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Ling X, Alexander GS, Molitoris J, Choi J, Schumaker L, Tran P, Mehra R, Gaykalova D, Ren L. Radiomic biomarkers of locoregional recurrence: prognostic insights from oral cavity squamous cell carcinoma preoperative CT scans. Front Oncol 2024; 14:1380599. [PMID: 38715772 PMCID: PMC11074368 DOI: 10.3389/fonc.2024.1380599] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Accepted: 04/04/2024] [Indexed: 05/15/2024] Open
Abstract
Introduction This study aimed to identify CT-based imaging biomarkers for locoregional recurrence (LR) in Oral Cavity Squamous Cell Carcinoma (OSCC) patients. Methods Computed tomography scans were collected from 78 patients with OSCC who underwent surgical treatment at a single medical center. We extracted 1,092 radiomic features from gross tumor volume in each patient's pre-treatment CT. Clinical characteristics were also obtained, including race, sex, age, tobacco and alcohol use, tumor staging, and treatment modality. A feature selection algorithm was used to eliminate the most redundant features, followed by a selection of the best subset of the Logistic regression model (LRM). The best LRM model was determined based on the best prediction accuracy in terms of the area under Receiver operating characteristic curve. Finally, significant radiomic features in the final LRM model were identified as imaging biomarkers. Results and discussion Two radiomics biomarkers, Large Dependence Emphasis (LDE) of the Gray Level Dependence Matrix (GLDM) and Long Run Emphasis (LRE) of the Gray Level Run Length Matrix (GLRLM) of the 3D Laplacian of Gaussian (LoG σ=3), have demonstrated the capability to preoperatively distinguish patients with and without LR, exhibiting exceptional testing specificity (1.00) and sensitivity (0.82). The group with LRE > 2.99 showed a 3-year recurrence-free survival rate of 0.81, in contrast to 0.49 for the group with LRE ≤ 2.99. Similarly, the group with LDE > 120 showed a rate of 0.82, compared to 0.49 for the group with LDE ≤ 120. These biomarkers broaden our understanding of using radiomics to predict OSCC progression, enabling personalized treatment plans to enhance patient survival.
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Affiliation(s)
- Xiao Ling
- Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, MD, United States
| | - Gregory S. Alexander
- Department of Radiation Oncology, Thomas Jefferson University, Philadelphia, PA, United States
| | - Jason Molitoris
- Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, MD, United States
| | - Jinhyuk Choi
- Department of Breast Surgery, Kosin University Gospel Hospital, Busan, Republic of Korea
| | - Lisa Schumaker
- Marlene and Stewart Greenebaum Comprehensive Cancer Center, University of Maryland School of Medicine, Baltimore, MD, United States
| | - Phuoc Tran
- Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, MD, United States
| | - Ranee Mehra
- Marlene and Stewart Greenebaum Comprehensive Cancer Center, University of Maryland School of Medicine, Baltimore, MD, United States
| | - Daria Gaykalova
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, United States
- Department of Otorhinolaryngology-Head and Neck Surgery, Marlene and Stewart Greenebaum Comprehensive Cancer Center, University of Maryland Medical Center, Baltimore, MD, United States
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD, United States
| | - Lei Ren
- Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, MD, United States
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Agelaki S, Boukovinas I, Athanasiadis I, Trimis G, Dimitriadis I, Poughias L, Morais E, Sabale U, Bencina G, Athanasopoulos C. A systematic literature review of the human papillomavirus prevalence in locally and regionally advanced and recurrent/metastatic head and neck cancers through the last decade: The "ALARM" study. Cancer Med 2024; 13:e6916. [PMID: 38247106 PMCID: PMC10905345 DOI: 10.1002/cam4.6916] [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: 07/18/2023] [Revised: 11/29/2023] [Accepted: 12/30/2023] [Indexed: 01/23/2024] Open
Abstract
AIMS The aim of this systematic literature review was to provide updated information on human papillomavirus (HPV) prevalence in locally and regionally advanced (LA) and recurrent/metastatic (RM) head and neck cancer (HNC) worldwide. METHODS Electronic searches were conducted on clinicaltrials.gov, MEDLINE/PubMed, Embase, and ASCO/ESMO journals of congresses for interventional studies (IS; Phase I-III trials) as well as MEDLINE and Embase for non-interventional studies (NIS) of LA/RM HNC published between January 01, 2010 and December 31, 2020. Criteria for study selection included: availability of HPV prevalence data for LA/RM HNC patients, patient enrollment from January 01, 2010 onward, and oropharyngeal cancer (OPC) included among HNC types. HPV prevalence per study was calculated as proportion of HPV+ over total number of enrolled patients. For overall HPV prevalence across studies, mean of reported HPV prevalence rates across studies and pooled estimate (sum of all HPV+ patients over sum of all patients enrolled) were assessed. RESULTS Eighty-one studies (62 IS; 19 NIS) were included, representing 9607 LA/RM HNC cases, with an overall mean (pooled) HPV prevalence of 32.6% (25.1%). HPV prevalence was 44.7% (44.0%) in LA and 24.3% (18.6%) in RM. Among 2714 LA/RM OPC patients from 52 studies with available data, mean (pooled) value was 55.8% (50.7%). The majority of data were derived from Northern America and Europe, with overall HPV prevalence of 46.0% (42.1%) and 24.7% (25.3%) across studies conducted exclusively in these geographic regions, respectively (Northern Europe: 31.9% [63.1%]). A "p16-based" assay was the most frequently reported HPV detection methodology (58.0%). CONCLUSION Over the last decade, at least one quarter of LA/RM HNC and half of OPC cases studied in IS and NIS were HPV+. This alarming burden is consistent with a potential implication of HPV in the pathogenesis of at least a subgroup of HNC, underscoring the relevance of HPV testing and prophylaxis to HNC prevention and management.
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Affiliation(s)
- Sofia Agelaki
- Laboratory of Translational Oncology, School of MedicineUniversity of CreteHerakleionGreece
- Department of Medical OncologyUniversity General Hospital of HerakleionHerakleionGreece
| | | | | | | | | | | | - Edith Morais
- MSD, Center for Observational and Real‐World Evidence (CORE)LyonFrance
| | - Ugne Sabale
- MSD, Center for Observational and Real‐World Evidence (CORE)StockholmSweden
| | - Goran Bencina
- MSD, Center for Observational and Real‐World Evidence (CORE)MadridSpain
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Lin Y, Yang Z, Chen J, Li M, Cai Z, Wang X, Zhai T, Lin Z. A contrast-enhanced CT radiomics-based model to identify candidates for deintensified chemoradiotherapy in locoregionally advanced nasopharyngeal carcinoma patients. Eur Radiol 2024; 34:1302-1313. [PMID: 37594526 DOI: 10.1007/s00330-023-09987-1] [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: 11/16/2022] [Revised: 06/05/2023] [Accepted: 06/12/2023] [Indexed: 08/19/2023]
Abstract
OBJECTIVES To develop a contrast-enhanced CT (CECT) radiomics-based model to identify locoregionally advanced nasopharyngeal carcinoma (LA-NPC) patients who would benefit from deintensified chemoradiotherapy. METHODS LA-NPC patients who received low-dose concurrent cisplatin therapy (cumulative: 150 mg/m2), were randomly divided into training and validation groups. 107 radiomics features based on the primary nasopharyngeal tumor were extracted from each pre-treatment CECT scan. Through Cox regression analysis, a radiomics model and patients' corresponding radiomics scores were created with predictive independent radiomics features. T stage (T) and radiomics score (R) were compared as predictive factors. Combining the N stage (N), a clinical model (T + N), and a substitution model (R + N) were constructed. RESULTS Training and validation groups consisted of 66 and 33 patients, respectively. Three significant independent radiomics features (flatness, mean, and gray level non-uniformity in gray level dependence matrix (GLDM-GLN)) were found. The radiomics score showed better predictive ability than the T stage (concordance index (C-index): 0.67 vs. 0.61, AUC: 0.75 vs. 0.60). The R + N model had better predictive performance and more effective risk stratification than the T + N model (C-index: 0.77 vs. 0.68, AUC: 0.80 vs. 0.70). The R + N model identified a low-risk group as deintensified chemoradiotherapy candidates in which no patient developed progression within 3 years, with 5-year progression-free survival (PFS) and overall survival (OS) both 90.7% (hazard ratio (HR) = 4.132, p = 0.018). CONCLUSION Our radiomics-based model combining radiomics score and N stage can identify specific LA-NPC candidates for whom de-escalation therapy can be performed without compromising therapeutic efficacy. CLINICAL RELEVANCE STATEMENT Our study shows that the radiomics-based model (R + N) can accurately stratify patients into different risk groups, with satisfactory prognosis in the low-risk group when treated with low-dose concurrent chemotherapy, providing new options for individualized de-escalation strategies. KEY POINTS • A radiomics score, consisting of 3 predictive radiomics features (flatness, mean, and GLDM-GLN) integrated with the N stage, can identify specific LA-NPC populations for deintensified treatment. • In the selection of LA-NPC candidates for de-intensified treatment, radiomics score extracted from primary nasopharyngeal tumors based on CECT can be superior to traditional T stage classification as a predictor.
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Affiliation(s)
- Yinbing Lin
- Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, 7 Raoping Road, Shantou, 515000, Guangdong, China
- Shantou University Medical College, 22 Xinling Road, Shantou 515000, 515041, Guangdong, China
| | - Zhining Yang
- Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, 7 Raoping Road, Shantou, 515000, Guangdong, China
- Nasopharyngeal Carcinoma Research Center, Shantou University Medical College, Shantou University, 7 Raoping Road, Shantou, 515000, Guangdong, China
| | - Jiechen Chen
- Shantou University Medical College, 22 Xinling Road, Shantou 515000, 515041, Guangdong, China
| | - Mei Li
- Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, 7 Raoping Road, Shantou, 515000, Guangdong, China
- Nasopharyngeal Carcinoma Research Center, Shantou University Medical College, Shantou University, 7 Raoping Road, Shantou, 515000, Guangdong, China
| | - Zeman Cai
- Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, 7 Raoping Road, Shantou, 515000, Guangdong, China
- Nasopharyngeal Carcinoma Research Center, Shantou University Medical College, Shantou University, 7 Raoping Road, Shantou, 515000, Guangdong, China
| | - Xiao Wang
- Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, 7 Raoping Road, Shantou, 515000, Guangdong, China
- Shantou University Medical College, 22 Xinling Road, Shantou 515000, 515041, Guangdong, China
| | - Tiantian Zhai
- Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, 7 Raoping Road, Shantou, 515000, Guangdong, China.
