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Bernatz S, Böth I, Ackermann J, Burck I, Mahmoudi S, Lenga L, Martin SS, Scholtz JE, Koch V, Grünewald LD, Koch I, Stöver T, Wild PJ, Winkelmann R, Vogl TJ, Pinto Dos Santos D. Does Dual-Energy Computed Tomography Material Decomposition Improve Radiomics Capability to Predict Survival in Head and Neck Squamous Cell Carcinoma Patients? A Preliminary Investigation. J Comput Assist Tomogr 2024; 48:323-333. [PMID: 38013237 DOI: 10.1097/rct.0000000000001551] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
OBJECTIVE Our study objective was to explore the additional value of dual-energy CT (DECT) material decomposition for squamous cell carcinoma of the head and neck (SCCHN) survival prognostication. METHODS A group of 50 SCCHN patients (male, 37; female, 13; mean age, 63.6 ± 10.82 years) with baseline head and neck DECT between September 2014 and August 2020 were retrospectively included. Primary tumors were segmented, radiomics features were extracted, and DECT material decomposition was performed. We used independent train and validation datasets with cross-validation and 100 independent iterations to identify prognostic signatures applying elastic net (EN) and random survival forest (RSF). Features were ranked and intercorrelated according to their prognostic importance. We benchmarked the models against clinical parameters. Intraclass correlation coefficients were used to analyze the interreader variation. RESULTS The exclusively radiomics-trained models achieved similar ( P = 0.947) prognostic performance of area under the curve (AUC) = 0.784 (95% confidence interval [CI], 0.775-0.812) (EN) and AUC = 0.785 (95% CI, 0.759-0.812) (RSF). The additional application of DECT material decomposition did not improve the model's performance (EN, P = 0.594; RSF, P = 0.198). In the clinical benchmark, the top averaged AUC value of 0.643 (95% CI, 0.611-0.675) was inferior to the quantitative imaging-biomarker models ( P < 0.001). A combined imaging and clinical model did not improve the imaging-based models ( P > 0.101). Shape features revealed high prognostic importance. CONCLUSIONS Radiomics AI applications may be used for SCCHN survival prognostication, but the spectral information of DECT material decomposition did not improve the model's performance in our preliminary investigation.
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Affiliation(s)
| | - Ines Böth
- From the Department of Diagnostic and Interventional Radiology
| | - Jörg Ackermann
- Department of Molecular Bioinformatics, Institute of Computer Science, Johann Wolfgang Goethe-University
| | - Iris Burck
- From the Department of Diagnostic and Interventional Radiology
| | | | - Lukas Lenga
- From the Department of Diagnostic and Interventional Radiology
| | - Simon S Martin
- From the Department of Diagnostic and Interventional Radiology
| | | | - Vitali Koch
- From the Department of Diagnostic and Interventional Radiology
| | | | - Ina Koch
- Department of Molecular Bioinformatics, Institute of Computer Science, Johann Wolfgang Goethe-University
| | - Timo Stöver
- Department of Otorhinolaryngology, University Hospital Frankfurt, Goethe University Frankfurt am Main
| | | | - Ria Winkelmann
- Dr Senckenberg Institute for Pathology, University Hospital Frankfurt, Goethe University Frankfurt am Main
| | - Thomas J Vogl
- From the Department of Diagnostic and Interventional Radiology
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Ghosh A, Yekeler E, Teixeira SR, Dalal D, States L. Role of MRI radiomics for the prediction of MYCN amplification in neuroblastomas. Eur Radiol 2023; 33:6726-6735. [PMID: 37178203 DOI: 10.1007/s00330-023-09628-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 02/18/2023] [Accepted: 02/26/2023] [Indexed: 05/15/2023]
Abstract
OBJECTIVES We evaluate MR radiomics and develop machine learning-based classifiers to predict MYCN amplification in neuroblastomas. METHODS A total of 120 patients with neuroblastomas and baseline MR imaging examination available were identified of whom 74 (mean age ± standard deviation [SD] of 6 years and 2 months ± 4 years and 9 months; 43 females and 31 males, 14 MYCN amplified) underwent imaging at our institution. This was therefore used to develop radiomics models. The model was tested in a cohort of children with the same diagnosis but imaged elsewhere (n = 46, mean age ± SD: 5 years 11 months ± 3 years 9 months, 26 females and 14 MYCN amplified). Whole tumour volumes of interest were adopted to extract first-order histogram and second-order radiomics features. Interclass correlation coefficient and maximum relevance and minimum redundancy algorithm were applied for feature selection. Logistic regression, support vector machine, and random forest were employed as the classifiers. Receiver operating characteristic (ROC) analysis was performed to evaluate the diagnostic accuracy of the classifiers on the external test set. RESULTS The logistic regression model and the random forest both showed an AUC of 0.75. The support vector machine classifier obtained an AUC of 0.78 on the test set with a sensitivity of 64% and a specificity of 72%. CONCLUSION The study provides preliminary retrospective evidence demonstrating the feasibility of MRI radiomics in predicting MYCN amplification in neuroblastomas. Future studies are needed to explore the correlation between other imaging features and genetic markers and to develop multiclass predictive models. KEY POINTS • MYCN amplification in neuroblastomas is an important determinant of disease prognosis. • Radiomics analysis of pre-treatment MR examinations can be used to predict MYCN amplification in neuroblastomas. • Radiomics machine learning models showed good generalisability to external test set, demonstrating reproducibility of the computational models.
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Affiliation(s)
- Adarsh Ghosh
- Department of Radiology, Cincinnati Children's Hospital and Medical Centre, Cincinnati, OH, USA.
- Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA, USA.
| | - Ensar Yekeler
- Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Sara Reis Teixeira
- Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Deepa Dalal
- Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Lisa States
- Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
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3
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Bernatz S, Koch V, Dos Santos DP, Ackermann J, Grünewald LD, Weitkamp I, Yel I, Martin SS, Lenga L, Scholtz JE, Vogl TJ, Mahmoudi S. Comparison of radiomics models and dual-energy material decomposition to decipher abdominal lymphoma in contrast-enhanced CT. Int J Comput Assist Radiol Surg 2023; 18:1829-1839. [PMID: 36877288 PMCID: PMC10497439 DOI: 10.1007/s11548-023-02854-w] [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: 07/26/2022] [Accepted: 02/10/2023] [Indexed: 03/07/2023]
Abstract
PURPOSE The radiologists' workload is increasing, and computational imaging techniques may have the potential to identify visually unequivocal lesions, so that the radiologist can focus on equivocal and critical cases. The purpose of this study was to assess radiomics versus dual-energy CT (DECT) material decomposition to objectively distinguish visually unequivocal abdominal lymphoma and benign lymph nodes. METHODS Retrospectively, 72 patients [m, 47; age, 63.5 (27-87) years] with nodal lymphoma (n = 27) or benign abdominal lymph nodes (n = 45) who had contrast-enhanced abdominal DECT between 06/2015 and 07/2019 were included. Three lymph nodes per patient were manually segmented to extract radiomics features and DECT material decomposition values. We used intra-class correlation analysis, Pearson correlation and LASSO to stratify a robust and non-redundant feature subset. Independent train and test data were applied on a pool of four machine learning models. Performance and permutation-based feature importance was assessed to increase the interpretability and allow for comparison of the models. Top performing models were compared by the DeLong test. RESULTS About 38% (19/50) and 36% (8/22) of the train and test set patients had abdominal lymphoma. Clearer entity clusters were seen in t-SNE plots using a combination of DECT and radiomics features compared to DECT features only. Top model performances of AUC = 0.763 (CI = 0.435-0.923) were achieved for the DECT cohort and AUC = 1.000 (CI = 1.000-1.000) for the radiomics feature cohort to stratify visually unequivocal lymphomatous lymph nodes. The performance of the radiomics model was significantly (p = 0.011, DeLong) superior to the DECT model. CONCLUSIONS Radiomics may have the potential to objectively stratify visually unequivocal nodal lymphoma versus benign lymph nodes. Radiomics seems superior to spectral DECT material decomposition in this use case. Therefore, artificial intelligence methodologies may not be restricted to centers with DECT equipment.
