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Lee J, Narang S, Martinez J, Rao G, Rao A. Association of graph-based spatial features with overall survival status of glioblastoma patients. Sci Rep 2023; 13:17046. [PMID: 37813981 PMCID: PMC10562480 DOI: 10.1038/s41598-023-44353-7] [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: 12/28/2022] [Accepted: 10/06/2023] [Indexed: 10/11/2023] Open
Abstract
Glioblastoma is the most common malignant brain tumor with less than 15 months median survival. To aid prognosis, there is a need for decision tools that leverage diagnostic modalities such as MRI to inform survival. In this study, we examine higher-order spatial proximity characteristics from habitats and propose two graph-based methods (minimum spanning tree and graph run-length matrix) to characterize spatial heterogeneity over tumor MRI-derived intensity habitats and assess their relationships with overall survival as well as the immune signature status of patients with glioblastoma. A data set of 74 patients was studied based on the availability of post-contrast T1-weighted and T2-weighted fluid attenuated inversion recovery (FLAIR) image data in The Cancer Image Archive (TCIA). We assessed the predictive value of MST- and GRLM-derived features from 2D images for prediction of 12-month survival status and immune signature status of patients with glioblastoma via a receiver operating characteristic curve analysis. For 12-month survival prediction using MST-based method, sensitivity and specificity were 0.82 and 0.79 respectively. For GRLM-based method, sensitivity and specificity were 0.73 and 0.77 respectively. For immune status, sensitivity and specificity were 0.91 and 0.69, respectively, for the GRLM-based method with an immune effector. Our results show that the proposed MST- and GRLM-derived features are predictive of 12-month survival status as well as the immune signature status of patients with glioblastoma. To our knowledge, this is the first application of MST- and GRLM-based proximity analyses for the study of radiologically-defined tumor habitats in glioblastoma.
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Affiliation(s)
- Joonsang Lee
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, 48109, USA.
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
| | - Shivali Narang
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Juan Martinez
- Department of Neurosurgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Ganesh Rao
- Department of Neurosurgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Arvind Rao
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, 48109, USA.
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, 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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Garcia DA, Jeans EB, Morris LK, Shiraishi S, Laughlin BS, Rong Y, Rwigema JCM, Foote RL, Herman MG, Qian J. A Radiomics-Based Classifier for the Progression of Oropharyngeal Cancer Treated with Definitive Radiotherapy. Cancers (Basel) 2023; 15:3715. [PMID: 37509376 PMCID: PMC10377821 DOI: 10.3390/cancers15143715] [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: 06/19/2023] [Revised: 07/14/2023] [Accepted: 07/20/2023] [Indexed: 07/30/2023] Open
Abstract
In this study, we investigated whether radiomics features from pre-treatment positron emission tomography (PET) images could be used to predict disease progression in patients with HPV-positive oropharyngeal cancer treated with definitive proton or x-ray radiotherapy. Machine learning models were built using a dataset from Mayo Clinic, Rochester, Minnesota (n = 72) and tested on a dataset from Mayo Clinic, Phoenix, Arizona (n = 22). A total of 71 clinical and radiomics features were considered. The Mann-Whitney U test was used to identify the top 2 clinical and top 20 radiomics features that were significantly different between progression and progression-free patients. Two dimensionality reduction methods were used to define two feature sets (manually filtered or machine-driven). A forward feature selection scheme was conducted on each feature set to build models of increased complexity (number of input features from 1 to 6) and evaluate model robustness and overfitting. The machine-driven features had superior performance and were less prone to overfitting compared to the manually filtered features. The four-variable Gaussian Naïve Bayes model using the 'Radiation Type' clinical feature and three machine-driven features achieved a training accuracy of 79% and testing accuracy of 77%. These results demonstrate that radiomics features can provide risk stratification beyond HPV-status to formulate individualized treatment and follow-up strategies.
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Affiliation(s)
- Darwin A Garcia
- Department of Radiation Oncology, Mayo Clinic, Rochester, MN 55905, USA
- Mayo Clinic Graduate School of Biomedical Sciences, Mayo Clinic, Rochester, MN 55905, USA
| | - Elizabeth B Jeans
- Department of Radiation Oncology, Mayo Clinic, Rochester, MN 55905, USA
| | - Lindsay K Morris
- Department of Radiation Oncology, Mayo Clinic, Rochester, MN 55905, USA
| | - Satomi Shiraishi
- Department of Radiation Oncology, Mayo Clinic, Rochester, MN 55905, USA
| | - Brady S Laughlin
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, USA
| | - Yi Rong
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, USA
| | | | - Robert L Foote
- Department of Radiation Oncology, Mayo Clinic, Rochester, MN 55905, USA
| | - Michael G Herman
- Department of Radiation Oncology, Mayo Clinic, Rochester, MN 55905, USA
| | - Jing Qian
- Department of Radiation Oncology, Mayo Clinic, Rochester, MN 55905, USA
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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: 1] [Impact Index Per Article: 1.0] [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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Kim SJ, Choi JY, Ahn YC, Ahn MJ, Moon SH. The prognostic value of radiomic features from pre- and post-treatment 18F-FDG PET imaging in patients with nasopharyngeal carcinoma. Sci Rep 2023; 13:8462. [PMID: 37231092 DOI: 10.1038/s41598-023-35582-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Accepted: 05/20/2023] [Indexed: 05/27/2023] Open
Abstract
Positron emission tomography/computed tomography (PET/CT) with 18F-fluorodeoxyglucose (FDG) is widely used for management of nasopharyngeal carcinoma (NPC). Combining the radiomic features of pre- and post-treatment FDG PET images may improve tumor characterization and prognostic predication. We investigated prognostic value of radiomic features from pre- and post-radiotherapy FDG PET images in patients with NPC. Quantitative radiomic features of primary tumors were extracted from the FDG PET images of 145 NPC patients and the delta values were also calculated. The study population was divided randomly into two groups, the training and test sets (7:3). A random survival forest (RSF) model was adopted to perform analyses of progression-free survival (PFS) and overall survival (OS). There were 37 (25.5%) cases of recurrence and 16 (11.0%) cases of death during a median follow-up period of 54.5 months. Both RSF models with clinical variables and radiomic PET features for PFS and OS showed comparable predictive performance to RSF models with clinical variables and conventional PET parameters. Tumoral radiomic features of pre- and post-treatment FDG PET and the corresponding delta values may predict PFS and OS in patients with NPC.
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Affiliation(s)
- Soo Jeong Kim
- Department of Nuclear Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, 29, Saemunan-ro, Jongno-gu, Seoul, 03181, Republic of Korea
| | - Joon Young Choi
- Department of Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81, Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
| | - Yong Chan Ahn
- Department of Radiation Oncology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Myung-Ju Ahn
- Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Seung Hwan Moon
- Department of Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81, Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.
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Lin Fracp P, Holloway L, Min Franzcr M, Lee Franzcr M, Fowler Franzcr A. Prognostic and predictive values of baseline and mid-treatment FDG-PET in oropharyngeal carcinoma treated with primary definitive (chemo)radiation and impact of HPV status: review of current literature and emerging roles. Radiother Oncol 2023; 184:109686. [PMID: 37142128 DOI: 10.1016/j.radonc.2023.109686] [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: 07/29/2022] [Revised: 04/16/2023] [Accepted: 04/24/2023] [Indexed: 05/06/2023]
Abstract
BACKGROUND AND PURPOSE This study provides a review of the literature assessing whether semiquantitative PET parameters acquired at baseline and/or during definitive (chemo)radiotherapy ("prePET" and "iPET") can predict survival outcomes in patients with oropharyngeal squamous cell carcinoma (OPC), and the impact of human papilloma virus (HPV) status. MATERIAL AND METHODS A literature search was carried out using PubMed and Embase between 2001 to 2021 in accordance with PRISMA. RESULTS The analysis included 22 FDG-PET/CT studies1-22, 19 pre-PET and 3 both pre-PET and iPET14,18,20,. The analysis involved 2646 patients, of which 1483 are HPV-positive (17 studies: 10 mixed and 7 HPV-positive only), 589 are HPV-negative, and 574 have unknown HPV status. Eighteen studies found significant correlations of survival outcomes with pre-PET parameters, most commonly primary or "Total" (combined primary and nodal) metabolic tumour volume and/or total lesional glycolysis. Two studies could not establish significant correlations and both employed SUVmax only. Two studies also could not establish significant correlations when taking into account of the HPV-positive population only. Because of the heterogeneity and lack of standardized methodology, no conclusions on optimal cut-off values can be drawn. Ten studies specifically evaluated HPV-positive patients: five showed positive correlation of pre-PET parameters and survival outcomes, but four of these studies did not include advanced T or N staging in multivariate analysis1,6,15,22, and two studies only showed positive correlations after excluding high risk patients with smoking history7 or adverse CT features22. Two studies found that prePET parameters predicted treatment outcomes only in HPV-negative but not HPV-positive patients10,16. Two studies found that iPET parameters could predict outcomes in HPV-positive patients but not prePET parameters14,18. CONCLUSION The current literature supports high pre-treatment metabolic burden prior to definitive (chemo)radiotherapy can predict poor treatment outcomes for HPV-negative OPC patients. Evidence is conflicting and currently does not support correlation in HPV-positive patients.
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Affiliation(s)
- Peter Lin Fracp
- Department of Nuclear Medicine and PET, Liverpool Hospital, Liverpool, NSW, Australia; South Western Sydney Clinical School, University of New South Wales, NSW, Australia; School of Medicine, Western Sydney University, NSW, Australia.
| | - Lois Holloway
- South Western Sydney Clinical School, University of New South Wales, NSW, Australia; School of Medicine, Western Sydney University, NSW, Australia; Cancer Therapy Centre, Liverpool Hospital, Liverpool, NSW, Australia; Ingham Institute of Applied Medical Research, Liverpool, NSW, Australia
| | - Myo Min Franzcr
- Department of Radiation Oncology, Sunshine Coast University Hospital, Queensland, Australia; Faculty of Science, Health, Education and Engineering, University of Sunshine Coast, Queensland, Australia
| | - Mark Lee Franzcr
- South Western Sydney Clinical School, University of New South Wales, NSW, Australia; Cancer Therapy Centre, Liverpool Hospital, Liverpool, NSW, Australia
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Radiomics Applications in Head and Neck Tumor Imaging: A Narrative Review. Cancers (Basel) 2023; 15:cancers15041174. [PMID: 36831517 PMCID: PMC9954362 DOI: 10.3390/cancers15041174] [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: 12/21/2022] [Revised: 01/31/2023] [Accepted: 02/08/2023] [Indexed: 02/16/2023] Open
Abstract
Recent advances in machine learning and artificial intelligence technology have ensured automated evaluation of medical images. As a result, quantifiable diagnostic and prognostic biomarkers have been created. We discuss radiomics applications for the head and neck region in this paper. Molecular characterization, categorization, prognosis and therapy recommendation are given special consideration. In a narrative manner, we outline the fundamental technological principles, the overall idea and usual workflow of radiomic analysis and what seem to be the present and potential challenges in normal clinical practice. Clinical oncology intends for all of this to ensure informed decision support for personalized and useful cancer treatment. Head and neck cancers present a unique set of diagnostic and therapeutic challenges. These challenges are brought on by the complicated anatomy and heterogeneity of the area under investigation. Radiomics has the potential to address these barriers. Future research must be interdisciplinary and focus on the study of certain oncologic functions and outcomes, with external validation and multi-institutional cooperation in order to achieve this.
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Prospective Investigation of 18FDG-PET/MRI with Intravoxel Incoherent Motion Diffusion-Weighted Imaging to Assess Survival in Patients with Oropharyngeal or Hypopharyngeal Carcinoma. Cancers (Basel) 2022; 14:cancers14246104. [PMID: 36551590 PMCID: PMC9775681 DOI: 10.3390/cancers14246104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2022] [Revised: 12/04/2022] [Accepted: 12/09/2022] [Indexed: 12/14/2022] Open
Abstract
To prospectively investigate the prognostic value of 18F-FDG PET/MRI in patients with oropharyngeal or hypopharyngeal squamous cell carcinomas (OHSCC) treated by chemoradiotherapy. The study cohort consisted of patients with OHSCC who had undergone integrated PET/MRI prior to chemoradiotherapy or radiotherapy. Imaging parameters derived from intravoxel incoherent motion (IVIM), dynamic contrast-enhanced MRI (DCE-MRI), and 18F-FDG PET were analyzed in relation to overall survival (OS) and recurrence-free survival (RFS). In multivariable analysis, T classification (p < 0.001), metabolic tumor volume (p = 0.013), and pseudo-diffusion coefficient (p = 0.008) were identified as independent risk factors for OS. The volume transfer rate constant (p = 0.015), initial area under the curve (p = 0.043), T classification (p = 0.018), and N classification (p = 0.018) were significant predictors for RFS. The Harrell’s c-indices of OS and RFS obtained from prognostic models incorporating clinical and PET/MRI predictors were significantly higher than those derived from the traditional TNM staging system (p = 0.001). The combination of clinical risk factors with functional parameters derived from IVIM and DCE-MRI plus metabolic PET parameters derived from 18F-FDG PET in integrated PET/MRI outperformed the information provided by traditional TNM staging in predicting the survival of patients with OHSCC.
