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Hipp N, Young KL, Apostolova II, Klutmann S, Hardewig I, Koglin N, Gallinat J, Buchert R. Increased off-target binding of [18F]florbetaben in the skull of women with reduced skull density. Nuklearmedizin 2024; 63:247-251. [PMID: 38387615 DOI: 10.1055/a-2246-5292] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/24/2024]
Abstract
AIM To investigate the relationship between off-target binding of the amyloid tracer [18F]florbetaben (FBB) in the skull and skull density. METHODS Forty-three consecutive patients were included retrospectively (age 70.2±7.5y, 42% females, 65% amyloid-positive). For each patient, CT skull density (in Hounsfield units) and (late) FBB uptake in the skull were obtained using an individual skull mask generated by warping the skull tissue probability map provided by the statistical parametric mapping software package (version SPM12) to the native patient space. Skull FBB uptake (mean of the 10% hottest voxels) was scaled to the individual median FBB uptake in the pons. The association between skull FBB uptake and skull density was tested by correlation analyses. Univariate analysis of variance (ANOVA) of skull FBB uptake with dichotomized skull density (low: ≤ median, high), sex (female, male) and amyloid-status (positive, negative) as between-subjects factors was used to assess the impact of sex and amyloid status. RESULTS There was a significant inverse correlation between skull FBB uptake and skull density (Pearson correlation coefficient -0.518, p < 0.001; Spearman rho -0.321, p = 0.036). The ANOVA confirmed the bone density effect on the FBB uptake in the skull (p = 0.019). In addition, sex (p = 0.012) and density*sex interaction (p = 0.016) had a significant impact. Skull FBB uptake was significantly higher in females with low skull density than for all other combinations of sex and skull density. Amyloid status did not reach statistical significance (p = 0.092). CONCLUSION Off-target binding of FBB in the skull is inversely associated with skull density. The relationship is mainly driven by females. Amyloid status does not have a major impact on skull FBB binding.
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Affiliation(s)
- Noah Hipp
- Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Kim Lea Young
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Ivayla Ilieva Apostolova
- Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Susanne Klutmann
- Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | | | | | - Jürgen Gallinat
- Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Ralph Buchert
- Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
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Erasmus LT, Strange CD, Ahuja J, Agrawal R, Shroff GS, Marom EM, Truong MT. Imaging of Lung Cancer Staging: TNM 9 Updates. Semin Ultrasound CT MR 2024:S0887-2171(24)00045-3. [PMID: 39069273 DOI: 10.1053/j.sult.2024.07.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/30/2024]
Abstract
Imaging plays a key role in clinical staging of lung cancer and guiding therapy. A thorough understanding of the staging system including the nomenclature and updates is necessary to tailor treatment plans and optimize patient care. The 9th edition of the Tumor, Node, Metastasis staging system for lung cancer has no changes for T classification and subdivides N2 and M1c categories. In nodal staging, N2 splits into N2a, ipsilateral mediastinal single station involvement and N2b, ipsilateral mediastinal multiple stations involvement. In the staging of multiple extrathoracic metastases, M1c splits into M1c1, multiple extrathoracic metastases in one organ system and M1c2, multiple extrathoracic metastases in multiple organ systems. Awareness of the proposed changes in TNM-9 staging classification is essential to provide methodical and accurate imaging interpretation.
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Affiliation(s)
- Lauren T Erasmus
- Department of Radiation Oncology, Baylor College of Medicine, Houston, TX
| | - Chad D Strange
- Department of Thoracic Imaging, University of Texas MD Anderson Cancer Center, Houston, TX
| | - Jitesh Ahuja
- Department of Thoracic Imaging, University of Texas MD Anderson Cancer Center, Houston, TX
| | - Rishi Agrawal
- Department of Thoracic Imaging, University of Texas MD Anderson Cancer Center, Houston, TX
| | - Girish S Shroff
- Department of Thoracic Imaging, University of Texas MD Anderson Cancer Center, Houston, TX
| | - Edith M Marom
- Department of Radiology, Chaim Sheba Medical Center, Tel Aviv, Israel
| | - Mylene T Truong
- Department of Thoracic Imaging, University of Texas MD Anderson Cancer Center, Houston, TX.
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3
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Amrane K, Meur CL, Thuillier P, Berthou C, Uguen A, Deandreis D, Bourhis D, Bourbonne V, Abgral R. Review on radiomic analysis in 18F-fluorodeoxyglucose positron emission tomography for prediction of melanoma outcomes. Cancer Imaging 2024; 24:87. [PMID: 38970050 PMCID: PMC11225300 DOI: 10.1186/s40644-024-00732-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Accepted: 06/24/2024] [Indexed: 07/07/2024] Open
Abstract
Over the past decade, several strategies have revolutionized the clinical management of patients with cutaneous melanoma (CM), including immunotherapy and targeted tyrosine kinase inhibitor (TKI)-based therapies. Indeed, immune checkpoint inhibitors (ICIs), alone or in combination, represent the standard of care for patients with advanced disease without an actionable mutation. Notably BRAF combined with MEK inhibitors represent the therapeutic standard for disease disclosing BRAF mutation. At the same time, FDG PET/CT has become part of the routine staging and evaluation of patients with cutaneous melanoma. There is growing interest in using FDG PET/CT measurements to predict response to ICI therapy and/or target therapy. While semiquantitative values such as standardized uptake value (SUV) are limited for predicting outcome, new measures including tumor metabolic volume, total lesion glycolysis and radiomics seem promising as potential imaging biomarkers for nuclear medicine. The aim of this review, prepared by an interdisciplinary group of experts, is to take stock of the current literature on radiomics approaches that could improve outcomes in CM.
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Affiliation(s)
- Karim Amrane
- Department of Oncology, Regional Hospital of Morlaix, Morlaix, 29600, France.
- Lymphocytes B et Autoimmunité, Inserm, UMR1227, Univ Brest, Inserm, LabEx IGO, Brest, France.
| | - Coline Le Meur
- Department of Radiotherapy, University Hospital of Brest, Brest, France
| | - Philippe Thuillier
- Department of Endocrinology, University Hospital of Brest, Brest, France
- UMR Inserm 1304 GETBO, University of Western Brittany, Brest, IFR 148, France
| | - Christian Berthou
- Lymphocytes B et Autoimmunité, Inserm, UMR1227, Univ Brest, Inserm, LabEx IGO, Brest, France
- Department of Hematology, University Hospital of Brest, Brest, France
| | - Arnaud Uguen
- Lymphocytes B et Autoimmunité, Inserm, UMR1227, Univ Brest, Inserm, LabEx IGO, Brest, France
- Department of Pathology, University Hospital of Brest, Brest, France
| | - Désirée Deandreis
- Department of Nuclear Medicine, Gustave Roussy Institute, University of Paris Saclay, Paris, France
| | - David Bourhis
- UMR Inserm 1304 GETBO, University of Western Brittany, Brest, IFR 148, France
- Department of Nuclear Medicine, University Hospital of Brest, Brest, France
| | - Vincent Bourbonne
- Department of Radiotherapy, University Hospital of Brest, Brest, France
- Inserm, UMR1101, LaTIM, University of Western Brittany, Brest, France
| | - Ronan Abgral
- UMR Inserm 1304 GETBO, University of Western Brittany, Brest, IFR 148, France
- Department of Nuclear Medicine, University Hospital of Brest, Brest, France
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4
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Ottaiano A, Grassi F, Sirica R, Genito E, Ciani G, Patanè V, Monti R, Belfiore MP, Urraro F, Santorsola M, Ponsiglione AM, Montella M, Cappabianca S, Reginelli A, Sansone M, Savarese G, Grassi R. Associations between Radiomics and Genomics in Non-Small Cell Lung Cancer Utilizing Computed Tomography and Next-Generation Sequencing: An Exploratory Study. Genes (Basel) 2024; 15:803. [PMID: 38927739 PMCID: PMC11202615 DOI: 10.3390/genes15060803] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2024] [Revised: 06/14/2024] [Accepted: 06/18/2024] [Indexed: 06/28/2024] Open
Abstract
BACKGROUND Radiomics, an evolving paradigm in medical imaging, involves the quantitative analysis of tumor features and demonstrates promise in predicting treatment responses and outcomes. This study aims to investigate the predictive capacity of radiomics for genetic alterations in non-small cell lung cancer (NSCLC). METHODS This exploratory, observational study integrated radiomic perspectives using computed tomography (CT) and genomic perspectives through next-generation sequencing (NGS) applied to liquid biopsies. Associations between radiomic features and genetic mutations were established using the Area Under the Receiver Operating Characteristic curve (AUC-ROC). Machine learning techniques, including Support Vector Machine (SVM) classification, aim to predict genetic mutations based on radiomic features. The prognostic impact of selected gene variants was assessed using Kaplan-Meier curves and Log-rank tests. RESULTS Sixty-six patients underwent screening, with fifty-seven being comprehensively characterized radiomically and genomically. Predominantly males (68.4%), adenocarcinoma was the prevalent histological type (73.7%). Disease staging is distributed across I/II (38.6%), III (31.6%), and IV (29.8%). Significant correlations were identified with mutations of ROS1 p.Thr145Pro (shape_Sphericity), ROS1 p.Arg167Gln (glszm_ZoneEntropy, firstorder_TotalEnergy), ROS1 p.Asp2213Asn (glszm_GrayLevelVariance, firstorder_RootMeanSquared), and ALK p.Asp1529Glu (glcm_Imc1). Patients with the ROS1 p.Thr145Pro variant demonstrated markedly shorter median survival compared to the wild-type group (9.7 months vs. not reached, p = 0.0143; HR: 5.35; 95% CI: 1.39-20.48). CONCLUSIONS The exploration of the intersection between radiomics and cancer genetics in NSCLC is not only feasible but also holds the potential to improve genetic predictions and enhance prognostic accuracy.
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Affiliation(s)
- Alessandro Ottaiano
- Istituto Nazionale Tumori di Napoli, IRCCS “G. Pascale”, 80131 Naples, Italy; (A.O.); (M.S.)
| | - Francesca Grassi
- Department of Precision Medicine, Università degli Studi della Campania “Luigi Vanvitelli”, 80138 Naples, Italy; (E.G.); (G.C.); (V.P.); (R.M.); (M.P.B.); (F.U.); (S.C.); (A.R.); (R.G.)
| | - Roberto Sirica
- AMES—Centro Polidiagnostico Strumentale, SRL, 80013 Naples, Italy; (R.S.); (G.S.)
| | - Emanuela Genito
- Department of Precision Medicine, Università degli Studi della Campania “Luigi Vanvitelli”, 80138 Naples, Italy; (E.G.); (G.C.); (V.P.); (R.M.); (M.P.B.); (F.U.); (S.C.); (A.R.); (R.G.)
| | - Giovanni Ciani
- Department of Precision Medicine, Università degli Studi della Campania “Luigi Vanvitelli”, 80138 Naples, Italy; (E.G.); (G.C.); (V.P.); (R.M.); (M.P.B.); (F.U.); (S.C.); (A.R.); (R.G.)
| | - Vittorio Patanè
- Department of Precision Medicine, Università degli Studi della Campania “Luigi Vanvitelli”, 80138 Naples, Italy; (E.G.); (G.C.); (V.P.); (R.M.); (M.P.B.); (F.U.); (S.C.); (A.R.); (R.G.)
| | - Riccardo Monti
- Department of Precision Medicine, Università degli Studi della Campania “Luigi Vanvitelli”, 80138 Naples, Italy; (E.G.); (G.C.); (V.P.); (R.M.); (M.P.B.); (F.U.); (S.C.); (A.R.); (R.G.)
| | - Maria Paola Belfiore
- Department of Precision Medicine, Università degli Studi della Campania “Luigi Vanvitelli”, 80138 Naples, Italy; (E.G.); (G.C.); (V.P.); (R.M.); (M.P.B.); (F.U.); (S.C.); (A.R.); (R.G.)
| | - Fabrizio Urraro
- Department of Precision Medicine, Università degli Studi della Campania “Luigi Vanvitelli”, 80138 Naples, Italy; (E.G.); (G.C.); (V.P.); (R.M.); (M.P.B.); (F.U.); (S.C.); (A.R.); (R.G.)
| | - Mariachiara Santorsola
- Istituto Nazionale Tumori di Napoli, IRCCS “G. Pascale”, 80131 Naples, Italy; (A.O.); (M.S.)
| | - Alfonso Maria Ponsiglione
- Department of Electrical Engineering and Information Technology, University of Naples “Federico II”, 80125 Naples, Italy; (A.M.P.); (M.S.)
| | - Marco Montella
- Pathology Unit, Department of Mental and Physical Health and Preventive Medicine, Università degli Studi della Campania “Luigi Vanvitelli”, 80138 Naples, Italy;
| | - Salvatore Cappabianca
- Department of Precision Medicine, Università degli Studi della Campania “Luigi Vanvitelli”, 80138 Naples, Italy; (E.G.); (G.C.); (V.P.); (R.M.); (M.P.B.); (F.U.); (S.C.); (A.R.); (R.G.)
| | - Alfonso Reginelli
- Department of Precision Medicine, Università degli Studi della Campania “Luigi Vanvitelli”, 80138 Naples, Italy; (E.G.); (G.C.); (V.P.); (R.M.); (M.P.B.); (F.U.); (S.C.); (A.R.); (R.G.)
| | - Mario Sansone
- Department of Electrical Engineering and Information Technology, University of Naples “Federico II”, 80125 Naples, Italy; (A.M.P.); (M.S.)
| | - Giovanni Savarese
- AMES—Centro Polidiagnostico Strumentale, SRL, 80013 Naples, Italy; (R.S.); (G.S.)
| | - Roberta Grassi
- Department of Precision Medicine, Università degli Studi della Campania “Luigi Vanvitelli”, 80138 Naples, Italy; (E.G.); (G.C.); (V.P.); (R.M.); (M.P.B.); (F.U.); (S.C.); (A.R.); (R.G.)