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Affiliation(s)
- Simon Bernatz
- Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Theodor-Stern-Kai 7, 60590 Frankfurt am Main, Germany
- Dr. Senckenberg Institute for Pathology, University Hospital Frankfurt, Goethe University Frankfurt am Main, 60590 Frankfurt am Main, Germany
| | - Vitali Koch
- Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Theodor-Stern-Kai 7, 60590 Frankfurt am Main, Germany
| | - Daniel Pinto Dos Santos
- Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Theodor-Stern-Kai 7, 60590 Frankfurt am Main, Germany
- Department of Diagnostic and Interventional Radiology, University of Cologne, Faculty of Medicine, University Hospital Cologne, Kerpener Str. 62, 50937 Cologne, Germany
| | - Jörg Ackermann
- Department of Molecular Bioinformatics, Institute of Computer Science, Johann Wolfgang Goethe-University, Robert-Mayer-Str. 11-15, 60325 Frankfurt am Main, Germany
| | - Leon D. Grünewald
- Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Theodor-Stern-Kai 7, 60590 Frankfurt am Main, Germany
| | - Inga Weitkamp
- Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Theodor-Stern-Kai 7, 60590 Frankfurt am Main, Germany
| | - Ibrahim Yel
- Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Theodor-Stern-Kai 7, 60590 Frankfurt am Main, Germany
| | - Simon S. Martin
- Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Theodor-Stern-Kai 7, 60590 Frankfurt am Main, Germany
| | - Lukas Lenga
- Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Theodor-Stern-Kai 7, 60590 Frankfurt am Main, Germany
| | - Jan-Erik Scholtz
- Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Theodor-Stern-Kai 7, 60590 Frankfurt am Main, Germany
| | - Thomas J. Vogl
- Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Theodor-Stern-Kai 7, 60590 Frankfurt am Main, Germany
| | - Scherwin Mahmoudi
- Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Theodor-Stern-Kai 7, 60590 Frankfurt am Main, Germany
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Liu J, Pasumarthi S, Duffy B, Gong E, Datta K, Zaharchuk G. One Model to Synthesize Them All: Multi-Contrast Multi-Scale Transformer for Missing Data Imputation. IEEE TRANSACTIONS ON MEDICAL IMAGING 2023; 42:2577-2591. [PMID: 37030684 PMCID: PMC10543020 DOI: 10.1109/tmi.2023.3261707] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Multi-contrast magnetic resonance imaging (MRI) is widely used in clinical practice as each contrast provides complementary information. However, the availability of each imaging contrast may vary amongst patients, which poses challenges to radiologists and automated image analysis algorithms. A general approach for tackling this problem is missing data imputation, which aims to synthesize the missing contrasts from existing ones. While several convolutional neural networks (CNN) based algorithms have been proposed, they suffer from the fundamental limitations of CNN models, such as the requirement for fixed numbers of input and output channels, the inability to capture long-range dependencies, and the lack of interpretability. In this work, we formulate missing data imputation as a sequence-to-sequence learning problem and propose a multi-contrast multi-scale Transformer (MMT), which can take any subset of input contrasts and synthesize those that are missing. MMT consists of a multi-scale Transformer encoder that builds hierarchical representations of inputs combined with a multi-scale Transformer decoder that generates the outputs in a coarse-to-fine fashion. The proposed multi-contrast Swin Transformer blocks can efficiently capture intra- and inter-contrast dependencies for accurate image synthesis. Moreover, MMT is inherently interpretable as it allows us to understand the importance of each input contrast in different regions by analyzing the in-built attention maps of Transformer blocks in the decoder. Extensive experiments on two large-scale multi-contrast MRI datasets demonstrate that MMT outperforms the state-of-the-art methods quantitatively and qualitatively.
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5
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Bernatz S, Böth I, Ackermann J, Burck I, Mahmoudi S, Lenga L, Martin SS, Scholtz JE, Koch V, Grünewald LD, Koch I, Stöver T, Wild PJ, Winkelmann R, Vogl TJ, Dos Santos DP. Radiomics for therapy-specific head and neck squamous cell carcinoma survival prognostication (part I). BMC Med Imaging 2023; 23:71. [PMID: 37268876 DOI: 10.1186/s12880-023-01034-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Accepted: 05/25/2023] [Indexed: 06/04/2023] Open
Abstract
BACKGROUND Treatment plans for squamous cell carcinoma of the head and neck (SCCHN) are individually decided in tumor board meetings but some treatment decision-steps lack objective prognostic estimates. Our purpose was to explore the potential of radiomics for SCCHN therapy-specific survival prognostication and to increase the models' interpretability by ranking the features based on their predictive importance. METHODS We included 157 SCCHN patients (male, 119; female, 38; mean age, 64.39 ± 10.71 years) with baseline head and neck CT between 09/2014 and 08/2020 in this retrospective study. Patients were stratified according to their treatment. Using independent training and test datasets with cross-validation and 100 iterations, we identified, ranked and inter-correlated prognostic signatures using elastic net (EN) and random survival forest (RSF). We benchmarked the models against clinical parameters. Inter-reader variation was analyzed using intraclass-correlation coefficients (ICC). RESULTS EN and RSF achieved top prognostication performances of AUC = 0.795 (95% CI 0.767-0.822) and AUC = 0.811 (95% CI 0.782-0.839). RSF prognostication slightly outperformed the EN for the complete (ΔAUC 0.035, p = 0.002) and radiochemotherapy (ΔAUC 0.092, p < 0.001) cohort. RSF was superior to most clinical benchmarking (p ≤ 0.006). The inter-reader correlation was moderate or high for all features classes (ICC ≥ 0.77 (± 0.19)). Shape features had the highest prognostic importance, followed by texture features. CONCLUSIONS EN and RSF built on radiomics features may be used for survival prognostication. The prognostically leading features may vary between treatment subgroups. This warrants further validation to potentially aid clinical treatment decision making in the future.
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Affiliation(s)
- Simon Bernatz
- Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Goethe University Frankfurt Am Main, Theodor-Stern-Kai 7, Frankfurt Am Main, 60590, Germany.
- Dr. Senckenberg Institute for Pathology, University Hospital Frankfurt, Goethe University Frankfurt Am Main, Frankfurt Am Main, 60590, Germany.
