1
|
Rothwell B, Bertolet A, Schuemann J. Proton FLASH-arc therapy (PFAT): A feasibility study for meeting FLASH dose-rate requirements in the clinic. Radiother Oncol 2025; 202:110623. [PMID: 39528113 DOI: 10.1016/j.radonc.2024.110623] [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: 04/30/2024] [Revised: 11/06/2024] [Accepted: 11/08/2024] [Indexed: 11/16/2024]
Abstract
BACKGROUND AND PURPOSE Proton arc therapy and FLASH radiotherapy (FLASH-RT) each offer unique advantages in proton therapy. However, clinical translation of FLASH-RT faces challenges in defining and delivering high dose rates. We propose the use of proton FLASH-arc therapy (PFAT) to leverage the benefits of arc while addressing FLASH delivery concerns by spatially fractionating dose delivery to healthy tissue. MATERIALS AND METHODS Treatment plans for an abdominal phantom and a clinical brain case were designed in OpenTPS, using monoenergetic beams within a 360-degree gantry rotation. Beams were optimized to achieve target coverage while maximizing spatial fractionation in non-target regions. The temporal dose delivery to healthy-tissue voxels, or in specified organs-at-risk (OARs), was constrained via selective spot removal in the beamlets matrix. The dose, LET, number of spots per voxel, and voxel-wise average dose rate were calculated for each PFAT plan and compared to a corresponding IMPT scenario. RESULTS PFAT plans demonstrated comparable dose conformity to IMPT, with LET hotspots shifted towards the target center. The number of spots influencing healthy-tissue voxels was reduced, leading to regions of substantially higher dose rates in many points outside the target. OAR dose-rate optimization in the brain plan resulted in dose rates exceeding 40 Gy/s in the majority of points in the brainstem. CONCLUSION The PFAT technique combines the advantages of FLASH and arc therapy, providing improved LET distributions and enhanced biological effect in the target, while achieving high dose rates in healthy tissue, thus reducing healthy tissue damage. This feasibility study demonstrates the capability of PFAT, setting the foundation for further optimization and application in diverse patient cases and complex geometries.
Collapse
Affiliation(s)
- Bethany Rothwell
- Physics Division, Department of Radiation Oncology, Massachusetts General Hospital & Harvard Medical School, Boston, MA, USA.
| | - Alejandro Bertolet
- Physics Division, Department of Radiation Oncology, Massachusetts General Hospital & Harvard Medical School, Boston, MA, USA.
| | - Jan Schuemann
- Physics Division, Department of Radiation Oncology, Massachusetts General Hospital & Harvard Medical School, Boston, MA, USA.
| |
Collapse
|
2
|
Burford-Eyre S, Aitkenhead A, Aylward JD, Henthorn NT, Ingram SP, Mackay R, Manger S, Merchant MJ, Sitch P, Warmenhoven JW, Appleby RB. Emulating the Delivery of Sawtooth Proton Arc Therapy Plans on a Cyclotron-Based Proton Beam Therapy System. Cancers (Basel) 2024; 16:3315. [PMID: 39409935 PMCID: PMC11475827 DOI: 10.3390/cancers16193315] [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: 07/31/2024] [Revised: 09/15/2024] [Accepted: 09/19/2024] [Indexed: 10/20/2024] Open
Abstract
Purpose: To evaluate and compare the deliverability of 'sawtooth' proton arc therapy (PAT) plans relative to static intensity modulated proton therapy (IMPT) at a cyclotron-based clinical facility. Methods: The delivery of single and dual arc Sawtooth PAT plans for an abdominal CT phantom and multiple clinical cases of brain, head and neck (H&N) and base of skull (BoS) targets was emulated under the step-and-shoot and continuous PAT delivery regimes and compared to that of a corresponding static IMPT plan. Results: Continuous PAT delivery increased the time associated with beam delivery and gantry movement in single/dual PAT plans by 4.86/7.34 min (brain), 7.51/12.40 min (BoS) and 6.59/10.57 min (H&N) on average relative to static IMPT. Step-and-shoot PAT increased this delivery time further by 4.79 min on average as the delivery was limited by gantry motion. Conclusions: The emulator can approximately model clinical sawtooth PAT delivery but requires experimental validation. No clear benefit was observed regarding beam-on time for sawtooth PAT relative to static IMPT.
Collapse
Affiliation(s)
- Samuel Burford-Eyre
- Department of Physics and Astronomy and the Cockcroft Institute, School of Natural Sciences, Faculty of Science and Engineering, The University of Manchester, Manchester M13 9PL, UK
| | - Adam Aitkenhead
- Christie Medical Physics and Engineering, The Christie NHS Foundation Trust, Manchester M20 4BX, UK
- Division of Cancer Sciences, Faculty of Biology Medicine and Heath, The University of Manchester, Manchester M13 9PL, UK
| | - Jack D. Aylward
- Department of Physics and Astronomy and the Cockcroft Institute, School of Natural Sciences, Faculty of Science and Engineering, The University of Manchester, Manchester M13 9PL, UK
- Christie Medical Physics and Engineering, The Christie NHS Foundation Trust, Manchester M20 4BX, UK
- School of Applied Sciences, University of the West of England, Bristol BS16 1QY, UK
| | - Nicholas T. Henthorn
- Christie Medical Physics and Engineering, The Christie NHS Foundation Trust, Manchester M20 4BX, UK
- Division of Cancer Sciences, Faculty of Biology Medicine and Heath, The University of Manchester, Manchester M13 9PL, UK
| | - Samuel P. Ingram
- Christie Medical Physics and Engineering, The Christie NHS Foundation Trust, Manchester M20 4BX, UK
- Division of Cancer Sciences, Faculty of Biology Medicine and Heath, The University of Manchester, Manchester M13 9PL, UK
| | - Ranald Mackay
- Christie Medical Physics and Engineering, The Christie NHS Foundation Trust, Manchester M20 4BX, UK
- Division of Cancer Sciences, Faculty of Biology Medicine and Heath, The University of Manchester, Manchester M13 9PL, UK
| | - Samuel Manger
- Christie Medical Physics and Engineering, The Christie NHS Foundation Trust, Manchester M20 4BX, UK
- Division of Cancer Sciences, Faculty of Biology Medicine and Heath, The University of Manchester, Manchester M13 9PL, UK
| | - Michael J. Merchant
- Division of Cancer Sciences, Faculty of Biology Medicine and Heath, The University of Manchester, Manchester M13 9PL, UK
| | - Peter Sitch
- Christie Medical Physics and Engineering, The Christie NHS Foundation Trust, Manchester M20 4BX, UK
- Division of Cancer Sciences, Faculty of Biology Medicine and Heath, The University of Manchester, Manchester M13 9PL, UK
| | - John-William Warmenhoven
- Christie Medical Physics and Engineering, The Christie NHS Foundation Trust, Manchester M20 4BX, UK
- Division of Cancer Sciences, Faculty of Biology Medicine and Heath, The University of Manchester, Manchester M13 9PL, UK
| | - Robert B. Appleby
- Department of Physics and Astronomy and the Cockcroft Institute, School of Natural Sciences, Faculty of Science and Engineering, The University of Manchester, Manchester M13 9PL, UK
| |
Collapse
|
3
|
Levi R, Mollura M, Savini G, Garoli F, Battaglia M, Ammirabile A, Cappellini LA, Superbi S, Grimaldi M, Barbieri R, Politi LS. A reference framework for standardization and harmonization of CT radiomics features on cadaveric sample. Sci Rep 2024; 14:19259. [PMID: 39164314 PMCID: PMC11336160 DOI: 10.1038/s41598-024-68158-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2024] [Accepted: 07/22/2024] [Indexed: 08/22/2024] Open
Abstract
Radiomics features (RFs) serve as quantitative metrics to characterize shape, density/intensity, and texture patterns in radiological images. Despite their promise, RFs exhibit reproducibility challenges across acquisition settings, thus limiting implementation into clinical practice. In this investigation, we evaluate the effects of different CT scanners and CT acquisition protocols (KV, mA, field-of-view, and reconstruction kernel settings) on RFs extracted from lumbar vertebrae of a cadaveric trunk. Employing univariate and multivariate Generalized Linear Models (GLM), we evaluated the impact of each acquisition parameter on RFs. Our findings indicate that variations in mA had negligible effects on RFs, while alterations in kV resulted in exponential changes in several RFs, notably First Order (94.4%), GLCM (87.5%), and NGTDM (100%). Moreover, we demonstrated that a tailored GLM model was superior to the ComBat algorithm in harmonizing CT images. GLM achieved R2 > 0.90 in 21 RFs (19.6%), contrasting ComBat's mean R2 above 0.90 in only 1 RF (0.9%). This pioneering study unveils the effects of CT acquisition parameters on bone RFs in cadaveric specimens, highlighting significant variations across parameters and scanner datasets. The proposed GLM model presents a robust solution for mitigating these differences, potentially advancing harmonization efforts in Radiomics-based studies across diverse CT protocols and vendors.
Collapse
Affiliation(s)
- Riccardo Levi
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini, Pieve Emanuele, 20072, Milan, Italy
- Neuroradiology Department, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, 20089, Milan, Italy
| | - Maximiliano Mollura
- Department of Electronic, Information and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133, Milan, Italy
| | - Giovanni Savini
- Neuroradiology Department, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, 20089, Milan, Italy
| | - Federico Garoli
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini, Pieve Emanuele, 20072, Milan, Italy
| | - Massimiliano Battaglia
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini, Pieve Emanuele, 20072, Milan, Italy
| | - Angela Ammirabile
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini, Pieve Emanuele, 20072, Milan, Italy
| | - Luca A Cappellini
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini, Pieve Emanuele, 20072, Milan, Italy
| | - Simona Superbi
- Neuroradiology Department, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, 20089, Milan, Italy
| | - Marco Grimaldi
- Neuroradiology Department, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, 20089, Milan, Italy
| | - Riccardo Barbieri
- Department of Electronic, Information and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133, Milan, Italy
| | - Letterio S Politi
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini, Pieve Emanuele, 20072, Milan, Italy.
