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Vaz SC, Woll JPP, Cardoso F, Groheux D, Cook GJR, Ulaner GA, Jacene H, Rubio IT, Schoones JW, Peeters MJV, Poortmans P, Mann RM, Graff SL, Dibble EH, de Geus-Oei LF. Joint EANM-SNMMI guideline on the role of 2-[ 18F]FDG PET/CT in no special type breast cancer : (endorsed by the ACR, ESSO, ESTRO, EUSOBI/ESR, and EUSOMA). Eur J Nucl Med Mol Imaging 2024; 51:2706-2732. [PMID: 38740576 PMCID: PMC11224102 DOI: 10.1007/s00259-024-06696-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Accepted: 03/20/2024] [Indexed: 05/16/2024]
Abstract
INTRODUCTION There is much literature about the role of 2-[18F]FDG PET/CT in patients with breast cancer (BC). However, there exists no international guideline with involvement of the nuclear medicine societies about this subject. PURPOSE To provide an organized, international, state-of-the-art, and multidisciplinary guideline, led by experts of two nuclear medicine societies (EANM and SNMMI) and representation of important societies in the field of BC (ACR, ESSO, ESTRO, EUSOBI/ESR, and EUSOMA). METHODS Literature review and expert discussion were performed with the aim of collecting updated information regarding the role of 2-[18F]FDG PET/CT in patients with no special type (NST) BC and summarizing its indications according to scientific evidence. Recommendations were scored according to the National Institute for Health and Care Excellence (NICE) criteria. RESULTS Quantitative PET features (SUV, MTV, TLG) are valuable prognostic parameters. In baseline staging, 2-[18F]FDG PET/CT plays a role from stage IIB through stage IV. When assessing response to therapy, 2-[18F]FDG PET/CT should be performed on certified scanners, and reported either according to PERCIST, EORTC PET, or EANM immunotherapy response criteria, as appropriate. 2-[18F]FDG PET/CT may be useful to assess early metabolic response, particularly in non-metastatic triple-negative and HER2+ tumours. 2-[18F]FDG PET/CT is useful to detect the site and extent of recurrence when conventional imaging methods are equivocal and when there is clinical and/or laboratorial suspicion of relapse. Recent developments are promising. CONCLUSION 2-[18F]FDG PET/CT is extremely useful in BC management, as supported by extensive evidence of its utility compared to other imaging modalities in several clinical scenarios.
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Affiliation(s)
- Sofia C Vaz
- Nuclear Medicine-Radiopharmacology, Champalimaud Clinical Center, Champalimaud Foundation, Lisbon, Portugal.
- Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands.
| | | | - Fatima Cardoso
- Breast Unit, Champalimaud Clinical Center, Champalimaud Foundation, Lisbon, Portugal
| | - David Groheux
- Nuclear Medicine Department, Saint-Louis Hospital, Paris, France
- University Paris-Diderot, INSERM U976, Paris, France
- Centre d'Imagerie Radio-Isotopique (CIRI), La Rochelle, France
| | - Gary J R Cook
- Department of Cancer Imaging, King's College London, London, UK
- King's College London and Guy's & St Thomas' PET Centre, London, UK
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Gary A Ulaner
- Molecular Imaging and Therapy, Hoag Family Cancer Institute, Newport Beach, CA, USA
- University of Southern California, Los Angeles, CA, USA
| | - Heather Jacene
- Dana-Farber Cancer Institute/Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Isabel T Rubio
- Breast Surgical Oncology, Clinica Universidad de Navarra, Madrid, Cancer Center Clinica Universidad de Navarra, Navarra, Spain
| | - Jan W Schoones
- Directorate of Research Policy, Leiden University Medical Center, Leiden, The Netherlands
| | - Marie-Jeanne Vrancken Peeters
- Department of Surgical Oncology, Netherlands Cancer Institute, Amsterdam, The Netherlands
- Department of Surgery, Amsterdam University Medical Center, Amsterdam, The Netherlands
| | - Philip Poortmans
- Department of Radiation Oncology, Iridium Netwerk, Antwerp, Belgium
- University of Antwerp, Wilrijk, Antwerp, Belgium
| | - Ritse M Mann
- Radiology Department, RadboudUMC, Nijmegen, The Netherlands
| | - Stephanie L Graff
- Lifespan Cancer Institute, Providence, Rhode Island, USA
- Legorreta Cancer Center at Brown University, Providence, Rhode Island, USA
| | - Elizabeth H Dibble
- Department of Diagnostic Imaging, The Warren Alpert Medical School of Brown University, Providence, Rhode Island, USA.
| | - Lioe-Fee de Geus-Oei
- Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands.
- Biomedical Photonic Imaging Group, University of Twente, Enschede, The Netherlands.
- Department of Radiation Science & Technology, Technical University of Delft, Delft, The Netherlands.
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Oliveira C, Oliveira F, Constantino C, Alves C, Brito MJ, Cardoso F, Costa DC. Baseline [ 18F]FDG PET/CT and MRI first-order breast tumor features do not improve pathological complete response prediction to neoadjuvant chemotherapy. Eur J Nucl Med Mol Imaging 2024:10.1007/s00259-024-06815-6. [PMID: 38922396 DOI: 10.1007/s00259-024-06815-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Accepted: 06/17/2024] [Indexed: 06/27/2024]
Abstract
PURPOSE To verify the ability of pretreatment [18F]FDG PET/CT and T1-weighed dynamic contrast-enhanced MRI to predict pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) in breast cancer (BC) patients. METHODS This retrospective study includes patients with BC of no special type submitted to baseline [18F]FDG PET/CT, NAC and surgery. [18F]FDG PET-based features reflecting intensity and heterogeneity of tracer uptake were extracted from the primary BC and suspicious axillary lymph nodes (ALN), for comparative analysis related to NAC response (pCR vs. non-pCR). Multivariate logistic regression was performed for response prediction combining the breast tumor-extracted PET-based features and clinicopathological features. A subanalysis was performed in a patients' subsample by adding breast tumor-extracted first-order MRI-based features to the multivariate logistic regression. RESULTS A total of 170 tumors from 168 patients were included. pCR was observed in 60/170 tumors (20/107 luminal B-like, 25/45 triple-negative and 15/18 HER2-enriched surrogate molecular subtypes). Higher intensity and higher heterogeneity of [18F]FDG uptake in the primary BC were associated with NAC response in HER2-negative tumors (immunohistochemistry score 0, 1 + or 2 + non-amplified by in situ hybridization). Also, higher intensity of tracer uptake was observed in ALN in the pCR group among HER2-negative tumors. No [18F]FDG PET-based features were associated with pCR in the other subgroup analyses. A subsample of 103 tumors was also submitted to extraction of MRI-based features. When combined with clinicopathological features, neither [18F]FDG PET nor MRI-based features had additional value for pCR prediction. The only significant predictors were estrogen receptor status, HER2 expression and grade. CONCLUSION Pretreatment [18F]FDG PET-based features from primary BC and ALN are not associated with response to NAC, except in HER2-negative tumors. As compared with pathological features, no breast tumor-extracted PET or MRI-based feature improved response prediction.
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Affiliation(s)
- Carla Oliveira
- Nuclear Medicine-Radiopharmacology, Champalimaud Clinical Centre/Champalimaud Foundation, Lisbon, Portugal.
| | - Francisco Oliveira
- Nuclear Medicine-Radiopharmacology, Champalimaud Clinical Centre/Champalimaud Foundation, Lisbon, Portugal
| | - Cláudia Constantino
- Nuclear Medicine-Radiopharmacology, Champalimaud Clinical Centre/Champalimaud Foundation, Lisbon, Portugal
| | - Celeste Alves
- Breast Unit, Champalimaud Clinical Centre/Champalimaud Foundation, Lisbon, Portugal
| | - Maria José Brito
- Breast Unit, Champalimaud Clinical Centre/Champalimaud Foundation, Lisbon, Portugal
- Pathology Department, Champalimaud Clinical Centre/Champalimaud Foundation, Lisbon, Portugal
| | - Fátima Cardoso
- Breast Unit, Champalimaud Clinical Centre/Champalimaud Foundation, Lisbon, Portugal
| | - Durval C Costa
- Nuclear Medicine-Radiopharmacology, Champalimaud Clinical Centre/Champalimaud Foundation, Lisbon, Portugal
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Katal S, McKay MJ, Taubman K. PET Molecular Imaging in Breast Cancer: Current Applications and Future Perspectives. J Clin Med 2024; 13:3459. [PMID: 38929989 PMCID: PMC11205053 DOI: 10.3390/jcm13123459] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2024] [Revised: 06/10/2024] [Accepted: 06/11/2024] [Indexed: 06/28/2024] Open
Abstract
Positron emission tomography (PET) plays a crucial role in breast cancer management. This review addresses the role of PET imaging in breast cancer care. We focus primarily on the utility of 18F-fluorodeoxyglucose (FDG) PET in staging, recurrence detection, and treatment response evaluation. Furthermore, we delve into the growing interest in precision therapy and the development of novel radiopharmaceuticals targeting tumor biology. This includes discussing the potential of PET/MRI and artificial intelligence in breast cancer imaging, offering insights into improved diagnostic accuracy and personalized treatment approaches.
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Affiliation(s)
- Sanaz Katal
- Medical Imaging Department, St. Vincent’s Hospital Melbourne, Fitzroy, VIC 3065, Australia;
| | - Michael J. McKay
- Northwest Regional Hospital, University of Tasmania, Burnie, TAS 7320, Australia;
- Northern Cancer Service, Northwest Regional Hospital, Burnie, TAS 7320, Australia
| | - Kim Taubman
- Medical Imaging Department, St. Vincent’s Hospital Melbourne, Fitzroy, VIC 3065, Australia;
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Vaz SC, Oliveira C, Teixeira R, Arias-Bouda LMP, Cardoso MJ, de Geus-Oei LF. The current role of nuclear medicine in breast cancer. Br J Radiol 2023; 96:20221153. [PMID: 37097285 PMCID: PMC10461286 DOI: 10.1259/bjr.20221153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 02/24/2023] [Accepted: 02/27/2023] [Indexed: 04/26/2023] Open
Abstract
Breast cancer is the most common cancer in females worldwide. Nuclear medicine plays an important role in patient management, not only in initial staging, but also during follow-up. Radiopharmaceuticals to study breast cancer have been used for over 50 years, and several of these are still used in clinical practice, according to the most recent guideline recommendations.In this critical review, an overview of nuclear medicine procedures used during the last decades is presented. Current clinical indications of each of the conventional nuclear medicine and PET/CT examinations are the focus of this review, and are objectively provided. Radionuclide therapies are also referred, mainly summarising the methods to palliate metastatic bone pain. Finally, recent developments and future perspectives in the field of nuclear medicine are discussed. In this context, the promising potential of new radiopharmaceuticals not only for diagnosis, but also for therapy, and the use of quantitative imaging features as potential biomarkers, are addressed.Despite the long way nuclear medicine has gone through, it looks like it will continue to benefit clinical practice, paving the way to improve healthcare provided to patients with breast cancer.
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Affiliation(s)
| | - Carla Oliveira
- Nuclear Medicine-Radiopharmacology, Champalimaud Clinical Center, Champalimaud Foundation, Lisbon, Portugal
| | - Ricardo Teixeira
- Nuclear Medicine-Radiopharmacology, Champalimaud Clinical Center, Champalimaud Foundation, Lisbon, Portugal
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Xu X, Sun X, Ma L, Zhang H, Ji W, Xia X, Lan X. 18F-FDG PET/CT radiomics signature and clinical parameters predict progression-free survival in breast cancer patients: A preliminary study. Front Oncol 2023; 13:1149791. [PMID: 36969043 PMCID: PMC10036789 DOI: 10.3389/fonc.2023.1149791] [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: 01/23/2023] [Accepted: 02/20/2023] [Indexed: 03/12/2023] Open
Abstract
IntroductionThis study aimed to investigate the feasibility of predicting progression-free survival (PFS) in breast cancer patients using pretreatment 18F-fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) radiomics signature and clinical parameters.MethodsBreast cancer patients who underwent 18F-FDG PET/CT imaging before treatment from January 2012 to December 2020 were eligible for study inclusion. Eighty-seven patients were randomly divided into training (n = 61) and internal test sets (n = 26) and an additional 25 patients were used as the external validation set. Clinical parameters, including age, tumor size, molecularsubtype, clinical TNM stage, and laboratory findings were collected. Radiomics features were extracted from preoperative PET/CT images. Least absolute shrinkage and selection operators were applied to shrink feature size and build a predictive radiomics signature. Univariate and multivariate Cox proportional hazards models and Kaplan-Meier analysis were used to assess the association of rad-score and clinical parameter with PFS. Nomograms were constructed to visualize survival prediction. C-index and calibration curve were used to evaluate nomogram performance.ResultsEleven radiomics features were selected to generate rad-score. The clinical model comprised three parameters: clinical M stage, CA125, and pathological N stage. Rad-score and clinical-model were significantly associated with PFS in the training set (P< 0.01) but not the test set. The integrated clinical-radiomics (ICR) model was significantly associated with PFS in both the training and test sets (P< 0.01). The ICR model nomogram had a significantly higher C-index than the clinical model and rad-score in the training and test sets. The C-index of the ICR model in the external validation set was 0.754 (95% confidence interval, 0.726–0.812). PFS significantly differed between the low- and high-risk groups stratified by the nomogram (P = 0.009). The calibration curve indicated the ICR model provided the greatest clinical benefit.ConclusionThe ICR model, which combined clinical parameters and preoperative 18F-FDG PET/CT imaging, was able to independently predict PFS in breast cancer patients and was superior to the clinical model alone and rad-score alone.
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Affiliation(s)
- Xiaojun Xu
- Department of Nuclear Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
- Key Laboratory of Biological Targeted Therapy of the Ministry of Education, Wuhan, China
| | - Xun Sun
- Department of Nuclear Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
- Key Laboratory of Biological Targeted Therapy of the Ministry of Education, Wuhan, China
| | - Ling Ma
- He Kang Corporate Management (SH) Co. Ltd, Shanghai, China
| | - Huangqi Zhang
- Department of Radiology, Affiliated Taizhou Hospital of Wenzhou Medical University, Taizhou, Zhejiang, China
| | - Wenbin Ji
- Department of Radiology, Affiliated Taizhou Hospital of Wenzhou Medical University, Taizhou, Zhejiang, China
| | - Xiaotian Xia
- Department of Nuclear Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
- Key Laboratory of Biological Targeted Therapy of the Ministry of Education, Wuhan, China
- *Correspondence: Xiaotian Xia, ; Xiaoli Lan,
| | - Xiaoli Lan
- Department of Nuclear Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
- Key Laboratory of Biological Targeted Therapy of the Ministry of Education, Wuhan, China
- *Correspondence: Xiaotian Xia, ; Xiaoli Lan,
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Dai J, Wang H, Xu Y, Chen X, Tian R. Clinical application of AI-based PET images in oncological patients. Semin Cancer Biol 2023; 91:124-142. [PMID: 36906112 DOI: 10.1016/j.semcancer.2023.03.005] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Revised: 02/28/2023] [Accepted: 03/07/2023] [Indexed: 03/11/2023]
Abstract
Based on the advantages of revealing the functional status and molecular expression of tumor cells, positron emission tomography (PET) imaging has been performed in numerous types of malignant diseases for diagnosis and monitoring. However, insufficient image quality, the lack of a convincing evaluation tool and intra- and interobserver variation in human work are well-known limitations of nuclear medicine imaging and restrict its clinical application. Artificial intelligence (AI) has gained increasing interest in the field of medical imaging due to its powerful information collection and interpretation ability. The combination of AI and PET imaging potentially provides great assistance to physicians managing patients. Radiomics, an important branch of AI applied in medical imaging, can extract hundreds of abstract mathematical features of images for further analysis. In this review, an overview of the applications of AI in PET imaging is provided, focusing on image enhancement, tumor detection, response and prognosis prediction and correlation analyses with pathology or specific gene mutations in several types of tumors. Our aim is to describe recent clinical applications of AI-based PET imaging in malignant diseases and to focus on the description of possible future developments.