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Wang F, Zhao Y, Xu J, Shao S, Yu D. Development and external validation of a radiomics combined with clinical nomogram for preoperative prediction prognosis of resectable pancreatic ductal adenocarcinoma patients. Front Oncol 2022; 12:1037672. [PMID: 36518321 PMCID: PMC9742428 DOI: 10.3389/fonc.2022.1037672] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Accepted: 11/02/2022] [Indexed: 11/25/2023] Open
Abstract
PURPOSE To develop and externally validate a prognosis nomogram based on contrast-enhanced computed tomography (CECT) combined clinical for preoperative prognosis prediction of patients with pancreatic ductal adenocarcinoma (PDAC). METHODS 184 patients from Center A with histopathologically confirmed PDAC who underwent CECT were included and allocated to training cohort (n=111) and internal validation cohort (n=28). The radiomic score (Rad - score) for predicting overall survival (OS) was constructed by using the least absolute shrinkage and selection operator (LASSO). Univariate and multivariable Cox regression analysis was used to construct clinic-pathologic features. Finally, a radiomics nomogram incorporating the Rad - score and clinical features was established. External validation was performed using Center B dataset (n = 45). The validation of nomogram was evaluated by calibration curve, Harrell's concordance index (C-index) and decision curve analysis (DCA). The Kaplan-Meier (K-M) method was used for OS analysis. RESULTS Univariate and multivariate analysis indicated that Rad - score, preoperative CA 19-9 and postoperative American Joint Committee on Cancer (AJCC) TNM stage were significant prognostic factors. The nomogram based on Rad - score and preoperative CA19-9 was found to exhibit excellent prediction ability: in the training cohort, C-index was superior to that of the preoperative CA19-9 (0.713 vs 0.616, P< 0.001) and AJCC TNM stage (0.713 vs 0.614, P< 0.001); the C-index was also had good performance in the validation cohort compared with CA19-9 (internal validation cohort: 0.694 vs 0.555, P< 0.001; external validation cohort: 0.684 vs 0.607, P< 0.001) and AJCC TNM stage (internal validation cohort: 0.694 vs 0.563, P< 0.001; external validation cohort: 0.684 vs 0.596, P< 0.001). The calibration plot and DCA showed excellent predictive accuracy in the validation cohort. CONCLUSION We established a well-designed nomogram to accurately predict OS of PDAC preoperatively. The nomogram showed a satisfactory prediction effect and was worthy of further evaluation in the future.
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Affiliation(s)
- Fangqing Wang
- Departments of Radiology, Qilu Hospital of Shandong University, Jinan, China
| | - Yuxuan Zhao
- Departments of Radiology, Qilu Hospital of Shandong University, Jinan, China
| | - Jianwei Xu
- Department of Pancreatic Surgery, Qilu Hospital of Shandong University, Jinan, China
| | - Sai Shao
- Shandong Provincial Hospital, Shandong University, Jinan, China
| | - Dexin Yu
- Departments of Radiology, Qilu Hospital of Shandong University, Jinan, China
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Noor A, Mair M, Cook L, Bolt H, Cheriyan S, Woods CM, Hopkins J, Ooi EH. Prognostic Value of
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F‐Fluoro‐Deoxyglucose‐Positron Emission Tomography Volumetric Parameters in Human Papillomavirus‐Related Oropharyngeal Squamous Cell Carcinoma. Laryngoscope 2022. [DOI: 10.1002/lary.30362] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Revised: 07/15/2022] [Accepted: 08/08/2022] [Indexed: 11/06/2022]
Affiliation(s)
- Anthony Noor
- Department of Otolaryngology, Head and Neck Surgery Flinders Medical Centre Bedford Park South Australia Australia
| | - Manish Mair
- Department of Otolaryngology, Head and Neck Surgery John Hunter Hospital Newcastle New South Wales Australia
| | - Lachlan Cook
- Department of Otolaryngology, Head and Neck Surgery Flinders Medical Centre Bedford Park South Australia Australia
- Flinders Health and Medical Research Institute, College of Medicine and Public Health Flinders University Adelaide South Australia Australia
| | - Harrison Bolt
- Department of Otolaryngology, Head and Neck Surgery Flinders Medical Centre Bedford Park South Australia Australia
| | - Sanith Cheriyan
- Department of Otolaryngology, Head and Neck Surgery Flinders Medical Centre Bedford Park South Australia Australia
| | - Charmaine M. Woods
- Department of Otolaryngology, Head and Neck Surgery Flinders Medical Centre Bedford Park South Australia Australia
- Flinders Health and Medical Research Institute, College of Medicine and Public Health Flinders University Adelaide South Australia Australia
| | - James Hopkins
- Department of Medical Imaging Flinders Medical Centre Bedford Park South Australia Australia
| | - Eng H. Ooi
- Department of Otolaryngology, Head and Neck Surgery Flinders Medical Centre Bedford Park South Australia Australia
- Flinders Health and Medical Research Institute, College of Medicine and Public Health Flinders University Adelaide South Australia Australia
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Dmytriw AA, Ortega C, Anconina R, Metser U, Liu ZA, Liu Z, Li X, Sananmuang T, Yu E, Joshi S, Waldron J, Huang SH, Bratman S, Hope A, Veit-Haibach P. Nasopharyngeal Carcinoma Radiomic Evaluation with Serial PET/CT: Exploring Features Predictive of Survival in Patients with Long-Term Follow-Up. Cancers (Basel) 2022; 14:cancers14133105. [PMID: 35804877 PMCID: PMC9264840 DOI: 10.3390/cancers14133105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Revised: 06/09/2022] [Accepted: 06/21/2022] [Indexed: 02/04/2023] Open
Abstract
Simple Summary Nasopharyngeal carcinoma (NPC) is a frequent head and neck cancer, especially in Asian countries. Our studies investigated the value of minable data derived from standard of care PET/CT imaging in patients with NPC. The here presented evaluation found that certain specific imaging features in this patient population can be potentially used to predict overall survival and progression free survival at different time points in those patients. Abstract Purpose: We aim determine the value of PET and CT radiomic parameters on survival with serial follow-up PET/CT in patients with nasopharyngeal carcinoma (NPC) for which curative intent therapy is undertaken. Methods: Patients with NPC and available pre-treatment as well as follow up PET/CT were included from 2005 to 2006 and were followed to 2021. Baseline demographic, radiological and outcome data were collected. Univariable Cox proportional hazard models were used to evaluate features from baseline and follow-up time points, and landmark analyses were performed for each time point. Results: Sixty patients were enrolled, and two-hundred and seventy-eight (278) PET/CT were at baseline and during follow-up. Thirty-eight percent (38%) were female, and sixty-two patients were male. All patients underwent curative radiation or chemoradiation therapy. The median follow-up was 11.72 years (1.26–14.86). Five-year and ten-year overall survivals (OSs) were 80.0% and 66.2%, and progression-free survival (PFS) was 90.0% and 74.4%. Time-dependent modelling suggested that, among others, PET gray-level zone length matrix (GLZLM) gray-level non-uniformity (GLNU) (HR 2.74 95% CI 1.06, 7.05) was significantly associated with OS. Landmark analyses suggested that CT parameters were most predictive at 15 month, whereas PET parameters were most predictive at time points 3, 6, 9 and 15 month. Conclusions: This study with long-term follow up data on NPC suggests that mainly PET-derived radiomic features are predictive for OS but not PFS in a time-dependent evaluation. Furthermore, CT radiomic measures may predict OS and PFS best at initial and long-term follow-up time points and PET measures may be more predictive in the interval. These modalities are commonly used in NPC surveillance, and prospective validation should be considered.
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Affiliation(s)
- Adam A. Dmytriw
- Department of Medical Imaging, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON M4N 3M5, Canada; (A.A.D.); (R.A.)
- Joint Department of Medical Imaging, Toronto General Hospital, University Health Network, University of Toronto, Toronto, ON M5G 2C4, Canada; (C.O.); (U.M.); (T.S.); (E.Y.); (S.J.)
| | - Claudia Ortega
- Joint Department of Medical Imaging, Toronto General Hospital, University Health Network, University of Toronto, Toronto, ON M5G 2C4, Canada; (C.O.); (U.M.); (T.S.); (E.Y.); (S.J.)
| | - Reut Anconina
- Department of Medical Imaging, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON M4N 3M5, Canada; (A.A.D.); (R.A.)
| | - Ur Metser
- Joint Department of Medical Imaging, Toronto General Hospital, University Health Network, University of Toronto, Toronto, ON M5G 2C4, Canada; (C.O.); (U.M.); (T.S.); (E.Y.); (S.J.)
| | - Zhihui A. Liu
- Department of Biostatistics, Princess Margaret Cancer Centre, University Health Network, University of Toronto, Toronto, ON M5G 2C1, Canada; (Z.A.L.); (Z.L.); (X.L.)
| | - Zijin Liu
- Department of Biostatistics, Princess Margaret Cancer Centre, University Health Network, University of Toronto, Toronto, ON M5G 2C1, Canada; (Z.A.L.); (Z.L.); (X.L.)
| | - Xuan Li
- Department of Biostatistics, Princess Margaret Cancer Centre, University Health Network, University of Toronto, Toronto, ON M5G 2C1, Canada; (Z.A.L.); (Z.L.); (X.L.)
| | - Thiparom Sananmuang
- Joint Department of Medical Imaging, Toronto General Hospital, University Health Network, University of Toronto, Toronto, ON M5G 2C4, Canada; (C.O.); (U.M.); (T.S.); (E.Y.); (S.J.)
- Department of Diagnostic and Therapeutic Radiology, Faculty of Medicine Ramathibodi Hospital, Mahidol University,270 Rama VI Road, Ratchathewi, Bangkok 10400, Thailand
| | - Eugene Yu
- Joint Department of Medical Imaging, Toronto General Hospital, University Health Network, University of Toronto, Toronto, ON M5G 2C4, Canada; (C.O.); (U.M.); (T.S.); (E.Y.); (S.J.)
| | - Sayali Joshi
- Joint Department of Medical Imaging, Toronto General Hospital, University Health Network, University of Toronto, Toronto, ON M5G 2C4, Canada; (C.O.); (U.M.); (T.S.); (E.Y.); (S.J.)
| | - John Waldron
- Department of Radiation Oncology, Princess Margaret Cancer Centre, University Health Network, University of Toronto, Toronto, ON M5G 2C1, Canada; (J.W.); (S.H.H.); (S.B.); (A.H.)
| | - Shao Hui Huang
- Department of Radiation Oncology, Princess Margaret Cancer Centre, University Health Network, University of Toronto, Toronto, ON M5G 2C1, Canada; (J.W.); (S.H.H.); (S.B.); (A.H.)
| | - Scott Bratman
- Department of Radiation Oncology, Princess Margaret Cancer Centre, University Health Network, University of Toronto, Toronto, ON M5G 2C1, Canada; (J.W.); (S.H.H.); (S.B.); (A.H.)
| | - Andrew Hope
- Department of Radiation Oncology, Princess Margaret Cancer Centre, University Health Network, University of Toronto, Toronto, ON M5G 2C1, Canada; (J.W.); (S.H.H.); (S.B.); (A.H.)
| | - Patrick Veit-Haibach
- Joint Department of Medical Imaging, Toronto General Hospital, University Health Network, University of Toronto, Toronto, ON M5G 2C4, Canada; (C.O.); (U.M.); (T.S.); (E.Y.); (S.J.)
- Correspondence: ; Tel.: +416-340-4800 (ext. 6085); Fax: 416-340-3900
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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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Role of Texture Analysis in Oropharyngeal Carcinoma: A Systematic Review of the Literature. Cancers (Basel) 2022; 14:cancers14102445. [PMID: 35626048 PMCID: PMC9139172 DOI: 10.3390/cancers14102445] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 05/02/2022] [Accepted: 05/10/2022] [Indexed: 11/16/2022] Open
Abstract
Simple Summary The incidence of squamous cell carcinomas of the oropharynx has rapidly increased in the last two decades due to human papilloma virus infection (HPV). HPV-positive and HPV-negative squamous cell tumours differ in radiological imaging, treatment, and prognosis; therefore, differential diagnosis is mandatory. Radiomics with texture analysis is an innovative technique that has been used increasingly in recent years to characterise the tissue heterogeneity of certain structures such as neoplasms or organs by measuring the spatial distribution of pixel values on radiological imaging. This review delineates the application of texture analysis in oropharyngeal tumours and explores how radiomics may potentially improve clinical decision-making. Abstract Human papilloma virus infection (HPV) is associated with the development of lingual and palatine tonsil carcinomas. Diagnosing, differentiating HPV-positive from HPV-negative cancers, and assessing the presence of lymph node metastases or recurrences by the visual interpretation of images is not easy. Texture analysis can provide structural information not perceptible to human eyes. A systematic literature search was performed on 16 February 2022 for studies with a focus on texture analysis in oropharyngeal cancers. We conducted the research on PubMed, Scopus, and Web of Science platforms. Studies were screened for inclusion according to the preferred reporting items for systematic reviews. Twenty-six studies were included in our review. Nineteen articles related specifically to the oropharynx and seven articles analysed the head and neck area with sections dedicated to the oropharynx. Six, thirteen, and seven articles used MRI, CT, and PET, respectively, as the imaging techniques by which texture analysis was performed. Regarding oropharyngeal tumours, this review delineates the applications of texture analysis in (1) the diagnosis, prognosis, and assessment of disease recurrence or persistence after therapy, (2) early differentiation of HPV-positive versus HPV-negative cancers, (3) the detection of cancers not visualised by imaging alone, and (4) the assessment of lymph node metastases from unknown primary carcinomas.
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Marr L, Haller B, Pyka T, Peeken JC, Jesinghaus M, Scheidhauer K, Friess H, Combs SE, Münch S. Predictive value of clinical and 18F-FDG-PET/CT derived imaging parameters in patients undergoing neoadjuvant chemoradiation for esophageal squamous cell carcinoma. Sci Rep 2022; 12:7148. [PMID: 35504955 PMCID: PMC9065158 DOI: 10.1038/s41598-022-11076-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Accepted: 04/12/2022] [Indexed: 12/24/2022] Open
Abstract
Aim of this study was to validate the prognostic impact of clinical parameters and baseline 18F-FDG-PET/CT derived textural features to predict histopathologic response and survival in patients with esophageal squamous cell carcinoma undergoing neoadjuvant chemoradiation (nCRT) and surgery. Between 2005 and 2014, 38 ESCC were treated with nCRT and surgery. For all patients, the 18F-FDG-PET-derived parameters metabolic tumor volume (MTV), SUVmax, contrast and busyness were calculated for the primary tumor using a SUV-threshold of 3. The parameter uniformity was calculated using contrast-enhanced computed tomography. Based on histopathological response to nCRT, patients were classified as good responders (< 10% residual tumor) (R) or non-responders (≥ 10% residual tumor) (NR). Regression analyses were used to analyse the association of clinical parameters and imaging parameters with treatment response and overall survival (OS). Good response to nCRT was seen in 27 patients (71.1%) and non-response was seen in 11 patients (28.9%). Grading was the only parameter predicting response to nCRT (Odds Ratio (OR) = 0.188, 95% CI: 0.040–0.883; p = 0.034). No association with histopathologic treatment response was seen for any of the evaluated imaging parameters including SUVmax, MTV, busyness, contrast and uniformity. Using multivariate Cox-regression analysis, the heterogeneity parameters busyness (Hazard Ratio (HR) = 1.424, 95% CI: 1.044–1.943; p = 0.026) and contrast (HR = 6.678, 95% CI: 1.969–22.643;p = 0.002) were independently associated with OS, while no independent association with OS was seen for SUVmax and MTV. In patients with ESCC undergoing nCRT and surgery, baseline 18F-FDG-PET/CT derived parameters could not predict histopathologic response to nCRT. However, the PET/CT derived features busyness and contrast were independently associated with OS and should be further investigated.