- Neuroradiology Department, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, 20089, Milan, Italy.
| |
Collapse
|
4
|
van der Reijd DJ, Guerendel C, Staal FCR, Busard MP, De Oliveira Taveira M, Klompenhouwer EG, Kuhlmann KFD, Moelker A, Verhoef C, Starmans MPA, Lambregts DMJ, Beets-Tan RGH, Benson S, Maas M. Independent validation of CT radiomics models in colorectal liver metastases: predicting local tumour progression after ablation. Eur Radiol 2024; 34:3635-3643. [PMID: 37987835 PMCID: PMC11166748 DOI: 10.1007/s00330-023-10417-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 07/07/2023] [Accepted: 09/10/2023] [Indexed: 11/22/2023]
Abstract
OBJECTIVES Independent internal and external validation of three previously published CT-based radiomics models to predict local tumor progression (LTP) after thermal ablation of colorectal liver metastases (CRLM). MATERIALS AND METHODS Patients with CRLM treated with thermal ablation were collected from two institutions to collect a new independent internal and external validation cohort. Ablation zones (AZ) were delineated on portal venous phase CT 2-8 weeks post-ablation. Radiomics features were extracted from the AZ and a 10 mm peri-ablational rim (PAR) of liver parenchyma around the AZ. Three previously published prediction models (clinical, radiomics, combined) were tested without retraining. LTP was defined as new tumor foci appearing next to the AZ up to 24 months post-ablation. RESULTS The internal cohort included 39 patients with 68 CRLM and the external cohort 52 patients with 78 CRLM. 34/146 CRLM developed LTP after a median follow-up of 24 months (range 5-139). The median time to LTP was 8 months (range 2-22). The combined clinical-radiomics model yielded a c-statistic of 0.47 (95%CI 0.30-0.64) in the internal cohort and 0.50 (95%CI 0.38-0.62) in the external cohort, compared to 0.78 (95%CI 0.65-0.87) in the previously published original cohort. The radiomics model yielded c-statistics of 0.46 (95%CI 0.29-0.63) and 0.39 (95%CI 0.28-0.52), and the clinical model 0.51 (95%CI 0.34-0.68) and 0.51 (95%CI 0.39-0.63) in the internal and external cohort, respectively. CONCLUSION The previously published results for prediction of LTP after thermal ablation of CRLM using clinical and radiomics models were not reproducible in independent internal and external validation. CLINICAL RELEVANCE STATEMENT Local tumour progression after thermal ablation of CRLM cannot yet be predicted with the use of CT radiomics of the ablation zone and peri-ablational rim. These results underline the importance of validation of radiomics results to test for reproducibility in independent cohorts. KEY POINTS • Previous research suggests CT radiomics models have the potential to predict local tumour progression after thermal ablation in colorectal liver metastases, but independent validation is lacking. • In internal and external validation, the previously published models were not able to predict local tumour progression after ablation. • Radiomics prediction models should be investigated in independent validation cohorts to check for reproducibility.
Collapse
Affiliation(s)
- Denise J van der Reijd
- Department of Radiology, Antoni Van Leeuwenhoek - The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
- GROW School for Oncology and Reproduction, Maastricht University, Universiteitssingel 40, 6229 ER, Maastricht, The Netherlands
| | - Corentin Guerendel
- Department of Radiology, Antoni Van Leeuwenhoek - The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
- GROW School for Oncology and Reproduction, Maastricht University, Universiteitssingel 40, 6229 ER, Maastricht, The Netherlands
| | - Femke C R Staal
- Department of Radiology, Antoni Van Leeuwenhoek - The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
- GROW School for Oncology and Reproduction, Maastricht University, Universiteitssingel 40, 6229 ER, Maastricht, The Netherlands
| | - Milou P Busard
- Department of Radiology, Antoni Van Leeuwenhoek - The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
| | - Mateus De Oliveira Taveira
- Department of Radiology, Antoni Van Leeuwenhoek - The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
| | - Elisabeth G Klompenhouwer
- Department of Radiology, Antoni Van Leeuwenhoek - The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
| | - Koert F D Kuhlmann
- Department of Surgery, Antoni Van Leeuwenhoek - The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
| | - Adriaan Moelker
- Department of Radiology and Nuclear Medicine, Erasmus MC Cancer Institute, University Hospital Rotterdam, Dr. Molewaterplein 40, 3015 GD, Rotterdam, The Netherlands
| | - Cornelis Verhoef
- Department of Surgical Oncology, Erasmus MC Cancer Institute, University Hospital Rotterdam, Dr. Molewaterplein 40, 3015 GD, Rotterdam, The Netherlands
| | - Martijn P A Starmans
- Department of Radiology and Nuclear Medicine, Erasmus MC Cancer Institute, University Hospital Rotterdam, Dr. Molewaterplein 40, 3015 GD, Rotterdam, The Netherlands
| | - Doenja M J Lambregts
- Department of Radiology, Antoni Van Leeuwenhoek - The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
- GROW School for Oncology and Reproduction, Maastricht University, Universiteitssingel 40, 6229 ER, Maastricht, The Netherlands
| | - Regina G H Beets-Tan
- Department of Radiology, Antoni Van Leeuwenhoek - The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
- GROW School for Oncology and Reproduction, Maastricht University, Universiteitssingel 40, 6229 ER, Maastricht, The Netherlands
- Institute of Regional Health Research, University of Southern Denmark, Campusvej 55, DK 5230, Odense M, Denmark
| | - Sean Benson
- Department of Radiology, Antoni Van Leeuwenhoek - The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
- Department of Cardiology, Amsterdam University Medical Centers, University of Amsterdam, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands
| | - Monique Maas
- Department of Radiology, Antoni Van Leeuwenhoek - The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands.
- GROW School for Oncology and Reproduction, Maastricht University, Universiteitssingel 40, 6229 ER, Maastricht, The Netherlands.
| |
Collapse
|
5
|
Scapicchio C, Imbriani M, Lizzi F, Quattrocchi M, Retico A, Saponaro S, Tenerani MI, Tofani A, Zafaranchi A, Fantacci ME. Investigation of a potential upstream harmonization based on image appearance matching to improve radiomics features robustness: a phantom study. Biomed Phys Eng Express 2024; 10:045006. [PMID: 38653209 DOI: 10.1088/2057-1976/ad41e7] [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: 10/27/2023] [Accepted: 04/23/2024] [Indexed: 04/25/2024]
Abstract
Objective. Radiomics is a promising valuable analysis tool consisting in extracting quantitative information from medical images. However, the extracted radiomics features are too sensitive to variations in used image acquisition and reconstruction parameters. This limited robustness hinders the generalizable validity of radiomics-assisted models. Our aim is to investigate a possible harmonization strategy based on matching image quality to improve feature robustness.Approach.We acquired CT scans of a phantom with two scanners across different dose levels and percentages of Iterative Reconstruction algorithms. The detectability index was used as a comprehensive task-based image quality metric. A statistical analysis based on the Intraclass Correlation Coefficient was performed to determine if matching image quality/appearance could enhance the robustness of radiomics features extracted from the phantom images. Additionally, an Artificial Neural Network was trained on these features to automatically classify the scanner used for image acquisition.Main results.We found that the ICC of the features across protocols providing a similar detectability index improves with respect to the ICC of the features across protocols providing a different detectability index. This improvement was particularly noticeable in features relevant for distinguishing between scanners.Significance.This preliminary study demonstrates that a harmonization based on image quality/appearance matching could improve radiomics features robustness and heterogeneous protocols can be used to obtain a similar image appearance in terms of the detectability index. Thus protocols with a lower dose level could be selected to reduce the amount of radiation dose delivered to the patient and simultaneously obtain a more robust quantitative analysis.
Collapse
Affiliation(s)
- Camilla Scapicchio
- Department of Physics, University of Pisa, Pisa, Italy
- National Institute for Nuclear Physics, Pisa Division, Italy
| | | | - Francesca Lizzi
- National Institute for Nuclear Physics, Pisa Division, Italy
| | | | | | - Sara Saponaro
- National Institute for Nuclear Physics, Pisa Division, Italy
| | - Maria Irene Tenerani
- Department of Physics, University of Pisa, Pisa, Italy
- National Institute for Nuclear Physics, Pisa Division, Italy
| | - Alessandro Tofani
- Medical Physics Department, Azienda Toscana Nord Ovest Area Nord, Lucca, Italy
| | - Arman Zafaranchi
- Department of Physics, University of Pisa, Pisa, Italy
- National Institute for Nuclear Physics, Pisa Division, Italy
- Department of Computer Science, University of Pisa, Pisa, Italy
| | - Maria Evelina Fantacci
- Department of Physics, University of Pisa, Pisa, Italy
- National Institute for Nuclear Physics, Pisa Division, Italy
| |
Collapse
|
6
|
Levi R, Mollura M, Savini G, Garoli F, Battaglia M, Ammirabile A, Cappellini LA, Superbi S, Grimaldi M, Barbieri R, Politi LS. CT Cadaveric dataset for Radiomics features stability assessment in lumbar vertebrae. Sci Data 2024; 11:366. [PMID: 38605079 PMCID: PMC11009306 DOI: 10.1038/s41597-024-03191-6] [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: 11/23/2023] [Accepted: 03/26/2024] [Indexed: 04/13/2024] Open
Abstract
Radiomics features (RFs) studies have showed limitations in the reproducibility of RFs in different acquisition settings. To date, reproducibility studies using CT images mainly rely on phantoms, due to the harness of patient exposure to X-rays. The provided CadAIver dataset has the aims of evaluating how CT scanner parameters effect radiomics features on cadaveric donor. The dataset comprises 112 unique CT acquisitions of a cadaveric truck acquired on 3 different CT scanners varying KV, mA, field-of-view, and reconstruction kernel settings. Technical validation of the CadAIver dataset comprises a comprehensive univariate and multivariate GLM approach to assess stability of each RFs extracted from lumbar vertebrae. The complete dataset is publicly available to be applied for future research in the RFs field, and could foster the creation of a collaborative open CT image database to increase the sample size, the range of available scanners, and the available body districts.