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Affiliation(s)
- Jiaona Dai
- Department of Nuclear Medicine, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Hui Wang
- Department of Nuclear Medicine, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Yuchao Xu
- School of Nuclear Science and Technology, University of South China, Hengyang City 421001, China
| | - Xiyang Chen
- Division of Vascular Surgery, Department of General Surgery, West China Hospital, Sichuan University, Chengdu 610041, China.
| | - Rong Tian
- Department of Nuclear Medicine, West China Hospital, Sichuan University, Chengdu 610041, China.
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7
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Oliveira C, Oliveira F, Vaz SC, Marques HP, Cardoso F. Prediction of pathological response after neoadjuvant chemotherapy using baseline FDG PET heterogeneity features in breast cancer. Br J Radiol 2023; 96:20220655. [PMID: 36867773 DOI: 10.1259/bjr.20220655] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/05/2023] Open
Abstract
Complete pathological response to neoadjuvant systemic treatment (NAST) in some subtypes of breast cancer (BC) has been used as a surrogate of long-term outcome. The possibility of predicting BC pathological response to NAST based on the baseline 18F-Fluorodeoxyglucose positron emission tomography (FDG PET), without the need of an interim study, is a focus of recent discussion. This review summarises the characteristics and results of the available studies regarding the potential impact of heterogeneity features of the primary tumour burden on baseline FDG PET in predicting pathological response to NAST in BC patients. Literature search was conducted on PubMed database and relevant data from each selected study were collected. A total of 13 studies were eligible for inclusion, all of them published over the last 5 years. Eight out of 13 analysed studies indicated an association between FDG PET-based tumour uptake heterogeneity features and prediction of response to NAST. When features associated with predicting response to NAST were derived, these varied between studies. Therefore, definitive reproducible findings across series were difficult to establish. This lack of consensus may reflect the heterogeneity and low number of included series. The clinical relevance of this topic justifies further investigation about the predictive role of baseline FDG PET.
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Affiliation(s)
- Carla Oliveira
- Nuclear Medicine-Radiopharmacology, Champalimaud Clinical Center/Champalimaud Foundation, Lisbon, Portugal
| | - Francisco Oliveira
- Nuclear Medicine-Radiopharmacology, Champalimaud Clinical Center/Champalimaud Foundation, Lisbon, Portugal
| | - Sofia C Vaz
- Nuclear Medicine-Radiopharmacology, Champalimaud Clinical Center/Champalimaud Foundation, Lisbon, Portugal
| | | | - Fátima Cardoso
- Breast Unit, Champalimaud Clinical Center/Champalimaud Foundation, Lisbon, Portugal
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Cárcamo Ibarra PM, López González UA, Esteban Hurtado A, Navas de la Cruz MA, Asensio Valero L, Diez Domingo S. Progress and current utility of radiomics in PET/CT study of non-metastatic breast cancer: A systematic review. Rev Esp Med Nucl Imagen Mol 2023; 42:83-92. [PMID: 36375751 DOI: 10.1016/j.remnie.2022.11.001] [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: 07/18/2022] [Revised: 08/13/2022] [Accepted: 08/18/2022] [Indexed: 11/13/2022]
Abstract
AIM To synthesize the current evidence of the usefulness of radiomics in PET/CT image analysis in local and locally advanced breast cancer. Also, to evaluate the methodological quality of the radiomic studies published. METHODS Systematic review of articles in different databases until 2021 using the terms "PET", "radiomics", "texture", "breast". Only articles with human data and that included a PET image were included. Studies with simulated data and with less than 20 patients were excluded. Were extracted sample size, radiotracer used, imaging technique, and radiomics characteristics from each article. The methodological quality of the studies was determined using the QUADAS-2 tool. RESULTS 18 articles were selected. The retrospective design was the most used. The most studied radiomic characteristic was SUVmax. Several radiomic parameters were correlated with tumor characterization, and tumor heterogeneity proved useful for predicting disease course and response to treatment. Most articles showed a high risk of bias, mainly from the patient selection. CONCLUSIONS A high probability of bias was observed in most of the published articles. Radiomics is a developing field and more studies are needed to demonstrate its usefulness in routine clinical practice. The QUADAS-2 tool allows critical assessment of the methodological quality of the available evidence. Despite its limitations, radiomics is shown to be an instrument that can help to achieve personalized oncologic management of breast cancer.
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Affiliation(s)
- P M Cárcamo Ibarra
- Servicio de Medicina Nuclear, Hospital Clínico Universitario de Valencia, Spain
| | - U A López González
- Servicio de Medicina Preventiva, Hospital Universitario Doctor Peset, Valencia, Spain
| | - A Esteban Hurtado
- Servicio de Medicina Nuclear, Hospital Universitario Doctor Peset, Valencia, Spain
| | - M A Navas de la Cruz
- Servicio de Medicina Nuclear, Hospital Universitario Doctor Peset, Valencia, Spain
| | - L Asensio Valero
- Servicio de Medicina Nuclear, Hospital Clínico Universitario de Valencia, Spain
| | - S Diez Domingo
- Servicio de Protección Radiológica, Hospital Clínico Universitario de Valencia, Valencia, Spain.
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Evaluation of functional and metabolic tumor volume using voxel-wise analysis in childhood rhabdomyosarcoma. Pediatr Radiol 2023; 53:438-449. [PMID: 36399161 PMCID: PMC9968707 DOI: 10.1007/s00247-022-05540-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 09/21/2022] [Accepted: 10/14/2022] [Indexed: 11/19/2022]
Abstract
BACKGROUND Cross-sectional imaging-based morphological characteristics of pediatric rhabdomyosarcoma have failed to predict outcomes. OBJECTIVE To evaluate the feasibility and possible value of generating tumor sub-volumes using voxel-wise analysis of metabolic and functional data from positron emission tomography/magnetic resonance imaging (PET/MR) or PET/computed tomography (CT) and MRI in rhabdomyosarcoma. MATERIALS AND METHODS Thirty-four examinations in 17 patients who received PET/MRI or PET/CT plus MRI were analyzed. The volume of interest included total tumor volume before and after therapy. Apparent diffusion coefficients (ADC) and standard uptake values (SUV) were determined voxel-wise. Voxels were assigned to three different groups based on ADC and SUV: "viable tumor tissue," "intermediate tissue" or "possible necrosis." In a second approach, data were grouped into three clusters using the Gaussian mixture model. The ratio of these clusters to total tumor volume and changes due to chemotherapy were correlated with clinical and histopathological data. RESULTS After chemotherapy, the proportion of voxels in the different groups changed significantly. A significant reduction of the proportion of voxels assigned to cluster 1 was found, from a mean of 36.4% to 2.5% (P < 0.001). There was a significant increase in the proportion of voxels in cluster 3 following chemotherapy from 24.8% to 81.6% (P = 0.02). The proportion of voxels in cluster 2 differed depending on the presence or absence of tumor recurrence, falling from 48% to 10% post-chemotherapy in the group with no tumor recurrence (P < 0.05) and from 29% to 23% (P > 0.05) in the group with tumor recurrence. CONCLUSION Voxel-wise evaluation of multimodal data in rhabdomyosarcoma is feasible. Our initial results suggest that the different distribution of sub-volumes before and after therapy may have prognostic significance.
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Chen R, Fu Y, Yi X, Pei Q, Zai H, Chen BT. Application of Radiomics in Predicting Treatment Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer: Strategies and Challenges. JOURNAL OF ONCOLOGY 2022; 2022:1590620. [PMID: 36471884 PMCID: PMC9719428 DOI: 10.1155/2022/1590620] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/20/2022] [Revised: 10/30/2022] [Accepted: 11/09/2022] [Indexed: 08/01/2023]
Abstract
Neoadjuvant chemoradiotherapy (nCRT) followed by total mesorectal excision is the standard treatment for locally advanced rectal cancer (LARC). A noninvasive preoperative prediction method should greatly assist in the evaluation of response to nCRT and for the development of a personalized strategy for patients with LARC. Assessment of nCRT relies on imaging and radiomics can extract valuable quantitative data from medical images. In this review, we examined the status of radiomic application for assessing response to nCRT in patients with LARC and indicated a potential direction for future research.
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Affiliation(s)
- Rui Chen
- Department of Radiology, Xiangya Hospital, Central South University, Changsha 410008, Hunan, China
| | - Yan Fu
- Department of Radiology, Xiangya Hospital, Central South University, Changsha 410008, Hunan, China
| | - Xiaoping Yi
- Department of Radiology, Xiangya Hospital, Central South University, Changsha 410008, Hunan, China
| | - Qian Pei
- Department of Radiology, Xiangya Hospital, Central South University, Changsha 410008, Hunan, China
| | - Hongyan Zai
- Department of Radiology, Xiangya Hospital, Central South University, Changsha 410008, Hunan, China
| | - Bihong T. Chen
- Department of Diagnostic Radiology, City of Hope National Medical Center, Duarte, CA, USA
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Urso L, Manco L, Castello A, Evangelista L, Guidi G, Castellani M, Florimonte L, Cittanti C, Turra A, Panareo S. PET-Derived Radiomics and Artificial Intelligence in Breast Cancer: A Systematic Review. Int J Mol Sci 2022; 23:13409. [PMID: 36362190 PMCID: PMC9653918 DOI: 10.3390/ijms232113409] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2022] [Revised: 10/27/2022] [Accepted: 10/28/2022] [Indexed: 08/13/2023] Open
Abstract
Breast cancer (BC) is a heterogeneous malignancy that still represents the second cause of cancer-related death among women worldwide. Due to the heterogeneity of BC, the correct identification of valuable biomarkers able to predict tumor biology and the best treatment approaches are still far from clear. Although molecular imaging with positron emission tomography/computed tomography (PET/CT) has improved the characterization of BC, these methods are not free from drawbacks. In recent years, radiomics and artificial intelligence (AI) have been playing an important role in the detection of several features normally unseen by the human eye in medical images. The present review provides a summary of the current status of radiomics and AI in different clinical settings of BC. A systematic search of PubMed, Web of Science and Scopus was conducted, including all articles published in English that explored radiomics and AI analyses of PET/CT images in BC. Several studies have demonstrated the potential role of such new features for the staging and prognosis as well as the assessment of biological characteristics. Radiomics and AI features appear to be promising in different clinical settings of BC, although larger prospective trials are needed to confirm and to standardize this evidence.
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Affiliation(s)
- Luca Urso
- Department of Translational Medicine, University of Ferrara, Via Aldo Moro 8, 44124 Ferrara, Italy
- Nuclear Medicine Unit, Oncological Medical and Specialist Department, University Hospital of Ferrara, 44124 Cona, Italy
| | - Luigi Manco
- Medical Physics Unit, Azienda USL of Ferrara, 44124 Ferrara, Italy
- Medical Physics Unit, University Hospital of Ferrara, 44124 Cona, Italy
| | - Angelo Castello
- Nuclear Medicine Unit, Fondazione IRCCS Ca’ Granda, Ospedale Maggiore Policlinico, 20122 Milan, Italy
| | - Laura Evangelista
- Department of Medicine DIMED, University of Padua, 35128 Padua, Italy
| | - Gabriele Guidi
- Medical Physics Unit, University Hospital of Modena, 41125 Modena, Italy
| | - Massimo Castellani
- Nuclear Medicine Unit, Fondazione IRCCS Ca’ Granda, Ospedale Maggiore Policlinico, 20122 Milan, Italy
| | - Luigia Florimonte
- Nuclear Medicine Unit, Fondazione IRCCS Ca’ Granda, Ospedale Maggiore Policlinico, 20122 Milan, Italy
| | - Corrado Cittanti
- Department of Translational Medicine, University of Ferrara, Via Aldo Moro 8, 44124 Ferrara, Italy
- Nuclear Medicine Unit, Oncological Medical and Specialist Department, University Hospital of Ferrara, 44124 Cona, Italy
| | - Alessandro Turra
- Medical Physics Unit, University Hospital of Ferrara, 44124 Cona, Italy
| | - Stefano Panareo
- Nuclear Medicine Unit, Oncology and Haematology Department, University Hospital of Modena, 41125 Modena, Italy
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12
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Cárcamo Ibarra P, López González U, Esteban Hurtado A, Navas de la Cruz M, Asensio Valero L, Diez Domingo S. Progreso y utilidad actual de la radiómica dentro del estudio PET/TC en cáncer de mama no metastásico: una revisión sistemática. Rev Esp Med Nucl Imagen Mol 2022. [DOI: 10.1016/j.remn.2022.08.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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13
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Wolsztynski E, O'Sullivan F, Eary JF. Spatially coherent modeling of 3D FDG-PET data for assessment of intratumoral heterogeneity and uptake gradients. J Med Imaging (Bellingham) 2022; 9:045003. [PMID: 35915767 PMCID: PMC9334646 DOI: 10.1117/1.jmi.9.4.045003] [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: 09/10/2021] [Accepted: 06/28/2022] [Indexed: 11/14/2022] Open
Abstract
Purpose: Radiomics have become invaluable for non-invasive cancer patient risk prediction, and the community now turns to exogenous assessment, e.g., from genomics, for interpretability of these agnostic analyses. Yet, some opportunities for clinically interpretable modeling of positron emission tomography (PET) imaging data remain unexplored, that could facilitate insightful characterization at voxel level. Approach: Here, we present a novel deformable tubular representation of the distribution of tracer uptake within a volume of interest, and derive interpretable prognostic summaries from it. This data-adaptive strategy yields a 3D-coherent and smooth model fit, and a profile curve describing tracer uptake as a function of voxel location within the volume. Local trends in uptake rates are assessed at each voxel via the calculation of gradients derived from this curve. Intratumoral heterogeneity can also be assessed directly from it. Results: We illustrate the added value of this approach over previous strategies, in terms of volume rendering and coherence of the structural representation of the data. We further demonstrate consistency of the implementation via simulations, and prognostic potential of heterogeneity and statistical summaries of the uptake gradients derived from the model on a clinical cohort of 158 sarcoma patients imaged with F 18 -fluorodeoxyglucose-PET, in multivariate prognostic models of patient survival. Conclusions: The proposed approach captures uptake characteristics consistently at any location, and yields a description of variations in uptake that holds prognostic value complementarily to structural heterogeneity. This creates opportunities for monitoring of local areas of greater interest within a tumor, e.g., to assess therapeutic response in avid locations.