- Nasopharyngeal Carcinoma Research Center, Shantou University Medical College, Shantou University, 7 Raoping Road, Shantou, 515000, Guangdong, China.
| | - Zhixiong Lin
- Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, 7 Raoping Road, Shantou, 515000, Guangdong, China.
- Nasopharyngeal Carcinoma Research Center, Shantou University Medical College, Shantou University, 7 Raoping Road, Shantou, 515000, Guangdong, China.
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Ren L, Ling X, Alexander G, Molitoris J, Choi J, Schumaker L, Mehra R, Gaykalova D. Radiomic Biomarkers of Locoregional Recurrence: Prognostic Insights from Oral Cavity Squamous Cell Carcinoma preoperative CT scans. RESEARCH SQUARE 2024:rs.3.rs-3857391. [PMID: 38343846 PMCID: PMC10854303 DOI: 10.21203/rs.3.rs-3857391/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
This study aimed to identify CT-based imaging biomarkers for locoregional recurrence (LR) in Oral Cavity Squamous Cell Carcinoma (OSCC) patients. Our study involved a retrospective review of 78 patients with OSCC who underwent surgical treatment at a single medical center. An approach involving feature selection and statistical model diagnostics was utilized to identify biomarkers. Two radiomics biomarkers, Large Dependence Emphasis (LDE) of the Gray Level Dependence Matrix (GLDM) and Long Run Emphasis (LRE) of the Gray Level Run Length Matrix (GLRLM) of the 3D Laplacian of Gaussian (LoG σ = 3), have demonstrated the capability to preoperatively distinguish patients with and without LR, exhibiting exceptional testing specificity (1.00) and sensitivity (0.82). The group with LRE > 2.99 showed a 3-year recurrence-free survival rate of 0.81, in contrast to 0.49 for the group with LRE ≤ 2.99. Similarly, the group with LDE > 120 showed a rate of 0.82, compared to 0.49 for the group with LDE ≤ 120. These biomarkers broaden our understanding of using radiomics to predict OSCC progression, enabling personalized treatment plans to enhance patient survival.
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Affiliation(s)
- Lei Ren
- University of Maryland School of Medicine
| | - Xiao Ling
- University of Maryland School of Medicine
| | | | | | | | | | | | - Daria Gaykalova
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University; Marlene & Stewart Greenebaum Comprehensive Cancer Center, University of Maryland Medical Center; Institute for Genome Sciences, U
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Choi JH, Choi JY, Woo SK, Moon JE, Lim CH, Park SB, Seo S, Ahn YC, Ahn MJ, Moon SH, Park JM. Prognostic Value of Radiomic Analysis Using Pre- and Post-Treatment 18F-FDG-PET/CT in Patients with Laryngeal Cancer and Hypopharyngeal Cancer. J Pers Med 2024; 14:71. [PMID: 38248772 PMCID: PMC10817325 DOI: 10.3390/jpm14010071] [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: 11/23/2023] [Revised: 12/20/2023] [Accepted: 01/04/2024] [Indexed: 01/23/2024] Open
Abstract
BACKGROUND The prognostic value of conducting 18F-FDG PET/CT imaging has yielded different results in patients with laryngeal cancer and hypopharyngeal cancer, but these results are controversial, and there is a lack of dedicated studies on each type of cancer. This study aimed to evaluate whether combining radiomic analysis of pre- and post-treatment 18F-FDG PET/CT imaging features and clinical parameters has additional prognostic value in patients with laryngeal cancer and hypopharyngeal cancer. METHODS From 2008 to 2016, data on patients diagnosed with cancer of the larynx and hypopharynx were retrospectively collected. The patients underwent pre- and post-treatment 18F-FDG PET/CT imaging. The values of ΔPre-Post PET were measured from the texture features. Least absolute shrinkage and selection operator (LASSO) Cox regression was used to select the most predictive features to formulate a Rad-score for both progression-free survival (PFS) and overall survival (OS). Kaplan-Meier curve analysis and Cox regression were employed to assess PFS and OS. Then, the concordance index (C-index) and calibration plot were used to evaluate the performance of the radiomics nomogram. RESULTS Study data were collected for a total of 91 patients. The mean follow-up period was 71.5 mo. (8.4-147.3). The Rad-score was formulated based on the texture parameters and was significantly associated with both PFS (p = 0.024) and OS (p = 0.009). When predicting PFS, only the Rad-score demonstrated a significant association (HR 2.1509, 95% CI [1.100-4.207], p = 0.025). On the other hand, age (HR 1.116, 95% CI [1.041-1.197], p = 0.002) and Rad-score (HR 33.885, 95% CI [2.891-397.175], p = 0.005) exhibited associations with OS. The Rad-score value showed good discrimination when it was combined with clinical parameters in both PFS (C-index 0.802-0.889) and OS (C-index 0.860-0.958). The calibration plots also showed a good agreement between the observed and predicted survival probabilities. CONCLUSIONS Combining clinical parameters with radiomics analysis of pre- and post-treatment 18F-FDG PET/CT parameters in patients with laryngeal cancer and hypopharyngeal cancer might have additional prognostic value.
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Affiliation(s)
- Joon Ho Choi
- Department of Nuclear Medicine, Soonchunhyang University Bucheon Hospital, Bucheon 14584, Republic of Korea
| | - Joon Young Choi
- Department of Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Republic of Korea
| | - Sang-Keun Woo
- Division of Applied RI, Korea Institutes of Radiological and Medical Sciences, Seoul 01812, Republic of Korea
| | - Ji Eun Moon
- Department of Biostatistics, Soonchunhyang University Bucheon Hospital, Bucheon 14584, Republic of Korea
| | - Chae Hong Lim
- Department of Nuclear Medicine, Soonchunhyang University Seoul Hospital, Seoul 04401, Republic of Korea
| | - Soo Bin Park
- Department of Nuclear Medicine, Soonchunhyang University Seoul Hospital, Seoul 04401, Republic of Korea
| | - Seongho Seo
- Department of Electronic Engineering, Pai Chai University, Daejeon 35345, Republic of Korea
| | - Yong Chan Ahn
- Department of Radiation Oncology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Republic of Korea
| | - Myung-Ju Ahn
- Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Republic of Korea
| | - Seung Hwan Moon
- Department of Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Republic of Korea
| | - Jung Mi Park
- Department of Nuclear Medicine, Soonchunhyang University Bucheon Hospital, Bucheon 14584, Republic of Korea
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Rogasch JMM, Shi K, Kersting D, Seifert R. Methodological evaluation of original articles on radiomics and machine learning for outcome prediction based on positron emission tomography (PET). Nuklearmedizin 2023; 62:361-369. [PMID: 37995708 PMCID: PMC10667066 DOI: 10.1055/a-2198-0545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Accepted: 10/25/2023] [Indexed: 11/25/2023]
Abstract
AIM Despite a vast number of articles on radiomics and machine learning in positron emission tomography (PET) imaging, clinical applicability remains limited, partly owing to poor methodological quality. We therefore systematically investigated the methodology described in publications on radiomics and machine learning for PET-based outcome prediction. METHODS A systematic search for original articles was run on PubMed. All articles were rated according to 17 criteria proposed by the authors. Criteria with >2 rating categories were binarized into "adequate" or "inadequate". The association between the number of "adequate" criteria per article and the date of publication was examined. RESULTS One hundred articles were identified (published between 07/2017 and 09/2023). The median proportion of articles per criterion that were rated "adequate" was 65% (range: 23-98%). Nineteen articles (19%) mentioned neither a test cohort nor cross-validation to separate training from testing. The median number of criteria with an "adequate" rating per article was 12.5 out of 17 (range, 4-17), and this did not increase with later dates of publication (Spearman's rho, 0.094; p = 0.35). In 22 articles (22%), less than half of the items were rated "adequate". Only 8% of articles published the source code, and 10% made the dataset openly available. CONCLUSION Among the articles investigated, methodological weaknesses have been identified, and the degree of compliance with recommendations on methodological quality and reporting shows potential for improvement. Better adherence to established guidelines could increase the clinical significance of radiomics and machine learning for PET-based outcome prediction and finally lead to the widespread use in routine clinical practice.
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Affiliation(s)
- Julian Manuel Michael Rogasch
- Department of Nuclear Medicine, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Berlin
| | - Kuangyu Shi
- Department of Nuclear Medicine, Inselspital University Hospital Bern, Bern, Switzerland
| | - David Kersting
- Department of Nuclear Medicine, University Hospital Essen, Essen, Germany
| | - Robert Seifert
- Department of Nuclear Medicine, University Hospital Essen, Essen, Germany
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Bianconi F, Salis R, Fravolini ML, Khan MU, Minestrini M, Filippi L, Marongiu A, Nuvoli S, Spanu A, Palumbo B. Performance Analysis of Six Semi-Automated Tumour Delineation Methods on [ 18F] Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography (FDG PET/CT) in Patients with Head and Neck Cancer. SENSORS (BASEL, SWITZERLAND) 2023; 23:7952. [PMID: 37766009 PMCID: PMC10537871 DOI: 10.3390/s23187952] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 09/01/2023] [Accepted: 09/15/2023] [Indexed: 09/29/2023]
Abstract
Background. Head and neck cancer (HNC) is the seventh most common neoplastic disorder at the global level. Contouring HNC lesions on [18F] Fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) scans plays a fundamental role for diagnosis, risk assessment, radiotherapy planning and post-treatment evaluation. However, manual contouring is a lengthy and tedious procedure which requires significant effort from the clinician. Methods. We evaluated the performance of six hand-crafted, training-free methods (four threshold-based, two algorithm-based) for the semi-automated delineation of HNC lesions on FDG PET/CT. This study was carried out on a single-centre population of n=103 subjects, and the standard of reference was manual segmentation generated by nuclear medicine specialists. Figures of merit were the Sørensen-Dice coefficient (DSC) and relative volume difference (RVD). Results. Median DSC ranged between 0.595 and 0.792, median RVD between -22.0% and 87.4%. Click and draw and Nestle's methods achieved the best segmentation accuracy (median DSC, respectively, 0.792 ± 0.178 and 0.762 ± 0.107; median RVD, respectively, -21.6% ± 1270.8% and -32.7% ± 40.0%) and outperformed the other methods by a significant margin. Nestle's method also resulted in a lower dispersion of the data, hence showing stronger inter-patient stability. The accuracy of the two best methods was in agreement with the most recent state-of-the art results. Conclusions. Semi-automated PET delineation methods show potential to assist clinicians in the segmentation of HNC lesions on FDG PET/CT images, although manual refinement may sometimes be needed to obtain clinically acceptable ROIs.