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Lucia F, Louis T, Cousin F, Bourbonne V, Visvikis D, Mievis C, Jansen N, Duysinx B, Le Pennec R, Nebbache M, Rehn M, Hamya M, Geier M, Salaun PY, Schick U, Hatt M, Coucke P, Hustinx R, Lovinfosse P. Multicentric development and evaluation of [ 18F]FDG PET/CT and CT radiomic models to predict regional and/or distant recurrence in early-stage non-small cell lung cancer treated by stereotactic body radiation therapy. Eur J Nucl Med Mol Imaging 2024; 51:1097-1108. [PMID: 37987783 DOI: 10.1007/s00259-023-06510-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Accepted: 11/03/2023] [Indexed: 11/22/2023]
Abstract
PURPOSE To develop machine learning models to predict regional and/or distant recurrence in patients with early-stage non-small cell lung cancer (ES-NSCLC) after stereotactic body radiation therapy (SBRT) using [18F]FDG PET/CT and CT radiomics combined with clinical and dosimetric parameters. METHODS We retrospectively collected 464 patients (60% for training and 40% for testing) from University Hospital of Liège and 63 patients from University Hospital of Brest (external testing set) with ES-NSCLC treated with SBRT between 2010 and 2020 and who had undergone pretreatment [18F]FDG PET/CT and planning CT. Radiomic features were extracted using the PyRadiomics toolbox®. The ComBat harmonization method was applied to reduce the batch effect between centers. Clinical, radiomic, and combined models were trained and tested using a neural network approach to predict regional and/or distant recurrence. RESULTS In the training (n = 273) and testing sets (n = 191 and n = 63), the clinical model achieved moderate performances to predict regional and/or distant recurrence with C-statistics from 0.53 to 0.59 (95% CI, 0.41, 0.67). The radiomic (original_firstorder_Entropy, original_gldm_LowGrayLevelEmphasis and original_glcm_DifferenceAverage) model achieved higher predictive ability in the training set and kept the same performance in the testing sets, with C-statistics from 0.70 to 0.78 (95% CI, 0.63, 0.88) while the combined model performs moderately well with C-statistics from 0.50 to 0.62 (95% CI, 0.37, 0.69). CONCLUSION Radiomic features extracted from pre-SBRT analog and digital [18F]FDG PET/CT outperform clinical parameters in the prediction of regional and/or distant recurrence and to discuss an adjuvant systemic treatment in ES-NSCLC. Prospective validation of our models should now be carried out.
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Affiliation(s)
- François Lucia
- Radiation Oncology Department, University Hospital, Brest, France.
- LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France.
- Division of Nuclear Medicine and Oncological Imaging, University Hospital of Liège, Liège, Belgium.
- Service de Radiothérapie, CHRU Morvan, 2 Avenue Foch, 29609 Cedex, Brest, France.
| | - Thomas Louis
- Division of Nuclear Medicine and Oncological Imaging, University Hospital of Liège, Liège, Belgium
| | - François Cousin
- Division of Nuclear Medicine and Oncological Imaging, University Hospital of Liège, Liège, Belgium
| | - Vincent Bourbonne
- Radiation Oncology Department, University Hospital, Brest, France
- LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France
| | | | - Carole Mievis
- Department of Radiotherapy Oncology, University Hospital of Liège, Liège, Belgium
| | - Nicolas Jansen
- Department of Radiotherapy Oncology, University Hospital of Liège, Liège, Belgium
| | | | - Romain Le Pennec
- Nuclear Medicine Department, University Hospital, Brest, France
- GETBO, INSERM, UMR 1304, University of Brest, UBO, Brest, France
| | - Malik Nebbache
- Radiation Oncology Department, University Hospital, Brest, France
| | - Martin Rehn
- Radiation Oncology Department, University Hospital, Brest, France
| | - Mohamed Hamya
- Radiation Oncology Department, University Hospital, Brest, France
| | - Margaux Geier
- Medical Oncology Department, University Hospital, Brest, France
| | - Pierre-Yves Salaun
- Nuclear Medicine Department, University Hospital, Brest, France
- GETBO, INSERM, UMR 1304, University of Brest, UBO, Brest, France
| | - Ulrike Schick
- Radiation Oncology Department, University Hospital, Brest, France
- LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France
| | - Mathieu Hatt
- LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France
| | - Philippe Coucke
- Department of Radiotherapy Oncology, University Hospital of Liège, Liège, Belgium
| | - Roland Hustinx
- Division of Nuclear Medicine and Oncological Imaging, University Hospital of Liège, Liège, Belgium
- GIGA-CRC In Vivo Imaging, University of Liège, Liège, Belgium
| | - Pierre Lovinfosse
- Division of Nuclear Medicine and Oncological Imaging, University Hospital of Liège, Liège, Belgium
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Carlier T, Frécon G, Mateus D, Rizkallah M, Kraeber-Bodéré F, Kanoun S, Blanc-Durand P, Itti E, Le Gouill S, Casasnovas RO, Bodet-Milin C, Bailly C. Prognostic Value of 18F-FDG PET Radiomics Features at Baseline in PET-Guided Consolidation Strategy in Diffuse Large B-Cell Lymphoma: A Machine-Learning Analysis from the GAINED Study. J Nucl Med 2024; 65:156-162. [PMID: 37945379 DOI: 10.2967/jnumed.123.265872] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Revised: 10/17/2023] [Indexed: 11/12/2023] Open
Abstract
The results of the GA in Newly Diagnosed Diffuse Large B-Cell Lymphoma (GAINED) study demonstrated the success of an 18F-FDG PET-driven approach to allow early identification-for intensification therapy-of diffuse large B-cell lymphoma patients with a high risk of relapse. Besides, some works have reported the prognostic value of baseline PET radiomics features (RFs). This work investigated the added value of such biomarkers on survival of patients involved in the GAINED protocol. Methods: Conventional PET features and RFs were computed from 18F-FDG PET at baseline and extracted using different volume definitions (patient level, largest lesion, and hottest lesion). Clinical features and the consolidation treatment information were also considered in the model. Two machine-learning pipelines were trained with 80% of patients and tested on the remaining 20%. The training was repeated 100 times to highlight the test set variability. For the 2-y progression-free survival (PFS) outcome, the pipeline included a data augmentation and an elastic net logistic regression model. Results for different feature groups were compared using the mean area under the curve (AUC). For the survival outcome, the pipeline included a Cox univariate model to select the features. Then, the model included a split between high- and low-risk patients using the median of a regression score based on the coefficients of a penalized Cox multivariate approach. The log-rank test P values over the 100 loops were compared with a Wilcoxon signed-ranked test. Results: In total, 545 patients were included for the 2-y PFS classification and 561 for survival analysis. Clinical features alone, consolidation features alone, conventional PET features, and RFs extracted at patient level achieved an AUC of, respectively, 0.65 ± 0.07, 0.64 ± 0.06, 0.60 ± 0.07, and 0.62 ± 0.07 (0.62 ± 0.07 for the largest lesion and 0.54 ± 0.07 for the hottest). Combining clinical features with the consolidation features led to the best AUC (0.72 ± 0.06). Adding conventional PET features or RFs did not improve the results. For survival, the log-rank P values of the model involving clinical and consolidation features together were significantly smaller than all combined-feature groups (P < 0.007). Conclusion: The results showed that a concatenation of multimodal features coupled with a simple machine-learning model does not seem to improve the results in terms of 2-y PFS classification and PFS prediction for patient treated according to the GAINED protocol.
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Affiliation(s)
- Thomas Carlier
- Nantes Université, INSERM, CNRS, CRCINA, Université d'Angers, Nantes, France
- Nuclear Medicine Department, University Hospital, Nantes, France
| | - Gauthier Frécon
- Nantes Université, INSERM, CNRS, CRCINA, Université d'Angers, Nantes, France
- Nuclear Medicine Department, University Hospital, Nantes, France
| | - Diana Mateus
- Laboratoire des Sciences Numériques de Nantes, Ecole Centrale de Nantes, CNRS UMR 6004, Nantes, France
| | - Mira Rizkallah
- Laboratoire des Sciences Numériques de Nantes, Ecole Centrale de Nantes, CNRS UMR 6004, Nantes, France
| | - Françoise Kraeber-Bodéré
- Nantes Université, INSERM, CNRS, CRCINA, Université d'Angers, Nantes, France
- Nuclear Medicine Department, University Hospital, Nantes, France
| | - Salim Kanoun
- Nuclear Medicine, Georges-François Leclerc Center, Dijon, France
| | - Paul Blanc-Durand
- Nuclear Medicine, CHU Henri Mondor, Paris-Est University, Créteil, France
| | - Emmanuel Itti
- Nuclear Medicine, CHU Henri Mondor, Paris-Est University, Créteil, France
| | - Steven Le Gouill
- Haematology Department, University Hospital, Nantes, France; and
| | | | - Caroline Bodet-Milin
- Nantes Université, INSERM, CNRS, CRCINA, Université d'Angers, Nantes, France
- Nuclear Medicine Department, University Hospital, Nantes, France
| | - Clément Bailly
- Nantes Université, INSERM, CNRS, CRCINA, Université d'Angers, Nantes, France;
- Nuclear Medicine Department, University Hospital, Nantes, France
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7
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Chen Y, Kan K, Liu S, Lin H, Lue K. Impact of respiratory motion on 18 F-FDG PET radiomics stability: Clinical evaluation with a digital PET scanner. J Appl Clin Med Phys 2023; 24:e14200. [PMID: 37937706 PMCID: PMC10691638 DOI: 10.1002/acm2.14200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Revised: 10/13/2023] [Accepted: 10/30/2023] [Indexed: 11/09/2023] Open
Abstract
PURPOSE 18 F-FDG PET quantitative features are susceptible to respiratory motion. However, studies using clinical patient data to explore the impact of respiratory motion on 18 F-FDG PET radiomic features are limited. In this study, we investigated the impact of respiratory motion on radiomics stability with clinical 18 F-FDG PET images using a data-driven gating (DDG) algorithm on the digital PET scanner. MATERIALS AND METHODS A total of 101 patients who underwent oncological 18 F-FDG PET scans were retrospectively included. A DDG algorithm combined with a motion compensation technique was used to extract the PET images with respiratory motion correction. 18 F-FDG-avid lesions from the thorax to the upper abdomen were analyzed on the non-DDG and DDG PET images. The lesions were segmented with a 40% threshold of the maximum standardized uptake. A total of 725 radiomic features were computed from the segmented lesions, including first-order, shape, texture, and wavelet features. The intraclass correlation coefficient (ICC) and coefficient of variation (COV) were calculated to evaluate feature stability. An ICC above 0.9 and a COV below 5% were considered high stability. RESULTS In total, 168 lesions with and without respiratory motion correction were analyzed. Our results indicated that most 18 F-FDG PET radiomic features are sensitive to respiratory motion. Overall, only 27 out of 725 (3.72%) radiomic features were identified as highly stable, including one from the first-order features (entropy), one from the shape features (sphericity), four from the gray-level co-occurrence matrix features (normalized and unnormalized inverse difference moment, joint entropy, and sum entropy), one from the gray-level run-length matrix features (run entropy), and 20 from the wavelet filter-based features. CONCLUSION Respiratory motion has a significant impact on 18 F-FDG PET radiomics stability. The highly stable features identified in our study may serve as potential candidates for further applications, such as machine learning modeling.