Collapse
Affiliation(s)
- Riccardo Levi
- Neuroradiology Department, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, 20089, Milan, Italy
| | - Maximiliano Mollura
- Department of Electronic, Information and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133, Milan, Italy
| | - Giovanni Savini
- Neuroradiology Department, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, 20089, Milan, Italy
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, 20072, Milan, Italy
| | - Federico Garoli
- Neuroradiology Department, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, 20089, Milan, Italy
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, 20072, Milan, Italy
| | - Massimiliano Battaglia
- Neuroradiology Department, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, 20089, Milan, Italy
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, 20072, Milan, Italy
| | - Angela Ammirabile
- Neuroradiology Department, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, 20089, Milan, Italy
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, 20072, Milan, Italy
| | - Luca A Cappellini
- Neuroradiology Department, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, 20089, Milan, Italy
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, 20072, Milan, Italy
| | - Simona Superbi
- Neuroradiology Department, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, 20089, Milan, Italy
| | - Marco Grimaldi
- Neuroradiology Department, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, 20089, Milan, Italy
| | - Riccardo Barbieri
- Department of Electronic, Information and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133, Milan, Italy
| | - Letterio S Politi
- Neuroradiology Department, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, 20089, Milan, Italy.
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, 20072, Milan, Italy.
| |
Collapse
|
7
|
Liu Z, Duan T, Zhang Y, Weng S, Xu H, Ren Y, Zhang Z, Han X. Radiogenomics: a key component of precision cancer medicine. Br J Cancer 2023; 129:741-753. [PMID: 37414827 PMCID: PMC10449908 DOI: 10.1038/s41416-023-02317-8] [Citation(s) in RCA: 36] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Revised: 05/02/2023] [Accepted: 06/12/2023] [Indexed: 07/08/2023] Open
Abstract
Radiogenomics, focusing on the relationship between genomics and imaging phenotypes, has been widely applied to address tumour heterogeneity and predict immune responsiveness and progression. It is an inevitable consequence of current trends in precision medicine, as radiogenomics costs less than traditional genetic sequencing and provides access to whole-tumour information rather than limited biopsy specimens. By providing voxel-by-voxel genetic information, radiogenomics can allow tailored therapy targeting a complete, heterogeneous tumour or set of tumours. In addition to quantifying lesion characteristics, radiogenomics can also be used to distinguish benign from malignant entities, as well as patient characteristics, to better stratify patients according to disease risk, thereby enabling more precise imaging and screening. Here, we have characterised the radiogenomic application in precision medicine using a multi-omic approach. we outline the main applications of radiogenomics in diagnosis, treatment planning and evaluations in the field of oncology with the aim of developing quantitative and personalised medicine. Finally, we discuss the challenges in the field of radiogenomics and the scope and clinical applicability of these methods.
Collapse
Affiliation(s)
- Zaoqu Liu
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, 450052, Zhengzhou, Henan, China
- Interventional Institute of Zhengzhou University, 450052, Zhengzhou, Henan, China
- Interventional Treatment and Clinical Research Center of Henan Province, 450052, Zhengzhou, Henan, China
| | - Tian Duan
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, 450052, Zhengzhou, Henan, China
| | - Yuyuan Zhang
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, 450052, Zhengzhou, Henan, China
| | - Siyuan Weng
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, 450052, Zhengzhou, Henan, China
| | - Hui Xu
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, 450052, Zhengzhou, Henan, China
| | - Yuqing Ren
- Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Zhengzhou University, 450052, Zhengzhou, Henan, China
| | - Zhenyu Zhang
- Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, 450052, Zhengzhou, Henan, China.
| | - Xinwei Han
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, 450052, Zhengzhou, Henan, China.
- Interventional Institute of Zhengzhou University, 450052, Zhengzhou, Henan, China.
- Interventional Treatment and Clinical Research Center of Henan Province, 450052, Zhengzhou, Henan, China.
| |
Collapse
|
8
|
Bach M, Aberle C, Depeursinge A, Jimenez-Del-Toro O, Schaer R, Flouris K, Konukoglu E, Müller H, Stieltjes B, Obmann MM. 3D-printed iodine-ink CT phantom for radiomics feature extraction - advantages and challenges. Med Phys 2023; 50:5682-5697. [PMID: 36945890 DOI: 10.1002/mp.16373] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Revised: 02/07/2023] [Accepted: 02/20/2023] [Indexed: 03/23/2023] Open
Abstract
BACKGROUND To test and validate novel CT techniques, such as texture analysis in radiomics, repeat measurements are required. Current anthropomorphic phantoms lack fine texture and true anatomic representation. 3D-printing of iodinated ink on paper is a promising phantom manufacturing technique. Previously acquired or artificially created CT data can be used to generate realistic phantoms. PURPOSE To present the design process of an anthropomorphic 3D-printed iodine ink phantom, highlighting the different advantages and pitfalls in its use. To analyze the phantom's X-ray attenuation properties, and the influences of the printing process on the imaging characteristics, by comparing it to the original input dataset. METHODS Two patient CT scans and artificially generated test patterns were combined in a single dataset for phantom printing and cropped to a size of 26 × 19 × 30 cm3 . This DICOM dataset was printed on paper using iodinated ink. The phantom was CT-scanned and compared to the original image dataset used for printing the phantom. The water-equivalent diameter of the phantom was compared to that of a patient cohort (N = 104). Iodine concentrations in the phantom were measured using dual-energy CT. 86 radiomics features were extracted from 10 repeat phantom scans and the input dataset. Features were compared using a histogram analysis and a PCA individually and overall, respectively. The frequency content was compared using the normalized spectrum modulus. RESULTS Low density structures are depicted incorrectly, while soft tissue structures show excellent visual accordance with the input dataset. Maximum deviations of around 30 HU between the original dataset and phantom HU values were observed. The phantom has X-ray attenuation properties comparable to a lightweight adult patient (∼54 kg, BMI 19 kg/m2 ). Iodine concentrations in the phantom varied between 0 and 50 mg/ml. PCA of radiomics features shows different tissue types separate in similar areas of PCA representation in the phantom scans as in the input dataset. Individual feature analysis revealed systematic shift of first order radiomics features compared to the original dataset, while some higher order radiomics features did not. The normalized frequency modulus |f(ω)| of the phantom data agrees well with the original data. However, all frequencies systematically occur more frequently in the phantom compared to the maximum of the spectrum modulus than in the original data set, especially for mid-frequencies (e.g., for ω = 0.3942 mm-1 , |f(ω)|original = 0.09 * |fmax |original and |f(ω)|phantom = 0.12 * |fmax |phantom ). CONCLUSIONS 3D-iodine-ink-printing technology can be used to print anthropomorphic phantoms with a water-equivalent diameter of a lightweight adult patient. Challenges include small residual air enclosures and the fidelity of HU values. For soft tissue, there is a good agreement between the HU values of the phantom and input data set. Radiomics texture features of the phantom scans are similar to the input data set, but systematic shifts of radiomics features in first order features, due to differences in HU values, need to be considered. The paper substrate influences the spatial frequency distribution of the phantom scans. This phantom type is of very limited use for dual-energy CT analyses.