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Affiliation(s)
- Lisa Marr
- Department of Radiation Oncology, Klinikum Rechts der Isar, Technical University of Munich (TUM), Ismaninger Str. 22, 81675, Munich, Germany
| | - Bernhard Haller
- Institute of Medical Informatics, Statistics and Epidemiology, Klinikum Rechts der Isar, Technical University of Munich (TUM), Ismaninger Str. 22, 81675, Munich, Germany
| | - Thomas Pyka
- Departement of Nuclear Medicine, Klinikum Rechts der Isar, Technical University of Munich (TUM), Ismaninger Str. 22, 81675, Munich, Germany.,Department of Nuclear Medicine, Inselspital Bern, Freiburgstr. 18, 3010, Bern, Switzerland
| | - Jan C Peeken
- Department of Radiation Oncology, Klinikum Rechts der Isar, Technical University of Munich (TUM), Ismaninger Str. 22, 81675, Munich, Germany.,German Cancer Consortium (DKTK), Partner Site Munich, Munich, Germany.,Institute of Radiation Medicine (IRM), Helmholtz Zentrum München (HMGU), Ingolstädter Landstraße 1, 85764, Oberschleißheim, Germany
| | - Moritz Jesinghaus
- Institute of Pathology, Klinikum Rechts der Isar, Technical University of Munich (TUM), Ismaninger Str. 22, 81675, Munich, Germany
| | - Klemens Scheidhauer
- Departement of Nuclear Medicine, Klinikum Rechts der Isar, Technical University of Munich (TUM), Ismaninger Str. 22, 81675, Munich, Germany
| | - Helmut Friess
- Department of Surgery, Klinikum Rechts der Isar, Technical University of Munich (TUM), Ismaninger Str. 22, 81675, Munich, Germany
| | - Stephanie E Combs
- Department of Radiation Oncology, Klinikum Rechts der Isar, Technical University of Munich (TUM), Ismaninger Str. 22, 81675, Munich, Germany.,German Cancer Consortium (DKTK), Partner Site Munich, Munich, Germany.,Institute of Radiation Medicine (IRM), Helmholtz Zentrum München (HMGU), Ingolstädter Landstraße 1, 85764, Oberschleißheim, Germany
| | - Stefan Münch
- Department of Radiation Oncology, Klinikum Rechts der Isar, Technical University of Munich (TUM), Ismaninger Str. 22, 81675, Munich, Germany. .,German Cancer Consortium (DKTK), Partner Site Munich, Munich, Germany.
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Zhou Y, Li J, Zhang X, Jia T, Zhang B, Dai N, Sang S, Deng S. Prognostic Value of Radiomic Features of 18F-FDG PET/CT in Patients With B-Cell Lymphoma Treated With CD19/CD22 Dual-Targeted Chimeric Antigen Receptor T Cells. Front Oncol 2022; 12:834288. [PMID: 35198451 PMCID: PMC8858981 DOI: 10.3389/fonc.2022.834288] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Accepted: 01/12/2022] [Indexed: 12/24/2022] Open
Abstract
ObjectiveIn the present study, we aimed to evaluate the prognostic value of PET/CT-derived radiomic features for patients with B-cell lymphoma (BCL), who were treated with CD19/CD22 dual-targeted chimeric antigen receptor (CAR) T cells. Moreover, we explored the relationship between baseline radiomic features and the occurrence probability of cytokine release syndrome (CRS).MethodsA total of 24 BCL patients who received 18F-FDG PET/CT before CAR T-cell infusion were enrolled in the present study. Radiomic features from PET and CT images were extracted using LIFEx software, and the least absolute shrinkage and selection operator (LASSO) regression was used to select the most useful predictive features of progression-free survival (PFS) and overall survival (OS). Receiver operating characteristic curves, Cox proportional hazards model, and Kaplan-Meier curves were conducted to assess the potential prognostic value.ResultsContrast extracted from neighbourhood grey-level different matrix (NGLDM) was an independent predictor of PFS (HR = 15.16, p = 0.023). MYC and BCL2 double-expressor (DE) was of prognostic significance for PFS (HR = 7.02, p = 0.047) and OS (HR = 10.37, p = 0.041). The combination of NGLDM_ContrastPET and DE yielded three risk groups with zero (n = 7), one (n = 11), or two (n = 6) factors (p < 0.0001 and p = 0.0004, for PFS and OS), respectively. The PFS was 85.7%, 63.6%, and 0%, respectively, and the OS was 100%, 90.9%, and 16.7%, respectively. Moreover, there was no significant association between PET/CT variables and CRS.ConclusionsIn conclusion, radiomic features extracted from baseline 18F-FDG PET/CT images in combination with genomic factors could predict the survival outcomes of BCL patients receiving CAR T-cell therapy.
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Affiliation(s)
- Yeye Zhou
- Department of Nuclear Medicine, the First Affiliated Hospital of Soochow University, Suzhou, China
| | - Jihui Li
- Department of Nuclear Medicine, the First Affiliated Hospital of Soochow University, Suzhou, China
| | - Xiaoyi Zhang
- Department of Nuclear Medicine, Changshu No. 2 People’s Hospital, Changshu, China
| | - Tongtong Jia
- Department of Nuclear Medicine, the First Affiliated Hospital of Soochow University, Suzhou, China
| | - Bin Zhang
- Department of Nuclear Medicine, the First Affiliated Hospital of Soochow University, Suzhou, China
| | - Na Dai
- Department of Nuclear Medicine, the First Affiliated Hospital of Soochow University, Suzhou, China
| | - Shibiao Sang
- Department of Nuclear Medicine, the First Affiliated Hospital of Soochow University, Suzhou, China
- *Correspondence: Shengming Deng, ; Shibiao Sang,
| | - Shengming Deng
- Department of Nuclear Medicine, the First Affiliated Hospital of Soochow University, Suzhou, China
- Nuclear Medicine Laboratory of Mianyang Central Hospital, Mianyang, China
- State Key Laboratory of Radiation Medicine and Protection, Soochow University, Suzhou, China
- *Correspondence: Shengming Deng, ; Shibiao Sang,
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Anan N, Zainon R, Tamal M. A review on advances in 18F-FDG PET/CT radiomics standardisation and application in lung disease management. Insights Imaging 2022; 13:22. [PMID: 35124733 PMCID: PMC8817778 DOI: 10.1186/s13244-021-01153-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Accepted: 12/23/2021] [Indexed: 02/06/2023] Open
Abstract
Radiomics analysis quantifies the interpolation of multiple and invisible molecular features present in diagnostic and therapeutic images. Implementation of 18-fluorine-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) radiomics captures various disorders in non-invasive and high-throughput manner. 18F-FDG PET/CT accurately identifies the metabolic and anatomical changes during cancer progression. Therefore, the application of 18F-FDG PET/CT in the field of oncology is well established. Clinical application of 18F-FDG PET/CT radiomics in lung infection and inflammation is also an emerging field. Combination of bioinformatics approaches or textual analysis allows radiomics to extract additional information to predict cell biology at the micro-level. However, radiomics texture analysis is affected by several factors associated with image acquisition and processing. At present, researchers are working on mitigating these interrupters and developing standardised workflow for texture biomarker establishment. This review article focuses on the application of 18F-FDG PET/CT in detecting lung diseases specifically on cancer, infection and inflammation. An overview of different approaches and challenges encountered on standardisation of 18F-FDG PET/CT technique has also been highlighted. The review article provides insights about radiomics standardisation and application of 18F-FDG PET/CT in lung disease management.
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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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Marcu LG, Marcu DC. Current Omics Trends in Personalised Head and Neck Cancer Chemoradiotherapy. J Pers Med 2021; 11:jpm11111094. [PMID: 34834445 PMCID: PMC8625829 DOI: 10.3390/jpm11111094] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Revised: 10/22/2021] [Accepted: 10/25/2021] [Indexed: 12/12/2022] Open
Abstract
Chemoradiotherapy remains the most common management of locally advanced head and neck cancer. While both treatment components have greatly developed over the years, the quality of life and long-term survival of patients undergoing treatment for head and neck malignancies are still poor. Research in head and neck oncology is equally focused on the improvement of tumour response to treatment and on the limitation of normal tissue toxicity. In this regard, personalised therapy through a multi-omics approach targeting patient management from diagnosis to treatment shows promising results. The aim of this paper is to discuss the latest results regarding the personalised approach to chemoradiotherapy of head and neck cancer by gathering the findings of the newest omics, involving radiotherapy (dosiomics), chemotherapy (pharmacomics), and medical imaging for treatment monitoring (radiomics). The incorporation of these omics into head and neck cancer management offers multiple viewpoints to treatment that represent the foundation of personalised therapy.
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Affiliation(s)
- Loredana G. Marcu
- Faculty of Informatics & Science, University of Oradea, 410087 Oradea, Romania
- Cancer Research Institute, University of South Australia, Adelaide, SA 5001, Australia
- Correspondence:
| | - David C. Marcu
- Faculty of Electrical Engineering & Information Technology, University of Oradea, 410087 Oradea, Romania;
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Prediction Model for Tumor Budding Status Using the Radiomic Features of F-18 Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography in Cervical Cancer. Diagnostics (Basel) 2021; 11:diagnostics11081517. [PMID: 34441452 PMCID: PMC8392321 DOI: 10.3390/diagnostics11081517] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Revised: 08/18/2021] [Accepted: 08/18/2021] [Indexed: 02/08/2023] Open
Abstract
OBJECTIVE To compare the radiomic features of F-18 fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) and intratumoral heterogeneity according to tumor budding (TB) status and to develop a prediction model for the TB status using the radiomic feature of 18F-FDG PET/CT in patients with cervical cancer. MATERIALS AND METHODS Seventy-six patients with cervical cancer who underwent radical hysterectomy and preoperative 18F-FDG PET/CT were included. We assessed the status of intratumoral budding (ITP) and peritumoral budding (PTB) in all available hematoxylin and eosin-stained specimens. Three conventional metabolic parameters and fifty-nine features were extracted and analyzed. Univariate analysis was used to identify significant metabolic parameters and radiomic findings for TB status. The prediction model for TB status was built using 3 machine learning classifiers (random forest, support vector machine, and neural network). RESULTS Univariate analysis led to the identification of 2 significant metabolic parameters and 12 significant radiomic features according to intratumoral budding (ITB) status. Among these parameters, following multivariate analysis for the ITB status, only compacity remained significant (odds ratio, 5.0047; 95% confidence interval, 1.1636-21.5253; p = 0.0305). Two conventional metabolic parameters and 25 radiomic features were selected by the Lasso regularization, and the prediction model for the ITB status had a mean area under the curve of 0.762 in the test dataset. CONCLUSION Radiomic features of 18F-FDG PET/CT were associated with the ITB status. The prediction model using radiomic features successfully predicted the TB status in patients with cervical cancer. The prediction models for the ITB status may contribute to personalized medicine in the management of patients with cervical cancer.
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Maleki F, Le WT, Sananmuang T, Kadoury S, Forghani R. Machine Learning Applications for Head and Neck Imaging. Neuroimaging Clin N Am 2021; 30:517-529. [PMID: 33039001 DOI: 10.1016/j.nic.2020.08.003] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
The head and neck (HN) consists of a large number of vital anatomic structures within a compact area. Imaging plays a central role in the diagnosis and management of major disorders affecting the HN. This article reviews the recent applications of machine learning (ML) in HN imaging with a focus on deep learning approaches. It categorizes ML applications in HN imaging into deep learning and traditional ML applications and provides examples of each category. It also discusses the main challenges facing the successful deployment of ML-based applications in the clinical setting and provides suggestions for addressing these challenges.
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Affiliation(s)
- Farhad Maleki
- Augmented Intelligence & Precision Health Laboratory (AIPHL), Department of Radiology & Research Institute of the McGill University Health Centre, 5252 Boulevard de Maisonneuve Ouest, Montreal, Quebec H4A 3S5, Canada
| | - William Trung Le
- Polytechnique Montreal, PO Box 6079, succ. Centre-ville, Montreal, Quebec H3C 3A7, Canada
| | - Thiparom Sananmuang
- Department of Diagnostic and Therapeutic Radiology and Research, Faculty of Medicine Ramathibodi Hospital, Ratchathewi, Bangkok 10400, Thailand
| | - Samuel Kadoury
- Polytechnique Montreal, PO Box 6079, succ. Centre-ville, Montreal, Quebec H3C 3A7, Canada; CHUM Research Center, 900 St Denis Street, Montreal, Quebec H2X 0A9, Canada
| | - Reza Forghani
- Augmented Intelligence & Precision Health Laboratory (AIPHL), Department of Radiology & Research Institute of the McGill University Health Centre, 5252 Boulevard de Maisonneuve Ouest, Montreal, Quebec H4A 3S5, Canada; Department of Radiology, McGill University, 1650 Cedar Avenue, Montreal, Quebec H3G1A4, Canada; Segal Cancer Centre, Lady Davis Institute for Medical Research, Jewish General Hospital, 3755 Cote Ste-Catherine Road, Montreal, Quebec H3T 1E2, Canada; Gerald Bronfman Department of Oncology, McGill University, Suite 720, 5100 Maisonneuve Boulevard West, Montreal, Quebec H4A3T2, Canada; Department of Otolaryngology, Head and Neck Surgery, Royal Victoria Hospital, McGill University Health Centre, 1001 boul. Decarie Boulevard, Montreal, Quebec H3A 3J1, Canada.