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Affiliation(s)
- Francesco Bianconi
- Department of Engineering, Università degli Studi di Perugia, Via Goffredo Duranti 93, 06125 Perugia, Italy; (M.L.F.); (M.U.K.)
| | - Roberto Salis
- Unit of Nuclear Medicine, Department of Medicine, Surgery and Pharmacy, University of Sassari, 07100 Sassari, Italy; (R.S.); (A.M.); (S.N.)
| | - Mario Luca Fravolini
- Department of Engineering, Università degli Studi di Perugia, Via Goffredo Duranti 93, 06125 Perugia, Italy; (M.L.F.); (M.U.K.)
| | - Muhammad Usama Khan
- Department of Engineering, Università degli Studi di Perugia, Via Goffredo Duranti 93, 06125 Perugia, Italy; (M.L.F.); (M.U.K.)
| | - Matteo Minestrini
- Section of Nuclear Medicine and Health Physics, Department of Medicine and Surgery, Università degli Studi di Perugia, Piazza Lucio Severi 1, 06132 Perugia, Italy; (M.M.); (B.P.)
| | - Luca Filippi
- Policlinico Tor Vergata Hospital, Viale Oxford 81, 00133 Rome, Italy;
| | - Andrea Marongiu
- Unit of Nuclear Medicine, Department of Medicine, Surgery and Pharmacy, University of Sassari, 07100 Sassari, Italy; (R.S.); (A.M.); (S.N.)
| | - Susanna Nuvoli
- Unit of Nuclear Medicine, Department of Medicine, Surgery and Pharmacy, University of Sassari, 07100 Sassari, Italy; (R.S.); (A.M.); (S.N.)
| | - Angela Spanu
- Unit of Nuclear Medicine, Department of Medicine, Surgery and Pharmacy, University of Sassari, 07100 Sassari, Italy; (R.S.); (A.M.); (S.N.)
| | - Barbara Palumbo
- Section of Nuclear Medicine and Health Physics, Department of Medicine and Surgery, Università degli Studi di Perugia, Piazza Lucio Severi 1, 06132 Perugia, Italy; (M.M.); (B.P.)
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Ling X, Alexander GS, Molitoris J, Choi J, Schumaker L, Mehra R, Gaykalova DA, Ren L. Identification of CT-based non-invasive Radiographic Biomarkers for Overall Survival Stratification in Oral Cavity Squamous Cell Carcinoma. RESEARCH SQUARE 2023:rs.3.rs-3263887. [PMID: 37674725 PMCID: PMC10479433 DOI: 10.21203/rs.3.rs-3263887/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/08/2023]
Abstract
This study addresses the limited non-invasive tools for Oral Cavity Squamous Cell Carcinoma OSCC survival prediction by identifying Computed Tomography (CT)-based biomarkers for improved prognosis. A retrospective analysis was conducted on data from 149 OSCC patients, including radiomics and clinical. An ensemble approach involving correlation analysis, score screening, and the Sparse-L1 algorithm was used to select functional features, which were then used to build Cox Proportional Hazards models (CPH). Our CPH achieved a 0.70 concordance index in testing. The model identified two CT-based radiomics features, Gradient-Neighboring-Gray-Tone-Difference-Matrix-Strength (GNS) and normalized-Wavelet-LLL-Gray-Level-Dependence-Matrix-Large-Dependence-High-Gray-Level-Emphasis (HLE), as well as smoking and alcohol usage, as survival biomarkers. The GNS group with values above 14 showed a hazard ratio of 0.12 and a 3-year survival rate of about 90%. Conversely, the GNS group with values less than or equal to 14 had a 49% survival rate. For normalized HLE, the high-end group (HLE > -0.415) had a hazard ratio of 2.41, resulting in a 3-year survival rate of 70%, while the low-end group (HLE <= -0.415) had a 36% survival rate. These findings contribute to our knowledge of how radiomics can be used to anticipate the outcome and tailor treatment plans from people with OSCC.
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Affiliation(s)
- Xiao Ling
- Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Gregory S. Alexander
- Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Jason Molitoris
- Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Jinhyuk Choi
- Department of Breast Surgery, Kosin University Gospel Hospital, Busan, KR
| | - Lisa Schumaker
- Marlene and Stewart Greenebaum Comprehensive Cancer Center, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Ranee Mehra
- Marlene and Stewart Greenebaum Comprehensive Cancer Center, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Daria A. Gaykalova
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, Maryland, USA
- Department of Otorhinolaryngology-Head and Neck Surgery, Marlene & Stewart Greenebaum Comprehensive Cancer Center, University of Maryland Medical Center, Baltimore, Maryland, USA
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA
| | - Lei Ren
- Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, Maryland, USA
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Philip MM, Welch A, McKiddie F, Nath M. A systematic review and meta-analysis of predictive and prognostic models for outcome prediction using positron emission tomography radiomics in head and neck squamous cell carcinoma patients. Cancer Med 2023; 12:16181-16194. [PMID: 37353996 PMCID: PMC10469753 DOI: 10.1002/cam4.6278] [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: 04/05/2023] [Revised: 06/07/2023] [Accepted: 06/11/2023] [Indexed: 06/25/2023] Open
Abstract
BACKGROUND Positron emission tomography (PET) images of head and neck squamous cell carcinoma (HNSCC) patients can assess the functional and biochemical processes at cellular levels. Therefore, PET radiomics-based prediction and prognostic models have the potentials to understand tumour heterogeneity and assist clinicians with diagnosis, prognosis and management of the disease. We conducted a systematic review of published modelling information to evaluate the usefulness of PET radiomics in the prediction and prognosis of HNSCC patients. METHODS We searched bibliographic databases (MEDLINE, Embase, Web of Science) from 2010 to 2021 and considered 31 studies with pre-defined inclusion criteria. We followed the CHARMS checklist for data extraction and performed quality assessment using the PROBAST tool. We conducted a meta-analysis to estimate the accuracy of the prediction and prognostic models using the diagnostic odds ratio (DOR) and average C-statistic, respectively. RESULTS Manual segmentation method followed by 40% of the maximum standardised uptake value (SUVmax ) thresholding is a commonly used approach. The area under the receiver operating curves of externally validated prediction models ranged between 0.60-0.87, 0.65-0.86 and 0.62-0.75 for overall survival, distant metastasis and recurrence, respectively. Most studies highlighted an overall high risk of bias (outcome definition, statistical methodologies and external validation of models) and high unclear concern in terms of applicability. The meta-analysis showed the estimated pooled DOR of 6.75 (95% CI: 4.45, 10.23) for prediction models and the C-statistic of 0.71 (95% CI: 0.67, 0.74) for prognostic models. CONCLUSIONS Both prediction and prognostic models using clinical variables and PET radiomics demonstrated reliable accuracy for detecting adverse outcomes in HNSCC, suggesting the prospect of PET radiomics in clinical settings for diagnosis, prognosis and management of HNSCC patients. Future studies of prediction and prognostic models should emphasise the quality of reporting, external model validation, generalisability to real clinical scenarios and enhanced reproducibility of results.
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Affiliation(s)
| | - Andy Welch
- Institute of Education in Healthcare and Medical Sciences, University of AberdeenAberdeenUK
| | | | - Mintu Nath
- Institute of Applied Health Sciences, University of AberdeenAberdeenUK
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10
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Lim CH, Choi JY, Choi JH, Lee JH, Lee J, Lim CW, Kim Z, Woo SK, Park SB, Park JM. Development and External Validation of 18F-FDG PET-Based Radiomic Model for Predicting Pathologic Complete Response after Neoadjuvant Chemotherapy in Breast Cancer. Cancers (Basel) 2023; 15:3842. [PMID: 37568658 PMCID: PMC10417050 DOI: 10.3390/cancers15153842] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 07/21/2023] [Accepted: 07/26/2023] [Indexed: 08/13/2023] Open
Abstract
The aim of our retrospective study is to develop and externally validate an 18F-FDG PET-derived radiomics model for predicting pathologic complete response (pCR) after neoadjuvant chemotherapy (NAC) in breast cancer patients. A total of 87 breast cancer patients underwent curative surgery after NAC at Soonchunhyang University Seoul Hospital and were randomly assigned to a training cohort and an internal validation cohort. Radiomic features were extracted from pretreatment PET images. A radiomic-score model was generated using the LASSO method. A combination model incorporating significant clinical variables was constructed. These models were externally validated in a separate cohort of 28 patients from Soonchunhyang University Buscheon Hospital. The model performances were assessed using area under the receiver operating characteristic (AUC). Seven radiomic features were selected to calculate the radiomic-score. Among clinical variables, human epidermal growth factor receptor 2 status was an independent predictor of pCR. The radiomic-score model achieved good discriminability, with AUCs of 0.963, 0.731, and 0.729 for the training, internal validation, and external validation cohorts, respectively. The combination model showed improved predictive performance compared to the radiomic-score model alone, with AUCs of 0.993, 0.772, and 0.906 in three cohorts, respectively. The 18F-FDG PET-derived radiomic-based model is useful for predicting pCR after NAC in breast cancer.
