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Haag F, Hertel A, Tharmaseelan H, Kuru M, Haselmann V, Brochhausen C, Schönberg SO, Froelich MF. Imaging-based characterization of tumoral heterogeneity for personalized cancer treatment. ROFO-FORTSCHR RONTG 2024; 196:262-272. [PMID: 37944935 DOI: 10.1055/a-2175-4622] [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: 11/12/2023]
Abstract
With personalized tumor therapy, understanding and addressing the heterogeneity of malignant tumors is becoming increasingly important. Heterogeneity can be found within one lesion (intralesional) and between several tumor lesions emerging from one primary tumor (interlesional). The heterogeneous tumor cells may show a different response to treatment due to their biology, which in turn influences the outcome of the affected patients and the choice of therapeutic agents. Therefore, both intra- and interlesional heterogeneity should be addressed at the diagnostic stage. While genetic and biological heterogeneity are important parameters in molecular tumor characterization and in histopathology, they are not yet addressed routinely in medical imaging. This article summarizes the recently established markers for tumor heterogeneity in imaging as well as heterogeneous/mixed response to therapy. Furthermore, a look at emerging markers is given. The ultimate goal of this overview is to provide comprehensive understanding of tumor heterogeneity and its implications for radiology and for communication with interdisciplinary teams in oncology. KEY POINTS:: · Tumor heterogeneity can be described within one lesion (intralesional) or between several lesions (interlesional).. · The heterogeneous biology of tumor cells can lead to a mixed therapeutic response and should be addressed in diagnostics and the therapeutic regime.. · Quantitative image diagnostics can be enhanced using AI, improved histopathological methods, and liquid profiling in the future..
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Affiliation(s)
- Florian Haag
- Institute of Clinical Radiology and Nuclear Medicine, University Medical Centre Mannheim, Germany
| | - Alexander Hertel
- Institute of Clinical Radiology and Nuclear Medicine, University Medical Centre Mannheim, Germany
| | - Hishan Tharmaseelan
- Institute of Clinical Radiology and Nuclear Medicine, University Medical Centre Mannheim, Germany
| | - Mustafa Kuru
- Institute of Clinical Radiology and Nuclear Medicine, University Medical Centre Mannheim, Germany
| | - Verena Haselmann
- Institute of Clinical Chemistry, Medical Faculty Mannheim of the University of Heidelberg, University Hospital Mannheim, Germany
| | - Christoph Brochhausen
- Institute of Pathology, University Medical Center Mannheim, Heidelberg University, Mannheim, Germany
| | - Stefan O Schönberg
- Institute of Clinical Radiology and Nuclear Medicine, University Medical Centre Mannheim, Germany
| | - Matthias F Froelich
- Institute of Clinical Radiology and Nuclear Medicine, University Medical Centre Mannheim, Germany
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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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Enhanced CT-based radiomics predicts pathological complete response after neoadjuvant chemotherapy for advanced adenocarcinoma of the esophagogastric junction: a two-center study. Insights Imaging 2022; 13:134. [PMID: 35976518 PMCID: PMC9385906 DOI: 10.1186/s13244-022-01273-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Accepted: 07/20/2022] [Indexed: 01/19/2023] Open
Abstract
Purpose This study aimed to develop and validate CT-based models to predict pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) for advanced adenocarcinoma of the esophagogastric junction (AEG). Methods Pre-NAC clinical and imaging data of AEG patients who underwent surgical resection after preoperative-NAC at two centers were retrospectively collected from November 2014 to September 2020. The dataset included training (n = 60) and external validation groups (n = 32). Three models, including CT-based radiomics, clinical and radiomics–clinical combined models, were established to differentiate pCR (tumor regression grade (TRG) = grade 0) and nonpCR (TRG = grade 1–3) patients. For the radiomics model, tumor-region-based radiomics features in the arterial and venous phases were extracted and selected. The naïve Bayes classifier was used to establish arterial- and venous-phase radiomics models. The selected candidate clinical factors were used to establish a clinical model, which was further incorporated into the radiomics–clinical combined model. ROC analysis, calibration and decision curves were used to assess the model performance. Results For the radiomics model, the AUC values obtained using the venous data were higher than those obtained using the arterial data (training: 0.751 vs. 0.736; validation: 0.768 vs. 0.750). Borrmann typing, tumor thickness and degree of differentiation were utilized to establish the clinical model (AUC-training: 0.753; AUC-validation: 0.848). The combination of arterial- and venous-phase radiomics and clinical factors further improved the discriminatory performance of the model (AUC-training: 0.838; AUC-validation: 0.902). The decision curve reflects the higher net benefit of the combined model. Conclusion The combination of CT imaging and clinical factors pre-NAC for advanced AEG could help stratify potential responsiveness to NAC. Supplementary Information The online version contains supplementary material available at 10.1186/s13244-022-01273-w.