- Frankfurt Cancer Institute (FCI), Frankfurt Am Main, 60590, Germany.
| | - Ines Böth
- Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Goethe University Frankfurt Am Main, Theodor-Stern-Kai 7, Frankfurt Am Main, 60590, Germany
| | - Jörg Ackermann
- Department of Molecular Bioinformatics, Institute of Computer Science, Johann Wolfgang Goethe-University, Robert-Mayer-Str. 11-15, Frankfurt Am Main, 60325, Germany
| | - Iris Burck
- Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Goethe University Frankfurt Am Main, Theodor-Stern-Kai 7, Frankfurt Am Main, 60590, Germany
| | - Scherwin Mahmoudi
- Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Goethe University Frankfurt Am Main, Theodor-Stern-Kai 7, Frankfurt Am Main, 60590, Germany
| | - Lukas Lenga
- Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Goethe University Frankfurt Am Main, Theodor-Stern-Kai 7, Frankfurt Am Main, 60590, Germany
| | - Simon S Martin
- Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Goethe University Frankfurt Am Main, Theodor-Stern-Kai 7, Frankfurt Am Main, 60590, Germany
| | - Jan-Erik Scholtz
- Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Goethe University Frankfurt Am Main, Theodor-Stern-Kai 7, Frankfurt Am Main, 60590, Germany
| | - Vitali Koch
- Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Goethe University Frankfurt Am Main, Theodor-Stern-Kai 7, Frankfurt Am Main, 60590, Germany
| | - Leon D Grünewald
- Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Goethe University Frankfurt Am Main, Theodor-Stern-Kai 7, Frankfurt Am Main, 60590, Germany
| | - Ina Koch
- Department of Molecular Bioinformatics, Institute of Computer Science, Johann Wolfgang Goethe-University, Robert-Mayer-Str. 11-15, Frankfurt Am Main, 60325, Germany
| | - Timo Stöver
- Department of Otorhinolaryngology, University Hospital Frankfurt, Goethe University Frankfurt Am Main, Theodor-Stern-Kai 7, Frankfurt Am Main, 60590, Germany
| | - Peter J Wild
- Dr. Senckenberg Institute for Pathology, University Hospital Frankfurt, Goethe University Frankfurt Am Main, Frankfurt Am Main, 60590, Germany
- Frankfurt Institute for Advanced Studies (FIAS), Frankfurt Am Main, 60438, Germany
| | - Ria Winkelmann
- Dr. Senckenberg Institute for Pathology, University Hospital Frankfurt, Goethe University Frankfurt Am Main, Frankfurt Am Main, 60590, Germany
| | - Thomas J Vogl
- Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Goethe University Frankfurt Am Main, Theodor-Stern-Kai 7, Frankfurt Am Main, 60590, Germany
| | - Daniel Pinto Dos Santos
- Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Goethe University Frankfurt Am Main, Theodor-Stern-Kai 7, Frankfurt Am Main, 60590, Germany
- Department of Diagnostic and Interventional Radiology, University of Cologne, Faculty of Medicine and University Hospital Cologne, Kerpener Str. 62, Cologne, 50937, Germany
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6
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Mahmoudi S, Bernatz S, Ackermann J, Koch V, Dos Santos DP, Grünewald LD, Yel I, Martin SS, Scholtz JE, Stehle A, Walter D, Zeuzem S, Wild PJ, Vogl TJ, Kinzler MN. Computed Tomography Radiomics to Differentiate Intrahepatic Cholangiocarcinoma and Hepatocellular Carcinoma. Clin Oncol (R Coll Radiol) 2023; 35:e312-e318. [PMID: 36804153 DOI: 10.1016/j.clon.2023.01.018] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Revised: 12/05/2022] [Accepted: 01/25/2023] [Indexed: 02/04/2023]
Abstract
AIMS Intrahepatic cholangiocarcinoma (iCCA) and hepatocellular carcinoma (HCC) differ in prognosis and treatment. We aimed to non-invasively differentiate iCCA and HCC by means of radiomics extracted from contrast-enhanced standard-of-care computed tomography (CT). MATERIALS AND METHODS In total, 94 patients (male, n = 68, mean age 63.3 ± 12.4 years) with histologically confirmed iCCA (n = 47) or HCC (n = 47) who underwent contrast-enhanced abdominal CT between August 2014 and November 2021 were retrospectively included. The enhancing tumour border was manually segmented in a clinically feasible way by defining three three-dimensional volumes of interest per tumour. Radiomics features were extracted. Intraclass correlation analysis and Pearson metrics were used to stratify robust and non-redundant features with further feature reduction by LASSO (least absolute shrinkage and selection operator). Independent training and testing datasets were used to build four different machine learning models. Performance metrics and feature importance values were computed to increase the models' interpretability. RESULTS The patient population was split into 65 patients for training (iCCA, n = 32) and 29 patients for testing (iCCA, n = 15). A final combined feature set of three radiomics features and the clinical features age and sex revealed a top test model performance of receiver operating characteristic (ROC) area under the curve (AUC) = 0.82 (95% confidence interval =0.66-0.98; train ROC AUC = 0.82) using a logistic regression classifier. The model was well calibrated, and the Youden J Index suggested an optimal cut-off of 0.501 to discriminate between iCCA and HCC with a sensitivity of 0.733 and a specificity of 0.857. CONCLUSIONS Radiomics-based imaging biomarkers can potentially help to non-invasively discriminate between iCCA and HCC.
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Affiliation(s)
- S Mahmoudi
- Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt am Main, Germany.
| | - S Bernatz
- Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt am Main, Germany; Dr. Senckenberg Institute for Pathology, University Hospital Frankfurt, Goethe University, Frankfurt am Main, Germany; Frankfurt Cancer Institute (FCI), Goethe University, Frankfurt am Main, Germany; University Cancer Center Frankfurt (UCT), University Hospital, Goethe University, Frankfurt am Main, Germany
| | - J Ackermann
- Department of Molecular Bioinformatics, Institute of Computer Science, Goethe University, Frankfurt am Main, Germany
| | - V Koch
- Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt am Main, Germany
| | - D P Dos Santos
- Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt am Main, Germany; Department of Diagnostic and Interventional Radiology, University Hospital Cologne, Cologne, Germany
| | - L D Grünewald
- Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt am Main, Germany
| | - I Yel
- Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt am Main, Germany
| | - S S Martin
- Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt am Main, Germany
| | - J-E Scholtz
- Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt am Main, Germany
| | - A Stehle
- Department of Internal Medicine I, University Hospital Frankfurt, Goethe University, Frankfurt am Main, Germany
| | - D Walter
- Department of Internal Medicine I, University Hospital Frankfurt, Goethe University, Frankfurt am Main, Germany
| | - S Zeuzem
- Department of Internal Medicine I, University Hospital Frankfurt, Goethe University, Frankfurt am Main, Germany
| | - P J Wild
- Dr. Senckenberg Institute for Pathology, University Hospital Frankfurt, Goethe University, Frankfurt am Main, Germany; Frankfurt Cancer Institute (FCI), Goethe University, Frankfurt am Main, Germany; Frankfurt Institute for Advanced Studies (FIAS), Frankfurt am Main, Germany
| | - T J Vogl
- Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt am Main, Germany
| | - M N Kinzler
- Department of Internal Medicine I, University Hospital Frankfurt, Goethe University, Frankfurt am Main, Germany
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7
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Bernatz S, Elenberger O, Ackermann J, Lenga L, Martin SS, Scholtz JE, Koch V, Grünewald LD, Herrmann Y, Kinzler MN, Stehle A, Koch I, Zeuzem S, Bankov K, Doering C, Reis H, Flinner N, Schulze F, Wild PJ, Hammerstingl R, Eichler K, Gruber-Rouh T, Vogl TJ, dos Santos DP, Mahmoudi S. CT-radiomics and clinical risk scores for response and overall survival prognostication in TACE HCC patients. Sci Rep 2023; 13:533. [PMID: 36631548 PMCID: PMC9834236 DOI: 10.1038/s41598-023-27714-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Accepted: 01/06/2023] [Indexed: 01/12/2023] Open
Abstract
We aimed to identify hepatocellular carcinoma (HCC) patients who will respond to repetitive transarterial chemoembolization (TACE) to improve the treatment algorithm. Retrospectively, 61 patients (mean age, 65.3 years ± 10.0 [SD]; 49 men) with 94 HCC mRECIST target-lesions who had three consecutive TACE between 01/2012 and 01/2020 were included. Robust and non-redundant radiomics features were extracted from the 24 h post-embolization CT. Five different clinical TACE-scores were assessed. Seven different feature selection methods and machine learning models were used. Radiomics, clinical and combined models were built to predict response to TACE on a lesion-wise and patient-wise level as well as its impact on overall-survival prognostication. 29 target-lesions of 19 patients were evaluated in the test set. Response rates were 37.9% (11/29) on the lesion-level and 42.1% (8/19) on the patient-level. Radiomics top lesion-wise response prognostications was AUC 0.55-0.67. Clinical scores revealed top AUCs of 0.65-0.69. The best working model combined the radiomic feature LargeDependenceHighGrayLevelEmphasis and the clinical score mHAP_II_score_group with AUC = 0.70, accuracy = 0.72. We transferred this model on a patient-level to achieve AUC = 0.62, CI = 0.41-0.83. The two radiomics-clinical features revealed overall-survival prognostication of C-index = 0.67. In conclusion, a random forest model using the radiomic feature LargeDependenceHighGrayLevelEmphasis and the clinical mHAP-II-score-group seems promising for TACE response prognostication.
