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Hatamikia S, George G, Schwarzhans F, Mahbod A, Woitek R. Breast MRI radiomics and machine learning-based predictions of response to neoadjuvant chemotherapy - How are they affected by variations in tumor delineation? Comput Struct Biotechnol J 2024; 23:52-63. [PMID: 38125296 PMCID: PMC10730996 DOI: 10.1016/j.csbj.2023.11.016] [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: 05/14/2023] [Revised: 11/08/2023] [Accepted: 11/08/2023] [Indexed: 12/23/2023] Open
Abstract
Manual delineation of volumes of interest (VOIs) by experts is considered the gold-standard method in radiomics analysis. However, it suffers from inter- and intra-operator variability. A quantitative assessment of the impact of variations in these delineations on the performance of the radiomics predictors is required to develop robust radiomics based prediction models. In this study, we developed radiomics models for the prediction of pathological complete response to neoadjuvant chemotherapy in patients with two different breast cancer subtypes based on contrast-enhanced magnetic resonance imaging acquired prior to treatment (baseline MRI scans). Different mathematical operations such as erosion, smoothing, dilation, randomization, and ellipse fitting were applied to the original VOIs delineated by experts to simulate variations of segmentation masks. The effects of such VOI modifications on various steps of the radiomics workflow, including feature extraction, feature selection, and prediction performance, were evaluated. Using manual tumor VOIs and radiomics features extracted from baseline MRI scans, an AUC of up to 0.96 and 0.89 was achieved for human epidermal growth receptor 2 positive and triple-negative breast cancer, respectively. For smoothing and erosion, VOIs yielded the highest number of robust features and the best prediction performance, while ellipse fitting and dilation lead to the lowest robustness and prediction performance for both breast cancer subtypes. At most 28% of the selected features were similar to manual VOIs when different VOI delineation data were used. Differences in VOI delineation affect different steps of radiomics analysis, and their quantification is therefore important for development of standardized radiomics research.
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Affiliation(s)
- Sepideh Hatamikia
- Danube Private University, Krems, Rathausplatz 1, Krems-Stein, AT-3500, Austria
- Austrian Center for Medical Innovation and Technology (ACMIT), Viktor Kaplan-Straße 2/1, Wiener Neustadt 2700, Austria
| | - Geevarghese George
- Danube Private University, Krems, Rathausplatz 1, Krems-Stein, AT-3500, Austria
| | - Florian Schwarzhans
- Danube Private University, Krems, Rathausplatz 1, Krems-Stein, AT-3500, Austria
| | - Amirreza Mahbod
- Danube Private University, Krems, Rathausplatz 1, Krems-Stein, AT-3500, Austria
| | - Ramona Woitek
- Danube Private University, Krems, Rathausplatz 1, Krems-Stein, AT-3500, Austria
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Pistel M, Brock L, Laun FB, Erber R, Weiland E, Uder M, Wenkel E, Ohlmeyer S, Bickelhaupt S. Stability of Radiomic Features against Variations in Lesion Segmentations Computed on Apparent Diffusion Coefficient Maps of Breast Lesions. Diagnostics (Basel) 2024; 14:1427. [PMID: 39001317 PMCID: PMC11241112 DOI: 10.3390/diagnostics14131427] [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: 04/18/2024] [Revised: 06/18/2024] [Accepted: 06/24/2024] [Indexed: 07/16/2024] Open
Abstract
Diffusion-weighted imaging (DWI) combined with radiomics can aid in the differentiation of breast lesions. Segmentation characteristics, however, might influence radiomic features. To evaluate feature stability, we implemented a standardized pipeline featuring shifts and shape variations of the underlying segmentations. A total of 103 patients were retrospectively included in this IRB-approved study after multiparametric diagnostic breast 3T MRI with a spin-echo diffusion-weighted sequence with echoplanar readout (b-values: 50, 750 and 1500 s/mm2). Lesion segmentations underwent shifts and shape variations, with >100 radiomic features extracted from apparent diffusion coefficient (ADC) maps for each variation. These features were then compared and ranked based on their stability, measured by the Overall Concordance Correlation Coefficient (OCCC) and Dynamic Range (DR). Results showed variation in feature robustness to segmentation changes. The most stable features, excluding shape-related features, were FO (Mean, Median, RootMeanSquared), GLDM (DependenceNonUniformity), GLRLM (RunLengthNonUniformity), and GLSZM (SizeZoneNonUniformity), which all had OCCC and DR > 0.95 for both shifting and resizing the segmentation. Perimeter, MajorAxisLength, MaximumDiameter, PixelSurface, MeshSurface, and MinorAxisLength were the most stable features in the Shape category with OCCC and DR > 0.95 for resizing. Considering the variability in radiomic feature stability against segmentation variations is relevant when interpreting radiomic analysis of breast DWI data.
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Affiliation(s)
- Mona Pistel
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91054 Erlangen, Germany
- Siemens Healthineers AG, 91052 Erlangen, Germany
| | - Luise Brock
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91054 Erlangen, Germany
| | - Frederik Bernd Laun
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91054 Erlangen, Germany
| | - Ramona Erber
- Institute of Pathology, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91054 Erlangen, Germany
| | - Elisabeth Weiland
- MR Application Predevelopment, Siemens Healthineers AG, 91052 Erlangen, Germany
| | - Michael Uder
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91054 Erlangen, Germany
| | - Evelyn Wenkel
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91054 Erlangen, Germany
- Radiologie München, 80331 München, Germany
| | - Sabine Ohlmeyer
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91054 Erlangen, Germany
| | - Sebastian Bickelhaupt
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91054 Erlangen, Germany
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Brown KH, Kerr BN, Pettigrew M, Connor K, Miller IS, Shiels L, Connolly C, McGarry C, Byrne AT, Butterworth KT. A comparative analysis of preclinical computed tomography radiomics using cone-beam and micro-computed tomography scanners. Phys Imaging Radiat Oncol 2024; 31:100615. [PMID: 39157293 PMCID: PMC11328005 DOI: 10.1016/j.phro.2024.100615] [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: 04/11/2024] [Revised: 07/17/2024] [Accepted: 07/20/2024] [Indexed: 08/20/2024] Open
Abstract
Background and purpose Radiomics analysis extracts quantitative data (features) from medical images. These features could potentially reflect biological characteristics and act as imaging biomarkers within precision medicine. However, there is a lack of cross-comparison and validation of radiomics outputs which is paramount for clinical implementation. In this study, we compared radiomics outputs across two computed tomography (CT)-based preclinical scanners. Materials and methods Cone beam CT (CBCT) and µCT scans were acquired using different preclinical CT imaging platforms. The reproducibility of radiomics features on each scanner was assessed using a phantom across imaging energies (40 & 60 kVp) and segmentation volumes (44-238 mm3). Retrospective mouse scans were used to compare feature reliability across varying tissue densities (lung, heart, bone), scanners and after voxel size harmonisation. Reliable features had an intraclass correlation coefficient (ICC) > 0.8. Results First order and GLCM features were the most reliable on both scanners across different volumes. There was an inverse relationship between tissue density and feature reliability, with the highest number of features in lung (CBCT=580, µCT=734) and lowest in bone (CBCT=110, µCT=560). Comparable features for lung and heart tissues increased when voxel sizes were harmonised. We have identified tissue-specific preclinical radiomics signatures in mice for the lung (133), heart (35), and bone (15). Conclusions Preclinical CBCT and µCT scans can be used for radiomics analysis to support the development of meaningful radiomics signatures. This study demonstrates the importance of standardisation and emphasises the need for multi-centre studies.