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Lin P, Min M, Lai K, Lee M, Holloway L, Xuan W, Bray V, Fowler A, Lee CS, Yong J. Mid-treatment Fluorodeoxyglucose Positron Emission Tomography in Human Papillomavirus-related Oropharyngeal Squamous Cell Carcinoma Treated with Primary Radiotherapy: Nodal Metabolic Response Rate can Predict Treatment Outcomes. Clin Oncol (R Coll Radiol) 2021; 33:e586-e598. [PMID: 34373179 DOI: 10.1016/j.clon.2021.07.011] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Revised: 06/05/2021] [Accepted: 07/16/2021] [Indexed: 11/26/2022]
Abstract
AIMS To evaluate whether biomarkers derived from fluorodeoxyglucose positron emission tomography-computed tomography (FDG PET-CT) performed prior to (prePET) and during the third week (interim PET; iPET) of radiotherapy can predict treatment outcomes in human papillomavirus (HPV)-positive oropharyngeal squamous cell carcinoma (OPC). MATERIALS AND METHODS This retrospective analysis included 46 patients with newly diagnosed OPC treated with definitive (chemo)radiation and all patients had confirmed positive HPV status (HPV+OPC) based on p16 immunohistochemistry. The maximum standardised uptake value (SUVmax), metabolic tumour volume (MTV) and total lesional glycolysis (TLG) of primary, index node (node with the highest TLG) and total lymph nodes and their median percentage (≥50%) reductions in iPET were analysed, and correlated with 5-year Kaplan-Meier and multivariable analyses (smoking, T4, N2b-3 and AJCC stage IV), including local failure-free survival, regional failure-free survival, locoregional failure-free survival (LRFFS), distant metastatic failure-free survival (DMFFS), disease-free survival (DFS) and overall survival. RESULTS There was no association of outcomes with prePET parameters observed on multivariate analysis. A complete metabolic response of primary tumour was seen in 13 patients; the negative predictive value for local failure was 100%. More than a 50% reduction in total nodal MTV provided the best predictor of outcomes, including LRFFS (88% versus 47.1%, P = 0.006, hazard ratio = 0.153) and DFS (78.2% versus 41.2%, P = 0.01, hazard ratio = 0.234). More than a 50% reduction in index node TLG was inversely related to DMFFS: a better nodal response was associated with a higher incidence of distant metastatic failure (66.7% versus 100%, P = 0.009, hazard ratio = 3.0). CONCLUSION The reduction (≥50%) of volumetric nodal metabolic burden can potentially identify a subgroup of HPV+OPC patients at low risk of locoregional failure but inversely at higher risk of distant metastatic failure and may have a role in individualised adaptive radiotherapy and systemic therapy.
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Affiliation(s)
- P Lin
- Department of Nuclear Medicine and PET, Liverpool Hospital, Liverpool, New South Wales, Australia; South Western Sydney Clinical School, University of New South Wales, New South Wales, Australia; School of Medicine, Western Sydney University, New South Wales, Australia.
| | - M Min
- Department of Radiation Oncology, Sunshine Coast University Hospital, Queensland, Australia; Faculty of Science, Health, Education and Engineering, University of Sunshine Coast, Queensland, Australia; Cancer Therapy Centre, Liverpool Hospital, Liverpool, New South Wales, Australia
| | - K Lai
- Department of Nuclear Medicine and PET, Liverpool Hospital, Liverpool, New South Wales, Australia; School of Medicine, Western Sydney University, New South Wales, Australia
| | - M Lee
- South Western Sydney Clinical School, University of New South Wales, New South Wales, Australia; Cancer Therapy Centre, Liverpool Hospital, Liverpool, New South Wales, Australia
| | - L Holloway
- South Western Sydney Clinical School, University of New South Wales, New South Wales, Australia; School of Medicine, Western Sydney University, New South Wales, Australia; Cancer Therapy Centre, Liverpool Hospital, Liverpool, New South Wales, Australia; Ingham Institute of Applied Medical Research, Liverpool, New South Wales, Australia
| | - W Xuan
- South Western Sydney Clinical School, University of New South Wales, New South Wales, Australia; Ingham Institute of Applied Medical Research, Liverpool, New South Wales, Australia
| | - V Bray
- Cancer Therapy Centre, Liverpool Hospital, Liverpool, New South Wales, Australia
| | - A Fowler
- Cancer Therapy Centre, Liverpool Hospital, Liverpool, New South Wales, Australia
| | - C S Lee
- South Western Sydney Clinical School, University of New South Wales, New South Wales, Australia; School of Medicine, Western Sydney University, New South Wales, Australia; Ingham Institute of Applied Medical Research, Liverpool, New South Wales, Australia; Department of Anatomical Pathology, Liverpool Hospital, Liverpool, New South Wales, Australia; Central Clinical School, University of Sydney, New South Wales, Australia
| | - J Yong
- Department of Anatomical Pathology, Liverpool Hospital, Liverpool, New South Wales, Australia
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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: 31] [Impact Index Per Article: 10.3] [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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A Phantom Study to Investigate Robustness and Reproducibility of Grey Level Co-Occurrence Matrix (GLCM)-Based Radiomics Features for PET. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11020535] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Quantification and classification of heterogeneous radiotracer uptake in Positron Emission Tomography (PET) using textural features (termed as radiomics) and artificial intelligence (AI) has the potential to be used as a biomarker of diagnosis and prognosis. However, textural features have been predicted to be strongly correlated with volume, segmentation and quantization, while the impact of image contrast and noise has not been assessed systematically. Further continuous investigations are required to update the existing standardization initiatives. This study aimed to investigate the relationships between textural features and these factors with 18F filled torso NEMA phantom to yield different contrasts and reconstructed with different durations to represent varying levels of noise. The phantom was also scanned with heterogeneous spherical inserts fabricated with 3D printing technology. All spheres were delineated using: (1) the exact boundaries based on their known diameters; (2) 40% fixed; and (3) adaptive threshold. Six textural features were derived from the gray level co-occurrence matrix (GLCM) using different quantization levels. The results indicate that homogeneity and dissimilarity are the most suitable for measuring PET tumor heterogeneity with quantization 64 provided that the segmentation method is robust to noise and contrast variations. To use these textural features as prognostic biomarkers, changes in textural features between baseline and treatment scans should always be reported along with the changes in volumes.
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24
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Chen YH, Wang TF, Chu SC, Lin CB, Wang LY, Lue KH, Liu SH, Chan SC. Incorporating radiomic feature of pretreatment 18F-FDG PET improves survival stratification in patients with EGFR-mutated lung adenocarcinoma. PLoS One 2020; 15:e0244502. [PMID: 33370365 PMCID: PMC7769431 DOI: 10.1371/journal.pone.0244502] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Accepted: 12/10/2020] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND To investigate the survival prognostic value of the radiomic features of 18F-FDG PET in patients who had EGFR (epidermal growth factor receptor) mutated lung adenocarcinoma and received targeted TKI (tyrosine kinase inhibitor) treatment. METHODS Fifty-one patients with stage III-IV lung adenocarcinoma and actionable EGFR mutation who received first-line TKI were retrospectively analyzed. All patients underwent pretreatment 18F-FDG PET/CT, and we calculated the PET-derived radiomic features. Cox proportional hazard model was used to examine the association between the radiomic features and the survival outcomes, including progression-free survival (PFS) and overall survival (OS). A score model was established according to the independent prognostic predictors and we compared this model to the TNM staging system using Harrell's concordance index (c-index). RESULTS Forty-eight patients (94.1%) experienced disease progression and 41 patients (80.4%) died. Primary tumor SUV entropy > 5.36, and presence of pleural effusion were independently associated with worse OS (both p < 0.001) and PFS (p = 0.001, and 0.003, respectively). We used these two survival predictors to devise a scoring system (score 0-2). Patients with a score of 1 or 2 had a worse survival than those with a score of 0 (HR for OS: 3.6, p = 0.006 for score 1, and HR: 21.8, p < 0.001 for score 2; HR for PFS: 2.2, p = 0.027 for score 1 and HR: 8.8, p < 0.001 for score 2). Our scoring system surpassed the TNM staging system (c-index = 0.691 versus 0.574, p = 0.013 for OS, and c-index = 0.649 versus 0.517, p = 0.004 for PFS). CONCLUSIONS In this preliminary study, combining PET radiomics with clinical risk factors may improve survival stratification in stage III-IV lung adenocarcinoma with actionable EFGR mutation. Our proposed scoring system may assist with optimization of individualized treatment strategies in these patients.
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Affiliation(s)
- Yu-Hung Chen
- Department of Nuclear Medicine, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien, Taiwan
- Department of Medicine, College of Medicine, Tzu Chi University, Hualien, Taiwan
| | - Tso-Fu Wang
- Department of Medicine, College of Medicine, Tzu Chi University, Hualien, Taiwan
- Department of Hematology and Oncology, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien, Taiwan
| | - Sung-Chao Chu
- Department of Medicine, College of Medicine, Tzu Chi University, Hualien, Taiwan
- Department of Hematology and Oncology, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien, Taiwan
| | - Chih-Bin Lin
- Department of Internal Medicine, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien, Taiwan
| | - Ling-Yi Wang
- Epidemiology and Biostatistics Consulting Center, Department of Medical Research and Department of Pharmacy, Tzu Chi General Hospital, Hualien, Taiwan
| | - Kun-Han Lue
- Department of Medical Imaging and Radiological Sciences, Tzu Chi University of Science and Technology, Hualien, Taiwan
| | - Shu-Hsin Liu
- Department of Nuclear Medicine, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien, Taiwan
- Department of Medical Imaging and Radiological Sciences, Tzu Chi University of Science and Technology, Hualien, Taiwan
| | - Sheng-Chieh Chan
- Department of Nuclear Medicine, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien, Taiwan
- Department of Medicine, College of Medicine, Tzu Chi University, Hualien, Taiwan
- * E-mail:
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25
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Correlation of texture feature analysis with bone marrow infiltration in initial staging of patients with lymphoma using 18F-fluorodeoxyglucose positron emission tomography combined with computed tomography. Pol J Radiol 2020; 85:e586-e594. [PMID: 33204373 PMCID: PMC7654316 DOI: 10.5114/pjr.2020.99833] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Accepted: 08/29/2020] [Indexed: 12/18/2022] Open
Abstract
Purpose To explore whether radiomic features of fluorine-18-fluorodeoxyglucose (18F-FDG) positron emission tomo-graphy-computed tomography (PET/CT) has association with bone marrow infiltration (BMI) in comparison to other conventional PET metrics. Material and methods Forty-four patients (with pathologically proven lymphoma disease) underwent staging 18F-FDG PET/CT scan. Primary tumour was semi-automatically or manually segmented with a threshold standardised uptake value (SUV) of 3. A total of 73 features were extracted from eight different textures. Spearman correlation was used to test the correlation of features with conventional quantitative metrics such as SUV, metabolic tumour volume, and total lesion glycolysis. Specificity and sensitivity (including 95% confidence intervals [CI]) for each of the studied parameters were derived using receiver operative characteristic (ROC) curves. Univariate and multivariate analyses were used to identify independent predictors associated with BMI. Results Correlation between conventional PET metrics and features ranged between 0.50 and 0.97 for positive correlation (33 significant association features) and ranged from -0.52 to -0.97 for inverse correlation (three significant association features) for both strong and moderate correlations. Analysis of ROC curves showed that high-intensity long-run emphasis 4 bin, high-intensity large zone emphasis 64 bin, long-run emphasis (LRE) 64 bin, large-zone emphasis 64 bin, max spectrum 8 bin, busyness 64 bin, and code similarity 32 and 64 bin were significant discriminators of BMI among other features (area under curve > 0.682, p < 0.05). Univariate analyses of texture features showed that code similarity and long-run emphasis (both 64 bin) were significant predictors of bone marrow involvement. Multivariate analyses revealed that LRE (64 bin, p = 0.031) with an odds ratio of 1.022 and 95% CI of (1.002-1.043) were independent variables for bone marrow involvement. Conclusions 18F-FDG PET/CT radiomic features are synergistic to visual assessment of BMI in patients diagnosed with lymphoma using 18F-FDG PET/CT. Further assessment of long-run emphasis is highly warranted.
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Ihara-Nishishita A, Norikane T, Mitamura K, Yamamoto Y, Fujimoto K, Takami Y, Ibuki E, Kudomi N, Hoshikawa H, Toyohara J, Nishiyama Y. Texture indices of 4'-[methyl- 11C]-thiothymidine uptake predict p16 status in patients with newly diagnosed oropharyngeal squamous cell carcinoma: comparison with 18F-FDG uptake. Eur J Hybrid Imaging 2020; 4:20. [PMID: 34191155 PMCID: PMC8218132 DOI: 10.1186/s41824-020-00090-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2020] [Accepted: 09/18/2020] [Indexed: 11/10/2022] Open
Abstract
Background In oropharyngeal squamous cell carcinoma (OPSCC), human papillomavirus (HPV)/p16 status is important as a prognostic biomarker. Purpose We evaluated the relationship between 4′-[methyl-11C]-thiothymidine (11C-4DST) and 18F-FDG PET texture indices and p16 status in patients with newly diagnosed OPSCC. Methods We retrospectively reviewed the collected data of 256 consecutive, previously untreated patients with primary head and neck tumors enrolled between November 2011 and October 2019. Complete data on both 11C-4DST and 18F-FDG PET/CT studies before therapy, patients with OPSCC, and p16 status were available for 34 patients. Six of them were excluded because they did not exhibit sufficient 11C-4DST and/or 18F-FDG tumor uptake to perform textural analysis. Finally, 28 patients with newly diagnosed OPSCC were investigated. The maximum standardized uptake value (SUVmax) and 6 texture indices (homogeneity, entropy, short-run emphasis, long-run emphasis, low gray-level zone emphasis, and high gray-level zone emphasis) were derived from PET images. The presence of p16 expression in tumor specimens was examined by immunohistochemistry and compared with the PET parameters. Results Using 11C-4DST, the expression of p16 was associated with a higher homogeneity (P = 0.012), lower short-run emphasis (P = 0.005), higher long-run emphasis (P = 0.009), and lower high-gray-level-zone emphasis (P = 0.042) values. There was no significant difference between 18F-FDG PET parameters and p16 status. Conclusion Texture indices of the primary tumor on 11C-4DST PET, but not 18F-FDG PET, may be of value in predicting the condition’s p16 status in patients with newly diagnosed OPSCC.