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Affiliation(s)
- Simon Bernatz
- University Hospital Frankfurt, Department of Diagnostic and Interventional Radiology, Goethe University Frankfurt am Main, Theodor-Stern-Kai 7, 60590, Frankfurt am Main, Germany. .,Dr. Senckenberg Institute for Pathology, University Hospital Frankfurt, Goethe University Frankfurt am Main, 60590, Frankfurt am Main, Germany. .,Frankfurt Cancer Institute (FCI), 60590, Frankfurt am Main, Germany.
| | - Oleg Elenberger
- grid.7839.50000 0004 1936 9721University Hospital Frankfurt, Department of Diagnostic and Interventional Radiology, Goethe University Frankfurt am Main, Theodor-Stern-Kai 7, 60590 Frankfurt am Main, Germany
| | - Jörg Ackermann
- grid.7839.50000 0004 1936 9721Department of Molecular Bioinformatics, Institute of Computer Science, Goethe University Frankfurt am Main, Robert-Mayer-Str. 11-15, 60325 Frankfurt am Main, Germany
| | - Lukas Lenga
- grid.7839.50000 0004 1936 9721University Hospital Frankfurt, Department of Diagnostic and Interventional Radiology, Goethe University Frankfurt am Main, Theodor-Stern-Kai 7, 60590 Frankfurt am Main, Germany
| | - Simon S. Martin
- grid.7839.50000 0004 1936 9721University Hospital Frankfurt, Department of Diagnostic and Interventional Radiology, Goethe University Frankfurt am Main, Theodor-Stern-Kai 7, 60590 Frankfurt am Main, Germany
| | - Jan-Erik Scholtz
- grid.7839.50000 0004 1936 9721University Hospital Frankfurt, Department of Diagnostic and Interventional Radiology, Goethe University Frankfurt am Main, Theodor-Stern-Kai 7, 60590 Frankfurt am Main, Germany
| | - Vitali Koch
- grid.7839.50000 0004 1936 9721University Hospital Frankfurt, Department of Diagnostic and Interventional Radiology, Goethe University Frankfurt am Main, Theodor-Stern-Kai 7, 60590 Frankfurt am Main, Germany
| | - Leon D. Grünewald
- grid.7839.50000 0004 1936 9721University Hospital Frankfurt, Department of Diagnostic and Interventional Radiology, Goethe University Frankfurt am Main, Theodor-Stern-Kai 7, 60590 Frankfurt am Main, Germany
| | - Yannis Herrmann
- grid.7839.50000 0004 1936 9721University Hospital Frankfurt, Department of Diagnostic and Interventional Radiology, Goethe University Frankfurt am Main, Theodor-Stern-Kai 7, 60590 Frankfurt am Main, Germany
| | - Maximilian N. Kinzler
- grid.411088.40000 0004 0578 8220Department of Internal Medicine I, University Hospital Frankfurt, Goethe University, Frankfurt am Main, Germany
| | - Angelika Stehle
- grid.411088.40000 0004 0578 8220Department of Internal Medicine I, University Hospital Frankfurt, Goethe University, Frankfurt am Main, Germany
| | - Ina Koch
- grid.7839.50000 0004 1936 9721Department of Molecular Bioinformatics, Institute of Computer Science, Goethe University Frankfurt am Main, Robert-Mayer-Str. 11-15, 60325 Frankfurt am Main, Germany
| | - Stefan Zeuzem
- grid.411088.40000 0004 0578 8220Department of Internal Medicine I, University Hospital Frankfurt, Goethe University, Frankfurt am Main, Germany
| | - Katrin Bankov
- grid.7839.50000 0004 1936 9721Dr. Senckenberg Institute for Pathology, University Hospital Frankfurt, Goethe University Frankfurt am Main, 60590 Frankfurt am Main, Germany
| | - Claudia Doering
- grid.7839.50000 0004 1936 9721Dr. Senckenberg Institute for Pathology, University Hospital Frankfurt, Goethe University Frankfurt am Main, 60590 Frankfurt am Main, Germany
| | - Henning Reis
- grid.7839.50000 0004 1936 9721Dr. Senckenberg Institute for Pathology, University Hospital Frankfurt, Goethe University Frankfurt am Main, 60590 Frankfurt am Main, Germany
| | - Nadine Flinner
- grid.7839.50000 0004 1936 9721Dr. Senckenberg Institute for Pathology, University Hospital Frankfurt, Goethe University Frankfurt am Main, 60590 Frankfurt am Main, Germany
| | - Falko Schulze
- grid.7839.50000 0004 1936 9721Dr. Senckenberg Institute for Pathology, University Hospital Frankfurt, Goethe University Frankfurt am Main, 60590 Frankfurt am Main, Germany
| | - Peter J. Wild
- grid.7839.50000 0004 1936 9721Dr. Senckenberg Institute for Pathology, University Hospital Frankfurt, Goethe University Frankfurt am Main, 60590 Frankfurt am Main, Germany ,grid.511198.5Frankfurt Cancer Institute (FCI), 60590 Frankfurt am Main, Germany ,grid.417999.b0000 0000 9260 4223Frankfurt Institute for Advanced Studies (FIAS), 60438 Frankfurt am Main, Germany
| | - Renate Hammerstingl
- grid.7839.50000 0004 1936 9721University Hospital Frankfurt, Department of Diagnostic and Interventional Radiology, Goethe University Frankfurt am Main, Theodor-Stern-Kai 7, 60590 Frankfurt am Main, Germany
| | - Katrin Eichler
- grid.7839.50000 0004 1936 9721University Hospital Frankfurt, Department of Diagnostic and Interventional Radiology, Goethe University Frankfurt am Main, Theodor-Stern-Kai 7, 60590 Frankfurt am Main, Germany
| | - Tatjana Gruber-Rouh
- grid.7839.50000 0004 1936 9721University Hospital Frankfurt, Department of Diagnostic and Interventional Radiology, Goethe University Frankfurt am Main, Theodor-Stern-Kai 7, 60590 Frankfurt am Main, Germany
| | - Thomas J. Vogl
- grid.7839.50000 0004 1936 9721University Hospital Frankfurt, Department of Diagnostic and Interventional Radiology, Goethe University Frankfurt am Main, Theodor-Stern-Kai 7, 60590 Frankfurt am Main, Germany
| | - Daniel Pinto dos Santos
- grid.7839.50000 0004 1936 9721University Hospital Frankfurt, Department of Diagnostic and Interventional Radiology, Goethe University Frankfurt am Main, Theodor-Stern-Kai 7, 60590 Frankfurt am Main, Germany ,grid.6190.e0000 0000 8580 3777Department of Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Kerpener Str. 62, 50937 Cologne, Germany
| | - Scherwin Mahmoudi
- grid.7839.50000 0004 1936 9721University Hospital Frankfurt, Department of Diagnostic and Interventional Radiology, Goethe University Frankfurt am Main, Theodor-Stern-Kai 7, 60590 Frankfurt am Main, Germany
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Nie K, Xiao Y. Radiomics in clinical trials: perspectives on standardization. Phys Med Biol 2022; 68. [PMID: 36384049 DOI: 10.1088/1361-6560/aca388] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Accepted: 11/16/2022] [Indexed: 11/17/2022]
Abstract
The term biomarker is used to describe a biological measure of the disease behavior. The existing imaging biomarkers are associated with the known tissue biological characteristics and follow a well-established roadmap to be implemented in routine clinical practice. Recently, a new quantitative imaging analysis approach named radiomics has emerged. It refers to the extraction of a large number of advanced imaging features with high-throughput computing. Extensive research has demonstrated its value in predicting disease behavior, progression, and response to therapeutic options. However, there are numerous challenges to establishing it as a clinically viable solution, including lack of reproducibility and transparency. The data-driven nature also does not offer insights into the underpinning biology of the observed relationships. As such, additional effort is needed to establish it as a qualified biomarker to inform clinical decisions. Here we review the technical difficulties encountered in the clinical applications of radiomics and current effort in addressing some of these challenges in clinical trial designs. By addressing these challenges, the true potential of radiomics can be unleashed.