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Affiliation(s)
- Chae Hong Lim
- Department of Nuclear Medicine, Soonchunhyang University Seoul Hospital, Seoul 04401, Republic of Korea;
| | - Joon Young Choi
- Department of Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Republic of Korea;
| | - Joon Ho Choi
- Department of Nuclear Medicine, Soonchunhyang University Bucheon Hospital, Bucheon 14584, Republic of Korea
| | - Jun-Hee Lee
- Department of Surgery, Soonchunhyang University Seoul Hospital, Seoul 04401, Republic of Korea
| | - Jihyoun Lee
- Department of Surgery, Soonchunhyang University Seoul Hospital, Seoul 04401, Republic of Korea
| | - Cheol Wan Lim
- Department of Surgery, Soonchunhyang University Bucheon Hospital, Bucheon 14584, Republic of Korea
| | - Zisun Kim
- Department of Surgery, Soonchunhyang University Bucheon Hospital, Bucheon 14584, Republic of Korea
| | - Sang-Keun Woo
- Division of Applied RI, Korea Institutes of Radiological and Medical Sciences, Seoul 01812, Republic of Korea
| | - Soo Bin Park
- Department of Nuclear Medicine, Soonchunhyang University Seoul Hospital, Seoul 04401, Republic of Korea;
| | - Jung Mi Park
- Department of Nuclear Medicine, Soonchunhyang University Bucheon Hospital, Bucheon 14584, Republic of Korea
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11
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Šedienė S, Kulakienė I, Urbonavičius BG, Korobeinikova E, Rudžianskas V, Povilonis PA, Jaselskė E, Adlienė D, Juozaitytė E. Development of a Model Based on Delta-Radiomic Features for the Optimization of Head and Neck Squamous Cell Carcinoma Patient Treatment. MEDICINA (KAUNAS, LITHUANIA) 2023; 59:1173. [PMID: 37374377 DOI: 10.3390/medicina59061173] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Revised: 05/25/2023] [Accepted: 06/09/2023] [Indexed: 06/29/2023]
Abstract
Background and Objectives: To our knowledge, this is the first study that investigated the prognostic value of radiomics features extracted from not only staging 18F-fluorodeoxyglucose positron emission tomography (FDG PET/CT) images, but also post-induction chemotherapy (ICT) PET/CT images. This study aimed to construct a training model based on radiomics features obtained from PET/CT in a cohort of patients with locally advanced head and neck squamous cell carcinoma treated with ICT, to predict locoregional recurrence, development of distant metastases, and the overall survival, and to extract the most significant radiomics features, which were included in the final model. Materials and Methods: This retrospective study analyzed data of 55 patients. All patients underwent PET/CT at the initial staging and after ICT. Along the classical set of 13 parameters, the original 52 parameters were extracted from each PET/CT study and an additional 52 parameters were generated as a difference between radiomics parameters before and after the ICT. Five machine learning algorithms were tested. Results: The Random Forest algorithm demonstrated the best performance (R2 0.963-0.998) in the majority of datasets. The strongest correlation in the classical dataset was between the time to disease progression and time to death (r = 0.89). Another strong correlation (r ≥ 0.8) was between higher-order texture indices GLRLM_GLNU, GLRLM_SZLGE, and GLRLM_ZLNU and standard PET parameters MTV, TLG, and SUVmax. Patients with a higher numerical expression of GLCM_ContrastVariance, extracted from the delta dataset, had a longer survival and longer time until progression (p = 0.001). Good correlations were observed between Discretized_SUVstd or Discretized_SUVSkewness and time until progression (p = 0.007). Conclusions: Radiomics features extracted from the delta dataset produced the most robust data. Most of the parameters had a positive impact on the prediction of the overall survival and the time until progression. The strongest single parameter was GLCM_ContrastVariance. Discretized_SUVstd or Discretized_SUVSkewness demonstrated a strong correlation with the time until progression.
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Affiliation(s)
- Severina Šedienė
- Department of Radiology of Lithuanian, University of Health Sciences, Eivenių g. 2, LT-50161 Kaunas, Lithuania
| | - Ilona Kulakienė
- Department of Radiology of Lithuanian, University of Health Sciences, Eivenių g. 2, LT-50161 Kaunas, Lithuania
| | - Benas Gabrielis Urbonavičius
- Department of Physics, Faculty of Mathematics and Natural Sciences, Kaunas University of Technology, Studentu g. 50, LT-51368 Kaunas, Lithuania
| | - Erika Korobeinikova
- Oncology Institute of Lithuanian, University of Health Sciences, Eiveniu g. 2, LT-50161 Kaunas, Lithuania
| | - Viktoras Rudžianskas
- Oncology Institute of Lithuanian, University of Health Sciences, Eiveniu g. 2, LT-50161 Kaunas, Lithuania
| | - Paulius Algirdas Povilonis
- Medical Academy of Lithuania, University of Health Sciences, A. Mickeviciaus g. 9, LT-44307 Kaunas, Lithuania
| | - Evelina Jaselskė
- Department of Physics, Faculty of Mathematics and Natural Sciences, Kaunas University of Technology, Studentu g. 50, LT-51368 Kaunas, Lithuania
| | - Diana Adlienė
- Department of Physics, Faculty of Mathematics and Natural Sciences, Kaunas University of Technology, Studentu g. 50, LT-51368 Kaunas, Lithuania
| | - Elona Juozaitytė
- Oncology Institute of Lithuanian, University of Health Sciences, Eiveniu g. 2, LT-50161 Kaunas, Lithuania
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Zhu F, Yang C, Xia Y, Wang J, Zou J, Zhao L, Zhao Z. CT-based radiomics models may predict the early efficacy of microwave ablation in malignant lung tumors. Cancer Imaging 2023; 23:60. [PMID: 37308918 DOI: 10.1186/s40644-023-00571-w] [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: 07/18/2022] [Accepted: 05/19/2023] [Indexed: 06/14/2023] Open
Abstract
PURPOSE To establish and validate radiomics models for predicting the early efficacy (less than 3 months) of microwave ablation (MWA) in malignant lung tumors. METHODS The study enrolled 130 malignant lung tumor patients (72 in the training cohort, 32 in the testing cohort, and 26 in the validation cohort) treated with MWA. Post-operation CT images were analyzed. To evaluate the therapeutic effect of ablation, three models were constructed by least absolute shrinkage and selection operator and logistic regression: the tumoral radiomics (T-RO), peritumoral radiomics (P-RO), and tumoral-peritumoral radiomics (TP-RO) models. Univariate and multivariate analyses were performed to identify clinical variables and radiomics features associated with early efficacy, which were incorporated into the combined radiomics (C-RO) model. The performance of the C-RO model was evaluated by the area under the receiver operating characteristic (ROC) curve (AUC), calibration curve, and decision curve analysis (DCA). The C-RO model was used to derive the best cutoff value of ROC and to distinguish the high-risk group (Nomo-score of C-RO model below than cutoff value) from the low-risk group (Nomo-score of C-RO model higher than cutoff value) for survival analysis of patients. RESULTS Four radiomics features were selected from the region of interest of tumoral and peritumoral CT images, which showed good performance for evaluating prognosis and early efficacy in three cohorts. The C-RO model had the highest AUC value in all models, and the C-RO model was better than the P-RO model (AUC in training, 0.896 vs. 0.740; p = 0.036). The DCA confirmed the clinical benefit of the C-RO model. Survival analysis revealed that in the C-RO model, the low-risk group defined by best cutoff value had significantly better progression-free survival than the high-risk group (p<0.05). CONCLUSIONS CT-based radiomics models in malignant lung tumor patients after MWA could be useful for individualized risk classification and treatment.
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Affiliation(s)
- Fandong Zhu
- Shaoxing University School of Medicine, Shaoxing, 312000, China
| | - Chen Yang
- Zhejiang University School of Medicine, Hangzhou, 310000, China
| | - Yang Xia
- Department of Radiology, Shaoxing Maternal and Child Health Hospital, Shaoxing, 312000, China
| | - Jianping Wang
- Department of Radiology, Shaoxing People's Hospital, Key Laboratory of Functional Molecular Imaging of Tumor and Interventional Diagnosis and Treatment of Shaoxing City, No. 568, North Zhongxing Road, Yuecheng District, Shaoxing, 312000, China
| | - Jiajun Zou
- Shaoxing University School of Medicine, Shaoxing, 312000, China
| | - Li Zhao
- Department of Radiology, Shaoxing People's Hospital, Key Laboratory of Functional Molecular Imaging of Tumor and Interventional Diagnosis and Treatment of Shaoxing City, No. 568, North Zhongxing Road, Yuecheng District, Shaoxing, 312000, China
| | - Zhenhua Zhao
- Department of Radiology, Shaoxing People's Hospital, Key Laboratory of Functional Molecular Imaging of Tumor and Interventional Diagnosis and Treatment of Shaoxing City, No. 568, North Zhongxing Road, Yuecheng District, Shaoxing, 312000, China.
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13
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Noortman WA, Aide N, Vriens D, Arkes LS, Slump CH, Boellaard R, Goeman JJ, Deroose CM, Machiels JP, Licitra LF, Lhommel R, Alessi A, Woff E, Goffin K, Le Tourneau C, Gal J, Temam S, Delord JP, van Velden FHP, de Geus-Oei LF. Development and External Validation of a PET Radiomic Model for Prognostication of Head and Neck Cancer. Cancers (Basel) 2023; 15:2681. [PMID: 37345017 DOI: 10.3390/cancers15102681] [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: 01/31/2023] [Revised: 04/30/2023] [Accepted: 05/03/2023] [Indexed: 06/23/2023] Open
Abstract
AIM To build and externally validate an [18F]FDG PET radiomic model to predict overall survival in patients with head and neck squamous cell carcinoma (HNSCC). METHODS Two multicentre datasets of patients with operable HNSCC treated with preoperative afatinib who underwent a baseline and evaluation [18F]FDG PET/CT scan were included (EORTC: n = 20, Unicancer: n = 34). Tumours were delineated, and radiomic features were extracted. Each cohort served once as a training and once as an external validation set for the prediction of overall survival. Supervised feature selection was performed using variable hunting with variable importance, selecting the top two features. A Cox proportional hazards regression model using selected radiomic features and clinical characteristics was fitted on the training dataset and validated in the external validation set. Model performances are expressed by the concordance index (C-index). RESULTS In both models, the radiomic model surpassed the clinical model with validation C-indices of 0.69 and 0.79 vs. 0.60 and 0.67, respectively. The model that combined the radiomic features and clinical variables performed best, with validation C-indices of 0.71 and 0.82. CONCLUSION Although assessed in two small but independent cohorts, an [18F]FDG-PET radiomic signature based on the evaluation scan seems promising for the prediction of overall survival for HNSSC treated with preoperative afatinib. The robustness and clinical applicability of this radiomic signature should be assessed in a larger cohort.