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Affiliation(s)
- Ayumi Ihara-Nishishita
- Department of Radiology, Faculty of Medicine, Kagawa University, 1750-1 Ikenobe, Miki-cho, Kita-gun, Kagawa, 761-0793, Japan
| | - Takashi Norikane
- Department of Radiology, Faculty of Medicine, Kagawa University, 1750-1 Ikenobe, Miki-cho, Kita-gun, Kagawa, 761-0793, Japan
| | - Katsuya Mitamura
- Department of Radiology, Faculty of Medicine, Kagawa University, 1750-1 Ikenobe, Miki-cho, Kita-gun, Kagawa, 761-0793, Japan
| | - Yuka Yamamoto
- Department of Radiology, Faculty of Medicine, Kagawa University, 1750-1 Ikenobe, Miki-cho, Kita-gun, Kagawa, 761-0793, Japan.
| | - Kengo Fujimoto
- Department of Radiology, Faculty of Medicine, Kagawa University, 1750-1 Ikenobe, Miki-cho, Kita-gun, Kagawa, 761-0793, Japan
| | - Yasukage Takami
- Department of Radiology, Faculty of Medicine, Kagawa University, 1750-1 Ikenobe, Miki-cho, Kita-gun, Kagawa, 761-0793, Japan
| | - Emi Ibuki
- Department of Diagnostic Pathology, Faculty of Medicine, Kagawa University, Kagawa, Japan
| | - Nobuyuki Kudomi
- Department of Medical Physics, Faculty of Medicine, Kagawa University, Kagawa, Japan
| | - Hiroshi Hoshikawa
- Department of Otolaryngology, Faculty of Medicine, Kagawa University, Kagawa, Japan
| | - Jun Toyohara
- Research Team for Neuroimaging, Tokyo Metropolitan Institute of Gerontology, Tokyo, Japan
| | - Yoshihiro Nishiyama
- Department of Radiology, Faculty of Medicine, Kagawa University, 1750-1 Ikenobe, Miki-cho, Kita-gun, Kagawa, 761-0793, Japan
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27
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Martens RM, Koopman T, Noij DP, Pfaehler E, Übelhör C, Sharma S, Vergeer MR, Leemans CR, Hoekstra OS, Yaqub M, Zwezerijnen GJ, Heymans MW, Peeters CFW, de Bree R, de Graaf P, Castelijns JA, Boellaard R. Predictive value of quantitative 18F-FDG-PET radiomics analysis in patients with head and neck squamous cell carcinoma. EJNMMI Res 2020; 10:102. [PMID: 32894373 PMCID: PMC7477048 DOI: 10.1186/s13550-020-00686-2] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2020] [Accepted: 08/13/2020] [Indexed: 01/06/2023] Open
Abstract
BACKGROUND Radiomics is aimed at image-based tumor phenotyping, enabling application within clinical-decision-support-systems to improve diagnostic accuracy and allow for personalized treatment. The purpose was to identify predictive 18-fluor-fluoro-2-deoxyglucose (18F-FDG) positron-emission tomography (PET) radiomic features to predict recurrence, distant metastasis, and overall survival in patients with head and neck squamous cell carcinoma treated with chemoradiotherapy. METHODS Between 2012 and 2018, 103 retrospectively (training cohort) and 71 consecutively included patients (validation cohort) underwent 18F-FDG-PET/CT imaging. The 434 extracted radiomic features were subjected, after redundancy filtering, to a projection resulting in outcome-independent meta-features (factors). Correlations between clinical, first-order 18F-FDG-PET parameters (e.g., SUVmean), and factors were assessed. Factors were combined with 18F-FDG-PET and clinical parameters in a multivariable survival regression and validated. A clinically applicable risk-stratification was constructed for patients' outcome. RESULTS Based on 124 retained radiomic features from 103 patients, 8 factors were constructed. Recurrence prediction was significantly most accurate by combining HPV-status, SUVmean, SUVpeak, factor 3 (histogram gradient and long-run-low-grey-level-emphasis), factor 4 (volume-difference, coarseness, and grey-level-non-uniformity), and factor 6 (histogram variation coefficient) (CI = 0.645). Distant metastasis prediction was most accurate assessing metabolic-active tumor volume (MATV)(CI = 0.627). Overall survival prediction was most accurate using HPV-status, SUVmean, SUVmax, factor 1 (least-axis-length, non-uniformity, high-dependence-of-high grey-levels), and factor 5 (aspherity, major-axis-length, inversed-compactness and, inversed-flatness) (CI = 0.764). CONCLUSIONS Combining HPV-status, first-order 18F-FDG-PET parameters, and complementary radiomic factors was most accurate for time-to-event prediction. Predictive phenotype-specific tumor characteristics and interactions might be captured and retained using radiomic factors, which allows for personalized risk stratification and optimizing personalized cancer care. TRIAL REGISTRATION Trial NL3946 (NTR4111), local ethics commission reference: Prediction 2013.191 and 2016.498. Registered 7 August 2013, https://www.trialregister.nl/trial/3946.
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Affiliation(s)
- Roland M Martens
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, De Boelelaan 1117, PO Box 7057, 1007, Amsterdam, MB, Netherlands.
| | - Thomas Koopman
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, De Boelelaan 1117, PO Box 7057, 1007, Amsterdam, MB, Netherlands
| | - Daniel P Noij
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, De Boelelaan 1117, PO Box 7057, 1007, Amsterdam, MB, Netherlands
| | - Elisabeth Pfaehler
- Department of Nuclear Medicine and Molecular Imaging, Medical Imaging Center, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Caroline Übelhör
- Department of Epidemiology and Biostatistics, Amsterdam University Medical Center, De Boelelaan, 1117, Amsterdam, Netherlands
| | - Sughandi Sharma
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, De Boelelaan 1117, PO Box 7057, 1007, Amsterdam, MB, Netherlands
| | - Marije R Vergeer
- Department of Radiation Oncology, Amsterdam University Medical Center, De Boelelaan, 1117, Amsterdam, Netherlands
| | - C René Leemans
- Department of Otolaryngology-Head and Neck Surgery, Amsterdam University Medical Center, De Boelelaan, 1117, Amsterdam, Netherlands
| | - Otto S Hoekstra
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, De Boelelaan 1117, PO Box 7057, 1007, Amsterdam, MB, Netherlands
| | - Maqsood Yaqub
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, De Boelelaan 1117, PO Box 7057, 1007, Amsterdam, MB, Netherlands
| | - Gerben J Zwezerijnen
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, De Boelelaan 1117, PO Box 7057, 1007, Amsterdam, MB, Netherlands
| | - Martijn W Heymans
- Department of Epidemiology and Biostatistics, Amsterdam University Medical Center, De Boelelaan, 1117, Amsterdam, Netherlands
| | - Carel F W Peeters
- Department of Epidemiology and Biostatistics, Amsterdam University Medical Center, De Boelelaan, 1117, Amsterdam, Netherlands
| | - Remco de Bree
- Department of Head and Neck Surgical Oncology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Pim de Graaf
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, De Boelelaan 1117, PO Box 7057, 1007, Amsterdam, MB, Netherlands
| | - Jonas A Castelijns
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, De Boelelaan 1117, PO Box 7057, 1007, Amsterdam, MB, Netherlands
| | - Ronald Boellaard
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, De Boelelaan 1117, PO Box 7057, 1007, Amsterdam, MB, Netherlands.,Department of Nuclear Medicine and Molecular Imaging, Medical Imaging Center, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
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Gao X, Tham IWK, Yan J. Quantitative accuracy of radiomic features of low-dose 18F-FDG PET imaging. Transl Cancer Res 2020; 9:4646-4655. [PMID: 35117828 PMCID: PMC8797853 DOI: 10.21037/tcr-20-1715] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2020] [Accepted: 07/08/2020] [Indexed: 01/12/2023]
Abstract
Background 18F-FDG PET based radiomics is promising for precision oncology imaging. This work aims to explore quantitative accuracies of radiomic features (RFs) for low-dose 18F-FDG PET imaging. Methods Twenty lung cancer patients were prospectively enrolled and underwent 18F-FDG PET/CT scans. Low-dose PET situations (true counts: 20×106, 15×106, 10×106, 7.5×106, 5×106, 2×106, 1×106, 0.5×106, 0.25×106) were simulated by randomly discarding counts from the acquired list-mode data. Each PET image was created using the scanner default reconstruction parameters. Each lesion volume of interest (VOI) was obtained via an adaptive contouring method with a threshold of 50% peak standardized uptake value (SUVpeak) in the PET images with full count data and VOIs were copied to the PET images at the reduced count level. Conventional SUV measures, features calculated from first-order statistics (FOS) and texture features (TFs) were calculated. Texture based RF include features calculated from gray-level co-occurrence matrix (GLCM), gray-level run length matrix (GLRLM), gray-level size zone matrix (GLSZM), neighboring gray-level dependence matrix (NGLDM) and neighbor gray-tone difference matrix (NGTDM). Bias percentage (BP) at different count levels for each RF was calculated. Results Fifty-seven lesions with a volume greater than 1.5 cm3 were found (mean volume, 25.7 cm3, volume range, 1.5–245.4 cm3). In comparison with normal total counts, mean SUV (SUVmean) in the lesions, normal lungs and livers, Entropy and sum entropy from GLCM, busyness from NGTDM and run-length non-uniformity from GLRLM were the most robust features, with a BP of 5% at the count level of 1×106 (equivalent to an effective dose of 0.04 mSv) RF including cluster shade from GLCM, long-run low grey-level emphasis, high grey-level run emphasis and short-run low grey-level emphasis from GLRM exhibited the worst performance with 50% of bias with 20×106 counts (equivalent to an effective dose of 0.8 mSv). Conclusions In terms of the lesions included in this study, SUVmean, entropy and sum entropy from GLCM, busyness from NGTDM and run-length non-uniformity from GLRLM were the least sensitive features to lowering count.
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Affiliation(s)
- Xin Gao
- Shanghai Universal Medical Imaging Diagnostic Center, Shanghai, China
| | - Ivan W K Tham
- ASTAR-NUS, Clinical Imaging Research Center, Singapore, Singapore.,Department of Radiation Oncology, National University Hospital, Singapore, Singapore.,Department of Radiation Oncology, Mount Elizabeth Novena Hospital, Singapore, Singapore
| | - Jianhua Yan
- Shanghai Key Laboratory for Molecular Imaging, Shanghai University of Medicine and Health Sciences, Shanghai, China.,Molecular Imaging Precision Medicine Collaborative Innovation Center, Shanxi Medical University, Taiyuan, China
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29
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Haider SP, Zeevi T, Baumeister P, Reichel C, Sharaf K, Forghani R, Kann BH, Judson BL, Prasad ML, Burtness B, Mahajan A, Payabvash S. Potential Added Value of PET/CT Radiomics for Survival Prognostication beyond AJCC 8th Edition Staging in Oropharyngeal Squamous Cell Carcinoma. Cancers (Basel) 2020; 12:cancers12071778. [PMID: 32635216 PMCID: PMC7407414 DOI: 10.3390/cancers12071778] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Revised: 06/29/2020] [Accepted: 06/30/2020] [Indexed: 12/18/2022] Open
Abstract
Accurate risk-stratification can facilitate precision therapy in oropharyngeal squamous cell carcinoma (OPSCC). We explored the potential added value of baseline positron emission tomography (PET)/computed tomography (CT) radiomic features for prognostication and risk stratification of OPSCC beyond the American Joint Committee on Cancer (AJCC) 8th edition staging scheme. Using institutional and publicly available datasets, we included OPSCC patients with known human papillomavirus (HPV) status, without baseline distant metastasis and treated with curative intent. We extracted 1037 PET and 1037 CT radiomic features quantifying lesion shape, imaging intensity, and texture patterns from primary tumors and metastatic cervical lymph nodes. Utilizing random forest algorithms, we devised novel machine-learning models for OPSCC progression-free survival (PFS) and overall survival (OS) using “radiomics” features, “AJCC” variables, and the “combined” set as input. We designed both single- (PET or CT) and combined-modality (PET/CT) models. Harrell’s C-index quantified survival model performance; risk stratification was evaluated in Kaplan–Meier analysis. A total of 311 patients were included. In HPV-associated OPSCC, the best “radiomics” model achieved an average C-index ± standard deviation of 0.62 ± 0.05 (p = 0.02) for PFS prediction, compared to 0.54 ± 0.06 (p = 0.32) utilizing “AJCC” variables. Radiomics-based risk-stratification of HPV-associated OPSCC was significant for PFS and OS. Similar trends were observed in HPV-negative OPSCC. In conclusion, radiomics imaging features extracted from pre-treatment PET/CT may provide complimentary information to the current AJCC staging scheme for survival prognostication and risk-stratification of HPV-associated OPSCC.
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Affiliation(s)
- Stefan P. Haider
- Section of Neuroradiology, Department of Radiology and Biomedical Imaging, Yale School of Medicine, 789 Howard Ave, New Haven, CT 06519, USA; (S.P.H.); (A.M.)
- Department of Otorhinolaryngology, University Hospital of Ludwig Maximilians Universität München, Marchioninistrasse 15, 81377 Munich, Germany; (P.B.); (C.R.); (K.S.)
| | - Tal Zeevi
- Center for Translational Imaging Analysis and Machine Learning, Department of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, New Haven, CT 06510, USA;
| | - Philipp Baumeister
- Department of Otorhinolaryngology, University Hospital of Ludwig Maximilians Universität München, Marchioninistrasse 15, 81377 Munich, Germany; (P.B.); (C.R.); (K.S.)
| | - Christoph Reichel
- Department of Otorhinolaryngology, University Hospital of Ludwig Maximilians Universität München, Marchioninistrasse 15, 81377 Munich, Germany; (P.B.); (C.R.); (K.S.)
| | - Kariem Sharaf
- Department of Otorhinolaryngology, University Hospital of Ludwig Maximilians Universität München, Marchioninistrasse 15, 81377 Munich, Germany; (P.B.); (C.R.); (K.S.)
| | - Reza Forghani
- Department of Diagnostic Radiology and Augmented Intelligence & Precision Health Laboratory, McGill University Health Centre & Research Institute, 1650 Cedar Avenue, Montreal, QC H3G 1A4, Canada;
| | - Benjamin H. Kann
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Harvard Medical School, 450 Brookline Avenue, Boston, MA 02215, USA;
| | - Benjamin L. Judson
- Division of Otolaryngology, Department of Surgery, Yale School of Medicine, 330 Cedar Street, New Haven, CT 06520, USA;
| | - Manju L. Prasad
- Department of Pathology, Yale School of Medicine, 310 Cedar Street, New Haven, CT 06520, USA;
| | - Barbara Burtness
- Section of Medical Oncology, Department of Internal Medicine, Yale School of Medicine, 25 York Street, New Haven, CT 06520, USA;
| | - Amit Mahajan
- Section of Neuroradiology, Department of Radiology and Biomedical Imaging, Yale School of Medicine, 789 Howard Ave, New Haven, CT 06519, USA; (S.P.H.); (A.M.)
| | - Seyedmehdi Payabvash
- Section of Neuroradiology, Department of Radiology and Biomedical Imaging, Yale School of Medicine, 789 Howard Ave, New Haven, CT 06519, USA; (S.P.H.); (A.M.)