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Affiliation(s)
- Eric Wolsztynski
- University College Cork, Statistics Department, Cork, Ireland.,Insight SFI Research Centre for Data Analytics, Cork, Ireland
| | - Finbarr O'Sullivan
- University College Cork, Statistics Department, Cork, Ireland.,Insight SFI Research Centre for Data Analytics, Cork, Ireland
| | - Janet F Eary
- National Cancer Institute, Bethesda, Maryland, United States
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14
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Mellinger AL, Muddiman DC, Gamcsik MP. Highlighting Functional Mass Spectrometry Imaging Methods in Bioanalysis. J Proteome Res 2022; 21:1800-1807. [PMID: 35749637 DOI: 10.1021/acs.jproteome.2c00220] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Most mass spectrometry imaging (MSI) methods provide a molecular map of tissue content but little information on tissue function. Mapping tissue function is possible using several well-known examples of "functional imaging" such as positron emission tomography and functional magnetic resonance imaging that can provide the spatial distribution of time-dependent biological processes. These functional imaging methods represent the net output of molecular networks influenced by local tissue environments that are difficult to predict from molecular/cellular content alone. However, for decades, MSI methods have also been demonstrated to provide functional imaging data on a variety of biological processes. In fact, MSI exceeds some of the classic functional imaging methods, demonstrating the ability to provide functional data from the nanoscale (subcellular) to whole tissue or organ level. This Perspective highlights several examples of how different MSI ionization and detection technologies can provide unprecedented detailed spatial maps of time-dependent biological processes, namely, nucleic acid synthesis, lipid metabolism, bioenergetics, and protein metabolism. By classifying various MSI methods under the umbrella of "functional MSI", we hope to draw attention to both the unique capabilities and accessibility with the aim of expanding this underappreciated field to include new approaches and applications.
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Affiliation(s)
- Allyson L Mellinger
- FTMS Laboratory for Human Health Research, Department of Chemistry, North Carolina State University, Raleigh, North Carolina 27695, United States
| | - David C Muddiman
- FTMS Laboratory for Human Health Research, Department of Chemistry, North Carolina State University, Raleigh, North Carolina 27695, United States.,Molecular Education, Technology and Research Innovation Center (METRIC), North Carolina State University, Raleigh, North Carolina 27695, United States
| | - Michael P Gamcsik
- UNC/NCSU Joint Department of Biomedical Engineering, Raleigh, North Carolina 27695, United States
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15
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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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16
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Urso L, Quartuccio N, Caracciolo M, Evangelista L, Schirone A, Frassoldati A, Arnone G, Panareo S, Bartolomei M. Impact on the long-term prognosis of FDG PET/CT in luminal-A and luminal-B breast cancer. Nucl Med Commun 2022; 43:212-219. [PMID: 35022378 PMCID: PMC10876173 DOI: 10.1097/mnm.0000000000001500] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Accepted: 10/01/2021] [Indexed: 02/07/2023]
Abstract
PURPOSE The aim of the present study was to explore the prognostic role of 2- deoxy-2-[18F]fluoro-D-glucose PET (FDG PET)/CT in recurrent luminal A and luminal B breast cancer. MATERIALS AND METHODS From two institutional databases, we retrospectively retrieved data about breast cancer patients undergoing FDG PET/CT between 2011 and 2018 for the assessment of recurrency. Molecular subtypes of breast cancer were defined based on the expression of estrogen, progesterone, human epidermal growth factor receptor 2 (HER2)-b receptors and proliferation index. Overall survival (OS, intended as the time from PET/CT and the time of death) was registered for each patient, by checking the medical charts. Parametric and survival analyses were computed. RESULTS Data of 179 patients were retrieved. Sixty-three patients had luminal A, 88 luminal B and 28 luminal B/He breast cancer. At the time of PET/CT scan, cancer antigen (CA) 15.3 levels was within the normal range in 119 patients, whereas it was increased in 60 patients. FDG PET/CT results were suggestive for disease recurrence in 114 (63.7%) patients. The median time lapse from the FDG PET/CT scan to the last clinical follow-up visit was 51 months (1-192 months). Patients with evidence of a PET/CT scan suggestive for disease recurrence showed a significantly shorter OS (P < 0.001) compared to patients with no PET/CT evidence of recurrence, in each subset of luminal breast cancer. Moreover, PET/CT was able to stratify the prognosis of patients independently from the level of tumor marker. CONCLUSION These data suggest that FDG PET/CT may be an attractive prognostic tool in recurrent breast cancer. Our study supports its prognostic role both in luminal A and B-type molecular subtypes, regardless of the CA 15.3 levels.
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Affiliation(s)
- Luca Urso
- Oncological Medical and Specialists Department, Nuclear Medicine Unit, University Hospital of Ferrara, Ferrara
| | - Natale Quartuccio
- Nuclear Medicine Unit, A.R.N.A.S. Ospedali Civico, Di Cristina e Benfratelli, Palermo
| | - Matteo Caracciolo
- Oncological Medical and Specialists Department, Nuclear Medicine Unit, University Hospital of Ferrara, Ferrara
| | - Laura Evangelista
- Department of Medicine DIMED, Nuclear Medicine Unit, University of Padova, Padova
| | - Alessio Schirone
- Oncological Medical and Specialists Department, Oncology Unit, University Hospital of Ferrara, Ferrara, Italy
| | - Antonio Frassoldati
- Oncological Medical and Specialists Department, Oncology Unit, University Hospital of Ferrara, Ferrara, Italy
| | - Gaspare Arnone
- Nuclear Medicine Unit, A.R.N.A.S. Ospedali Civico, Di Cristina e Benfratelli, Palermo
| | - Stefano Panareo
- Oncological Medical and Specialists Department, Nuclear Medicine Unit, University Hospital of Ferrara, Ferrara
| | - Mirco Bartolomei
- Oncological Medical and Specialists Department, Nuclear Medicine Unit, University Hospital of Ferrara, Ferrara
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Bouron C, Mathie C, Seegers V, Morel O, Jézéquel P, Lasla H, Guillerminet C, Girault S, Lacombe M, Sher A, Lacoeuille F, Patsouris A, Testard A. Prognostic Value of Metabolic, Volumetric and Textural Parameters of Baseline [ 18F]FDG PET/CT in Early Triple-Negative Breast Cancer. Cancers (Basel) 2022; 14:cancers14030637. [PMID: 35158904 PMCID: PMC8833829 DOI: 10.3390/cancers14030637] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2021] [Revised: 01/22/2022] [Accepted: 01/23/2022] [Indexed: 02/04/2023] Open
Abstract
Simple Summary The aim of this study was to evaluate PET/CT parameters to determine different prognostic groups in TNBC, in order to select patients with a high risk of relapse, for whom therapeutic escalation can be considered. We have demonstrated that the MTV, TLG and entropy of the primary breast lesion could be of interest to predict the prognostic outcome of TNBC patients. Abstract (1) Background: triple-negative breast cancer (TNBC) remains a clinical and therapeutic challenge primarily affecting young women with poor prognosis. TNBC is currently treated as a single entity but presents a very diverse profile in terms of prognosis and response to treatment. Positron emission tomography/computed tomography (PET/CT) with 18F-fluorodeoxyglucose ([18F]FDG) is gaining importance for the staging of breast cancers. TNBCs often show high [18F]FDG uptake and some studies have suggested a prognostic value for metabolic and volumetric parameters, but no study to our knowledge has examined textural features in TNBC. The objective of this study was to evaluate the association between metabolic, volumetric and textural parameters measured at the initial [18F]FDG PET/CT and disease-free survival (DFS) and overall survival (OS) in patients with nonmetastatic TBNC. (2) Methods: all consecutive nonmetastatic TNBC patients who underwent a [18F]FDG PET/CT examination upon diagnosis between 2012 and 2018 were retrospectively included. The metabolic and volumetric parameters (SUVmax, SUVmean, SUVpeak, MTV, and TLG) and the textural features (entropy, homogeneity, SRE, LRE, LGZE, and HGZE) of the primary tumor were collected. (3) Results: 111 patients were enrolled (median follow-up: 53.6 months). In the univariate analysis, high TLG, MTV and entropy values of the primary tumor were associated with lower DFS (p = 0.008, p = 0.006 and p = 0.025, respectively) and lower OS (p = 0.002, p = 0.001 and p = 0.046, respectively). The discriminating thresholds for two-year DFS were calculated as 7.5 for MTV, 55.8 for TLG and 2.6 for entropy. The discriminating thresholds for two-year OS were calculated as 9.3 for MTV, 57.4 for TLG and 2.67 for entropy. In the multivariate analysis, lymph node involvement in PET/CT was associated with lower DFS (p = 0.036), and the high MTV of the primary tumor was correlated with lower OS (p = 0.014). (4) Conclusions: textural features associated with metabolic and volumetric parameters of baseline [18F]FDG PET/CT have a prognostic value for identifying high-relapse-risk groups in early TNBC patients.
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Affiliation(s)
- Clément Bouron
- Department of Nuclear Medicine, ICO Pays de la Loire, 15 rue André Boquel, 49055 Angers, France; (O.M.); (C.G.); (S.G.); (M.L.); (A.S.); (A.T.)
- Department of Nuclear Medicine, University Hospital of Angers, 4 rue Larrey, 49100 Angers, France;
- Correspondence:
| | - Clara Mathie
- Department of Medical Oncology, ICO Pays de la Loire, 15 rue André Boquel, 49055 Angers, France; (C.M.); (A.P.)
| | - Valérie Seegers
- Research and Statistics Department, ICO Pays de la Loire, 15 rue André Boquel, 49055 Angers, France;
| | - Olivier Morel
- Department of Nuclear Medicine, ICO Pays de la Loire, 15 rue André Boquel, 49055 Angers, France; (O.M.); (C.G.); (S.G.); (M.L.); (A.S.); (A.T.)
| | - Pascal Jézéquel
- Omics Data Science Unit, ICO Pays de la Loire, Bd Jacques Monod, CEDEX, 44805 Saint-Herblain, France; (P.J.); (H.L.)
- CRCINA, UMR 1232 INSERM, Université de Nantes, Université d’Angers, Institut de Recherche en Santé, 8 Quai Moncousu—BP 70721, CEDEX 1, 44007 Nantes, France
| | - Hamza Lasla
- Omics Data Science Unit, ICO Pays de la Loire, Bd Jacques Monod, CEDEX, 44805 Saint-Herblain, France; (P.J.); (H.L.)
| | - Camille Guillerminet
- Department of Nuclear Medicine, ICO Pays de la Loire, 15 rue André Boquel, 49055 Angers, France; (O.M.); (C.G.); (S.G.); (M.L.); (A.S.); (A.T.)
- Department of Medical Physics, ICO Pays de la Loire, 15 rue André Boquel, 49055 Angers, France
| | - Sylvie Girault
- Department of Nuclear Medicine, ICO Pays de la Loire, 15 rue André Boquel, 49055 Angers, France; (O.M.); (C.G.); (S.G.); (M.L.); (A.S.); (A.T.)
| | - Marie Lacombe
- Department of Nuclear Medicine, ICO Pays de la Loire, 15 rue André Boquel, 49055 Angers, France; (O.M.); (C.G.); (S.G.); (M.L.); (A.S.); (A.T.)
| | - Avigaelle Sher
- Department of Nuclear Medicine, ICO Pays de la Loire, 15 rue André Boquel, 49055 Angers, France; (O.M.); (C.G.); (S.G.); (M.L.); (A.S.); (A.T.)
| | - Franck Lacoeuille
- Department of Nuclear Medicine, University Hospital of Angers, 4 rue Larrey, 49100 Angers, France;
- CRCINA, University of Nantes and Angers, INSERM UMR1232 équipe 17, 49055 Angers, France
| | - Anne Patsouris
- Department of Medical Oncology, ICO Pays de la Loire, 15 rue André Boquel, 49055 Angers, France; (C.M.); (A.P.)
- INSERM UMR1232 équipe 12, 49055 Angers, France
| | - Aude Testard
- Department of Nuclear Medicine, ICO Pays de la Loire, 15 rue André Boquel, 49055 Angers, France; (O.M.); (C.G.); (S.G.); (M.L.); (A.S.); (A.T.)
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Önner H, Coskun N, Erol M, Eren Karanis Mİ. Association of 18F-FDG PET/CT textural features with immunohistochemical characteristics in invasive ductal breast cancer. Rev Esp Med Nucl Imagen Mol 2022; 41:11-16. [PMID: 34991831 DOI: 10.1016/j.remnie.2020.12.009] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2020] [Accepted: 10/18/2020] [Indexed: 12/12/2022]
Abstract
OBJECTıVES: This study investigates whether textural features (TFs) extracted from 18F-FDG positron emission tomography/computed tomography (PET/CT) are associated with immunohistochemical characteristics (IHCs) of invasive ductal breast carcinoma (IDBC). MATERIALS AND METHODS The relationship of TFs with IHCs [estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor-2 (HER-2), Ki-67 proliferation index, and histological grades] from solely excised primary tumors were evaluated for a more accurate assessment. Therefore patients with early-stage IDBC who underwent pre-operative 18F-FDG PET/CT scan for staging were included in this retrospective study. The clinical staging was performed according to the 8th edition of the American Joint Committee on Cancer. Maximum standardized uptake value (SUVmax) and 37TFs of the primary tumor were extracted from 18F-FDG PET/CT. Spearman's rank correlation test was used to evaluate the correlation between TFs and SUVmax. Receiver operating characteristic curves were generated to define the diagnostic performance of each parameter. Among these parameters, those with the highest diagnostic performance were included in the multivariate logistic regression model to identify the independent predictors of histopathological characteristics. RESULTS A total of 124 patients were included. Histogram-uniformity, grey-level co-occurrence matrix (GLCM), GLCM-energy, and GLCM-homogeneity showed a strong negative correlation with SUVmax, while grey-level run-length matrix (GLRLM), GLRLM-SRHGE, grey-level zone length matrix (GLZLM), GLZLM-HGZE, GLRLM-HGRE, GLCM-entropy, GLCM-contrast, histogram-entropy, and GLCM-dissimilarity showed a strong positive correlation. Some of the TFs were independently associated with ER-negativity, PR-negativity, HER-2-positivity, and increased Ki-67 proliferation index (GLCM-contrast, GLZLM-GLNU, histogram-uniformity, and shape-sphericity respectively). While SUVmax had an independent association with high-grade and triple-negativity, GLZLM-SZLGE, a high-order TF that shows the distribution of the short homogeneous zones with low grey-levels, had an independent association with axillary lymph node metastasis. CONCLUSIONS ER-negative, PR-negative, HER-2-positive, triple-negative, high-grade, highly proliferative, and high-stage tumors were found to be more glycolytic and metabolically heterogeneous. These findings suggest that the use of TFs in addition to SUVmax may improve the prognostic value of 18F-FDG PET/CT in IDBC, as certain TFs were independently associated with many IHCs and predicted axillary lymph node involvement.
