1
|
Horng H, Scott C, Winham S, Jensen M, Pantalone L, Mankowski W, Kerlikowske K, Vachon CM, Kontos D, Shinohara RT. Multivariate testing and effect size measures for batch effect evaluation in radiomic features. Sci Rep 2024; 14:13923. [PMID: 38886407 PMCID: PMC11183083 DOI: 10.1038/s41598-024-64208-z] [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: 01/28/2024] [Accepted: 06/06/2024] [Indexed: 06/20/2024] Open
Abstract
While precision medicine applications of radiomics analysis are promising, differences in image acquisition can cause "batch effects" that reduce reproducibility and affect downstream predictive analyses. Harmonization methods such as ComBat have been developed to correct these effects, but evaluation methods for quantifying batch effects are inconsistent. In this study, we propose the use of the multivariate statistical test PERMANOVA and the Robust Effect Size Index (RESI) to better quantify and characterize batch effects in radiomics data. We evaluate these methods in both simulated and real radiomics features extracted from full-field digital mammography (FFDM) data. PERMANOVA demonstrated higher power than standard univariate statistical testing, and RESI was able to interpretably quantify the effect size of site at extremely large sample sizes. These methods show promise as more powerful and interpretable methods for the detection and quantification of batch effects in radiomics studies.
Collapse
Affiliation(s)
- Hannah Horng
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Department of Radiology, Center for Biomedical Image Computing and Analysis (CBICA), University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Penn Statistics in Imaging Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, 19104, USA.
| | | | | | | | - Lauren Pantalone
- Department of Radiology, Center for Biomedical Image Computing and Analysis (CBICA), University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Walter Mankowski
- Department of Radiology, Center for Biomedical Image Computing and Analysis (CBICA), University of Pennsylvania, Philadelphia, PA, 19104, USA
| | | | | | - Despina Kontos
- Department of Radiology, Center for Biomedical Image Computing and Analysis (CBICA), University of Pennsylvania, Philadelphia, PA, 19104, USA
- Center for Innovation in Imaging Biomarkers and Integrated Diagnostics (CIMBID), Columbia University, New York, NY, 10027, USA
| | - Russell T Shinohara
- Department of Radiology, Center for Biomedical Image Computing and Analysis (CBICA), University of Pennsylvania, Philadelphia, PA, 19104, USA
- Penn Statistics in Imaging Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, 19104, USA
| |
Collapse
|
2
|
Thomas-Odenthal F, Stein F, Vogelbacher C, Alexander N, Bechdolf A, Bermpohl F, Bröckel K, Brosch K, Correll CU, Evermann U, Falkenberg I, Fallgatter A, Flinkenflügel K, Grotegerd D, Hahn T, Hautzinger M, Jansen A, Juckel G, Krug A, Lambert M, Leicht G, Leopold K, Meinert S, Mikolas P, Mulert C, Nenadić I, Pfarr JK, Reif A, Ringwald K, Ritter P, Stamm T, Straube B, Teutenberg L, Thiel K, Usemann P, Winter A, Wroblewski A, Dannlowski U, Bauer M, Pfennig A, Kircher T. Larger putamen in individuals at risk and with manifest bipolar disorder. Psychol Med 2024:1-11. [PMID: 38801091 DOI: 10.1017/s0033291724001193] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
BACKGROUND Individuals at risk for bipolar disorder (BD) have a wide range of genetic and non-genetic risk factors, like a positive family history of BD or (sub)threshold affective symptoms. Yet, it is unclear whether these individuals at risk and those diagnosed with BD share similar gray matter brain alterations. METHODS In 410 male and female participants aged 17-35 years, we compared gray matter volume (3T MRI) between individuals at risk for BD (as assessed using the EPIbipolar scale; n = 208), patients with a DSM-IV-TR diagnosis of BD (n = 87), and healthy controls (n = 115) using voxel-based morphometry in SPM12/CAT12. We applied conjunction analyses to identify similarities in gray matter volume alterations in individuals at risk and BD patients, relative to healthy controls. We also performed exploratory whole-brain analyses to identify differences in gray matter volume among groups. ComBat was used to harmonize imaging data from seven sites. RESULTS Both individuals at risk and BD patients showed larger volumes in the right putamen than healthy controls. Furthermore, individuals at risk had smaller volumes in the right inferior occipital gyrus, and BD patients had larger volumes in the left precuneus, compared to healthy controls. These findings were independent of course of illness (number of lifetime manic and depressive episodes, number of hospitalizations), comorbid diagnoses (major depressive disorder, attention-deficit hyperactivity disorder, anxiety disorder, eating disorder), familial risk, current disease severity (global functioning, remission status), and current medication intake. CONCLUSIONS Our findings indicate that alterations in the right putamen might constitute a vulnerability marker for BD.
Collapse
Affiliation(s)
- Florian Thomas-Odenthal
- Department of Psychiatry and Psychotherapy, Philipps-Universität Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), Universities of Marburg and Gießen, Marburg, Germany
| | - Frederike Stein
- Department of Psychiatry and Psychotherapy, Philipps-Universität Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), Universities of Marburg and Gießen, Marburg, Germany
| | - Christoph Vogelbacher
- Center for Mind, Brain and Behavior (CMBB), Universities of Marburg and Gießen, Marburg, Germany
- Translational Clinical Psychology, Department of Psychology, Philipps-University Marburg, Marburg, Germany
| | - Nina Alexander
- Department of Psychiatry and Psychotherapy, Philipps-Universität Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), Universities of Marburg and Gießen, Marburg, Germany
| | - Andreas Bechdolf
- Department of Psychiatry, Psychotherapy and Psychosomatic Medicine, Vivantes Hospital Am Urban and Vivantes Hospital Im Friedrichshain, Berlin, Germany
- Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin, Campus Mitte, Berlin, Germany
| | - Felix Bermpohl
- Department of Psychiatry and Neuroscience, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Kyra Bröckel
- Department of Psychiatry and Psychotherapy, Faculty of Medicine, TUD Dresden University of Technology, Dresden, Germany
| | - Katharina Brosch
- Department of Psychiatry and Psychotherapy, Philipps-Universität Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), Universities of Marburg and Gießen, Marburg, Germany
- Department of Psychiatry, Zucker Hillside Hospital, Northwell Health, Glen Oaks, NY, USA
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Manhasset, NY, USA
| | - Christoph U Correll
- Department of Psychiatry, Zucker Hillside Hospital, Northwell Health, Glen Oaks, NY, USA
- Department of Child and Adolescent Psychiatry, Charité Universitätsmedizin Berlin, Berlin, Germany
- Department of Psychiatry and Molecular Medicine, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
- Center for Psychiatric Neuroscience, Feinstein Institute for Medical Research, Manhasset, NY, USA
| | - Ulrika Evermann
- Department of Psychiatry and Psychotherapy, Philipps-Universität Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), Universities of Marburg and Gießen, Marburg, Germany
| | - Irina Falkenberg
- Department of Psychiatry and Psychotherapy, Philipps-Universität Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), Universities of Marburg and Gießen, Marburg, Germany
| | - Andreas Fallgatter
- Department of Psychiatry and Psychotherapy, University of Tübingen, Germany; German Center for Mental Health (DZPG), partner site Tübingen, Germany
| | - Kira Flinkenflügel
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Dominik Grotegerd
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Tim Hahn
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Martin Hautzinger
- Department of Psychology, Clinical Psychology and Psychotherapy, Eberhard Karls University, Tübingen, Germany
| | - Andreas Jansen
- Department of Psychiatry and Psychotherapy, Philipps-Universität Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), Universities of Marburg and Gießen, Marburg, Germany
- Core-Facility BrainImaging, Faculty of Medicine, Philipps-Universität Marburg, Marburg, Germany
| | - Georg Juckel
- Department of Psychiatry, Ruhr University Bochum, Bochum, Germany
| | - Axel Krug
- Department of Psychiatry and Psychotherapy, University Hospital of Bonn, Bonn, Germany
| | - Martin Lambert
- Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Gregor Leicht
- Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Karolina Leopold
- Department of Psychiatry, Psychotherapy and Psychosomatic Medicine, Vivantes Hospital Am Urban and Vivantes Hospital Im Friedrichshain, Berlin, Germany
- Department of Psychiatry and Psychotherapy, Faculty of Medicine, TUD Dresden University of Technology, Dresden, Germany
| | - Susanne Meinert
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
- Institute for Translational Neuroscience, University of Münster, Münster, Germany
| | - Pavol Mikolas
- Department of Psychiatry and Psychotherapy, Faculty of Medicine, TUD Dresden University of Technology, Dresden, Germany
| | - Christoph Mulert
- Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Center for Psychiatry, Justus Liebig University, Giessen, Germany
| | - Igor Nenadić
- Department of Psychiatry and Psychotherapy, Philipps-Universität Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), Universities of Marburg and Gießen, Marburg, Germany
| | - Julia-Katharina Pfarr
- Department of Psychiatry and Psychotherapy, Philipps-Universität Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), Universities of Marburg and Gießen, Marburg, Germany
| | - Andreas Reif
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital Frankfurt, Frankfurt, Germany
| | - Kai Ringwald
- Department of Psychiatry and Psychotherapy, Philipps-Universität Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), Universities of Marburg and Gießen, Marburg, Germany
| | - Philipp Ritter
- Department of Psychiatry and Psychotherapy, Faculty of Medicine, TUD Dresden University of Technology, Dresden, Germany
| | - Thomas Stamm
- Department of Psychiatry and Neuroscience, Charité Universitätsmedizin Berlin, Berlin, Germany
- Department of Clinical Psychiatry and Psychotherapy Brandenburg Medical School, Neuruppin, Germany
| | - Benjamin Straube
- Department of Psychiatry and Psychotherapy, Philipps-Universität Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), Universities of Marburg and Gießen, Marburg, Germany
| | - Lea Teutenberg
- Department of Psychiatry and Psychotherapy, Philipps-Universität Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), Universities of Marburg and Gießen, Marburg, Germany
| | - Katharina Thiel
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Paula Usemann
- Department of Psychiatry and Psychotherapy, Philipps-Universität Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), Universities of Marburg and Gießen, Marburg, Germany
| | - Alexandra Winter
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Adrian Wroblewski
- Department of Psychiatry and Psychotherapy, Philipps-Universität Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), Universities of Marburg and Gießen, Marburg, Germany
| | - Udo Dannlowski
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Michael Bauer
- Department of Psychiatry and Psychotherapy, Faculty of Medicine, TUD Dresden University of Technology, Dresden, Germany
| | - Andrea Pfennig
- Department of Psychiatry and Psychotherapy, Faculty of Medicine, TUD Dresden University of Technology, Dresden, Germany
| | - Tilo Kircher
- Department of Psychiatry and Psychotherapy, Philipps-Universität Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), Universities of Marburg and Gießen, Marburg, Germany
| |
Collapse
|
3
|
Khodabakhshi Z, Motisi L, Bink A, Broglie MA, Rupp NJ, Fleischmann M, von der Grün J, Guckenberger M, Tanadini-Lang S, Balermpas P. MRI-based radiomics for predicting histology in malignant salivary gland tumors: methodology and "proof of principle". Sci Rep 2024; 14:9945. [PMID: 38688932 PMCID: PMC11061101 DOI: 10.1038/s41598-024-60200-9] [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: 12/01/2023] [Accepted: 04/18/2024] [Indexed: 05/02/2024] Open
Abstract
Defining the exact histological features of salivary gland malignancies before treatment remains an unsolved problem that compromises the ability to tailor further therapeutic steps individually. Radiomics, a new methodology to extract quantitative information from medical images, could contribute to characterizing the individual cancer phenotype already before treatment in a fast and non-invasive way. Consequently, the standardization and implementation of radiomic analysis in the clinical routine work to predict histology of salivary gland cancer (SGC) could also provide improvements in clinical decision-making. In this study, we aimed to investigate the potential of radiomic features as imaging biomarker to distinguish between high grade and low-grade salivary gland malignancies. We have also investigated the effect of image and feature level harmonization on the performance of radiomic models. For this study, our dual center cohort consisted of 126 patients, with histologically proven SGC, who underwent curative-intent treatment in two tertiary oncology centers. We extracted and analyzed the radiomics features of 120 pre-therapeutic MRI images with gadolinium (T1 sequences), and correlated those with the definitive post-operative histology. In our study the best radiomic model achieved average AUC of 0.66 and balanced accuracy of 0.63. According to the results, there is significant difference between the performance of models based on MRI intensity normalized images + harmonized features and other models (p value < 0.05) which indicates that in case of dealing with heterogeneous dataset, applying the harmonization methods is beneficial. Among radiomic features minimum intensity from first order, and gray level-variance from texture category were frequently selected during multivariate analysis which indicate the potential of these features as being used as imaging biomarker. The present bicentric study presents for the first time the feasibility of implementing MR-based, handcrafted radiomics, based on T1 contrast-enhanced sequences and the ComBat harmonization method in an effort to predict the formal grading of salivary gland carcinoma with satisfactory performance.
Collapse
Affiliation(s)
- Zahra Khodabakhshi
- Department of Radiation Oncology, Zurich University Hospital, Zurich, Switzerland
| | - Laura Motisi
- Department of Radiation Oncology, Zurich University Hospital, Zurich, Switzerland
| | - Andrea Bink
- Department of Neuroradadiology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Martina A Broglie
- Department of Otorhinolaryngology, Zurich University Hospital, Zurich, Switzerland
| | - Niels J Rupp
- Department of Pathology and Molecular Pathology, University Hospital Zurich, Zurich, Switzerland
| | - Maximilian Fleischmann
- Department of Radiation Oncology, J.W. Goethe University Hospital Frankfurt, Frankfurt, Germany
| | - Jens von der Grün
- Department of Radiation Oncology, Zurich University Hospital, Zurich, Switzerland
| | | | | | - Panagiotis Balermpas
- Department of Radiation Oncology, Zurich University Hospital, Zurich, Switzerland.
| |
Collapse
|
4
|
Khodabakhshi Z, Gabrys H, Wallimann P, Guckenberger M, Andratschke N, Tanadini-Lang S. Magnetic resonance imaging radiomic features stability in brain metastases: Impact of image preprocessing, image-, and feature-level harmonization. Phys Imaging Radiat Oncol 2024; 30:100585. [PMID: 38799810 PMCID: PMC11127267 DOI: 10.1016/j.phro.2024.100585] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Revised: 04/23/2024] [Accepted: 05/02/2024] [Indexed: 05/29/2024] Open
Abstract
Background and purpose Magnetic resonance imaging (MRI) scans are highly sensitive to acquisition and reconstruction parameters which affect feature stability and model generalizability in radiomic research. This work aims to investigate the effect of image pre-processing and harmonization methods on the stability of brain MRI radiomic features and the prediction performance of radiomic models in patients with brain metastases (BMs). Materials and methods Two T1 contrast enhanced brain MRI data-sets were used in this study. The first contained 25 BMs patients with scans at two different time points and was used for features stability analysis. The effect of gray level discretization (GLD), intensity normalization (Z-score, Nyul, WhiteStripe, and in house-developed method named N-Peaks), and ComBat harmonization on features stability was investigated and features with intraclass correlation coefficient >0.8 were considered as stable. The second data-set containing 64 BMs patients was used for a classification task to investigate the informativeness of stable features and the effects of harmonization methods on radiomic model performance. Results Applying fixed bin number (FBN) GLD, resulted in higher number of stable features compare to fixed bin size (FBS) discretization (10 ± 5.5 % higher). `Harmonization in feature domain improved the stability for non-normalized and normalized images with Z-score and WhiteStripe methods. For the classification task, keeping the stable features resulted in good performance only for normalized images with N-Peaks along with FBS discretization. Conclusions To develop a robust MRI based radiomic model we recommend using an intensity normalization method based on a reference tissue (e.g N-Peaks) and then using FBS discretization.