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Affiliation(s)
- Wyanne A Noortman
- Section of Nuclear Medicine, Department of Radiology, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands
- TechMed Centre, University of Twente, 7522 NB Enschede, The Netherlands
| | - Nicolas Aide
- Nuclear Medicine Department, Centre Hospitalier Universitaire de Caen, 14000 Caen, France
| | - Dennis Vriens
- Section of Nuclear Medicine, Department of Radiology, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands
| | - Lisa S Arkes
- Section of Nuclear Medicine, Department of Radiology, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands
- Technical Medicine, Delft University of Technology, 2628 CD Delft, The Netherlands
| | - Cornelis H Slump
- TechMed Centre, University of Twente, 7522 NB Enschede, The Netherlands
| | - Ronald Boellaard
- Amsterdam University Medical Center, 1081 HV Amsterdam, The Netherlands
| | - Jelle J Goeman
- Department of Biomedical Data Sciences, Leiden University Medical Center, 2300 RC Leiden, The Netherlands
| | - Christophe M Deroose
- Nuclear Medicine and Molecular Imaging, Department of Imaging & Pathology, University Hospitals Leuven, KU Leuven, 3000 Leuven, Belgium
| | - Jean-Pascal Machiels
- Department of Medical Oncology, Institut Roi Albert II, Cliniques Universitaires Saint-Luc, 1200 Brussels, Belgium
- Institute for Experimental and Clinical Research (IREC, pôle MIRO), Université Catholique de Louvain (UCLouvain), 1200 Brussels, Belgium
| | - Lisa F Licitra
- Department of Head and Neck Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori, University of Milan, 20133 Milan, Italy
| | - Renaud Lhommel
- Division of Nuclear Medicine, Institut de Recherche Clinique, Cliniques Universitaires Saint Luc, 1200 Brussels, Belgium
| | - Alessandra Alessi
- Department of Nuclear Medicine-PET Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy
| | - Erwin Woff
- Nuclear Medicine Department, Institut Jules Bordet, Université Libre de Bruxelles (ULB), Hôpital Universitaire de Bruxelles (H.U.B.), 1070 Bruxelles, Belgium
| | - Karolien Goffin
- Nuclear Medicine and Molecular Imaging, Department of Imaging & Pathology, University Hospitals Leuven, KU Leuven, 3000 Leuven, Belgium
| | - Christophe Le Tourneau
- Department of Drug Development and Innovation, Institut Curie, Paris-Saclay University, 75005 Paris, France
| | - Jocelyn Gal
- Epidemiology and Biostatistics Department, Centre Antoine Lacassagne, University Côte d'Azur, 06100 Nice, France
| | - Stéphane Temam
- Department of Head and Neck Surgery Gustave Roussy, 94805 Villejuif, France
| | | | - Floris H P van Velden
- Section of Nuclear Medicine, Department of Radiology, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands
| | - Lioe-Fee de Geus-Oei
- Section of Nuclear Medicine, Department of Radiology, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands
- TechMed Centre, University of Twente, 7522 NB Enschede, The Netherlands
- Department of Radiation Science & Technology, Delft University of Technology, 2628 CD Delft, The Netherlands
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Mori M, Deantoni C, Olivieri M, Spezi E, Chiara A, Baroni S, Picchio M, Del Vecchio A, Di Muzio NG, Fiorino C, Dell'Oca I. External validation of an 18F-FDG-PET radiomic model predicting survival after radiotherapy for oropharyngeal cancer. Eur J Nucl Med Mol Imaging 2023; 50:1329-1336. [PMID: 36604325 DOI: 10.1007/s00259-022-06098-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Accepted: 12/24/2022] [Indexed: 01/06/2023]
Abstract
PURPOSE/OBJECTIVE The purpose of the study is to externally validate published 18F-FDG-PET radiomic models for outcome prediction in patients with oropharyngeal cancer treated with chemoradiotherapy. MATERIAL/METHODS Outcome data and pre-radiotherapy PET images of 100 oropharyngeal cancer patients (stage IV:78) treated with concomitant chemotherapy to 66-69 Gy/30 fr were available. Tumors were segmented using a previously validated semi-automatic method; 450 radiomic features (RF) were extracted according to IBSI (Image Biomarker Standardization Initiative) guidelines. Only one model for cancer-specific survival (CSS) prediction was suitable to be independently tested, according to our criteria. This model, in addition to HPV status, SUVmean and SUVmax, included two independent meta-factors (Fi), resulting from combining selected RF clusters. In a subgroup of 66 patients with complete HPV information, the global risk score R was computed considering the original coefficients and was tested by Cox regression as predictive of CSS. Independently, only the radiomic risk score RF derived from Fi was tested on the same subgroup to learn about the radiomics contribution to the model. The metabolic tumor volume (MTV) was also tested as a single predictor and its prediction performances were compared to the global and radiomic models. Finally, the validation of MTV and the radiomic score RF were also tested on the entire dataset. RESULTS Regarding the analysis of the subgroup with HPV information, with a median follow-up of 41.6 months, seven patients died due to cancer. R was confirmed to be associated to CSS (p value = 0.05) with a C-index equal 0.75 (95% CI=0.62-0.85). The best cut-off value (equal to 0.15) showed high ability in patient stratification (p=0.01, HR=7.4, 95% CI=1.6-11.4). The 5-year CSS for R were 97% (95% CI: 93-100%) vs 74% (56-92%) for low- and high-risk groups, respectively. RF and MTV alone were also significantly associated to CSS for the subgroup with an almost identical C-index. According to best cut-off value (RF>0.12 and MTV>15.5cc), the 5-year CSS were 96% (95% CI: 89-100%) vs 65% (36-94%) and 97% (95% CI: 88-100%) vs 77% (58-93%) for RF and MTV, respectively. Results regarding RF and MTV were confirmed in the overall group. CONCLUSION A previously published PET radiomic model for CSS prediction was independently validated. Performances of the model were similar to the ones of using only the MTV, without improvement of prediction accuracy.
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Affiliation(s)
- Martina Mori
- Department of Medical Physics, San Raffaele Scientific Institute, Milano, Italy
| | - Chiara Deantoni
- Department of Radiotherapy, San Raffaele Scientific Institute, Milano, Italy
| | - Michela Olivieri
- Department of Medical Physics, San Raffaele Scientific Institute, Milano, Italy
| | - Emiliano Spezi
- School of Engineering, Cardiff University, Cardiff, UK
- Department of Medical Physics, Velindre Cancer Centre, Cardiff, UK
| | - Anna Chiara
- Department of Radiotherapy, San Raffaele Scientific Institute, Milano, Italy
| | - Simone Baroni
- Department of Radiotherapy, San Raffaele Scientific Institute, Milano, Italy
| | - Maria Picchio
- Department of Nuclear Medicine, San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
| | | | - Nadia Gisella Di Muzio
- Department of Radiotherapy, San Raffaele Scientific Institute, Milano, Italy
- Vita-Salute San Raffaele University, Milan, Italy
| | - Claudio Fiorino
- Department of Medical Physics, San Raffaele Scientific Institute, Milano, Italy.
| | - Italo Dell'Oca
- Department of Radiotherapy, San Raffaele Scientific Institute, Milano, Italy
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15
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Yuan H, Kang B, Sun K, Qin S, Ji C, Wang X. CT-based radiomics nomogram for differentiation of adrenal hyperplasia from lipid-poor adenoma: an exploratory study. BMC Med Imaging 2023; 23:4. [PMID: 36611159 PMCID: PMC9826591 DOI: 10.1186/s12880-022-00951-x] [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: 08/12/2022] [Accepted: 12/14/2022] [Indexed: 01/09/2023] Open
Abstract
BACKGROUND To establish and verify a radiomics nomogram for differentiating isolated micronodular adrenal hyperplasia (iMAD) from lipid-poor adenoma (LPA) based on computed tomography (CT)-extracted radiomic features. METHODS A total of 148 patients with iMAD or LPA were divided into three cohorts: a training cohort (n = 72; 37 iMAD and 35 LPA), a validation cohort (n = 36; 22 iMAD and 14 LPA), and an external validation cohort (n = 40; 20 iMAD and 20 LPA). Radiomics features were extracted from contrast-enhanced and non-contrast CT images. The least absolute shrinkage and selection operator (LASSO) method was applied to develop a triphasic radiomics model and unenhanced radiomics model using reproducible radiomics features. A clinical model was constructed using certain laboratory variables and CT findings. Radiomics nomogram was established by selected radiomics signature and clinical factors. Nomogram performance was assessed by calibration curve, the areas under receiver operating characteristic curves (AUC), and decision curve analysis (DCA). RESULTS Eleven and eight extracted features were finally selected to construct an unenhanced radiomics model and a triphasic radiomics model, respectively. There was no significant difference in AUC between the two models in the external validation cohort (0.838 vs. 0.843, p = 0.949). The radiomics nomogram inclusive of the unenhanced model, maximum diameter, and aldosterone showed the AUC of 0.951, 0.938, and 0.893 for the training, validation, and external validation cohorts, respectively. The nomogram showed good calibration, and the DCA demonstrated the superiority of the nomogram compared with the clinical factors model alone in terms of clinical usefulness. CONCLUSIONS A radiomics nomogram based on unenhanced CT images and clinical variables showed favorable performance for distinguishing iMAD from LPA. In addition, an efficient unenhanced model can help avoid extra contrast-enhanced scanning and radiation risk.