- Correspondence: ; Tel.: +1-(203)-214-4650
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Haider SP, Burtness B, Yarbrough WG, Payabvash S. Applications of radiomics in precision diagnosis, prognostication and treatment planning of head and neck squamous cell carcinomas. CANCERS OF THE HEAD & NECK 2020; 5:6. [PMID: 32391171 PMCID: PMC7197186 DOI: 10.1186/s41199-020-00053-7] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/10/2020] [Accepted: 03/09/2020] [Indexed: 12/15/2022]
Abstract
Recent advancements in computational power, machine learning, and artificial intelligence technology have enabled automated evaluation of medical images to generate quantitative diagnostic and prognostic biomarkers. Such objective biomarkers are readily available and have the potential to improve personalized treatment, precision medicine, and patient selection for clinical trials. In this article, we explore the merits of the most recent addition to the “-omics” concept for the broader field of head and neck cancer – “Radiomics”. This review discusses radiomics studies focused on (molecular) characterization, classification, prognostication and treatment guidance for head and neck squamous cell carcinomas (HNSCC). We review the underlying hypothesis, general concept and typical workflow of radiomic analysis, and elaborate on current and future challenges to be addressed before routine clinical application.
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Affiliation(s)
- Stefan P Haider
- 1Department of Radiology and Biomedical Imaging, Division of Neuroradiology, Yale School of Medicine, New Haven, CT USA.,2Department of Otorhinolaryngology, University Hospital of Ludwig Maximilians University of Munich, Munich, Germany
| | - Barbara Burtness
- 3Department of Internal Medicine, Division of Medical Oncology, Yale School of Medicine, New Haven, CT USA
| | - Wendell G Yarbrough
- 4Department of Otolaryngology/Head and Neck Surgery, University of North Carolina School of Medicine, Chapel Hill, NC USA
| | - Seyedmehdi Payabvash
- 1Department of Radiology and Biomedical Imaging, Division of Neuroradiology, Yale School of Medicine, New Haven, CT USA
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Lin HC, Chan SC, Cheng NM, Liao CT, Hsu CL, Wang HM, Lin CY, Chang JTC, Ng SH, Yang LY, Yen TC. Pretreatment 18F-FDG PET/CT texture parameters provide complementary information to Epstein-Barr virus DNA titers in patients with metastatic nasopharyngeal carcinoma. Oral Oncol 2020; 104:104628. [DOI: 10.1016/j.oraloncology.2020.104628] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2019] [Revised: 02/05/2020] [Accepted: 03/02/2020] [Indexed: 01/07/2023]
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Guha A, Connor S, Anjari M, Naik H, Siddiqui M, Cook G, Goh V. Radiomic analysis for response assessment in advanced head and neck cancers, a distant dream or an inevitable reality? A systematic review of the current level of evidence. Br J Radiol 2020; 93:20190496. [PMID: 31682155 PMCID: PMC7055439 DOI: 10.1259/bjr.20190496] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2019] [Revised: 10/19/2019] [Accepted: 10/29/2019] [Indexed: 01/06/2023] Open
Abstract
OBJECTIVE The recent increase in publications on radiomic analysis as means to produce diagnostic and predictive biomarkers in head and neck cancers (HNCC) reveal complicated and often conflicting results. The objective of this paper is to systematically review the published data, and evaluate the current level of evidence accumulated that would determine clinical application. METHODS Data sources: Articles in the English language available on the Ovid-MEDLINE and Embase databases were used for the literature search. Study selection:Studies which evaluated the role of radiomics as a predictive or prognostic tool for response assessment in HNCC were included in this review.Study appraisal and synthesis methods: The authors set-out to perform a meta-analysis, however given the small number of studies retrieved that presented adequate data, combined with excessive methodological heterogeneity, we could only perform a structured descriptive systematic review summarizing the key findings. Independent extraction of articles was performed by two authors using predefined data fields and any disagreement was resolved by consensus. RESULTS Though most papers concluded that radiomics is an effective predictive and prognostic biomarker in the management of HNCC, significant heterogeneity exists in the study methodology and statistical modelling; thus precluding accurate mathematical comparison or the ability to make clear recommendations going forwards. Moreover, most studies have not been validated and the reproducibility of their results will be a challenge. CONCLUSION Until robust external validation studies on the reproducibility and accuracy of radiomic analysis methods on HNCC are carried out, the current level of evidence remains low, with the authors advising caution against hasty implementation of these tools in the multidisciplinary clinic. ADVANCES IN KNOWLEDGE This review is the first attempt to critically analyze the merits and demerits of currently published literature on tumour heterogeneity studies in HNCC, and identifies specific loop holes that need to be addressed by research groups, for a meaningful clinical translation of this potential biomarker.
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Affiliation(s)
| | - Steve Connor
- Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom
| | - Mustafa Anjari
- Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom
| | - Harish Naik
- Grant Medical college and JJ Group of hospitals, Mumbai, India
| | - Musib Siddiqui
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Gary Cook
- Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom
| | - Vicky Goh
- Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom
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Textural features of hypoxia PET predict survival in head and neck cancer during chemoradiotherapy. Eur J Nucl Med Mol Imaging 2019; 47:1056-1064. [PMID: 31773233 DOI: 10.1007/s00259-019-04609-9] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2019] [Accepted: 11/07/2019] [Indexed: 01/24/2023]
Abstract
PURPOSE The aim of this study was to investigate whether textural features of tumour hypoxia, assessed with serial [18F]fluoromisonidazole (FMISO)-PET, were able to predict clinical outcome in patients with head and neck squamous cell carcinoma (HNSCC, T1-4, N+, M0) during chemoradiotherapy (CRT). METHODS In a preliminary evaluation of a prospective trial, tumour hypoxia was evaluated in 29 patients via serial FMISO-PET before and during CRT. All patients received an initial [18F]fluorodeoxyglucose (FDG)-PET before CRT, and tumour regions were defined on this FDG-PET. The first-order metrics tumour-to-background ratio (TBRmean, TBRmax, TBRpeak), coefficient of variation, total lesion uptake and integral non-uniformity were calculated for all scans. Further, 3 second-order (textural) features from two grey-level matrices were calculated, as well as differential non-uniformity (udiff). Prognostic value was examined by median split for group separation (GS) in Kaplan-Meier estimates and correlated with overall survival (OS), quantified via log-rank tests (p ≤ 0.05) and group-relative hazard ratios (HR). RESULTS Within a median follow-up of 29.6 months (95% CI: 16.8-48.0 months), no first-order metrics predicted OS with a significant GS (all p > 0.05) on any FMISO-PET scan. Only udiff before and in week 2 during CRT (p = 0.03, HR = 10.8 and p = 0.05, HR = 5.2) and non-uniformity from grey-level run length matrix in week 2 separated prognostic groups (p = 0.05, HR = 5.3); lower values were correlated with better OS. Further, the decrease in udiff from before CRT to week 2 was correlated with better OS (p = 0.04, HR = 9.4). FDG-PET before CRT did not predict outcome in any measure. CONCLUSIONS Textural features on FMISO-PET scans before CRT, in week 2 and, to a limited degree, the change of features during CRT, were able to identify head and neck squamous cell carcinoma patients with better OS, suggesting that a higher homogeneity of the degree of hypoxia in tumours could correlate with a better outcome after CRT.
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Li L, Mu W, Wang Y, Liu Z, Liu Z, Wang Y, Ma W, Kong Z, Wang S, Zhou X, Wei W, Cheng X, Lin Y, Tian J. A Non-invasive Radiomic Method Using 18F-FDG PET Predicts Isocitrate Dehydrogenase Genotype and Prognosis in Patients With Glioma. Front Oncol 2019; 9:1183. [PMID: 31803608 PMCID: PMC6869373 DOI: 10.3389/fonc.2019.01183] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2019] [Accepted: 10/21/2019] [Indexed: 01/15/2023] Open
Abstract
Purpose: We aimed to analyze 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) images via the radiomic method to develop a model and validate the potential value of features reflecting glioma metabolism for predicting isocitrate dehydrogenase (IDH) genotype and prognosis. Methods: PET images of 127 patients were retrospectively analyzed. A series of quantitative features reflecting the metabolic heterogeneity of the tumors were extracted, and a radiomic signature was generated using the support vector machine method. A combined model that included clinical characteristics and the radiomic signature was then constructed by multivariate logistic regression to predict the IDH genotype status, and the model was evaluated and verified by receiver operating characteristic (ROC) curves and calibration curves. Finally, Kaplan-Meier curves and log-rank tests were used to analyze overall survival (OS) according to the predicted result. Results: The generated radiomic signature was significantly associated with IDH genotype (p < 0.05) and could achieve large areas under the ROC curve of 0.911 and 0.900 on the training and validation cohorts, respectively, with the incorporation of age and type of tumor metabolism. The good agreement of the calibration curves in the validation cohort further validated the efficacy of the constructed model. Moreover, the predicted results showed a significant difference in OS between high- and low-risk groups (p < 0.001). Conclusions: Our results indicate that the 18F-FDG metabolism-related features could effectively predict the IDH genotype of gliomas and stratify the OS of patients with different prognoses.
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Affiliation(s)
- Longfei Li
- Collaborative Innovation Center for Internet Healthcare, Zhengzhou University, Zhengzhou, China.,CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Wei Mu
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Yaning Wang
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Zhenyu Liu
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Zehua Liu
- Collaborative Innovation Center for Internet Healthcare, Zhengzhou University, Zhengzhou, China.,CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Yu Wang
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Wenbin Ma
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Ziren Kong
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Shuo Wang
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Xuezhi Zhou
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, China
| | - Wei Wei
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, China.,School of Electronics and Information, Xi'an Polytechnic University, Xi'an, China
| | - Xin Cheng
- Department of Nuclear Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yusong Lin
- Collaborative Innovation Center for Internet Healthcare, Zhengzhou University, Zhengzhou, China.,School of Software, Zhengzhou University, Zhengzhou, China
| | - Jie Tian
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China.,Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University, Beijing, China
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Moan JM, Amdal CD, Malinen E, Svestad JG, Bogsrud TV, Dale E. The prognostic role of 18F-fluorodeoxyglucose PET in head and neck cancer depends on HPV status. Radiother Oncol 2019; 140:54-61. [DOI: 10.1016/j.radonc.2019.05.019] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2019] [Revised: 05/13/2019] [Accepted: 05/15/2019] [Indexed: 11/25/2022]
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Prediction of Overall Survival and Progression-Free Survival by the 18F-FDG PET/CT Radiomic Features in Patients with Primary Gastric Diffuse Large B-Cell Lymphoma. CONTRAST MEDIA & MOLECULAR IMAGING 2019; 2019:5963607. [PMID: 31777473 PMCID: PMC6875372 DOI: 10.1155/2019/5963607] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/19/2019] [Accepted: 09/25/2019] [Indexed: 02/05/2023]
Abstract
Purpose. To determine whether the radiomic features of 18F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) contribute to prognosis prediction in primary gastric diffuse large B-cell lymphoma (PG-DLBCL) patients. Methods. This retrospective study included 35 PG-DLBCL patients who underwent PET/CT scans at West China Hospital before curative treatment. The volume of interest (VOI) was drawn around the tumor, and radiomic analysis of the PET and CT images, within the same VOI, was conducted. The metabolic and textural features of PET and CT images were evaluated. Correlations of the extracted features with the overall survival (OS) and progression-free survival (PFS) were evaluated. Univariate and multivariate analyses were conducted to assess the prognostic value of the radiomic parameters. Results. In the univariate model, many of the textural features, including kurtosis and volume, extracted from the PET and CT datasets were significantly associated with survival (5 for OS and 7 for PFS (PET); 7 for OS and 14 for PFS (CT)). Multivariate analysis identified kurtosis (hazard ratio (HR): 28.685, 95% confidence interval (CI): 2.067–398.152, p=0.012), metabolic tumor volume (MTV) (HR: 26.152, 95% CI: 2.089–327.392, p=0.011), and gray-level nonuniformity (GLNU) (HR: 14.642, 95% CI: 2.661–80.549, p=0.002) in PET and sphericity (HR: 11.390, 95% CI: 1.360–95.371, p=0.025) and kurtosis (HR: 11.791, 95% CI: 1.583–87.808, p=0.016), gray-level nonuniformity (GLNU) (HR: 6.934, 95% CI: 1.069–44.981, p=0.042), and high gray-level zone emphasis (HGZE) (HR: 9.805, 95% CI: 1.359–70.747, p=0.024) in CT as independent prognostic factors. Conclusion. 18F-FDG PET/CT radiomic features are potentially useful for survival prediction in PG-DLBCL patients. However, studies with larger cohorts are needed to confirm the clinical prognostication of these parameters.