Collapse
Affiliation(s)
- Zahra Khodabakhshi
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Hubert Gabrys
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Philipp Wallimann
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Matthias Guckenberger
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Nicolaus Andratschke
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Stephanie Tanadini-Lang
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| |
Collapse
|
5
|
Fiste O, Gkiozos I, Charpidou A, Syrigos NK. Artificial Intelligence-Based Treatment Decisions: A New Era for NSCLC. Cancers (Basel) 2024; 16:831. [PMID: 38398222 PMCID: PMC10887017 DOI: 10.3390/cancers16040831] [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: 01/31/2024] [Revised: 02/12/2024] [Accepted: 02/17/2024] [Indexed: 02/25/2024] Open
Abstract
Non-small cell lung cancer (NSCLC) is the leading cause of cancer-related mortality among women and men, in developed countries, despite the public health interventions including tobacco-free campaigns, screening and early detection methods, recent therapeutic advances, and ongoing intense research on novel antineoplastic modalities. Targeting oncogenic driver mutations and immune checkpoint inhibition has indeed revolutionized NSCLC treatment, yet there still remains the unmet need for robust and standardized predictive biomarkers to accurately inform clinical decisions. Artificial intelligence (AI) represents the computer-based science concerned with large datasets for complex problem-solving. Its concept has brought a paradigm shift in oncology considering its immense potential for improved diagnosis, treatment guidance, and prognosis. In this review, we present the current state of AI-driven applications on NSCLC management, with a particular focus on radiomics and pathomics, and critically discuss both the existing limitations and future directions in this field. The thoracic oncology community should not be discouraged by the likely long road of AI implementation into daily clinical practice, as its transformative impact on personalized treatment approaches is undeniable.
Collapse
Affiliation(s)
- Oraianthi Fiste
- Oncology Unit, Third Department of Internal Medicine and Laboratory, Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece; (I.G.); (A.C.); (N.K.S.)
| | | | | | | |
Collapse
|
6
|
Shang Y, Chen W, Li G, Huang Y, Wang Y, Kui X, Li M, Zheng H, Zhao W, Liu J. Computed Tomography-derived intratumoral and peritumoral radiomics in predicting EGFR mutation in lung adenocarcinoma. LA RADIOLOGIA MEDICA 2023; 128:1483-1496. [PMID: 37749461 PMCID: PMC10700425 DOI: 10.1007/s11547-023-01722-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/15/2023] [Accepted: 09/04/2023] [Indexed: 09/27/2023]
Abstract
OBJECTIVE To investigate the value of Computed Tomography (CT) radiomics derived from different peritumoral volumes of interest (VOIs) in predicting epidermal growth factor receptor (EGFR) mutation status in lung adenocarcinoma patients. MATERIALS AND METHODS A retrospective cohort of 779 patients who had pathologically confirmed lung adenocarcinoma were enrolled. 640 patients were randomly divided into a training set, a validation set, and an internal testing set (3:1:1), and the remaining 139 patients were defined as an external testing set. The intratumoral VOI (VOI_I) was manually delineated on the thin-slice CT images, and seven peritumoral VOIs (VOI_P) were automatically generated with 1, 2, 3, 4, 5, 10, and 15 mm expansion along the VOI_I. 1454 radiomic features were extracted from each VOI. The t-test, the least absolute shrinkage and selection operator (LASSO), and the minimum redundancy maximum relevance (mRMR) algorithm were used for feature selection, followed by the construction of radiomics models (VOI_I model, VOI_P model and combined model). The performance of the models were evaluated by the area under the curve (AUC). RESULTS 399 patients were classified as EGFR mutant (EGFR+), while 380 were wild-type (EGFR-). In the training and validation sets, internal and external testing sets, VOI4 (intratumoral and peritumoral 4 mm) model achieved the best predictive performance, with AUCs of 0.877, 0.727, and 0.701, respectively, outperforming the VOI_I model (AUCs of 0.728, 0.698, and 0.653, respectively). CONCLUSIONS Radiomics extracted from peritumoral region can add extra value in predicting EGFR mutation status of lung adenocarcinoma patients, with the optimal peritumoral range of 4 mm.
Collapse
Affiliation(s)
- Youlan Shang
- Department of Radiology, The Second Xiangya Hospital, Central South University, No. 139 Middle Renmin Road, Changsha, 410011, Hunan, People's Republic of China
| | - Weidao Chen
- Infervision, Chaoyang District, Beijing, 100025, China
| | - Ge Li
- Department of Radiology, Xiangya Hospital, Central South University, No. 87 Xiangya Rd, Changsha, 410008, Hunan, People's Republic of China
| | - Yijie Huang
- Department of Radiology, The Second Xiangya Hospital, Central South University, No. 139 Middle Renmin Road, Changsha, 410011, Hunan, People's Republic of China
| | - Yisong Wang
- Department of Radiology, The Second Xiangya Hospital, Central South University, No. 139 Middle Renmin Road, Changsha, 410011, Hunan, People's Republic of China
| | - Xiaoyan Kui
- School of Computer Science and Engineering, Central South University, Changsha, 410083, Hunan, People's Republic of China
| | - Ming Li
- Department of Radiology, Huadong Hospital Affiliated to Fudan University, Shanghai, People's Republic of China
| | - Hairong Zheng
- Paul C. Lauterbur Research Center for Biomedical Imaging, Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, People's Republic of China
| | - Wei Zhao
- Department of Radiology, The Second Xiangya Hospital, Central South University, No. 139 Middle Renmin Road, Changsha, 410011, Hunan, People's Republic of China.
- Paul C. Lauterbur Research Center for Biomedical Imaging, Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, People's Republic of China.
- Clinical Research Center for Medical Imaging in Hunan Province, Changsha, Hunan Province, People's Republic of China.
| | - Jun Liu
- Department of Radiology, The Second Xiangya Hospital, Central South University, No. 139 Middle Renmin Road, Changsha, 410011, Hunan, People's Republic of China.
- Clinical Research Center for Medical Imaging in Hunan Province, Changsha, Hunan Province, People's Republic of China.
| |
Collapse
|
7
|
Wu H, Liu X, Peng L, Yang Y, Zhou Z, Du D, Xu H, Lv W, Lu L. Optimal batch determination for improved harmonization and prognostication of multi-center PET/CT radiomics feature in head and neck cancer. Phys Med Biol 2023; 68:225014. [PMID: 37844604 DOI: 10.1088/1361-6560/ad03d1] [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: 05/06/2023] [Accepted: 10/16/2023] [Indexed: 10/18/2023]
Abstract
Objective. To determine the optimal approach for identifying and mitigating batch effects in PET/CT radiomics features, and further improve the prognosis of patients with head and neck cancer (HNC), this study investigated the performance of three batch harmonization methods.Approach. Unsupervised harmonization identified the batch labels by K-means clustering. Supervised harmonization regarding the image acquisition factors (center, manufacturer, scanner, filter kernel) as known/given batch labels, and Combat harmonization was then implemented separately and sequentially based on the batch labels, i.e. harmonizing features among batches determined by each factor individually or harmonizing features among batches determined by multiple factors successively. Extensive experiments were conducted to predict overall survival (OS) on public PET/CT datasets that contain 800 patients from 9 centers.Main results. In the external validation cohort, results show that compared to original models without harmonization, Combat harmonization would be beneficial in OS prediction with C-index of 0.687-0.740 versus 0.684-0.767. Supervised harmonization slightly outperformed unsupervised harmonization in all models (C-index: 0.692-0.767 versus 0.684-0.750). Separate harmonization outperformed sequential harmonization in CT_m+clinic and CT_cm+clinic models with C-index of 0.752 and 0.722, respectively, while sequential harmonization involved clinical features in PET_rs+clinic model further improving the performance and achieving the highest C-index of 0.767.Significance. Optimal batch determination especially sequential harmonization for Combat holds the potential to improve the prognostic power of radiomics model in multi-center HNC dataset with PET/CT imaging.
Collapse
Affiliation(s)
- Huiqin Wu
- Department of Medical Imaging, Guangdong Second Provincial General Hospital, Guangzhou, Guangdong, 518037, People's Republic of China
- School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, 510515, People's Republic of China
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, Guangdong, 510515, People's Republic of China
| | - Xiaohui Liu
- School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, 510515, People's Republic of China
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, Guangdong, 510515, People's Republic of China
| | - Lihong Peng
- School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, 510515, People's Republic of China
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, Guangdong, 510515, People's Republic of China
| | - Yuling Yang
- School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, 510515, People's Republic of China
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, Guangdong, 510515, People's Republic of China
| | - Zidong Zhou
- School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, 510515, People's Republic of China
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, Guangdong, 510515, People's Republic of China
| | - Dongyang Du
- School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, 510515, People's Republic of China
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, Guangdong, 510515, People's Republic of China
| | - Hui Xu
- School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, 510515, People's Republic of China
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, Guangdong, 510515, People's Republic of China
| | - Wenbing Lv
- School of Information and Yunnan Key Laboratory of Intelligent Systems and Computing, Yunnan University, Kunming, Yunnan, 650504, People's Republic of China
| | - Lijun Lu
- School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, 510515, People's Republic of China
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, Guangdong, 510515, People's Republic of China
- Pazhou Lab, Guangzhou 510330, People's Republic of China
| |
Collapse
|
8
|
Khodabakhshi Z, Amini M, Hajianfar G, Oveisi M, Shiri I, Zaidi H. Dual-Centre Harmonised Multimodal Positron Emission Tomography/Computed Tomography Image Radiomic Features and Machine Learning Algorithms for Non-small Cell Lung Cancer Histopathological Subtype Phenotype Decoding. Clin Oncol (R Coll Radiol) 2023; 35:713-725. [PMID: 37599160 DOI: 10.1016/j.clon.2023.08.003] [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: 09/27/2022] [Revised: 06/10/2023] [Accepted: 08/05/2023] [Indexed: 08/22/2023]
Abstract
AIMS We aimed to build radiomic models for classifying non-small cell lung cancer (NSCLC) histopathological subtypes through a dual-centre dataset and comprehensively evaluate the effect of ComBat harmonisation on the performance of single- and multimodality radiomic models. MATERIALS AND METHODS A public dataset of NSCLC patients from two independent centres was used. Two image fusion methods, namely guided filtering-based fusion and image fusion based on visual saliency map and weighted least square optimisation, were used. Radiomic features were extracted from each scan, including first-order, texture and moment-invariant features. Subsequently, ComBat harmonisation was applied to the extracted features from computed tomography (CT), positron emission tomography (PET) and fused images to correct the centre effect. For feature selection, least absolute shrinkage and selection operator (Lasso) and recursive feature elimination (RFE) were investigated. For machine learning, logistic regression (LR), support vector machine (SVM) and AdaBoost were evaluated for classifying NSCLC subtypes. Training and evaluation of the models were carried out in a robust framework to offset plausible errors and performance was reported using area under the curve, balanced accuracy, sensitivity and specificity before and after harmonisation. N-way ANOVA was used to assess the effect of different factors on the performance of the models. RESULTS Support vector machine fed with selected features by recursive feature elimination from a harmonised PET feature set achieved the highest performance (area under the curve = 0.82) in classifying NSCLC histopathological subtypes. Although the performance of the models did not significantly improve for CT images after harmonisation, the performance of PET and guided filtering-based fusion feature signatures significantly improved for almost all models. Although the selection of the image modality and feature selection methods was effective on the performance of the model (ANOVA P-values <0.001), machine learning and harmonisation did not change the performance significantly (ANOVA P-values = 0.839 and 0.292, respectively). CONCLUSION This study confirmed the potential of radiomic analysis on PET, CT and hybrid images for histopathological classification of NSCLC subtypes.
Collapse
Affiliation(s)
- Z Khodabakhshi
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Science, Tehran, Iran
| | - M Amini
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
| | - G Hajianfar
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
| | - M Oveisi
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Science, Tehran, Iran; Comprehensive Cancer Centre, School of Cancer & Pharmaceutical Sciences, Faculty of Life Sciences & Medicine, Kings College London, London, UK; Department of Computer Science, University of British Columbia, Vancouver, BC, Canada
| | - I Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
| | - H Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland; Geneva University Neurocenter, Geneva University, Geneva, Switzerland; Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands; Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark.
| |
Collapse
|
9
|
Cobo M, Menéndez Fernández-Miranda P, Bastarrika G, Lloret Iglesias L. Enhancing radiomics and Deep Learning systems through the standardization of medical imaging workflows. Sci Data 2023; 10:732. [PMID: 37865635 PMCID: PMC10590396 DOI: 10.1038/s41597-023-02641-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 10/12/2023] [Indexed: 10/23/2023] Open
Affiliation(s)
- Miriam Cobo
- Advanced Computing and e-Science Group, Institute of Physics of Cantabria (IFCA), CSIC - UC, Santander, Spain.
| | | | - Gorka Bastarrika
- Clínica Universidad de Navarra, Department of Radiology, Pamplona, Spain
| | - Lara Lloret Iglesias
- Advanced Computing and e-Science Group, Institute of Physics of Cantabria (IFCA), CSIC - UC, Santander, Spain
| |
Collapse
|
10
|
Prosper AE, Kammer MN, Maldonado F, Aberle DR, Hsu W. Expanding Role of Advanced Image Analysis in CT-detected Indeterminate Pulmonary Nodules and Early Lung Cancer Characterization. Radiology 2023; 309:e222904. [PMID: 37815447 PMCID: PMC10623199 DOI: 10.1148/radiol.222904] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 03/23/2023] [Accepted: 03/30/2023] [Indexed: 10/11/2023]
Abstract
The implementation of low-dose chest CT for lung screening presents a crucial opportunity to advance lung cancer care through early detection and interception. In addition, millions of pulmonary nodules are incidentally detected annually in the United States, increasing the opportunity for early lung cancer diagnosis. Yet, realization of the full potential of these opportunities is dependent on the ability to accurately analyze image data for purposes of nodule classification and early lung cancer characterization. This review presents an overview of traditional image analysis approaches in chest CT using semantic characterization as well as more recent advances in the technology and application of machine learning models using CT-derived radiomic features and deep learning architectures to characterize lung nodules and early cancers. Methodological challenges currently faced in translating these decision aids to clinical practice, as well as the technical obstacles of heterogeneous imaging parameters, optimal feature selection, choice of model, and the need for well-annotated image data sets for the purposes of training and validation, will be reviewed, with a view toward the ultimate incorporation of these potentially powerful decision aids into routine clinical practice.