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Affiliation(s)
- Ke Nie
- Rutgers-Cancer Institute of New Jersey, Rutgers-Robert Wood Johnson Medical School, Department of Radiation Oncology, New Brunswick, NJ, 08901, United States of America
| | - Ying Xiao
- University of Pennsylvania, Department of Radiation Oncology, 3400 Civic Center Blvd, TRC-2 West Philadelphia, PA 19104, United States of America
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Mahmoudi S, Koch V, Santos DPD, Ackermann J, Grünewald LD, Weitkamp I, Yel I, Martin SS, Albrecht MH, Scholtz JE, Vogl TJ, Bernatz S. Imaging biomarkers to stratify lymph node metastases in abdominal CT - Is radiomics superior to dual-energy material decomposition? Eur J Radiol Open 2022; 10:100459. [PMID: 36561422 PMCID: PMC9763741 DOI: 10.1016/j.ejro.2022.100459] [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: 09/29/2022] [Revised: 11/16/2022] [Accepted: 11/30/2022] [Indexed: 12/13/2022] Open
Abstract
Purpose To assess the potential of radiomic features in comparison to dual-energy CT (DECT) material decomposition to objectively stratify abdominal lymph node metastases. Materials and methods In this retrospective study, we included 81 patients (m, 57; median age, 65 (interquartile range, 58.7-73.3) years) with either lymph node metastases (n = 36) or benign lymph nodes (n = 45) who underwent contrast-enhanced abdominal DECT between 06/2015-07/2019. All malignant lymph nodes were classified as unequivocal according to RECIST criteria and confirmed by histopathology, PET-CT or follow-up imaging. Three investigators segmented lymph nodes to extract DECT and radiomics features. Intra-class correlation analysis was applied to stratify a robust feature subset with further feature reduction by Pearson correlation analysis and LASSO. Independent training and testing datasets were applied on four different machine learning models. We calculated the performance metrics and permutation-based feature importance values to increase interpretability of the models. DeLong test was used to compare the top performing models. Results Distance matrices and t-SNE plots revealed clearer clusters using a combination of DECT and radiomic features compared to DECT features only. Feature reduction by LASSO excluded all DECT features of the combined feature cohort. The top performing radiomic features model (AUC = 1.000; F1 = 1.000; precision = 1.000; Random Forest) was significantly superior to the top performing DECT features model (AUC = 0.942; F1 = 0.762; precision = 0.800; Stochastic Gradient Boosting) (DeLong < 0.001). Conclusion Imaging biomarkers have the potential to stratify unequivocal lymph node metastases. Radiomics models were superior to DECT material decomposition and may serve as a support tool to facilitate stratification of abdominal lymph node metastases.
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Key Words
- ADB, AdaBoost
- AUC, Area under the curve
- Abdominal imaging
- CT, Computed tomography
- CTDI, Computed tomography dose index
- DECT, Dual-energy computed tomography
- DICOM, Digital Imaging and Communications in Medicine
- DLP, Dose-length product
- Dual-energy computed tomography
- GLCM, Gray Level Co-occurrence Matrix
- GLDM, Gray Level Dependence Matrix
- GLRLM, Gray Level Run Length Matrix
- GLSZM, Gray Level Size Zone Matrix
- HU, Hounsfield Units
- ICC, Intra-class correlation coefficient
- ID%, Normalized iodine uptake
- ID, Iodine density
- LR, Logistic Regression
- Lymph node metastasis
- Machine Learning
- NGTDM, Neighboring Gray Tone Difference Matrix
- Oncology
- PET, Positron emission tomography
- RF, Random Forest
- ROC, Receiver operating characteristics
- ROI, Region of interest
- Radiomics
- SGB, Stochastic Gradient Boosting
- VOI, Volume of interest
- mGy, Milligray
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Affiliation(s)
- Scherwin Mahmoudi
- University Hospital Frankfurt, Department of Diagnostic and Interventional Radiology, Theodor-Stern-Kai 7, 60590 Frankfurt, Germany
| | - Vitali Koch
- University Hospital Frankfurt, Department of Diagnostic and Interventional Radiology, Theodor-Stern-Kai 7, 60590 Frankfurt, Germany
| | - Daniel Pinto Dos Santos
- University Hospital Frankfurt, Department of Diagnostic and Interventional Radiology, Theodor-Stern-Kai 7, 60590 Frankfurt, Germany
- University Hospital Cologne, Department of Diagnostic and Interventional Radiology, Kerpener Str. 62, 50937 Cologne, Germany
| | - Jörg Ackermann
- Department of Molecular Bioinformatics, Institute of Computer Science, Johann Wolfgang Goethe-University, Robert-Mayer-Str. 11-15, 60325 Frankfurt am Main, Germany
| | - Leon D. Grünewald
- University Hospital Frankfurt, Department of Diagnostic and Interventional Radiology, Theodor-Stern-Kai 7, 60590 Frankfurt, Germany
| | - Inga Weitkamp
- University Hospital Frankfurt, Department of Diagnostic and Interventional Radiology, Theodor-Stern-Kai 7, 60590 Frankfurt, Germany
| | - Ibrahim Yel
- University Hospital Frankfurt, Department of Diagnostic and Interventional Radiology, Theodor-Stern-Kai 7, 60590 Frankfurt, Germany
| | - Simon S. Martin
- University Hospital Frankfurt, Department of Diagnostic and Interventional Radiology, Theodor-Stern-Kai 7, 60590 Frankfurt, Germany
| | - Moritz H. Albrecht
- University Hospital Frankfurt, Department of Diagnostic and Interventional Radiology, Theodor-Stern-Kai 7, 60590 Frankfurt, Germany
| | - Jan-Erik Scholtz
- University Hospital Frankfurt, Department of Diagnostic and Interventional Radiology, Theodor-Stern-Kai 7, 60590 Frankfurt, Germany
| | - Thomas J. Vogl
- University Hospital Frankfurt, Department of Diagnostic and Interventional Radiology, Theodor-Stern-Kai 7, 60590 Frankfurt, Germany
| | - Simon Bernatz
- University Hospital Frankfurt, Department of Diagnostic and Interventional Radiology, Theodor-Stern-Kai 7, 60590 Frankfurt, Germany
- Dr. Senckenberg Institute for Pathology, University Hospital Frankfurt, Goethe University Frankfurt am Main, 60590 Frankfurt am Main, Germany
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A Review of Radiomics and Artificial Intelligence and Their Application in Veterinary Diagnostic Imaging. Vet Sci 2022; 9:vetsci9110620. [PMID: 36356097 PMCID: PMC9693121 DOI: 10.3390/vetsci9110620] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2022] [Revised: 10/31/2022] [Accepted: 10/31/2022] [Indexed: 11/09/2022] Open
Abstract
Simple Summary The goal of this paper is to provide an overview of current radiomic and AI applications in veterinary diagnostic imaging. We discuss the essential elements of AI for veterinary practitioners with the aim of helping them make informed decisions in applying AI technologies to their practices and that veterinarians will play an integral role in ensuring the appropriate uses and suitable curation of data. The expertise of veterinary professionals will be vital to ensuring suitable data and, subsequently, AI that meets the needs of the profession. Abstract Great advances have been made in human health care in the application of radiomics and artificial intelligence (AI) in a variety of areas, ranging from hospital management and virtual assistants to remote patient monitoring and medical diagnostics and imaging. To improve accuracy and reproducibility, there has been a recent move to integrate radiomics and AI as tools to assist clinical decision making and to incorporate it into routine clinical workflows and diagnosis. Although lagging behind human medicine, the use of radiomics and AI in veterinary diagnostic imaging is becoming more frequent with an increasing number of reported applications. The goal of this paper is to provide an overview of current radiomic and AI applications in veterinary diagnostic imaging.