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Affiliation(s)
- Yu‐Hung Chen
- Department of Nuclear MedicineHualien Tzu Chi HospitalBuddhist Tzu Chi Medical FoundationHualienTaiwan
- School of MedicineCollege of MedicineTzu Chi UniversityHualienTaiwan
- Department of Medical Imaging and Radiological SciencesTzu Chi University of Science and TechnologyHualienTaiwan
| | - Kuo‐Yi Kan
- Department of Nuclear MedicineFu Jen Catholic University HospitalNew Taipei CityTaiwan
| | - Shu‐Hsin Liu
- Department of Nuclear MedicineHualien Tzu Chi HospitalBuddhist Tzu Chi Medical FoundationHualienTaiwan
- Department of Medical Imaging and Radiological SciencesTzu Chi University of Science and TechnologyHualienTaiwan
| | - Hsin‐Hon Lin
- Department of Medical Imaging and Radiological SciencesCollege of MedicineChang Gung UniversityTaoyuanTaiwan
- Department of Nuclear MedicineChang Gung Memorial HospitalLinkouTaiwan
| | - Kun‐Han Lue
- Department of Medical Imaging and Radiological SciencesTzu Chi University of Science and TechnologyHualienTaiwan
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8
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Huang W, Tao Z, Younis MH, Cai W, Kang L. Nuclear medicine radiomics in digestive system tumors: Concept, applications, challenges, and future perspectives. VIEW 2023; 4:20230032. [PMID: 38179181 PMCID: PMC10766416 DOI: 10.1002/viw.20230032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Accepted: 07/20/2023] [Indexed: 01/06/2024] Open
Abstract
Radiomics aims to develop novel biomarkers and provide relevant deeper subvisual information about pathology, immunophenotype, and tumor microenvironment. It uses automated or semiautomated quantitative analysis of high-dimensional images to improve characterization, diagnosis, and prognosis. Recent years have seen a rapid increase in radiomics applications in nuclear medicine, leading to some promising research results in digestive system oncology, which have been driven by big data analysis and the development of artificial intelligence. Although radiomics advances one step further toward the non-invasive precision medical analysis, it is still a step away from clinical application and faces many challenges. This review article summarizes the available literature on digestive system tumors regarding radiomics in nuclear medicine. First, we describe the workflow and steps involved in radiomics analysis. Subsequently, we discuss the progress in clinical application regarding the utilization of radiomics for distinguishing between various diseases and evaluating their prognosis, and demonstrate how radiomics advances this field. Finally, we offer our viewpoint on how the field can progress by addressing the challenges facing clinical implementation.
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Affiliation(s)
- Wenpeng Huang
- Department of Nuclear Medicine, Peking University First Hospital, Beijing, China
| | - Zihao Tao
- Department of Nuclear Medicine, Peking University First Hospital, Beijing, China
| | - Muhsin H. Younis
- Departments of Radiology and Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Weibo Cai
- Departments of Radiology and Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Lei Kang
- Department of Nuclear Medicine, Peking University First Hospital, Beijing, China
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9
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Erasmus LT, Strange TA, Agrawal R, Strange CD, Ahuja J, Shroff GS, Truong MT. Lung Cancer Staging: Imaging and Potential Pitfalls. Diagnostics (Basel) 2023; 13:3359. [PMID: 37958255 PMCID: PMC10649001 DOI: 10.3390/diagnostics13213359] [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: 09/20/2023] [Revised: 10/22/2023] [Accepted: 10/31/2023] [Indexed: 11/15/2023] Open
Abstract
Lung cancer is the leading cause of cancer deaths in men and women in the United States. Accurate staging is needed to determine prognosis and devise effective treatment plans. The International Association for the Study of Lung Cancer (IASLC) has made multiple revisions to the tumor, node, metastasis (TNM) staging system used by the Union for International Cancer Control and the American Joint Committee on Cancer to stage lung cancer. The eighth edition of this staging system includes modifications to the T classification with cut points of 1 cm increments in tumor size, grouping of lung cancers associated with partial or complete lung atelectasis or pneumonitis, grouping of tumors with involvement of a main bronchus regardless of distance from the carina, and upstaging of diaphragmatic invasion to T4. The N classification describes the spread to regional lymph nodes and no changes were proposed for TNM-8. In the M classification, metastatic disease is divided into intra- versus extrathoracic metastasis, and single versus multiple metastases. In order to optimize patient outcomes, it is important to understand the nuances of the TNM staging system, the strengths and weaknesses of various imaging modalities used in lung cancer staging, and potential pitfalls in image interpretation.
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Affiliation(s)
- Lauren T. Erasmus
- Department of Anatomy and Cell Biology, Faculty of Sciences, McGill University, Montreal, QC H3A 0G4, Canada;
| | - Taylor A. Strange
- Department of Pathology, University of Texas Medical Branch, Galveston, TX 77555, USA;
| | - Rishi Agrawal
- Department of Thoracic Radiology, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; (R.A.); (C.D.S.); (J.A.)
| | - Chad D. Strange
- Department of Thoracic Radiology, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; (R.A.); (C.D.S.); (J.A.)
| | - Jitesh Ahuja
- Department of Thoracic Radiology, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; (R.A.); (C.D.S.); (J.A.)
| | - Girish S. Shroff
- Department of Thoracic Radiology, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; (R.A.); (C.D.S.); (J.A.)
| | - Mylene T. Truong
- Department of Thoracic Radiology, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; (R.A.); (C.D.S.); (J.A.)
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10
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Mattoli MV, Cocciolillo F, Chiacchiaretta P, Dotta F, Trevisi G, Carrarini C, Thomas A, Sensi S, Pizzi AD, Nicola ADD, Crosta AD, Mammarella N, Padovani A, Pilotto A, Moda F, Tiraboschi P, Martino G, Bonanni L. Combined 18F-FDG PET-CT markers in dementia with Lewy bodies. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2023; 15:e12515. [PMID: 38145190 PMCID: PMC10746864 DOI: 10.1002/dad2.12515] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 11/10/2023] [Accepted: 11/20/2023] [Indexed: 12/26/2023]
Abstract
INTRODUCTION 18F-Fluoro-deoxyglucose-positron emission tomography (FDG-PET) is a supportive biomarker in dementia with Lewy bodies (DLB) diagnosis and its advanced analysis methods, including radiomics and machine learning (ML), were developed recently. The aim of this study was to evaluate the FDG-PET diagnostic performance in predicting a DLB versus Alzheimer's disease (AD) diagnosis. METHODS FDG-PET scans were visually and semi-quantitatively analyzed in 61 patients. Radiomics and ML analyses were performed, building five ML models: (1) clinical features; (2) visual and semi-quantitative PET features; (3) radiomic features; (4) all PET features; and (5) overall features. RESULTS At follow-up, 34 patients had DLB and 27 had AD. At visual analysis, DLB PET signs were significantly more frequent in DLB, having the highest diagnostic accuracy (86.9%). At semi-quantitative analysis, the right precuneus, superior parietal, lateral occipital, and primary visual cortices showed significantly reduced uptake in DLB. The ML model 2 had the highest diagnostic accuracy (84.3%). DISCUSSION FDG-PET is a valuable tool in DLB diagnosis, having visual and semi-quantitative analyses with the highest diagnostic accuracy at ML analyses.
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Affiliation(s)
- Maria Vittoria Mattoli
- Department of NeuroscienceImaging and Clinical SciencesUniversity G. d'Annunzio of Chieti‐PescaraChietiItaly
- Nuclear Medicine UnitPresidio Ospedaliero Santo SpiritoPescaraItaly
| | - Fabrizio Cocciolillo
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed EmatologiaUOC di Medicina Nucleare, Fondazione Policlinico Universitario Agostino Gemelli IRCCSRomeItaly
| | - Piero Chiacchiaretta
- Department of Innovative Technologies in Medicine and DentistryUniversity G. d'Annunzio of Chieti – PescaraChietiItaly
- Advanced Computing Core, Center for Advanced Studies and Technology ‐ C.A.S.TUniversity G. d'Annunzio of Chieti – PescaraChietiItaly
| | - Francesco Dotta
- Department of Innovative Technologies in Medicine and DentistryUniversity G. d'Annunzio of Chieti – PescaraChietiItaly
| | - Gianluca Trevisi
- Department of NeuroscienceImaging and Clinical SciencesUniversity G. d'Annunzio of Chieti‐PescaraChietiItaly
| | - Claudia Carrarini
- Department of NeuroscienceCatholic University of Sacred HeartRomeItaly
- IRCCS San RaffaeleRomeItaly
| | - Astrid Thomas
- Department of NeuroscienceImaging and Clinical SciencesUniversity G. d'Annunzio of Chieti‐PescaraChietiItaly
| | - Stefano Sensi
- Department of NeuroscienceImaging and Clinical SciencesUniversity G. d'Annunzio of Chieti‐PescaraChietiItaly
| | - Andrea Delli Pizzi
- Department of Innovative Technologies in Medicine and DentistryUniversity G. d'Annunzio of Chieti – PescaraChietiItaly
| | | | - Adolfo Di Crosta
- Department of Psychological ScienceHumanities and TerritoryUniversity “G. d'Annunzio” of Chieti‐PescaraChietiItaly
- Department of Medicine and Aging SciencesUniversity G d'Annunzio of Chieti‐PescaraChietiItaly
| | - Nicola Mammarella
- Department of Psychological ScienceHumanities and TerritoryUniversity “G. d'Annunzio” of Chieti‐PescaraChietiItaly
| | - Alessandro Padovani
- Neurology Unit, Department of Clinical and Experimental SciencesUniversity of BresciaBresciaItaly
| | - Andrea Pilotto
- Neurology Unit, Department of Clinical and Experimental SciencesUniversity of BresciaBresciaItaly
- Parkinson's Disease Rehabilitation CentreFERB ONLUS‐S. Isidoro HospitalTrescore BalnearioBergamoItaly
| | - Fabio Moda
- Division of Neurology 5 and NeuropathologyFondazione IRCCS Istituto Neurologico Carlo BestaMilanItaly
| | - Pietro Tiraboschi
- Division of Neurology 5 and NeuropathologyFondazione IRCCS Istituto Neurologico Carlo BestaMilanItaly
| | - Gianluigi Martino
- Department of Radiological Sciences, Nuclear Medicine UniteSS. Annunziata HospitalVia dei Vestini 31ChietiItaly
| | - Laura Bonanni
- Department of Medicine and Aging SciencesUniversity G d'Annunzio of Chieti‐PescaraChietiItaly
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11
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Lucia F, Bourbonne V, Pleyers C, Dupré PF, Miranda O, Visvikis D, Pradier O, Abgral R, Mervoyer A, Classe JM, Rousseau C, Vos W, Hermesse J, Gennigens C, De Cuypere M, Kridelka F, Schick U, Hatt M, Hustinx R, Lovinfosse P. Multicentric development and evaluation of 18F-FDG PET/CT and MRI radiomics models to predict para-aortic lymph node involvement in locally advanced cervical cancer. Eur J Nucl Med Mol Imaging 2023; 50:2514-2528. [PMID: 36892667 DOI: 10.1007/s00259-023-06180-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Accepted: 02/27/2023] [Indexed: 03/10/2023]
Abstract
PURPOSE To develop machine learning models to predict para-aortic lymph node (PALN) involvement in patients with locally advanced cervical cancer (LACC) before chemoradiotherapy (CRT) using 18F-FDG PET/CT and MRI radiomics combined with clinical parameters. METHODS We retrospectively collected 178 patients (60% for training and 40% for testing) in 2 centers and 61 patients corresponding to 2 further external testing cohorts with LACC between 2010 to 2022 and who had undergone pretreatment analog or digital 18F-FDG PET/CT, pelvic MRI and surgical PALN staging. Only primary tumor volumes were delineated. Radiomics features were extracted using the Radiomics toolbox®. The ComBat harmonization method was applied to reduce the batch effect between centers. Different prediction models were trained using a neural network approach with either clinical, radiomics or combined models. They were then evaluated on the testing and external validation sets and compared. RESULTS In the training set (n = 102), the clinical model achieved a good prediction of the risk of PALN involvement with a C-statistic of 0.80 (95% CI 0.71, 0.87). However, it performed in the testing (n = 76) and external testing sets (n = 30 and n = 31) with C-statistics of only 0.57 to 0.67 (95% CI 0.36, 0.83). The ComBat-radiomic (GLDZM_HISDE_PET_FBN64 and Shape_maxDiameter2D3_PET_FBW0.25) and ComBat-combined (FIGO 2018 and same radiomics features) models achieved very high predictive ability in the training set and both models kept the same performance in the testing sets, with C-statistics from 0.88 to 0.96 (95% CI 0.76, 1.00) and 0.85 to 0.92 (95% CI 0.75, 0.99), respectively. CONCLUSIONS Radiomic features extracted from pre-CRT analog and digital 18F-FDG PET/CT outperform clinical parameters in the decision to perform a para-aortic node staging or an extended field irradiation to PALN. Prospective validation of our models should now be carried out.