Collapse
Affiliation(s)
- Ashley Elizabeth Prosper
- From the Department of Radiological Sciences, David Geffen School of
Medicine at UCLA, 924 Westwood Blvd, Suite 420, Los Angeles, CA 90024 (A.E.P.,
D.R.A., W.H.); Division of Allergy, Pulmonary and Critical Care Medicine,
Department of Medicine, Vanderbilt University Medical Center, Nashville, Tenn
(M.N.K., F.M.); and Department of Bioengineering, UCLA Samueli School of
Engineering, Los Angeles, Calif (D.R.A., W.H.)
| | - Michael N. Kammer
- From the Department of Radiological Sciences, David Geffen School of
Medicine at UCLA, 924 Westwood Blvd, Suite 420, Los Angeles, CA 90024 (A.E.P.,
D.R.A., W.H.); Division of Allergy, Pulmonary and Critical Care Medicine,
Department of Medicine, Vanderbilt University Medical Center, Nashville, Tenn
(M.N.K., F.M.); and Department of Bioengineering, UCLA Samueli School of
Engineering, Los Angeles, Calif (D.R.A., W.H.)
| | - Fabien Maldonado
- From the Department of Radiological Sciences, David Geffen School of
Medicine at UCLA, 924 Westwood Blvd, Suite 420, Los Angeles, CA 90024 (A.E.P.,
D.R.A., W.H.); Division of Allergy, Pulmonary and Critical Care Medicine,
Department of Medicine, Vanderbilt University Medical Center, Nashville, Tenn
(M.N.K., F.M.); and Department of Bioengineering, UCLA Samueli School of
Engineering, Los Angeles, Calif (D.R.A., W.H.)
| | - Denise R. Aberle
- From the Department of Radiological Sciences, David Geffen School of
Medicine at UCLA, 924 Westwood Blvd, Suite 420, Los Angeles, CA 90024 (A.E.P.,
D.R.A., W.H.); Division of Allergy, Pulmonary and Critical Care Medicine,
Department of Medicine, Vanderbilt University Medical Center, Nashville, Tenn
(M.N.K., F.M.); and Department of Bioengineering, UCLA Samueli School of
Engineering, Los Angeles, Calif (D.R.A., W.H.)
| | - William Hsu
- From the Department of Radiological Sciences, David Geffen School of
Medicine at UCLA, 924 Westwood Blvd, Suite 420, Los Angeles, CA 90024 (A.E.P.,
D.R.A., W.H.); Division of Allergy, Pulmonary and Critical Care Medicine,
Department of Medicine, Vanderbilt University Medical Center, Nashville, Tenn
(M.N.K., F.M.); and Department of Bioengineering, UCLA Samueli School of
Engineering, Los Angeles, Calif (D.R.A., W.H.)
| |
Collapse
|
11
|
Bartholomeus GA, van Amsterdam WAC, Harder AMD, Willemink MJ, van Hamersvelt RW, de Jong PA, Leiner T. Robustness of pulmonary nodule radiomic features on computed tomography as a function of varying radiation dose levels-a multi-dose in vivo patient study. Eur Radiol 2023; 33:7044-7055. [PMID: 37074424 PMCID: PMC10511375 DOI: 10.1007/s00330-023-09643-8] [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: 05/12/2022] [Revised: 03/16/2023] [Accepted: 03/28/2023] [Indexed: 04/20/2023]
Abstract
OBJECTIVE Analysis of textural features of pulmonary nodules in chest CT, also known as radiomics, has several potential clinical applications, such as diagnosis, prognostication, and treatment response monitoring. For clinical use, it is essential that these features provide robust measurements. Studies with phantoms and simulated lower dose levels have demonstrated that radiomic features can vary with different radiation dose levels. This study presents an in vivo stability analysis of radiomic features for pulmonary nodules against varying radiation dose levels. METHODS Nineteen patients with a total of thirty-five pulmonary nodules underwent four chest CT scans at different radiation dose levels (60, 33, 24, and 15 mAs) in a single session. The nodules were manually delineated. To assess the robustness of features, we calculated the intra-class correlation coefficient (ICC). To visualize the effect of milliampere-second variation on groups of features, a linear model was fitted to each feature. We calculated bias and calculated the R2 value as a measure of goodness of fit. RESULTS A small minority of 15/100 (15%) radiomic features were considered stable (ICC > 0.9). Bias increased and R2 decreased at lower dose, but shape features seemed to be more robust to milliampere-second variations than other feature classes. CONCLUSION A large majority of pulmonary nodule radiomic features were not inherently robust to radiation dose level variations. For a subset of features, it was possible to correct this variability by a simple linear model. However, the correction became increasingly less accurate at lower radiation dose levels. CLINICAL RELEVANCE STATEMENT Radiomic features provide a quantitative description of a tumor based on medical imaging such as computed tomography (CT). These features are potentially useful in several clinical tasks such as diagnosis, prognosis prediction, treatment effect monitoring, and treatment effect estimation. KEY POINTS • The vast majority of commonly used radiomic features are strongly influenced by variations in radiation dose level. • A small minority of radiomic features, notably the shape feature class, are robust against dose-level variations according to ICC calculations. • A large subset of radiomic features can be corrected by a linear model taking into account only the radiation dose level.
Collapse
Affiliation(s)
| | | | | | - Martin J Willemink
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA
| | | | - Pim A de Jong
- University Medical Center Utrecht, Utrecht, the Netherlands
| | - Tim Leiner
- University Medical Center Utrecht, Utrecht, the Netherlands
- Mayo Clinic, Rochester, MN, USA
| |
Collapse
|
12
|
Chen M, Copley SJ, Viola P, Lu H, Aboagye EO. Radiomics and artificial intelligence for precision medicine in lung cancer treatment. Semin Cancer Biol 2023; 93:97-113. [PMID: 37211292 DOI: 10.1016/j.semcancer.2023.05.004] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 04/14/2023] [Accepted: 05/17/2023] [Indexed: 05/23/2023]
Abstract
Lung cancer is the leading cause of cancer-related deaths worldwide. It exhibits, at the mesoscopic scale, phenotypic characteristics that are generally indiscernible to the human eye but can be captured non-invasively on medical imaging as radiomic features, which can form a high dimensional data space amenable to machine learning. Radiomic features can be harnessed and used in an artificial intelligence paradigm to risk stratify patients, and predict for histological and molecular findings, and clinical outcome measures, thereby facilitating precision medicine for improving patient care. Compared to tissue sampling-driven approaches, radiomics-based methods are superior for being non-invasive, reproducible, cheaper, and less susceptible to intra-tumoral heterogeneity. This review focuses on the application of radiomics, combined with artificial intelligence, for delivering precision medicine in lung cancer treatment, with discussion centered on pioneering and groundbreaking works, and future research directions in the area.
Collapse
Affiliation(s)
- Mitchell Chen
- Department of Surgery and Cancer, The Commonwealth Building, Du Cane Road, Hammersmith Campus, Imperial College, London W12 0NN, UK; Imperial College Healthcare NHS Trust, Hammersmith Hospital, Du Cane Road, London W12 0HS, UK
| | - Susan J Copley
- Department of Surgery and Cancer, The Commonwealth Building, Du Cane Road, Hammersmith Campus, Imperial College, London W12 0NN, UK; Imperial College Healthcare NHS Trust, Hammersmith Hospital, Du Cane Road, London W12 0HS, UK
| | - Patrizia Viola
- North West London Pathology, Charing Cross Hospital, Fulham Palace Rd, London W6 8RF, UK
| | - Haonan Lu
- Department of Surgery and Cancer, The Commonwealth Building, Du Cane Road, Hammersmith Campus, Imperial College, London W12 0NN, UK
| | - Eric O Aboagye
- Department of Surgery and Cancer, The Commonwealth Building, Du Cane Road, Hammersmith Campus, Imperial College, London W12 0NN, UK.
| |
Collapse
|
13
|
Chen K, Wang J, Li S, Zhou W, Xu W. Predictive value of 18F-FDG PET/CT-based radiomics model for neoadjuvant chemotherapy efficacy in breast cancer: a multi-scanner/center study with external validation. Eur J Nucl Med Mol Imaging 2023; 50:1869-1880. [PMID: 36808002 DOI: 10.1007/s00259-023-06150-2] [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: 10/31/2022] [Accepted: 02/12/2023] [Indexed: 02/23/2023]
Abstract
PURPOSE To develop and validate the predictive value of an 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) model for breast cancer neoadjuvant chemotherapy (NAC) efficacy based on the tumor-to-liver ratio (TLR) radiomic features and multiple data pre-processing methods. METHODS One hundred and ninety-three breast cancer patients from multiple centers were retrospectively included in this study. According to the endpoint of NAC, we divided the patients into pathological complete remission (pCR) and non-pCR groups. All patients underwent 18F-FDG PET/CT imaging before NAC treatment, and CT and PET images volume of interest (VOI) segmentation by manual segmentation and semi-automated absolute threshold segmentation, respectively. Then, feature extraction of VOI was performed with the pyradiomics package. A total of 630 models were created based on the source of radiomic features, the elimination of the batch effect approach, and the discretization method. The differences in data pre-processing approaches were compared and analyzed to identify the best-performing model, which was further tested by the permutation test. RESULTS A variety of data pre-processing methods contributed in varying degrees to the improvement of model effects. Among them, TLR radiomic features and Combat and Limma methods that eliminate batch effects could enhance the model prediction overall, and data discretization could be used as a potential method that can further optimize the model. A total of seven excellent models were selected and then based on the AUC of each model in the four test sets and their standard deviations, we selected the optimal model. The optimal model predicted AUC between 0.7 and 0.77 for the four test groups, with p-values less than 0.05 for the permutation test. CONCLUSION It is necessary to enhance the predictive effect of the model by eliminating confounding factors through data pre-processing. The model developed in this way is effective in predicting the efficacy of NAC for breast cancer.
Collapse
Affiliation(s)
- Kun Chen
- Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Huanhuxi Road, Hexi Distinct, 300060, Tianjin, China
- Tianjin's Clinical Research Center for Cancer, Tianjin, 300060, China
- Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Tianjin, 300060, China
| | - Jian Wang
- Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Huanhuxi Road, Hexi Distinct, 300060, Tianjin, China
- Tianjin's Clinical Research Center for Cancer, Tianjin, 300060, China
- Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Tianjin, 300060, China
| | - Shuai Li
- Tianjin Key Laboratory of Technologies Enabling Development of Clinical Therapeutics and Diagnostics, School of Pharmacy, Tianjin Medical University, Tianjin, 300070, People's Republic of China
| | - Wen Zhou
- Tianjin Key Laboratory of Technologies Enabling Development of Clinical Therapeutics and Diagnostics, School of Pharmacy, Tianjin Medical University, Tianjin, 300070, People's Republic of China.
| | - Wengui Xu
- Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Huanhuxi Road, Hexi Distinct, 300060, Tianjin, China.
- Tianjin's Clinical Research Center for Cancer, Tianjin, 300060, China.
- Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Tianjin, 300060, China.
| |
Collapse
|
14
|
Bouhamama A, Leporq B, Faraz K, Foy JP, Boussageon M, Pérol M, Ortiz-Cuaran S, Ghiringhelli F, Saintigny P, Beuf O, Pilleul F. Radiomics combined with transcriptomics to predict response to immunotherapy from patients treated with PD-1/PD-L1 inhibitors for advanced NSCLC. FRONTIERS IN RADIOLOGY 2023; 3:1168448. [PMID: 37492391 PMCID: PMC10365090 DOI: 10.3389/fradi.2023.1168448] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Accepted: 03/31/2023] [Indexed: 07/27/2023]
Abstract
Introduction In this study, we aim to build radiomics and multiomics models based on transcriptomics and radiomics to predict the response from patients treated with the PD-L1 inhibitor. Materials and methods One hundred and ninety-five patients treated with PD-1/PD-L1 inhibitors were included. For all patients, 342 radiomic features were extracted from pretreatment computed tomography scans. The training set was built with 110 patients treated at the Léon Bérard Cancer Center. An independent validation cohort was built with the 85 patients treated in Dijon. The two sets were dichotomized into two classes, patients with disease control and those considered non-responders, in order to predict the disease control at 3 months. Various models were trained with different feature selection methods, and different classifiers were evaluated to build the models. In a second exploratory step, we used transcriptomics to enrich the database and develop a multiomic signature of response to immunotherapy in a 54-patient subgroup. Finally, we considered the HOT/COLD status. We first trained a radiomic model to predict the HOT/COLD status and then prototyped a hybrid model integrating radiomics and the HOT/COLD status to predict the response to immunotherapy. Results Radiomic signature for 3 months' progression-free survival (PFS) classification: The most predictive model had an area under the receiver operating characteristic curve (AUROC) of 0.94 on the training set and 0.65 on the external validation set. This model was obtained with the t-test selection method and with a support vector machine (SVM) classifier. Multiomic signature for PFS classification: The most predictive model had an AUROC of 0.95 on the training set and 0.99 on the validation set. Radiomic model to predict the HOT/COLD status: the most predictive model had an AUROC of 0.93 on the training set and 0.86 on the validation set. HOT/COLD radiomic hybrid model for PFS classification: the most predictive model had an AUROC of 0.93 on the training set and 0.90 on the validation set. Conclusion In conclusion, radiomics could be used to predict the response to immunotherapy in non-small-cell lung cancer patients. The use of transcriptomics or the HOT/COLD status, together with radiomics, may improve the working of the prediction models.
Collapse
Affiliation(s)
- Amine Bouhamama
- Department of Radiology, Centre Léon Bérard, Lyon, France
- Creatis, University Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, CNRS, Inserm, Creatis, UMR 5220, U1206, Lyon, France
| | - Benjamin Leporq
- Creatis, University Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, CNRS, Inserm, Creatis, UMR 5220, U1206, Lyon, France
| | - Khuram Faraz
- Creatis, University Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, CNRS, Inserm, Creatis, UMR 5220, U1206, Lyon, France
| | - Jean-Philippe Foy
- Department of Oral and Maxillofacial Surgery, Sorbonne Université, Pitié-Salpêtrière Hospital, APHP, Paris, France
| | | | - Maurice Pérol
- Department of Medical Oncology, Centre Léon Bérard, Lyon, France
| | - Sandra Ortiz-Cuaran
- CRCL, University Lyon, Claude Bernard Lyon 1 University, Inserm 1052, CNRS 5286, Centre Léon Bérard, Cancer Research Center of Lyon, Lyon, France
| | | | - Pierre Saintigny
- Department of Medical Oncology, Centre Léon Bérard, Lyon, France
- CRCL, University Lyon, Claude Bernard Lyon 1 University, Inserm 1052, CNRS 5286, Centre Léon Bérard, Cancer Research Center of Lyon, Lyon, France
| | - Olivier Beuf
- Creatis, University Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, CNRS, Inserm, Creatis, UMR 5220, U1206, Lyon, France
| | - Frank Pilleul
- Department of Radiology, Centre Léon Bérard, Lyon, France
- Creatis, University Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, CNRS, Inserm, Creatis, UMR 5220, U1206, Lyon, France
| |
Collapse
|
15
|
A Comprehensive Survey on the Progress, Process, and Challenges of Lung Cancer Detection and Classification. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:5905230. [PMID: 36569180 PMCID: PMC9788902 DOI: 10.1155/2022/5905230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 10/17/2022] [Accepted: 11/09/2022] [Indexed: 12/23/2022]
Abstract
Lung cancer is the primary reason of cancer deaths worldwide, and the percentage of death rate is increasing step by step. There are chances of recovering from lung cancer by detecting it early. In any case, because the number of radiologists is limited and they have been working overtime, the increase in image data makes it hard for them to evaluate the images accurately. As a result, many researchers have come up with automated ways to predict the growth of cancer cells using medical imaging methods in a quick and accurate way. Previously, a lot of work was done on computer-aided detection (CADe) and computer-aided diagnosis (CADx) in computed tomography (CT) scan, magnetic resonance imaging (MRI), and X-ray with the goal of effective detection and segmentation of pulmonary nodule, as well as classifying nodules as malignant or benign. But still, no complete comprehensive review that includes all aspects of lung cancer has been done. In this paper, every aspect of lung cancer is discussed in detail, including datasets, image preprocessing, segmentation methods, optimal feature extraction and selection methods, evaluation measurement matrices, and classifiers. Finally, the study looks into several lung cancer-related issues with possible solutions.
Collapse
|
16
|
Luo L, Li A, Fu S, Du W, He LN, Zhang X, Wang Y, Zhou Y, Yunpeng Y, Li Z, Hong S. [Cuproptosis-related immune gene signature predicts clinical benefits from anti-PD-1/PD-L1 therapy in non-small-cell lung cancer. Immunol Res 2022; 71:213-228. [PMID: 36434349 DOI: 10.1007/s12026-022-09335-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Accepted: 10/24/2022] [Indexed: 11/26/2022]
Abstract
Non-small-cell lung cancer (NSCLC) remains the major cause of cancer-related death. Immune checkpoint inhibition has become the cornerstone treatment for NSCLC. Cuproptosis is a newly identified form of cell death relying on mitochondrial respiration that might play a role in shaping tumor immune microenvironment (TIME). The clinical significance of cuproptosis-related genes (CRGs) remains unclear and warrant investigation. The current study extracted RNA sequencing profiles and corresponding clinical information from six aggregated datasets from the Gene Expression Omnibus (GEO) repository as the training set, and from The Cancer Genome Atlas (TCGA) database as the testing set. Cuproptosis-related immune genes (CRIMGs) were obtained through coexpression analysis, univariate Cox regression analysis, and LASSO analysis for overall survival (OS) association analysis. Consensus clustering was employed to divide the subjects into clusters. Stepwise multivariate Cox regression was used to establish the prognostic CRIMG_score from the CRIMGs. A 17-gene prediction signature was established that informed patients' OS both in the training and testing datasets (p < 0.001). The predictive value of the signature in terms of immunotherapeutic responses was assessed in two publicly available NSCLC immunotherapy datasets (POPLAR and OAK studies) and an internal dataset from Sun Yat-sen University Cancer Center (ORIENT-11 study). Patients in the high-risk group displayed worse survival, a characteristic suppressive tumor immune microenvironment, and low immunotherapeutic benefits compared to those in the low-risk group. Collectively, the CRIMG_score established herein could serve as a promising indicator of prognosis and immunotherapeutic response in patients with NSCLC.