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Affiliation(s)
- Kathryn H Brown
- Patrick G. Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, United Kingdom
| | - Brianna N Kerr
- Patrick G. Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, United Kingdom
| | - Mihaela Pettigrew
- Patrick G. Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, United Kingdom
| | - Kate Connor
- Department of Physiology and Medical Physics and Centre for Systems Medicine, Royal College of Surgeons in Ireland, Dublin 2, Ireland
| | - Ian S Miller
- Department of Physiology and Medical Physics and Centre for Systems Medicine, Royal College of Surgeons in Ireland, Dublin 2, Ireland
- National Preclinical Imaging Centre, Royal College of Surgeons in Ireland, Dublin 2, Ireland
| | - Liam Shiels
- Department of Physiology and Medical Physics and Centre for Systems Medicine, Royal College of Surgeons in Ireland, Dublin 2, Ireland
| | - Colum Connolly
- Department of Physiology and Medical Physics and Centre for Systems Medicine, Royal College of Surgeons in Ireland, Dublin 2, Ireland
| | - Conor McGarry
- Patrick G. Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, United Kingdom
- Northern Ireland Cancer Centre, Belfast Health & Social Care Trust, Belfast, United Kingdom
| | - Annette T Byrne
- Department of Physiology and Medical Physics and Centre for Systems Medicine, Royal College of Surgeons in Ireland, Dublin 2, Ireland
- National Preclinical Imaging Centre, Royal College of Surgeons in Ireland, Dublin 2, Ireland
| | - Karl T Butterworth
- Patrick G. Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, United Kingdom
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Xie H, Huang W, Li S, Huang M, Luo C, Li S, Cui C, Ma H, Li H, Liu L, Wang X, Fu G. Radiomics-based lymph nodes prognostic models from three MRI regions in nasopharyngeal carcinoma. Heliyon 2024; 10:e31557. [PMID: 38803981 PMCID: PMC11128517 DOI: 10.1016/j.heliyon.2024.e31557] [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: 01/30/2024] [Revised: 05/17/2024] [Accepted: 05/17/2024] [Indexed: 05/29/2024] Open
Abstract
Accurate prediction of the prognosis of nasopharyngeal carcinoma (NPC) is important for treatment. Lymph nodes metastasis is an important predictor for distant failure and regional recurrence in patients with NPC. Traditionally, subjective radiological evaluation increases concerns regarding the accuracy and consistency of predictions. Radiomics is an objective and quantitative evaluation algorithm for medical images. This retrospective analysis was conducted based on the data of 729 patients newly diagnosed with NPC without distant metastases to evaluate the performance of radiomics pretreatment using magnetic resonance imaging (MRI)-determined metastatic lymph nodes models to predict NPC prognosis with three delineation methods. Radiomics features were extracted from all lymph nodes (ALN), largest lymph node (LLN), and largest slice of the largest lymph node (LSLN) to generate three radiomics signatures. The radiomics signatures, clinical model, and radiomics-clinic merged models were developed in training cohort for predicting overall survival (OS). The results showed that LSLN signature with clinical factors predicted OS with high accuracy and robustness using pretreatment MR-determined metastatic lymph nodes (C-index [95 % confidence interval]: 0.762[0.760-0.763]), providing a new tool for treatment planning in NPC.
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Affiliation(s)
- Hui Xie
- Department of Radiology, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Wenjie Huang
- Department of Radiology, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Shaolong Li
- Department of Radiology, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Manqian Huang
- Department of Radiology, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Chao Luo
- Department of Radiology, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Shuqi Li
- Department of Radiology, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Chunyan Cui
- Department of Radiology, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Huali Ma
- Department of Radiology, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Haojiang Li
- Department of Radiology, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Lizhi Liu
- Department of Radiology, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Xiaoyi Wang
- Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), Haikou, China
| | - Gui Fu
- Department of Radiology, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China
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Dang LH, Hung SH, Le NTN, Chuang WK, Wu JY, Huang TC, Le NQK. Enhancing Nasopharyngeal Carcinoma Survival Prediction: Integrating Pre- and Post-Treatment MRI Radiomics with Clinical Data. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024:10.1007/s10278-024-01109-7. [PMID: 38689151 DOI: 10.1007/s10278-024-01109-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 03/26/2024] [Accepted: 04/02/2024] [Indexed: 05/02/2024]
Abstract
Recurrences are frequent in nasopharyngeal carcinoma (NPC) despite high remission rates with treatment, leading to considerable morbidity. This study aimed to develop a prediction model for NPC survival by harnessing both pre- and post-treatment magnetic resonance imaging (MRI) radiomics in conjunction with clinical data, focusing on 3-year progression-free survival (PFS) as the primary outcome. Our comprehensive approach involved retrospective clinical and MRI data collection of 276 eligible NPC patients from three independent hospitals (180 in the training cohort, 46 in the validation cohort, and 50 in the external cohort) who underwent MRI scans twice, once within 2 months prior to treatment and once within 10 months after treatment. From the contrast-enhanced T1-weighted images before and after treatment, 3404 radiomics features were extracted. These features were not only derived from the primary lesion but also from the adjacent lymph nodes surrounding the tumor. We conducted appropriate feature selection pipelines, followed by Cox proportional hazards models for survival analysis. Model evaluation was performed using receiver operating characteristic (ROC) analysis, the Kaplan-Meier method, and nomogram construction. Our study unveiled several crucial predictors of NPC survival, notably highlighting the synergistic combination of pre- and post-treatment data in both clinical and radiomics assessments. Our prediction model demonstrated robust performance, with an accuracy of AUCs of 0.66 (95% CI: 0.536-0.779) in the training cohort, 0.717 (95% CI: 0.536-0.883) in the testing cohort, and 0.827 (95% CI: 0.684-0.948) in validation cohort in prognosticating patient outcomes. Our study presented a novel and effective prediction model for NPC survival, leveraging both pre- and post-treatment clinical data in conjunction with MRI features. Its constructed nomogram provides potentially significant implications for NPC research, offering clinicians a valuable tool for individualized treatment planning and patient counseling.
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Affiliation(s)
- Luong Huu Dang
- Department of Otolaryngology, Faculty of Medicine, University of Medicine and Pharmacy at Ho Chi Minh City, Ho Chi Minh City, Vietnam
| | - Shih-Han Hung
- Department of Otolaryngology, School of Medicine, Taipei Medical University, Taipei, Taiwan
- Department of Otolaryngology, Wan Fang Hospital, Taipei, Taiwan
- International Ph.D. Program in Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Nhi Thao Ngoc Le
- International Ph.D. Program in Biomedical Engineering, College of Biomedical Engineering, Taipei, Taiwan
| | - Wei-Kai Chuang
- Department of Radiation Oncology, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Jeng-You Wu
- Department of Radiation Oncology, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
| | - Ting-Chieh Huang
- Department of Otolaryngology, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan
| | - Nguyen Quoc Khanh Le
- Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.