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Affiliation(s)
- François Lucia
- Radiation Oncology Department, University Hospital, Brest, France.
- LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France.
- Division of Nuclear Medicine and Oncological Imaging, University Hospital of Liège, Liège, Belgium.
| | - Vincent Bourbonne
- Radiation Oncology Department, University Hospital, Brest, France
- LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France
| | - Clémence Pleyers
- Department of Radiotherapy Oncology, University Hospital of Liège, Liège, Belgium
| | | | - Omar Miranda
- Radiation Oncology Department, University Hospital, Brest, France
| | | | - Olivier Pradier
- Radiation Oncology Department, University Hospital, Brest, France
- LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France
| | - Ronan Abgral
- Nuclear Medicine Department, University Hospital, Brest, France
- EA GETBO 3878, IFR 148, University of Brest, UBO, Brest, France
| | - Augustin Mervoyer
- Department of Radiation Oncology, Institut de Cancérologie de l'Ouest Centre René Gauducheau, Saint Herblain, France
| | - Jean-Marc Classe
- Department of Surgical Oncology, Institut de Cancérologie de l'Ouest Centre René Gauducheau, Saint Herblain, France
| | - Caroline Rousseau
- Université de Nantes, CNRS, Inserm, CRCINA, F-44000, Nantes, France
- ICO René Gauducheau, F-44800, Saint-Herblain, France
| | - Wim Vos
- Radiomics SA, Liège, Belgium
| | - Johanne Hermesse
- Department of Radiotherapy Oncology, University Hospital of Liège, Liège, Belgium
| | - Christine Gennigens
- Department of Medical Oncology, University Hospital of Liège, Liège, Belgium
| | | | - Frédéric Kridelka
- Department of Gynecology, University Hospital of Liège, Liège, Belgium
| | - Ulrike Schick
- Radiation Oncology Department, University Hospital, Brest, France
- LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France
| | - Mathieu Hatt
- LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France
| | - Roland Hustinx
- Division of Nuclear Medicine and Oncological Imaging, University Hospital of Liège, Liège, Belgium
| | - Pierre Lovinfosse
- Division of Nuclear Medicine and Oncological Imaging, University Hospital of Liège, Liège, Belgium
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12
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Long ZC, Ding XC, Zhang XB, Sun PP, Hao FR, Li ZR, Hu M. The Efficacy of Pretreatment 18F-FDG PET-CT-Based Deep Learning Network Structure to Predict Survival in Nasopharyngeal Carcinoma. Clin Med Insights Oncol 2023; 17:11795549231171793. [PMID: 37251551 PMCID: PMC10214083 DOI: 10.1177/11795549231171793] [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: 11/28/2022] [Accepted: 04/10/2023] [Indexed: 05/31/2023] Open
Abstract
Background Previous studies have shown that the 5-year survival rates of patients with nasopharyngeal carcinoma (NPC) were still not ideal despite great improvement in NPC treatments. To achieve individualized treatment of NPC, we have been looking for novel models to predict the prognosis of patients with NPC. The objective of this study was to use a novel deep learning network structural model to predict the prognosis of patients with NPC and to compare it with the traditional PET-CT model combining metabolic parameters and clinical factors. Methods A total of 173 patients were admitted to 2 institutions between July 2014 and April 2020 for the retrospective study; each received a PET-CT scan before treatment. The least absolute shrinkage and selection operator (LASSO) was employed to select some features, including SUVpeak-P, T3, age, stage II, MTV-P, N1, stage III and pathological type, which were associated with overall survival (OS) of patients. We constructed 2 survival prediction models: an improved optimized adaptive multimodal task (a 3D Coordinate Attention Convolutional Autoencoder and an uncertainty-based jointly Optimizing Cox Model, CACA-UOCM for short) and a clinical model. The predictive power of these models was assessed using the Harrell Consistency Index (C index). Overall survival of patients with NPC was compared by Kaplan-Meier and Log-rank tests. Results The results showed that CACA-UOCM model could estimate OS (C index, 0.779 for training, 0.774 for validation, and 0.819 for testing) and divide patients into low and high mortality risk groups, which were significantly associated with OS (P < .001). However, the C-index of the model based only on clinical variables was only 0.42. Conclusions The deep learning network model based on 18F-FDG PET/CT can serve as a reliable and powerful predictive tool for NPC and provide therapeutic strategies for individual treatment.
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Affiliation(s)
- Zi-Chan Long
- Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Xing-Chen Ding
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Xian-Bin Zhang
- Department of General Surgery and Integrated Chinese and Western Medicine, Institute of Precision Diagnosis and Treatment of Gastrointestinal Tumors, Carson International Cancer Center, Shenzhen University General Hospital, Shenzhen University, Shenzhen, China
| | - Peng-Peng Sun
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Fu-Rong Hao
- Department of Radiation Oncology, Weifang People's Hospital, Weifang, China
| | | | - Man Hu
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
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13
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Amrane K, Thuillier P, Bourhis D, Le Meur C, Quere C, Leclere JC, Ferec M, Jestin-Le Tallec V, Doucet L, Alemany P, Salaun PY, Metges JP, Schick U, Abgral R. Prognostic value of pre-therapeutic FDG-PET radiomic analysis in gastro-esophageal junction cancer. Sci Rep 2023; 13:5789. [PMID: 37031233 PMCID: PMC10082755 DOI: 10.1038/s41598-023-31587-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2022] [Accepted: 03/14/2023] [Indexed: 04/10/2023] Open
Abstract
The main aim of this study was to evaluate the prognostic value of radiomic approach in pre-therapeutic 18F-fluorodeoxyglucose positron-emission tomography (FDG-PET/CT) in a large cohort of patients with gastro-esophageal junction cancer (GEJC). This was a retrospective monocenter study including 97 consecutive patients with GEJC who underwent a pre-therapeutic FDG-PET and were followed up for 3 years. Standard first-order radiomic PET indices including SUVmax, SUVmean, SUVpeak, MTV and TLG and 32 textural features (TFs) were calculated using LIFEx software on PET imaging. Prognostic significance of these parameters was assessed in univariate and multivariate analysis. Relapse-free survival (RFS) and overall survival (OS) were respectively chosen as primary and secondary endpoints. An internal validation cohort was used by randomly drawing one-third of included patients. The main characteristics of this cohort were: median age of 65 years [41-88], sex ratio H/F = 83/14, 81.5% of patients with a histopathology of adenocarcinoma and 43.3% with a stage IV disease. The median follow-up was 28.5 months [4.2-108.5]. Seventy-seven (79.4%) patients had locoregional or distant progression or recurrence and 71 (73.2%) died. In univariate analysis, SUVmean, Histogram-Entropy and 2 TFs (GLCM-Homogeneity and GLCM-Energy) were significantly correlated with RFS and OS, as well as 2 others TFs (GLRLM-LRE and GLRLM-GLNU) with OS only. In multivariate analysis, Histogram-Entropy remained an independent prognostic factor of both RFS and OS whereas SUVmean was an independent prognostic factor of OS only. These results were partially confirmed in our internal validation cohort of 33 patients. Our results suggest that radiomic approach reveals independent prognostic factors for survival in patients with GEJC.
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Affiliation(s)
- Karim Amrane
- Department of Oncology, Regional Hospital of Morlaix, Morlaix, France.
| | - Philippe Thuillier
- Department of Endocrinology, University Hospital of Brest, Brest, France
- UMR Inserm 1304 GETBO, IFR 148, University of Western Brittany, Brest, France
| | - David Bourhis
- UMR Inserm 1304 GETBO, IFR 148, University of Western Brittany, Brest, France
- Department of Nuclear Medicine, University Hospital of Brest, 2 Avenue Foch, 29609, Brest Cedex, France
| | - Coline Le Meur
- Department of Oncology, University Hospital of Brest, Brest, France
| | - Chloe Quere
- Department of Nuclear Medicine, University Hospital of Brest, 2 Avenue Foch, 29609, Brest Cedex, France
| | | | - Marc Ferec
- Department of Gastroenterology, Regional Hospital of Morlaix, Morlaix, France
| | | | - Laurent Doucet
- Department of Pathology, University Hospital of Brest, Brest, France
| | - Pierre Alemany
- Department of Pathology, Ouestpathology Brest, Brest, France
| | - Pierre-Yves Salaun
- UMR Inserm 1304 GETBO, IFR 148, University of Western Brittany, Brest, France
- Department of Nuclear Medicine, University Hospital of Brest, 2 Avenue Foch, 29609, Brest Cedex, France
| | | | - Ulrike Schick
- Department of Radiotherapy, University Hospital of Brest, Brest, France
| | - Ronan Abgral
- UMR Inserm 1304 GETBO, IFR 148, University of Western Brittany, Brest, France.
- Department of Nuclear Medicine, University Hospital of Brest, 2 Avenue Foch, 29609, Brest Cedex, France.
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Andrearczyk V, Oreiller V, Abobakr M, Akhavanallaf A, Balermpas P, Boughdad S, Capriotti L, Castelli J, Le Rest CC, Decazes P, Correia R, El-Habashy D, Elhalawani H, Fuller CD, Jreige M, Khamis Y, La Greca A, Mohamed A, Naser M, Prior JO, Ruan S, Tanadini-Lang S, Tankyevych O, Salimi Y, Vallières M, Vera P, Visvikis D, Wahid K, Zaidi H, Hatt M, Depeursinge A. Overview of the HECKTOR Challenge at MICCAI 2022: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT. HEAD AND NECK TUMOR SEGMENTATION AND OUTCOME PREDICTION : THIRD CHALLENGE, HECKTOR 2022, HELD IN CONJUNCTION WITH MICCAI 2022, SINGAPORE, SEPTEMBER 22, 2022, PROCEEDINGS. HEAD AND NECK TUMOR SEGMENTATION CHALLENGE (3RD : 2022 : SINGAPOR... 2023; 13626:1-30. [PMID: 37195050 PMCID: PMC10171217 DOI: 10.1007/978-3-031-27420-6_1] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/18/2023]
Abstract
This paper presents an overview of the third edition of the HEad and neCK TumOR segmentation and outcome prediction (HECKTOR) challenge, organized as a satellite event of the 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 2022. The challenge comprises two tasks related to the automatic analysis of FDG-PET/CT images for patients with Head and Neck cancer (H&N), focusing on the oropharynx region. Task 1 is the fully automatic segmentation of H&N primary Gross Tumor Volume (GTVp) and metastatic lymph nodes (GTVn) from FDG-PET/CT images. Task 2 is the fully automatic prediction of Recurrence-Free Survival (RFS) from the same FDG-PET/CT and clinical data. The data were collected from nine centers for a total of 883 cases consisting of FDG-PET/CT images and clinical information, split into 524 training and 359 test cases. The best methods obtained an aggregated Dice Similarity Coefficient (DSCagg) of 0.788 in Task 1, and a Concordance index (C-index) of 0.682 in Task 2.