Collapse
|
17
|
Improved generalized ComBat methods for harmonization of radiomic features. Sci Rep 2022; 12:19009. [PMID: 36348002 PMCID: PMC9643436 DOI: 10.1038/s41598-022-23328-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Accepted: 10/29/2022] [Indexed: 11/10/2022] Open
Abstract
Radiomic approaches in precision medicine are promising, but variation associated with image acquisition factors can result in severe biases and low generalizability. Multicenter datasets used in these studies are often heterogeneous in multiple imaging parameters and/or have missing information, resulting in multimodal radiomic feature distributions. ComBat is a promising harmonization tool, but it only harmonizes by single/known variables and assumes standardized input data are normally distributed. We propose a procedure that sequentially harmonizes for multiple batch effects in an optimized order, called OPNested ComBat. Furthermore, we propose to address bimodality by employing a Gaussian Mixture Model (GMM) grouping considered as either a batch variable (OPNested + GMM) or as a protected clinical covariate (OPNested - GMM). Methods were evaluated on features extracted with CapTK and PyRadiomics from two public lung computed tomography (CT) datasets. We found that OPNested ComBat improved harmonization performance over standard ComBat. OPNested + GMM ComBat exhibited the best harmonization performance but the lowest predictive performance, while OPNested - GMM ComBat showed poorer harmonization performance, but the highest predictive performance. Our findings emphasize that improved harmonization performance is no guarantee of improved predictive performance, and that these methods show promise for superior standardization of datasets heterogeneous in multiple or unknown imaging parameters and greater generalizability.
Collapse
|
18
|
Cui Y, Yin FF. Impact of image quality on radiomics applications. Phys Med Biol 2022; 67. [DOI: 10.1088/1361-6560/ac7fd7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 07/08/2022] [Indexed: 11/12/2022]
Abstract
Abstract
Radiomics features extracted from medical images have been widely reported to be useful in the patient specific outcome modeling for variety of assessment and prediction purposes. Successful application of radiomics features as imaging biomarkers, however, is dependent on the robustness of the approach to the variation in each step of the modeling workflow. Variation in the input image quality is one of the main sources that impacts the reproducibility of radiomics analysis when a model is applied to broader range of medical imaging data. The quality of medical image is generally affected by both the scanner related factors such as image acquisition/reconstruction settings and the patient related factors such as patient motion. This article aimed to review the published literatures in this field that reported the impact of various imaging factors on the radiomics features through the change in image quality. The literatures were categorized by different imaging modalities and also tabulated based on the imaging parameters and the class of radiomics features included in the study. Strategies for image quality standardization were discussed based on the relevant literatures and recommendations for reducing the impact of image quality variation on the radiomics in multi-institutional clinical trial were summarized at the end of this article.
Collapse
|
19
|
Radiomics-Based Deep Learning Prediction of Overall Survival in Non-Small-Cell Lung Cancer Using Contrast-Enhanced Computed Tomography. Cancers (Basel) 2022; 14:cancers14153798. [PMID: 35954461 PMCID: PMC9367244 DOI: 10.3390/cancers14153798] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2022] [Revised: 08/01/2022] [Accepted: 08/02/2022] [Indexed: 12/22/2022] Open
Abstract
Simple Summary The five-year survival rate of non-small-cell lung cancer (NSCLC), which accounts for 85% of all lung cancer cases, is only 10–20%. A reliable prediction model of overall survival (OS) that integrates imaging and clinical data is required. Overall, 492 patients with NSCLC from two hospitals were enrolled in this study. The compensation method was applied to reduce the variation of imaging features among different hospitals. We constructed a deep learning prediction model, DeepSurv, based on computed tomography radiomics and key clinical features to generate a personalized survival curve for each patient. The results of DeepSurv showed a good performance in discriminating high and low risk of survival. Furthermore, the generated personalized survival curves could be intuitively applied for individual OS prediction in clinical practice. We concluded that the proposed prediction model could benefit physicians, patients, and caregivers in managing NSCLC and facilitate personalized medicine. Abstract Patient outcomes of non-small-cell lung cancer (NSCLC) vary because of tumor heterogeneity and treatment strategies. This study aimed to construct a deep learning model combining both radiomic and clinical features to predict the overall survival of patients with NSCLC. To improve the reliability of the proposed model, radiomic analysis complying with the Image Biomarker Standardization Initiative and the compensation approach to integrate multicenter datasets were performed on contrast-enhanced computed tomography (CECT) images. Pretreatment CECT images and the clinical data of 492 patients with NSCLC from two hospitals were collected. The deep neural network architecture, DeepSurv, with the input of radiomic and clinical features was employed. The performance of survival prediction model was assessed using the C-index and area under the curve (AUC) 8, 12, and 24 months after diagnosis. The performance of survival prediction that combined eight radiomic features and five clinical features outperformed that solely based on radiomic or clinical features. The C-index values of the combined model achieved 0.74, 0.75, and 0.75, respectively, and AUC values of 0.76, 0.74, and 0.73, respectively, 8, 12, and 24 months after diagnosis. In conclusion, combining the traits of pretreatment CECT images, lesion characteristics, and treatment strategies could effectively predict the survival of patients with NSCLC using a deep learning model.
Collapse
|
20
|
Ferrández MC, Eertink JJ, Golla SSV, Wiegers SE, Zwezerijnen GJC, Pieplenbosch S, Zijlstra JM, Boellaard R. Combatting the effect of image reconstruction settings on lymphoma [ 18F]FDG PET metabolic tumor volume assessment using various segmentation methods. EJNMMI Res 2022; 12:44. [PMID: 35904645 PMCID: PMC9338209 DOI: 10.1186/s13550-022-00916-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Accepted: 07/18/2022] [Indexed: 11/15/2022] Open
Abstract
Background [18F]FDG PET-based metabolic tumor volume (MTV) is a promising prognostic marker for lymphoma patients. The aim of this study is to assess the sensitivity of several MTV segmentation methods to variations in image reconstruction methods and the ability of ComBat to improve MTV reproducibility. Methods Fifty-six lesions were segmented from baseline [18F]FDG PET scans of 19 lymphoma patients. For each scan, EARL1 and EARL2 standards and locally clinically preferred reconstruction protocols were applied. Lesions were delineated using 9 semiautomatic segmentation methods: fixed threshold based on standardized uptake value (SUV), (SUV = 4, SUV = 2.5), relative threshold (41% of SUVmax [41M], 50% of SUVpeak [A50P]), majority vote-based methods that select voxels detected by at least 2 (MV2) and 3 (MV3) out of the latter 4 methods, Nestle thresholding, and methods that identify the optimal method based on SUVmax (L2A, L2B). MTVs from EARL2 and locally clinically preferred reconstructions were compared to those from EARL1. Finally, different versions of ComBat were explored to harmonize the data.
Results MTVs from the SUV4.0 method were least sensitive to the use of different reconstructions (MTV ratio: median = 1.01, interquartile range = [0.96–1.10]). After ComBat harmonization, an improved agreement of MTVs among different reconstructions was found for most segmentation methods. The regular implementation of ComBat (‘Regular ComBat’) using non-transformed distributions resulted in less accurate and precise MTV alignments than a version using log-transformed datasets (‘Log-transformed ComBat’). Conclusion MTV depends on both segmentation method and reconstruction methods. ComBat reduces reconstruction dependent MTV variability, especially when log-transformation is used to account for the non-normal distribution of MTVs. Supplementary Information The online version contains supplementary material available at 10.1186/s13550-022-00916-9.
Collapse
Affiliation(s)
- Maria C Ferrández
- Cancer Center Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands.
| | - Jakoba J Eertink
- Cancer Center Amsterdam, Department of Hematology, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Sandeep S V Golla
- Cancer Center Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Sanne E Wiegers
- Cancer Center Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Gerben J C Zwezerijnen
- Cancer Center Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Simone Pieplenbosch
- Cancer Center Amsterdam, Department of Hematology, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Josée M Zijlstra
- Cancer Center Amsterdam, Department of Hematology, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Ronald Boellaard
- Cancer Center Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands
| |
Collapse
|
21
|
Singh A, Horng H, Roshkovan L, Weeks JK, Hershman M, Noël P, Luna JM, Cohen EA, Pantalone L, Shinohara RT, Bauml JM, Thompson JC, Aggarwal C, Carpenter EL, Katz SI, Kontos D. Development of a robust radiomic biomarker of progression-free survival in advanced non-small cell lung cancer patients treated with first-line immunotherapy. Sci Rep 2022; 12:9993. [PMID: 35705618 PMCID: PMC9200843 DOI: 10.1038/s41598-022-14160-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Accepted: 05/05/2022] [Indexed: 12/03/2022] Open
Abstract
We aim to determine the feasibility of a novel radiomic biomarker that can integrate with other established clinical prognostic factors to predict progression-free survival (PFS) in patients with non-small cell lung cancer (NSCLC) undergoing first-line immunotherapy. Our study includes 107 patients with stage 4 NSCLC treated with pembrolizumab-based therapy (monotherapy: 30%, combination chemotherapy: 70%). The ITK-SNAP software was used for 3D tumor volume segmentation from pre-therapy CT scans. Radiomic features (n = 102) were extracted using the CaPTk software. Impact of heterogeneity introduced by image physical dimensions (voxel spacing parameters) and acquisition parameters (contrast enhancement and CT reconstruction kernel) was mitigated by resampling the images to the minimum voxel spacing parameters and harmonization by a nested ComBat technique. This technique was initialized with radiomic features, clinical factors of age, sex, race, PD-L1 expression, ECOG status, body mass index (BMI), smoking status, recurrence event and months of progression-free survival, and image acquisition parameters as batch variables. Two phenotypes were identified using unsupervised hierarchical clustering of harmonized features. Prognostic factors, including PDL1 expression, ECOG status, BMI and smoking status, were combined with radiomic phenotypes in Cox regression models of PFS and Kaplan Meier (KM) curve-fitting. Cox model based on clinical factors had a c-statistic of 0.57, which increased to 0.63 upon addition of phenotypes derived from harmonized features. There were statistically significant differences in survival outcomes stratified by clinical covariates, as measured by the log-rank test (p = 0.034), which improved upon addition of phenotypes (p = 0.00022). We found that mitigation of heterogeneity by image resampling and nested ComBat harmonization improves prognostic value of phenotypes, resulting in better prediction of PFS when added to other prognostic variables.
Collapse
Affiliation(s)
- Apurva Singh
- Department of Radiology, Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Rm D702 Richards Bldg., 3700 Hamilton Walk, Philadelphia, PA, 19104, USA
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Hannah Horng
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Leonid Roshkovan
- Department of Radiology, Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Rm D702 Richards Bldg., 3700 Hamilton Walk, Philadelphia, PA, 19104, USA
| | - Joanna K Weeks
- Department of Radiology, Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Rm D702 Richards Bldg., 3700 Hamilton Walk, Philadelphia, PA, 19104, USA
| | - Michelle Hershman
- Department of Radiology, Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Rm D702 Richards Bldg., 3700 Hamilton Walk, Philadelphia, PA, 19104, USA
| | - Peter Noël
- Department of Radiology, Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Rm D702 Richards Bldg., 3700 Hamilton Walk, Philadelphia, PA, 19104, USA
| | - José Marcio Luna
- Department of Radiology, Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Rm D702 Richards Bldg., 3700 Hamilton Walk, Philadelphia, PA, 19104, USA
| | - Eric A Cohen
- Department of Radiology, Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Rm D702 Richards Bldg., 3700 Hamilton Walk, Philadelphia, PA, 19104, USA
| | - Lauren Pantalone
- Department of Radiology, Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Rm D702 Richards Bldg., 3700 Hamilton Walk, Philadelphia, PA, 19104, USA
| | - Russell T Shinohara
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Joshua M Bauml
- Department of Medicine, Division of Hematology-Oncology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Jeffrey C Thompson
- Department of Medicine, Division of Hematology-Oncology, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Medicine, Pulmonary, Allergy and Critical Care Medicine, Thoracic Oncology Group, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Charu Aggarwal
- Department of Medicine, Division of Hematology-Oncology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Erica L Carpenter
- Department of Medicine, Division of Hematology-Oncology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Sharyn I Katz
- Department of Radiology, Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Rm D702 Richards Bldg., 3700 Hamilton Walk, Philadelphia, PA, 19104, USA
| | - Despina Kontos
- Department of Radiology, Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Rm D702 Richards Bldg., 3700 Hamilton Walk, Philadelphia, PA, 19104, USA.
| |
Collapse
|
22
|
Zhu YQ, Liu C, Mo Y, Dong H, Huang C, Duan YN, Tang LL, Chu YY, Qin J. Radiomics for differentiating minimally invasive adenocarcinoma from precursor lesions in pure ground-glass opacities on chest computed tomography. Br J Radiol 2022; 95:20210768. [PMID: 35262392 PMCID: PMC10996418 DOI: 10.1259/bjr.20210768] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Revised: 02/27/2022] [Accepted: 03/04/2022] [Indexed: 12/14/2022] Open
Abstract
OBJECTIVE To explore the correlation between radiomic features and the pathology of pure ground-glass opacities (pGGOs), we established a radiomics model for predicting the pathological subtypes of minimally invasive adenocarcinoma (MIA) and precursor lesions. METHODS CT images of 1521 patients with lung adenocarcinoma or precursor lesions appearing as pGGOs on CT in our hospital (The Third Affiliated Hospital of Sun Yat-sen University) from January 2015 to March 2021 were analyzed retrospectively and selected based on inclusion and exclusion criteria. pGGOs were divided into an atypical adenomatous hyperplasia (AAH)/adenocarcinoma in situ (AIS) group and an MIA group. Radiomic features were extracted from the original and preprocessed images of the region of interest. ANOVA and least absolute shrinkage and selection operator feature selection algorithm were used for feature selection. Logistic regression algorithm was used to construct radiomics prediction model. Receiver operating characteristic curves were used to evaluate the classification efficiency. RESULTS 129 pGGOs were included. 2107 radiomic features were extracted from each region of interest. 18 radiomic features were eventually selected for model construction. The area under the curve of the radiomics model was 0.884 [95% confidence interval (CI), 0.818-0.949] in the training set and 0.872 (95% CI, 0.756-0.988) in the test set, with a sensitivity of 72.73%, specificity of 88.24% and accuracy of 79.47%. The decision curve indicated that the model had a high net benefit rate. CONCLUSION The prediction model for pathological subtypes of MIA and precursor lesions in pGGOs demonstrated a high diagnostic accuracy. ADVANCES IN KNOWLEDGE We focused on lesions appearing as pGGOs on CT and revealed the differences in radiomic features between MIA and precursor lesions. We constructed a radiomics prediction model and improved the diagnostic accuracy for the pathology of MIA and precursor lesions.
Collapse
Affiliation(s)
- Yan-qiu Zhu
- Department of Radiology, The Third Affiliated Hospital of Sun
Yat-sen University, No. 600 Tianhe Road, Tianhe District,
Guangzhou, China
| | - Chaohui Liu
- Department of Research Collaboration, R&D Center, Beijing
Deepwise & League of PHD Technology Co. Ltd,
Beijing, China
| | - Yan Mo
- Department of Research Collaboration, R&D Center, Beijing
Deepwise & League of PHD Technology Co. Ltd,
Beijing, China
| | - Hao Dong
- Department of Research Collaboration, R&D Center, Beijing
Deepwise & League of PHD Technology Co. Ltd,
Beijing, China
| | - Chencui Huang
- Department of Research Collaboration, R&D Center, Beijing
Deepwise & League of PHD Technology Co. Ltd,
Beijing, China
| | - Ya-ni Duan
- Department of Radiology, The Third Affiliated Hospital of Sun
Yat-sen University, No. 600 Tianhe Road, Tianhe District,
Guangzhou, China
| | - Lei-lei Tang
- Department of Radiology, The Third Affiliated Hospital of Sun
Yat-sen University, No. 600 Tianhe Road, Tianhe District,
Guangzhou, China
| | - Yuan-yuan Chu
- Department of Radiology, The Third Affiliated Hospital of Sun
Yat-sen University, No. 600 Tianhe Road, Tianhe District,
Guangzhou, China
| | - Jie Qin
- Department of Radiology, The Third Affiliated Hospital of Sun
Yat-sen University, No. 600 Tianhe Road, Tianhe District,
Guangzhou, China
| |
Collapse
|
23
|
Nan Y, Ser JD, Walsh S, Schönlieb C, Roberts M, Selby I, Howard K, Owen J, Neville J, Guiot J, Ernst B, Pastor A, Alberich-Bayarri A, Menzel MI, Walsh S, Vos W, Flerin N, Charbonnier JP, van Rikxoort E, Chatterjee A, Woodruff H, Lambin P, Cerdá-Alberich L, Martí-Bonmatí L, Herrera F, Yang G. Data harmonisation for information fusion in digital healthcare: A state-of-the-art systematic review, meta-analysis and future research directions. AN INTERNATIONAL JOURNAL ON INFORMATION FUSION 2022; 82:99-122. [PMID: 35664012 PMCID: PMC8878813 DOI: 10.1016/j.inffus.2022.01.001] [Citation(s) in RCA: 38] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/24/2021] [Revised: 12/22/2021] [Accepted: 01/07/2022] [Indexed: 05/13/2023]
Abstract
Removing the bias and variance of multicentre data has always been a challenge in large scale digital healthcare studies, which requires the ability to integrate clinical features extracted from data acquired by different scanners and protocols to improve stability and robustness. Previous studies have described various computational approaches to fuse single modality multicentre datasets. However, these surveys rarely focused on evaluation metrics and lacked a checklist for computational data harmonisation studies. In this systematic review, we summarise the computational data harmonisation approaches for multi-modality data in the digital healthcare field, including harmonisation strategies and evaluation metrics based on different theories. In addition, a comprehensive checklist that summarises common practices for data harmonisation studies is proposed to guide researchers to report their research findings more effectively. Last but not least, flowcharts presenting possible ways for methodology and metric selection are proposed and the limitations of different methods have been surveyed for future research.
