the EANM Physics Committee and the SNMMI Physics, Instrumentation and Data Sciences Council. Eur J Nucl Med Mol Imaging 2023; 50:352-375. [PMID: 36326868 PMCID: PMC9816255 DOI: 10.1007/s00259-022-06001-6] [Citation(s) in RCA: 40] [Impact Index Per Article: 40.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Accepted: 10/09/2022] [Indexed: 11/05/2022]
Abstract
PURPOSE The purpose of this guideline is to provide comprehensive information on best practices for robust radiomics analyses for both hand-crafted and deep learning-based approaches. METHODS In a cooperative effort between the EANM and SNMMI, we agreed upon current best practices and recommendations for relevant aspects of radiomics analyses, including study design, quality assurance, data collection, impact of acquisition and reconstruction, detection and segmentation, feature standardization and implementation, as well as appropriate modelling schemes, model evaluation, and interpretation. We also offer an outlook for future perspectives. CONCLUSION Radiomics is a very quickly evolving field of research. The present guideline focused on established findings as well as recommendations based on the state of the art. Though this guideline recognizes both hand-crafted and deep learning-based radiomics approaches, it primarily focuses on the former as this field is more mature. This guideline will be updated once more studies and results have contributed to improved consensus regarding the application of deep learning methods for radiomics. Although methodological recommendations in the present document are valid for most medical image modalities, we focus here on nuclear medicine, and specific recommendations when necessary are made for PET/CT, PET/MR, and quantitative SPECT.
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Affiliation(s)
- M Hatt
- LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France
| | | | - A Rahmim
- Departments of Radiology and Physics, University of British Columbia, Vancouver, BC, Canada
| | - T J Bradshaw
- Department of Radiology, University of Wisconsin, Madison, WI, USA
| | - P F Costa
- Department of Nuclear Medicine, West German Cancer Center, University of Duisburg-Essen and German Cancer Consortium (DKTK)-University Hospital Essen, Essen, Germany
| | | | - R Seifert
- Department of Nuclear Medicine, West German Cancer Center, University of Duisburg-Essen and German Cancer Consortium (DKTK)-University Hospital Essen, Essen, Germany.
- Department of Nuclear Medicine, Münster University Hospital, Münster, Germany.
| | - A Zwanenburg
- OncoRay-National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany
- National Center for Tumor Diseases (NCT/UCC), Dresden, Germany
- German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - I El Naqa
- Department of Machine Learning, Moffitt Cancer Center, Tampa, FL, 33626, USA
| | - P E Kinahan
- Imaging Research Laboratory, PET/CT Physics, Department of Radiology, UW Medical Center, University of Washington, Seattle, WA, USA
| | - F Tixier
- LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France
| | - A K Jha
- McKelvey School of Engineering and Mallinckrodt Institute of Radiology, Washington University in St. Louis, Saint Louis, MO, USA
| | - D Visvikis
- LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France
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Hung KF, Ai QYH, Wong LM, Yeung AWK, Li DTS, Leung YY. Current Applications of Deep Learning and Radiomics on CT and CBCT for Maxillofacial Diseases. Diagnostics (Basel) 2022; 13:diagnostics13010110. [PMID: 36611402 PMCID: PMC9818323 DOI: 10.3390/diagnostics13010110] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 12/23/2022] [Accepted: 12/24/2022] [Indexed: 12/31/2022] Open
Abstract
The increasing use of computed tomography (CT) and cone beam computed tomography (CBCT) in oral and maxillofacial imaging has driven the development of deep learning and radiomics applications to assist clinicians in early diagnosis, accurate prognosis prediction, and efficient treatment planning of maxillofacial diseases. This narrative review aimed to provide an up-to-date overview of the current applications of deep learning and radiomics on CT and CBCT for the diagnosis and management of maxillofacial diseases. Based on current evidence, a wide range of deep learning models on CT/CBCT images have been developed for automatic diagnosis, segmentation, and classification of jaw cysts and tumors, cervical lymph node metastasis, salivary gland diseases, temporomandibular (TMJ) disorders, maxillary sinus pathologies, mandibular fractures, and dentomaxillofacial deformities, while CT-/CBCT-derived radiomics applications mainly focused on occult lymph node metastasis in patients with oral cancer, malignant salivary gland tumors, and TMJ osteoarthritis. Most of these models showed high performance, and some of them even outperformed human experts. The models with performance on par with human experts have the potential to serve as clinically practicable tools to achieve the earliest possible diagnosis and treatment, leading to a more precise and personalized approach for the management of maxillofacial diseases. Challenges and issues, including the lack of the generalizability and explainability of deep learning models and the uncertainty in the reproducibility and stability of radiomic features, should be overcome to gain the trust of patients, providers, and healthcare organizers for daily clinical use of these models.
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Affiliation(s)
- Kuo Feng Hung
- Oral and Maxillofacial Surgery, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China
| | - Qi Yong H. Ai
- Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Lun M. Wong
- Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Andy Wai Kan Yeung
- Oral and Maxillofacial Radiology, Applied Oral Sciences and Community Dental Care, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China
| | - Dion Tik Shun Li
- Oral and Maxillofacial Surgery, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China
| | - Yiu Yan Leung
- Oral and Maxillofacial Surgery, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China
- Correspondence:
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