- AIBioMed Research Group, Taipei Medical University, Taipei, Taiwan.
- Translational Imaging Research Center, Taipei Medical University Hospital, Taipei, Taiwan.
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Al-Mubarak H, Bane O, Gillingham N, Kyriakakos C, Abboud G, Cuevas J, Gonzalez J, Meilika K, Horowitz A, Huang HHV, Daza J, Fauveau V, Badani K, Viswanath SE, Taouli B, Lewis S. Characterization of renal masses with MRI-based radiomics: assessment of inter-package and inter-observer reproducibility in a prospective pilot study. Abdom Radiol (NY) 2024:10.1007/s00261-024-04212-z. [PMID: 38467854 DOI: 10.1007/s00261-024-04212-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 01/17/2024] [Accepted: 01/17/2024] [Indexed: 03/13/2024]
Abstract
OBJECTIVES To evaluate radiomics features' reproducibility using inter-package/inter-observer measurement analysis in renal masses (RMs) based on MRI and to employ machine learning (ML) models for RM characterization. METHODS 32 Patients (23M/9F; age 61.8 ± 10.6 years) with RMs (25 renal cell carcinomas (RCC)/7 benign masses; mean size, 3.43 ± 1.73 cm) undergoing resection were prospectively recruited. All patients underwent 1.5 T MRI with T2-weighted (T2-WI), diffusion-weighted (DWI)/apparent diffusion coefficient (ADC), and pre-/post-contrast-enhanced T1-weighted imaging (T1-WI). RMs were manually segmented using volume of interest (VOI) on T2-WI, DWI/ADC, and T1-WI pre-/post-contrast imaging (1-min, 3-min post-injection) by two independent observers using two radiomics software packages for inter-package and inter-observer assessments of shape/histogram/texture features common to both packages (104 features; n = 26 patients). Intra-class correlation coefficients (ICCs) were calculated to assess inter-observer and inter-package reproducibility of radiomics measurements [good (ICC ≥ 0.8)/moderate (ICC = 0.5-0.8)/poor (ICC < 0.5)]. ML models were employed using reproducible features (between observers and packages, ICC > 0.8) to distinguish RCC from benign RM. RESULTS Inter-package comparisons demonstrated that radiomics features from T1-WI-post-contrast had the highest proportion of good/moderate ICCs (54.8-58.6% for T1-WI-1 min), while most features extracted from T2-WI, T1-WI-pre-contrast, and ADC exhibited poor ICCs. Inter-observer comparisons found that radiomics measurements from T1-WI pre/post-contrast and T2-WI had the greatest proportion of features with good/moderate ICCs (95.3-99.1% T1-WI-post-contrast 1-min), while ADC measurements yielded mostly poor ICCs. ML models generated an AUC of 0.71 [95% confidence interval = 0.67-0.75] for diagnosis of RCC vs. benign RM. CONCLUSION Radiomics features extracted from T1-WI-post-contrast demonstrated greater inter-package and inter-observer reproducibility compared to ADC, with fair accuracy for distinguishing RCC from benign RM. CLINICAL RELEVANCE Knowledge of reproducibility of MRI radiomics features obtained on renal masses will aid in future study design and may enhance the diagnostic utility of radiomics models for renal mass characterization.
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Affiliation(s)
- Haitham Al-Mubarak
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Octavia Bane
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, Mount Sinai Hospital, New York, NY, USA
| | - Nicolas Gillingham
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, Mount Sinai West, New York, NY, 10019, USA
| | - Christopher Kyriakakos
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, Mount Sinai Hospital, New York, NY, USA
| | - Ghadi Abboud
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, Mount Sinai Hospital, New York, NY, USA
| | - Jordan Cuevas
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, Mount Sinai Hospital, New York, NY, USA
| | - Janette Gonzalez
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, Mount Sinai Hospital, New York, NY, USA
| | - Kirolos Meilika
- Department of Urology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Amir Horowitz
- Precision Immunology Institute/Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Hsin-Hui Vivien Huang
- Department of Population Sciences and Health Policy, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jorge Daza
- Department of Urology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Precision Immunology Institute/Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Valentin Fauveau
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ketan Badani
- Department of Urology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Satish E Viswanath
- Department of Biomedical Engineering, School of Medicine, Case School of Engineering, Case Western Reserve University, Cleveland, OH, USA
- Department of Radiology, Case School of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Bachir Taouli
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, Mount Sinai Hospital, New York, NY, USA
| | - Sara Lewis
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, Mount Sinai Hospital, New York, NY, USA.
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, One Gustave Levy Place, Box 1234, New York, NY, 10029, USA.
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Liu CJ, Zhang L, Sun Y, Geng L, Wang R, Shi KM, Wan JX. Application of CT and MRI images based on an artificial intelligence algorithm for predicting lymph node metastasis in breast cancer patients: a meta-analysis. BMC Cancer 2023; 23:1134. [PMID: 37993845 PMCID: PMC10666295 DOI: 10.1186/s12885-023-11638-z] [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: 07/20/2023] [Accepted: 11/13/2023] [Indexed: 11/24/2023] Open
Abstract
BACKGROUND This study aimed to comprehensively evaluate the accuracy and effect of computed tomography (CT) and magnetic resonance imaging (MRI) based on artificial intelligence (AI) algorithms for predicting lymph node metastasis in breast cancer patients. METHODS We systematically searched the PubMed, Embase and Cochrane Library databases for literature from inception to June 2023 using keywords that included 'artificial intelligence', 'CT,' 'MRI', 'breast cancer' and 'lymph nodes'. Studies that met the inclusion criteria were screened and their data were extracted for analysis. The main outcome measures included sensitivity, specificity, positive likelihood ratio, negative likelihood ratio and area under the curve (AUC). RESULTS A total of 16 studies were included in the final meta-analysis, covering 4,764 breast cancer patients. Among them, 11 studies used the manual algorithm MRI to calculate breast cancer risk, which had a sensitivity of 0.85 (95% confidence interval [CI] 0.79-0.90; p < 0.001; I2 = 75.3%), specificity of 0.81 (95% CI 0.66-0.83; p < 0.001; I2 = 0%), a positive likelihood ratio of 4.6 (95% CI 4.0-4.8), a negative likelihood ratio of 0.18 (95% CI 0.13-0.26) and a diagnostic odds ratio of 25 (95% CI 17-38). Five studies used manual algorithm CT to calculate breast cancer risk, which had a sensitivity of 0.88 (95% CI 0.79-0.94; p < 0.001; I2 = 87.0%), specificity of 0.80 (95% CI 0.69-0.88; p < 0.001; I2 = 91.8%), a positive likelihood ratio of 4.4 (95% CI 2.7-7.0), a negative likelihood ratio of 0.15 (95% CI 0.08-0.27) and a diagnostic odds ratio of 30 (95% CI 12-72). For MRI and CT, the AUC after study pooling was 0.85 (95% CI 0.82-0.88) and 0.91 (95% CI 0.88-0.93), respectively. CONCLUSION Computed tomography and MRI images based on an AI algorithm have good diagnostic accuracy in predicting lymph node metastasis in breast cancer patients and have the potential for clinical application.