Collapse
Affiliation(s)
- Yang Nan
- National Heart and Lung Institute, Imperial College London, London, Northern Ireland UK
| | - Javier Del Ser
- Department of Communications Engineering, University of the Basque Country UPV/EHU, Bilbao 48013, Spain
- TECNALIA, Basque Research and Technology Alliance (BRTA), Derio 48160, Spain
| | - Simon Walsh
- National Heart and Lung Institute, Imperial College London, London, Northern Ireland UK
| | - Carola Schönlieb
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, Northern Ireland UK
| | - Michael Roberts
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, Northern Ireland UK
- Oncology R&D, AstraZeneca, Cambridge, Northern Ireland UK
| | - Ian Selby
- Department of Radiology, University of Cambridge, Cambridge, Northern Ireland UK
| | - Kit Howard
- Clinical Data Interchange Standards Consortium, Austin, TX, United States of America
| | - John Owen
- Clinical Data Interchange Standards Consortium, Austin, TX, United States of America
| | - Jon Neville
- Clinical Data Interchange Standards Consortium, Austin, TX, United States of America
| | - Julien Guiot
- University Hospital of Liège (CHU Liège), Respiratory medicine department, Liège, Belgium
- University of Liege, Department of clinical sciences, Pneumology-Allergology, Liège, Belgium
| | - Benoit Ernst
- University Hospital of Liège (CHU Liège), Respiratory medicine department, Liège, Belgium
- University of Liege, Department of clinical sciences, Pneumology-Allergology, Liège, Belgium
| | | | | | - Marion I. Menzel
- Technische Hochschule Ingolstadt, Ingolstadt, Germany
- GE Healthcare GmbH, Munich, Germany
| | - Sean Walsh
- Radiomics (Oncoradiomics SA), Liège, Belgium
| | - Wim Vos
- Radiomics (Oncoradiomics SA), Liège, Belgium
| | - Nina Flerin
- Radiomics (Oncoradiomics SA), Liège, Belgium
| | | | | | - Avishek Chatterjee
- Department of Precision Medicine, Maastricht University, Maastricht, The Netherlands
| | - Henry Woodruff
- Department of Precision Medicine, Maastricht University, Maastricht, The Netherlands
| | - Philippe Lambin
- Department of Precision Medicine, Maastricht University, Maastricht, The Netherlands
| | - Leonor Cerdá-Alberich
- Medical Imaging Department, Hospital Universitari i Politècnic La Fe, Valencia, Spain
| | - Luis Martí-Bonmatí
- Medical Imaging Department, Hospital Universitari i Politècnic La Fe, Valencia, Spain
| | - Francisco Herrera
- Department of Computer Sciences and Artificial Intelligence, Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI) University of Granada, Granada, Spain
- Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Guang Yang
- National Heart and Lung Institute, Imperial College London, London, Northern Ireland UK
- Cardiovascular Research Centre, Royal Brompton Hospital, London, Northern Ireland UK
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, Northern Ireland UK
| |
Collapse
|
24
|
Generalized ComBat harmonization methods for radiomic features with multi-modal distributions and multiple batch effects. Sci Rep 2022; 12:4493. [PMID: 35296726 PMCID: PMC8927332 DOI: 10.1038/s41598-022-08412-9] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Accepted: 03/07/2022] [Indexed: 01/20/2023] Open
Abstract
Radiomic features have a wide range of clinical applications, but variability due to image acquisition factors can affect their performance. The harmonization tool ComBat is a promising solution but is limited by inability to harmonize multimodal distributions, unknown imaging parameters, and multiple imaging parameters. In this study, we propose two methods for addressing these limitations. We propose a sequential method that allows for harmonization of radiomic features by multiple imaging parameters (Nested ComBat). We also employ a Gaussian Mixture Model (GMM)-based method (GMM ComBat) where scans are split into groupings based on the shape of the distribution used for harmonization as a batch effect and subsequent harmonization by a known imaging parameter. These two methods were evaluated on features extracted with CapTK and PyRadiomics from two public lung computed tomography datasets. We found that Nested ComBat exhibited similar performance to standard ComBat in reducing the percentage of features with statistically significant differences in distribution attributable to imaging parameters. GMM ComBat improved harmonization performance over standard ComBat (− 11%, − 10% for Lung3/CAPTK, Lung3/PyRadiomics harmonizing by kernel resolution). Features harmonized with a variant of the Nested method and the GMM split method demonstrated similar c-statistics and Kaplan–Meier curves when used in survival analyses.
Collapse
|
25
|
Cabini RF, Brero F, Lancia A, Stelitano C, Oneta O, Ballante E, Puppo E, Mariani M, Alì E, Bartolomeo V, Montesano M, Merizzoli E, Aluia D, Agustoni F, Stella GM, Sun R, Bianchini L, Deutsch E, Figini S, Bortolotto C, Preda L, Lascialfari A, Filippi AR. Preliminary report on harmonization of features extraction process using the ComBat tool in the multi-center “Blue Sky Radiomics” study on stage III unresectable NSCLC. Insights Imaging 2022; 13:38. [PMID: 35254525 PMCID: PMC8901939 DOI: 10.1186/s13244-022-01171-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Accepted: 01/28/2022] [Indexed: 11/10/2022] Open
Abstract
Abstract
Background and purpose
In the retrospective-prospective multi-center "Blue Sky Radiomics” study (NCT04364776), we plan to test a pre-defined radiomic signature in a series of stage III unresectable NSCLC patients undergoing chemoradiotherapy and maintenance immunotherapy. As a necessary preliminary step, we explore the influence of different image-acquisition parameters on radiomic features’ reproducibility and apply methods for harmonization.
Material and methods
We identified the primary lung tumor on two computed tomography (CT) series for each patient, acquired before and after chemoradiation with i.v. contrast medium and with different scanners. Tumor segmentation was performed by two oncological imaging specialists (thoracic radiologist and radio-oncologist) using the Oncentra Masterplan® software. We extracted 42 radiomic features from the specific ROIs (LIFEx). To assess the impact of different acquisition parameters on features extraction, we used the Combat tool with nonparametric adjustment and the longitudinal version (LongComBat).
Results
We defined 14 CT acquisition protocols for the harmonization process. Before harmonization, 76% of the features were significantly influenced by these protocols. After, all extracted features resulted in being independent of the acquisition parameters. In contrast, 5% of the LongComBat harmonized features still depended on acquisition protocols.
Conclusions
We reduced the impact of different CT acquisition protocols on radiomic features extraction in a group of patients enrolled in a radiomic study on stage III NSCLC. The harmonization process appears essential for the quality of radiomic data and for their reproducibility.
ClinicalTrials.gov Identifier: NCT04364776, First Posted:April 28, 2020, Actual Study Start Date: April 15, 2020, https://clinicaltrials.gov/ct2/show/NCT04364776.
Collapse
|
26
|
Impact of feature harmonization on radiogenomics analysis: Prediction of EGFR and KRAS mutations from non-small cell lung cancer PET/CT images. Comput Biol Med 2022; 142:105230. [DOI: 10.1016/j.compbiomed.2022.105230] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Revised: 12/23/2021] [Accepted: 01/07/2022] [Indexed: 12/13/2022]
|
27
|
Cho HH, Kim CK, Park H. Overview of radiomics in prostate imaging and future directions. Br J Radiol 2022; 95:20210539. [PMID: 34797688 PMCID: PMC8978251 DOI: 10.1259/bjr.20210539] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Recent advancements in imaging technology and analysis methods have led to an analytic framework known as radiomics. This framework extracts comprehensive high-dimensional features from imaging data and performs data mining to build analytical models for improved decision-support. Its features include many categories spanning texture and shape; thus, it can provide abundant information for precision medicine. Many studies of prostate radiomics have shown promising results in the assessment of pathological features, prediction of treatment response, and stratification of risk groups. Herein, we aimed to provide a general overview of radiomics procedures, discuss technical issues, explain various clinical applications, and suggest future research directions, especially for prostate imaging.
Collapse
Affiliation(s)
- Hwan-Ho Cho
- Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, Korea.,Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, Korea
| | - Chan Kyo Kim
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Hyunjin Park
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, Korea.,School of Electronic and Electrical Engineering, Sungkyunkwan University, Suwon, Korea
| |
Collapse
|
28
|
Zhang X, Zhang Y, Zhang G, Qiu X, Tan W, Yin X, Liao L. Deep Learning With Radiomics for Disease Diagnosis and Treatment: Challenges and Potential. Front Oncol 2022; 12:773840. [PMID: 35251962 PMCID: PMC8891653 DOI: 10.3389/fonc.2022.773840] [Citation(s) in RCA: 37] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Accepted: 01/17/2022] [Indexed: 12/12/2022] Open
Abstract
The high-throughput extraction of quantitative imaging features from medical images for the purpose of radiomic analysis, i.e., radiomics in a broad sense, is a rapidly developing and emerging research field that has been attracting increasing interest, particularly in multimodality and multi-omics studies. In this context, the quantitative analysis of multidimensional data plays an essential role in assessing the spatio-temporal characteristics of different tissues and organs and their microenvironment. Herein, recent developments in this method, including manually defined features, data acquisition and preprocessing, lesion segmentation, feature extraction, feature selection and dimension reduction, statistical analysis, and model construction, are reviewed. In addition, deep learning-based techniques for automatic segmentation and radiomic analysis are being analyzed to address limitations such as rigorous workflow, manual/semi-automatic lesion annotation, and inadequate feature criteria, and multicenter validation. Furthermore, a summary of the current state-of-the-art applications of this technology in disease diagnosis, treatment response, and prognosis prediction from the perspective of radiology images, multimodality images, histopathology images, and three-dimensional dose distribution data, particularly in oncology, is presented. The potential and value of radiomics in diagnostic and therapeutic strategies are also further analyzed, and for the first time, the advances and challenges associated with dosiomics in radiotherapy are summarized, highlighting the latest progress in radiomics. Finally, a robust framework for radiomic analysis is presented and challenges and recommendations for future development are discussed, including but not limited to the factors that affect model stability (medical big data and multitype data and expert knowledge in medical), limitations of data-driven processes (reproducibility and interpretability of studies, different treatment alternatives for various institutions, and prospective researches and clinical trials), and thoughts on future directions (the capability to achieve clinical applications and open platform for radiomics analysis).
Collapse
Affiliation(s)
- Xingping Zhang
- Institute of Advanced Cyberspace Technology, Guangzhou University, Guangzhou, China
- Department of New Networks, Peng Cheng Laboratory, Shenzhen, China
| | - Yanchun Zhang
- Institute of Advanced Cyberspace Technology, Guangzhou University, Guangzhou, China
- Department of New Networks, Peng Cheng Laboratory, Shenzhen, China
| | - Guijuan Zhang
- Department of Respiratory Medicine, First Affiliated Hospital of Gannan Medical University, Ganzhou, China
| | - Xingting Qiu
- Department of Radiology, First Affiliated Hospital of Gannan Medical University, Ganzhou, China
| | - Wenjun Tan
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Shenyang, China
| | - Xiaoxia Yin
- Institute of Advanced Cyberspace Technology, Guangzhou University, Guangzhou, China
| | - Liefa Liao
- School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou, China
| |
Collapse
|
29
|
Reproducibility of radiomic features in CT images of NSCLC patients: an integrative analysis on the impact of acquisition and reconstruction parameters. Eur Radiol Exp 2022; 6:2. [PMID: 35075539 PMCID: PMC8786992 DOI: 10.1186/s41747-021-00258-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Accepted: 12/16/2021] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND We investigated to what extent tube voltage, scanner model, and reconstruction algorithm affect radiomic feature reproducibility in a single-institution retrospective database of computed tomography images of non-small-cell lung cancer patients. METHODS This study was approved by the Institutional Review Board (UID 2412). Images of 103 patients were considered, being acquired on either among two scanners, at 100 or 120 kVp. For each patient, images were reconstructed with six iterative blending levels, and 1414 features were extracted from each reconstruction. At univariate analysis, Wilcoxon-Mann-Whitney test was applied to evaluate feature differences within scanners and voltages, whereas the impact of the reconstruction was established with the overall concordance correlation coefficient (OCCC). A multivariable mixed model was also applied to investigate the independent contribution of each acquisition/reconstruction parameter. Univariate and multivariable analyses were combined to analyse feature behaviour. RESULTS Scanner model and voltage did not affect features significantly. The reconstruction blending level showed a significant impact at both univariate analysis (154/1414 features yielding an OCCC < 0.85) and multivariable analysis, with most features (1042/1414) revealing a systematic trend with the blending level (multiple comparisons adjusted p < 0.05). Reproducibility increased in association to image processing with smooth filters, nonetheless specific investigation in relation to clinical endpoints should be performed to ensure that textural information is not removed. CONCLUSIONS Combining univariate and multivariable models is allowed to identify features for which corrections may be applied to reduce the trend with the algorithm and increase reproducibility. Subsequent clustering may be applied to eliminate residual redundancy.
Collapse
|
30
|
Yoon JH, Sun SH, Xiao M, Yang H, Lu L, Li Y, Schwartz LH, Zhao B. Convolutional Neural Network Addresses the Confounding Impact of CT Reconstruction Kernels on Radiomics Studies. Tomography 2021; 7:877-892. [PMID: 34941646 PMCID: PMC8707549 DOI: 10.3390/tomography7040074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Revised: 10/31/2021] [Accepted: 11/29/2021] [Indexed: 11/20/2022] Open
Abstract
Achieving high feature reproducibility while preserving biological information is one of the main challenges for the generalizability of current radiomics studies. Non-clinical imaging variables, such as reconstruction kernels, have shown to significantly impact radiomics features. In this study, we retrain an open-source convolutional neural network (CNN) to harmonize computerized tomography (CT) images with various reconstruction kernels to improve feature reproducibility and radiomic model performance using epidermal growth factor receptor (EGFR) mutation prediction in lung cancer as a paradigm. In the training phase, the CNN was retrained and tested on 32 lung cancer patients’ CT images between two different groups of reconstruction kernels (smooth and sharp). In the validation phase, the retrained CNN was validated on an external cohort of 223 lung cancer patients’ CT images acquired using different CT scanners and kernels. The results showed that the retrained CNN could be successfully applied to external datasets with different CT scanner parameters, and harmonization of reconstruction kernels from sharp to smooth could significantly improve the performance of radiomics model in predicting EGFR mutation status in lung cancer. In conclusion, the CNN based method showed great potential in improving feature reproducibility and generalizability by harmonizing medical images with heterogeneous reconstruction kernels.