Collapse
Affiliation(s)
- Michael Bach
- Clinic of Radiology and Nuclear Medicine, University Hospital Basel, University of Basel, Basel, Switzerland
| | - Christoph Aberle
- Clinic of Radiology and Nuclear Medicine, University Hospital Basel, University of Basel, Basel, Switzerland
| | - Adrien Depeursinge
- University of Applied Sciences Western Switzerland (HES-SO) Valais, Sierre, Switzerland
- Department of Nuclear Medicine and Molecular Imaging, Lausanne University Hospital, Lausanne, Switzerland
| | | | - Roger Schaer
- University of Applied Sciences Western Switzerland (HES-SO) Valais, Sierre, Switzerland
| | | | | | - Henning Müller
- University of Applied Sciences Western Switzerland (HES-SO) Valais, Sierre, Switzerland
- Faculty of Medicine, University of Geneva (UNIGE), Geneva, Switzerland
| | - Bram Stieltjes
- Clinic of Radiology and Nuclear Medicine, University Hospital Basel, University of Basel, Basel, Switzerland
| | - Markus M Obmann
- Clinic of Radiology and Nuclear Medicine, University Hospital Basel, University of Basel, Basel, Switzerland
| |
Collapse
|
9
|
Cavaliere C, Baldi D, Brancato V, Aiello M, Salvatore M. A customized anthropomorphic 3D-printed phantom to reproducibility assessment in computed tomography: an oncological case study. Front Oncol 2023; 13:1123796. [PMID: 37700836 PMCID: PMC10493384 DOI: 10.3389/fonc.2023.1123796] [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: 12/14/2022] [Accepted: 08/07/2023] [Indexed: 09/14/2023] Open
Abstract
Introduction Studies on computed tomography (CT) reproducibility at different acquisition parameters have to take into account radiation dose administered and related ethical issues. 3D-printed phantoms provide the possibility to investigate these features deeply and to foster CT research, also taking advantage by outperforming new generation scanners. The aim of this study is to propose a new anthropomorphic 3D-printed phantom for chest lesions, tailored on a real patient CT scan, to investigate the variability of volume and Hounsfield Unit (HU) measurements at different CT acquisition parameters. Methods The chest CT of a 75-year-old patient with a paramediastinal lung lesion was segmented based on an eight-compartment approach related to HU ranges (air lung, lung interstitium, fat, muscle, vascular, skin, bone, and lesion). From each mask produced, the 3D.stl model was exported and linked to a different printing infill value, based on a preliminary test and HU ratios derived from the patient scan. Fused deposition modeling (FDM) technology printing was chosen with filament materials in polylactic acid (PLA). Phantom was acquired at 50 mAs and three different tube voltages of 80, 100, and 120 kVp on two different scanners, namely, Siemens Somatom Force (Siemens Healthineers, Erlangen, Germany; same setting of real patient for 80 kVp acquisition) and GE 750 HD CT (GE Healthcare, Chicago, IL). The same segmentation workflow was then applied on each phantom acquisition after coregistration pipeline, and Dice Similarity Coefficient (DSC) and HU averages were extracted and compared for each compartment. Results DSC comparison among real patient versus phantom scans at different kVp, and on both CT scanners, demonstrated a good overlap of different compartments and lesion vascularization with a higher similarity for lung and lesion masks for each setting (about 0.9 and 0.8, respectively). Although mean HU was not comparable with real data, due to the PLA material, the proportion of intensity values for each compartment remains respected. Discussion The proposed approach demonstrated the reliability of 3D-printed technology for personalized approaches in CT research, opening to the application of the same workflow to other oncological fields.
Collapse
|
10
|
Arapi V, Hardt-Stremayr A, Weiss S, Steinbrener J. Bridging the simulation-to-real gap for AI-based needle and target detection in robot-assisted ultrasound-guided interventions. Eur Radiol Exp 2023; 7:30. [PMID: 37332035 DOI: 10.1186/s41747-023-00344-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Accepted: 04/05/2023] [Indexed: 06/20/2023] Open
Abstract
BACKGROUND Artificial intelligence (AI)-powered, robot-assisted, and ultrasound (US)-guided interventional radiology has the potential to increase the efficacy and cost-efficiency of interventional procedures while improving postsurgical outcomes and reducing the burden for medical personnel. METHODS To overcome the lack of available clinical data needed to train state-of-the-art AI models, we propose a novel approach for generating synthetic ultrasound data from real, clinical preoperative three-dimensional (3D) data of different imaging modalities. With the synthetic data, we trained a deep learning-based detection algorithm for the localization of needle tip and target anatomy in US images. We validated our models on real, in vitro US data. RESULTS The resulting models generalize well to unseen synthetic data and experimental in vitro data making the proposed approach a promising method to create AI-based models for applications of needle and target detection in minimally invasive US-guided procedures. Moreover, we show that by one-time calibration of the US and robot coordinate frames, our tracking algorithm can be used to accurately fine-position the robot in reach of the target based on 2D US images alone. CONCLUSIONS The proposed data generation approach is sufficient to bridge the simulation-to-real gap and has the potential to overcome data paucity challenges in interventional radiology. The proposed AI-based detection algorithm shows very promising results in terms of accuracy and frame rate. RELEVANCE STATEMENT This approach can facilitate the development of next-generation AI algorithms for patient anatomy detection and needle tracking in US and their application to robotics. KEY POINTS • AI-based methods show promise for needle and target detection in US-guided interventions. • Publicly available, annotated datasets for training AI models are limited. • Synthetic, clinical-like US data can be generated from magnetic resonance or computed tomography data. • Models trained with synthetic US data generalize well to real in vitro US data. • Target detection with an AI model can be used for fine positioning of the robot.
Collapse
Affiliation(s)
- Visar Arapi
- Control of Networked Systems Research Group, Institute of Smart Systems Technologies, University of Klagenfurt, Klagenfurt, Austria.
| | - Alexander Hardt-Stremayr
- Control of Networked Systems Research Group, Institute of Smart Systems Technologies, University of Klagenfurt, Klagenfurt, Austria
| | - Stephan Weiss
- Control of Networked Systems Research Group, Institute of Smart Systems Technologies, University of Klagenfurt, Klagenfurt, Austria
| | - Jan Steinbrener
- Control of Networked Systems Research Group, Institute of Smart Systems Technologies, University of Klagenfurt, Klagenfurt, Austria
| |
Collapse
|
11
|
Aymerich M, Riveira-Martín M, García-Baizán A, González-Pena M, Sebastià C, López-Medina A, Mesa-Álvarez A, Tardágila de la Fuente G, Méndez-Castrillón M, Berbel-Rodríguez A, Matos-Ugas AC, Berenguer R, Sabater S, Otero-García M. Pilot Study for the Assessment of the Best Radiomic Features for Bosniak Cyst Classification Using Phantom and Radiologist Inter-Observer Selection. Diagnostics (Basel) 2023; 13:diagnostics13081384. [PMID: 37189486 DOI: 10.3390/diagnostics13081384] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 04/05/2023] [Accepted: 04/09/2023] [Indexed: 05/17/2023] Open
Abstract
Since the Bosniak cysts classification is highly reader-dependent, automated tools based on radiomics could help in the diagnosis of the lesion. This study is an initial step in the search for radiomic features that may be good classifiers of benign-malignant Bosniak cysts in machine learning models. A CCR phantom was used through five CT scanners. Registration was performed with ARIA software, while Quibim Precision was used for feature extraction. R software was used for the statistical analysis. Robust radiomic features based on repeatability and reproducibility criteria were chosen. Excellent correlation criteria between different radiologists during lesion segmentation were imposed. With the selected features, their classification ability in benignity-malignity terms was assessed. From the phantom study, 25.3% of the features were robust. For the study of inter-observer correlation (ICC) in the segmentation of cystic masses, 82 subjects were prospectively selected, finding 48.4% of the features as excellent regarding concordance. Comparing both datasets, 12 features were established as repeatable, reproducible, and useful for the classification of Bosniak cysts and could serve as initial candidates for the elaboration of a classification model. With those features, the Linear Discriminant Analysis model classified the Bosniak cysts in terms of benignity or malignancy with 88.2% accuracy.
Collapse
Affiliation(s)
- María Aymerich
- Diagnostic Imaging Research Group, Galicia Sur Health Research Institute, Hospital Álvaro Cunqueiro, 36312 Vigo, Spain
| | - Mercedes Riveira-Martín
- Medical Physics Research Group, Galicia Sur Health Research Institute, Hospital Álvaro Cunqueiro, 36312 Vigo, Spain
| | - Alejandra García-Baizán
- Diagnostic Imaging Research Group, Galicia Sur Health Research Institute, Hospital Álvaro Cunqueiro, 36312 Vigo, Spain
- Radiology Department, Hospital Álvaro Cunqueiro, 36312 Vigo, Spain
| | - Mariña González-Pena
- Diagnostic Imaging Research Group, Galicia Sur Health Research Institute, Hospital Álvaro Cunqueiro, 36312 Vigo, Spain
| | - Carmen Sebastià
- Centre de Diagnòstic per la Imatge Clínic, Hospital Clínic de Barcelona, 08036 Barcelona, Spain
| | - Antonio López-Medina
- Medical Physics Research Group, Galicia Sur Health Research Institute, Hospital Álvaro Cunqueiro, 36312 Vigo, Spain
- Radiophysics Department, Hospital do Meixoeiro, 36214 Vigo, Spain
| | - Alicia Mesa-Álvarez
- Radiology Department, Hospital Universitario Central de Asturias, 33011 Oviedo, Spain
| | | | - Marta Méndez-Castrillón
- Diagnostic Imaging Research Group, Galicia Sur Health Research Institute, Hospital Álvaro Cunqueiro, 36312 Vigo, Spain
- Radiology Department, Hospital Álvaro Cunqueiro, 36312 Vigo, Spain
| | - Andrea Berbel-Rodríguez
- Diagnostic Imaging Research Group, Galicia Sur Health Research Institute, Hospital Álvaro Cunqueiro, 36312 Vigo, Spain
- Radiology Department, Hospital Álvaro Cunqueiro, 36312 Vigo, Spain
| | - Alejandra C Matos-Ugas
- Diagnostic Imaging Research Group, Galicia Sur Health Research Institute, Hospital Álvaro Cunqueiro, 36312 Vigo, Spain
- Radiology Department, Hospital Álvaro Cunqueiro, 36312 Vigo, Spain
| | - Roberto Berenguer
- Radiation Oncology, Complejo Hospitalario Universitario de Albacete, 02006 Albacete, Spain
| | - Sebastià Sabater
- Radiation Oncology, Complejo Hospitalario Universitario de Albacete, 02006 Albacete, Spain
| | - Milagros Otero-García
- Diagnostic Imaging Research Group, Galicia Sur Health Research Institute, Hospital Álvaro Cunqueiro, 36312 Vigo, Spain
- Radiology Department, Hospital Álvaro Cunqueiro, 36312 Vigo, Spain
| |
Collapse
|
12
|
Zhang Z, Yang M, Li H, Chen S, Wang J, Xu L. An Innovative Low-dose CT Inpainting Algorithm based on Limited-angle Imaging Inpainting Model. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2023; 31:131-152. [PMID: 36373341 DOI: 10.3233/xst-221260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
BACKGROUND With the popularity of computed tomography (CT) technique, an increasing number of patients are receiving CT scans. Simultaneously, the public's attention to CT radiation dose is also increasing. How to obtain CT images suitable for clinical diagnosis while reducing the radiation dose has become the focus of researchers. OBJECTIVE To demonstrate that limited-angle CT imaging technique can be used to acquire lower dose CT images, we propose a generative adversarial network-based image inpainting model-Low-dose imaging and Limited-angle imaging inpainting Model (LDLAIM), this method can effectively restore low-dose CT images with limited-angle imaging, which verifies that limited-angle CT imaging technique can be used to acquire low-dose CT images. METHODS In this work, we used three datasets, including chest and abdomen dataset, head dataset and phantom dataset. They are used to synthesize low-dose and limited-angle CT images for network training. During training stage, we divide each dataset into training set, validation set and testing set according to the ratio of 8:1:1, and use the validation set to validate after finishing an epoch training, and use the testing set to test after finishing all the training. The proposed method is based on generative adversarial networks(GANs), which consists of a generator and a discriminator. The generator consists of residual blocks and encoder-decoder, and uses skip connection. RESULTS We use SSIM, PSNR and RMSE to evaluate the performance of the proposed method. In the chest and abdomen dataset, the mean SSIM, PSNR and RMSE of the testing set are 0.984, 35.385 and 0.017, respectively. In the head dataset, the mean SSIM, PSNR and RMSE of the testing set are 0.981, 38.664 and 0.011, respectively. In the phantom dataset, the mean SSIM, PSNR and RMSE of the testing set are 0.977, 33.468 and 0.022, respectively. By comparing the experimental results of other algorithms in these three datasets, it can be found that the proposed method is superior to other algorithms in these indicators. Meanwhile, the proposed method also achieved the highest score in the subjective quality score. CONCLUSIONS Experimental results show that the proposed method can effectively restore CT images when both low-dose CT imaging techniques and limited-angle CT imaging techniques are used simultaneously. This work proves that the limited-angle CT imaging technique can be used to reduce the CT radiation dose, and also provides a new idea for the research of low-dose CT imaging.