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Affiliation(s)
- Cheng-Jie Liu
- Department of Information Center, Lianyungang Human Resources and Social Security Bureau, Lianyungang, 222000, JiangSu, China
| | - Lei Zhang
- Department of Information System, Lianyungang 149 Hospital, Lianyungang, 222000, Jiangsu, China
| | - Yi Sun
- Department of Medical Imaging, The Second People's Hospital of Lianyungang, 161 Xingfu Road, Haizhou District, Lianyungang, 222000, Jiangsu, China
| | - Lei Geng
- Department of Medical Imaging, The Second People's Hospital of Lianyungang, 161 Xingfu Road, Haizhou District, Lianyungang, 222000, Jiangsu, China
| | - Rui Wang
- Department of Medical Imaging, The Second People's Hospital of Lianyungang, 161 Xingfu Road, Haizhou District, Lianyungang, 222000, Jiangsu, China
| | - Kai-Min Shi
- Department of Information Center, Lianyungang Shuangcheng Information Technology Co., Ltd, Lianyungang, 222000, China
| | - Jin-Xin Wan
- Department of Medical Imaging, The Second People's Hospital of Lianyungang, 161 Xingfu Road, Haizhou District, Lianyungang, 222000, Jiangsu, China.
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8
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Verde F, Stanzione A, Cuocolo R, Romeo V, Di Stasi M, Ugga L, Mainenti PP, D'Armiento M, Sarno L, Guida M, Brunetti A, Maurea S. Segmentation methods applied to MRI-derived radiomic analysis for the prediction of placenta accreta spectrum in patients with placenta previa. Abdom Radiol (NY) 2023; 48:3207-3215. [PMID: 37439841 DOI: 10.1007/s00261-023-03963-5] [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: 01/15/2023] [Revised: 05/15/2023] [Accepted: 05/16/2023] [Indexed: 07/14/2023]
Abstract
PURPOSE To retrospectively evaluate the performance of different manual segmentation methods of placenta MR images for predicting Placenta Accreta Spectrum (PAS) disorders in patients with placenta previa (PP) using a Machine Learning (ML) Radiomics analysis. METHODS 64 patients (n=41 with PAS and n= 23 without PAS) with PP who underwent MRI examination for suspicion of PAS were retrospectively selected. All MRI examinations were acquired on a 1.5 T using T2-weighted (T2w) sequences on axial, sagittal and coronal planes. Ten different manual segmentation methods were performed on sagittal placental T2-weighted images obtaining five sets of 2D regions of interest (ROIs) and five sets of 3D volumes of interest (VOIs) from each patient. In detail, ROIs and VOIs were positioned on the following areas: placental tissue, retroplacental myometrium, cervix, placenta with underneath myometrium, placenta with underneath myometrium and cervix. For feature stability testing, the same process was repeated on 30 randomly selected placental MRI examinations by two additional radiologists, working independently and blinded to the original segmentation. Radiomic features were extracted from all available ROIs and VOIs. 100 iterations of 5-fold cross-validation with nested feature selection, based on recursive feature elimination, were subsequently run on each ROI/VOI to identify the best-performing method to classify instances correctly. RESULTS Among the segmentation methods, the best performance in predicting PAS was obtained by the VOIs covering the retroplacental myometrium (Mean validation score: 0.761, standard deviation: 0.116). CONCLUSION Our preliminary results show that the VOI including the retroplacental myometrium using T2w images seems to be the best method when segmenting images for the development of ML radiomics predictive models to identify PAS in patients with PP.
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Affiliation(s)
- Francesco Verde
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Via S. Pansini, 5, 80123, Naples, Italy.
| | - Arnaldo Stanzione
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Via S. Pansini, 5, 80123, Naples, Italy
| | - Renato Cuocolo
- Department of Medicine, Surgery, and Dentistry, University of Salerno, Baronissi, Italy
| | - Valeria Romeo
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Via S. Pansini, 5, 80123, Naples, Italy
| | - Martina Di Stasi
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Via S. Pansini, 5, 80123, Naples, Italy
| | - Lorenzo Ugga
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Via S. Pansini, 5, 80123, Naples, Italy
| | - Pier Paolo Mainenti
- Institute of Biostructures and Bioimaging of the National Council of Research (CNR), Naples, Italy
| | - Maria D'Armiento
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Via S. Pansini, 5, 80123, Naples, Italy
| | - Laura Sarno
- Department of Neuroscience, Reproductive and Dentistry Sciences, University of Naples "Federico II", Naples, Italy
| | - Maurizio Guida
- Department of Neuroscience, Reproductive and Dentistry Sciences, University of Naples "Federico II", Naples, Italy
| | - Arturo Brunetti
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Via S. Pansini, 5, 80123, Naples, Italy
| | - Simone Maurea
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Via S. Pansini, 5, 80123, Naples, Italy
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9
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Gülbay M. A radiomics-based logistic regression model for the assessment of emphysema severity. Tuberk Toraks 2023; 71:290-298. [PMID: 37740632 PMCID: PMC10795240 DOI: 10.5578/tt.20239710] [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: 09/24/2023] Open
Abstract
Introduction The aim of this study is to develop a model that differentiates between the radiological patterns of severe and mild emphysema using radiomics parameters, as well as to examine the parameters included in the model. Materials and Methods Over the last 12 months, a total of 354 patients were screened based on the presence of terms such as “Fleischner”, “CLE”, and “centriacinar” in their thoracic CT reports, culminating in a study population of 82 patients. The study population was divided into Group 1 (Fleischner mild and moderate; n= 45) and Group 2 (Fleischner confluent and advanced destructive; n= 37). Volumetric segmentation was performed, focusing on the upper lobe segments of both lungs. From these segmented volumes, radiomics parameters including shape, size, first-order, and second-order features were calculated. The best model parameters were selected based on the Bayesian Information Criterion and further optimized through grid search. The final model was tested using 1000 iterations of bootstrap resampling. Results In the training set, performance metrics were calculated with a sensitivity of 0.862, specificity of 0.870, accuracy of 0.863, and AUC of 0.910. Correspondingly, in the test set, these values were sensitivity= 0.848; specificity= 0.865; accuracy= 0.857; and AUC= 0.907. Conclusion The logistic regression model, composed of radiomics parameters and trained on a limited number of cases, effectively differentiated between mild and severe radiological patterns of emphysema using computed tomography images.