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Affiliation(s)
- Hongtao Yuan
- grid.27255.370000 0004 1761 1174Shandong Provincial Hospital, Shandong University, Jinan, Shandong China
| | - Bing Kang
- grid.460018.b0000 0004 1769 9639Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong China
| | - Kui Sun
- grid.460018.b0000 0004 1769 9639Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong China ,grid.410638.80000 0000 8910 6733School of Medicine, Shandong First Medical University, Jinan, Shandong China
| | - Songnan Qin
- grid.27255.370000 0004 1761 1174Shandong Provincial Hospital, Shandong University, Jinan, Shandong China
| | - Congshan Ji
- grid.460018.b0000 0004 1769 9639Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong China
| | - Ximing Wang
- grid.460018.b0000 0004 1769 9639Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong China
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Kudoh T, Haga A, Kudoh K, Takahashi A, Sasaki M, Kudo Y, Ikushima H, Miyamoto Y. Radiomics analysis of [ 18F]-fluoro-2-deoxyglucose positron emission tomography for the prediction of cervical lymph node metastasis in tongue squamous cell carcinoma. Oral Radiol 2023; 39:41-50. [PMID: 35254609 DOI: 10.1007/s11282-022-00600-7] [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: 12/12/2021] [Accepted: 02/10/2022] [Indexed: 01/07/2023]
Abstract
OBJECTIVES This study aimed to create a predictive model for cervical lymph node metastasis (CLNM) in patients with tongue squamous cell carcinoma (SCC) based on radiomics features detected by [18F]-fluoro-2-deoxyglucose (18F-FDG) positron emission tomography (PET). METHODS A total of 40 patients with tongue SCC who underwent 18F-FDG PET imaging during their first medical examination were enrolled. During the follow-up period (mean 28 months), 20 patients had CLNM, including six with late CLNM, whereas the remaining 20 patients did not have CLNM. Radiomics features were extracted from 18F-FDG PET images of all patients irrespective of metal artifact, and clinicopathological factors were obtained from the medical records. Late CLNM was defined as the CLNM that occurred after major treatment. The least absolute shrinkage and selection operator (LASSO) model was used for radiomics feature selection and sequential data fitting. The receiver operating characteristic curve analysis was used to assess the predictive performance of the 18F-FDG PET-based model and clinicopathological factors model (CFM) for CLNM. RESULTS Six radiomics features were selected from LASSO analysis. The average values of the area under the curve (AUC), accuracy, sensitivity, and specificity of radiomics analysis for predicting CLNM from 18F-FDG PET images were 0.79, 0.68, 0.65, and 0.70, respectively. In contrast, those of the CFM were 0.54, 0.60, 0.60, and 0.60, respectively. The 18F-FDG PET-based model showed significantly higher AUC than that of the CFM. CONCLUSIONS The 18F-FDG PET-based model has better potential for diagnosing CLNM and predicting late CLNM in patients with tongue SCC than the CFM.
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Affiliation(s)
- Takaharu Kudoh
- Department of Oral Surgery, Tokushima University Graduate School of Biomedical Sciences, Kuramoto-cho, Tokushima, Japan.
| | - Akihiro Haga
- Department of Medical Image Informatics, Tokushima University Graduate School of Biomedical Sciences, Kuramoto-cho, Tokushima, Japan
| | - Keiko Kudoh
- Department of Oral Surgery, Tokushima University Graduate School of Biomedical Sciences, Kuramoto-cho, Tokushima, Japan
| | - Akira Takahashi
- Department of Oral Surgery, Tokushima University Graduate School of Biomedical Sciences, Kuramoto-cho, Tokushima, Japan
| | - Motoharu Sasaki
- Department of Therapeutic Radiology, Tokushima University Graduate School of Biomedical Sciences, Kuramoto-cho, Tokushima, Japan
| | - Yasusei Kudo
- Department of Oral Bioscience, Tokushima University Graduate School of Biomedical Sciences, Kuramoto-cho, Tokushima, Japan
| | - Hitoshi Ikushima
- Department of Therapeutic Radiology, Tokushima University Graduate School of Biomedical Sciences, Kuramoto-cho, Tokushima, Japan
| | - Youji Miyamoto
- Department of Oral Surgery, Tokushima University Graduate School of Biomedical Sciences, Kuramoto-cho, Tokushima, Japan
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Eertink JJ, Heymans MW, Zwezerijnen GJC, Zijlstra JM, de Vet HCW, Boellaard R. External validation: a simulation study to compare cross-validation versus holdout or external testing to assess the performance of clinical prediction models using PET data from DLBCL patients. EJNMMI Res 2022; 12:58. [PMID: 36089634 PMCID: PMC9464671 DOI: 10.1186/s13550-022-00931-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Accepted: 08/27/2022] [Indexed: 11/16/2022] Open
Abstract
Aim Clinical prediction models need to be validated. In this study, we used simulation data to compare various internal and external validation approaches to validate models. Methods Data of 500 patients were simulated using distributions of metabolic tumor volume, standardized uptake value, the maximal distance between the largest lesion and another lesion, WHO performance status and age of 296 diffuse large B cell lymphoma patients. These data were used to predict progression after 2 years based on an existing logistic regression model. Using the simulated data, we applied cross-validation, bootstrapping and holdout (n = 100). We simulated new external datasets (n = 100, n = 200, n = 500) and simulated stage-specific external datasets (1), varied the cut-off for high-risk patients (2) and the false positive and false negative rates (3) and simulated a dataset with EARL2 characteristics (4). All internal and external simulations were repeated 100 times. Model performance was expressed as the cross-validated area under the curve (CV-AUC ± SD) and calibration slope. Results The cross-validation (0.71 ± 0.06) and holdout (0.70 ± 0.07) resulted in comparable model performances, but the model had a higher uncertainty using a holdout set. Bootstrapping resulted in a CV-AUC of 0.67 ± 0.02. The calibration slope was comparable for these internal validation approaches. Increasing the size of the test set resulted in more precise CV-AUC estimates and smaller SD for the calibration slope. For test datasets with different stages, the CV-AUC increased as Ann Arbor stages increased. As expected, changing the cut-off for high risk and false positive- and negative rates influenced the model performance, which is clearly shown by the low calibration slope. The EARL2 dataset resulted in similar model performance and precision, but calibration slope indicated overfitting. Conclusion In case of small datasets, it is not advisable to use a holdout or a very small external dataset with similar characteristics. A single small testing dataset suffers from a large uncertainty. Therefore, repeated CV using the full training dataset is preferred instead. Our simulations also demonstrated that it is important to consider the impact of differences in patient population between training and test data, which may ask for adjustment or stratification of relevant variables.
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Somasundaram A, Vállez García D, Pfaehler E, van Sluis J, Dierckx RAJO, de Vries EGE, Boellaard R. Mitigation of noise-induced bias of PET radiomic features. PLoS One 2022; 17:e0272643. [PMID: 36006959 PMCID: PMC9409510 DOI: 10.1371/journal.pone.0272643] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Accepted: 07/22/2022] [Indexed: 11/20/2022] Open
Abstract
Introduction One major challenge in PET radiomics is its sensitivity to noise. Low signal-to-noise ratio (SNR) affects not only the precision but also the accuracy of quantitative metrics extracted from the images resulting in noise-induced bias. This phantom study aims to identify the radiomic features that are robust to noise in terms of precision and accuracy and to explore some methods that might help to correct noise-induced bias. Methods A phantom containing three 18F-FDG filled 3D printed inserts, reflecting heterogeneous tracer uptake and realistic tumor shapes, was used in the study. The three different phantom inserts were filled and scanned with three different tumor-to-background ratios, simulating a total of nine different tumors. From the 40-minute list-mode data, ten frames each for 5 s, 10 s, 30 s, and 120 s frame duration were reconstructed to generate images with different noise levels. Under these noise conditions, the precision and accuracy of the radiomic features were analyzed using intraclass correlation coefficient (ICC) and similarity distance metric (SDM) respectively. Based on the ICC and SDM values, the radiomic features were categorized into four groups: poor, moderate, good, and excellent precision and accuracy. A “difference image” created by subtracting two statistically equivalent replicate images was used to develop a model to correct the noise-induced bias. Several regression methods (e.g., linear, exponential, sigmoid, and power-law) were tested. The best fitting model was chosen based on Akaike information criteria. Results Several radiomic features derived from low SNR images have high repeatability, with 68% of radiomic features having ICC ≥ 0.9 for images with a frame duration of 5 s. However, most features show a systematic bias that correlates with the increase in noise level. Out of 143 features with noise-induced bias, the SDM values were improved based on a regression model (53 features to excellent and 67 to good) indicating that the noise-induced bias of these features can be, at least partially, corrected. Conclusion To have a predictive value, radiomic features should reflect tumor characteristics and be minimally affected by noise. The present study has shown that it is possible to correct for noise-induced bias, at least in a subset of the features, using a regression model based on the local image noise estimates.
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Affiliation(s)
- Ananthi Somasundaram
- Department of Nuclear Medicine and Molecular Imaging, Medical Imaging Center, University Medical Center Groningen, Groningen, The Netherlands
- * E-mail:
| | - David Vállez García
- Department of Nuclear Medicine and Molecular Imaging, Medical Imaging Center, University Medical Center Groningen, Groningen, The Netherlands
- Department of Radiology and Nuclear Medicine, Amsterdam UMC–Location VU University Medical Center, Amsterdam, The Netherlands
| | - Elisabeth Pfaehler
- Department of Nuclear Medicine, University Hospital Juelich, Aachen, Germany
| | - Joyce van Sluis
- Department of Nuclear Medicine and Molecular Imaging, Medical Imaging Center, University Medical Center Groningen, Groningen, The Netherlands
| | - Rudi A. J. O. Dierckx
- Department of Nuclear Medicine and Molecular Imaging, Medical Imaging Center, University Medical Center Groningen, Groningen, The Netherlands
| | - Elisabeth G. E. de Vries
- Department of Medical Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Ronald Boellaard
- Department of Nuclear Medicine and Molecular Imaging, Medical Imaging Center, University Medical Center Groningen, Groningen, The Netherlands
- Department of Radiology and Nuclear Medicine, Amsterdam UMC–Location VU University Medical Center, Amsterdam, The Netherlands
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Morland D, Triumbari EKA, Boldrini L, Gatta R, Pizzuto D, Annunziata S. Radiomics in Oncological PET Imaging: A Systematic Review—Part 1, Supradiaphragmatic Cancers. Diagnostics (Basel) 2022; 12:diagnostics12061329. [PMID: 35741138 PMCID: PMC9221970 DOI: 10.3390/diagnostics12061329] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 05/25/2022] [Accepted: 05/26/2022] [Indexed: 12/10/2022] Open
Abstract
Radiomics is an upcoming field in nuclear oncology, both promising and technically challenging. To summarize the already undertaken work on supradiaphragmatic neoplasia and assess its quality, we performed a literature search in the PubMed database up to 18 February 2022. Inclusion criteria were: studies based on human data; at least one specified tumor type; supradiaphragmatic malignancy; performing radiomics on PET imaging. Exclusion criteria were: studies only based on phantom or animal data; technical articles without a clinically oriented question; fewer than 30 patients in the training cohort. A review database containing PMID, year of publication, cancer type, and quality criteria (number of patients, retrospective or prospective nature, independent validation cohort) was constructed. A total of 220 studies met the inclusion criteria. Among them, 119 (54.1%) studies included more than 100 patients, 21 studies (9.5%) were based on prospectively acquired data, and 91 (41.4%) used an independent validation set. Most studies focused on prognostic and treatment response objectives. Because the textural parameters and methods employed are very different from one article to another, it is complicated to aggregate and compare articles. New contributions and radiomics guidelines tend to help improving quality of the reported studies over the years.