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Affiliation(s)
- Vincent Andrearczyk
- Institute of Informatics, HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland, Sierre, Switzerland
| | - Valentin Oreiller
- Institute of Informatics, HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland, Sierre, Switzerland
- Department of Nuclear Medicine and Molecular Imaging, Lausanne University Hospital (CHUV), Rue du Bugnon 46, 1011 Lausanne, Switzerland
| | - Moamen Abobakr
- The University of Texas MD Anderson Cancer Center, Houston, USA
| | | | | | - Sarah Boughdad
- Department of Nuclear Medicine and Molecular Imaging, Lausanne University Hospital (CHUV), Rue du Bugnon 46, 1011 Lausanne, Switzerland
| | - Leo Capriotti
- Center Henri Becquerel, LITIS Laboratory, University of Rouen Normandy, Rouen, France
| | - Joel Castelli
- Radiotherapy Department, Cancer Institute Eugène Marquis, Rennes, France
- INSERM, U1099, Rennes, France
- University of Rennes 1, LTSI, Rennes, France
| | - Catherine Cheze Le Rest
- Centre Hospitalier Universitaire de Poitiers (CHUP), Poitiers, France
- LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France
| | - Pierre Decazes
- Center Henri Becquerel, LITIS Laboratory, University of Rouen Normandy, Rouen, France
| | - Ricardo Correia
- Department of Nuclear Medicine and Molecular Imaging, Lausanne University Hospital (CHUV), Rue du Bugnon 46, 1011 Lausanne, Switzerland
| | - Dina El-Habashy
- The University of Texas MD Anderson Cancer Center, Houston, USA
| | - Hesham Elhalawani
- Cleveland Clinic Foundation, Department of Radiation Oncology, Cleveland, OH, USA
| | | | - Mario Jreige
- Department of Nuclear Medicine and Molecular Imaging, Lausanne University Hospital (CHUV), Rue du Bugnon 46, 1011 Lausanne, Switzerland
| | - Yornna Khamis
- The University of Texas MD Anderson Cancer Center, Houston, USA
| | | | | | - Mohamed Naser
- The University of Texas MD Anderson Cancer Center, Houston, USA
| | - John O Prior
- Department of Nuclear Medicine and Molecular Imaging, Lausanne University Hospital (CHUV), Rue du Bugnon 46, 1011 Lausanne, Switzerland
| | - Su Ruan
- Center Henri Becquerel, LITIS Laboratory, University of Rouen Normandy, Rouen, France
| | | | - Olena Tankyevych
- Centre Hospitalier Universitaire de Poitiers (CHUP), Poitiers, France
- LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France
| | | | - Martin Vallières
- Department of Computer Science, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Pierre Vera
- Center Henri Becquerel, LITIS Laboratory, University of Rouen Normandy, Rouen, France
| | | | - Kareem Wahid
- The University of Texas MD Anderson Cancer Center, Houston, USA
| | - Habib Zaidi
- Geneva University Hospital, Geneva, Switzerland
| | - Mathieu Hatt
- LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France
| | - Adrien Depeursinge
- Institute of Informatics, HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland, Sierre, Switzerland
- Department of Nuclear Medicine and Molecular Imaging, Lausanne University Hospital (CHUV), Rue du Bugnon 46, 1011 Lausanne, Switzerland
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15
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Duff LM, Scarsbrook AF, Ravikumar N, Frood R, van Praagh GD, Mackie SL, Bailey MA, Tarkin JM, Mason JC, van der Geest KSM, Slart RHJA, Morgan AW, Tsoumpas C. An Automated Method for Artifical Intelligence Assisted Diagnosis of Active Aortitis Using Radiomic Analysis of FDG PET-CT Images. Biomolecules 2023; 13:343. [PMID: 36830712 PMCID: PMC9953018 DOI: 10.3390/biom13020343] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 01/30/2023] [Accepted: 02/01/2023] [Indexed: 02/12/2023] Open
Abstract
The aim of this study was to develop and validate an automated pipeline that could assist the diagnosis of active aortitis using radiomic imaging biomarkers derived from [18F]-Fluorodeoxyglucose Positron Emission Tomography-Computed Tomography (FDG PET-CT) images. The aorta was automatically segmented by convolutional neural network (CNN) on FDG PET-CT of aortitis and control patients. The FDG PET-CT dataset was split into training (43 aortitis:21 control), test (12 aortitis:5 control) and validation (24 aortitis:14 control) cohorts. Radiomic features (RF), including SUV metrics, were extracted from the segmented data and harmonized. Three radiomic fingerprints were constructed: A-RFs with high diagnostic utility removing highly correlated RFs; B used principal component analysis (PCA); C-Random Forest intrinsic feature selection. The diagnostic utility was evaluated with accuracy and area under the receiver operating characteristic curve (AUC). Several RFs and Fingerprints had high AUC values (AUC > 0.8), confirmed by balanced accuracy, across training, test and external validation datasets. Good diagnostic performance achieved across several multi-centre datasets suggests that a radiomic pipeline can be generalizable. These findings could be used to build an automated clinical decision tool to facilitate objective and standardized assessment regardless of observer experience.
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Affiliation(s)
- Lisa M. Duff
- School of Medicine, University of Leeds, Leeds LS2 9JT, UK
- Institute of Medical and Biological Engineering, University of Leeds, Leeds LS2 9JT, UK
| | - Andrew F. Scarsbrook
- School of Medicine, University of Leeds, Leeds LS2 9JT, UK
- Department of Radiology, St. James University Hospital, Leeds LS9 7TF, UK
| | - Nishant Ravikumar
- School of Medicine, University of Leeds, Leeds LS2 9JT, UK
- Center for Computational Imaging and Simulation Technologies in Biomedicine, University of Leeds, Leeds LS2 9JT, UK
| | - Russell Frood
- School of Medicine, University of Leeds, Leeds LS2 9JT, UK
- Department of Radiology, St. James University Hospital, Leeds LS9 7TF, UK
| | - Gijs D. van Praagh
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, 9713 GZ Groningen, The Netherlands
| | - Sarah L. Mackie
- School of Medicine, University of Leeds, Leeds LS2 9JT, UK
- NIHR Leeds Biomedical Research Centre and NIHR Leeds MedTech and In Vitro Diagnostics Co-Operative, Leeds Teaching Hospitals NHS Trust, Leeds LS7 4SA, UK
| | - Marc A. Bailey
- School of Medicine, University of Leeds, Leeds LS2 9JT, UK
- The Leeds Vascular Institute, Leeds General Infirmary, Leeds LS2 9NS, UK
| | - Jason M. Tarkin
- Division of Cardiovascular Medicine, University of Cambridge, Cambridge CB2 0QQ, UK
| | - Justin C. Mason
- National Heart and Lung Institute, Imperial College London, London SW3 6LY, UK
| | - Kornelis S. M. van der Geest
- Department of Rheumatology and Clinical Immunology, University of Groningen, University Medical Center Groningen, 9713 GZ Groningen, The Netherlands
| | - Riemer H. J. A. Slart
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, 9713 GZ Groningen, The Netherlands
- Department of Biomedical Photonic Imaging, Faculty of Science and Technology, University of Twente, 7522 NB Enschede, The Netherlands
| | - Ann W. Morgan
- School of Medicine, University of Leeds, Leeds LS2 9JT, UK
- NIHR Leeds Biomedical Research Centre and NIHR Leeds MedTech and In Vitro Diagnostics Co-Operative, Leeds Teaching Hospitals NHS Trust, Leeds LS7 4SA, UK
| | - Charalampos Tsoumpas
- School of Medicine, University of Leeds, Leeds LS2 9JT, UK
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, 9713 GZ Groningen, The Netherlands
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16
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Xu H, Abdallah N, Marion JM, Chauvet P, Tauber C, Carlier T, Lu L, Hatt M. Radiomics prognostic analysis of PET/CT images in a multicenter head and neck cancer cohort: investigating ComBat strategies, sub-volume characterization, and automatic segmentation. Eur J Nucl Med Mol Imaging 2023; 50:1720-1734. [PMID: 36690882 DOI: 10.1007/s00259-023-06118-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Accepted: 01/16/2023] [Indexed: 01/25/2023]
Abstract
PURPOSE This study aimed to investigate the impact of several ComBat harmonization strategies, intra-tumoral sub-volume characterization, and automatic segmentations for progression-free survival (PFS) prediction through radiomics modeling for patients with head and neck cancer (HNC) in PET/CT images. METHODS The HECKTOR MICCAI 2021 challenge set containing PET/CT images and clinical data of 325 oropharynx HNC patients was exploited. A total of 346 IBSI-compliant radiomic features were extracted for each patient's primary tumor volume defined by the reference manual contours. Modeling relied on least absolute shrinkage Cox regression (Lasso-Cox) for feature selection (FS) and Cox proportional-hazards (CoxPH) models were built to predict PFS. Within this methodological framework, 8 different strategies for ComBat harmonization were compared, including before or after FS, in feature groups separately or all features directly, and with center or clustering-determined labels. Features extracted from tumor sub-volume clustering were also investigated for their prognostic additional value. Finally, 3 automatic segmentations (2 threshold-based and a 3D U-Net) were also compared. All results were evaluated with the concordance index (C-index). RESULTS Radiomics features without harmonization, combined with clinical factors, led to models with C-index values of 0.69 in the testing set. The best version of ComBat harmonization, i.e., after FS, for feature groups separately and relying on clustering-determined labels, achieved a C-index of 0.71. The use of features extracted from tumor sub-volumes further improved the C-index to 0.72. Models that relied on the automatic segmentations yielded close but slightly lower prognostic performance (0.67-0.70) compared to reference contours. CONCLUSION A standard radiomics pipeline allowed for prediction of PFS in a multicenter HNC cohort. Applying a specific strategy of ComBat harmonization improved the performance. The extraction of intra-tumoral sub-volume features and automatic segmentation could contribute to the improvement and automation of prognosis modeling, respectively.
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Affiliation(s)
- Hui Xu
- School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, 1023 Shatai Road, Guangzhou, 510515, Guangdong, China.,LaTIM, INSERM, UMR 1101, University Brest, Brest, France
| | | | | | | | - Clovis Tauber
- INSERM U930, Université François Rabelais de Tours, Tours, France
| | - Thomas Carlier
- Nuclear Medicine Department, CHU and CRCINA, INSERM, CNRS, Univ Angers, Univ Nantes, Nantes, France
| | - Lijun Lu
- School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, 1023 Shatai Road, Guangzhou, 510515, Guangdong, China. .,Pazhou Lab, Guangzhou, 510330, China.
| | - Mathieu Hatt
- LaTIM, INSERM, UMR 1101, University Brest, Brest, France
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17
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Hatt M, Krizsan AK, Rahmim A, Bradshaw TJ, Costa PF, Forgacs A, Seifert R, Zwanenburg A, El Naqa I, Kinahan PE, Tixier F, Jha AK, Visvikis D. Joint EANM/SNMMI guideline on radiomics in nuclear medicine : Jointly supported by the EANM Physics Committee and the SNMMI Physics, Instrumentation and Data Sciences Council. Eur J Nucl Med Mol Imaging 2023; 50:352-375. [PMID: 36326868 PMCID: PMC9816255 DOI: 10.1007/s00259-022-06001-6] [Citation(s) in RCA: 40] [Impact Index Per Article: 40.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Accepted: 10/09/2022] [Indexed: 11/05/2022]
Abstract
PURPOSE The purpose of this guideline is to provide comprehensive information on best practices for robust radiomics analyses for both hand-crafted and deep learning-based approaches. METHODS In a cooperative effort between the EANM and SNMMI, we agreed upon current best practices and recommendations for relevant aspects of radiomics analyses, including study design, quality assurance, data collection, impact of acquisition and reconstruction, detection and segmentation, feature standardization and implementation, as well as appropriate modelling schemes, model evaluation, and interpretation. We also offer an outlook for future perspectives. CONCLUSION Radiomics is a very quickly evolving field of research. The present guideline focused on established findings as well as recommendations based on the state of the art. Though this guideline recognizes both hand-crafted and deep learning-based radiomics approaches, it primarily focuses on the former as this field is more mature. This guideline will be updated once more studies and results have contributed to improved consensus regarding the application of deep learning methods for radiomics. Although methodological recommendations in the present document are valid for most medical image modalities, we focus here on nuclear medicine, and specific recommendations when necessary are made for PET/CT, PET/MR, and quantitative SPECT.
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Affiliation(s)
- M Hatt
- LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France
| | | | - A Rahmim
- Departments of Radiology and Physics, University of British Columbia, Vancouver, BC, Canada
| | - T J Bradshaw
- Department of Radiology, University of Wisconsin, Madison, WI, USA
| | - P F Costa
- Department of Nuclear Medicine, West German Cancer Center, University of Duisburg-Essen and German Cancer Consortium (DKTK)-University Hospital Essen, Essen, Germany
| | | | - R Seifert
- Department of Nuclear Medicine, West German Cancer Center, University of Duisburg-Essen and German Cancer Consortium (DKTK)-University Hospital Essen, Essen, Germany.