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Affiliation(s)
- Mutlu Gülbay
- Clinic of Radiology, Ankara Bilkent City Hospital, Ankara, Türkiye
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10
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Brown KH, Payan N, Osman S, Ghita M, Walls GM, Patallo IS, Schettino G, Prise KM, McGarry CK, Butterworth KT. Development and optimisation of a preclinical cone beam computed tomography-based radiomics workflow for radiation oncology research. Phys Imaging Radiat Oncol 2023; 26:100446. [PMID: 37252250 PMCID: PMC10213103 DOI: 10.1016/j.phro.2023.100446] [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: 02/24/2023] [Revised: 05/03/2023] [Accepted: 05/08/2023] [Indexed: 05/31/2023] Open
Abstract
Background and purpose Radiomics features derived from medical images have the potential to act as imaging biomarkers to improve diagnosis and predict treatment response in oncology. However, the complex relationships between radiomics features and the biological characteristics of tumours are yet to be fully determined. In this study, we developed a preclinical cone beam computed tomography (CBCT) radiomics workflow with the aim to use in vivo models to further develop radiomics signatures. Materials and methods CBCT scans of a mouse phantom were acquired using onboard imaging from a small animal radiotherapy research platform (SARRP, Xstrahl). The repeatability and reproducibility of radiomics outputs were compared across different imaging protocols, segmentation sizes, pre-processing parameters and materials. Robust features were identified and used to compare scans of two xenograft mouse tumour models (A549 and H460). Results Changes to the radiomics workflow significantly impact feature robustness. Preclinical CBCT radiomics analysis is feasible with 119 stable features identified from scans imaged at 60 kV, 25 bin width and 0.26 mm slice thickness. Large variation in segmentation volumes reduced the number of reliable radiomics features for analysis. Standardization in imaging and analysis parameters is essential in preclinical radiomics analysis to improve accuracy of outputs, leading to more consistent and reproducible findings. Conclusions We present the first optimised workflow for preclinical CBCT radiomics to identify imaging biomarkers. Preclinical radiomics has the potential to maximise the quantity of data captured in in vivo experiments and could provide key information supporting the wider application of radiomics.
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Affiliation(s)
- Kathryn H. Brown
- Patrick G. Johnston Centre for Cancer Research, Queen’s University Belfast, Northern Ireland, UK
| | - Neree Payan
- Patrick G. Johnston Centre for Cancer Research, Queen’s University Belfast, Northern Ireland, UK
| | - Sarah Osman
- University College London Hospitals NHS Foundation Trust Department of Radiotherapy, London, UK
| | - Mihaela Ghita
- Patrick G. Johnston Centre for Cancer Research, Queen’s University Belfast, Northern Ireland, UK
| | - Gerard M. Walls
- Patrick G. Johnston Centre for Cancer Research, Queen’s University Belfast, Northern Ireland, UK
- Cancer Centre, Belfast Health & Social Care Trust, Lisburn Road, Belfast BT9 7AB, Northern Ireland, UK
| | | | | | - Kevin M. Prise
- Patrick G. Johnston Centre for Cancer Research, Queen’s University Belfast, Northern Ireland, UK
| | - Conor K. McGarry
- Patrick G. Johnston Centre for Cancer Research, Queen’s University Belfast, Northern Ireland, UK
- Cancer Centre, Belfast Health & Social Care Trust, Lisburn Road, Belfast BT9 7AB, Northern Ireland, UK
| | - Karl T. Butterworth
- Patrick G. Johnston Centre for Cancer Research, Queen’s University Belfast, Northern Ireland, UK
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11
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Wei Q, Chen Z, Tang Y, Chen W, Zhong L, Mao L, Hu S, Wu Y, Deng K, Yang W, Liu X. External validation and comparison of MR-based radiomics models for predicting pathological complete response in locally advanced rectal cancer: a two-centre, multi-vendor study. Eur Radiol 2023; 33:1906-1917. [PMID: 36355199 DOI: 10.1007/s00330-022-09204-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Revised: 09/20/2022] [Accepted: 09/25/2022] [Indexed: 11/11/2022]
Abstract
OBJECTIVES The aim of this study was two-fold: (1) to develop and externally validate a multiparameter MR-based machine learning model to predict the pathological complete response (pCR) in locally advanced rectal cancer (LARC) patients after neoadjuvant chemoradiotherapy (nCRT), and (2) to compare different classifiers' discriminative performance for pCR prediction. METHODS This retrospective study includes 151 LARC patients divided into internal (centre A, n = 100) and external validation set (centre B, n = 51). The clinical and MR radiomics features were derived to construct clinical, radiomics, and clinical-radiomics model. Random forest (RF), support vector machine (SVM), logistic regression (LR), K-nearest neighbor (KNN), naive Bayes (NB), and extreme gradient boosting (XGBoost) were used as classifiers. The predictive performance was assessed using the receiver operating characteristic (ROC) curve. RESULTS Eleven radiomics and four clinical features were chosen as pCR-related signatures. In the radiomics model, the RF algorithm achieved 74.0% accuracy (an AUC of 0.863) and 84.4% (an AUC of 0.829) in the internal and external validation sets. In the clinical-radiomics model, RF algorithm exhibited high and stable predictive performance in the internal and external validation datasets with an AUC of 0.906 (87.3% sensitivity, 73.7% specificity, 76.0% accuracy) and 0.872 (77.3% sensitivity, 88.2% specificity, 86.3% accuracy), respectively. RF showed a better predictive performance than the other classifiers in the external validation datasets of three models. CONCLUSIONS The multiparametric clinical-radiomics model combined with RF algorithm is optimal for predicting pCR in the internal and external sets, and might help improve clinical stratifying management of LARC patients. KEY POINTS • A two-centre study showed that radiomics analysis of pre- and post-nCRT multiparameter MR images could predict pCR in patients with LARC. • The combined model was superior to the clinical and radiomics model in predicting pCR in locally advanced rectal cancer. • The RF classifier performed best in the current study.
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Affiliation(s)
- Qiurong Wei
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510120, China
| | - Zeli Chen
- Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China
| | - Yehuan Tang
- Department of Radiology, First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, China
| | - Weicui Chen
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510120, China
| | - Liming Zhong
- Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China
| | - Liting Mao
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510120, China
| | - Shaowei Hu
- Department of Pathology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510120, China
| | - Yuankui Wu
- Department of Medical Imaging, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
| | - Kan Deng
- Clinical Science, Philips Healthcare, Guangzhou, China
| | - Wei Yang
- Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China.
| | - Xian Liu
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510120, China.