Collapse
Affiliation(s)
- Ziheng Zhang
- Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui, China
- University of Science and Technology of China, Hefei, Anhui, China
| | - Minghan Yang
- Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui, China
| | - Huijuan Li
- Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui, China
- University of Science and Technology of China, Hefei, Anhui, China
| | - Shuai Chen
- Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui, China
| | - Jianye Wang
- Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui, China
| | - Lei Xu
- The First Affiliated Hospitalof University of Science and Technology of China, Hefei, Anhui, China
| |
Collapse
|
13
|
Nie K, Xiao Y. Radiomics in clinical trials: perspectives on standardization. Phys Med Biol 2022; 68. [PMID: 36384049 DOI: 10.1088/1361-6560/aca388] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Accepted: 11/16/2022] [Indexed: 11/17/2022]
Abstract
The term biomarker is used to describe a biological measure of the disease behavior. The existing imaging biomarkers are associated with the known tissue biological characteristics and follow a well-established roadmap to be implemented in routine clinical practice. Recently, a new quantitative imaging analysis approach named radiomics has emerged. It refers to the extraction of a large number of advanced imaging features with high-throughput computing. Extensive research has demonstrated its value in predicting disease behavior, progression, and response to therapeutic options. However, there are numerous challenges to establishing it as a clinically viable solution, including lack of reproducibility and transparency. The data-driven nature also does not offer insights into the underpinning biology of the observed relationships. As such, additional effort is needed to establish it as a qualified biomarker to inform clinical decisions. Here we review the technical difficulties encountered in the clinical applications of radiomics and current effort in addressing some of these challenges in clinical trial designs. By addressing these challenges, the true potential of radiomics can be unleashed.
Collapse
Affiliation(s)
- Ke Nie
- Rutgers-Cancer Institute of New Jersey, Rutgers-Robert Wood Johnson Medical School, Department of Radiation Oncology, New Brunswick, NJ, 08901, United States of America
| | - Ying Xiao
- University of Pennsylvania, Department of Radiation Oncology, 3400 Civic Center Blvd, TRC-2 West Philadelphia, PA 19104, United States of America
| |
Collapse
|
14
|
A Review of Radiomics and Artificial Intelligence and Their Application in Veterinary Diagnostic Imaging. Vet Sci 2022; 9:vetsci9110620. [PMID: 36356097 PMCID: PMC9693121 DOI: 10.3390/vetsci9110620] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2022] [Revised: 10/31/2022] [Accepted: 10/31/2022] [Indexed: 11/09/2022] Open
Abstract
Simple Summary The goal of this paper is to provide an overview of current radiomic and AI applications in veterinary diagnostic imaging. We discuss the essential elements of AI for veterinary practitioners with the aim of helping them make informed decisions in applying AI technologies to their practices and that veterinarians will play an integral role in ensuring the appropriate uses and suitable curation of data. The expertise of veterinary professionals will be vital to ensuring suitable data and, subsequently, AI that meets the needs of the profession. Abstract Great advances have been made in human health care in the application of radiomics and artificial intelligence (AI) in a variety of areas, ranging from hospital management and virtual assistants to remote patient monitoring and medical diagnostics and imaging. To improve accuracy and reproducibility, there has been a recent move to integrate radiomics and AI as tools to assist clinical decision making and to incorporate it into routine clinical workflows and diagnosis. Although lagging behind human medicine, the use of radiomics and AI in veterinary diagnostic imaging is becoming more frequent with an increasing number of reported applications. The goal of this paper is to provide an overview of current radiomic and AI applications in veterinary diagnostic imaging.
Collapse
|
15
|
Wuyckens S, Saint-Guillain M, Janssens G, Zhao L, Li X, Ding X, Sterpin E, Lee JA, Souris K. Treatment planning in arc proton therapy: Comparison of several optimization problem statements and their corresponding solvers. Comput Biol Med 2022; 148:105609. [DOI: 10.1016/j.compbiomed.2022.105609] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Revised: 05/06/2022] [Accepted: 05/09/2022] [Indexed: 11/03/2022]
|
16
|
Avery E, Sanelli PC, Aboian M, Payabvash S. Radiomics: A Primer on Processing Workflow and Analysis. Semin Ultrasound CT MR 2022; 43:142-146. [PMID: 35339254 PMCID: PMC8961004 DOI: 10.1053/j.sult.2022.02.003] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Quantitative analysis of medical images can provide objective tools for diagnosis, prognostication, and disease monitoring. Radiomics refers to automated extraction of a large number of quantitative features from medical images for characterization of underlying pathologies. In this review, we will discuss the principles of radiomics, image preprocessing, feature extraction workflow, and statistical analysis. We will also address the limitations and future directions of radiomics.
Collapse
Affiliation(s)
- Emily Avery
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT
| | - Pina C Sanelli
- Northwell Health, and Feinstein Institute for Medical Research, Manhasset, NY
| | - Mariam Aboian
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT
| | - Seyedmehdi Payabvash
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT.
| |
Collapse
|
17
|
Zullino S, Paglialonga A, Dastrù W, Longo DL, Aime S. XNAT-PIC: Extending XNAT to Preclinical Imaging Centers. J Digit Imaging 2022; 35:860-875. [PMID: 35304674 PMCID: PMC9485318 DOI: 10.1007/s10278-022-00612-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Revised: 02/07/2022] [Accepted: 02/15/2022] [Indexed: 11/22/2022] Open
Abstract
Molecular imaging generates large volumes of heterogeneous biomedical imagery with an impelling need of guidelines for handling image data. Although several successful solutions have been implemented for human epidemiologic studies, few and limited approaches have been proposed for animal population studies. Preclinical imaging research deals with a variety of machinery yielding tons of raw data but the current practices to store and distribute image data are inadequate. Therefore, standard tools for the analysis of large image datasets need to be established. In this paper, we present an extension of XNAT for Preclinical Imaging Centers (XNAT-PIC). XNAT is a worldwide used, open-source platform for securely hosting, sharing, and processing of clinical imaging studies. Despite its success, neither tools for importing large, multimodal preclinical image datasets nor pipelines for processing whole imaging studies are yet available in XNAT. In order to overcome these limitations, we have developed several tools to expand the XNAT core functionalities for supporting preclinical imaging facilities. Our aim is to streamline the management and exchange of image data within the preclinical imaging community, thereby enhancing the reproducibility of the results of image processing and promoting open science practices.
