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Abstract
With the ongoing advances in imaging techniques, increasing volumes of anatomical and functional data are being generated as part of the routine clinical workflow. This surge of available imaging data coincides with increasing research in quantitative imaging, particularly in the domain of imaging features. An important and novel approach is radiomics, where high-dimensional image properties are extracted from routine medical images. The fundamental principle of radiomics is the hypothesis that biomedical images contain predictive information, not discernible to the human eye, that can be mined through quantitative image analysis. In this review, a general outline of radiomics and artificial intelligence (AI) will be provided, along with prominent use cases in immunotherapy (e.g. response and adverse event prediction) and targeted therapy (i.e. radiogenomics). While the increased use and development of radiomics and AI in immuno-oncology is highly promising, the technology is still in its early stages, and different challenges still need to be overcome. Nevertheless, novel AI algorithms are being constructed with an ever-increasing scope of applications.
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Affiliation(s)
- Z. Bodalal
- Department of Radiology, Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
| | - I. Wamelink
- Department of Radiology, Netherlands Cancer Institute, Amsterdam, The Netherlands
- Technical Medicine, University of Twente, Enschede, The Netherlands
| | - S. Trebeschi
- Department of Radiology, Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
| | - R.G.H. Beets-Tan
- Department of Radiology, Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
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Trebeschi S, Drago SG, Birkbak NJ, Kurilova I, Cǎlin AM, Delli Pizzi A, Lalezari F, Lambregts DMJ, Rohaan MW, Parmar C, Rozeman EA, Hartemink KJ, Swanton C, Haanen JBAG, Blank CU, Smit EF, Beets-Tan RGH, Aerts HJWL. Predicting response to cancer immunotherapy using noninvasive radiomic biomarkers. Ann Oncol 2020; 30:998-1004. [PMID: 30895304 PMCID: PMC6594459 DOI: 10.1093/annonc/mdz108] [Citation(s) in RCA: 305] [Impact Index Per Article: 76.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Abstract
INTRODUCTION Immunotherapy is regarded as one of the major breakthroughs in cancer treatment. Despite its success, only a subset of patients responds-urging the quest for predictive biomarkers. We hypothesize that artificial intelligence (AI) algorithms can automatically quantify radiographic characteristics that are related to and may therefore act as noninvasive radiomic biomarkers for immunotherapy response. PATIENTS AND METHODS In this study, we analyzed 1055 primary and metastatic lesions from 203 patients with advanced melanoma and non-small-cell lung cancer (NSCLC) undergoing anti-PD1 therapy. We carried out an AI-based characterization of each lesion on the pretreatment contrast-enhanced CT imaging data to develop and validate a noninvasive machine learning biomarker capable of distinguishing between immunotherapy responding and nonresponding. To define the biological basis of the radiographic biomarker, we carried out gene set enrichment analysis in an independent dataset of 262 NSCLC patients. RESULTS The biomarker reached significant performance on NSCLC lesions (up to 0.83 AUC, P < 0.001) and borderline significant for melanoma lymph nodes (0.64 AUC, P = 0.05). Combining these lesion-wide predictions on a patient level, immunotherapy response could be predicted with an AUC of up to 0.76 for both cancer types (P < 0.001), resulting in a 1-year survival difference of 24% (P = 0.02). We found highly significant associations with pathways involved in mitosis, indicating a relationship between increased proliferative potential and preferential response to immunotherapy. CONCLUSIONS These results indicate that radiographic characteristics of lesions on standard-of-care imaging may function as noninvasive biomarkers for response to immunotherapy, and may show utility for improved patient stratification in both neoadjuvant and palliative settings.
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Affiliation(s)
- S Trebeschi
- Department of Radiology, Netherlands Cancer Institute, Amsterdam; GROW School of Oncology and Developmental Biology, Maastricht, The Netherlands; Departments of Radiation Oncology; Radiology, Dana Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
| | - S G Drago
- Department of Radiology, Netherlands Cancer Institute, Amsterdam; Department of Radiology, Milano-Bicocca University, San Gerardo Hospital, Monza, Italy
| | - N J Birkbak
- The Francis Crick Institute, London; University College London, London, UK; Department of Molecular Medicine, Aarhus University, Aarhus, Denmark
| | - I Kurilova
- Department of Radiology, Netherlands Cancer Institute, Amsterdam; GROW School of Oncology and Developmental Biology, Maastricht, The Netherlands
| | - A M Cǎlin
- Department of Radiology, Netherlands Cancer Institute, Amsterdam; Affidea Romania, Cluj-Napoca, Romania
| | - A Delli Pizzi
- Department of Radiology, Netherlands Cancer Institute, Amsterdam; ITAB Institute for Advanced Biomedical Technologies, University G. d'Annunzio, Chieti, Italy
| | - F Lalezari
- Department of Radiology, Netherlands Cancer Institute, Amsterdam
| | - D M J Lambregts
- Department of Radiology, Netherlands Cancer Institute, Amsterdam
| | | | - C Parmar
- Departments of Radiation Oncology; Radiology, Dana Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
| | | | | | - C Swanton
- The Francis Crick Institute, London; University College London, London, UK
| | | | | | - E F Smit
- Thoracic Oncology, Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - R G H Beets-Tan
- Department of Radiology, Netherlands Cancer Institute, Amsterdam; GROW School of Oncology and Developmental Biology, Maastricht, The Netherlands
| | - H J W L Aerts
- Department of Radiology, Netherlands Cancer Institute, Amsterdam; Departments of Radiation Oncology; Radiology, Dana Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, USA.