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Stamoulou E, Spanakis C, Manikis GC, Karanasiou G, Grigoriadis G, Foukakis T, Tsiknakis M, Fotiadis DI, Marias K. Harmonization Strategies in Multicenter MRI-Based Radiomics. J Imaging 2022; 8:303. [PMID: 36354876 PMCID: PMC9695920 DOI: 10.3390/jimaging8110303] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 10/28/2022] [Accepted: 10/31/2022] [Indexed: 08/13/2023] Open
Abstract
Radiomics analysis is a powerful tool aiming to provide diagnostic and prognostic patient information directly from images that are decoded into handcrafted features, comprising descriptors of shape, size and textural patterns. Although radiomics is gaining momentum since it holds great promise for accelerating digital diagnostics, it is susceptible to bias and variation due to numerous inter-patient factors (e.g., patient age and gender) as well as inter-scanner ones (different protocol acquisition depending on the scanner center). A variety of image and feature based harmonization methods has been developed to compensate for these effects; however, to the best of our knowledge, none of these techniques has been established as the most effective in the analysis pipeline so far. To this end, this review provides an overview of the challenges in optimizing radiomics analysis, and a concise summary of the most relevant harmonization techniques, aiming to provide a thorough guide to the radiomics harmonization process.
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Affiliation(s)
- Elisavet Stamoulou
- Computational BioMedicine Laboratory (CBML), Foundation for Research and Technology—Hellas (FORTH), 700 13 Heraklion, Greece
| | - Constantinos Spanakis
- Computational BioMedicine Laboratory (CBML), Foundation for Research and Technology—Hellas (FORTH), 700 13 Heraklion, Greece
| | - Georgios C. Manikis
- Computational BioMedicine Laboratory (CBML), Foundation for Research and Technology—Hellas (FORTH), 700 13 Heraklion, Greece
- Department of Oncology-Pathology, Karolinska Institutet, 171 77 Stockholm, Sweden
| | - Georgia Karanasiou
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, 451 10 Ioannina, Greece
| | - Grigoris Grigoriadis
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, 451 10 Ioannina, Greece
| | - Theodoros Foukakis
- Department of Oncology-Pathology, Karolinska Institutet, 171 77 Stockholm, Sweden
| | - Manolis Tsiknakis
- Computational BioMedicine Laboratory (CBML), Foundation for Research and Technology—Hellas (FORTH), 700 13 Heraklion, Greece
- Department of Electrical & Computer Engineering, Hellenic Mediterranean University, 714 10 Heraklion, Greece
| | - Dimitrios I. Fotiadis
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, 451 10 Ioannina, Greece
- Department of Biomedical Research, Institute of Molecular Biology and Biotechnology—FORTH, University Campus of Ioannina, 451 15 Ioannina, Greece
| | - Kostas Marias
- Computational BioMedicine Laboratory (CBML), Foundation for Research and Technology—Hellas (FORTH), 700 13 Heraklion, Greece
- Department of Electrical & Computer Engineering, Hellenic Mediterranean University, 714 10 Heraklion, Greece
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Phantom Study on the Robustness of MR Radiomics Features: Comparing the Applicability of 3D Printed and Biological Phantoms. Diagnostics (Basel) 2022; 12:diagnostics12092196. [PMID: 36140598 PMCID: PMC9497898 DOI: 10.3390/diagnostics12092196] [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: 07/04/2022] [Revised: 08/24/2022] [Accepted: 08/29/2022] [Indexed: 11/17/2022] Open
Abstract
The objectives of our study were to (a) evaluate the feasibility of using 3D printed phantoms in magnetic resonance imaging (MR) in assessing the robustness and repeatability of radiomic parameters and (b) to compare the results obtained from the 3D printed phantoms to metrics obtained in biological phantoms. To this end, three different 3D phantoms were printed: a Hilbert cube (5 × 5 × 5 cm3) and two cubic quick response (QR) code phantoms (a large phantom (large QR) (5 × 5 × 4 cm3) and a small phantom (small QR) (4 × 4 × 3 cm3)). All 3D printed and biological phantoms (kiwis, tomatoes, and onions) were scanned thrice on clinical 1.5 T and 3 T MR with 1 mm and 2 mm isotropic resolution. Subsequent analyses included analyses of several radiomics indices (RI), their repeatability and reliability were calculated using the coefficient of variation (CV), the relative percentage difference (RPD), and the interclass coefficient (ICC) parameters. Additionally, the readability of QR codes obtained from the MR images was examined with several mobile phones and algorithms. The best repeatability (CV ≤ 10%) is reported for the acquisition protocols with the highest spatial resolution. In general, the repeatability and reliability of RI were better in data obtained at 1.5 T (CV = 1.9) than at 3 T (CV = 2.11). Furthermore, we report good agreements between results obtained for the 3D phantoms and biological phantoms. Finally, analyses of the read-out rate of the QR code revealed better texture analyses for images with a spatial resolution of 1 mm than 2 mm. In conclusion, 3D printing techniques offer a unique solution to create textures for analyzing the reliability of radiomic data from MR scans.