Collapse
Affiliation(s)
- Sara Zullino
- Molecular Imaging Center, Department of Molecular Biotechnology and Health Sciences, University of Torino, Torino, Italy.,Euro-BioImaging ERIC, Torino, Italy
| | - Alessandro Paglialonga
- Molecular Imaging Center, Department of Molecular Biotechnology and Health Sciences, University of Torino, Torino, Italy
| | - Walter Dastrù
- Molecular Imaging Center, Department of Molecular Biotechnology and Health Sciences, University of Torino, Torino, Italy.,Euro-BioImaging ERIC, Torino, Italy
| | - Dario Livio Longo
- Institute of Biostructures and Bioimaging (IBB), Italian National Research Council (CNR), Via Nizza 52, 10126, Torino, Italy.
| | - Silvio Aime
- Molecular Imaging Center, Department of Molecular Biotechnology and Health Sciences, University of Torino, Torino, Italy.,Euro-BioImaging ERIC, Torino, Italy
| |
Collapse
|
18
|
Li Y, Reyhan M, Zhang Y, Wang X, Zhou J, Zhang Y, Yue NJ, Nie K. The impact of phantom design and material-dependence on repeatability and reproducibility of CT-based radiomics features. Med Phys 2022; 49:1648-1659. [PMID: 35103332 DOI: 10.1002/mp.15491] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Revised: 12/26/2021] [Accepted: 12/29/2021] [Indexed: 12/28/2022] Open
Abstract
PURPOSE To understand the design of radiomics phantom and material-dependence on repeatability and reproducibility of CT radiomics features METHODS: : A radiomics phantom consisting of various materials with uniformity, textural and biological components, was constructed. The phantom was scanned with different manufacturer CT scanners and the scans were repeated multiple times on the same scanner with different acquisition settings as kVp, mAs, orientation, field of view (FOV), slice thickness, pitch, reconstruction kernels and acquisition mode. A total of 72 phantom scans were included. For each scan, 18 different regions of interest (ROI) were contoured and 708 radiomics features were extracted from each ROI via an open source radiomics tool, IBEX. To relate the phantom data to patient data, the radiomics features from different phantom materials were compared with those extracted from 50 patients' images of five disease sites as brain, head-and-neck, breast, liver and lung cases using box-plots comparison and principal component analysis (PCA). The temporal stability of imaging features was then evaluated with respect to a controlled scenario (test-retest) via the intra-class correlation coefficient (ICC). The reproducibility of radiomics features with respect to different scanners or acquisition settings were further evaluated with concordance correlation coefficients (CCC). RESULTS Among all phantom materials, the biological component had feature values closest to human tissues, especially for tumors in brain and liver. The textural component showed similar ranges of variation to lung lesions, particularly for cartridges of rice, cereal, and the 3D-printed textural phantom with fine and rough-grid. It also showed that certain materials, such as polystyrene foam, plaster and peanuts, did not have comparable values to human tissue and could be excluded for future phantom design. High repeatability was observed in the test-retest study as indicated by an ICC value of 0.998 ± 0.020. All materials were used for feature stability analysis. For the inter-scanner study, shape-related features were the most-reliable category with 94% of features having CCC ≥0.9, while GOH were the least-reliable with only 14.6% meeting the criteria. For the intra-scanner study, the reproducibility of CT-based radiomics features showed material-dependence. In general, the instability of radiomics features introduced by kVp, mAs, pitch, acquisition mode and orientation were relatively mild. However, the homogeneous materials were more vulnerable to those changes compared to materials with textural patterns. Regardless of material compositions, resolution parameters like FOV and slice thickness, could have large impact on feature stability. Switching between standard and bone reconstruction kernels could also result significant changes to feature reproducibility. CONCLUSION We have built a radiomics phantom using materials that cover a wide span of tumor textures seen in oncological CT images. The designed phantom presents a preliminary opportunity for investigating reproducibility of radiomics features and the reproducibility can be material dependent. Thus, in the radiomics quality assurance design, it is important to choose appropriate materials that can provide a close range of radiomics features to patients with specific disease sites dependency taken into consideration. This article is protected by copyright. All rights reserved.
Collapse
Affiliation(s)
- Yanjing Li
- Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, New Brunswick, NJ, 08901
| | - Meral Reyhan
- Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, New Brunswick, NJ, 08901
| | - Yin Zhang
- Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, New Brunswick, NJ, 08901
| | - Xiao Wang
- Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, New Brunswick, NJ, 08901
| | - Jinghao Zhou
- Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, New Brunswick, NJ, 08901
| | - Yang Zhang
- Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, New Brunswick, NJ, 08901
| | - Ning J Yue
- Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, New Brunswick, NJ, 08901
| | - Ke Nie
- Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, New Brunswick, NJ, 08901
| |
Collapse
|
19
|
Edalat-Javid M, Shiri I, Hajianfar G, Abdollahi H, Arabi H, Oveisi N, Javadian M, Shamsaei Zafarghandi M, Malek H, Bitarafan-Rajabi A, Oveisi M, Zaidi H. Cardiac SPECT radiomic features repeatability and reproducibility: A multi-scanner phantom study. J Nucl Cardiol 2021; 28:2730-2744. [PMID: 32333282 DOI: 10.1007/s12350-020-02109-0] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2019] [Accepted: 03/12/2020] [Indexed: 11/30/2022]
Abstract
BACKGROUND The aim of this work was to assess the robustness of cardiac SPECT radiomic features against changes in imaging settings, including acquisition, and reconstruction parameters. METHODS Four commercial SPECT and SPECT/CT cameras were used to acquire images of a static cardiac phantom mimicking typical myorcardial perfusion imaging using 185 MBq of 99mTc. The effects of different image acquisition and reconstruction parameters, including number of views, view matrix size, attenuation correction, as well as image reconstruction related parameters (algorithm, number of iterations, number of subsets, type of post-reconstruction filter, and its associated parameters, including filter order and cut-off frequency) were studied. In total, 5,063 transverse views were reconstructed by varying the aforementioned factors. Eighty-seven radiomic features including first-, second-, and high-order textures were extracted from these images. To assess reproducibility and repeatability, the coefficient of variation (COV), as a widely adopted metric, was measured for each of the radiomic features over the different imaging settings. RESULTS The Inverse Difference Moment Normalized (IDMN) and Inverse Difference Normalized (IDN) features from the Gray Level Co-occurrence Matrix (GLCM), Run Percentage (RP) from the Gray Level Co-occurrence Matrix (GLRLM), Zone Entropy (ZE) from the Gray Level Size Zone Matrix (GLSZM), and Dependence Entropy (DE) from the Gray Level Dependence Matrix (GLDM) feature sets were the only features that exhibited high reproducibility (COV ≤ 5%) against changes in all imaging settings. In addition, Large Area Low Gray Level Emphasis (LALGLE), Small Area Low Gray Level Emphasis (SALGLE) and Low Gray Level Zone Emphasis (LGLZE) from GLSZM, and Small Dependence Low Gray Level Emphasis (SDLGLE) from GLDM feature sets turned out to be less reproducible (COV > 20%) against changes in imaging settings. The GLRLM (31.88%) and GLDM feature set (54.2%) had the highest (COV < 5%) and lowest (COV > 20%) number of the reproducible features, respectively. Matrix size had the largest impact on feature variability as most of the features were not repeatable when matrix size was modified with 82.8% of them having a COV > 20%. CONCLUSION The repeatability and reproducibility of SPECT/CT cardiac radiomic features under different imaging settings is feature-dependent. Different image acquisition and reconstruction protocols have variable effects on radiomic features. The radiomic features exhibiting low COV are potential candidates for future clinical studies.
Collapse
Affiliation(s)
- Mohammad Edalat-Javid
- Department of Energy Engineering and Physics, Amir Kabir University of Technology, Tehran, Iran
| | - Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, 1211, Geneva 4, Switzerland
| | - Ghasem Hajianfar
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Science, Tehran, Iran
| | - Hamid Abdollahi
- Department of Radiologic Sciences and Medical Physics, Faculty of Allied Medicine, Kerman University, Kerman, Iran
| | - Hossein Arabi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, 1211, Geneva 4, Switzerland
| | - Niki Oveisi
- School of Population and Public Health, The University of British Columbia, Vancouver, BC, V6T 1Z4, Canada
| | - Mohammad Javadian
- Department of Computer Engineering, Faculty of Information Technology, Kermanshah University of Technology, Kermanshah, Iran
| | | | - Hadi Malek
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Science, Tehran, Iran
| | - Ahmad Bitarafan-Rajabi
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Science, Tehran, Iran
- Cardiovascular Intervention Research Center, Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran
- Echocardiography Research Center, Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Mehrdad Oveisi
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Science, Tehran, Iran
- Department of Computer Science, University of British Columbia, Vancouver, BC, Canada
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, 1211, Geneva 4, Switzerland.
- Geneva University Neurocenter, Geneva University, 1205, 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.
| |
Collapse
|
20
|
A Multicentre Evaluation of Dosiomics Features Reproducibility, Stability and Sensitivity. Cancers (Basel) 2021; 13:cancers13153835. [PMID: 34359737 PMCID: PMC8345157 DOI: 10.3390/cancers13153835] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 07/28/2021] [Accepted: 07/29/2021] [Indexed: 12/24/2022] Open
Abstract
Simple Summary Dosiomics is born directly as an extension of radiomics: it entails extracting features from the patients’ three-dimensional (3D) radiotherapy dose distribution rather than from conventional medical images to obtain specific spatial and statistical information. Dosiomic studies, in a multicentre setting, require assessing the features’ stability to dose calculation settings and the features’ capability in distinguishing different dose distributions. This study provides the first multicentre evaluation of the dosiomic features in terms of reproducibility, stability and sensitivity across various dose distributions obtained from multiple technologies and techniques and considering different dose calculation algorithms of TPS and two different resolutions of the dose grid. Harmonisation strategies to account for a possible variation in the dose distribution due to these confounding factors should be adopted when investigating a correlation between dosiomic features and clinical outcomes in multicentre studies. Abstract Dosiomics is a texture analysis method to produce dose features that encode the spatial 3D distribution of radiotherapy dose. Dosiomic studies, in a multicentre setting, require assessing the features’ stability to dose calculation settings and the features’ capability in distinguishing different dose distributions. Dose distributions were generated by eight Italian centres on a shared image dataset acquired on a dedicated phantom. Treatment planning protocols, in terms of planning target volume coverage and dose–volume constraints to the organs at risk, were shared among the centres to produce comparable dose distributions for measuring reproducibility/stability and sensitivity of dosiomic features. In addition, coefficient of variation (CV) was employed to evaluate the dosiomic features’ variation. We extracted 38,160 features from 30 different dose distributions from six regions of interest, grouped by four features’ families. A selected group of features (CV < 3 for the reproducibility/stability studies, CV > 1 for the sensitivity studies) were identified to support future multicentre studies, assuring both stable features when dose distributions variation is minimal and sensitive features when dose distribution variations need to be clearly identified. Dosiomic is a promising tool that could support multicentre studies, especially for predictive models, and encode the spatial and statistical characteristics of the 3D dose distribution.