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Affiliation(s)
- David Morland
- Nuclear Medicine Unit, TracerGLab, Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168 Rome, Italy; (E.K.A.T.); (D.P.); (S.A.)
- Service de Médecine Nucléaire, Institut Godinot, 51100 Reims, France
- Laboratoire de Biophysique, UFR de Médecine, Université de Reims Champagne-Ardenne, 51100 Reims, France
- CReSTIC (Centre de Recherche en Sciences et Technologies de l’Information et de la Communication), EA 3804, Université de Reims Champagne-Ardenne, 51100 Reims, France
- Correspondence:
| | - Elizabeth Katherine Anna Triumbari
- Nuclear Medicine Unit, TracerGLab, Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168 Rome, Italy; (E.K.A.T.); (D.P.); (S.A.)
| | - Luca Boldrini
- Radiotherapy Unit, Radiomics, Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168 Rome, Italy; (L.B.); (R.G.)
| | - Roberto Gatta
- Radiotherapy Unit, Radiomics, Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168 Rome, Italy; (L.B.); (R.G.)
- Department of Clinical and Experimental Sciences, University of Brescia, 25121 Brescia, Italy
- Department of Oncology, Lausanne University Hospital, 1011 Lausanne, Switzerland
| | - Daniele Pizzuto
- Nuclear Medicine Unit, TracerGLab, Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168 Rome, Italy; (E.K.A.T.); (D.P.); (S.A.)
| | - Salvatore Annunziata
- Nuclear Medicine Unit, TracerGLab, Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168 Rome, Italy; (E.K.A.T.); (D.P.); (S.A.)
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20
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Sheikhbahaei S, Subramaniam RM, Solnes LB. 2-Deoxy-2-[18F] Fluoro-d-Glucose PET/Computed Tomography. PET Clin 2022; 17:307-317. [DOI: 10.1016/j.cpet.2021.12.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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21
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Jimenez JE, Dai D, Xu G, Zhao R, Li T, Pan T, Wang L, Lin Y, Wang Z, Jaffray D, Hazle JD, Macapinlac HA, Wu J, Lu Y. Lesion-Based Radiomics Signature in Pretherapy 18F-FDG PET Predicts Treatment Response to Ibrutinib in Lymphoma. Clin Nucl Med 2022; 47:209-218. [PMID: 35020640 PMCID: PMC8851692 DOI: 10.1097/rlu.0000000000004060] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
PURPOSE The aim of this study was to develop a pretherapy PET/CT-based prediction model for treatment response to ibrutinib in lymphoma patients. PATIENTS AND METHODS One hundred sixty-nine lymphoma patients with 2441 lesions were studied retrospectively. All eligible lymphomas on pretherapy 18F-FDG PET images were contoured and segmented for radiomic analysis. Lesion- and patient-based responsiveness to ibrutinib was determined retrospectively using the Lugano classification. PET radiomic features were extracted. A radiomic model was built to predict ibrutinib response. The prognostic significance of the radiomic model was evaluated independently in a test cohort and compared with conventional PET metrics: SUVmax, metabolic tumor volume, and total lesion glycolysis. RESULTS The radiomic model had an area under the receiver operating characteristic curve (ROC AUC) of 0.860 (sensitivity, 92.9%, specificity, 81.4%; P < 0.001) for predicting response to ibrutinib, outperforming the SUVmax (ROC AUC, 0.519; P = 0.823), metabolic tumor volume (ROC AUC, 0.579; P = 0.412), total lesion glycolysis (ROC AUC, 0.576; P = 0.199), and a composite model built using all 3 (ROC AUC, 0.562; P = 0.046). The radiomic model increased the probability of accurately predicting ibrutinib-responsive lesions from 84.8% (pretest) to 96.5% (posttest). At the patient level, the model's performance (ROC AUC = 0.811; P = 0.007) was superior to that of conventional PET metrics. Furthermore, the radiomic model showed robustness when validated in treatment subgroups: first (ROC AUC, 0.916; P < 0.001) versus second or greater (ROC AUC, 0.842; P < 0.001) line of defense and single treatment (ROC AUC, 0.931; P < 0.001) versus multiple treatments (ROC AUC, 0.824; P < 0.001). CONCLUSIONS We developed and validated a pretherapy PET-based radiomic model to predict response to treatment with ibrutinib in a diverse cohort of lymphoma patients.
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Affiliation(s)
- Jorge E Jimenez
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Dong Dai
- Department of Nuclear Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Guofan Xu
- Department of Nuclear Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Ruiyang Zhao
- Department of Electrical and Computer Engineering, Rice University, Houston, TX
| | - Tengfei Li
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Tinsu Pan
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Linghua Wang
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Yingyan Lin
- Department of Electrical and Computer Engineering, Rice University, Houston, TX
| | - Zhangyang Wang
- Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX
| | - David Jaffray
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - John D. Hazle
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Homer A. Macapinlac
- Department of Nuclear Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Jia Wu
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Yang Lu
- Department of Nuclear Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX
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22
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Zhang X, Zhang Y, Zhang G, Qiu X, Tan W, Yin X, Liao L. Deep Learning With Radiomics for Disease Diagnosis and Treatment: Challenges and Potential. Front Oncol 2022; 12:773840. [PMID: 35251962 PMCID: PMC8891653 DOI: 10.3389/fonc.2022.773840] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Accepted: 01/17/2022] [Indexed: 12/12/2022] Open
Abstract
The high-throughput extraction of quantitative imaging features from medical images for the purpose of radiomic analysis, i.e., radiomics in a broad sense, is a rapidly developing and emerging research field that has been attracting increasing interest, particularly in multimodality and multi-omics studies. In this context, the quantitative analysis of multidimensional data plays an essential role in assessing the spatio-temporal characteristics of different tissues and organs and their microenvironment. Herein, recent developments in this method, including manually defined features, data acquisition and preprocessing, lesion segmentation, feature extraction, feature selection and dimension reduction, statistical analysis, and model construction, are reviewed. In addition, deep learning-based techniques for automatic segmentation and radiomic analysis are being analyzed to address limitations such as rigorous workflow, manual/semi-automatic lesion annotation, and inadequate feature criteria, and multicenter validation. Furthermore, a summary of the current state-of-the-art applications of this technology in disease diagnosis, treatment response, and prognosis prediction from the perspective of radiology images, multimodality images, histopathology images, and three-dimensional dose distribution data, particularly in oncology, is presented. The potential and value of radiomics in diagnostic and therapeutic strategies are also further analyzed, and for the first time, the advances and challenges associated with dosiomics in radiotherapy are summarized, highlighting the latest progress in radiomics. Finally, a robust framework for radiomic analysis is presented and challenges and recommendations for future development are discussed, including but not limited to the factors that affect model stability (medical big data and multitype data and expert knowledge in medical), limitations of data-driven processes (reproducibility and interpretability of studies, different treatment alternatives for various institutions, and prospective researches and clinical trials), and thoughts on future directions (the capability to achieve clinical applications and open platform for radiomics analysis).
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Affiliation(s)
- Xingping Zhang
- Institute of Advanced Cyberspace Technology, Guangzhou University, Guangzhou, China
- Department of New Networks, Peng Cheng Laboratory, Shenzhen, China
| | - Yanchun Zhang
- Institute of Advanced Cyberspace Technology, Guangzhou University, Guangzhou, China
- Department of New Networks, Peng Cheng Laboratory, Shenzhen, China
| | - Guijuan Zhang
- Department of Respiratory Medicine, First Affiliated Hospital of Gannan Medical University, Ganzhou, China
| | - Xingting Qiu
- Department of Radiology, First Affiliated Hospital of Gannan Medical University, Ganzhou, China
| | - Wenjun Tan
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Shenyang, China
| | - Xiaoxia Yin
- Institute of Advanced Cyberspace Technology, Guangzhou University, Guangzhou, China
| | - Liefa Liao
- School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou, China
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23
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Early Response Prediction of Multiparametric Functional MRI and 18F-FDG-PET in Patients with Head and Neck Squamous Cell Carcinoma Treated with (Chemo)Radiation. Cancers (Basel) 2022; 14:cancers14010216. [PMID: 35008380 PMCID: PMC8750157 DOI: 10.3390/cancers14010216] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2021] [Revised: 12/20/2021] [Accepted: 12/23/2021] [Indexed: 12/14/2022] Open
Abstract
Simple Summary Patients with locally-advanced head and neck squamous cell carcinoma (HNSCC) have variable responses to (chemo)radiotherapy. A reliable early prediction of outcomes allows for enhancing treatment efficacy and follow-up monitoring. Early tumoral changes can be captured by functional imaging (DWI/IVIM/DCE/18F-FDG-PET-CT) parameters, which allow for the construction of accurate patient-specific prognostic models for locoregional recurrence-free survival, distant metastasis-free survival and overall survival. We also present clinical applicable risk stratification in high/medium/low risks for these patient outcomes. This can enable personalized treatment (adaptation) management early on during treatment, improve counseling and enhance patient-specific post-therapy monitoring. Abstract Background: Patients with locally-advanced head and neck squamous cell carcinoma (HNSCC) have variable responses to (chemo)radiotherapy. A reliable prediction of outcomes allows for enhancing treatment efficacy and follow-up monitoring. Methods: Fifty-seven histopathologically-proven HNSCC patients with curative (chemo)radiotherapy were prospectively included. All patients had an MRI (DW,-IVIM, DCE-MRI) and 18F-FDG-PET/CT before and 10 days after start-treatment (intratreatment). Primary tumor functional imaging parameters were extracted. Univariate and multivariate analysis were performed to construct prognostic models and risk stratification for 2 year locoregional recurrence-free survival (LRFFS), distant metastasis-free survival (DMFS) and overall survival (OS). Model performance was measured by the cross-validated area under the receiver operating characteristic curve (AUC). Results: The best LRFFS model contained the pretreatment imaging parameters ADC_kurtosis, Kep and SUV_peak, and intratreatment imaging parameters change (Δ) Δ-ADC_skewness, Δ-f, Δ-SUV_peak and Δ-total lesion glycolysis (TLG) (AUC = 0.81). Clinical parameters did not enhance LRFFS prediction. The best DMFS model contained pretreatment ADC_kurtosis and SUV_peak (AUC = 0.88). The best OS model contained gender, HPV-status, N-stage, pretreatment ADC_skewness, D, f, metabolic-active tumor volume (MATV), SUV_mean and SUV_peak (AUC = 0.82). Risk stratification in high/medium/low risk was significantly prognostic for LRFFS (p = 0.002), DMFS (p < 0.001) and OS (p = 0.003). Conclusions: Intratreatment functional imaging parameters capture early tumoral changes that only provide prognostic information regarding LRFFS. The best LRFFS model consisted of pretreatment, intratreatment and Δ functional imaging parameters; the DMFS model consisted of only pretreatment functional imaging parameters, and the OS model consisted ofHPV-status, gender and only pretreatment functional imaging parameters. Accurate clinically applicable risk stratification calculators can enable personalized treatment (adaptation) management, early on during treatment, improve counseling and enhance patient-specific post-therapy monitoring.