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Ibrahim A, Vallières M, Woodruff H, Primakov S, Beheshti M, Keek S, Refaee T, Sanduleanu S, Walsh S, Morin O, Lambin P, Hustinx R, Mottaghy FM. Radiomics Analysis for Clinical Decision Support in Nuclear Medicine. Semin Nucl Med 2019; 49:438-449. [DOI: 10.1053/j.semnuclmed.2019.06.005] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
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Wong CK, Chan SC, Ng SH, Hsieh CH, Cheng NM, Yen TC, Liao CT. Textural features on 18F-FDG PET/CT and dynamic contrast-enhanced MR imaging for predicting treatment response and survival of patients with hypopharyngeal carcinoma. Medicine (Baltimore) 2019; 98:e16608. [PMID: 31415354 PMCID: PMC6831375 DOI: 10.1097/md.0000000000016608] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
The utility of multimodality molecular imaging for predicting treatment response and survival of patients with hypopharyngeal carcinoma remains unclear. Here, we sought to investigate whether the combination of different molecular imaging parameters may improve outcome prediction in this patient group.Patients with pathologically proven hypopharyngeal carcinoma scheduled to undergo chemoradiotherapy (CRT) were deemed eligible. Besides clinical data, parameters obtained from pretreatment 2-deoxy-2-[fluorine-18]fluoro-D-glucose positron emission tomography/computed tomography (F-FDG PET/CT), dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI), and diffusion-weighted MRI were analyzed in relation to treatment response, recurrence-free survival (RFS), and overall survival (OS).A total of 61 patients with advanced-stage disease were examined. After CRT, 36% of the patients did not achieve a complete response. Total lesion glycolysis (TLG) and texture feature entropy were found to predict treatment response. The transfer constant (K), TLG, and entropy were associated with RFS, whereas K, blood plasma volume (Vp), standardized uptake value (SUV), and entropy were predictors of OS. Different scoring systems based on the sum of PET- or MRI-derived prognosticators enabled patient stratification into distinct prognostic groups (P <.0001). The complete response rate of patients with a score of 2 was significantly lower than those of patients with a score 1 or 0 (14.7% vs 58.9% vs 75.7%, respectively, P = .007, respectively). The combination of PET- and DCE-MRI-derived independent risk factors allowed a better survival stratification than the TNM staging system (P <.0001 vs .691, respectively).Texture features on F-FDG PET/CT and DCE-MRI are clinically useful to predict treatment response and survival in patients with hypopharyngeal carcinoma. Their combined use in prognostic scoring systems may help these patients benefit from tailored treatment and obtain better oncological results.
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Affiliation(s)
| | - Sheng-Chieh Chan
- Department of Nuclear Medicine, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien
| | | | - Chia-Hsun Hsieh
- Division of Medical Oncology, Department of Internal Medicine, Linkou Chang Gung Memorial Hospital and Chang Gung University, Taoyuan
| | - Nai-Ming Cheng
- Department of Nuclear Medicine, Keelung Chang Gung Memorial Hospital, Keelung
| | | | - Chun-Ta Liao
- Department of Otorhinolaryngology, Linkou Chang Gung Memorial Hospital and Chang Gung University, Taoyuan, Taiwan
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Prognostic Value of Functional Parameters of 18F-FDG-PET Images in Patients with Primary Renal/Adrenal Lymphoma. CONTRAST MEDIA & MOLECULAR IMAGING 2019; 2019:2641627. [PMID: 31427906 PMCID: PMC6683818 DOI: 10.1155/2019/2641627] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/30/2018] [Revised: 05/05/2019] [Accepted: 07/09/2019] [Indexed: 02/07/2023]
Abstract
Objectives The aim of this study is to explore the textural features that may identify the morphological changes in the lymphoma region and predict the prognosis of patients with primary renal lymphoma (PRL) and primary adrenal lymphoma (PAL). Methods This retrospective study comprised nineteen non-Hodgkin's lymphoma (NHL) patients undergoing 18F-FDG-PET/CT at West China Hospital from December 2013 to May 2017. 18F-FDG-PET images were reviewed independently by two board certificated radiologists of nuclear medicine, and the texture features were extracted from LifeX packages. The prognostic value of PET FDG-uptake parameters, patients' baseline characteristics, and textural parameters were analyzed using Kaplan–Meier analysis. Cox regression analysis was used to identify the independent prognostic factors among the imaging and clinical features. Results The overall survival of included patients was 18.84 ± 13.40 (mean ± SD) months. Univariate Cox analyses found that the tumor stage, GLCM (gray-level co-occurrence matrix) entropy, GLZLM_GLNU (gray-level nonuniformity), and GLZLM_ZLNU (zone length nonuniformity), values were significant predictors for OS. Among them, GLRLM_RLNU ≥216.6 demonstrated association with worse OS at multivariate analysis (HR 9.016, 95% CI 1.041–78.112, p=0.046). Conclusions The texture analysis of 18F-FDG-PET images could potentially serve as a noninvasive strategy to predict the overall survival of patients with PRL and PAL.
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Feeding the Data Monster: Data Science in Head and Neck Cancer for Personalized Therapy. J Am Coll Radiol 2019; 16:1695-1701. [PMID: 31238024 DOI: 10.1016/j.jacr.2019.05.045] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2019] [Revised: 05/18/2019] [Accepted: 05/28/2019] [Indexed: 01/14/2023]
Abstract
OBJECTIVE Head and neck carcinomas are clinically challenging malignancies because of tumor heterogeneities and resilient tumor subvolumes that require individualized treatment planning and delivery for an improved outcome. Although current approaches to diagnosis and therapy have boosted locoregional control, the long-term survival in this patient group remains unchanged over the last decades. A new approach to head and neck cancer management is therefore needed to better identify patient subgroups that are responsive to specific therapies. The aim of this article is to review the current status of knowledge and practice utilizing big data toward personalized therapy in head and neck cancers based on CT and PET imaging modalities. METHODS Literature published in English since 2000 was searched using Medline. Additional articles were retrieved via pearling of identified literature. Publications were reviewed and summarized in tabulated format. RESULTS Studies based on big data in head and neck cancer are limited; however, the field of radiomics is under continuous development and provides valuable input for personalized treatment. Using PET/PET CT biomarkers for patient treatment individualization and response prediction seems promising, especially in regard to detection of hypoxia and clonogenic cancer stem cells. Literature shows that macroscopic changes in medical images (whether structural or functional) are correlated with biologic and biochemical changes within a tumor. CONCLUSION Current trends in data science suggest that the ideal model for decision support in head and neck cancers should be based on human-machine collaboration, namely, on (1) software-based algorithms, (2) physician innovation collaboratives, and (3) clinician mix optimization.
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Combining the radiomic features and traditional parameters of 18F-FDG PET with clinical profiles to improve prognostic stratification in patients with esophageal squamous cell carcinoma treated with neoadjuvant chemoradiotherapy and surgery. Ann Nucl Med 2019; 33:657-670. [DOI: 10.1007/s12149-019-01380-7] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2019] [Accepted: 06/03/2019] [Indexed: 12/13/2022]
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Fujima N, Shimizu Y, Yoshida D, Kano S, Mizumachi T, Homma A, Yasuda K, Onimaru R, Sakai O, Kudo K, Shirato H. Machine-Learning-Based Prediction of Treatment Outcomes Using MR Imaging-Derived Quantitative Tumor Information in Patients with Sinonasal Squamous Cell Carcinomas: A Preliminary Study. Cancers (Basel) 2019; 11:cancers11060800. [PMID: 31185611 PMCID: PMC6627127 DOI: 10.3390/cancers11060800] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2019] [Revised: 06/02/2019] [Accepted: 06/06/2019] [Indexed: 02/06/2023] Open
Abstract
The purpose of this study was to determine the predictive power for treatment outcome of a machine-learning algorithm combining magnetic resonance imaging (MRI)-derived data in patients with sinonasal squamous cell carcinomas (SCCs). Thirty-six primary lesions in 36 patients were evaluated. Quantitative morphological parameters and intratumoral characteristics from T2-weighted images, tumor perfusion parameters from arterial spin labeling (ASL) and tumor diffusion parameters of five diffusion models from multi-b-value diffusion-weighted imaging (DWI) were obtained. Machine learning by a non-linear support vector machine (SVM) was used to construct the best diagnostic algorithm for the prediction of local control and failure. The diagnostic accuracy was evaluated using a 9-fold cross-validation scheme, dividing patients into training and validation sets. Classification criteria for the division of local control and failure in nine training sets could be constructed with a mean sensitivity of 0.98, specificity of 0.91, positive predictive value (PPV) of 0.94, negative predictive value (NPV) of 0.97, and accuracy of 0.96. The nine validation data sets showed a mean sensitivity of 1.0, specificity of 0.82, PPV of 0.86, NPV of 1.0, and accuracy of 0.92. In conclusion, a machine-learning algorithm using various MR imaging-derived data can be helpful for the prediction of treatment outcomes in patients with sinonasal SCCs.
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Affiliation(s)
- Noriyuki Fujima
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, Sapporo 060-8638, Hokkaido, Japan.
| | - Yukie Shimizu
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, Sapporo 060-8638, Hokkaido, Japan.
| | - Daisuke Yoshida
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, Sapporo 060-8638, Hokkaido, Japan.
| | - Satoshi Kano
- Department of Otolaryngology-Head and Neck Surgery, Hokkaido University Graduate School of Medicine, Sapporo 060-8638, Hokkaido, Japan.
| | - Takatsugu Mizumachi
- Department of Otolaryngology-Head and Neck Surgery, Hokkaido University Graduate School of Medicine, Sapporo 060-8638, Hokkaido, Japan.
| | - Akihiro Homma
- Department of Otolaryngology-Head and Neck Surgery, Hokkaido University Graduate School of Medicine, Sapporo 060-8638, Hokkaido, Japan.
| | - Koichi Yasuda
- Department of Radiation Medicine, Hokkaido University Graduate School of Medicine, Sapporo 060-8638, Hokkaido, Japan.
| | - Rikiya Onimaru
- Department of Radiation Medicine, Hokkaido University Graduate School of Medicine, Sapporo 060-8638, Hokkaido, Japan.
| | - Osamu Sakai
- Departments of Radiology, Otolaryngology-Head and Neck Surgery, and Radiation Oncology, Boston Medical Center, Boston University School of Medicine, Boston, MA 02118, USA.
| | - Kohsuke Kudo
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, Sapporo 060-8638, Hokkaido, Japan.
| | - Hiroki Shirato
- Department of Radiation Medicine, Hokkaido University Graduate School of Medicine, Sapporo 060-8638, Hokkaido, Japan.
- The Global Station for Quantum Medical Science and Engineering, Global Institution for Collaborative Research and Education, Sapporo 060-0808, Hokkaido, Japan.
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Prognostic Value of Tumor Heterogeneity and SUVmax of Pretreatment 18F-FDG PET/CT for Salivary Gland Carcinoma With High-Risk Histology. Clin Nucl Med 2019; 44:351-358. [DOI: 10.1097/rlu.0000000000002530] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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44
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Gouw ZAR, La Fontaine MD, van Kranen S, van de Kamer JB, Vogel WV, van Werkhoven E, Sonke JJ, Al-Mamgani A. The Prognostic Value of Baseline 18F-FDG PET/CT in Human Papillomavirus–Positive Versus Human Papillomavirus–Negative Patients With Oropharyngeal Cancer. Clin Nucl Med 2019; 44:e323-e328. [DOI: 10.1097/rlu.0000000000002531] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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45
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Thuillier P, Bourhis D, Schick U, Alavi Z, Guezennec C, Robin P, Kerlan V, Salaun PY, Abgral R. Diagnostic value of positron-emission tomography textural indices for malignancy of 18F-fluorodeoxyglucose-avid adrenal lesions. THE QUARTERLY JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING : OFFICIAL PUBLICATION OF THE ITALIAN ASSOCIATION OF NUCLEAR MEDICINE (AIMN) [AND] THE INTERNATIONAL ASSOCIATION OF RADIOPHARMACOLOGY (IAR), [AND] SECTION OF THE SOCIETY OF RADIOPHARMACEUTICAL CHEMISTRY AND BIOLOGY 2019; 65:79-87. [PMID: 30916534 DOI: 10.23736/s1824-4785.19.03138-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
BACKGROUND PET Textural indices could have an add-on diagnostic value for diagnosis of malignancy in patients with FDG-avid adrenal lesions. METHODS Consecutive patients referred for a FDG-PET/CT to our nuclear medicine department from June 2012 to June 2017 were retrospectively screened. Inclusion criteria were: patients with a FDG-avid adrenal lesion (uptake≥liver background); malignant/benign lesion confirmed histologically or with follow-up imaging examination. Pheochromocytomas were not included in the analysis. For each adrenal lesion, 5 quantitative PET parameters (SUV<inf>max</inf>, MTV, TLG, TLR<inf>max</inf> and TLRmean<inf>)</inf> were calculated. Thirty-seven textural indices were extracted using LIFEx software®. Diagnostic performance to determine malignancy was assessed with a ROC analysis. Parameters with a significantly AUC>0.5 were selected and groups of highly correlated (r>0.8) parameters were created. A scoring system combining PET and textural indices was examined. RESULTS PET textural indices were calculated for 53 lesions (37 malignant, 16 benign). Three PET metabolic parameters (SUV<inf>max</inf>, TLR<inf>max</inf>, TLRmean) and 13 textural indices had an AUC>0.5. Seven groups of highly correlated parameters (r>0.8) were extracted. For PET parameters, SUV<inf>max</inf> had the best AUC (0.89 95% CI [0.79-0.98]; cut-off=7.0). For textural indices, ZLNU had the best AUC (0.87 95% CI [0.78-0.96]; cut-off=34.7) and specificity of 100%. Three scores combining the best four textural indices alone (Contrast<inf>GLCM</inf>, LRHGE, SZE and ZLNU) or with one PET parameters (SUV<inf>max</inf>, TLR<inf>max</inf>) were developed but did not increase the diagnostic performance (AUC≤0.89). ZLNU was the best parameter to distinguish primary adrenal cancer from adrenal metastases in malignant lesions (P<0.001). CONCLUSIONS Our study highlighted excellent diagnostic performance of several PET textural indices comparable to that of PET metabolic parameters. However, our results did not find any additional diagnostic value of textural indices when combined with metabolic parameters.