Collapse
|
21
|
Cuocolo R, Stanzione A, Castaldo A, De Lucia DR, Imbriaco M. Quality control and whole-gland, zonal and lesion annotations for the PROSTATEx challenge public dataset. Eur J Radiol 2021; 138:109647. [PMID: 33721767 DOI: 10.1016/j.ejrad.2021.109647] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Revised: 02/26/2021] [Accepted: 03/09/2021] [Indexed: 12/22/2022]
Abstract
PURPOSE Radiomic features are promising quantitative parameters that can be extracted from medical images and employed to build machine learning predictive models. However, generalizability is a key concern, encouraging the use of public image datasets. We performed a quality assessment of the PROSTATEx training dataset and provide publicly available lesion, whole-gland, and zonal anatomy segmentation masks. METHOD Two radiology residents and two experienced board-certified radiologists reviewed the 204 prostate MRI scans (330 lesions) included in the training dataset. The quality of provided lesion coordinate was scored using the following scale: 0 = perfectly centered, 1 = within lesion, 2 = within the prostate without lesion, 3 = outside the prostate. All clearly detectable lesions were segmented individually slice-by-slice on T2-weighted and apparent diffusion coefficient images. With the same methodology, volumes of interest including the whole gland, transition, and peripheral zones were annotated. RESULTS Of the 330 available lesion identifiers, 3 were duplicates (1%). From the remaining, 218 received score = 0, 74 score = 1, 31 score = 2 and 4 score = 3. Overall, 299 lesions were verified and segmented. Independently of lesion coordinate score and other issues (e.g., lesion coordinates falling outside DICOM images, artifacts etc.), the whole prostate gland and zonal anatomy were also manually annotated for all cases. CONCLUSION While several issues were encountered evaluating the original PROSTATEx dataset, the improved quality and availability of lesion, whole-gland and zonal segmentations will increase its potential utility as a common benchmark in prostate MRI radiomics.
Collapse
Affiliation(s)
- Renato Cuocolo
- Department of Clinical Medicine and Surgery, University of Naples "Federico II", Naples, Italy; Laboratory of Augmented Reality for Health Monitoring (ARHeMLab), Department of Electrical Engineering and Information Technology, University of Naples "Federico II", Naples, Italy
| | - Arnaldo Stanzione
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples, Italy.
| | - Anna Castaldo
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples, Italy
| | | | - Massimo Imbriaco
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples, Italy
| |
Collapse
|
22
|
Badic B, Tixier F, Cheze Le Rest C, Hatt M, Visvikis D. Radiogenomics in Colorectal Cancer. Cancers (Basel) 2021; 13:cancers13050973. [PMID: 33652647 PMCID: PMC7956421 DOI: 10.3390/cancers13050973] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2021] [Revised: 02/07/2021] [Accepted: 02/20/2021] [Indexed: 12/12/2022] Open
Abstract
Simple Summary Colorectal carcinoma is characterized by intratumoral heterogeneity that can be assessed by radiogenomics. Radiomics, high-throughput quantitative data extracted from medical imaging, combined with molecular analysis, through genomic and transcriptomic data, is expected to lead to significant advances in personalized medicine. However, a radiogenomics approach in colorectal cancer is still in its early stages and many problems remain to be solved. Here we review the progress and challenges in this field at its current stage, as well as future developments. Abstract The steady improvement of high-throughput technologies greatly facilitates the implementation of personalized precision medicine. Characterization of tumor heterogeneity through image-derived features—radiomics and genetic profile modifications—genomics, is a rapidly evolving field known as radiogenomics. Various radiogenomics studies have been dedicated to colorectal cancer so far, highlighting the potential of these approaches to enhance clinical decision-making. In this review, a general outline of colorectal radiogenomics literature is provided, discussing the current limitations and suggested further developments.
Collapse
Affiliation(s)
- Bogdan Badic
- National Institute of Health and Medical Research, LaTIM—Laboratory of Medical Information Processing (INSERM LaTIM), UMR 1101, Université Bretagne Occidentale, 29238 Brest, France; (F.T.); (C.C.L.R.); (M.H.); (D.V.)
- Correspondence: ; Tel.: +33-298-347-215
| | - Florent Tixier
- National Institute of Health and Medical Research, LaTIM—Laboratory of Medical Information Processing (INSERM LaTIM), UMR 1101, Université Bretagne Occidentale, 29238 Brest, France; (F.T.); (C.C.L.R.); (M.H.); (D.V.)
| | - Catherine Cheze Le Rest
- National Institute of Health and Medical Research, LaTIM—Laboratory of Medical Information Processing (INSERM LaTIM), UMR 1101, Université Bretagne Occidentale, 29238 Brest, France; (F.T.); (C.C.L.R.); (M.H.); (D.V.)
- Department of Nuclear Medicine, University Hospital of Poitiers, 86021 Poitiers, France
| | - Mathieu Hatt
- National Institute of Health and Medical Research, LaTIM—Laboratory of Medical Information Processing (INSERM LaTIM), UMR 1101, Université Bretagne Occidentale, 29238 Brest, France; (F.T.); (C.C.L.R.); (M.H.); (D.V.)
| | - Dimitris Visvikis
- National Institute of Health and Medical Research, LaTIM—Laboratory of Medical Information Processing (INSERM LaTIM), UMR 1101, Université Bretagne Occidentale, 29238 Brest, France; (F.T.); (C.C.L.R.); (M.H.); (D.V.)
| |
Collapse
|
23
|
Beaumont H, Iannessi A, Bertrand AS, Cucchi JM, Lucidarme O. Harmonization of radiomic feature distributions: impact on classification of hepatic tissue in CT imaging. Eur Radiol 2021; 31:6059-6068. [PMID: 33459855 DOI: 10.1007/s00330-020-07641-8] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Revised: 11/23/2020] [Accepted: 12/17/2020] [Indexed: 12/15/2022]
Abstract
OBJECTIVES Following the craze for radiomic features (RF), their lack of reliability raised the question of the generalizability of classification models. Inter-site harmonization of images therefore becomes a central issue. We compared RF harmonization processing designed to detect liver diseases in CT images. METHODS We retrospectively analyzed 76 multi-center portal CT series of non-diseased (NDL) and diseased liver (DL) patients. In each series, we positioned volumes of interest in spleen and liver, then extracted 9 RF (histogram and texture). We evaluated two RF harmonization approaches. First, in each series, we computed the Z-score of liver measurements based on those computed in the spleen. Second, we evaluated the ComBat method according to each imaging center; parameters were computed in the spleen and applied to the liver. We compared RF distributions and classification performances before/after harmonization. We classified NDL versus spleen and versus DL tissues. RESULTS The RF distributions were all different between liver and spleen (p < 0.05). The Z-score harmonization outperformed for the detection of liver versus spleen: AUC = 93.1% (p < 0.001). For the detection of DL versus NDL, in a case/control setting, we found no differences between the harmonizations: mean AUC = 73.6% (p = 0.49). Using the whole datasets, the performances were improved using ComBat (p = 0.05) AUC = 82.4% and degraded with Z-score AUC = 67.4% (p = 0.008). CONCLUSIONS Data harmonization requires to first focus on data structuring to not degrade the performances of subsequent classifications. Liver tissue classification after harmonization of spleen-based RF is a promising strategy for improving the detection of DL tissue. KEY POINTS • Variability of acquisition parameter makes radiomics of CT features non-reproducible. • Data harmonization can help circumvent the inter-site variability of acquisition protocols. • Inter-site harmonization must be carefully implemented and requires designing consistent data sets.
Collapse
Affiliation(s)
| | | | | | - Jean Michel Cucchi
- Centre Hospitalier Princesse Grâce, Avenue Pasteur, 98000, Monaco, Monaco
| | | |
Collapse
|
24
|
Röhrich S, Hofmanninger J, Prayer F, Müller H, Prosch H, Langs G. Prospects and Challenges of Radiomics by Using Nononcologic Routine Chest CT. Radiol Cardiothorac Imaging 2020; 2:e190190. [PMID: 33778599 DOI: 10.1148/ryct.2020190190] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2019] [Revised: 03/10/2020] [Accepted: 04/21/2020] [Indexed: 02/06/2023]
Abstract
Chest CT scans are one of the most common medical imaging procedures. The automatic extraction and quantification of imaging features may help in diagnosis, prognosis of, or treatment decision in cardiovascular, pulmonary, and metabolic diseases. However, an adequate sample size as a statistical necessity for radiomics studies is often difficult to achieve in prospective trials. By exploiting imaging data from clinical routine, a much larger amount of data could be used than in clinical trials. Still, there is only little literature on the implementation of radiomics in clinical routine chest CT scans. Reasons are heterogeneous CT scanning protocols and the resulting technical variability (eg, different slice thicknesses, reconstruction kernels or timings after contrast material administration) in routine CT imaging data. This review summarizes the recent state of the art of studies aiming to develop quantifiable imaging biomarkers at chest CT, such as for osteoporosis, chronic obstructive pulmonary disease, interstitial lung disease, and coronary artery disease. This review explains solutions to overcome heterogeneity in routine data such as the use of imaging repositories, the standardization of radiomic features, algorithmic approaches to improve feature stability, test-retest studies, and the evolution of deep learning for modeling radiomics features. Supplemental material is available for this article. © RSNA, 2020 See also the commentary by Kay in this issue.