Collapse
Affiliation(s)
- Jin H. Yoon
- Department of Radiology, New York Presbyterian Hospital, Columbia University Irving Medical Center, New York, NY 10039, USA; (J.H.Y.); (S.H.S.); (H.Y.); (L.H.S.); (B.Z.)
| | - Shawn H. Sun
- Department of Radiology, New York Presbyterian Hospital, Columbia University Irving Medical Center, New York, NY 10039, USA; (J.H.Y.); (S.H.S.); (H.Y.); (L.H.S.); (B.Z.)
| | - Manjun Xiao
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha 410011, China;
| | - Hao Yang
- Department of Radiology, New York Presbyterian Hospital, Columbia University Irving Medical Center, New York, NY 10039, USA; (J.H.Y.); (S.H.S.); (H.Y.); (L.H.S.); (B.Z.)
| | - Lin Lu
- Department of Radiology, New York Presbyterian Hospital, Columbia University Irving Medical Center, New York, NY 10039, USA; (J.H.Y.); (S.H.S.); (H.Y.); (L.H.S.); (B.Z.)
- Correspondence: (L.L.); (Y.L.); Tel.: +1-212-342-3018 (L.L.)
| | - Yajun Li
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha 410011, China;
- Correspondence: (L.L.); (Y.L.); Tel.: +1-212-342-3018 (L.L.)
| | - Lawrence H. Schwartz
- Department of Radiology, New York Presbyterian Hospital, Columbia University Irving Medical Center, New York, NY 10039, USA; (J.H.Y.); (S.H.S.); (H.Y.); (L.H.S.); (B.Z.)
| | - Binsheng Zhao
- Department of Radiology, New York Presbyterian Hospital, Columbia University Irving Medical Center, New York, NY 10039, USA; (J.H.Y.); (S.H.S.); (H.Y.); (L.H.S.); (B.Z.)
| |
Collapse
|
31
|
Radiomics for Predicting Lung Cancer Outcomes Following Radiotherapy: A Systematic Review. Clin Oncol (R Coll Radiol) 2021; 34:e107-e122. [PMID: 34763965 DOI: 10.1016/j.clon.2021.10.006] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Revised: 08/24/2021] [Accepted: 10/14/2021] [Indexed: 12/13/2022]
Abstract
Lung cancer's radiomic phenotype may potentially inform clinical decision-making with respect to radical radiotherapy. At present there are no validated biomarkers available for the individualisation of radical radiotherapy in lung cancer and the mortality rate of this disease remains the highest of all other solid tumours. MEDLINE was searched using the terms 'radiomics' and 'lung cancer' according to the Preferred Reporting Items for Systematic Reviews and Met-Analyses (PRISMA) guidance. Radiomics studies were defined as those manuscripts describing the extraction and analysis of at least 10 quantifiable imaging features. Only those studies assessing disease control, survival or toxicity outcomes for patients with lung cancer following radical radiotherapy ± chemotherapy were included. Study titles and abstracts were reviewed by two independent reviewers. The Radiomics Quality Score was applied to the full text of included papers. Of 244 returned results, 44 studies met the eligibility criteria for inclusion. End points frequently reported were local (17%), regional (17%) and distant control (31%), overall survival (79%) and pulmonary toxicity (4%). Imaging features strongly associated with clinical outcomes include texture features belonging to the subclasses Gray level run length matrix, Gray level co-occurrence matrix and kurtosis. The median cohort size for model development was 100 (15-645); in the 11 studies with external validation in a separate independent population, the median cohort size was 84 (21-295). The median number of imaging features extracted was 184 (10-6538). The median Radiomics Quality Score was 11% (0-47). Patient-reported outcomes were not incorporated within any studies identified. No studies externally validated a radiomics signature in a registered prospective study. Imaging-derived indices attained through radiomic analyses could equip thoracic oncologists with biomarkers for treatment response, patterns of failure, normal tissue toxicity and survival in lung cancer. Based on routine scans, their non-invasive nature and cost-effectiveness are major advantages over conventional pathological assessment. Improved tools are required for the appraisal of radiomics studies, as significant barriers to clinical implementation remain, such as standardisation of input scan data, quality of reporting and external validation of signatures in randomised, interventional clinical trials.
Collapse
|
32
|
Kendrick J, Francis R, Hassan GM, Rowshanfarzad P, Jeraj R, Kasisi C, Rusanov B, Ebert M. Radiomics for Identification and Prediction in Metastatic Prostate Cancer: A Review of Studies. Front Oncol 2021; 11:771787. [PMID: 34790581 PMCID: PMC8591174 DOI: 10.3389/fonc.2021.771787] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Accepted: 10/11/2021] [Indexed: 12/21/2022] Open
Abstract
Metastatic Prostate Cancer (mPCa) is associated with a poor patient prognosis. mPCa spreads throughout the body, often to bones, with spatial and temporal variations that make the clinical management of the disease difficult. The evolution of the disease leads to spatial heterogeneity that is extremely difficult to characterise with solid biopsies. Imaging provides the opportunity to quantify disease spread. Advanced image analytics methods, including radiomics, offer the opportunity to characterise heterogeneity beyond what can be achieved with simple assessment. Radiomics analysis has the potential to yield useful quantitative imaging biomarkers that can improve the early detection of mPCa, predict disease progression, assess response, and potentially inform the choice of treatment procedures. Traditional radiomics analysis involves modelling with hand-crafted features designed using significant domain knowledge. On the other hand, artificial intelligence techniques such as deep learning can facilitate end-to-end automated feature extraction and model generation with minimal human intervention. Radiomics models have the potential to become vital pieces in the oncology workflow, however, the current limitations of the field, such as limited reproducibility, are impeding their translation into clinical practice. This review provides an overview of the radiomics methodology, detailing critical aspects affecting the reproducibility of features, and providing examples of how artificial intelligence techniques can be incorporated into the workflow. The current landscape of publications utilising radiomics methods in the assessment and treatment of mPCa are surveyed and reviewed. Associated studies have incorporated information from multiple imaging modalities, including bone scintigraphy, CT, PET with varying tracers, multiparametric MRI together with clinical covariates, spanning the prediction of progression through to overall survival in varying cohorts. The methodological quality of each study is quantified using the radiomics quality score. Multiple deficits were identified, with the lack of prospective design and external validation highlighted as major impediments to clinical translation. These results inform some recommendations for future directions of the field.
Collapse
Affiliation(s)
- Jake Kendrick
- School of Physics, Mathematics and Computing, University of Western Australia, Perth, WA, Australia
| | - Roslyn Francis
- Medical School, University of Western Australia, Crawley, WA, Australia
- Department of Nuclear Medicine, Sir Charles Gairdner Hospital, Perth, WA, Australia
| | - Ghulam Mubashar Hassan
- School of Physics, Mathematics and Computing, University of Western Australia, Perth, WA, Australia
| | - Pejman Rowshanfarzad
- School of Physics, Mathematics and Computing, University of Western Australia, Perth, WA, Australia
| | - Robert Jeraj
- Department of Medical Physics, University of Wisconsin, Madison, WI, United States
- Faculty of Mathematics and Physics, University of Ljubljana, Ljubljana, Slovenia
| | - Collin Kasisi
- Department of Nuclear Medicine, Sir Charles Gairdner Hospital, Perth, WA, Australia
| | - Branimir Rusanov
- School of Physics, Mathematics and Computing, University of Western Australia, Perth, WA, Australia
| | - Martin Ebert
- School of Physics, Mathematics and Computing, University of Western Australia, Perth, WA, Australia
- Department of Radiation Oncology, Sir Charles Gairdner Hospital, Perth, WA, Australia
- 5D Clinics, Claremont, WA, Australia
| |
Collapse
|
33
|
Tabatabaei M, Tasorian B, Goyal M, Moini A, Sotoudeh H. Feasibility of Radiomics to Differentiate Coronavirus Disease 2019 (COVID-19) from H1N1 Influenza Pneumonia on Chest Computed Tomography: A Proof of Concept. IRANIAN JOURNAL OF MEDICAL SCIENCES 2021; 46:420-427. [PMID: 34840382 PMCID: PMC8611216 DOI: 10.30476/ijms.2021.88036.1858] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Revised: 01/09/2021] [Accepted: 01/20/2021] [Indexed: 01/19/2023]
Abstract
BACKGROUND Chest computed tomography (CT) plays an essential role in diagnosing coronavirus disease 2019 (COVID-19). However, CT findings are often nonspecific among different viral pneumonia conditions. The differentiation between COVID-19 and influenza can be challenging when seasonal influenza concurs with the COVID-19 pandemic. This study was conducted to test the ability of radiomics-artificial intelligence (AI) to perform this task. METHODS In this retrospective study, chest CT images from 47 patients with COVID-19 (after February 2020) and 19 patients with H1N1 influenza (before September 2019) pneumonia were collected from three hospitals affiliated with Arak University of Medical Sciences, Arak, Iran. All pulmonary lesions were segmented on CT images. Multiple radiomics features were extracted from the lesions and used to develop support-vector machine (SVM), k-nearest neighbor (k-NN), decision tree, neural network, adaptive boosting (AdaBoost), and random forest. RESULTS The patients with COVID-19 and H1N1 influenza were not significantly different in age and sex (P=0.13 and 0.99, respectively). Nonetheless, the average time between initial symptoms/hospitalization and chest CT was shorter in the patients with COVID-19 (P=0.001 and 0.01, respectively). After the implementation of the inclusion and exclusion criteria, 453 pulmonary lesions were included in this study. On the harmonized features, random forest yielded the highest performance (area under the curve=0.97, sensitivity=89%, precision=90%, F1 score=89%, and classification accuracy=89%). CONCLUSION In our preliminary study, radiomics feature extraction, conjoined with AI, especially random forest and neural network, appeared to yield very promising results in the differentiation between COVID-19 and H1N1 influenza on chest CT.
Collapse
Affiliation(s)
- Mohsen Tabatabaei
- Health Information Management, Office of Vice Chancellor for Research, Arak University of Medical Sciences, Arak, Iran
| | - Baharak Tasorian
- Internal Medicine Department, Arak University of Medical Sciences, Arak, Iran
| | - Manu Goyal
- Postdoctoral Research Associate in Medical Imaging at Dartmouth College, Hanover, NH 03755 USA
| | - Abdollatif Moini
- Department of Internal Medicine of Amir Al Momenin Hospital, Arak University of Medical Sciences, Arak, Iran
| | | |
Collapse
|
34
|
Whitney HM, Li H, Ji Y, Liu P, Giger ML. Multi-Stage Harmonization for Robust AI across Breast MR Databases. Cancers (Basel) 2021; 13:cancers13194809. [PMID: 34638294 PMCID: PMC8508003 DOI: 10.3390/cancers13194809] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Revised: 09/16/2021] [Accepted: 09/18/2021] [Indexed: 12/22/2022] Open
Abstract
Simple Summary Batch harmonization of radiomic features extracted from magnetic resonance images of breast lesions from two databases was applied to an artificial intelligence/machine learning classification workflow. Training and independent test sets from the two databases, as well as the combination of them, were used in pre-harmonization and post-harmonization forms to investigate the generalizability of performance in the task of distinguishing between malignant and benign lesions. Most training and independent test scenarios were statistically equivalent, demonstrating that batch harmonization with feature selection harmonization can potentially develop generalizable classification models. Abstract Radiomic features extracted from medical images may demonstrate a batch effect when cases come from different sources. We investigated classification performance using training and independent test sets drawn from two sources using both pre-harmonization and post-harmonization features. In this retrospective study, a database of thirty-two radiomic features, extracted from DCE-MR images of breast lesions after fuzzy c-means segmentation, was collected. There were 944 unique lesions in Database A (208 benign lesions, 736 cancers) and 1986 unique lesions in Database B (481 benign lesions, 1505 cancers). The lesions from each database were divided by year of image acquisition into training and independent test sets, separately by database and in combination. ComBat batch harmonization was conducted on the combined training set to minimize the batch effect on eligible features by database. The empirical Bayes estimates from the feature harmonization were applied to the eligible features of the combined independent test set. The training sets (A, B, and combined) were then used in training linear discriminant analysis classifiers after stepwise feature selection. The classifiers were then run on the A, B, and combined independent test sets. Classification performance was compared using pre-harmonization features to post-harmonization features, including their corresponding feature selection, evaluated using the area under the receiver operating characteristic curve (AUC) as the figure of merit. Four out of five training and independent test scenarios demonstrated statistically equivalent classification performance when compared pre- and post-harmonization. These results demonstrate that translation of machine learning techniques with batch data harmonization can potentially yield generalizable models that maintain classification performance.
Collapse
Affiliation(s)
- Heather M. Whitney
- Department of Radiology, The University of Chicago, Chicago, IL 60637, USA; (H.L.); (Y.J.)
- Department of Physics, Wheaton College, Wheaton, IL 60187, USA
- Correspondence: (H.M.W.); (M.L.G.)
| | - Hui Li
- Department of Radiology, The University of Chicago, Chicago, IL 60637, USA; (H.L.); (Y.J.)
| | - Yu Ji
- Department of Radiology, The University of Chicago, Chicago, IL 60637, USA; (H.L.); (Y.J.)
- Tianjin Medical University Cancer Institute and Hospital, Tianjin 300060, China;
| | - Peifang Liu
- Tianjin Medical University Cancer Institute and Hospital, Tianjin 300060, China;
| | - Maryellen L. Giger
- Department of Radiology, The University of Chicago, Chicago, IL 60637, USA; (H.L.); (Y.J.)
- Correspondence: (H.M.W.); (M.L.G.)
| |
Collapse
|
35
|
Flaus A, Habouzit V, De Leiris N, Vuillez JP, Leccia MT, Perrot JL, Prevot N, Cachin F. FDG PET biomarkers for prediction of survival in metastatic melanoma prior to anti-PD1 immunotherapy. Sci Rep 2021; 11:18795. [PMID: 34552135 PMCID: PMC8458464 DOI: 10.1038/s41598-021-98310-3] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Accepted: 08/31/2021] [Indexed: 12/22/2022] Open
Abstract
Our aim was to analyse whether biomarkers extracted from baseline 18F-FDG PET before anti-PD1 treatment contribute to prognostic survival information for early risk stratification in metastatic melanoma. Fifty-six patients, without prior systemic treatment, BRAF wild type, explored using 18F-FDG PET were included retrospectively. Our primary endpoint was overall survival (OS). Total metabolic tumoral volume (MTV) and forty-one IBSI compliant parameters were extracted from PET. Parameters associated with outcome were evaluated by a cox regression model and when significant helped build a prognostic score. Median follow-up was 22.1 months and 21 patients died. Total MTV and long zone emphasis (LZE) correlated with shorter OS and served to define three risk categories for the prognostic score. For low, intermediate and high risk groups, survival rates were respectively 91.1% (IC 95 80–1), 56.1% (IC 95 37.1–85) and 19% (IC 95 0.06–60.2) and hazard ratios were respectively 0.11 (IC 95 0.025–0.46), P = 0.0028, 1.2 (IC 95 0.48–2.8), P = 0.74 and 5.9 (IC 95 2.5–14), P < 0.0001. To conclude, a prognostic score based on total MTV and LZE separated metastatic melanoma patients in 3 categories with dramatically different outcomes. Innovative therapies should be tested in the group with the lowest prognosis score for future clinical trials.