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Orlhac F, Nioche C, Klyuzhin I, Rahmim A, Buvat I. Radiomics in PET Imaging:: A Practical Guide for Newcomers. PET Clin 2021; 16:597-612. [PMID: 34537132 DOI: 10.1016/j.cpet.2021.06.007] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
Radiomics has undergone considerable development in recent years. In PET imaging, very promising results concerning the ability of handcrafted features to predict the biological characteristics of lesions and to assess patient prognosis or response to treatment have been reported in the literature. This article presents a checklist for designing a reliable radiomic study, gives an overview of the steps of the pipeline, and outlines approaches for data harmonization. Tips are provided for critical reading of the content of articles. The advantages and limitations of handcrafted radiomics compared with deep-learning approaches for the characterization of PET images are also discussed.
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Affiliation(s)
- Fanny Orlhac
- Institut Curie Centre de Recherche, Centre Universitaire, Bat 101B, Rue Henri Becquerel, CS 90030, 91401 Orsay Cedex, France.
| | - Christophe Nioche
- Institut Curie Centre de Recherche, Centre Universitaire, Bat 101B, Rue Henri Becquerel, CS 90030, 91401 Orsay Cedex, France
| | - Ivan Klyuzhin
- Department of Integrative Oncology, BC Cancer Research Institute, 675 West 10th Avenue, Vancouver, BC V5Z 1L3, Canada; Department of Radiology, University of British Columbia, 675 West 10th Avenue, Vancouver, BC V5Z 1L3, Canada
| | - Arman Rahmim
- Department of Integrative Oncology, BC Cancer Research Institute, 675 West 10th Avenue, Vancouver, BC V5Z 1L3, Canada; Department of Radiology, University of British Columbia, 675 West 10th Avenue, Vancouver, BC V5Z 1L3, Canada
| | - Irène Buvat
- Institut Curie Centre de Recherche, Centre Universitaire, Bat 101B, Rue Henri Becquerel, CS 90030, 91401 Orsay Cedex, France
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Orel VE, Ashykhmin A, Golovko T, Rykhalskyi O, Orel VB. Texture Analysis of Tumor and Peritumoral Tissues Based on 18F-Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography Hybrid Imaging in Patients With Rectal Cancer. J Comput Assist Tomogr 2021; 45:820-828. [PMID: 34469907 DOI: 10.1097/rct.0000000000001218] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
OBJECTIVE This study aimed to determine whether texture parameters could be used in differentiation between the tumor and the peritumoral tissues based on hybrid 18F-Fluorodeoxyglucose positron emission tomography/computed tomography imaging for patients with rectal cancer. METHODS Seven parameters, including heterogeneity, entropy, energy, skewness, kurtosis, standard deviation, and average brightness, were extracted from positron emission tomography/computed tomography scans of 22 patients (12 male and 10 female; mean age, 61 ± 2 years). RESULTS The peritumoral tissue had a significantly lower value of the heterogeneity parameter (23%) than the tumor. Tumor size (r = -0.48, P < 0.05) and extramural venous invasion scores (r = 0.64, P < 0.05) correlated with heterogeneity in the peritumoral tissue. There were significant differences (P < 0.05) in the correlation coefficients between men and women. CONCLUSIONS Therefore, we provided additional quantitative information to differentiate the tumor from the peritumoral tissue and indicated possible application for extramural venous invasion evaluation in rectal cancer.