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19
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Ceriani L, Milan L, Cascione L, Gritti G, Dalmasso F, Esposito F, Pirosa MC, Schär S, Bruno A, Dirnhofer S, Giovanella L, Hayoz S, Mamot C, Rambaldi A, Chauvie S, Zucca E. Generation and validation of a PET radiomics model that predicts survival in diffuse large B cell lymphoma treated with R-CHOP14: A SAKK 38/07 trial post-hoc analysis. Hematol Oncol 2021; 40:11-21. [PMID: 34714558 DOI: 10.1002/hon.2935] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Revised: 10/01/2021] [Accepted: 10/04/2021] [Indexed: 12/24/2022]
Abstract
Functional parameters from positron emission tomography (PET) seem promising biomarkers in various lymphoma subtypes. This study investigated the prognostic value of PET radiomics in diffuse large B-cell lymphoma (DLBCL) patients treated with R-CHOP given either every 14 (testing set) or 21 days (validation set). Using the PyRadiomics Python package, 107 radiomics features were extracted from baseline PET scans of 133 patients enrolled in the Swiss Group for Clinical Cancer Research 38/07 prospective clinical trial (SAKK 38/07) [ClinicalTrial.gov identifier: NCT00544219]. The international prognostic indices, the main clinical parameters and standard PET metrics, together with 52 radiomics uncorrelated features (selected using the Spearman correlation test) were included in a least absolute shrinkage and selection operator (LASSO) Cox regression to assess their impact on progression-free (PFS), cause-specific (CSS), and overall survival (OS). A linear combination of the resulting parameters generated a prognostic radiomics score (RS) whose area under the curve (AUC) was calculated by receiver operating characteristic analysis. The RS efficacy was validated in an independent cohort of 107 DLBCL patients. LASSO Cox regression identified four radiomics features predicting PFS in SAKK 38/07. The derived RS showed a significant capability to foresee PFS in both testing (AUC, 0.709; p < 0.001) and validation (AUC, 0.706; p < 0.001) sets. RS was significantly associated also with CSS and OS in testing (CSS: AUC, 0.721; p < 0.001; OS: AUC, 0.740; p < 0.001) and validation (CSS: AUC, 0.763; p < 0.0001; OS: AUC, 0.703; p = 0.004) sets. The RS allowed risk classification of patients with significantly different PFS, CSS, and OS in both cohorts showing better predictive accuracy respect to clinical international indices. PET-derived radiomics may improve the prediction of outcome in DLBCL patients.
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Affiliation(s)
- Luca Ceriani
- Nuclear Medicine and PET/CT Centre, Imaging Institute of Southern Switzerland, Ente Ospedaliero Cantonale, Bellinzona, Switzerland.,Faculty of Biomedical Sciences, Institute of Oncology Research, Università della Svizzera Italiana, Bellinzona, Switzerland
| | - Lisa Milan
- Nuclear Medicine and PET/CT Centre, Imaging Institute of Southern Switzerland, Ente Ospedaliero Cantonale, Bellinzona, Switzerland
| | - Luciano Cascione
- Faculty of Biomedical Sciences, Institute of Oncology Research, Università della Svizzera Italiana, Bellinzona, Switzerland.,SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Giuseppe Gritti
- Hematology Unit, Azienda Ospedaliera Papa Giovanni XXIII, Bergamo, Italy
| | | | - Fabiana Esposito
- Medical Oncology, Oncology Institute of Southern Switzerland, Ente Ospedaliero Cantonale, Bellinzona, Switzerland
| | - Maria Cristina Pirosa
- Medical Oncology, Oncology Institute of Southern Switzerland, Ente Ospedaliero Cantonale, Bellinzona, Switzerland
| | - Sämi Schär
- Swiss Group for Clinical Cancer Research (SAKK) Coordinating Center, Bern, Switzerland
| | - Andrea Bruno
- Department of Nuclear Medicine, Azienda Ospedaliera Papa Giovanni XXIII, Bergamo, Italy
| | - Stephan Dirnhofer
- Institute of Medical Genetics and Pathology, University Hospital Basel, University of Basel, Switzerland
| | - Luca Giovanella
- Nuclear Medicine and PET/CT Centre, Imaging Institute of Southern Switzerland, Ente Ospedaliero Cantonale, Bellinzona, Switzerland.,Department of Nuclear Medicine, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Stefanie Hayoz
- Swiss Group for Clinical Cancer Research (SAKK) Coordinating Center, Bern, Switzerland
| | - Christoph Mamot
- Division of Oncology, Cantonal Hospital Aarau, Aarau, Switzerland
| | - Alessandro Rambaldi
- Hematology Unit, Azienda Ospedaliera Papa Giovanni XXIII, Bergamo, Italy.,Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
| | - Stephane Chauvie
- Medical Physics Unit, Santa Croce e Carlo Hospital, Cuneo, Italy
| | - Emanuele Zucca
- Faculty of Biomedical Sciences, Institute of Oncology Research, Università della Svizzera Italiana, Bellinzona, Switzerland.,Medical Oncology, Oncology Institute of Southern Switzerland, Ente Ospedaliero Cantonale, Bellinzona, Switzerland.,Department of Medical Oncology, Bern University Hospital and University of Bern, Bern, Switzerland
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20
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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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Preoperative Texture Analysis Using 11C-Methionine Positron Emission Tomography Predicts Survival after Surgery for Glioma. Diagnostics (Basel) 2021; 11:diagnostics11020189. [PMID: 33525709 PMCID: PMC7911154 DOI: 10.3390/diagnostics11020189] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2021] [Revised: 01/26/2021] [Accepted: 01/26/2021] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND Positron emission tomography with 11C-methionine (MET) is well established in the diagnostic work-up of malignant brain tumors. Texture analysis is a novel technique for extracting information regarding relationships among surrounding voxels, in order to quantify their inhomogeneity. This study evaluated whether the texture analysis of MET uptake has prognostic value for patients with glioma. METHODS We retrospectively analyzed adults with glioma who had undergone preoperative metabolic imaging at a single center. Tumors were delineated using a threshold of 1.3-fold of the mean standardized uptake value for the contralateral cortex, and then processed to calculate the texture features in glioma. RESULTS The study included 42 patients (median age: 56 years). The World Health Organization classifications were grade II (7 patients), grade III (17 patients), and grade IV (18 patients). Sixteen (16.1%) all-cause deaths were recorded during the median follow-up of 18.8 months. The univariate analyses revealed that overall survival (OS) was associated with age (hazard ratio (HR) 1.04, 95% confidence interval (CI) 1.01-1.08, p = 0.0093), tumor grade (HR 3.64, 95% CI 1.63-9.63, p = 0.0010), genetic status (p < 0.0001), low gray-level run emphasis (LGRE, calculated from the gray-level run-length matrix) (HR 2.30 × 1011, 95% CI 737.11-4.23 × 1019, p = 0.0096), and correlation (calculated from the gray-level co-occurrence matrix) (HR 5.17, 95% CI 1.07-20.93, p = 0.041). The multivariate analyses revealed OS was independently associated with LGRE and correlation. The survival curves were also significantly different (both log-rank p < 0.05). CONCLUSION Textural features obtained using preoperative MET positron emission tomography may compliment the semi-quantitative assessment for prognostication in glioma cases.
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Satoh Y, Hirata K, Tamada D, Funayama S, Onishi H. Texture Analysis in the Diagnosis of Primary Breast Cancer: Comparison of High-Resolution Dedicated Breast Positron Emission Tomography (dbPET) and Whole-Body PET/CT. Front Med (Lausanne) 2021; 7:603303. [PMID: 33425949 PMCID: PMC7793660 DOI: 10.3389/fmed.2020.603303] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2020] [Accepted: 12/02/2020] [Indexed: 12/02/2022] Open
Abstract
Objective: This retrospective study aimed to compare the ability to classify tumor characteristics of breast cancer (BC) of positron emission tomography (PET)-derived texture features between dedicated breast PET (dbPET) and whole-body PET/computed tomography (CT). Methods: Forty-four BCs scanned by both high-resolution ring-shaped dbPET and whole-body PET/CT were analyzed. The primary BC was extracted with a standardized uptake value (SUV) threshold segmentation method. On both dbPET and PET/CT images, 38 texture features were computed; their ability to classify tumor characteristics such as tumor (T)-category, lymph node (N)-category, molecular subtype, and Ki67 levels was compared. The texture features were evaluated using univariate and multivariate analyses following principal component analysis (PCA). AUC values were used to evaluate the diagnostic power of the computed texture features to classify BC characteristics. Results: Some texture features of dbPET and PET/CT were different between Tis-1 and T2-4 and between Luminal A and other groups, respectively. No association with texture features was found in the N-category or Ki67 level. In contrast, receiver-operating characteristic analysis using texture features' principal components showed that the AUC for classification of any BC characteristics were equally good for both dbPET and whole-body PET/CT. Conclusions: PET-based texture analysis of dbPET and whole-body PET/CT may have equally good classification power for BC.
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Affiliation(s)
- Yoko Satoh
- Yamanashi PET Imaging Clinic, Yamanashi, Japan.,Department of Radiology, University of Yamanashi, Yamanashi, Japan
| | - Kenji Hirata
- Department of Diagnostic Imaging, School of Medicine, Hokkaido University, Sapporo, Japan
| | - Daiki Tamada
- Department of Radiology, University of Yamanashi, Yamanashi, Japan
| | - Satoshi Funayama
- Department of Radiology, University of Yamanashi, Yamanashi, Japan
| | - Hiroshi Onishi
- Department of Radiology, University of Yamanashi, Yamanashi, Japan
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In Vivo Optical Metabolic Imaging of Long-Chain Fatty Acid Uptake in Orthotopic Models of Triple-Negative Breast Cancer. Cancers (Basel) 2021; 13:cancers13010148. [PMID: 33466329 PMCID: PMC7794847 DOI: 10.3390/cancers13010148] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Revised: 12/23/2020] [Accepted: 12/31/2020] [Indexed: 12/16/2022] Open
Abstract
Simple Summary A dysregulated metabolism is a hallmark of cancer. Once understood, tumor metabolic reprogramming can lead to targetable vulnerabilities, spurring the development of novel treatment strategies. Beyond the common observation that tumors rely heavily on glucose, building evidence indicates that a subset of tumors use lipids to maintain their proliferative or metastatic phenotype. This study developed an intra-vital microscopy method to quantify lipid uptake in breast cancer murine models using a fluorescently labeled palmitate molecule, Bodipy FL c16. This work highlights optical imaging’s ability to both measure metabolic endpoints non-destructively and repeatedly, as well as inform small animal metabolic phenotyping beyond in vivo optical imaging of breast cancer alone. Abstract Targeting a tumor’s metabolic dependencies is a clinically actionable therapeutic approach; however, identifying subtypes of tumors likely to respond remains difficult. The use of lipids as a nutrient source is of particular importance, especially in breast cancer. Imaging techniques offer the opportunity to quantify nutrient use in preclinical tumor models to guide development of new drugs that restrict uptake or utilization of these nutrients. We describe a fast and dynamic approach to image fatty acid uptake in vivo and demonstrate its relevance to study both tumor metabolic reprogramming directly, as well as the effectiveness of drugs targeting lipid metabolism. Specifically, we developed a quantitative optical approach to spatially and longitudinally map the kinetics of long-chain fatty acid uptake in in vivo murine models of breast cancer using a fluorescently labeled palmitate molecule, Bodipy FL c16. We chose intra-vital microscopy of mammary tumor windows to validate our approach in two orthotopic breast cancer models: a MYC-overexpressing, transgenic, triple-negative breast cancer (TNBC) model and a murine model of the 4T1 family. Following injection, Bodipy FL c16 fluorescence increased and reached its maximum after approximately 30 min, with the signal remaining stable during the 30–80 min post-injection period. We used the fluorescence at 60 min (Bodipy60), the mid-point in the plateau region, as a summary parameter to quantify Bodipy FL c16 fluorescence in subsequent experiments. Using our imaging platform, we observed a two- to four-fold decrease in fatty acid uptake in response to the downregulation of the MYC oncogene, consistent with findings from in vitro metabolic assays. In contrast, our imaging studies report an increase in fatty acid uptake with tumor aggressiveness (6NR, 4T07, and 4T1), and uptake was significantly decreased after treatment with a fatty acid transport inhibitor, perphenazine, in both normal mammary pads and in the most aggressive 4T1 tumor model. Our approach fills an important gap between in vitro assays providing rich metabolic information at static time points and imaging approaches visualizing metabolism in whole organs at a reduced resolution.
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Optimal method for metabolic tumour volume assessment of cervical cancers with inter-observer agreement on [18F]-fluoro-deoxy-glucose positron emission tomography with computed tomography. Eur J Nucl Med Mol Imaging 2020; 48:2009-2023. [PMID: 33313962 PMCID: PMC8113292 DOI: 10.1007/s00259-020-05136-8] [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: 07/01/2020] [Accepted: 11/24/2020] [Indexed: 12/24/2022]
Abstract
PURPOSE Cervical cancer metabolic tumour volume (MTV) derived from [18F]-FDG PET/CT has a role in prognostication and therapy planning. There is no standard method of outlining MTV on [18F]-FDG PET/CT. The aim of this study was to assess the optimal method to outline primary cervical tumours on [18F]-FDG PET/CT using MRI-derived tumour volumes as the reference standard. METHODS 81 consecutive cervical cancer patients with pre-treatment staging MRI and [18F]-FDG PET/CT imaging were included. MRI volumes were compared with different PET segmentation methods. Method 1 measured MTVs at different SUVmax thresholds ranging from 20 to 60% (MTV20-MTV60) with bladder masking and manual adjustment when required. Method 2 created an isocontour around the tumour prior to different SUVmax thresholds being applied. Method 3 used an automated gradient method. Inter-observer agreement of MTV, following manual adjustment when required, was recorded. RESULTS For method 1, the MTV25 and MTV30 were closest to the MRI volumes for both readers (mean percentage change from MRI volume of 2.9% and 13.4% for MTV25 and - 13.1% and - 2.0% for MTV30 for readers 1 and 2). 70% of lesions required manual adjustment at MTV25 compared with 45% at MTV30. There was excellent inter-observer agreement between MTV30 to MTV60 (ICC ranged from 0.898-0.976 with narrow 95% confidence intervals (CIs)) and moderate agreement at lower thresholds (ICC estimates of 0.534 and 0.617, respectively for the MTV20 and MTV25 with wide 95% CIs). Bladder masking was performed in 86% of cases overall. For method 2, excellent correlation was demonstrated at MTV25 and MTV30 (mean % change from MRI volume of -3.9% and - 8.6% for MTV25 and - 16.9% and 19% for MTV30 for readers 1 and 2, respectively). This method also demonstrated excellent ICC across all thresholds with no manual adjustment. Method 3 demonstrated excellent ICC of 0.96 (95% CI 0.94-0.97) but had a mean percentage difference from the MRI volume of - 19.1 and - 18.2% for readers 1 and 2, respectively. 21% required manual adjustment for both readers. CONCLUSION MTV30 provides the optimal correlation with MRI volume taking into consideration the excellent inter-reader agreement and less requirement for manual adjustment.