Collapse
Affiliation(s)
- Sebastian Röhrich
- Computational Imaging Research Laboratory (J.H., G.L) of the Department of Biomedical Imaging and Image-guided Therapy (S.R., F.P., H.P.), Medical University of Vienna, Währinger Gürtel 18-20, 1090 Vienna, Austria; and Department of Information Systems, University of Applied Sciences of Western Switzerland, Sierre, Switzerland (H.M.)
| | - Johannes Hofmanninger
- Computational Imaging Research Laboratory (J.H., G.L) of the Department of Biomedical Imaging and Image-guided Therapy (S.R., F.P., H.P.), Medical University of Vienna, Währinger Gürtel 18-20, 1090 Vienna, Austria; and Department of Information Systems, University of Applied Sciences of Western Switzerland, Sierre, Switzerland (H.M.)
| | - Florian Prayer
- Computational Imaging Research Laboratory (J.H., G.L) of the Department of Biomedical Imaging and Image-guided Therapy (S.R., F.P., H.P.), Medical University of Vienna, Währinger Gürtel 18-20, 1090 Vienna, Austria; and Department of Information Systems, University of Applied Sciences of Western Switzerland, Sierre, Switzerland (H.M.)
| | - Henning Müller
- Computational Imaging Research Laboratory (J.H., G.L) of the Department of Biomedical Imaging and Image-guided Therapy (S.R., F.P., H.P.), Medical University of Vienna, Währinger Gürtel 18-20, 1090 Vienna, Austria; and Department of Information Systems, University of Applied Sciences of Western Switzerland, Sierre, Switzerland (H.M.)
| | - Helmut Prosch
- Computational Imaging Research Laboratory (J.H., G.L) of the Department of Biomedical Imaging and Image-guided Therapy (S.R., F.P., H.P.), Medical University of Vienna, Währinger Gürtel 18-20, 1090 Vienna, Austria; and Department of Information Systems, University of Applied Sciences of Western Switzerland, Sierre, Switzerland (H.M.)
| | - Georg Langs
- Computational Imaging Research Laboratory (J.H., G.L) of the Department of Biomedical Imaging and Image-guided Therapy (S.R., F.P., H.P.), Medical University of Vienna, Währinger Gürtel 18-20, 1090 Vienna, Austria; and Department of Information Systems, University of Applied Sciences of Western Switzerland, Sierre, Switzerland (H.M.)
| |
Collapse
|
25
|
van Timmeren JE, Cester D, Tanadini-Lang S, Alkadhi H, Baessler B. Radiomics in medical imaging-"how-to" guide and critical reflection. Insights Imaging 2020; 11:91. [PMID: 32785796 PMCID: PMC7423816 DOI: 10.1186/s13244-020-00887-2] [Citation(s) in RCA: 731] [Impact Index Per Article: 146.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Accepted: 06/22/2020] [Indexed: 02/06/2023] Open
Abstract
Radiomics is a quantitative approach to medical imaging, which aims at enhancing the existing data available to clinicians by means of advanced mathematical analysis. Through mathematical extraction of the spatial distribution of signal intensities and pixel interrelationships, radiomics quantifies textural information by using analysis methods from the field of artificial intelligence. Various studies from different fields in imaging have been published so far, highlighting the potential of radiomics to enhance clinical decision-making. However, the field faces several important challenges, which are mainly caused by the various technical factors influencing the extracted radiomic features.The aim of the present review is twofold: first, we present the typical workflow of a radiomics analysis and deliver a practical "how-to" guide for a typical radiomics analysis. Second, we discuss the current limitations of radiomics, suggest potential improvements, and summarize relevant literature on the subject.
Collapse
Affiliation(s)
- Janita E van Timmeren
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Raemistrasse 100, 8091, Zurich, Switzerland
| | - Davide Cester
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Raemistrasse 100, 8091, Zurich, Switzerland
| | - Stephanie Tanadini-Lang
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Raemistrasse 100, 8091, Zurich, Switzerland
| | - Hatem Alkadhi
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Raemistrasse 100, 8091, Zurich, Switzerland
| | - Bettina Baessler
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Raemistrasse 100, 8091, Zurich, Switzerland.
| |
Collapse
|
26
|
Song J, Yin Y, Wang H, Chang Z, Liu Z, Cui L. A review of original articles published in the emerging field of radiomics. Eur J Radiol 2020; 127:108991. [PMID: 32334372 DOI: 10.1016/j.ejrad.2020.108991] [Citation(s) in RCA: 101] [Impact Index Per Article: 20.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2019] [Revised: 03/06/2020] [Accepted: 04/03/2020] [Indexed: 12/18/2022]
Abstract
PURPOSE To determine the characteristics of and trends in research in the emerging field of radiomics through bibliometric and hotspot analyses of relevant original articles published between 2013 and 2018. METHODS We evaluated 553 original articles concerning radiomics, published in a total of 61 peer-reviewed journals between 2013 and 2018. The following information was retrieved for each article: radiological subspecialty, imaging technique(s), machine learning technique(s), sample size, study setting and design, statistical result(s), study purpose, software used for feature calculation, funding declarations, author number, first author's affiliation, study origin, and journal name. Qualitative and quantitative analyses were performed for the manually extracted data for identification and visualization of the trends in radiomics research. RESULTS The annual growth rate in the number of published papers was 177.82% (p < 0.001). The characteristics and trends of research hotspots in the field of radiomics were clarified and visualized in this study. It was found that the field of radiomics is at a more mature stage for lung, breast, and prostate cancers than for other sites. Radiomics studies primarily focused on radiological characterization (215) and monitoring (182). Logistic regression and LASSO were the two most commonly used techniques for feature selection. Non-clinical researchers without a medical background dominated radiomics studies (70.52%), the vast majority of which only highlighted positive results (97.80%) while downplaying negative findings. CONCLUSIONS The reporting of quantifiable knowledge about the characteristics and trajectories of radiomics can inform researchers about the gaps in the field of radiomics and guide its future direction.
Collapse
Affiliation(s)
- Jiangdian Song
- School of Medical Informatics, China Medical University, No.77 Puhe Road, Shenyang North New Area, Shenyang, 110122, Liaoning Province, PR China; School of Medicine, Department of Radiology, Stanford University, 1201 Welch Rd Lucas Center PS055, Palo Alto, CA 94306, United States.
| | - Yanjie Yin
- School of Medical Informatics, China Medical University, No.77 Puhe Road, Shenyang North New Area, Shenyang, 110122, Liaoning Province, PR China
| | - Hairui Wang
- Department of Radiology, Shengjing Hospital of China Medical University, No.36 Sanhao Street, Heping District Shenyang, 110004, Liaoning, Province, PR China
| | - Zhihui Chang
- Department of Radiology, Shengjing Hospital of China Medical University, No.36 Sanhao Street, Heping District Shenyang, 110004, Liaoning, Province, PR China
| | - Zhaoyu Liu
- Department of Radiology, Shengjing Hospital of China Medical University, No.36 Sanhao Street, Heping District Shenyang, 110004, Liaoning, Province, PR China
| | - Lei Cui
- School of Medical Informatics, China Medical University, No.77 Puhe Road, Shenyang North New Area, Shenyang, 110122, Liaoning Province, PR China.
| |
Collapse
|
27
|
Bologna M, Corino V, Mainardi L. Technical Note: Virtual phantom analyses for preprocessing evaluation and detection of a robust feature set for MRI-radiomics of the brain. Med Phys 2019; 46:5116-5123. [PMID: 31539450 PMCID: PMC6899873 DOI: 10.1002/mp.13834] [Citation(s) in RCA: 51] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2019] [Revised: 08/26/2019] [Accepted: 09/13/2019] [Indexed: 11/14/2022] Open
Abstract
Purpose The purpose of the paper was to use a virtual phantom to identify a set of radiomic features from T1‐weighted and T2‐weighted magnetic resonance imaging (MRI) of the brain which is stable to variations in image acquisition parameters and to evaluate the effect of image preprocessing on radiomic features stability. Methods Stability to different sources of variability (time of repetition and echo, voxel size, random noise and intensity non‐uniformity) was evaluated for both T1‐weighted and T2‐weighted MRI images. A set of 107 radiomic features, accounting for shape and size, first order statistics, and textural features was used. Feature stability was quantified using intraclass correlation coefficient (ICC). For each source of variability, stability was evaluated before and after preprocessing (Z‐score normalization, resampling, gaussian filtering and bias field correction). Features that have ICC > 0.75 in all the analysis of variability are selected as stable features. Last, the robust feature sets were tested on images acquired with random simulation parameters to assess their generalizability to unseen conditions. Results Preprocessing significantly increased the robustness of radiomic features to the different sources of variability. When preprocessing is applied, a set of 67 and 61 features resulted as stable for T1‐weighted and T2‐wieghted images respectively, over 80% of which were confirmed by the analysis on the images acquired with random simulation parameters. Conclusion A set of MRI‐radiomic features, robust to changes in TR/TE/PS/ST, was identified. This set of features may be used in radiomic analyses based on T1‐weighted and T2‐weighted MRI images.
Collapse
Affiliation(s)
- Marco Bologna
- Biosignals, Bioimaging and Bioinformatics Laboratory (B3-Lab), Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Milan, Italy
| | - Valentina Corino
- Biosignals, Bioimaging and Bioinformatics Laboratory (B3-Lab), Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Milan, Italy
| | - Luca Mainardi
- Biosignals, Bioimaging and Bioinformatics Laboratory (B3-Lab), Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Milan, Italy
| |
Collapse
|