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A Pipeline for the Implementation and Visualization of Explainable Machine Learning for Medical Imaging Using Radiomics Features. SENSORS 2022; 22:s22145205. [PMID: 35890885 PMCID: PMC9318445 DOI: 10.3390/s22145205] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 06/19/2022] [Accepted: 06/24/2022] [Indexed: 02/01/2023]
Abstract
Machine learning (ML) models have been shown to predict the presence of clinical factors from medical imaging with remarkable accuracy. However, these complex models can be difficult to interpret and are often criticized as “black boxes”. Prediction models that provide no insight into how their predictions are obtained are difficult to trust for making important clinical decisions, such as medical diagnoses or treatment. Explainable machine learning (XML) methods, such as Shapley values, have made it possible to explain the behavior of ML algorithms and to identify which predictors contribute most to a prediction. Incorporating XML methods into medical software tools has the potential to increase trust in ML-powered predictions and aid physicians in making medical decisions. Specifically, in the field of medical imaging analysis the most used methods for explaining deep learning-based model predictions are saliency maps that highlight important areas of an image. However, they do not provide a straightforward interpretation of which qualities of an image area are important. Here, we describe a novel pipeline for XML imaging that uses radiomics data and Shapley values as tools to explain outcome predictions from complex prediction models built with medical imaging with well-defined predictors. We present a visualization of XML imaging results in a clinician-focused dashboard that can be generalized to various settings. We demonstrate the use of this workflow for developing and explaining a prediction model using MRI data from glioma patients to predict a genetic mutation.
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14
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Zhan PC, Lyu PJ, Li Z, Liu X, Wang HX, Liu NN, Zhang Y, Huang W, Chen Y, Gao JB. CT-Based Radiomics Analysis for Noninvasive Prediction of Perineural Invasion of Perihilar Cholangiocarcinoma. Front Oncol 2022; 12:900478. [PMID: 35795043 PMCID: PMC9252420 DOI: 10.3389/fonc.2022.900478] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2022] [Accepted: 05/20/2022] [Indexed: 12/02/2022] Open
Abstract
Purpose The study aimed to construct and evaluate a CT-Based radiomics model for noninvasive detecting perineural invasion (PNI) of perihilar cholangiocarcinoma (pCCA) preoperatively. Materials and Methods From February 2012 to October 2021, a total of 161 patients with pCCA who underwent resection were retrospectively enrolled in this study. Patients were allocated into the training cohort and the validation cohort according to the diagnostic time. Venous phase images of contrast-enhanced CT were used for radiomics analysis. The intraclass correlation efficient (ICC), the correlation analysis, and the least absolute shrinkage and selection operator (LASSO) regression were applied to select radiomics features and built radiomics signature. Logistic regression analyses were performed to establish a clinical model, a radiomics model, and a combined model. The performance of the predictive models was measured by area under the receiver operating characteristic curve (AUC), and pairwise ROC comparisons between models were tested using the Delong method. Finally, the model with the best performance was presented as a nomogram, and its calibration and clinical usefulness were assessed. Results Finally, 15 radiomics features were selected to build a radiomics signature, and three models were developed through logistic regression. In the training cohort, the combined model showed a higher predictive capability (AUC = 0.950) than the radiomics model and the clinical model (AUC: radiomics = 0.914, clinical = 0.756). However, in the validation cohort, the AUC of the radiomics model (AUC = 0.885) was significantly higher than the other two models (AUC: combined = 0.791, clinical = 0.567). After comprehensive consideration, the radiomics model was chosen to develop the nomogram. The calibration curve and decision curve analysis (DCA) suggested that the nomogram had a good consistency and clinical utility. Conclusion We developed a CT-based radiomics model with good performance to noninvasively predict PNI of pCCA preoperatively.
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Affiliation(s)
- Peng-Chao Zhan
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Henan Key Laboratory of Imaging Diagnosis and Treatment for Digestive System Tumor, Zhengzhou, China
| | - Pei-jie Lyu
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Zhen Li
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Xing Liu
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Hui-Xia Wang
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Na-Na Liu
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Henan Key Laboratory of Imaging Diagnosis and Treatment for Digestive System Tumor, Zhengzhou, China
| | - Yuyuan Zhang
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Wenpeng Huang
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Henan Key Laboratory of Imaging Diagnosis and Treatment for Digestive System Tumor, Zhengzhou, China
| | - Yan Chen
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jian-bo Gao
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Henan Key Laboratory of Imaging Diagnosis and Treatment for Digestive System Tumor, Zhengzhou, China
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Scalco E, Rizzo G, Mastropietro A. The stability of oncologic MRI radiomic features and the potential role of deep learning: a review. Phys Med Biol 2022; 67. [DOI: 10.1088/1361-6560/ac60b9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Accepted: 03/24/2022] [Indexed: 11/11/2022]
Abstract
Abstract
The use of MRI radiomic models for the diagnosis, prognosis and treatment response prediction of tumors has been increasingly reported in literature. However, its widespread adoption in clinics is hampered by issues related to features stability. In the MRI radiomic workflow, the main factors that affect radiomic features computation can be found in the image acquisition and reconstruction phase, in the image pre-processing steps, and in the segmentation of the region of interest on which radiomic indices are extracted. Deep Neural Networks (DNNs), having shown their potentiality in the medical image processing and analysis field, can be seen as an attractive strategy to partially overcome the issues related to radiomic stability and mitigate their impact. In fact, DNN approaches can be prospectively integrated in the MRI radiomic workflow to improve image quality, obtain accurate and reproducible segmentations and generate standardized images. In this review, DNN methods that can be included in the image processing steps of the radiomic workflow are described and discussed, in the light of a detailed analysis of the literature in the context of MRI radiomic reliability.
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16
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Robustness and reproducibility of radiomics in T2 weighted images from magnetic resonance image guided linear accelerator in a phantom study. Phys Med 2022; 96:130-139. [PMID: 35287100 DOI: 10.1016/j.ejmp.2022.03.002] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Revised: 02/07/2022] [Accepted: 03/04/2022] [Indexed: 12/13/2022] Open
Abstract
PURPOSE Quantitative radiomics features extracted from medical images have been shown to provide value in predicting clinical outcomes. The study for robustness and reproducibility of radiomics features obtained with magnetic resonance image guided linear accelerator (MR-Linac) is insufficient. The objective of this work was to investigate the stability of radiomics features extracted from T2-weighted images of MR-Linac for five common effect factors. MATERIALS AND METHOD In this work, ten jellies, five fruits/vegetables, and a dynamic phantom were used to evaluate the impact of test-retest, intraobserver, varied thicknesses, radiation, and motion. These phantoms were scanned on a 1.5 T MRI system of MR-Linac. For test-retest data, the phantoms were scanned twice with repositioning within 15 min. To assess for intraobserver comparison, the segmentation of MR images was repeated by one observer in a double-blind manner. Three slice thicknesses (1.2 mm, 2.4 mm, and 4.8 mm) were used to select robust features that were insensitive to different thicknesses. The effect of radiation on features was studied by acquiring images when the beam was on. Common movement images of patients during radiotherapy were simulated by a dynamic phantom with five motion states to study the motion effect. A total of 1409 radiomics features, including shape features, first-order features, and texture features, were extracted from the original, wavelet, square, logarithmic, exponential and gradient images. The robustness and reproducibility features were evaluated using the concordance correlation coefficient (CCC). RESULT The intraobserver group had the most robust features (936/1079, 86.7%), while the group of motion effects had the lowest robustness (56/936, 6.0%), followed by the group of different thickness cohorts (374/936, 40.0%). The stability of features in the test-retest and radiation groups was 1072 of 1312 (81.7%) and 810 of 936 (86.5%), respectively. Overall, 25 of 1409 (2.4%) radiomics features remained robust in all five tests, mostly focusing on the image type of the wavelet. The number of stable features extracted from when the beam was on was less than that extracted when the beam was off. Shape features were the most robust of all of the features in all of the groups, excluding the motion group. CONCLUSION Compared with other factors fewer features remained robust to the effect of motion. This result emphasizes the need to consider the effect of respiration motion. The study for T2-weighted images from MR-Linac under different conditions will help us to build a robust predictive model applicable for radiotherapy.