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Martin-Gonzalez P, de Mariscal EG, Martino ME, Gordaliza PM, Peligros I, Carreras JL, Calvo FA, Pascau J, Desco M, Muñoz-Barrutia A. Association of visual and quantitative heterogeneity of 18F-FDG PET images with treatment response in locally advanced rectal cancer: A feasibility study. PLoS One 2020; 15:e0242597. [PMID: 33253194 PMCID: PMC7704000 DOI: 10.1371/journal.pone.0242597] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2020] [Accepted: 11/05/2020] [Indexed: 12/14/2022] Open
Abstract
Background and purpose Few tools are available to predict tumor response to treatment. This retrospective study assesses visual and automatic heterogeneity from 18F-FDG PET images as predictors of response in locally advanced rectal cancer. Methods This study included 37 LARC patients who underwent an 18F-FDG PET before their neoadjuvant therapy. One expert segmented the tumor from the PET images. Blinded to the patient´s outcome, two experts established by consensus a visual score for tumor heterogeneity. Metabolic and texture parameters were extracted from the tumor area. Multivariate binary logistic regression with cross-validation was used to estimate the clinical relevance of these features. Area under the ROC Curve (AUC) of each model was evaluated. Histopathological tumor regression grade was the ground-truth. Results Standard metabolic parameters could discriminate 50.1% of responders (AUC = 0.685). Visual heterogeneity classification showed correct assessment of the response in 75.4% of the sample (AUC = 0.759). Automatic quantitative evaluation of heterogeneity achieved a similar predictive capacity (73.1%, AUC = 0.815). Conclusion A response prediction model in LARC based on tumor heterogeneity (assessed either visually or with automatic texture measurement) shows that texture features may complement the information provided by the metabolic parameters and increase prediction accuracy.
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Affiliation(s)
- Paula Martin-Gonzalez
- Departamento de Bioingeniería e Ingeniería Aeroespacial, Universidad Carlos III de Madrid, Madrid, Spain
| | - Estibaliz Gomez de Mariscal
- Departamento de Bioingeniería e Ingeniería Aeroespacial, Universidad Carlos III de Madrid, Madrid, Spain.,Instituto de Investigación, Sanitaria Gregorio Marañón, Madrid, Spain
| | - M Elena Martino
- Departamento de Bioingeniería e Ingeniería Aeroespacial, Universidad Carlos III de Madrid, Madrid, Spain.,Instituto de Investigación, Sanitaria Gregorio Marañón, Madrid, Spain
| | - Pedro M Gordaliza
- Departamento de Bioingeniería e Ingeniería Aeroespacial, Universidad Carlos III de Madrid, Madrid, Spain.,Instituto de Investigación, Sanitaria Gregorio Marañón, Madrid, Spain
| | - Isabel Peligros
- Instituto de Investigación, Sanitaria Gregorio Marañón, Madrid, Spain.,Department of Pathology, Hospital General Universitario Gregorio Marañón, Madrid, Spain.,School of Medicine, Universidad Complutense, Madrid, Spain
| | - Jose Luis Carreras
- Instituto de Investigación, Sanitaria Gregorio Marañón, Madrid, Spain.,Department of Pathology, Hospital General Universitario Gregorio Marañón, Madrid, Spain.,Department of Radiology and Medical Physics, Hospital General Universitario Gregorio Marañón, Madrid, Spain
| | - Felipe A Calvo
- Instituto de Investigación, Sanitaria Gregorio Marañón, Madrid, Spain.,School of Medicine, Universidad Complutense, Madrid, Spain.,Department of Oncology, Hospital General Universitario Gregorio Marañón, Madrid, Spain
| | - Javier Pascau
- Departamento de Bioingeniería e Ingeniería Aeroespacial, Universidad Carlos III de Madrid, Madrid, Spain.,Instituto de Investigación, Sanitaria Gregorio Marañón, Madrid, Spain
| | - Manuel Desco
- Departamento de Bioingeniería e Ingeniería Aeroespacial, Universidad Carlos III de Madrid, Madrid, Spain.,Instituto de Investigación, Sanitaria Gregorio Marañón, Madrid, Spain.,Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain.,Centro de Investigaciones Cardiovasculares Carlos III (CNIC), Madrid, Spain
| | - Arrate Muñoz-Barrutia
- Departamento de Bioingeniería e Ingeniería Aeroespacial, Universidad Carlos III de Madrid, Madrid, Spain.,Instituto de Investigación, Sanitaria Gregorio Marañón, Madrid, Spain
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Chen S, Shu Z, Li Y, Chen B, Tang L, Mo W, Shao G, Shao F. Machine Learning-Based Radiomics Nomogram Using Magnetic Resonance Images for Prediction of Neoadjuvant Chemotherapy Efficacy in Breast Cancer Patients. Front Oncol 2020; 10:1410. [PMID: 32923392 PMCID: PMC7456979 DOI: 10.3389/fonc.2020.01410] [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: 11/30/2019] [Accepted: 07/03/2020] [Indexed: 12/18/2022] Open
Abstract
Purpose: The construction and validation of a radiomics nomogram based on machine learning using magnetic resonance image (MRI) for predicting the efficacy of neoadjuvant chemotherapy (NACT) in patients with breast cancer (BCa). Methods: This retrospective investigation consisted of 158 patients who were diagnosed with BCa and underwent MRI before NACT, of which 33 patients experienced pathological complete response (pCR) by the postoperative pathological examination. The patients with BCa were divided into the training set (n = 110) and test set (n = 48) randomly. The features were selected by the maximum relevance minimum redundancy (mRMR) and absolute shrinkage and selection operator (LASSO) algorithm in the training set. In return, the radiomics signature was established using machine learning. The predictive score of each patient was calculated using the radiomics signature formula. Finally, the predictive scores and clinical factors were used to perform the multivariate logistic regression and construct the nomogram. Receiver operating characteristics (ROC) analyses were used to assess and validate the diagnostic accuracy of the nomogram in the test set. Lastly, the usefulness of the nomogram was confirmed via decision curve analysis (DCA). Results: The radiomics signature was well-discriminated in the training set [AUC 0.835, specificity 71.32%, and sensitivity 82.61%], and test set (AUC 0.834, specificity 73.21%, and sensitivity 80%). Containing the radiomics signature and hormone status, the radiomics nomogram showed good calibration and discrimination in the training set [AUC 0.888, specificity 79.31%, and sensitivity 86.96%] and test set (AUC 0.879, specificity 82.19%, and sensitivity 83.57%). The decision curve indicated the clinical usefulness of our nomogram. Conclusion: Our radiomics nomogram showed good discrimination in patients with BCa who experience pCR after NACT. The model may aid physicians in predicting how specific patients may respond to BCa treatments in the future.
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Affiliation(s)
- Shujun Chen
- Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China.,Institute of Cancer and Basic Medicine (IBMC), Chinese Academy of Sciences, Hangzhou, China.,Department of Radiology, Zhejiang Cancer Hospital, Hangzhou, China
| | - Zhenyu Shu
- Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital of Hangzhou Medical College, Hangzhou, China
| | - Yongfeng Li
- Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China.,Institute of Cancer and Basic Medicine (IBMC), Chinese Academy of Sciences, Hangzhou, China.,Department of Breast Surgery, Zhejiang Cancer Hospital, Hangzhou, China
| | - Bo Chen
- Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China.,Institute of Cancer and Basic Medicine (IBMC), Chinese Academy of Sciences, Hangzhou, China.,Department of Pathology, Zhejiang Cancer Hospital, Hangzhou, China
| | - Lirong Tang
- Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China.,Institute of Cancer and Basic Medicine (IBMC), Chinese Academy of Sciences, Hangzhou, China.,Department of Radiology, Zhejiang Cancer Hospital, Hangzhou, China
| | - Wenju Mo
- Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China.,Institute of Cancer and Basic Medicine (IBMC), Chinese Academy of Sciences, Hangzhou, China.,Department of Breast Surgery, Zhejiang Cancer Hospital, Hangzhou, China
| | - Guoliang Shao
- Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China.,Institute of Cancer and Basic Medicine (IBMC), Chinese Academy of Sciences, Hangzhou, China.,Department of Radiology, Zhejiang Cancer Hospital, Hangzhou, China
| | - Feng Shao
- Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China.,Institute of Cancer and Basic Medicine (IBMC), Chinese Academy of Sciences, Hangzhou, China.,Department of Gynecological Oncology, Zhejiang Cancer Hospital, Hangzhou, China
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Kim H, Lee J, Kang BJ, Kim SH. What shear wave elastography parameter best differentiates breast cancer and predicts its histologic aggressiveness? Ultrasonography 2020; 40:265-273. [PMID: 32660207 PMCID: PMC7994732 DOI: 10.14366/usg.20007] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2020] [Accepted: 06/15/2020] [Indexed: 01/09/2023] Open
Abstract
Purpose This study aimed to identify useful shear wave elastography (SWE) parameters for differentiating breast cancer and predicting associated immunohistochemical factors and subtypes. Methods From November 2018 to February 2019, a total of 211 breast lesions from 190 patients who underwent conventional breast ultrasonography and SWE were included. The Breast Imaging Reporting and Data System categories and qualitative and quantitative SWE parameters for each lesion were obtained. Pathologic results including immunohistochemical factors were evaluated. The diagnostic performance of each parameter and its correlation with histological characteristics, immunohistochemical factors, and subtypes of breast cancer were analyzed using analysis of variance, the independent t test, the Fisher exact test, logistic regression analysis, and the DeLong method. Results Among 211 breast lesions, 82 were malignant, and 129 were benign. Of the SWE parameters, Emax showed the highest area under the curve (AUC) for differentiating malignant from benign lesions (AUC, 0.891; cut-off>50.85). Poor tumor differentiation and progesterone receptor-negativity were correlated with higher SDmean and Emax (P<0.05). Ki-67-positive breast cancer showed higher SDmean and a heterogeneous color distribution (P<0.05). Ki-67 and cytokeratin 5/6-positive breast cancers showed higher Emax/Efat ratios (P<0.05). Luminal B, human epidermal growth factor receptor 2-enriched, and triple-negative (non-basal) subtypes showed somewhat higher SDmean values than the luminal A and triple-negative (basal) subtypes (P=0.028). Conclusion Emax is a reliable parameter for differentiating malignancies from benign breast lesions. In addition, high stiffness and SDmean values in tumors measured on SWE could be used to predict poorly differentiated, progesterone receptor-negative, or Ki-67-positive breast cancer.
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Affiliation(s)
- Hyunjin Kim
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Jeongmin Lee
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Bong Joo Kang
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Sung Hun Kim
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
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Diagnostic classification of solitary pulmonary nodules using support vector machine model based on 2-[18F]fluoro-2-deoxy-D-glucose PET/computed tomography texture features. Nucl Med Commun 2020; 41:560-566. [DOI: 10.1097/mnm.0000000000001193] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
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29
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Li J, Chekkoury A, Prakash J, Glasl S, Vetschera P, Koberstein-Schwarz B, Olefir I, Gujrati V, Omar M, Ntziachristos V. Spatial heterogeneity of oxygenation and haemodynamics in breast cancer resolved in vivo by conical multispectral optoacoustic mesoscopy. LIGHT, SCIENCE & APPLICATIONS 2020; 9:57. [PMID: 32337021 PMCID: PMC7154032 DOI: 10.1038/s41377-020-0295-y] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/28/2019] [Revised: 02/10/2020] [Accepted: 03/19/2020] [Indexed: 05/11/2023]
Abstract
The characteristics of tumour development and metastasis relate not only to genomic heterogeneity but also to spatial heterogeneity, associated with variations in the intratumoural arrangement of cell populations, vascular morphology and oxygen and nutrient supply. While optical (photonic) microscopy is commonly employed to visualize the tumour microenvironment, it assesses only a few hundred cubic microns of tissue. Therefore, it is not suitable for investigating biological processes at the level of the entire tumour, which can be at least four orders of magnitude larger. In this study, we aimed to extend optical visualization and resolve spatial heterogeneity throughout the entire tumour volume. We developed an optoacoustic (photoacoustic) mesoscope adapted to solid tumour imaging and, in a pilot study, offer the first insights into cancer optical contrast heterogeneity in vivo at an unprecedented resolution of <50 μm throughout the entire tumour mass. Using spectral methods, we resolve unknown patterns of oxygenation, vasculature and perfusion in three types of breast cancer and showcase different levels of structural and functional organization. To our knowledge, these results are the most detailed insights of optical signatures reported throughout entire tumours in vivo, and they position optoacoustic mesoscopy as a unique investigational tool linking microscopic and macroscopic observations.