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12
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Wang S, Pang X, de Keyzer F, Feng Y, Swinnen JV, Yu J, Ni Y. AI-based MRI auto-segmentation of brain tumor in rodents, a multicenter study. Acta Neuropathol Commun 2023; 11:11. [PMID: 36641470 PMCID: PMC9840251 DOI: 10.1186/s40478-023-01509-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Accepted: 01/06/2023] [Indexed: 01/15/2023] Open
Abstract
Automatic segmentation of rodent brain tumor on magnetic resonance imaging (MRI) may facilitate biomedical research. The current study aims to prove the feasibility for automatic segmentation by artificial intelligence (AI), and practicability of AI-assisted segmentation. MRI images, including T2WI, T1WI and CE-T1WI, of brain tumor from 57 WAG/Rij rats in KU Leuven and 46 mice from the cancer imaging archive (TCIA) were collected. A 3D U-Net architecture was adopted for segmentation of tumor bearing brain and brain tumor. After training, these models were tested with both datasets after Gaussian noise addition. Reduction of inter-observer disparity by AI-assisted segmentation was also evaluated. The AI model segmented tumor-bearing brain well for both Leuven and TCIA datasets, with Dice similarity coefficients (DSCs) of 0.87 and 0.85 respectively. After noise addition, the performance remained unchanged when the signal-noise ratio (SNR) was higher than two or eight, respectively. For the segmentation of tumor lesions, AI-based model yielded DSCs of 0.70 and 0.61 for Leuven and TCIA datasets respectively. Similarly, the performance is uncompromised when the SNR was over two and eight respectively. AI-assisted segmentation could significantly reduce the inter-observer disparities and segmentation time in both rats and mice. Both AI models for segmenting brain or tumor lesions could improve inter-observer agreement and therefore contributed to the standardization of the following biomedical studies.
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Affiliation(s)
- Shuncong Wang
- grid.5596.f0000 0001 0668 7884Biomedical Group, Campus Gasthuisberg, KU Leuven, 3000 Leuven, Belgium
| | - Xin Pang
- grid.5596.f0000 0001 0668 7884Biomedical Group, Campus Gasthuisberg, KU Leuven, 3000 Leuven, Belgium ,grid.5596.f0000 0001 0668 7884Faculty of Economics and Business, KU Leuven, 3000 Leuven, Belgium
| | - Frederik de Keyzer
- grid.5596.f0000 0001 0668 7884Department of Radiology, University Hospitals Leuven, KU Leuven, Herestraat 49, 3000 Leuven, Belgium
| | - Yuanbo Feng
- grid.5596.f0000 0001 0668 7884Biomedical Group, Campus Gasthuisberg, KU Leuven, 3000 Leuven, Belgium
| | - Johan V. Swinnen
- grid.5596.f0000 0001 0668 7884Biomedical Group, Campus Gasthuisberg, KU Leuven, 3000 Leuven, Belgium
| | - Jie Yu
- grid.5596.f0000 0001 0668 7884Biomedical Group, Campus Gasthuisberg, KU Leuven, 3000 Leuven, Belgium
| | - Yicheng Ni
- grid.5596.f0000 0001 0668 7884Biomedical Group, Campus Gasthuisberg, KU Leuven, 3000 Leuven, Belgium
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13
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Haraguchi T, Kobayashi Y, Hirahara D, Kobayashi T, Takaya E, Nagai MT, Tomita H, Okamoto J, Kanemaki Y, Tsugawa K. Radiomics model of diffusion-weighted whole-body imaging with background signal suppression (DWIBS) for predicting axillary lymph node status in breast cancer. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2023; 31:627-640. [PMID: 37038802 DOI: 10.3233/xst-230009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2023]
Abstract
BACKGROUND In breast cancer diagnosis and treatment, non-invasive prediction of axillary lymph node (ALN) metastasis can help avoid complications related to sentinel lymph node biopsy. OBJECTIVE This study aims to develop and evaluate machine learning models using radiomics features extracted from diffusion-weighted whole-body imaging with background signal suppression (DWIBS) examination for predicting the ALN status. METHODS A total of 100 patients with histologically proven, invasive, clinically N0 breast cancer who underwent DWIBS examination consisting of short tau inversion recovery (STIR) and DWIBS sequences before surgery were enrolled. Radiomic features were calculated using segmented primary lesions in DWIBS and STIR sequences and were divided into training (n = 75) and test (n = 25) datasets based on the examination date. Using the training dataset, optimal feature selection was performed using the least absolute shrinkage and selection operator algorithm, and the logistic regression model and support vector machine (SVM) classifier model were constructed with DWIBS, STIR, or a combination of DWIBS and STIR sequences to predict ALN status. Receiver operating characteristic curves were used to assess the prediction performance of radiomics models. RESULTS For the test dataset, the logistic regression model using DWIBS, STIR, and a combination of both sequences yielded an area under the curve (AUC) of 0.765 (95% confidence interval: 0.548-0.982), 0.801 (0.597-1.000), and 0.779 (0.567-0.992), respectively, whereas the SVM classifier model using DWIBS, STIR, and a combination of both sequences yielded an AUC of 0.765 (0.548-0.982), 0.757 (0.538-0.977), and 0.779 (0.567-0.992), respectively. CONCLUSIONS Use of machine learning models incorporating with the quantitative radiomic features derived from the DWIBS and STIR sequences can potentially predict ALN status.
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Affiliation(s)
- Takafumi Haraguchi
- Department of Advanced Biomedical Imaging and Informatics, St. Marianna University School of Medicine, Sugao, Miyamae-ku, Kawasaki, Kanagawa, Japan
| | - Yasuyuki Kobayashi
- Department of Medical Information and Communication Technology Research, St. Marianna University School of Medicine, Sugao, Miyamae-ku, Kawasaki, Kanagawa, Japan
| | - Daisuke Hirahara
- Department of Medical Information and Communication Technology Research, St. Marianna University School of Medicine, Sugao, Miyamae-ku, Kawasaki, Kanagawa, Japan
- Department of AI Research Lab, Harada Academy, Higashitaniyama, Kagoshima, Kagoshima, Japan
| | - Tatsuaki Kobayashi
- Department of Medical Information and Communication Technology Research, St. Marianna University School of Medicine, Sugao, Miyamae-ku, Kawasaki, Kanagawa, Japan
| | - Eichi Takaya
- Department of Medical Information and Communication Technology Research, St. Marianna University School of Medicine, Sugao, Miyamae-ku, Kawasaki, Kanagawa, Japan
- AI Lab, Tohoku University Hospital, Seiryomachi, Aoba-ku, Sendai, Miyagi, Japan
- School of Science for Open and Environmental Systems, Graduate School of Science and Technology, Keio University, Hiyoshi, Kohoku-ku, Yokohama, Kanagawa, Japan
| | - Mariko Takishita Nagai
- Division of Breast and Endocrine Surgery, Department of Surgery, St. Marianna University School of Medicine, Sugao, Miyamae-ku, Kawasaki, Kanagawa, Japan
| | - Hayato Tomita
- Department of Radiology, St. Marianna University School of Medicine, Sugao, Miyamae-ku, Kawasaki, Kanagawa, Japan