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Nguyen-Kim T, Trebeschi S, Pouw J, Milanese G, Topff L, Bodalal Z, Mangana J, Frauenfelder T, Haanen JBAG, Blank C, Aerts HJWL, Beets-Tan R, Dummer R. Deep learning radiomics distinguishes intrapulmonary disease from metastases in immunotherapy-treated melanoma patients. Ann Oncol 2019. [DOI: 10.1093/annonc/mdz253.126] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
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Valentinitsch A, Trebeschi S, Kaesmacher J, Lorenz C, Löffler MT, Zimmer C, Baum T, Kirschke JS. Opportunistic osteoporosis screening in multi-detector CT images via local classification of textures. Osteoporos Int 2019; 30:1275-1285. [PMID: 30830261 PMCID: PMC6546649 DOI: 10.1007/s00198-019-04910-1] [Citation(s) in RCA: 57] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/17/2018] [Accepted: 02/18/2019] [Indexed: 11/23/2022]
Abstract
UNLABELLED Our study proposed an automatic pipeline for opportunistic osteoporosis screening using 3D texture features and regional vBMD using multi-detector CT images. A combination of different local and global texture features outperformed the global vBMD and showed high discriminative power to identify patients with vertebral fractures. INTRODUCTION Many patients at risk for osteoporosis undergo computed tomography (CT) scans, usable for opportunistic (non-dedicated) screening. We compared the performance of global volumetric bone mineral density (vBMD) with a random forest classifier based on regional vBMD and 3D texture features to separate patients with and without osteoporotic fractures. METHODS In total, 154 patients (mean age 64 ± 8.5, male; n = 103) were included in this retrospective single-center analysis, who underwent contrast-enhanced CT for other reasons than osteoporosis screening. Patients were dichotomized regarding prevalent vertebral osteoporotic fractures (noFX, n = 101; FX, n = 53). Vertebral bodies were automatically segmented, and trabecular vBMD was calculated with a dedicated phantom. For 3D texture analysis, we extracted gray-level co-occurrence matrix Haralick features (HAR), histogram of gradients (HoG), local binary patterns (LBP), and wavelets (WL). Fractured vertebrae were excluded for texture-feature and vBMD data extraction. The performance to identify patients with prevalent osteoporotic vertebral fractures was evaluated in a fourfold cross-validation. RESULTS The random forest classifier showed a high discriminatory power (AUC = 0.88). Parameters of all vertebral levels significantly contributed to this classification. Importantly, the AUC of the proposed algorithm was significantly higher than that of volumetric global BMD alone (AUC = 0.64). CONCLUSION The presented classifier combining 3D texture features and regional vBMD including the complete thoracolumbar spine showed high discriminatory power to identify patients with vertebral fractures and had a better diagnostic performance than vBMD alone.
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Affiliation(s)
- A. Valentinitsch
- 0000000123222966grid.6936.aDepartment of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technische Universität München, München, Germany
| | - S. Trebeschi
- 0000000123222966grid.6936.aDepartment of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technische Universität München, München, Germany
| | - J. Kaesmacher
- 0000000123222966grid.6936.aDepartment of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technische Universität München, München, Germany
| | - C. Lorenz
- Philips Research Hamburg, Hamburg, Germany
| | - M. T. Löffler
- 0000000123222966grid.6936.aDepartment of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technische Universität München, München, Germany
| | - C. Zimmer
- 0000000123222966grid.6936.aDepartment of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technische Universität München, München, Germany
| | - T. Baum
- 0000000123222966grid.6936.aDepartment of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technische Universität München, München, Germany
| | - J. S. Kirschke
- 0000000123222966grid.6936.aDepartment of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technische Universität München, München, Germany
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Beckers R, Trebeschi S, Maas M, Schnerr R, Sijmons J, Beets G, Houwers J, Beets-Tan R, Lambregts D. CT texture analysis in colorectal liver metastases and the surrounding liver parenchyma and its potential as an imaging biomarker of disease aggressiveness, response and survival. Eur J Radiol 2018; 102:15-21. [DOI: 10.1016/j.ejrad.2018.02.031] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2017] [Revised: 01/10/2018] [Accepted: 02/26/2018] [Indexed: 12/20/2022]
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Van Griethuysen J, Trebeschi S, Beets-Tan R, Lambregts D, Lahaye M, Bakers F, Peeters N, Vliegen R, Voest E, Aerts H. Radiomics signature of primary diffusion MR for treatment response prediction in rectal carcinoma. Eur J Cancer 2017. [DOI: 10.1016/s0959-8049(17)30268-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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