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Affiliation(s)
- Jiao Li
- School of Precision Instruments and Optoelectronics Engineering, Tianjin University, No.92, Weijin Road, Nankai District, 300072 Tianjin, China
- Institute of Biological and Medical Imaging, Helmholtz Zentrum München, Ingolstädter Landstr. 1, D-85764 Neuherberg, Germany
- Chair of Biological Imaging, TranslaTUM, Technische Universität München, Ismaningerstr. 22, D-81675 Munich, Germany
| | - Andrei Chekkoury
- Institute of Biological and Medical Imaging, Helmholtz Zentrum München, Ingolstädter Landstr. 1, D-85764 Neuherberg, Germany
- Chair of Biological Imaging, TranslaTUM, Technische Universität München, Ismaningerstr. 22, D-81675 Munich, Germany
| | - Jaya Prakash
- Institute of Biological and Medical Imaging, Helmholtz Zentrum München, Ingolstädter Landstr. 1, D-85764 Neuherberg, Germany
- Chair of Biological Imaging, TranslaTUM, Technische Universität München, Ismaningerstr. 22, D-81675 Munich, Germany
- Department of Instrumentation and Applied Physics, Indian Institute of Science Bangalore, CV Raman Rd, Bengaluru, 560012 Karnataka India
| | - Sarah Glasl
- Institute of Biological and Medical Imaging, Helmholtz Zentrum München, Ingolstädter Landstr. 1, D-85764 Neuherberg, Germany
- Chair of Biological Imaging, TranslaTUM, Technische Universität München, Ismaningerstr. 22, D-81675 Munich, Germany
| | - Paul Vetschera
- Institute of Biological and Medical Imaging, Helmholtz Zentrum München, Ingolstädter Landstr. 1, D-85764 Neuherberg, Germany
- Chair of Biological Imaging, TranslaTUM, Technische Universität München, Ismaningerstr. 22, D-81675 Munich, Germany
| | - Benno Koberstein-Schwarz
- Institute of Biological and Medical Imaging, Helmholtz Zentrum München, Ingolstädter Landstr. 1, D-85764 Neuherberg, Germany
- Chair of Biological Imaging, TranslaTUM, Technische Universität München, Ismaningerstr. 22, D-81675 Munich, Germany
| | - Ivan Olefir
- Institute of Biological and Medical Imaging, Helmholtz Zentrum München, Ingolstädter Landstr. 1, D-85764 Neuherberg, Germany
- Chair of Biological Imaging, TranslaTUM, Technische Universität München, Ismaningerstr. 22, D-81675 Munich, Germany
| | - Vipul Gujrati
- Institute of Biological and Medical Imaging, Helmholtz Zentrum München, Ingolstädter Landstr. 1, D-85764 Neuherberg, Germany
- Chair of Biological Imaging, TranslaTUM, Technische Universität München, Ismaningerstr. 22, D-81675 Munich, Germany
| | - Murad Omar
- Institute of Biological and Medical Imaging, Helmholtz Zentrum München, Ingolstädter Landstr. 1, D-85764 Neuherberg, Germany
- Chair of Biological Imaging, TranslaTUM, Technische Universität München, Ismaningerstr. 22, D-81675 Munich, Germany
| | - Vasilis Ntziachristos
- Institute of Biological and Medical Imaging, Helmholtz Zentrum München, Ingolstädter Landstr. 1, D-85764 Neuherberg, Germany
- Chair of Biological Imaging, TranslaTUM, Technische Universität München, Ismaningerstr. 22, D-81675 Munich, Germany
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Yamagishi Y, Yamasaki T, Ishida J, Moriya T, Einama T, Koiwai T, Fukumura-Koga M, Kono T, Hayashi K, Ueno H, Yamamoto J, Tsuda H. Utility of 18F-Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography Fusion Imaging for Prediction of Metastasis to Sentinel and Nonsentinel Nodes in Patients with Clinically Node-Negative Breast Cancer. Ann Surg Oncol 2020; 27:2698-2710. [PMID: 32124121 PMCID: PMC7334280 DOI: 10.1245/s10434-020-08269-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2019] [Indexed: 01/03/2023]
Abstract
Purpose 18F-Fluorodeoxyglucose positron emission tomography/computed tomography fusion imaging (18F-FDG PET/CT) is an important diagnostic tool in breast cancer. The utility of maximum standardized uptake values (SUVmax) of primary tumors has been evaluated to predict sentinel node (SN) and non-SN metastasis in clinically node-negative (cN0) patients. Patients and Methods 18F-FDG PET/CT was performed on 414 cN0 patients. The following parameters were evaluated: SUVmax at 60 min (SUVmax1), SUVmax at 120 min (SUVmax2), percent change between SUVmax1 and SUVmax2 (ΔSUVmax%), SN metastasis foci maximum size (SN meta size), and ratio of metastatic SNs to total SNs or SN ratio (SNR). It was assessed whether these were risk factors for SN metastasis. The relationship between these parameters and the status of SN and/or non-SN metastasis was retrospectively explored to predict non-SN metastasis. Results All SUV parameters significantly correlated with pathological T factor (pT), nuclear grade, lymphatic invasion (Ly), and Ki-67 labeling index. On multivariate analysis, pT and Ly were independent predictive factors for SN metastasis. In SN meta-positive cases, SN meta size, SNR, and ΔSUVmax% were predictors for non-SN metastasis on univariate analyses, and the former two were independent predictors on multivariate analysis. The combination of SUVmax2 and ΔSUVmax% was an independent predictor of non-SN metastasis (P = 0.0312) and was associated with prediction of non-SN metastasis negative status with high probability (92.3%). Conclusions In patients with cN0 breast cancer, SUV parameters of the primary tumor were correlated with pathological features. The combination of SUVmax2 and ΔSUVmax% may be useful for predicting non-SN metastasis. Electronic supplementary material The online version of this article (10.1245/s10434-020-08269-0) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Yoji Yamagishi
- Department of Basic Pathology, National Defense Medical College, Tokorozawa, Japan.,Department of Surgery, National Defense Medical College, Tokorozawa, Japan
| | - Tamio Yamasaki
- Department of Surgery, National Defense Medical College, Tokorozawa, Japan
| | - Jiro Ishida
- Tokorozawa PET Diagnostic Imaging Clinic, Tokorozawa, Japan
| | - Tomoyuki Moriya
- Department of Surgery, National Defense Medical College, Tokorozawa, Japan.,Sugiura Breast Gastroenterology Clinic, Tokorozawa, Japan
| | - Takahiro Einama
- Department of Surgery, National Defense Medical College, Tokorozawa, Japan
| | - Tomomi Koiwai
- Department of Surgery, National Defense Medical College, Tokorozawa, Japan
| | | | - Takako Kono
- Department of Basic Pathology, National Defense Medical College, Tokorozawa, Japan
| | - Katsumi Hayashi
- Department of Radiology, National Defense Medical College, Tokorozawa, Japan
| | - Hideki Ueno
- Department of Surgery, National Defense Medical College, Tokorozawa, Japan
| | - Junji Yamamoto
- Department of Surgery, National Defense Medical College, Tokorozawa, Japan.,Department of Surgery, New Tokyo Hospital, Matsudo, Japan
| | - Hitoshi Tsuda
- Department of Basic Pathology, National Defense Medical College, Tokorozawa, Japan.
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Gao J, Huang X, Meng H, Zhang M, Zhang X, Lin X, Li B. Performance of Multiparametric Functional Imaging and Texture Analysis in Predicting Synchronous Metastatic Disease in Pancreatic Ductal Adenocarcinoma Patients by Hybrid PET/MR: Initial Experience. Front Oncol 2020; 10:198. [PMID: 32158690 PMCID: PMC7052324 DOI: 10.3389/fonc.2020.00198] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2019] [Accepted: 02/05/2020] [Indexed: 12/16/2022] Open
Abstract
Objectives: To assess the imaging biomarkers of glucose metabolic activity and diffusion-weighted imaging (DWI) derived from pretreatment integrated 18F-fluorodeoxyglucose positron emission tomography-magnetic resonance (18F-FDG PET/MR) imaging as potential predictive factors of metastasis in patients with pancreatic ductal adenocarcinoma (PDAC). Patients and Methods: We retrospectively included 17 consecutive patients with pathologically confirmed PDAC by pretreatment 18F-FDG PET/MR. The study subjects were divided into a non-metastatic group (M0, six cases) and a metastatic group (M1, 11 cases). The 18F-FDG PET/MR images were reviewed independently by two board certificated nuclear medicine physicians and one radiologist. Conventional characteristics and quantitative parameters from both PET and apparent diffusion coefficient (ADC) were assessed. The texture features were extracted from LIFEx packages (www.lifexsoft.org), and a 3D tumor volume of interest was manually drawn on fused PET/ADC images. Chi-square tests, independent-samples t-tests and Mann-Whitney U-tests were used to compare the differences in single parameters between the two groups. A logistic regression analysis was performed to determine independent predictors. A receiver operating characteristic (ROC) curve analysis was performed to assess the discriminatory power of the selected parameters. Correlations between metabolic parameters and ADC features were calculated with Spearman's rank correlation coefficient test. Results: For conventional parameters, univariable analysis demonstrated that the M1 group had a significantly larger size and a higher peak of standardized uptake value (SUVpeak), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) than those of the M0 group (p < 0.05 for all). TLG remained significant predictor in the multivariable analysis, but there were no significant differences for the area under the ROC curve (AUC) among the four conventional features in differential diagnoses (p > 0.05 for all). For the texture features, there were four features from the PET image and 13 from the ADC map that showed significant differences between the two groups. Multivariate analysis indicated that one feature from PET and three from the ADC were significant predictors. TLG was associated with ADC-GLRLM_GLNU (r = 0.659), ADC-GLRLM_LRHGE (r = 0.762), and PET-GLRLM_LRHGE (r = 0.806). Conclusions: Multiple parameters and texture features of primary tumors from 18F-FDG PET/MR images maybe reliable biomarkers to predict synchronous metastatic disease for the pretreatment PDAC.
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Affiliation(s)
- Jing Gao
- Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xinyun Huang
- Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Hongping Meng
- Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Miao Zhang
- Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiaozhe Zhang
- Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiaozhu Lin
- Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Biao Li
- Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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32
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Sollini M, Cozzi L, Ninatti G, Antunovic L, Cavinato L, Chiti A, Kirienko M. PET/CT radiomics in breast cancer: Mind the step. Methods 2020; 188:122-132. [PMID: 31978538 DOI: 10.1016/j.ymeth.2020.01.007] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2019] [Revised: 01/08/2020] [Accepted: 01/14/2020] [Indexed: 12/22/2022] Open
Abstract
The aim of the present review was to assess the current status of positron emission tomography/computed tomography (PET/CT) radiomics research in breast cancer, and in particular to analyze the strengths and weaknesses of the published papers in order to identify challenges and suggest possible solutions and future research directions. Various combinations of the terms "breast", "radiomic", "PET", "radiomics", "texture", and "textural" were used for the literature search, extended until 8 July 2019, within the PubMed/MEDLINE database. Twenty-six articles fulfilling the inclusion/exclusion criteria were retrieved in full text and analyzed. The studies had technical and clinical objectives, including diagnosis, biological characterization (correlation with histology, molecular subtypes and IHC marker expression), prediction of response to neoadjuvant chemotherapy, staging, and outcome prediction. We reviewed and discussed the selected investigations following the radiomics workflow steps related to the clinical, technical, analysis, and reporting issues. Most of the current evidence on the clinical role of PET/CT radiomics in breast cancer is at the feasibility level. Harmonized methods in image acquisition, post-processing and features calculation, predictive models and classifiers trained and validated on sufficiently representative datasets, adherence to consensus guidelines, and transparent reporting will give validity and generalizability to the results.
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Affiliation(s)
- Martina Sollini
- Nuclear Medicine, Humanitas Clinical and Research Center - IRCCS, Rozzano (Milan), Italy; Department of Biomedical Sciences, Humanitas University, Pieve Emanuele (Milan), Italy
| | - Luca Cozzi
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele (Milan), Italy; Radiation Oncology, Humanitas Clinical and Research Center - IRCCS, Rozzano (Milan), Italy
| | - Gaia Ninatti
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele (Milan), Italy
| | - Lidija Antunovic
- Nuclear Medicine, Humanitas Clinical and Research Center - IRCCS, Rozzano (Milan), Italy
| | - Lara Cavinato
- Nuclear Medicine, Humanitas Clinical and Research Center - IRCCS, Rozzano (Milan), Italy
| | - Arturo Chiti
- Nuclear Medicine, Humanitas Clinical and Research Center - IRCCS, Rozzano (Milan), Italy; Department of Biomedical Sciences, Humanitas University, Pieve Emanuele (Milan), Italy
| | - Margarita Kirienko
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele (Milan), Italy.
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Bousse A, Courdurier M, Emond E, Thielemans K, Hutton BF, Irarrazaval P, Visvikis D. PET Reconstruction With Non-Negativity Constraint in Projection Space: Optimization Through Hypo-Convergence. IEEE TRANSACTIONS ON MEDICAL IMAGING 2020; 39:75-86. [PMID: 31170066 DOI: 10.1109/tmi.2019.2920109] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Standard positron emission tomography (PET) reconstruction techniques are based on maximum-likelihood (ML) optimization methods, such as the maximum-likelihood expectation-maximization (MLEM) algorithm and its variations. Most methodologies rely on a positivity constraint on the activity distribution image. Although this constraint is meaningful from a physical point of view, it can be a source of bias for low-count/high-background PET, which can compromise accurate quantification. Existing methods that allow for negative values in the estimated image usually utilize a modified log-likelihood, and therefore break the data statistics. In this paper, we propose to incorporate the positivity constraint on the projections only, by approximating the (penalized) log-likelihood function by an adequate sequence of objective functions that are easily maximized without constraint. This sequence is constructed such that there is hypo-convergence (a type of convergence that allows the convergence of the maximizers under some conditions) to the original log-likelihood, hence allowing us to achieve maximization with positivity constraint on the projections using simple settings. A complete proof of convergence under weak assumptions is given. We provide results of experiments on simulated data where we compare our methodology with the alternative direction method of multipliers (ADMM) method, showing that our algorithm converges to a maximizer, which stays in the desired feasibility set, with faster convergence than ADMM. We also show that this approach reduces the bias, as compared with MLEM images, in necrotic tumors-which are characterized by cold regions surrounded by hot structures-while reconstructing similar activity values in hot regions.
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Cook GJR, Goh V. What can artificial intelligence teach us about the molecular mechanisms underlying disease? Eur J Nucl Med Mol Imaging 2019; 46:2715-2721. [PMID: 31190176 PMCID: PMC6879441 DOI: 10.1007/s00259-019-04370-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2019] [Accepted: 05/23/2019] [Indexed: 12/24/2022]
Abstract
While molecular imaging with positron emission tomography or single-photon emission computed tomography already reports on tumour molecular mechanisms on a macroscopic scale, there is increasing evidence that there are multiple additional features within medical images that can further improve tumour characterization, treatment prediction and prognostication. Early reports have already revealed the power of radiomics to personalize and improve patient management and outcomes. What remains unclear is how these additional metrics relate to underlying molecular mechanisms of disease. Furthermore, the ability to deal with increasingly large amounts of data from medical images and beyond in a rapid, reproducible and transparent manner is essential for future clinical practice. Here, artificial intelligence (AI) may have an impact. AI encompasses a broad range of 'intelligent' functions performed by computers, including language processing, knowledge representation, problem solving and planning. While rule-based algorithms, e.g. computer-aided diagnosis, have been in use for medical imaging since the 1990s, the resurgent interest in AI is related to improvements in computing power and advances in machine learning (ML). In this review we consider why molecular and cellular processes are of interest and which processes have already been exposed to AI and ML methods as reported in the literature. Non-small-cell lung cancer is used as an exemplar and the focus of this review as the most common tumour type in which AI and ML approaches have been tested and to illustrate some of the concepts.
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Affiliation(s)
- Gary J R Cook
- Cancer Imaging Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, SE1 7EH, UK.