| | - Jun Okamoto
- Department of Radiology, St. Marianna University School of Medicine, Sugao, Miyamae-ku, Kawasaki, Kanagawa, Japan
| | - Yoshihide Kanemaki
- Department of Radiology, Breast and Imaging Center, St. Marianna University School of Medicine, Manpukuji, Asao-ku, Kawasaki, Kanagawa, Japan
| | - Koichiro Tsugawa
- Division of Breast and Endocrine Surgery, Department of Surgery, St. Marianna University School of Medicine, Sugao, Miyamae-ku, Kawasaki, Kanagawa, Japan
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14
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Tan E, Merchant K, Kn BP, Cs A, Zhao JJ, Saffari SE, Tan PH, Tang PH. CT-based morphologic and radiomics features for the classification of MYCN gene amplification status in pediatric neuroblastoma. Childs Nerv Syst 2022; 38:1487-1495. [PMID: 35460355 DOI: 10.1007/s00381-022-05534-3] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Accepted: 04/13/2022] [Indexed: 11/24/2022]
Abstract
PURPOSE MYCN onco-gene amplification in neuroblastoma confers patients to the high-risk disease category for which prognosis is poor and more aggressive multimodal treatment is indicated. This retrospective study leverages machine learning techniques to develop a computed tomography (CT)-based model incorporating semantic and non-semantic features for non-invasive prediction of MYCN amplification status in pediatric neuroblastoma. METHODS From 2009 to 2020, 54 pediatric patients treated for neuroblastoma at a specialized children's hospital with pre-treatment contrast-enhanced CT and MYCN status were identified (training cohort, n = 44; testing cohort, n = 10). Six morphologic features and 107 quantitative gray-level texture radiomics features extracted from manually drawn volume-of-interest were analyzed. Following feature selection and class balancing, the final predictive model was developed with eXtreme Gradient Boosting (XGBoost) algorithm. Accumulated local effects (ALE) plots were used to explore main effects of the predictive features. Tumor texture maps were also generated for visualization of radiomics features. RESULTS One morphologic and 2 radiomics features were selected for model building. The XGBoost model from the training cohort yielded an area under the receiver operating characteristics curve (AUC-ROC) of 0.930 (95% CI, 0.85-1.00), optimized F1-score of 0.878, and Matthews correlation coefficient (MCC) of 0.773. Evaluation on the testing cohort returned AUC-ROC of 0.880 (95% CI, 0.64-1.00), optimized F1-score of 0.933, and MCC of 0.764. ALE plots and texture maps showed higher "GreyLevelNonUniformity" values, lower "Strength" values, and higher number of image-defined risk factors contribute to higher predicted probability of MYCN amplification. CONCLUSION The machine learning model reliably classified MYCN amplification in pediatric neuroblastoma and shows potential as a surrogate imaging biomarker.
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Affiliation(s)
- Eelin Tan
- Department of Diagnostic & Interventional Imaging, KK Womens' and Childrens' Hospital, 100 Bukit Timah Rd, Singapore, 229899, Singapore.
| | - Khurshid Merchant
- Department of Pathology and Laboratory Medicine, KK Womens' and Childrens' Hospital, 100 Bukit Timah Rd, Singapore, 229899, Singapore
| | - Bhanu Prakash Kn
- Bioinformatics Institute, A*Star, 30 Biopolis Street, #07-01 Matrix, Singapore, 138671, Singapore
| | - Arvind Cs
- Bioinformatics Institute, A*Star, 30 Biopolis Street, #07-01 Matrix, Singapore, 138671, Singapore
| | - Joseph J Zhao
- Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Dr, Singapore, 117597, Singapore
| | - Seyed Ehsan Saffari
- Center for Quantitative Medicine, Duke-NUS Graduate Medical School, 8 College Rd, Singapore, 169857, Singapore
| | - Poh Hwa Tan
- Department of Diagnostic & Interventional Imaging, KK Womens' and Childrens' Hospital, 100 Bukit Timah Rd, Singapore, 229899, Singapore
| | - Phua Hwee Tang
- Department of Diagnostic & Interventional Imaging, KK Womens' and Childrens' Hospital, 100 Bukit Timah Rd, Singapore, 229899, Singapore
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15
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Bos P, van den Brekel MWM, Taghavi M, Gouw ZAR, Al-Mamgani A, Waktola S, J W L Aerts H, Beets-Tan RGH, Castelijns JA, Jasperse B. Largest diameter delineations can substitute 3D tumor volume delineations for radiomics prediction of human papillomavirus status on MRI's of oropharyngeal cancer. Phys Med 2022; 101:36-43. [PMID: 35882094 DOI: 10.1016/j.ejmp.2022.07.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 07/11/2022] [Accepted: 07/13/2022] [Indexed: 11/16/2022] Open
Abstract
PURPOSE Laborious and time-consuming tumor segmentations are one of the factors that impede adoption of radiomics in the clinical routine. This study investigates model performance using alternative tumor delineation strategies in models predictive of human papillomavirus (HPV) in oropharyngeal squamous cell carcinoma (OPSCC). METHODS Of 153 OPSCC patients, HPV status was determined using p16/p53 immunohistochemistry. MR-based radiomic features were extracted within 3D delineations by an inexperienced observer, experienced radiologist or radiation oncologist, and within a 2D delineation of the largest axial tumor diameter and 3D spheres within the tumor. First, logistic regression prediction models were constructed and tested separately for each of these six delineation strategies. Secondly, the model trained on experienced delineations was tested using these delineation strategies. The latter methodology was repeated with the omission of shape features. Model performance was evaluated using area under the curve (AUC), sensitivity and specificity. RESULTS Models constructed and tested using single-slice delineations (AUC/Sensitivity/Specificity: 0.84/0.75/0.84) perform better compared to 3D experienced observer delineations (AUC/Sensitivity/Specificity: 0.76/0.76/0.71), where models based on 4 mm sphere delineations (AUC/Sensitivity/Specificity: 0.77/0.59/0.71) show similar performance. Similar performance was found when experienced and largest diameter delineations (AUC/Sens/Spec: 0.76/0.75/0.65 vs 0.76/0.69/0.69) was used to test the model constructed using experienced delineations without shape features. CONCLUSION Alternative delineations can substitute labor and time intensive full tumor delineations in a model that predicts HPV status in OPSCC. These faster delineations may improve adoption of radiomics in the clinical setting. Future research should evaluate whether these alternative delineations are valid in other radiomics models.
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Affiliation(s)
- Paula Bos
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, the Netherlands; Department of Head and Neck Oncology and Surgery, The Netherlands Cancer Institute, Amsterdam, the Netherlands; GROW School for Oncology and Developmental Biology, University of Maastricht, Maastricht, the Netherlands.