- Department of Nuclear Medicine, Münster University Hospital, Münster, Germany.
| | - A Zwanenburg
- OncoRay-National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany
- National Center for Tumor Diseases (NCT/UCC), Dresden, Germany
- German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - I El Naqa
- Department of Machine Learning, Moffitt Cancer Center, Tampa, FL, 33626, USA
| | - P E Kinahan
- Imaging Research Laboratory, PET/CT Physics, Department of Radiology, UW Medical Center, University of Washington, Seattle, WA, USA
| | - F Tixier
- LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France
| | - A K Jha
- McKelvey School of Engineering and Mallinckrodt Institute of Radiology, Washington University in St. Louis, Saint Louis, MO, USA
| | - D Visvikis
- LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France
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18
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Hannequin P, Decroisette C, Kermanach P, Berardi G, Bourbonne V. FDG PET and CT radiomics in diagnosis and prognosis of non-small-cell lung cancer. Transl Lung Cancer Res 2022; 11:2051-2063. [PMID: 36386457 PMCID: PMC9641045 DOI: 10.21037/tlcr-22-158] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 08/22/2022] [Indexed: 09/13/2023]
Abstract
BACKGROUND 18F-FDG PET and CT radiomics has been the object of a wide research for over 20 years but its contribution to clinical practice remains not yet well established. We have investigated its impact versus that of only histo-clinical data, for the routine management of non-small-cell lung cancer (NSCLC). METHODS Our patients were retrospectively considered. They all had a FDG PET-CT and immuno-histo-chemistry (IHC) to assess PD-L1 expression at the beginning of the disease. A prognosis univariate and multivariate Cox survival analyses was performed for overall survival (OS) and progression free survival (PFS) prediction, including a training/testing procedure. Two sets of 47 PET and 47 CT radiomics features (RFs) were extracted. Difference between RFs according to PD-L1 expression, the histology status and the stage level were tested using suited non parametric statistical tests and the receiver operating characteristics (ROC) curve and the area under curve (AUC). RESULTS From 2017 to 2019, 212 NSCLC patients treated in our institution were included. The main conventional prognostic variables were stage and gender with a low added prognostic value in the models including PET and CT RFs. Neither PET nor CT RFs were significant to separate the different levels of PD-L1 expression. Several RFs differ between adenocarcinoma (ADC) and squamous cell carcinoma (SCC) tumours and a large number of PET and CT RFs are significantly linked to patient stage. CONCLUSIONS In our population, PET and CT RFs show their intrinsic power to predict survival but do not significantly improve OS and PFS prediction in the different multivariate models, in comparison to conventional data. It would seem necessary to carry out one's own survival analysis before determining a radiomics signature.
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Affiliation(s)
- Pascal Hannequin
- Annecy Nuclear Medicine Center, Le Pericles, B Allée de la Mandallaz, Metz-Tessy, France
| | - Chantal Decroisette
- Pneumology Department, CHANGE Annecy, 1 Avenue de l’hôpital, Metz-Tessy, France
| | - Pascale Kermanach
- Mont Blanc Histo-Pathology Laboratory, 40 Route de l’Aiglière, Argonay, France
| | - Giulia Berardi
- Pneumology Department, University Hospital la Tronche, Boulevard de la Chantourne, La Tronche, France
| | - Vincent Bourbonne
- Radiation Oncology Department, University Hospital, 2 Avenue Foch, Brest, France
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19
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Schwenck J, Kneilling M, Riksen NP, la Fougère C, Mulder DJ, Slart RJHA, Aarntzen EHJG. A role for artificial intelligence in molecular imaging of infection and inflammation. Eur J Hybrid Imaging 2022; 6:17. [PMID: 36045228 PMCID: PMC9433558 DOI: 10.1186/s41824-022-00138-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Accepted: 05/16/2022] [Indexed: 12/03/2022] Open
Abstract
The detection of occult infections and low-grade inflammation in clinical practice remains challenging and much depending on readers’ expertise. Although molecular imaging, like [18F]FDG PET or radiolabeled leukocyte scintigraphy, offers quantitative and reproducible whole body data on inflammatory responses its interpretation is limited to visual analysis. This often leads to delayed diagnosis and treatment, as well as untapped areas of potential application. Artificial intelligence (AI) offers innovative approaches to mine the wealth of imaging data and has led to disruptive breakthroughs in other medical domains already. Here, we discuss how AI-based tools can improve the detection sensitivity of molecular imaging in infection and inflammation but also how AI might push the data analysis beyond current application toward predicting outcome and long-term risk assessment.
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20
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Visvikis D, Lambin P, Beuschau Mauridsen K, Hustinx R, Lassmann M, Rischpler C, Shi K, Pruim J. Application of artificial intelligence in nuclear medicine and molecular imaging: a review of current status and future perspectives for clinical translation. Eur J Nucl Med Mol Imaging 2022; 49:4452-4463. [PMID: 35809090 PMCID: PMC9606092 DOI: 10.1007/s00259-022-05891-w] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Accepted: 06/25/2022] [Indexed: 02/06/2023]
Abstract
Artificial intelligence (AI) will change the face of nuclear medicine and molecular imaging as it will in everyday life. In this review, we focus on the potential applications of AI in the field, both from a physical (radiomics, underlying statistics, image reconstruction and data analysis) and a clinical (neurology, cardiology, oncology) perspective. Challenges for transferability from research to clinical practice are being discussed as is the concept of explainable AI. Finally, we focus on the fields where challenges should be set out to introduce AI in the field of nuclear medicine and molecular imaging in a reliable manner.
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Affiliation(s)
| | - Philippe Lambin
- The D-Lab, Department of Precision Medicine, GROW - School for Oncology, Maastricht University Medical Center (MUMC +), Maastricht, The Netherlands.,Department of Radiology and Nuclear Medicine, GROW - School for Oncology, Maastricht University Medical Center (MUMC +), Maastricht, The Netherlands
| | - Kim Beuschau Mauridsen
- Center of Functionally Integrative Neuroscience and MindLab, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark.,Department of Nuclear Medicine, University of Bern, Bern, Switzerland
| | - Roland Hustinx
- GIGA-CRC in Vivo Imaging, University of Liège, GIGA, Avenue de l'Hôpital 11, 4000, Liege, Belgium
| | - Michael Lassmann
- Klinik Und Poliklinik Für Nuklearmedizin, Universitätsklinikum Würzburg, Würzburg, Germany
| | - Christoph Rischpler
- Department of Nuclear Medicine, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Kuangyu Shi
- Department of Nuclear Medicine, University of Bern, Bern, Switzerland.,Department of Informatics, Technical University of Munich, Munich, Germany
| | - Jan Pruim
- Medical Imaging Center, Dept. of Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
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21
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Léost F, Delpon G, Garcion E, Gestin JF, Hatt M, Potiron V, Rbah-Vidal L, Supiot S. « Adaptation of the tumour and its ecosystem to radiotherapies: Mechanisms, imaging and therapeutic approaches » XIVe édition du workshop organisé par le réseau « Vectorisation, Imagerie, Radiothérapies » du Cancéropôle Grand-Ouest, 22–25 septembre 2021, Le Bono, France. Bull Cancer 2022; 109:1088-1093. [DOI: 10.1016/j.bulcan.2022.06.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 06/03/2022] [Accepted: 06/07/2022] [Indexed: 10/16/2022]
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22
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Salihoğlu YS, Uslu Erdemir R, Aydur Püren B, Özdemir S, Uyulan Ç, Ergüzel TT, Tekin HO. Diagnostic Performance of Machine Learning Models Based on 18F-FDG PET/CT Radiomic Features in the Classification of Solitary Pulmonary Nodules. Mol Imaging Radionucl Ther 2022; 31:82-88. [PMID: 35770958 PMCID: PMC9246312 DOI: 10.4274/mirt.galenos.2021.43760] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022] Open
Abstract
Objectives This study aimed to evaluate the ability of 18fluorine-fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) radiomic features combined with machine learning methods to distinguish between benign and malignant solitary pulmonary nodules (SPN). Methods Data of 48 patients with SPN detected on 18F-FDG PET/CT scan were evaluated retrospectively. The texture feature extraction from PET/CT images was performed using an open-source application (LIFEx). Deep learning and classical machine learning algorithms were used to build the models. Final diagnosis was confirmed by pathology and follow-up was accepted as the reference. The performances of the models were assessed by the following metrics: Sensitivity, specificity, accuracy, and area under the receiver operator characteristic curve (AUC). Results The predictive models provided reasonable performance for the differential diagnosis of SPNs (AUCs ~0.81). The accuracy and AUC of the radiomic models were similar to the visual interpretation. However, when compared to the conventional evaluation, the sensitivity of the deep learning model (88% vs. 83%) and specificity of the classic learning model were higher (86% vs. 79%). Conclusion Machine learning based on 18F-FDG PET/CT texture features can contribute to the conventional evaluation to distinguish between benign and malignant lung nodules.
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Affiliation(s)
- Yavuz Sami Salihoğlu
- Çanakkale Onsekiz Mart University Faculty of Medicine, Department of Nuclear Medicine, Çanakkale, Turkey
| | - Rabiye Uslu Erdemir
- Zonguldak Bülent Ecevit University Faculty of Medicine, Department of Nuclear Medicine, Zonguldak, Turkey
| | - Büşra Aydur Püren
- Çanakkale Onsekiz Mart University Faculty of Medicine, Department of Nuclear Medicine, Çanakkale, Turkey
| | - Semra Özdemir
- Çanakkale Onsekiz Mart University Faculty of Medicine, Department of Nuclear Medicine, Çanakkale, Turkey
| | - Çağlar Uyulan
- İzmir Katip Çelebi University Faculty of Engineering and Architecture, Department of Mechanical Engineering, İzmir, Turkey
| | - Türker Tekin Ergüzel
- Üsküdar University Faculty of Natural Sciences, Department of Software Engineering, İstanbul, Turkey
| | - Hüseyin Ozan Tekin
- University of Sharjah, College of Health Sciences, Department of Medical Diagnostic Imaging, Sharjah, United Arab Emirates
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23
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Manafi-Farid R, Askari E, Shiri I, Pirich C, Asadi M, Khateri M, Zaidi H, Beheshti M. [ 18F]FDG-PET/CT radiomics and artificial intelligence in lung cancer: Technical aspects and potential clinical applications. Semin Nucl Med 2022; 52:759-780. [PMID: 35717201 DOI: 10.1053/j.semnuclmed.2022.04.004] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 04/10/2022] [Accepted: 04/13/2022] [Indexed: 02/07/2023]
Abstract
Lung cancer is the second most common cancer and the leading cause of cancer-related death worldwide. Molecular imaging using [18F]fluorodeoxyglucose Positron Emission Tomography and/or Computed Tomography ([18F]FDG-PET/CT) plays an essential role in the diagnosis, evaluation of response to treatment, and prediction of outcomes. The images are evaluated using qualitative and conventional quantitative indices. However, there is far more information embedded in the images, which can be extracted by sophisticated algorithms. Recently, the concept of uncovering and analyzing the invisible data extracted from medical images, called radiomics, is gaining more attention. Currently, [18F]FDG-PET/CT radiomics is growingly evaluated in lung cancer to discover if it enhances the diagnostic performance or implication of [18F]FDG-PET/CT in the management of lung cancer. In this review, we provide a short overview of the technical aspects, as they are discussed in different articles of this special issue. We mainly focus on the diagnostic performance of the [18F]FDG-PET/CT-based radiomics and the role of artificial intelligence in non-small cell lung cancer, impacting the early detection, staging, prediction of tumor subtypes, biomarkers, and patient's outcomes.
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Affiliation(s)
- Reyhaneh Manafi-Farid
- Research Center for Nuclear Medicine, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Emran Askari
- Department of Nuclear Medicine, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
| | - Christian Pirich
- Division of Molecular Imaging and Theranostics, Department of Nuclear Medicine, University Hospital Salzburg, Paracelsus Medical University, Salzburg, Austria
| | - Mahboobeh Asadi
- Research Center for Nuclear Medicine, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Maziar Khateri
- Research Center for Nuclear Medicine, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland; Geneva University Neurocenter, Geneva University, Geneva, Switzerland; Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands; Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark
| | - Mohsen Beheshti
- Division of Molecular Imaging and Theranostics, Department of Nuclear Medicine, University Hospital Salzburg, Paracelsus Medical University, Salzburg, Austria.
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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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Morland D, Triumbari EKA, Boldrini L, Gatta R, Pizzuto D, Annunziata S. Radiomics in Oncological PET Imaging: A Systematic Review-Part 2, Infradiaphragmatic Cancers, Blood Malignancies, Melanoma and Musculoskeletal Cancers. Diagnostics (Basel) 2022; 12:diagnostics12061330. [PMID: 35741139 PMCID: PMC9222024 DOI: 10.3390/diagnostics12061330] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 05/19/2022] [Accepted: 05/20/2022] [Indexed: 12/04/2022] Open
Abstract
The objective of this review was to summarize published radiomics studies dealing with infradiaphragmatic cancers, blood malignancies, melanoma, and musculoskeletal cancers, and assess their quality. PubMed database was searched from January 1990 to February 2022 for articles performing radiomics on PET imaging of at least 1 specified tumor type. Exclusion criteria includd: non-oncological studies; supradiaphragmatic tumors; reviews, comments, cases reports; phantom or animal studies; technical articles without a clinically oriented question; studies including <30 patients in the training cohort. The review database contained PMID, first author, year of publication, cancer type, number of patients, study design, independent validation cohort and objective. This database was completed twice by the same person; discrepant results were resolved by a third reading of the articles. A total of 162 studies met inclusion criteria; 61 (37.7%) studies included >100 patients, 13 (8.0%) were prospective and 61 (37.7%) used an independent validation set. The most represented cancers were esophagus, lymphoma, and cervical cancer (n = 24, n = 24 and n = 19 articles, respectively). Most studies focused on 18F-FDG, and prognostic and response to treatment objectives. Although radiomics and artificial intelligence are technically challenging, new contributions and guidelines help improving research quality over the years and pave the way toward personalized medicine.