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Yousefirizi F, Pierre Decazes, Amyar A, Ruan S, Saboury B, Rahmim A. AI-Based Detection, Classification and Prediction/Prognosis in Medical Imaging:: Towards Radiophenomics. PET Clin 2021; 17:183-212. [PMID: 34809866 DOI: 10.1016/j.cpet.2021.09.010] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Artificial intelligence (AI) techniques have significant potential to enable effective, robust, and automated image phenotyping including the identification of subtle patterns. AI-based detection searches the image space to find the regions of interest based on patterns and features. There is a spectrum of tumor histologies from benign to malignant that can be identified by AI-based classification approaches using image features. The extraction of minable information from images gives way to the field of "radiomics" and can be explored via explicit (handcrafted/engineered) and deep radiomics frameworks. Radiomics analysis has the potential to be used as a noninvasive technique for the accurate characterization of tumors to improve diagnosis and treatment monitoring. This work reviews AI-based techniques, with a special focus on oncological PET and PET/CT imaging, for different detection, classification, and prediction/prognosis tasks. We also discuss needed efforts to enable the translation of AI techniques to routine clinical workflows, and potential improvements and complementary techniques such as the use of natural language processing on electronic health records and neuro-symbolic AI techniques.
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Affiliation(s)
- Fereshteh Yousefirizi
- Department of Integrative Oncology, BC Cancer Research Institute, 675 West 10th Avenue, Vancouver, British Columbia V5Z 1L3, Canada.
| | - Pierre Decazes
- Department of Nuclear Medicine, Henri Becquerel Centre, Rue d'Amiens - CS 11516 - 76038 Rouen Cedex 1, France; QuantIF-LITIS, Faculty of Medicine and Pharmacy, Research Building - 1st floor, 22 boulevard Gambetta, 76183 Rouen Cedex, France
| | - Amine Amyar
- QuantIF-LITIS, Faculty of Medicine and Pharmacy, Research Building - 1st floor, 22 boulevard Gambetta, 76183 Rouen Cedex, France; General Electric Healthcare, Buc, France
| | - Su Ruan
- QuantIF-LITIS, Faculty of Medicine and Pharmacy, Research Building - 1st floor, 22 boulevard Gambetta, 76183 Rouen Cedex, France
| | - Babak Saboury
- Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD, USA; Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, Baltimore, MD, USA; Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, PA, USA
| | - Arman Rahmim
- Department of Integrative Oncology, BC Cancer Research Institute, 675 West 10th Avenue, Vancouver, British Columbia V5Z 1L3, Canada; Department of Radiology, University of British Columbia, Vancouver, British Columbia, Canada; Department of Physics, University of British Columbia, Vancouver, British Columbia, Canada
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25
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A Systematic Review of PET Textural Analysis and Radiomics in Cancer. Diagnostics (Basel) 2021; 11:diagnostics11020380. [PMID: 33672285 PMCID: PMC7926413 DOI: 10.3390/diagnostics11020380] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 02/10/2021] [Accepted: 02/19/2021] [Indexed: 12/12/2022] Open
Abstract
Background: Although many works have supported the utility of PET radiomics, several authors have raised concerns over the robustness and replicability of the results. This study aimed to perform a systematic review on the topic of PET radiomics and the used methodologies. Methods: PubMed was searched up to 15 October 2020. Original research articles based on human data specifying at least one tumor type and PET image were included, excluding those that apply only first-order statistics and those including fewer than 20 patients. Each publication, cancer type, objective and several methodological parameters (number of patients and features, validation approach, among other things) were extracted. Results: A total of 290 studies were included. Lung (28%) and head and neck (24%) were the most studied cancers. The most common objective was prognosis/treatment response (46%), followed by diagnosis/staging (21%), tumor characterization (18%) and technical evaluations (15%). The average number of patients included was 114 (median = 71; range 20–1419), and the average number of high-order features calculated per study was 31 (median = 26, range 1–286). Conclusions: PET radiomics is a promising field, but the number of patients in most publications is insufficient, and very few papers perform in-depth validations. The role of standardization initiatives will be crucial in the upcoming years.
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Wang X, Li BB. Deep Learning in Head and Neck Tumor Multiomics Diagnosis and Analysis: Review of the Literature. Front Genet 2021; 12:624820. [PMID: 33643386 PMCID: PMC7902873 DOI: 10.3389/fgene.2021.624820] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2020] [Accepted: 01/07/2021] [Indexed: 12/24/2022] Open
Abstract
Head and neck tumors are the sixth most common neoplasms. Multiomics integrates multiple dimensions of clinical, pathologic, radiological, and biological data and has the potential for tumor diagnosis and analysis. Deep learning (DL), a type of artificial intelligence (AI), is applied in medical image analysis. Among the DL techniques, the convolution neural network (CNN) is used for image segmentation, detection, and classification and in computer-aided diagnosis. Here, we reviewed multiomics image analysis of head and neck tumors using CNN and other DL neural networks. We also evaluated its application in early tumor detection, classification, prognosis/metastasis prediction, and the signing out of the reports. Finally, we highlighted the challenges and potential of these techniques.
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Affiliation(s)
- Xi Wang
- Department of Oral Pathology, Peking University School and Hospital of Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Laboratory for Digital and Material Technology of Stomatology & Beijing Key Laboratory of Digital Stomatology, Beijing, China.,Research Unit of Precision Pathologic Diagnosis in Tumors of the Oral and Maxillofacial Regions, Chinese Academy of Medical Sciences, Beijing, China
| | - Bin-Bin Li
- Department of Oral Pathology, Peking University School and Hospital of Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Laboratory for Digital and Material Technology of Stomatology & Beijing Key Laboratory of Digital Stomatology, Beijing, China.,Research Unit of Precision Pathologic Diagnosis in Tumors of the Oral and Maxillofacial Regions, Chinese Academy of Medical Sciences, Beijing, China
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27
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Peng Z, Wang Y, Wang Y, Jiang S, Fan R, Zhang H, Jiang W. Application of radiomics and machine learning in head and neck cancers. Int J Biol Sci 2021; 17:475-486. [PMID: 33613106 PMCID: PMC7893590 DOI: 10.7150/ijbs.55716] [Citation(s) in RCA: 46] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Accepted: 12/10/2020] [Indexed: 02/07/2023] Open
Abstract
With the continuous development of medical image informatics technology, more and more high-throughput quantitative data could be extracted from digital medical images, which has resulted in a new kind of omics-Radiomics. In recent years, in addition to genomics, proteomics and metabolomics, radiomic has attracted the interest of more and more researchers. Compared to other omics, radiomics can be perfectly integrated with clinical data, even with the pathology and molecular biomarker, so that the study can be closer to the clinical reality and more revealing of the tumor development. Mass data will also be generated in this process. Machine learning, due to its own characteristics, has a unique advantage in processing massive radiomic data. By analyzing mass amounts of data with strong clinical relevance, people can construct models that more accurately reflect tumor development and progression, thereby providing the possibility of personalized and sequential treatment of patients. As one of the cancer types whose treatment and diagnosis rely on imaging examination, radiomics has a very broad application prospect in head and neck cancers (HNC). Until now, there have been some notable results in HNC. In this review, we will introduce the concepts and workflow of radiomics and machine learning and their current applications in head and neck cancers, as well as the directions and applications of artificial intelligence in the treatment and diagnosis of HNC.
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Affiliation(s)
- Zhouying Peng
- Department of Otolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha 410078, Hunan, China
| | - Yumin Wang
- Department of Otolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha 410078, Hunan, China
| | - Yaxuan Wang
- Department of Otolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha 410078, Hunan, China
| | - Sijie Jiang
- Department of Otolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha 410078, Hunan, China
| | - Ruohao Fan
- Department of Otolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha 410078, Hunan, China
| | - Hua Zhang
- Department of Otolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha 410078, Hunan, China
| | - Weihong Jiang
- Department of Otolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha 410078, Hunan, China
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Paterson C, Hargreaves S, Rumley CN. Functional Imaging to Predict Treatment Response in Head and Neck Cancer: How Close are We to Biologically Adaptive Radiotherapy? Clin Oncol (R Coll Radiol) 2020; 32:861-873. [PMID: 33127234 DOI: 10.1016/j.clon.2020.10.004] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Revised: 09/28/2020] [Accepted: 10/05/2020] [Indexed: 02/07/2023]
Abstract
It is increasingly recognised that head and neck cancer represents a spectrum of disease with a differential response to standard treatments. Although prognostic factors are well established, they do not reliably predict response. The ability to predict response early during radiotherapy would allow adaptation of treatment: intensifying treatment for those not responding adequately or de-intensifying remaining therapy for those likely to achieve a complete response. Functional imaging offers such an opportunity. Changes in parameters obtained with functional magnetic resonance imaging or positron emission tomography-computed tomography during treatment have been found to be predictive of disease control in head and neck cancer. Although many questions remain unanswered regarding the optimal implementation of these techniques, current, maturing and future studies may provide the much-needed homogeneous cohorts with larger sample sizes and external validation of parameters. With a stepwise and collaborative approach, we may be able to develop imaging biomarkers that allow us to deliver personalised, biologically adaptive radiotherapy for head and neck cancer.
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Affiliation(s)
- C Paterson
- Beatson West of Scotland Cancer Centre, Glasgow, UK.
| | | | - C N Rumley
- Department of Radiation Oncology, Townsville University Hospital, Douglas, Australia; South Western Clinical School, University of New South Wales, Sydney, Australia; Ingham Institute for Applied Medical Research, Sydney, Australia
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