- King's College London & Guy's and St Thomas' PET Centre, St Thomas' Hospital, London, UK.
| | - Vicky Goh
- Cancer Imaging Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, SE1 7EH, UK
- Radiology Department, Guy's and St Thomas' Hospitals NHS Trust, London, UK
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35
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Cheze Le Rest C, Hustinx R. Are radiomics ready for clinical prime-time in PET/CT imaging? 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; 63:347-354. [DOI: 10.23736/s1824-4785.19.03210-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
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36
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Yamagishi Y, Koiwai T, Yamasaki T, Einama T, Fukumura M, Hiratsuka M, Kono T, Hayashi K, Ishida J, Ueno H, Tsuda H. Dual time point 18F-fluorodeoxyglucose positron emission tomography/computed tomography fusion imaging ( 18F-FDG PET/CT) in primary breast cancer. BMC Cancer 2019; 19:1146. [PMID: 31775675 PMCID: PMC6882358 DOI: 10.1186/s12885-019-6315-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2018] [Accepted: 10/31/2019] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND To evaluate the clinicopathological and prognostic significance of the percentage change between maximum standardized uptake value (SUVmax) at 60 min (SUVmax1) and SUVmax at 120 min (SUVmax2) (ΔSUVmax%) using dual time point 18F-fluorodeoxyglucose emission tomography/computed tomography (18F-FDG PET/CT) in breast cancer. METHODS Four hundred and sixty-four patients with primary breast cancer underwent 18F-FDG PET/CT for preoperative staging. ΔSUVmax% was defined as (SUVmax2 - SUVmax1) / SUVmax1 × 100. We explored the optimal cutoff value of SUVmax parameters (SUVmax1 and ΔSUVmax%) referring to the event of relapse by using receiver operator characteristic curves. The clinicopathological and prognostic significances of the SUVmax1 and ΔSUVmax% were analyzed by Cox's univariate and multivariate analyses. RESULTS The optimal cutoff values of SUVmax1 and ΔSUVmax% were 3.4 and 12.5, respectively. Relapse-free survival (RFS) curves were significantly different between high and low SUVmax1 groups (P = 0.0003) and also between high and low ΔSUVmax% groups (P = 0.0151). In Cox multivariate analysis for RFS, SUVmax1 was an independent prognostic factor (P = 0.0267) but ΔSUVmax% was not (P = 0.152). There was a weak correlation between SUVmax1 and ΔSUVmax% (P < 0.0001, R2 = 0.166). On combining SUVmax1 and ΔSUVmax%, the subgroups of high SUVmax1 and high ΔSUVmax% showed significantly worse prognosis than the other groups in terms of RFS (P = 0.0002). CONCLUSION Dual time point 18F-FDG PET/CT evaluation can be a useful method for predicting relapse in patients with breast cancer. The combination of SUVmax1 and ΔSUVmax% was able to identify subgroups with worse prognosis more accurately than SUVmax1 alone.
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Affiliation(s)
- Yoji Yamagishi
- Department of Basic Pathology, National Defense Medical College, 3-2 Namiki, Tokorozawa, Saitama, 359-8513, Japan.,Department of Surgery, National Defense Medical College, 3-2 Namiki, Tokorozawa, Saitama, 359-8513, Japan
| | - Tomomi Koiwai
- Department of Surgery, National Defense Medical College, 3-2 Namiki, Tokorozawa, Saitama, 359-8513, Japan
| | - Tamio Yamasaki
- Department of Surgery, National Defense Medical College, 3-2 Namiki, Tokorozawa, Saitama, 359-8513, Japan
| | - Takahiro Einama
- Department of Surgery, National Defense Medical College, 3-2 Namiki, Tokorozawa, Saitama, 359-8513, Japan
| | - Makiko Fukumura
- Department of Surgery, National Defense Medical College, 3-2 Namiki, Tokorozawa, Saitama, 359-8513, Japan
| | - Miyuki Hiratsuka
- Department of Surgery, National Defense Medical College, 3-2 Namiki, Tokorozawa, Saitama, 359-8513, Japan
| | - Takako Kono
- Department of Basic Pathology, National Defense Medical College, 3-2 Namiki, Tokorozawa, Saitama, 359-8513, Japan
| | - Katsumi Hayashi
- Department of Radiology, National Defense Medical College, 3-2 Namiki, Tokorozawa, Saitama, 359-8513, Japan
| | - Jiro Ishida
- Tokorozawa PET Diagnostic Imaging Clinic, 7-5 Higashisumiyoshi, Tokorozawa, Saitama, 359-1124, Japan
| | - Hideki Ueno
- Department of Surgery, National Defense Medical College, 3-2 Namiki, Tokorozawa, Saitama, 359-8513, Japan
| | - Hitoshi Tsuda
- Department of Basic Pathology, National Defense Medical College, 3-2 Namiki, Tokorozawa, Saitama, 359-8513, Japan.
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Ou X, Zhang J, Wang J, Pang F, Wang Y, Wei X, Ma X. Radiomics based on 18 F-FDG PET/CT could differentiate breast carcinoma from breast lymphoma using machine-learning approach: A preliminary study. Cancer Med 2019; 9:496-506. [PMID: 31769230 PMCID: PMC6970046 DOI: 10.1002/cam4.2711] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2019] [Revised: 10/02/2019] [Accepted: 10/03/2019] [Indexed: 02/05/2023] Open
Abstract
PURPOSE Our study assessed the ability 18 F-fluorodeoxyglucose (FDG) positron emission tomography (PET)/computed tomography (CT) radiomics to differentiate breast carcinoma from breast lymphoma using machine-learning approach. METHODS Sixty-five breast nodules from 44 patients diagnosed as breast carcinoma or breast lymphoma were included. Standardized uptake value (SUV) and radiomic features from CT and PET images were extracted using local image features extraction software. Six discriminative models including PETa (based on clinical, SUV and radiomic features from PET images), PETb (SUV and radiomic features from PET images), PETc (radiomic features only from PET images), CTa (clinical and radiomic features from CT images), CTb (radiomic features only from CT images), and SUV model were generated using least absolute shrinkage and selection operator method and linear discriminant analysis. The areas under the receiver operating characteristic curve (AUCs), accuracy, sensitivity, and specificity were calculated to evaluate the discriminative ability of these models. RESULTS PETa and CTa models showed the best ability to differentiation in training and validation group (AUCs of 0.867 and 0.806 for PETa model, AUCs of 0.891 and 0.759 for CTa model, respectively). CONCLUSION Models based on clinical, SUV, and radiomic features of 18 F-FDG PET/CT images could accurately discriminate breast carcinoma from breast lymphoma.
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Affiliation(s)
- Xuejin Ou
- Department of Biotherapy, West China Hospital and State Key Laboratory of Biotherapy, Sichuan University, Chengdu, PR China.,Department of Thoracic Oncology, West China Hospital, Sichuan University, Chengdu, PR China
| | - Jing Zhang
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, PR China.,Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, PR China
| | - Jian Wang
- School of Computer Science, Nanjing University of Science and Technology, Nanjing, PR China
| | - Fuwen Pang
- Department of Nuclear Medicine, West China Hospital, Sichuan University, Chengdu, P.R. China
| | - Yongsheng Wang
- Department of Thoracic Oncology, West China Hospital, Sichuan University, Chengdu, PR China.,State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, PR China
| | - Xiawei Wei
- Laboratory of Aging Research and Nanotoxicology, State Key Laboratory of Biotherapy, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, Sichuan, PR China
| | - Xuelei Ma
- Department of Biotherapy, West China Hospital and State Key Laboratory of Biotherapy, Sichuan University, Chengdu, PR China.,State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, PR China
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38
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Nakajo M, Jinguji M, Aoki M, Tani A, Sato M, Yoshiura T. The clinical value of texture analysis of dual-time-point 18F-FDG-PET/CT imaging to differentiate between 18F-FDG-avid benign and malignant pulmonary lesions. Eur Radiol 2019; 30:1759-1769. [DOI: 10.1007/s00330-019-06463-7] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2019] [Revised: 08/01/2019] [Accepted: 09/18/2019] [Indexed: 12/16/2022]
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Hotta M, Minamimoto R, Miwa K. 11C-methionine-PET for differentiating recurrent brain tumor from radiation necrosis: radiomics approach with random forest classifier. Sci Rep 2019; 9:15666. [PMID: 31666650 PMCID: PMC6821731 DOI: 10.1038/s41598-019-52279-2] [Citation(s) in RCA: 53] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2019] [Accepted: 10/15/2019] [Indexed: 12/22/2022] Open
Abstract
Differentiating recurrent brain tumor from radiation necrosis is often difficult. This study aims to investigate the efficacy of 11C-methionine (MET)-PET radiomics for distinguishing recurrent brain tumor from radiation necrosis, as compared with conventional tumor-to-normal cortex (T/N) ratio evaluation. We enrolled 41 patients with metastatic brain tumor or glioma treated using radiation therapy who underwent MET-PET. The area with a standardized uptake value > 1.3 times that of the normal brain cortex was contoured. Forty-two PET features were extracted and used in a random forest classifier and the diagnostic performance was evaluated using a 10-fold cross-validation scheme. Gini index was measured to identify relevant PET parameters for classification. The reference standard was surgical histopathological analysis or more than 6 months of follow-up with MRI. Forty-four lesions were used for the analysis. Thirty-three and 11 lesions were confirmed as recurrent brain tumor and radiation necrosis, respectively. Radiomics and T/N ratio evaluation showed sensitivities of 90.1% and 60.6%, and specificities of 93.9% and 72.7% with areas under the curve of 0.98 and 0.73, respectively. Gray level co-occurrence matrix dissimilarity was the most pertinent feature for diagnosis. MET-PET radiomics yielded excellent outcome for differentiating recurrent brain tumor from radiation necrosis, which outperformed T/N ratio evaluation.
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Affiliation(s)
- Masatoshi Hotta
- Department of Radiology, National Center for Global Health and Medicine, 1-21-1, Toyama, Shinjuku-ku, Tokyo, 162-8655, Japan.
| | - Ryogo Minamimoto
- Department of Radiology, National Center for Global Health and Medicine, 1-21-1, Toyama, Shinjuku-ku, Tokyo, 162-8655, Japan
| | - Kenta Miwa
- Department of Radiological Sciences, School of Health Science, International University of Health and Welfare, 2600-1 Kitakanemaru, Ohtawara city, Tochigi, 324-850, Japan
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40
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Tello Galán MJ, García Vicente AM, Pérez Beteta J, Amo Salas M, Jiménez Londoño GA, Pena Pardo FJ, Soriano Castrejón ÁM, Pérez García VM. Global heterogeneity assessed with 18F-FDG PET/CT. Relation with biological variables and prognosis in locally advanced breast cancer. Rev Esp Med Nucl Imagen Mol 2019. [DOI: 10.1016/j.remnie.2019.03.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Impact of Point-Spread Function Reconstruction on 68Ga-DOTATATE PET/CT Quantitative Imaging Parameters. AJR Am J Roentgenol 2019; 213:683-688. [PMID: 31120789 DOI: 10.2214/ajr.18.21067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
OBJECTIVE. The objective of our study was to investigate the impact of point-spread function (PSF) reconstruction and lesion size on 68Ga-tetraazacyclododecanetetraacetic acid-DPhe1-Tyr3-octreotate (DOTATATE) PET/CT quantitative parameters. MATERIALS AND METHODS. A total of 38 patients with 42 68Ga-DOTATATE PET/CT studies and 125 lesions were included. Scans were reconstructed with and without PSF modulation. For each lesion, the maximum standardized uptake value (SUVmax) and peak standardized uptake value (SUVpeak), metabolic tumor volume (MTV), total lesion somatostatin avidity, and tumor somatostatin receptor expression heterogeneity using the AUC method were measured. Intraclass correlation coefficient (ICC) and Bland-Altman analyses were used to compare PSF and non-PSF values. A subgroup analysis was performed to determine the impact of lesion size. RESULTS. Of the 125 lesions, 51 were in the liver, 31 in lymph nodes, 17 in bone, eight in pancreas, four in lung, and 14 in other sites. The ICCs between PSF and non-PSF values were excellent for SUVmax, SUVpeak, MTV, and total lesion somatostatin avidity (ICC = 0.97-0.99), and the ICC was good for tumor somatostatin receptor expression heterogeneity (ICC = 0.81). Comparison of PSF with non-PSF values showed a bias (mean percentage change ± SD) of 27.5% ± 14.7% for SUVmax, 15.5% ± 9.5% for SUVpeak, -18.6% ± 37.6% for MTV, 0.8% ± 28.1% for total lesion somatostatin avidity, and -7.1% ± 11.0% for tumor somatostatin receptor expression heterogeneity. Comparison of PSF with non-PSF values for lesions less than 2 cm (n = 75) showed corresponding biases greater than those for lesions 2 cm or larger (n = 50). CONCLUSION. PSF reconstruction effected higher values for SUVmax and SUVpeak, produced decreased values for tumor somatostatin receptor expression heterogeneity, and had a variable effect on MTV and total lesion somatostatin avidity depending on lesion size.
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Karacavus S, Yılmaz B, Tasdemir A, Kayaaltı Ö, Kaya E, İçer S, Ayyıldız O. Can Laws Be a Potential PET Image Texture Analysis Approach for Evaluation of Tumor Heterogeneity and Histopathological Characteristics in NSCLC? J Digit Imaging 2019; 31:210-223. [PMID: 28685320 DOI: 10.1007/s10278-017-9992-3] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
We investigated the association between the textural features obtained from 18F-FDG images, metabolic parameters (SUVmax, SUVmean, MTV, TLG), and tumor histopathological characteristics (stage and Ki-67 proliferation index) in non-small cell lung cancer (NSCLC). The FDG-PET images of 67 patients with NSCLC were evaluated. MATLAB technical computing language was employed in the extraction of 137 features by using first order statistics (FOS), gray-level co-occurrence matrix (GLCM), gray-level run length matrix (GLRLM), and Laws' texture filters. Textural features and metabolic parameters were statistically analyzed in terms of good discrimination power between tumor stages, and selected features/parameters were used in the automatic classification by k-nearest neighbors (k-NN) and support vector machines (SVM). We showed that one textural feature (gray-level nonuniformity, GLN) obtained using GLRLM approach and nine textural features using Laws' approach were successful in discriminating all tumor stages, unlike metabolic parameters. There were significant correlations between Ki-67 index and some of the textural features computed using Laws' method (r = 0.6, p = 0.013). In terms of automatic classification of tumor stage, the accuracy was approximately 84% with k-NN classifier (k = 3) and SVM, using selected five features. Texture analysis of FDG-PET images has a potential to be an objective tool to assess tumor histopathological characteristics. The textural features obtained using Laws' approach could be useful in the discrimination of tumor stage.