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Affiliation(s)
- David Morland
- Unità di Medicina Nucleare, TracerGLab, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Roma, 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
- Unità di Medicina Nucleare, TracerGLab, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Roma, Italy; (E.K.A.T.); (D.P.); (S.A.)
| | - Luca Boldrini
- Unità di Radioterapia Oncologica, Radiomics, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Roma, Italy; (L.B.); (R.G.)
| | - Roberto Gatta
- Unità di Radioterapia Oncologica, Radiomics, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Roma, 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
- Unità di Medicina Nucleare, TracerGLab, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Roma, Italy; (E.K.A.T.); (D.P.); (S.A.)
| | - Salvatore Annunziata
- Unità di Medicina Nucleare, TracerGLab, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Roma, Italy; (E.K.A.T.); (D.P.); (S.A.)
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Filippi L, Bianconi F, Schillaci O, Spanu A, Palumbo B. The Role and Potential of 18F-FDG PET/CT in Malignant Melanoma: Prognostication, Monitoring Response to Targeted and Immunotherapy, and Radiomics. Diagnostics (Basel) 2022; 12:diagnostics12040929. [PMID: 35453977 PMCID: PMC9028862 DOI: 10.3390/diagnostics12040929] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2022] [Revised: 04/06/2022] [Accepted: 04/07/2022] [Indexed: 12/17/2022] Open
Abstract
Novel therapeutic approaches, consisting of immune check-point inhibitors (ICIs) and molecularly targeted therapy, have thoroughly changed the clinical management of malignant melanoma (MM), the most frequent and deadly skin cancer. Since only 30–40% of MM patients respond to ICIs, imaging biomarkers suitable for the pre-therapeutic stratification and response assessment are warmly welcome. In this scenario, positron emission computed tomography (PET/CT) with 18F-fluorodeoxyglucose (18F-FDG) has been successfully utilized for advanced MM staging and therapy response evaluation. Furthermore, several PET-derived parameters (SUVmax, MTV, TLG) were particularly impactful for the prognostic evaluation of patients submitted to targeted and immunotherapy. In this review, we performed a web-based and desktop research on the clinical applications of 18F-FDG PET/CT in MM, with a particular emphasis on the various metabolic criteria developed for interpreting PET/CT scan in patients undergoing immunotherapy or targeted therapy or a combination of both. Furthermore, the emerging role of radiomics, a quantitative approach to medical imaging applying analysis methodology derived by the field of artificial intelligence, was examined in the peculiar context, putting a particular emphasis on the potential of this discipline to support clinicians in the delicate process of building patient-tailored pathways of care.
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Affiliation(s)
- Luca Filippi
- Nuclear Medicine Unit, “Santa Maria Goretti” Hospital, Via Antonio Canova, 04100 Latina, Italy
- Correspondence: ; Tel.: +39-077-3655-3591
| | - Francesco Bianconi
- Department of Engineering, Università Degli Studi di Perugia, Via Goffredo Duranti 93, 06135 Perugia, Italy;
| | - Orazio Schillaci
- Department of Biomedicine and Prevention, University Tor Vergata, Viale Oxford 81, 00133 Rome, Italy;
- IRCCS Neuromed, 86077 Pozzilli, Italy
| | - Angela Spanu
- Unit of Nuclear Medicine, Department of Medical, Surgical and Experimental Sciences, University of Sassari, Viale San Pietro 8, 07100 Sassari, Italy;
| | - Barbara Palumbo
- Section of Nuclear Medicine and Health Physics, Department of Medicine and Surgery, Università Degli Studi di Perugia, Piazza Lucio Severi 1, 06132 Perugia, Italy;
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Jiang C, Huang X, Li A, Teng Y, Ding C, Chen J, Xu J, Zhou Z. Radiomics signature from [ 18F]FDG PET images for prognosis predication of primary gastrointestinal diffuse large B cell lymphoma. Eur Radiol 2022; 32:5730-5741. [PMID: 35298676 DOI: 10.1007/s00330-022-08668-9] [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: 10/18/2021] [Revised: 02/13/2022] [Accepted: 02/17/2022] [Indexed: 11/26/2022]
Abstract
OBJECTIVES To investigate the prognostic value of PET radiomics feature in the prognosis of patients with primary gastrointestinal diffuse large B cell lymphoma (PGI-DLBCL) treated with R-CHOP-like regimen. METHODS A total of 140 PGI-DLBCL patients who underwent pre-therapy [18F] FDG PET/CT were enrolled in this retrospective analysis. PET radiomics features obtained from patients in the training cohort were subjected to three machine learning methods and Pearson's correlation test for feature selection. Support vector machine (SVM) was used to build a radiomics signature classifier associated with progression-free survival (PFS) and overall survival (OS). A multivariate Cox proportional hazards regression model was established to predict survival outcomes. RESULTS A total of 1421 PET radiomics features were extracted and reduced to 5 features to build a radiomics signature which was significantly associated with PFS and OS (p < 0.05). The combined model incorporating radiomics signatures, metabolic metrics, and clinical risk factors showed high C-indices in both the training (PFS: 0.825, OS: 0.834) and validation sets (PFS: 0.831, OS: 0.877). Decision curve analysis (DCA) demonstrated that the combined models achieved the most net benefit across a wider reasonable range of threshold probabilities for predicting PFS and OS. CONCLUSION The newly developed radiomics signatures obtained by the ensemble strategy were independent predictors of PFS and OS for PGI-DLBCL patients. Moreover, the combined model with clinical and metabolic factors was able to predict patient prognosis and may enable personalized treatment decision-making. KEY POINTS • Radiomics signatures generated from the optimal radiomics feature set from the [18F]FDG PET images can predict the survival of PGI-DLBCL patients. • The optimal radiomics feature set is constructed by integrating the feature selection outputs of LASSO, RF, Xgboost, and PC methods. • Combined models incorporating radiomics signatures from18F-FDG PET images, metabolic parameters, and clinical factors outperformed clinical models, and NCCN-IPI.
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Affiliation(s)
- Chong Jiang
- Department of Nuclear Medicine, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, 210000, China
| | - Xiangjun Huang
- The Key Laboratory of Broadband Wireless Communication and Sensor Network Technology (Ministry of Education), Nanjing University of Posts and Telecommunications, Nanjing, China
| | - Ang Li
- The Key Laboratory of Broadband Wireless Communication and Sensor Network Technology (Ministry of Education), Nanjing University of Posts and Telecommunications, Nanjing, China
| | - Yue Teng
- Department of Nuclear Medicine, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, 210000, China
| | - Chongyang Ding
- Department of Nuclear Medicine, The First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing, China
| | - Jianxin Chen
- The Key Laboratory of Broadband Wireless Communication and Sensor Network Technology (Ministry of Education), Nanjing University of Posts and Telecommunications, Nanjing, China
| | - Jingyan Xu
- Department of Hematology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, No. 321, Zhongshan Road, Nanjing City, Jiangsu Province, 210008, China.
| | - Zhengyang Zhou
- Department of Nuclear Medicine, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, 210000, China.
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Moummad I, Jaudet C, Lechervy A, Valable S, Raboutet C, Soilihi Z, Thariat J, Falzone N, Lacroix J, Batalla A, Corroyer-Dulmont A. The Impact of Resampling and Denoising Deep Learning Algorithms on Radiomics in Brain Metastases MRI. Cancers (Basel) 2021; 14:cancers14010036. [PMID: 35008198 PMCID: PMC8750741 DOI: 10.3390/cancers14010036] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 12/14/2021] [Accepted: 12/18/2021] [Indexed: 01/06/2023] Open
Abstract
Simple Summary Due to the central role of magnetic resonance Imaging (MRI) in the management of patients with cancer, waiting lists exceed clinically relevant delays. For this reason, many research groups and MRI manufacturers develop algorithms as resampling and denoising models to allow faster acquisition time without deterioration in image quality. Whereas these algorithms are available in all new MRI, it is not clear how they will impact image features as well as the validity of statistical model of radiomics which use deep images characteristics to predict treatment outcome. The aim of this study was to develop resampling and denoising deep learning (DL) models and evaluate their impact on radiomics from post-Gd-T1w-MRI brain images with brain metastases. We show that resampling and denoising DL models reconstruct low resolution and noised MRI images acquired quickly into high quality images. While fast acquisition loses most of the radiomic-features and invalidates predictive radiomic models, DL models restore these parameters. Abstract Background: Magnetic resonance imaging (MRI) is predominant in the therapeutic management of cancer patients, unfortunately, patients have to wait a long time to get an appointment for examination. Therefore, new MRI devices include deep-learning (DL) solutions to save acquisition time. However, the impact of these algorithms on intensity and texture parameters has been poorly studied. The aim of this study was to evaluate the impact of resampling and denoising DL models on radiomics. Methods: Resampling and denoising DL model was developed on 14,243 T1 brain images from 1.5T-MRI. Radiomics were extracted from 40 brain metastases from 11 patients (2049 images). A total of 104 texture features of DL images were compared to original images with paired t-test, Pearson correlation and concordance-correlation-coefficient (CCC). Results: When two times shorter image acquisition shows strong disparities with the originals concerning the radiomics, with significant differences and loss of correlation of 79.81% and 48.08%, respectively. Interestingly, DL models restore textures with 46.15% of unstable parameters and 25.96% of low CCC and without difference for the first-order intensity parameters. Conclusions: Resampling and denoising DL models reconstruct low resolution and noised MRI images acquired quickly into high quality images. While fast MRI acquisition loses most of the radiomic features, DL models restore these parameters.
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Affiliation(s)
- Ilyass Moummad
- Medical Physics Department, CLCC François Baclesse, 14000 Caen, France; (I.M.); (C.J.); (Z.S.); (A.B.)
| | - Cyril Jaudet
- Medical Physics Department, CLCC François Baclesse, 14000 Caen, France; (I.M.); (C.J.); (Z.S.); (A.B.)
| | - Alexis Lechervy
- UMR GREYC, Normandie University, UNICAEN, ENSICAEN, CNRS, 14000 Caen, France;
| | - Samuel Valable
- ISTCT/CERVOxy Group, Normandie University, UNICAEN, CEA, CNRS, 14000 Caen, France;
| | - Charlotte Raboutet
- Radiology Department, CLCC François Baclesse, 14000 Caen, France; (C.R.); (J.L.)
| | - Zamila Soilihi
- Medical Physics Department, CLCC François Baclesse, 14000 Caen, France; (I.M.); (C.J.); (Z.S.); (A.B.)
| | - Juliette Thariat
- Radiotherapy Department, CLCC François Baclesse, 14000 Caen, France;
| | - Nadia Falzone
- GenesisCare Theranostics, Building 1 & 11, The Mill, 41-43 Bourke Road, Alexandria, NSW 2015, Australia;
| | - Joëlle Lacroix
- Radiology Department, CLCC François Baclesse, 14000 Caen, France; (C.R.); (J.L.)
| | - Alain Batalla
- Medical Physics Department, CLCC François Baclesse, 14000 Caen, France; (I.M.); (C.J.); (Z.S.); (A.B.)
| | - Aurélien Corroyer-Dulmont
- Medical Physics Department, CLCC François Baclesse, 14000 Caen, France; (I.M.); (C.J.); (Z.S.); (A.B.)