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Abstract
Radiomics describes the extraction of multiple features from medical images, including molecular imaging modalities, that with bioinformatic approaches, provide additional clinically relevant information that may be invisible to the human eye. This information may complement standard radiological interpretation with data that may better characterize a disease or that may provide predictive or prognostic information. Progressing from predefined image features, often describing heterogeneity of voxel intensities within a volume of interest, there is increasing use of machine learning to classify disease characteristics and deep learning methods based on artificial neural networks that can learn features without a priori definition and without the need for preprocessing of images. There have been advances in standardization and harmonization of methods to a level that should support multicenter studies. However, in this relatively early phase of research in the field, there are limited aspects that have been adopted into routine practice. Most of the reports in the molecular imaging field describe radiomic approaches in cancer using 18F-fluorodeoxyglucose positron emission tomography (18F-FDG-PET). In this review, we will describe radiomics in molecular imaging and summarize the pertinent literature in lung cancer where reports are most prevalent and mature.
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Affiliation(s)
- Gary J R Cook
- Cancer Imaging Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK; King's College London & Guy's and St Thomas' PET Centre, St Thomas' Hospital, London, UK.
| | - Vicky Goh
- Cancer Imaging Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK; Radiology Department, Guy's and St Thomas' Hospitals NHS Trust, London, UK
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Espedal H, Fonnes T, Fasmer KE, Krakstad C, Haldorsen IS. Imaging of Preclinical Endometrial Cancer Models for Monitoring Tumor Progression and Response to Targeted Therapy. Cancers (Basel) 2019; 11:cancers11121885. [PMID: 31783595 PMCID: PMC6966645 DOI: 10.3390/cancers11121885] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Revised: 11/22/2019] [Accepted: 11/25/2019] [Indexed: 12/11/2022] Open
Abstract
Endometrial cancer is the most common gynecologic malignancy in industrialized countries. Most patients are cured by surgery; however, about 15% of the patients develop recurrence with limited treatment options. Patient-derived tumor xenograft (PDX) mouse models represent useful tools for preclinical evaluation of new therapies and biomarker identification. Preclinical imaging by magnetic resonance imaging (MRI), positron emission tomography-computed tomography (PET-CT), single-photon emission computed tomography (SPECT) and optical imaging during disease progression enables visualization and quantification of functional tumor characteristics, which may serve as imaging biomarkers guiding targeted therapies. A critical question, however, is whether the in vivo model systems mimic the disease setting in patients to such an extent that the imaging biomarkers may be translatable to the clinic. The primary objective of this review is to give an overview of current and novel preclinical imaging methods relevant for endometrial cancer animal models. Furthermore, we highlight how these advanced imaging methods depict pathogenic mechanisms important for tumor progression that represent potential targets for treatment in endometrial cancer.
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Affiliation(s)
- Heidi Espedal
- Department of Clinical Medicine, University of Bergen, 5021 Bergen, Norway;
- Mohn Medical Imaging and Visualization Centre, Department of Radiology, Haukeland University Hospital, 5021 Bergen, Norway
- Correspondence: (H.E.); (I.S.H.)
| | - Tina Fonnes
- Centre for Cancer Biomarkers, Department of Clinical Science, University of Bergen, 5021 Bergen, Norway; (T.F.); (C.K.)
- Department of Obstetrics and Gynecology, Haukeland University Hospital, 5021 Bergen, Norway
| | - Kristine E. Fasmer
- Department of Clinical Medicine, University of Bergen, 5021 Bergen, Norway;
- Mohn Medical Imaging and Visualization Centre, Department of Radiology, Haukeland University Hospital, 5021 Bergen, Norway
| | - Camilla Krakstad
- Centre for Cancer Biomarkers, Department of Clinical Science, University of Bergen, 5021 Bergen, Norway; (T.F.); (C.K.)
- Department of Obstetrics and Gynecology, Haukeland University Hospital, 5021 Bergen, Norway
| | - Ingfrid S. Haldorsen
- Department of Clinical Medicine, University of Bergen, 5021 Bergen, Norway;
- Mohn Medical Imaging and Visualization Centre, Department of Radiology, Haukeland University Hospital, 5021 Bergen, Norway
- Correspondence: (H.E.); (I.S.H.)
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