Collapse
Affiliation(s)
- A Flaus
- Nuclear Medecine Department, Saint-Etienne University Hospital, University of Saint-Etienne, Saint-Etienne, France. .,Nuclear Medicine Department, East Group Hospital, Hospices Civils de Lyon, Lyon, France. .,Service de Medecine Nucléaire, Hôpital Nord, CHU de Saint-Etienne, 42 055, Saint-Etienne, Cedex 2, France.
| | - V Habouzit
- Nuclear Medecine Department, Saint-Etienne University Hospital, University of Saint-Etienne, Saint-Etienne, France
| | - N De Leiris
- Nuclear Medecine Department, CHU Grenoble Alpes, University Grenoble Alpes, Grenoble, France.,Laboratoire Radiopharmaceutiques Biocliniques, University Grenoble Alpes, INSERM, CHU Grenoble Alpes, 38000, Grenoble, France
| | - J P Vuillez
- Nuclear Medecine Department, CHU Grenoble Alpes, University Grenoble Alpes, Grenoble, France.,Laboratoire Radiopharmaceutiques Biocliniques, University Grenoble Alpes, INSERM, CHU Grenoble Alpes, 38000, Grenoble, France
| | - M T Leccia
- Dermatology Department, CHU Grenoble Alpes, University Grenoble Alpes, Grenoble, France
| | - J L Perrot
- Dermatology Department, Saint-Etienne University Hospital, University of Saint-Etienne, Saint-Etienne, France
| | - N Prevot
- Nuclear Medecine Department, Saint-Etienne University Hospital, University of Saint-Etienne, Saint-Etienne, France
| | - F Cachin
- Nuclear Medicine Department, Jean Perrin Cancer Center of Clermont-Ferrand, Clermont-Ferrand, France
| |
Collapse
|
36
|
Orlhac F, Eertink JJ, Cottereau AS, Zijlstra JM, Thieblemont C, Meignan MA, Boellaard R, Buvat I. A guide to ComBat harmonization of imaging biomarkers in multicenter studies. J Nucl Med 2021; 63:172-179. [PMID: 34531263 DOI: 10.2967/jnumed.121.262464] [Citation(s) in RCA: 89] [Impact Index Per Article: 29.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Revised: 08/26/2021] [Indexed: 11/16/2022] Open
Abstract
The impact of PET image acquisition and reconstruction parameters on SUV measurements or radiomic feature values is widely documented. This "scanner" effect is detrimental to the design and validation of predictive or prognostic models and limits the use of large multicenter cohorts. To reduce the impact of this scanner effect, the ComBat method has been proposed and is now used in various contexts. The purpose of this article is to explain and illustrate the use of ComBat based on practical examples. We also give examples in which the ComBat assumptions are not met; thus, ComBat should not be used.
Collapse
Affiliation(s)
- Fanny Orlhac
- Institut Curie, Universite PSL, Inserm, U1288 LITO, Universite Paris Saclay, France
| | - Jakoba J Eertink
- Amsterdam UMC, Vrije Universiteit Amsterdam, department of Hematology, Cancer Center, Netherlands
| | | | - Josee M Zijlstra
- Amsterdam UMC, Vrije Universiteit Amsterdam, department of Hematology, Cancer Center, Netherlands
| | - Catherine Thieblemont
- APHP, Hopital Saint-Louis, Service d'hemato-oncologie, DMU DHI, Universite de Paris, France
| | - Michel A Meignan
- AP-HP, Universite Paris-Est, Hopital Henri Mondor, Lysa Imaging, France
| | - Ronald Boellaard
- Amsterdam UMC, Vrije Universiteit Amsterdam, department of Radiology and Nuclear Medicine, Cancer Center, Netherlands
| | - Irene Buvat
- Institut Curie, Universite PSL, Inserm, U1288 LITO, Universite Paris Saclay, France
| |
Collapse
|
37
|
Tang X, Qian WL, Yan WF, Pang T, Gong YL, Yang ZG. Radiomic assessment as a method for predicting tumor mutation burden (TMB) of bladder cancer patients: a feasibility study. BMC Cancer 2021; 21:823. [PMID: 34271855 PMCID: PMC8285848 DOI: 10.1186/s12885-021-08569-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Accepted: 07/07/2021] [Indexed: 02/08/2023] Open
Abstract
Background Tumor mutation burden (TMB) is an emerging prognostic biomarker of immunotherapy for bladder cancer (BLCA). We aim at investigating radiomic features’ value in predicting the TMB status of BLCA patients. Methods Totally, 75 patients with BLCA were enrolled. Radiomic features extracted from the volume of interest of preoperative pelvic contrast-enhanced computed tomography (CECT) were obtained for each case. Unsupervised hierarchical clustering analysis was performed based on radiomic features. Sequential univariate Logistic regression, the least absolute shrinkage and selection operator (LASSO) regression and the backward stepwise regression were used to develop a TMB-predicting model using radiomic features. Results The unsupervised clustering analysis divided the total cohort into two groups, i.e., group A (32.0%) and B (68.0%). Patients in group A had a significantly larger proportion of having high TMB against those in group B (66.7% vs. 41.2%, p = 0.039), indicating the intrinsic ability of radiomic features in TMB-predicting. In univariate analysis, 27 radiomic features could predict TMB. Based on six radiomic features selected by logistic and LASSO regression, a TMB-predicting model was built and visualized by nomogram. The area under the ROC curve of the model reached 0.853. Besides, the calibration curve and the decision curve also revealed the good performance of the model. Conclusions Our work firstly proved the feasibility of using radiomics to predict TMB for patients with BLCA. The predictive model based on radiomic features from pelvic CECT has a promising ability to predict TMB. Future study with a larger cohort is needed to verify our findings. Supplementary Information The online version contains supplementary material available at 10.1186/s12885-021-08569-y.
Collapse
Affiliation(s)
- Xin Tang
- Department of Radiology, West China Hospital, Sichuan University, 37# Guo Xue Xiang, Chengdu, 610041, Sichuan, China
| | - Wen-Lei Qian
- Department of Radiology, West China Hospital, Sichuan University, 37# Guo Xue Xiang, Chengdu, 610041, Sichuan, China
| | - Wei-Feng Yan
- Department of Radiology, West China Hospital, Sichuan University, 37# Guo Xue Xiang, Chengdu, 610041, Sichuan, China
| | - Tong Pang
- Department of Radiology, West China Hospital, Sichuan University, 37# Guo Xue Xiang, Chengdu, 610041, Sichuan, China
| | - You-Ling Gong
- Department of Thoracic Oncology and State Key Laboratory of Biotherapy, Cancer Center, West China Hospital, Sichuan University, 37# Guo Xue Xiang, Chengdu, 610041, Sichuan, China.
| | - Zhi-Gang Yang
- Department of Radiology, West China Hospital, Sichuan University, 37# Guo Xue Xiang, Chengdu, 610041, Sichuan, China.
| |
Collapse
|
38
|
Fiz F, Costa G, Gennaro N, la Bella L, Boichuk A, Sollini M, Politi LS, Balzarini L, Torzilli G, Chiti A, Viganò L. Contrast Administration Impacts CT-Based Radiomics of Colorectal Liver Metastases and Non-Tumoral Liver Parenchyma Revealing the "Radiological" Tumour Microenvironment. Diagnostics (Basel) 2021; 11:diagnostics11071162. [PMID: 34202253 PMCID: PMC8305553 DOI: 10.3390/diagnostics11071162] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Revised: 06/11/2021] [Accepted: 06/22/2021] [Indexed: 12/29/2022] Open
Abstract
The impact of the contrast medium on the radiomic textural features (TF) extracted from the CT scan is unclear. We investigated the modification of TFs of colorectal liver metastases (CLM), peritumoral tissue, and liver parenchyma. One hundred and sixty-two patients with 409 CLMs undergoing resection (2017–2020) into a single institution were considered. We analyzed the following volumes of interest (VOIs): The CLM (Tumor-VOI); a 5-mm parenchyma rim around the CLM (Margin-VOI); and a 2-mL sample of parenchyma distant from CLM (Liver-VOI). Forty-five TFs were extracted from each VOI (LIFEx®®). Contrast enhancement affected most TFs of the Tumor-VOI (71%) and Margin-VOI (62%), and part of those of the Liver-VOI (44%, p = 0.010). After contrast administration, entropy increased and energy decreased in the Tumor-VOI (0.93 ± 0.10 vs. 0.85 ± 0.14 in pre-contrast; 0.14 ± 0.03 vs. 0.18 ± 0.04, p < 0.001) and Margin-VOI (0.89 ± 0.11 vs. 0.85 ± 0.12; 0.16 ± 0.04 vs. 0.18 ± 0.04, p < 0.001), while remaining stable in the Liver-VOI. Comparing the VOIs, pre-contrast Tumor and Margin-VOI had similar entropy and energy (0.85/0.18 for both), while Liver-VOI had lower values (0.76/0.21, p < 0.001). In the portal phase, a gradient was observed (entropy: Tumor > Margin > Liver; energy: Tumor < Margin < Liver, p < 0.001). Contrast enhancement affected TFs of CLM, while it did not modify entropy and energy of parenchyma. TFs of the peritumoral tissue had modifications similar to the Tumor-VOI despite its radiological aspect being equal to non-tumoral parenchyma.
Collapse
Affiliation(s)
- Francesco Fiz
- Nuclear Medicine Unit, IRCCS Humanitas Research Hospital, Rozzano, 20089 Milan, Italy; (M.S.); (A.C.)
- Correspondence: (F.F.); (L.V.); Tel.: +39-02-8224-7361 (L.V.)
| | - Guido Costa
- Division of Hepatobiliary and General Surgery, Department of Surgery, IRCCS Humanitas Research Hospital, Rozzano, 20089 Milan, Italy; (G.C.); (G.T.)
| | - Nicolò Gennaro
- Department of Diagnostic Imaging, IRCCS Humanitas Research Hospital, Rozzano, 20089 Milan, Italy; (N.G.); (L.S.P.); (L.B.)
| | - Ludovico la Bella
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20089 Milan, Italy; (L.l.B.); (A.B.)
| | - Alexandra Boichuk
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20089 Milan, Italy; (L.l.B.); (A.B.)
| | - Martina Sollini
- Nuclear Medicine Unit, IRCCS Humanitas Research Hospital, Rozzano, 20089 Milan, Italy; (M.S.); (A.C.)
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20089 Milan, Italy; (L.l.B.); (A.B.)
| | - Letterio S. Politi
- Department of Diagnostic Imaging, IRCCS Humanitas Research Hospital, Rozzano, 20089 Milan, Italy; (N.G.); (L.S.P.); (L.B.)
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20089 Milan, Italy; (L.l.B.); (A.B.)
| | - Luca Balzarini
- Department of Diagnostic Imaging, IRCCS Humanitas Research Hospital, Rozzano, 20089 Milan, Italy; (N.G.); (L.S.P.); (L.B.)
| | - Guido Torzilli
- Division of Hepatobiliary and General Surgery, Department of Surgery, IRCCS Humanitas Research Hospital, Rozzano, 20089 Milan, Italy; (G.C.); (G.T.)
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20089 Milan, Italy; (L.l.B.); (A.B.)
| | - Arturo Chiti
- Nuclear Medicine Unit, IRCCS Humanitas Research Hospital, Rozzano, 20089 Milan, Italy; (M.S.); (A.C.)
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20089 Milan, Italy; (L.l.B.); (A.B.)
| | - Luca Viganò
- Division of Hepatobiliary and General Surgery, Department of Surgery, IRCCS Humanitas Research Hospital, Rozzano, 20089 Milan, Italy; (G.C.); (G.T.)
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20089 Milan, Italy; (L.l.B.); (A.B.)
- Correspondence: (F.F.); (L.V.); Tel.: +39-02-8224-7361 (L.V.)
| |
Collapse
|
39
|
Comment on Ibrahim et al. The Effects of In-Plane Spatial Resolution on CT-Based Radiomic Features' Stability with and without ComBat Harmonization. Cancers 2021, 13, 1848. Cancers (Basel) 2021; 13:cancers13123037. [PMID: 34207102 PMCID: PMC8234074 DOI: 10.3390/cancers13123037] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Revised: 05/10/2021] [Accepted: 05/10/2021] [Indexed: 12/22/2022] Open
|
40
|
Impact of Preprocessing and Harmonization Methods on the Removal of Scanner Effects in Brain MRI Radiomic Features. Cancers (Basel) 2021; 13:cancers13123000. [PMID: 34203896 PMCID: PMC8232807 DOI: 10.3390/cancers13123000] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Revised: 05/06/2021] [Accepted: 06/07/2021] [Indexed: 12/22/2022] Open
Abstract
Simple Summary As a rapid-development research field, radiomics-based analysis has been applied to many clinical problems. However, the reproducibility of the radiomics studies remain challenging especially when data suffers from scanner effects, a kind of non-biological variations introduced by different image acquiring settings. This study aims to investigate how the image preprocessing methods (N4 bias field correction and image resampling) and the harmonization methods (intensity normalization methods working on images and ComBat method working on radiomic features) help to remove the scanner effects and improve the radiomics reproducibility in brain MRI radiomics. Abstract In brain MRI radiomics studies, the non-biological variations introduced by different image acquisition settings, namely scanner effects, affect the reliability and reproducibility of the radiomics results. This paper assesses how the preprocessing methods (including N4 bias field correction and image resampling) and the harmonization methods (either the six intensity normalization methods working on brain MRI images or the ComBat method working on radiomic features) help to remove the scanner effects and improve the radiomic feature reproducibility in brain MRI radiomics. The analyses were based on in vitro datasets (homogeneous and heterogeneous phantom data) and in vivo datasets (brain MRI images collected from healthy volunteers and clinical patients with brain tumors). The results show that the ComBat method is essential and vital to remove scanner effects in brain MRI radiomic studies. Moreover, the intensity normalization methods, while not able to remove scanner effects at the radiomic feature level, still yield more comparable MRI images and improve the robustness of the harmonized features to the choice among ComBat implementations.
Collapse
|
41
|
Prediction of Human Papillomavirus (HPV) Association of Oropharyngeal Cancer (OPC) Using Radiomics: The Impact of the Variation of CT Scanner. Cancers (Basel) 2021; 13:cancers13092269. [PMID: 34066857 PMCID: PMC8125906 DOI: 10.3390/cancers13092269] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2021] [Revised: 04/29/2021] [Accepted: 05/06/2021] [Indexed: 11/16/2022] Open
Abstract
Simple Summary Recent studies exploring the application of radiomics features in medicine have shown promising results. However, variation in imaging parameters may impact the robustness of these features. Feature robustness may then in turn affect the prediction performance of the machine learning models built upon these features. While numerous studies have tested feature robustness against a variety of imaging parameters, the extent to which feature robustness affects predictions remains unclear. A particularly notable application of radiomics in clinical oncology is the prediction of Human Papillomavirus (HPV) association in Oropharyngeal cancer. In this study we explore how CT scanner type affects the performance of radiomics features for HPV association prediction and highlight the need to implement precautionary approaches so as to minimize this effect. Abstract Studies have shown that radiomic features are sensitive to the variability of imaging parameters (e.g., scanner models), and one of the major challenges in these studies lies in improving the robustness of quantitative features against the variations in imaging datasets from multi-center studies. Here, we assess the impact of scanner choice on computed tomography (CT)-derived radiomic features to predict the association of oropharyngeal squamous cell carcinoma with human papillomavirus (HPV). This experiment was performed on CT image datasets acquired from two different scanner manufacturers. We demonstrate strong scanner dependency by developing a machine learning model to classify HPV status from radiological images. These experiments reveal the effect of scanner manufacturer on the robustness of radiomic features, and the extent of this dependency is reflected in the performance of HPV prediction models. The results of this study highlight the importance of implementing an appropriate approach to reducing the impact of imaging parameters on radiomic features and consequently on the machine learning models, without removing features which are deemed non-robust but may contain learning information.
Collapse
|
42
|
Reiazi R, Abbas E, Famiyeh P, Rezaie A, Kwan JYY, Patel T, Bratman SV, Tadic T, Liu FF, Haibe-Kains B. The impact of the variation of imaging parameters on the robustness of Computed Tomography radiomic features: A review. Comput Biol Med 2021; 133:104400. [PMID: 33930766 DOI: 10.1016/j.compbiomed.2021.104400] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Revised: 03/23/2021] [Accepted: 04/11/2021] [Indexed: 12/13/2022]
Abstract
The field of radiomics is at the forefront of personalized medicine. However, there is concern that high variation in imaging parameters will impact robustness of radiomic features and subsequently the performance of the predictive models built upon them. Therefore, our review aims to evaluate the impact of imaging parameters on the robustness of radiomic features. We also provide insights into the validity and discrepancy of different methodologies applied to investigate the robustness of radiomic features. We selected 47 papers based on our predefined inclusion criteria and grouped these papers by the imaging parameter under investigation: (i) scanner parameters, (ii) acquisition parameters and (iii) reconstruction parameters. Our review highlighted that most of the imaging parameters are disruptive parameters, and shape along with First order statistics were reported as the most robust radiomic features against variation in imaging parameters. This review identified inconsistencies related to the methodology of the reviewed studies such as the metrics used for robustness, the feature extraction techniques, the reporting style, and their outcome inclusion. We hope this review will aid the scientific community in conducting research in a way that is more reproducible and avoids the pitfalls of previous analyses.