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Affiliation(s)
- Philippe Thuillier
- Department of Endocrinology, University Hospital of Brest, Brest, France - .,EA GETBO 3878, University Hospital of Brest, Brest, France -
| | - David Bourhis
- EA GETBO 3878, University Hospital of Brest, Brest, France.,Department of Nuclear Medicine, University Hospital of Brest, Brest, France
| | - Ulrike Schick
- Department of Radiotherapy, University Hospital of Brest, Brest, France
| | - Zarrin Alavi
- EA-3878, INSERM CIC-1412 Medical University Hospital of Brest, Brest, France
| | - Catherine Guezennec
- EA GETBO 3878, University Hospital of Brest, Brest, France.,Department of Nuclear Medicine, University Hospital of Brest, Brest, France
| | - Philippe Robin
- EA GETBO 3878, University Hospital of Brest, Brest, France.,Department of Nuclear Medicine, University Hospital of Brest, Brest, France
| | - Véronique Kerlan
- Department of Endocrinology, University Hospital of Brest, Brest, France.,EA GETBO 3878, University Hospital of Brest, Brest, France
| | - Pierre-Yve Salaun
- EA GETBO 3878, University Hospital of Brest, Brest, France.,Department of Nuclear Medicine, University Hospital of Brest, Brest, France
| | - Ronan Abgral
- EA GETBO 3878, University Hospital of Brest, Brest, France.,Department of Nuclear Medicine, University Hospital of Brest, Brest, France
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46
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Inter-observer and segmentation method variability of textural analysis in pre-therapeutic FDG PET/CT in head and neck cancer. PLoS One 2019; 14:e0214299. [PMID: 30921388 PMCID: PMC6438585 DOI: 10.1371/journal.pone.0214299] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2018] [Accepted: 03/11/2019] [Indexed: 12/25/2022] Open
Abstract
Aim Characterizing tumor heterogeneity with textural indices extracted from 18F-fluorodeoxyglucose positron emission tomography (FDG PET/CT) is of growing interest in oncology. Several series showed promising results to predict survival in patients with head and neck squamous cell carcinoma (HNSCC), analyzing various tumor segmentation methods and textural indices. This preliminary study aimed at assessing the inter-observer and inter-segmentation method variability of textural indices in HNSCC pre-therapeutic FDG PET/CT. Materials and methods Consecutive patients with HNSCC referred in our department for a pre-therapeutic FDG PET/CT from January to March 2016 were retrospectively included. Two nuclear medicine physicians separately segmented all tumors using 3 different segmentation methods: a relative standardized uptake value (SUV) threshold (40%SUVmax), a signal-to-noise adaptive SUV threshold (DAISNE) and an image gradient-based method (PET-EDGE). SUV and metabolic tumor volume were recorded. Thirty-one textural indices were calculated using LIFEx software (www.lifexsoft.org). After correlation analysis, selected indices’ inter-segmentation method and inter-observer variability were calculated. Results Forty-three patients (mean age 63.8±9.3y) were analyzed. Due to a too small segmented tumor volume of interest, textural analysis could not be performed in 6, 11 and 15 cases with respectively DAISNE, 40%SUVmax and PET-EDGE segmentation methods. Five independent textural indices were selected (Homogeneity, Correlation, Entropy, Busyness and LZLGE). There was a high inter-contouring method variability for Homogeneity, Correlation, Entropy and LZLGE (p<0.0001 for each index). The inter-observer reproducibility analysis revealed an excellent agreement for 3 indices (Homogeneity, Correlation and Entropy) with an intraclass correlation coefficient higher than 0.90 for the 3 methods. Conclusions This preliminary study showed a high variability of 4 out of 5 textural indices (Homogeneity, Correlation, Entropy and LZLGE) extracted from pre-therapeutic FDG PET/CT in HNSCC using 3 different contouring methods. However, for each method, there was an excellent agreement between observers for 3 of these textural indices (Homogeneity, Correlation and Entropy).
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47
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Branchini M, Zorz A, Zucchetta P, Bettinelli A, De Monte F, Cecchin D, Paiusco M. Impact of acquisition count statistics reduction and SUV discretization on PET radiomic features in pediatric 18F-FDG-PET/MRI examinations. Phys Med 2019; 59:117-126. [PMID: 30928060 DOI: 10.1016/j.ejmp.2019.03.005] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/01/2018] [Revised: 03/02/2019] [Accepted: 03/07/2019] [Indexed: 01/09/2023] Open
Abstract
PURPOSE The evaluation of features robustness with respect to acquisition and post-processing parameter changes is fundamental for the reliability of radiomics studies. The aim of this study was to investigate the sensitivity of PET radiomic features to acquisition statistics reduction and standardized-uptake-volume (SUV) discretization in PET/MRI pediatric examinations. METHODS Twenty-seven lesions were detected from the analysis of twenty-one 18F-FDG-PET/MRI pediatric examinations. By decreasing the count-statistics of the original list-mode data (3 MBq/kg), injected activity reduction was simulated. Two SUV discretization approaches were applied: 1) resampling lesion SUV range into fixed bins numbers (FBN); 2) rounding lesion SUV into fixed bin size (FBS). One hundred and six radiomic features were extracted. Intraclass Correlation Coefficient (ICC), Spearman correlation coefficient and coefficient-of-variation (COV) were calculated to assess feature reproducibility between low tracer activities and full tracer activity feature values. RESULTS More than 70% of Shape and first order features, and around 70% and 40% of textural features, when using FBS and FBN methods respectively, resulted robust till 1.2 MBk/kg. Differences in median features reproducibility (ICC) between FBS and FBN datasets were statistically significant for every activity level independently from bin number/size, with higher values for FBS. Differences in median Spearman coefficient (i.e. patient ranking according to feature values) were not statistically significant, varying the intensity resolution (i.e. bin number/size) for either FBS and FBN methods. CONCLUSIONS For each simulated count-statistic level, robust PET radiomic features were determined for pediatric PET/MRI examinations. A larger number of robust features were detected when using FBS methods.
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Affiliation(s)
- Marco Branchini
- Medical Physics Department, Veneto Institute of Oncology IOV - IRCCS, Padova, Italy.
| | - Alessandra Zorz
- Medical Physics Department, Veneto Institute of Oncology IOV - IRCCS, Padova, Italy
| | - Pietro Zucchetta
- Nuclear Medicine Unit, Department of Medicine DIMED, University Hospital of Padua, Padova, Italy
| | - Andrea Bettinelli
- Medical Physics Department, Veneto Institute of Oncology IOV - IRCCS, Padova, Italy
| | - Francesca De Monte
- Medical Physics Department, Veneto Institute of Oncology IOV - IRCCS, Padova, Italy
| | - Diego Cecchin
- Nuclear Medicine Unit, Department of Medicine DIMED, University Hospital of Padua, Padova, Italy
| | - Marta Paiusco
- Medical Physics Department, Veneto Institute of Oncology IOV - IRCCS, Padova, Italy
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48
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Miller TA, Robinson KR, Li H, Seiwert TY, Haraf DJ, Lan L, Giger ML, Ginat DT. Prognostic value of pre-treatment CT texture analysis in combination with change in size of the primary tumor in response to induction chemotherapy for HPV-positive oropharyngeal squamous cell carcinoma. Quant Imaging Med Surg 2019; 9:399-408. [PMID: 31032187 DOI: 10.21037/qims.2019.03.08] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Background To determine the additive value of quantitative radiomic texture features in predicting progression in human papillomavirus (HPV)-positive oropharyngeal squamous cell carcinoma (OPSCC) based on pre-treatment CT. Methods Retrospective analysis of a single-center cohort of adult patients enrolled in a response-adapted radiation volume de-escalation trial treated with induction chemotherapy. Texture analysis of HPV-positive OPSCC was performed via primary tumor site contouring on pre-treatment contrast-enhanced CT scans. Percent change in size of the tumor in response to induction chemotherapy based on RECIST 1.1 criteria and progression free survival were clinically determined for this cohort. Receiver operating characteristic (ROC) analysis was performed to compare the accuracy of percent change in tumor size after induction chemotherapy with a combination of change in tumor size and radiomic texture features for predicting tumor progression. Results Radiomic texture analysis of the primary tumors in 38 patients with OPSCC depicted on pre-treatment neck CT scans using skewness and entropy in combination with percent change in tumor size after induction chemotherapy yielded a statistically significant increase in accuracy for predicting tumor progression over change in tumor size alone, with an area under the curve of 0.80 versus 0.56 (one-tailed P=0.0087). Conclusions This pilot study suggests that disease progression in patients with HPV-positive OPSCC is more accurately predicted using a combination of texture features on pre-treatment CT scans, along with change in tumor size compared to change in tumor size alone and could therefore serve as a radiomic texture signature.
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Affiliation(s)
- Tamari A Miller
- 1Pritzker School of Medicine, 2Department of Radiology, 3Section of Hematology-Oncology, Department of Medicine, 4Department of Radiation and Cellular Oncology, The University of Chicago, Chicago, IL 60637, USA
| | - Kayla R Robinson
- 1Pritzker School of Medicine, 2Department of Radiology, 3Section of Hematology-Oncology, Department of Medicine, 4Department of Radiation and Cellular Oncology, The University of Chicago, Chicago, IL 60637, USA
| | - Hui Li
- 1Pritzker School of Medicine, 2Department of Radiology, 3Section of Hematology-Oncology, Department of Medicine, 4Department of Radiation and Cellular Oncology, The University of Chicago, Chicago, IL 60637, USA
| | - Tanguy Y Seiwert
- 1Pritzker School of Medicine, 2Department of Radiology, 3Section of Hematology-Oncology, Department of Medicine, 4Department of Radiation and Cellular Oncology, The University of Chicago, Chicago, IL 60637, USA
| | - Daniel J Haraf
- 1Pritzker School of Medicine, 2Department of Radiology, 3Section of Hematology-Oncology, Department of Medicine, 4Department of Radiation and Cellular Oncology, The University of Chicago, Chicago, IL 60637, USA
| | - Li Lan
- 1Pritzker School of Medicine, 2Department of Radiology, 3Section of Hematology-Oncology, Department of Medicine, 4Department of Radiation and Cellular Oncology, The University of Chicago, Chicago, IL 60637, USA
| | - Maryellen L Giger
- 1Pritzker School of Medicine, 2Department of Radiology, 3Section of Hematology-Oncology, Department of Medicine, 4Department of Radiation and Cellular Oncology, The University of Chicago, Chicago, IL 60637, USA
| | - Daniel T Ginat
- 1Pritzker School of Medicine, 2Department of Radiology, 3Section of Hematology-Oncology, Department of Medicine, 4Department of Radiation and Cellular Oncology, The University of Chicago, Chicago, IL 60637, USA
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Lu H, Arshad M, Thornton A, Avesani G, Cunnea P, Curry E, Kanavati F, Liang J, Nixon K, Williams ST, Hassan MA, Bowtell DDL, Gabra H, Fotopoulou C, Rockall A, Aboagye EO. A mathematical-descriptor of tumor-mesoscopic-structure from computed-tomography images annotates prognostic- and molecular-phenotypes of epithelial ovarian cancer. Nat Commun 2019; 10:764. [PMID: 30770825 PMCID: PMC6377605 DOI: 10.1038/s41467-019-08718-9] [Citation(s) in RCA: 105] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2018] [Accepted: 01/24/2019] [Indexed: 12/11/2022] Open
Abstract
The five-year survival rate of epithelial ovarian cancer (EOC) is approximately 35-40% despite maximal treatment efforts, highlighting a need for stratification biomarkers for personalized treatment. Here we extract 657 quantitative mathematical descriptors from the preoperative CT images of 364 EOC patients at their initial presentation. Using machine learning, we derive a non-invasive summary-statistic of the primary ovarian tumor based on 4 descriptors, which we name "Radiomic Prognostic Vector" (RPV). RPV reliably identifies the 5% of patients with median overall survival less than 2 years, significantly improves established prognostic methods, and is validated in two independent, multi-center cohorts. Furthermore, genetic, transcriptomic and proteomic analysis from two independent datasets elucidate that stromal phenotype and DNA damage response pathways are activated in RPV-stratified tumors. RPV and its associated analysis platform could be exploited to guide personalized therapy of EOC and is potentially transferrable to other cancer types.
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Affiliation(s)
- Haonan Lu
- Ovarian Cancer Action Research Centre, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, W12 0HS, UK
- Cancer Imaging Centre, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, W12 0HS, UK
| | - Mubarik Arshad
- Cancer Imaging Centre, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, W12 0HS, UK
| | - Andrew Thornton
- Ovarian Cancer Action Research Centre, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, W12 0HS, UK
| | - Giacomo Avesani
- Cancer Imaging Centre, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, W12 0HS, UK
| | - Paula Cunnea
- Ovarian Cancer Action Research Centre, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, W12 0HS, UK
| | - Ed Curry
- Ovarian Cancer Action Research Centre, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, W12 0HS, UK
| | - Fahdi Kanavati
- Cancer Imaging Centre, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, W12 0HS, UK
| | - Jack Liang
- Cancer Imaging Centre, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, W12 0HS, UK
| | - Katherine Nixon
- Ovarian Cancer Action Research Centre, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, W12 0HS, UK
| | - Sophie T Williams
- Ovarian Cancer Action Research Centre, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, W12 0HS, UK
| | - Mona Ali Hassan
- Ovarian Cancer Action Research Centre, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, W12 0HS, UK
| | - David D L Bowtell
- Peter MacCallum Cancer Centre, Melbourne, 3010, VIC, Australia
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, 3010, VIC, Australia
| | - Hani Gabra
- Ovarian Cancer Action Research Centre, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, W12 0HS, UK
- Early Clinical Development, iMED Biotech Unit, AstraZeneca, Cambridge, SG8 6HB, UK
| | - Christina Fotopoulou
- Ovarian Cancer Action Research Centre, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, W12 0HS, UK
| | - Andrea Rockall
- Cancer Imaging Centre, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, W12 0HS, UK
- Department of Radiology, Imperial College Healthcare NHS Trust, London, W12 0HS, UK
- Department of Radiology, The Royal Marsden NHS Foundation Trust, London, SW3 6JJ, UK
| | - Eric O Aboagye
- Cancer Imaging Centre, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, W12 0HS, UK.
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50
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Radiomics Analysis of PET and CT Components of PET/CT Imaging Integrated with Clinical Parameters: Application to Prognosis for Nasopharyngeal Carcinoma. Mol Imaging Biol 2019; 21:954-964. [DOI: 10.1007/s11307-018-01304-3] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
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