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Demircioğlu A. Evaluation of the dependence of radiomic features on the machine learning model. Insights Imaging 2022; 13:28. [PMID: 35201534 PMCID: PMC8873309 DOI: 10.1186/s13244-022-01170-2] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2021] [Accepted: 02/03/2022] [Indexed: 12/03/2022] Open
Abstract
BACKGROUND In radiomic studies, several models are often trained with different combinations of feature selection methods and classifiers. The features of the best model are usually considered relevant to the problem, and they represent potential biomarkers. Features selected from statistically similarly performing models are generally not studied. To understand the degree to which the selected features of these statistically similar models differ, 14 publicly available datasets, 8 feature selection methods, and 8 classifiers were used in this retrospective study. For each combination of feature selection and classifier, a model was trained, and its performance was measured with AUC-ROC. The best-performing model was compared to other models using a DeLong test. Models that were statistically similar were compared in terms of their selected features. RESULTS Approximately 57% of all models analyzed were statistically similar to the best-performing model. Feature selection methods were, in general, relatively unstable (0.58; range 0.35-0.84). The features selected by different models varied largely (0.19; range 0.02-0.42), although the selected features themselves were highly correlated (0.71; range 0.4-0.92). CONCLUSIONS Feature relevance in radiomics strongly depends on the model used, and statistically similar models will generally identify different features as relevant. Considering features selected by a single model is misleading, and it is often not possible to directly determine whether such features are candidate biomarkers.
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Affiliation(s)
- Aydin Demircioğlu
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstraße 55, 45157, Essen, Germany.
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Ferro M, de Cobelli O, Musi G, del Giudice F, Carrieri G, Busetto GM, Falagario UG, Sciarra A, Maggi M, Crocetto F, Barone B, Caputo VF, Marchioni M, Lucarelli G, Imbimbo C, Mistretta FA, Luzzago S, Vartolomei MD, Cormio L, Autorino R, Tătaru OS. Radiomics in prostate cancer: an up-to-date review. Ther Adv Urol 2022; 14:17562872221109020. [PMID: 35814914 PMCID: PMC9260602 DOI: 10.1177/17562872221109020] [Citation(s) in RCA: 53] [Impact Index Per Article: 26.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2021] [Accepted: 05/30/2022] [Indexed: 12/24/2022] Open
Abstract
Prostate cancer (PCa) is the most common worldwide diagnosed malignancy in male population. The diagnosis, the identification of aggressive disease, and the post-treatment follow-up needs a more comprehensive and holistic approach. Radiomics is the extraction and interpretation of images phenotypes in a quantitative manner. Radiomics may give an advantage through advancements in imaging modalities and through the potential power of artificial intelligence techniques by translating those features into clinical outcome prediction. This article gives an overview on the current evidence of methodology and reviews the available literature on radiomics in PCa patients, highlighting its potential for personalized treatment and future applications.
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Affiliation(s)
- Matteo Ferro
- Department of Urology, European Institute of Oncology, IRCCS, Milan, Italy, via Ripamonti 435 Milano, Italy
| | - Ottavio de Cobelli
- Department of Urology, European Institute of Oncology, IRCCS, Milan, Italy; Department of Oncology and Hematology-Oncology, Università degli Studi di Milano, Milan, Italy
| | - Gennaro Musi
- Department of Urology, European Institute of Oncology, IRCCS, Milan, Italy; Department of Oncology and Hematology-Oncology, Università degli Studi di Milano, Milan, Italy
| | - Francesco del Giudice
- Department of Urology, Policlinico Umberto I, Sapienza University of Rome, Rome, Italy
| | - Giuseppe Carrieri
- Department of Urology and Organ Transplantation, University of Foggia, Foggia, Italy
| | - Gian Maria Busetto
- Department of Urology and Organ Transplantation, University of Foggia, Foggia, Italy
| | | | - Alessandro Sciarra
- Department of Urology, Policlinico Umberto I, Sapienza University of Rome, Rome, Italy
| | - Martina Maggi
- Department of Urology, Policlinico Umberto I, Sapienza University of Rome, Rome, Italy
| | - Felice Crocetto
- Department of Neurosciences, Reproductive Sciences and Odontostomatology, University of Naples ‘Federico II’, Naples, Italy
| | - Biagio Barone
- Department of Neurosciences, Reproductive Sciences and Odontostomatology, University of Naples ‘Federico II’, Naples, Italy
| | - Vincenzo Francesco Caputo
- Department of Neurosciences, Reproductive Sciences and Odontostomatology, University of Naples ‘Federico II’, Naples, Italy
| | - Michele Marchioni
- Department of Medical, Oral and Biotechnological Sciences, G. d’Annunzio, University of Chieti, Chieti, Italy; Urology Unit, ‘SS. Annunziata’ Hospital, Chieti, Italy
- Department of Urology, ASL Abruzzo 2, Chieti, Italy
| | - Giuseppe Lucarelli
- Department of Emergency and Organ Transplantation, Urology, Andrology and Kidney Transplantation Unit, University of Bari, Bari, Italy
| | - Ciro Imbimbo
- Department of Neurosciences, Reproductive Sciences and Odontostomatology, University of Naples ‘Federico II’, Naples, Italy
| | - Francesco Alessandro Mistretta
- Department of Urology, European Institute of Oncology, IRCCS, Milan, Italy
- Università degli Studi di Milano, Milan, Italy
| | - Stefano Luzzago
- Department of Urology, European Institute of Oncology, IRCCS, Milan, Italy
- Università degli Studi di Milano, Milan, Italy
| | - Mihai Dorin Vartolomei
- Department of Cell and Molecular Biology, George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Târgu Mures, Târgu Mures, Romania
- Department of Urology, Medical University of Vienna, Vienna, Austria
| | - Luigi Cormio
- Urology and Renal Transplantation Unit, Department of Medical and Surgical Sciences, University of Foggia, Foggia, Italy
- Urology Unit, Bonomo Teaching Hospital, Foggia, Italy
| | | | - Octavian Sabin Tătaru
- Institution Organizing University Doctoral Studies, I.O.S.U.D., George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Târgu Mures, Târgu Mures, Romania
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Demircioğlu A. Measuring the bias of incorrect application of feature selection when using cross-validation in radiomics. Insights Imaging 2021; 12:172. [PMID: 34817740 PMCID: PMC8613324 DOI: 10.1186/s13244-021-01115-1] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Accepted: 10/25/2021] [Indexed: 12/27/2022] Open
Abstract
BACKGROUND Many studies in radiomics are using feature selection methods to identify the most predictive features. At the same time, they employ cross-validation to estimate the performance of the developed models. However, if the feature selection is performed before the cross-validation, data leakage can occur, and the results can be biased. To measure the extent of this bias, we collected ten publicly available radiomics datasets and conducted two experiments. First, the models were developed by incorrectly applying the feature selection prior to cross-validation. Then, the same experiment was conducted by applying feature selection correctly within cross-validation to each fold. The resulting models were then evaluated against each other in terms of AUC-ROC, AUC-F1, and Accuracy. RESULTS Applying the feature selection incorrectly prior to the cross-validation showed a bias of up to 0.15 in AUC-ROC, 0.29 in AUC-F1, and 0.17 in Accuracy. CONCLUSIONS Incorrect application of feature selection and cross-validation can lead to highly biased results for radiomic datasets.
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Affiliation(s)
- Aydin Demircioğlu
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstr. 55, 45147, Essen, Germany.
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