Collapse
Affiliation(s)
- Reza Reiazi
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada; Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada; Department of Radiation Oncology, University of Toronto, Toronto, Ontario, Canada
| | - Engy Abbas
- Joint Department of Medical Imaging, University of Toronto, Toronto, Canada
| | - Petra Famiyeh
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada; Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Aria Rezaie
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | - Jennifer Y Y Kwan
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | - Tirth Patel
- Techna Institute, University Health Network, Toronto, Ontario, Canada
| | - Scott V Bratman
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada; Department of Radiation Oncology, University of Toronto, Toronto, Ontario, Canada
| | - Tony Tadic
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada; Department of Radiation Oncology, University of Toronto, Toronto, Ontario, Canada
| | - Fei-Fei Liu
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada; Department of Radiation Oncology, University of Toronto, Toronto, Ontario, Canada
| | - Benjamin Haibe-Kains
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada; Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada; Ontario Institute for Cancer Research, Toronto, Ontario, Canada; Department of Computer Science, University of Toronto, Toronto, Ontario, Canada; Vector Institute, Toronto, Ontario, Canada.
| |
Collapse
|
43
|
Zhao B. Understanding Sources of Variation to Improve the Reproducibility of Radiomics. Front Oncol 2021; 11:633176. [PMID: 33854969 PMCID: PMC8039446 DOI: 10.3389/fonc.2021.633176] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Accepted: 03/01/2021] [Indexed: 12/12/2022] Open
Abstract
Radiomics is the method of choice for investigating the association between cancer imaging phenotype, cancer genotype and clinical outcome prediction in the era of precision medicine. The fast dispersal of this new methodology has benefited from the existing advances of the core technologies involved in radiomics workflow: image acquisition, tumor segmentation, feature extraction and machine learning. However, despite the rapidly increasing body of publications, there is no real clinical use of a developed radiomics signature so far. Reasons are multifaceted. One of the major challenges is the lack of reproducibility and generalizability of the reported radiomics signatures (features and models). Sources of variation exist in each step of the workflow; some are controllable or can be controlled to certain degrees, while others are uncontrollable or even unknown. Insufficient transparency in reporting radiomics studies further prevents translation of the developed radiomics signatures from the bench to the bedside. This review article first addresses sources of variation, which is illustrated using demonstrative examples. Then, it reviews a number of published studies and progresses made to date in the investigation and improvement of feature reproducibility and model performance. Lastly, it discusses potential strategies and practical considerations to reduce feature variability and improve the quality of radiomics study. This review focuses on CT image acquisition, tumor segmentation, quantitative feature extraction, and the disease of lung cancer.
Collapse
Affiliation(s)
- Binsheng Zhao
- Department of Radiology, Columbia University Irving Medical Center, New York, NY, United States
| |
Collapse
|
44
|
Castiglioni I, Rundo L, Codari M, Di Leo G, Salvatore C, Interlenghi M, Gallivanone F, Cozzi A, D'Amico NC, Sardanelli F. AI applications to medical images: From machine learning to deep learning. Phys Med 2021; 83:9-24. [PMID: 33662856 DOI: 10.1016/j.ejmp.2021.02.006] [Citation(s) in RCA: 143] [Impact Index Per Article: 47.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Revised: 02/09/2021] [Accepted: 02/13/2021] [Indexed: 12/13/2022] Open
Abstract
PURPOSE Artificial intelligence (AI) models are playing an increasing role in biomedical research and healthcare services. This review focuses on challenges points to be clarified about how to develop AI applications as clinical decision support systems in the real-world context. METHODS A narrative review has been performed including a critical assessment of articles published between 1989 and 2021 that guided challenging sections. RESULTS We first illustrate the architectural characteristics of machine learning (ML)/radiomics and deep learning (DL) approaches. For ML/radiomics, the phases of feature selection and of training, validation, and testing are described. DL models are presented as multi-layered artificial/convolutional neural networks, allowing us to directly process images. The data curation section includes technical steps such as image labelling, image annotation (with segmentation as a crucial step in radiomics), data harmonization (enabling compensation for differences in imaging protocols that typically generate noise in non-AI imaging studies) and federated learning. Thereafter, we dedicate specific sections to: sample size calculation, considering multiple testing in AI approaches; procedures for data augmentation to work with limited and unbalanced datasets; and the interpretability of AI models (the so-called black box issue). Pros and cons for choosing ML versus DL to implement AI applications to medical imaging are finally presented in a synoptic way. CONCLUSIONS Biomedicine and healthcare systems are one of the most important fields for AI applications and medical imaging is probably the most suitable and promising domain. Clarification of specific challenging points facilitates the development of such systems and their translation to clinical practice.
Collapse
Affiliation(s)
- Isabella Castiglioni
- Department of Physics, Università degli Studi di Milano-Bicocca, Piazza della Scienza 3, 20126 Milano, Italy; Institute of Biomedical Imaging and Physiology, National Research Council, Via Fratelli Cervi 93, 20090 Segrate, Italy.
| | - Leonardo Rundo
- Department of Radiology, Box 218, Cambridge Biomedical Campus, Cambridge CB2 0QQ, United Kingdom; Cancer Research UK Cambridge Centre, University of Cambridge Li Ka Shing Centre, Robinson Way, Cambridge CB2 0RE, United Kingdom.
| | - Marina Codari
- Department of Radiology, Stanford University School of Medicine, Stanford University, 300 Pasteur Drive, Stanford, CA, USA.
| | - Giovanni Di Leo
- Unit of Radiology, IRCCS Policlinico San Donato, Via Rodolfo Morandi 30, 20097 San Donato Milanese, Italy.
| | - Christian Salvatore
- Scuola Universitaria Superiore IUSS Pavia, Piazza della Vittoria 15, 27100 Pavia, Italy; DeepTrace Technologies S.r.l., Via Conservatorio 17, 20122 Milano, Italy.
| | - Matteo Interlenghi
- DeepTrace Technologies S.r.l., Via Conservatorio 17, 20122 Milano, Italy.
| | - Francesca Gallivanone
- Institute of Biomedical Imaging and Physiology, National Research Council, Via Fratelli Cervi 93, 20090 Segrate, Italy.
| | - Andrea Cozzi
- Department of Biomedical Sciences for Health, Università degli Studi di Milano, Via Luigi Mangiagalli 31, 20133 Milano, Italy.
| | - Natascha Claudia D'Amico
- Department of Diagnostic Imaging and Stereotactic Radiosurgery, Centro Diagnostico Italiano S.p.A., Via Saint Bon 20, 20147 Milano, Italy; Unit of Computer Systems and Bioinformatics, Department of Engineering, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, 00128 Roma, Italy.
| | - Francesco Sardanelli
- Unit of Radiology, IRCCS Policlinico San Donato, Via Rodolfo Morandi 30, 20097 San Donato Milanese, Italy; Department of Biomedical Sciences for Health, Università degli Studi di Milano, Via Luigi Mangiagalli 31, 20133 Milano, Italy.
| |
Collapse
|
45
|
Artificial Intelligence and the Medical Physicist: Welcome to the Machine. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11041691] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Artificial intelligence (AI) is a branch of computer science dedicated to giving machines or computers the ability to perform human-like cognitive functions, such as learning, problem-solving, and decision making. Since it is showing superior performance than well-trained human beings in many areas, such as image classification, object detection, speech recognition, and decision-making, AI is expected to change profoundly every area of science, including healthcare and the clinical application of physics to healthcare, referred to as medical physics. As a result, the Italian Association of Medical Physics (AIFM) has created the “AI for Medical Physics” (AI4MP) group with the aims of coordinating the efforts, facilitating the communication, and sharing of the knowledge on AI of the medical physicists (MPs) in Italy. The purpose of this review is to summarize the main applications of AI in medical physics, describe the skills of the MPs in research and clinical applications of AI, and define the major challenges of AI in healthcare.
Collapse
|
46
|
Abstract
Carrying out large multicenter studies is one of the key goals to be achieved towards a faster transfer of the radiomics approach in the clinical setting. This requires large-scale radiomics data analysis, hence the need for integrating radiomic features extracted from images acquired in different centers. This is challenging as radiomic features exhibit variable sensitivity to differences in scanner model, acquisition protocols and reconstruction settings, which is similar to the so-called 'batch-effects' in genomics studies. In this review we discuss existing methods to perform data integration with the aid of reducing the unwanted variation associated with batch effects. We also discuss the future potential role of deep learning methods in providing solutions for addressing radiomic multicentre studies.
Collapse
Affiliation(s)
- R Da-Ano
- LaTiM, INSERM, UMR 1101, Univ Brest, Brest, France
| | - D Visvikis
- LaTiM, INSERM, UMR 1101, Univ Brest, Brest, France
- equally contributed
| | - M Hatt
- LaTiM, INSERM, UMR 1101, Univ Brest, Brest, France
- equally contributed
| |
Collapse
|
47
|
How can we combat multicenter variability in MR radiomics? Validation of a correction procedure. Eur Radiol 2020; 31:2272-2280. [PMID: 32975661 DOI: 10.1007/s00330-020-07284-9] [Citation(s) in RCA: 81] [Impact Index Per Article: 20.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2020] [Revised: 07/06/2020] [Accepted: 09/10/2020] [Indexed: 10/23/2022]
Abstract
OBJECTIVE Test a practical realignment approach to compensate the technical variability of MR radiomic features. METHODS T1 phantom images acquired on 2 scanners, FLAIR and contrast-enhanced T1-weighted (CE-T1w) images of 18 brain tumor patients scanned on both 1.5-T and 3-T scanners, and 36 T2-weighted (T2w) images of prostate cancer patients scanned in one of two centers were investigated. The ComBat procedure was used for harmonizing radiomic features. Differences in statistical distributions in feature values between 1.5- and 3-T images were tested before and after harmonization. The prostate studies were used to determine the impact of harmonization to distinguish between Gleason grades (GGs). RESULTS In the phantom data, 40 out of 42 radiomic feature values were significantly different between the 2 scanners before harmonization and none after. In white matter regions, the statistical distributions of features were significantly different (p < 0.05) between the 1.5- and 3-T images for 37 out of 42 features in both FLAIR and CE-T1w images. After harmonization, no statistically significant differences were observed. In brain tumors, 41 (FLAIR) or 36 (CE-T1w) out of 42 features were significantly different between the 1.5- and 3-T images without harmonization, against 1 (FLAIR) or none (CE-T1w) with harmonization. In prostate studies, 636 radiomic features were significantly different between GGs after harmonization against 461 before. The ability to distinguish between GGs using radiomic features was increased after harmonization. CONCLUSION ComBat harmonization efficiently removes inter-center technical inconsistencies in radiomic feature values and increases the sensitivity of studies using data from several scanners. KEY POINTS • Radiomic feature values obtained using different MR scanners or imaging protocols can be harmonized by combining off-the-shelf image standardization and feature realignment procedures. • Harmonized radiomic features enable one to pool data from different scanners and centers without a substantial loss of statistical power caused by intra- and inter-center variability. • The proposed realignment method is applicable to radiomic features from different MR sequences and tumor types and does not rely on any phantom acquisition.
Collapse
|
48
|
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: 541] [Impact Index Per Article: 135.3] [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
|
49
|
Dercle L, Henry T, Carré A, Paragios N, Deutsch E, Robert C. Reinventing radiation therapy with machine learning and imaging bio-markers (radiomics): State-of-the-art, challenges and perspectives. Methods 2020; 188:44-60. [PMID: 32697964 DOI: 10.1016/j.ymeth.2020.07.003] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2020] [Revised: 07/02/2020] [Accepted: 07/06/2020] [Indexed: 12/14/2022] Open
Abstract
Radiation therapy is a pivotal cancer treatment that has significantly progressed over the last decade due to numerous technological breakthroughs. Imaging is now playing a critical role on deployment of the clinical workflow, both for treatment planning and treatment delivery. Machine-learning analysis of predefined features extracted from medical images, i.e. radiomics, has emerged as a promising clinical tool for a wide range of clinical problems addressing drug development, clinical diagnosis, treatment selection and implementation as well as prognosis. Radiomics denotes a paradigm shift redefining medical images as a quantitative asset for data-driven precision medicine. The adoption of machine-learning in a clinical setting and in particular of radiomics features requires the selection of robust, representative and clinically interpretable biomarkers that are properly evaluated on a representative clinical data set. To be clinically relevant, radiomics must not only improve patients' management with great accuracy but also be reproducible and generalizable. Hence, this review explores the existing literature and exposes its potential technical caveats, such as the lack of quality control, standardization, sufficient sample size, type of data collection, and external validation. Based upon the analysis of 165 original research studies based on PET, CT-scan, and MRI, this review provides an overview of new concepts, and hypotheses generating findings that should be validated. In particular, it describes evolving research trends to enhance several clinical tasks such as prognostication, treatment planning, response assessment, prediction of recurrence/relapse, and prediction of toxicity. Perspectives regarding the implementation of an AI-based radiotherapy workflow are presented.
Collapse
Affiliation(s)
- Laurent Dercle
- Department of Radiology, New York Presbyterian Hospital, Columbia University Medical Center, New York, USA
| | - Theophraste Henry
- Molecular Radiotherapy and Innovative Therapeutics, INSERM UMR1030, Gustave Roussy Cancer Campus, Université Paris Saclay, Villejuif, France; Department of Nuclear Medicine and Endocrine Oncology, Gustave Roussy Cancer Campus, Villejuif, France
| | - Alexandre Carré
- Molecular Radiotherapy and Innovative Therapeutics, INSERM UMR1030, Gustave Roussy Cancer Campus, Université Paris Saclay, Villejuif, France; Department of Radiation Oncology, Gustave Roussy Cancer Campus, Villejuif, France
| | | | - Eric Deutsch
- Molecular Radiotherapy and Innovative Therapeutics, INSERM UMR1030, Gustave Roussy Cancer Campus, Université Paris Saclay, Villejuif, France; Department of Radiation Oncology, Gustave Roussy Cancer Campus, Villejuif, France
| | - Charlotte Robert
- Molecular Radiotherapy and Innovative Therapeutics, INSERM UMR1030, Gustave Roussy Cancer Campus, Université Paris Saclay, Villejuif, France; Department of Radiation Oncology, Gustave Roussy Cancer Campus, Villejuif, France.
| |
Collapse
|
50
|
Fornacon-Wood I, Faivre-Finn C, O'Connor JPB, Price GJ. Radiomics as a personalized medicine tool in lung cancer: Separating the hope from the hype. Lung Cancer 2020; 146:197-208. [PMID: 32563015 PMCID: PMC7383235 DOI: 10.1016/j.lungcan.2020.05.028] [Citation(s) in RCA: 65] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2020] [Revised: 05/18/2020] [Accepted: 05/23/2020] [Indexed: 12/24/2022]
Abstract
Radiomics has become a popular image analysis method in the last few years. Its key hypothesis is that medical images harbor biological, prognostic and predictive information that is not revealed upon visual inspection. In contrast to previous work with a priori defined imaging biomarkers, radiomics instead calculates image features at scale and uses statistical methods to identify those most strongly associated to outcome. This builds on years of research into computer aided diagnosis and pattern recognition. While the potential of radiomics to aid personalized medicine is widely recognized, several technical limitations exist which hinder biomarker translation. Aspects of the radiomic workflow lack repeatability or reproducibility under particular circumstances, which is a key requirement for the translation of imaging biomarkers into clinical practice. One of the most commonly studied uses of radiomics is for personalized medicine applications in Non-Small Cell Lung Cancer (NSCLC). In this review, we summarize reported methodological limitations in CT based radiomic analyses together with suggested solutions. We then evaluate the current NSCLC radiomics literature to assess the risk associated with accepting the published conclusions with respect to these limitations. We review different complementary scoring systems and initiatives that can be used to critically appraise data from radiomics studies. Wider awareness should improve the quality of ongoing and future radiomics studies and advance their potential as clinically relevant biomarkers for personalized medicine in patients with NSCLC.
Collapse
Affiliation(s)
| | - Corinne Faivre-Finn
- Division of Cancer Sciences, University of Manchester, Manchester, UK; Department of Radiation Oncology, The Christie Hospital NHS Foundation Trust, Manchester, UK
| | - James P B O'Connor
- Division of Cancer Sciences, University of Manchester, Manchester, UK; Department of Radiology, The Christie Hospital NHS Foundation Trust, Manchester, UK
| | - Gareth J Price
- Division of Cancer Sciences, University of Manchester, Manchester, UK
| |
Collapse
|