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Affiliation(s)
- Seyhan Karacavus
- Department of Nuclear Medicine, Saglık Bilimleri University, Kayseri Training and Research Hospital, 38010, Kayseri, Turkey. .,Department of Biomedical Engineering, Erciyes University, Engineering Faculty, Kayseri, Turkey.
| | - Bülent Yılmaz
- Department of Electrical and Electronics Engineering, Abdullah Gül University, Engineering Faculty, Kayseri, Turkey
| | - Arzu Tasdemir
- Department of Pathology, Saglik Bilimleri University, Kayseri Training and Research Hospital, Kayseri, Turkey
| | - Ömer Kayaaltı
- Department of Computer Technologies, Erciyes University, Develi Hüseyin Şahin Vocational College, Kayseri, Turkey
| | - Eser Kaya
- Department of Nuclear Medicine, Acibadem University, School of Medicine, İstanbul, Turkey
| | - Semra İçer
- Department of Biomedical Engineering, Erciyes University, Engineering Faculty, Kayseri, Turkey
| | - Oguzhan Ayyıldız
- Department of Electrical and Electronics Engineering, Abdullah Gül University, Engineering Faculty, Kayseri, Turkey
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Tello Galán MJ, García Vicente AM, Pérez Beteta J, Amo Salas M, Jiménez Londoño GA, Pena Pardo FJ, Soriano Castrejón ÁM, Pérez García VM. Global heterogeneity assessed with 18F-FDG PET/CT. Relation with biological variables and prognosis in locally advanced breast cancer. Rev Esp Med Nucl Imagen Mol 2019; 38:290-297. [PMID: 31427247 DOI: 10.1016/j.remn.2019.02.004] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2018] [Revised: 02/07/2019] [Accepted: 02/26/2019] [Indexed: 02/07/2023]
Abstract
AIM To analyze the relationship between measurements of global heterogeneity, obtained from 18F-FDG PET/CT, with biological variables, and their predictive and prognostic role in patients with locally advanced breast cancer (LABC). MATERIAL AND METHODS 68 patients from a multicenter and prospective study, with LABC and a baseline 18F-FDG PET/CT were included. Immunohistochemical profile [estrogen receptors (ER) and progesterone receptors (PR), expression of the HER-2 oncogene, Ki-67 proliferation index and tumor histological grade], response to neoadjuvant chemotherapy (NC), overall survival (OS) and disease-free survival (DFS) were obtained as clinical variables. Three-dimensional segmentation of the lesions, providing SUV, volumetric [metabolic tumor volume (MTV) and total lesion glycolysis (TLG)] and global heterogeneity variables [coefficient of variation (COV) and SUVmean/SUVmax ratio], as well as sphericity was performed. The correlation between the results obtained with the immunohistochemical profile, the response to NC and survival was also analyzed. RESULTS Of the patients included, 62 received NC. Only 18 responded. 13 patients relapsed and 11 died during follow-up. ER negative tumors had a lower COV (p=0.018) as well as those with high Ki-67 (p=0.001) and high risk phenotype (p=0.033) compared to the rest. No PET variable showed association with the response to NC nor OS. There was an inverse relationship between sphericity with DFS (p=0.041), so, for every tenth that sphericity increases, the risk of recurrence decreases by 37%. CONCLUSIONS Breast tumors in our LABC dataset behaved as homogeneous and spherical lesions. Larger volumes were associated with a lower sphericity. Global heterogeneity variables and sphericity do not seem to have a predictive role in response to NC nor in OS. More spherical tumors with less variation in gray intensity between voxels showed a lower risk of recurrence.
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Affiliation(s)
- M J Tello Galán
- Servicio de Medicina Nuclear. Hospital General Universitario de Ciudad Real, España.
| | - A M García Vicente
- Servicio de Medicina Nuclear. Hospital General Universitario de Ciudad Real, España
| | - J Pérez Beteta
- Instituto de Matemática Aplicada a la Ciencia y la Ingeniería. Universidad de Castilla La Mancha, Ciudad Real, España
| | - M Amo Salas
- Departamento de Matemáticas. Universidad de Castilla La Mancha, Ciudad Real, España
| | - G A Jiménez Londoño
- Servicio de Medicina Nuclear. Hospital General Universitario de Ciudad Real, España
| | - F J Pena Pardo
- Servicio de Medicina Nuclear. Hospital General Universitario de Ciudad Real, España
| | | | - V M Pérez García
- Instituto de Matemática Aplicada a la Ciencia y la Ingeniería. Universidad de Castilla La Mancha, Ciudad Real, España
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Biological correlates of tumor perfusion and its heterogeneity in newly diagnosed breast cancer using dynamic first-pass 18F-FDG PET/CT. Eur J Nucl Med Mol Imaging 2019; 47:1103-1115. [DOI: 10.1007/s00259-019-04422-4] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2019] [Accepted: 07/01/2019] [Indexed: 12/30/2022]
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45
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Chang CC, Chen CJ, Hsu WL, Chang SM, Huang YF, Tyan YC. Prognostic Significance of Metabolic Parameters and Textural Features on 18F-FDG PET/CT in Invasive Ductal Carcinoma of Breast. Sci Rep 2019; 9:10946. [PMID: 31358786 PMCID: PMC6662792 DOI: 10.1038/s41598-019-46813-5] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2018] [Accepted: 06/25/2019] [Indexed: 12/19/2022] Open
Abstract
To investigate the prognostic significance of metabolic parameters and texture analysis on 18F-fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) in patients with breast invasive ductal carcinoma (IDC), from August 2005 to May 2015, IDC patients who had undergone pre-treatment FDG PET/CT were enrolled. The metabolic parameters, including maximal standardized uptake value of breast tumor (SUVbt) and ipsilateral axillary lymph node (SUVln), metabolic tumor volume (MTVbt) and total lesion glycolysis (TLGbt) of breast tumor, whole-body MTV (MTVwb) and whole-body TLG (TLGwb) were recorded. Nine textural features of tumor (four co-occurrence matrices and five SUV-based statistics) were measured. The prognostic significance of above parameters and clinical factors was assessed by univariate and multivariate analyses. Thirty-five patients were enrolled. Patients with low and high MTVwb had 5-year progression-free survival (PFS) of 81.0 and 14.3% (p < 0.0001). The 5-year overall survival for low and high MTVwb was 88.5% and 43.6% (p = 0.0005). Multivariate analyses showed MTVwb was an independent prognostic factor for PFS (HR: 8.29, 95% CI: 2.17–31.64, p = 0.0020). The SUV, TLG and textural features were not independently predictive. Elevated MTVwb was an independent predictor for shorter PFS in patients with breast IDC.
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Affiliation(s)
- Chin-Chuan Chang
- Department of Nuclear Medicine, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan.,Graduate Institute of Clinical Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan.,Center for Cancer Research, Kaohsiung Medical University, Kaohsiung, Taiwan.,Neuroscience Research Center, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Chao-Jung Chen
- Departments of Nuclear Medicine, Yuan's General Hospital, Kaohsiung, Taiwan.,Department of Health Business Administration, Meiho University, Pingtung, Taiwan
| | - Wen-Ling Hsu
- Department of Nuclear Medicine, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan.,Department of Medical Imaging and Radiological Sciences, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Shu-Min Chang
- Department of Nuclear Medicine, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan.,Graduate Institute of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Ying-Fong Huang
- Department of Nuclear Medicine, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan.,Department of Medical Imaging and Radiological Sciences, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Yu-Chang Tyan
- Center for Cancer Research, Kaohsiung Medical University, Kaohsiung, Taiwan. .,Department of Medical Imaging and Radiological Sciences, Kaohsiung Medical University, Kaohsiung, Taiwan. .,Graduate Institute of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan. .,Institute of Medical Science and Technology, National Sun Yat-sen University, Kaohsiung, Taiwan. .,Department of Medical Research, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan.
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Paydary K, Seraj SM, Zadeh MZ, Emamzadehfard S, Shamchi SP, Gholami S, Werner TJ, Alavi A. The Evolving Role of FDG-PET/CT in the Diagnosis, Staging, and Treatment of Breast Cancer. Mol Imaging Biol 2019. [PMID: 29516387 DOI: 10.1007/s11307-018-1181-3] [Citation(s) in RCA: 83] [Impact Index Per Article: 16.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
The applications of 2-deoxy-2-[18F]fluoro-D-glucose positron emission tomography/X-ray computed tomography (PET/CT) in the management of patients with breast cancer have been extensively studied. According to these studies, PET/CT is not routinely performed for the diagnosis of primary breast cancer, although PET/CT in specific subtypes of breast cancer correlates with histopathologic features of the primary tumor. PET/CT can detect metastases to mediastinal, axial, and internal mammary nodes, but it cannot replace the sentinel node biopsy. In detection of distant metastases, this imaging tool may have a better accuracy in detecting lytic bone metastases compared to bone scintigraphy. Thus, PET/CT is recommended when advanced-stage disease is suspected, and conventional modalities are inconclusive. Also, PET/CT has a high sensitivity and specificity to detect loco-regional recurrence and is recommended in asymptomatic patients with rising tumor markers. Numerous studies support the future role of PET/CT in prediction of response to neoadjuvant chemotherapy (NAC). PET/CT has a higher diagnostic value for prognostic risk stratification in comparison with conventional modalities. With the continuing research on the treatment planning and evaluation of patients with breast cancer, the role of PET/CT can be further extended.
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Affiliation(s)
- Koosha Paydary
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | | | | | | | | | - Saeid Gholami
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Thomas J Werner
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Abass Alavi
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA. .,Division of Nuclear Medicine, Department of Radiology, Hospital of the University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, 19104, USA.
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Prediction of Chemotherapy Response of Osteosarcoma Using Baseline 18F-FDG Textural Features Machine Learning Approaches with PCA. CONTRAST MEDIA & MOLECULAR IMAGING 2019; 2019:3515080. [PMID: 31427908 PMCID: PMC6681577 DOI: 10.1155/2019/3515080] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/15/2019] [Revised: 06/20/2019] [Accepted: 07/10/2019] [Indexed: 11/17/2022]
Abstract
Purpose Patients with high-grade osteosarcoma undergo several chemotherapy cycles before surgical intervention. Response to chemotherapy, however, is affected by intratumor heterogeneity. In this study, we assessed the ability of a machine learning approach using baseline 18F-fluorodeoxyglucose (18F-FDG) positron emitted tomography (PET) textural features to predict response to chemotherapy in osteosarcoma patients. Materials and Methods This study included 70 osteosarcoma patients who received neoadjuvant chemotherapy. Quantitative characteristics of the tumors were evaluated by standard uptake value (SUV), total lesion glycolysis (TLG), and metabolic tumor volume (MTV). Tumor heterogeneity was evaluated using textural analysis of 18F-FDG PET scan images. Assessments were performed at baseline and after chemotherapy using 18F-FDG PET; 18F-FDG textural features were evaluated using the Chang-Gung Image Texture Analysis toolbox. To predict the chemotherapy response, several features were chosen using the principal component analysis (PCA) feature selection method. Machine learning was performed using linear support vector machine (SVM), random forest, and gradient boost methods. The ability to predict chemotherapy response was evaluated using the area under the receiver operating characteristic curve (AUC). Results AUCs of the baseline 18F-FDG features SUVmax, TLG, MTV, 1st entropy, and gray level co-occurrence matrix entropy were 0.553, 0538, 0.536, 0.538, and 0.543, respectively. However, AUCs of the machine learning features linear SVM, random forest, and gradient boost were 0.72, 0.78, and 0.82, respectively. Conclusion We found that a machine learning approach based on 18F-FDG textural features could predict the chemotherapy response using baseline PET images. This early prediction of the chemotherapy response may aid in determining treatment plans for osteosarcoma patients.
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Machine Learning Methods for Optimal Radiomics-Based Differentiation Between Recurrence and Inflammation: Application to Nasopharyngeal Carcinoma Post-therapy PET/CT Images. Mol Imaging Biol 2019; 22:730-738. [DOI: 10.1007/s11307-019-01411-9] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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49
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Gemoll T, Miroll E, Klein O, Lischka A, Eravci M, Thorns C, Habermann JK. Spatial UBE2N protein expression indicates genomic instability in colorectal cancers. BMC Cancer 2019; 19:710. [PMID: 31319803 PMCID: PMC6639966 DOI: 10.1186/s12885-019-5856-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2018] [Accepted: 06/19/2019] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND One major hallmark of colorectal cancers (CRC) is genomic instability with its contribution to tumor heterogeneity and therapy resistance. To facilitate the investigation of intra-sample phenotypes and the de novo identification of tumor sub-populations, imaging mass spectrometry (IMS) provides a powerful technique to elucidate the spatial distribution patterns of peptides and proteins in tissue sections. METHODS In the present study, we analyzed an in-house compiled tissue microarray (n = 60) comprising CRCs and control tissues by IMS. After obtaining protein profiles through direct analysis of tissue sections, two validation sets were used for immunohistochemical evaluation. RESULTS A total of 28 m/z values in the mass range 800-3500 Da distinguished euploid from aneuploid CRCs (p < 0.001, ROC AUC values < 0.385 or > 0.635). After liquid chromatograph-mass spectrometry identification, UBE2N could be successfully validated by immunohistochemistry in the initial sample cohort (p = 0.0274, ROC AUC = 0.7937) and in an independent sample set of 90 clinical specimens (p = 0.0070, ROC AUC = 0.6957). CONCLUSIONS The results showed that FFPE protein expression profiling of surgically resected CRC tissue extracts by MALDI-TOF MS has potential value for improved molecular classification. Particularly, the protein expression of UBE2N was validated in an independent clinical cohort to distinguish euploid from aneuploid CRCs.
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Affiliation(s)
- Timo Gemoll
- Section for Translational Surgical Oncology and Biobanking, Department of Surgery, University of Lübeck and University Medical Center Schleswig-Holstein, Campus Lübeck, Lübeck, Germany.
| | - Elena Miroll
- Section for Translational Surgical Oncology and Biobanking, Department of Surgery, University of Lübeck and University Medical Center Schleswig-Holstein, Campus Lübeck, Lübeck, Germany
| | - Oliver Klein
- Berlin-Brandenburg Center for Regenerative Therapies, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Annette Lischka
- Section for Translational Surgical Oncology and Biobanking, Department of Surgery, University of Lübeck and University Medical Center Schleswig-Holstein, Campus Lübeck, Lübeck, Germany
| | - Murat Eravci
- Institute of Chemistry and Biochemistry, Biochemistry, Freie Universität Berlin, Berlin, Germany
| | - Christoph Thorns
- Institute of Pathology, University Medical Center Schleswig-Holstein, Campus Lübeck, Lübeck, Germany
| | - Jens K Habermann
- Section for Translational Surgical Oncology and Biobanking, Department of Surgery, University of Lübeck and University Medical Center Schleswig-Holstein, Campus Lübeck, Lübeck, Germany.,Interdisciplinary Center for Biobanking-Lübeck (ICB-L), University of Lübeck, Lübeck, Germany
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50
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Comparison of the volumetric and radiomics findings of 18F-FDG PET/CT images with immunohistochemical prognostic factors in local/locally advanced breast cancer. Nucl Med Commun 2019; 40:764-772. [DOI: 10.1097/mnm.0000000000001019] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
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