- ISTCT/CERVOxy Group, Normandie University, UNICAEN, CEA, CNRS, 14000 Caen, France;
- Correspondence:
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Yousefirizi F, Pierre Decazes, Amyar A, Ruan S, Saboury B, Rahmim A. AI-Based Detection, Classification and Prediction/Prognosis in Medical Imaging:: Towards Radiophenomics. PET Clin 2021; 17:183-212. [PMID: 34809866 DOI: 10.1016/j.cpet.2021.09.010] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Artificial intelligence (AI) techniques have significant potential to enable effective, robust, and automated image phenotyping including the identification of subtle patterns. AI-based detection searches the image space to find the regions of interest based on patterns and features. There is a spectrum of tumor histologies from benign to malignant that can be identified by AI-based classification approaches using image features. The extraction of minable information from images gives way to the field of "radiomics" and can be explored via explicit (handcrafted/engineered) and deep radiomics frameworks. Radiomics analysis has the potential to be used as a noninvasive technique for the accurate characterization of tumors to improve diagnosis and treatment monitoring. This work reviews AI-based techniques, with a special focus on oncological PET and PET/CT imaging, for different detection, classification, and prediction/prognosis tasks. We also discuss needed efforts to enable the translation of AI techniques to routine clinical workflows, and potential improvements and complementary techniques such as the use of natural language processing on electronic health records and neuro-symbolic AI techniques.
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Affiliation(s)
- Fereshteh Yousefirizi
- Department of Integrative Oncology, BC Cancer Research Institute, 675 West 10th Avenue, Vancouver, British Columbia V5Z 1L3, Canada.
| | - Pierre Decazes
- Department of Nuclear Medicine, Henri Becquerel Centre, Rue d'Amiens - CS 11516 - 76038 Rouen Cedex 1, France; QuantIF-LITIS, Faculty of Medicine and Pharmacy, Research Building - 1st floor, 22 boulevard Gambetta, 76183 Rouen Cedex, France
| | - Amine Amyar
- QuantIF-LITIS, Faculty of Medicine and Pharmacy, Research Building - 1st floor, 22 boulevard Gambetta, 76183 Rouen Cedex, France; General Electric Healthcare, Buc, France
| | - Su Ruan
- QuantIF-LITIS, Faculty of Medicine and Pharmacy, Research Building - 1st floor, 22 boulevard Gambetta, 76183 Rouen Cedex, France
| | - Babak Saboury
- Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD, USA; Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, Baltimore, MD, USA; Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, PA, USA
| | - Arman Rahmim
- Department of Integrative Oncology, BC Cancer Research Institute, 675 West 10th Avenue, Vancouver, British Columbia V5Z 1L3, Canada; Department of Radiology, University of British Columbia, Vancouver, British Columbia, Canada; Department of Physics, University of British Columbia, Vancouver, British Columbia, Canada
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Sepehri S, Tankyevych O, Iantsen A, Visvikis D, Hatt M, Cheze Le Rest C. Accurate Tumor Delineation vs. Rough Volume of Interest Analysis for 18F-FDG PET/CT Radiomics-Based Prognostic Modeling inNon-Small Cell Lung Cancer. Front Oncol 2021; 11:726865. [PMID: 34733779 PMCID: PMC8560021 DOI: 10.3389/fonc.2021.726865] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Accepted: 09/20/2021] [Indexed: 12/24/2022] Open
Abstract
Background The aim of this work was to investigate the ability of building prognostic models in non-small cell lung cancer (NSCLC) using radiomic features from positron emission tomography and computed tomography with 2-deoxy-2-[fluorine-18]fluoro-d-glucose (18F-FDG PET/CT) images based on a "rough" volume of interest (VOI) containing the tumor instead of its accurate delineation, which is a significant time-consuming bottleneck of radiomics analyses. Methods A cohort of 138 patients with stage II-III NSCLC treated with radiochemotherapy recruited retrospectively (n = 87) and prospectively (n = 51) was used. Two approaches were compared: firstly, the radiomic features were extracted from the delineated primary tumor volumes in both PET (using the automated fuzzy locally adaptive Bayesian, FLAB) and CT (using a semi-automated approach with 3D Slicer™) components. Both delineations were carried out within previously manually defined "rough" VOIs containing the tumor and the surrounding tissues, which were exploited for the second approach: the same features were extracted from this alternative VOI. Both sets for features were then combined with the clinical variables and processed through the same machine learning (ML) pipelines using the retrospectively recruited patients as the training set and the prospectively recruited patients as the testing set. Logistic regression (LR), random forest (RF), and support vector machine (SVM), as well as their consensus through averaging the output probabilities, were considered for feature selection and modeling for overall survival (OS) prediction as a binary classification (either median OS or 6 months OS). The resulting models were compared in terms of balanced accuracy, sensitivity, and specificity. Results Overall, better performance was achieved using the features from delineated tumor volumes. This was observed consistently across ML algorithms and for the two clinical endpoints. However, the loss of performance was not significant, especially when a consensus of the three ML algorithms was considered (0.89 vs. 0.88 and 0.78 vs. 0.77). Conclusion Our findings suggest that it is feasible to achieve similar levels of prognostic accuracy in radiomics-based modeling by relying on a faster and easier VOI definition, skipping a time-consuming tumor delineation step, thus facilitating automation of the whole radiomics workflow. The associated cost is a loss of performance in the resulting models, although this loss can be greatly mitigated when a consensus of several models is relied upon.
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Affiliation(s)
| | - Olena Tankyevych
- LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France.,University Hospital Poitiers, Nuclear Medicine Department, Poitiers, France
| | | | | | - Mathieu Hatt
- LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France
| | - Catherine Cheze Le Rest
- LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France.,University Hospital Poitiers, Nuclear Medicine Department, Poitiers, France
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Shao X, Niu R, Shao X, Gao J, Shi Y, Jiang Z, Wang Y. Application of dual-stream 3D convolutional neural network based on 18F-FDG PET/CT in distinguishing benign and invasive adenocarcinoma in ground-glass lung nodules. EJNMMI Phys 2021; 8:74. [PMID: 34727258 PMCID: PMC8561359 DOI: 10.1186/s40658-021-00423-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2021] [Accepted: 10/25/2021] [Indexed: 12/31/2022] Open
Abstract
Purpose This work aims to train, validate, and test a dual-stream three-dimensional convolutional neural network (3D-CNN) based on fluorine 18 (18F)-fluorodeoxyglucose (FDG) PET/CT to distinguish benign lesions and invasive adenocarcinoma (IAC) in ground-glass nodules (GGNs). Methods We retrospectively analyzed patients with suspicious GGNs who underwent 18F-FDG PET/CT in our hospital from November 2011 to November 2020. The patients with benign lesions or IAC were selected for this study. According to the ratio of 7:3, the data were randomly divided into training data and testing data. Partial image feature extraction software was used to segment PET and CT images, and the training data after using the data augmentation were used for the training and validation (fivefold cross-validation) of the three CNNs (PET, CT, and PET/CT networks). Results A total of 23 benign nodules and 92 IAC nodules from 106 patients were included in this study. In the training set, the performance of PET network (accuracy, sensitivity, and specificity of 0.92 ± 0.02, 0.97 ± 0.03, and 0.76 ± 0.15) was better than the CT network (accuracy, sensitivity, and specificity of 0.84 ± 0.03, 0.90 ± 0.07, and 0.62 ± 0.16) (especially accuracy was significant, P-value was 0.001); in the testing set, the performance of both networks declined. However, the accuracy and sensitivity of PET network were still higher than that of CT network (0.76 vs. 0.67; 0.85 vs. 0.70). For dual-stream PET/CT network, its performance was almost the same as PET network in the training set (P-value was 0.372–1.000), while in the testing set, although its performance decreased, the accuracy and sensitivity (0.85 and 0.96) were still higher than both CT and PET networks. Moreover, the accuracy of PET/CT network was higher than two nuclear medicine physicians [physician 1 (3-year experience): 0.70 and physician 2 (10-year experience): 0.73]. Conclusion The 3D-CNN based on 18F-FDG PET/CT can be used to distinguish benign lesions and IAC in GGNs, and the performance is better when both CT and PET images are used together. Supplementary Information The online version contains supplementary material available at 10.1186/s40658-021-00423-1.
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Affiliation(s)
- Xiaonan Shao
- Department of Nuclear Medicine, The Third Affiliated Hospital of Soochow University, Changzhou, 213003, China.,Changzhou Key Laboratory of Molecular Imaging, Changzhou, 213003, China
| | - Rong Niu
- Department of Nuclear Medicine, The Third Affiliated Hospital of Soochow University, Changzhou, 213003, China.,Changzhou Key Laboratory of Molecular Imaging, Changzhou, 213003, China
| | - Xiaoliang Shao
- Department of Nuclear Medicine, The Third Affiliated Hospital of Soochow University, Changzhou, 213003, China.,Changzhou Key Laboratory of Molecular Imaging, Changzhou, 213003, China
| | - Jianxiong Gao
- Department of Nuclear Medicine, The Third Affiliated Hospital of Soochow University, Changzhou, 213003, China.,Changzhou Key Laboratory of Molecular Imaging, Changzhou, 213003, China
| | - Yunmei Shi
- Department of Nuclear Medicine, The Third Affiliated Hospital of Soochow University, Changzhou, 213003, China.,Changzhou Key Laboratory of Molecular Imaging, Changzhou, 213003, China
| | - Zhenxing Jiang
- Department of Radiology, The Third Affiliated Hospital of Soochow University, Changzhou, 213003, China
| | - Yuetao Wang
- Department of Nuclear Medicine, The Third Affiliated Hospital of Soochow University, Changzhou, 213003, China. .,Changzhou Key Laboratory of Molecular Imaging, Changzhou, 213003, China.
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Jaudet C, Weyts K, Lechervy A, Batalla A, Bardet S, Corroyer-Dulmont A. The Impact of Artificial Intelligence CNN Based Denoising on FDG PET Radiomics. Front Oncol 2021; 11:692973. [PMID: 34504782 PMCID: PMC8421788 DOI: 10.3389/fonc.2021.692973] [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: 04/09/2021] [Accepted: 07/26/2021] [Indexed: 12/22/2022] Open
Abstract
Background With a constantly increasing number of diagnostic images performed each year, Artificial Intelligence (AI) denoising methods offer an opportunity to respond to the growing demand. However, it may affect information in the image in an unknown manner. This study quantifies the effect of AI-based denoising on FDG PET textural information in comparison to a convolution with a standard gaussian postfilter (EARL1). Methods The study was carried out on 113 patients who underwent a digital FDG PET/CT (VEREOS, Philips Healthcare). 101 FDG avid lesions were segmented semi-automatically by a nuclear medicine physician. VOIs in the liver and lung as reference organs were contoured. PET textural features were extracted with pyradiomics. Texture features from AI denoised and EARL1 versus original PET images were compared with a Concordance Correlation Coefficient (CCC). Features with CCC values ≥ 0.85 threshold were considered concordant. Scatter plots of variable pairs with R2 coefficients of the more relevant features were computed. A Wilcoxon signed rank test to compare the absolute values between AI denoised and original images was performed. Results The ratio of concordant features was 90/104 (86.5%) in AI denoised versus 46/104 (44.2%) with EARL1 denoising. In the reference organs, the concordant ratio for AI and EARL1 denoised images was low, respectively 12/104 (11.5%) and 7/104 (6.7%) in the liver, 26/104 (25%) and 24/104 (23.1%) in the lung. SUVpeak was stable after the application of both algorithms in comparison to SUVmax. Scatter plots of variable pairs showed that AI filtering affected more lower versus high intensity regions unlike EARL1 gaussian post filters, affecting both in a similar way. In lesions, the majority of texture features 79/100 (79%) were significantly (p<0.05) different between AI denoised and original PET images. Conclusions Applying an AI-based denoising on FDG PET images maintains most of the lesion’s texture information in contrast to EARL1-compatible Gaussian filter. Predictive features of a trained model could be thus the same, however with an adapted threshold. Artificial intelligence based denoising in PET is a very promising approach as it adapts the denoising in function of the tissue type, preserving information where it should.
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Affiliation(s)
- Cyril Jaudet
- Medical Physics Department, CLCC François Baclesse, Caen, France
| | - Kathleen Weyts
- Nuclear Medicine Department, CLCC François Baclesse, Caen, France
| | - Alexis Lechervy
- UMR GREYC, Normandie Univ, UNICAEN, ENSICAEN, CNRS, Caen, France
| | - Alain Batalla
- Medical Physics Department, CLCC François Baclesse, Caen, France
| | - Stéphane Bardet
- Nuclear Medicine Department, CLCC François Baclesse, Caen, France
| | - Aurélien Corroyer-Dulmont
- Medical Physics Department, CLCC François Baclesse, Caen, France.,Normandie Univ, UNICAEN, CEA, CNRS, ISTCT/CERVOxy group, GIP CYCERON, Caen, France
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Bouchelouche K, Sathekge MM. Letter from the Editors. Semin Nucl Med 2021; 51:99-101. [PMID: 33509375 DOI: 10.1053/j.semnuclmed.2020.11.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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