| | - Michiel W M van den Brekel
- Department of Head and Neck Oncology and Surgery, The Netherlands Cancer Institute, Amsterdam, the Netherlands; Department of Oral and Maxillofacial Surgery, Amsterdam University Medical Center (AUMC), Amsterdam, the Netherlands
| | - Marjaneh Taghavi
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Zeno A R Gouw
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Abrahim Al-Mamgani
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Selam Waktola
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Hugo J W L Aerts
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, the Netherlands; Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, United States; Radiology and Nuclear Medicine, CARIM & GROW, Maastricht University, Maastricht, the Netherlands
| | - Regina G H Beets-Tan
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, the Netherlands; GROW School for Oncology and Developmental Biology, University of Maastricht, Maastricht, the Netherlands; Department of Regional Health Research, University of Southern Denmark, Denmark
| | - Jonas A Castelijns
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Bas Jasperse
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, the Netherlands; Department of Radiology, Amsterdam University Medical Center, Amsterdam the Netherlands
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Pei W, Wang C, Liao H, Chen X, Wei Y, Huang X, Liang X, Bao H, Su D, Jin G. MRI-based random survival Forest model improves prediction of progression-free survival to induction chemotherapy plus concurrent Chemoradiotherapy in Locoregionally Advanced nasopharyngeal carcinoma. BMC Cancer 2022; 22:739. [PMID: 35794590 PMCID: PMC9261049 DOI: 10.1186/s12885-022-09832-6] [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: 12/06/2021] [Accepted: 06/27/2022] [Indexed: 12/08/2022] Open
Abstract
Background The present study aimed to explore the application value of random survival forest (RSF) model and Cox model in predicting the progression-free survival (PFS) among patients with locoregionally advanced nasopharyngeal carcinoma (LANPC) after induction chemotherapy plus concurrent chemoradiotherapy (IC + CCRT). Methods Eligible LANPC patients underwent magnetic resonance imaging (MRI) scan before treatment were subjected to radiomics feature extraction. Radiomics and clinical features of patients in the training cohort were subjected to RSF analysis to predict PFS and were tested in the testing cohort. The performance of an RSF model with clinical and radiologic predictors was assessed with the area under the receiver operating characteristic (ROC) curve (AUC) and Delong test and compared with Cox models based on clinical and radiologic parameters. Further, the Kaplan-Meier method was used for risk stratification of patients. Results A total of 294 LANPC patients (206 in the training cohort; 88 in the testing cohort) were enrolled and underwent magnetic resonance imaging (MRI) scans before treatment. The AUC value of the clinical Cox model, radiomics Cox model, clinical + radiomics Cox model, and clinical + radiomics RSF model in predicting 3- and 5-year PFS for LANPC patients was [0.545 vs 0.648 vs 0.648 vs 0.899 (training cohort), and 0.566 vs 0.736 vs 0.730 vs 0.861 (testing cohort); 0.556 vs 0.604 vs 0.611 vs 0.897 (training cohort), and 0.591 vs 0.661 vs 0.676 vs 0.847 (testing cohort), respectively]. Delong test showed that the RSF model and the other three Cox models were statistically significant, and the RSF model markedly improved prediction performance (P < 0.001). Additionally, the PFS of the high-risk group was lower than that of the low-risk group in the RSF model (P < 0.001), while comparable in the Cox model (P > 0.05). Conclusion The RSF model may be a potential tool for prognostic prediction and risk stratification of LANPC patients. Supplementary Information The online version contains supplementary material available at 10.1186/s12885-022-09832-6.
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Innocenti T, Danti G, Lynch EN, Dragoni G, Gottin M, Fedeli F, Palatresi D, Biagini MR, Milani S, Miele V, Galli A. Higher volume growth rate is associated with development of worrisome features in patients with branch duct-intraductal papillary mucinous neoplasms. World J Clin Cases 2022; 10:5667-5679. [PMID: 35979097 PMCID: PMC9258377 DOI: 10.12998/wjcc.v10.i17.5667] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Revised: 03/18/2022] [Accepted: 04/09/2022] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Branch duct-intraductal papillary mucinous neoplasms (BD-IPMNs) are the most common pancreatic cystic tumours and have a low risk of malignant transformation. Current guidelines only evaluate cyst diameter as an important risk factor but it is not always easy to measure, especially when comparing different methods. On the other side, cyst volume is a new parameter with low inter-observer variability and is highly reproducible over time.
AIM To assess both diameter and volume growth rate of BD-IPMNs and evaluate their correlation with the development of malignant characteristics.
METHODS Computed tomography scans and magnetic resonance imaging exams were retrospectively reviewed. The diameter was measured on three planes, while the volume was calculated by segmentation: The volume of the entire cyst was determined by manually drawing a region of interest along the edge of the neoplasm on each consecutive slice covering the whole lesion; therefore, a three-dimensional volume of interest was finally obtained with the calculated value expressed in cm3. Changes in size over time were measured. The development of worrisome features was evaluated.
RESULTS We evaluated exams of 98 patients across a 40.5-mo median follow-up time. Ten patients developed worrisome features. Cysts at baseline were significantly larger in patients who developed worrisome features (diameters P = 0.0035, P = 0.00652, P = 0.00424; volume P = 0.00222). Volume growth rate was significantly higher in patients who developed worrisome features (1.12 cm3/year vs 0 cm3/year, P = 0.0001); diameter growth rate was higher as well, but the difference did not always reach statistical significance. Volume but not diameter growth rate in the first year of follow-up was higher in patients who developed worrisome features (0.46 cm3/year vs 0 cm3/year, P = 0.00634).
CONCLUSION The measurement of baseline volume and its variation over time is a reliable tool for the follow-up of BD-IPMNs. Volume measurement could be a better tool than diameter measurement to predict the development of worrisome features.
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Affiliation(s)
- Tommaso Innocenti
- Gastroenterology Research Unit, Department of Experimental and Clinical Biomedical Sciences "Mario Serio”, University of Florence, Florence 50134, Italy
| | - Ginevra Danti
- Emergency Radiology Unit, Department of Services, Careggi University Hospital, Florence 50134, Italy
| | - Erica Nicola Lynch
- Gastroenterology Research Unit, Department of Experimental and Clinical Biomedical Sciences "Mario Serio”, University of Florence, Florence 50134, Italy
| | - Gabriele Dragoni
- Gastroenterology Research Unit, Department of Experimental and Clinical Biomedical Sciences "Mario Serio”, University of Florence, Florence 50134, Italy
- Department of Medical Biotechnologies, University of Siena, Siena 53100, Italy
| | - Matteo Gottin
- Gastroenterology Research Unit, Department of Experimental and Clinical Biomedical Sciences "Mario Serio”, University of Florence, Florence 50134, Italy
| | - Filippo Fedeli
- Emergency Radiology Unit, Department of Services, Careggi University Hospital, Florence 50134, Italy
| | - Daniele Palatresi
- Emergency Radiology Unit, Department of Services, Careggi University Hospital, Florence 50134, Italy
| | - Maria Rosa Biagini
- Gastroenterology Research Unit, Department of Experimental and Clinical Biomedical Sciences "Mario Serio”, University of Florence, Florence 50134, Italy
| | - Stefano Milani
- Gastroenterology Research Unit, Department of Experimental and Clinical Biomedical Sciences "Mario Serio”, University of Florence, Florence 50134, Italy
| | - Vittorio Miele
- Emergency Radiology Unit, Department of Services, Careggi University Hospital, Florence 50134, Italy
| | - Andrea Galli
- Gastroenterology Research Unit, Department of Experimental and Clinical Biomedical Sciences "Mario Serio”, University of Florence, Florence 50134, Italy
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18
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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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Automated segmentation of the fractured vertebrae on CT and its applicability in a radiomics model to predict fracture malignancy. Sci Rep 2022; 12:6735. [PMID: 35468985 PMCID: PMC9038736 DOI: 10.1038/s41598-022-10807-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Accepted: 04/13/2022] [Indexed: 11/08/2022] Open
Abstract
Although CT radiomics has shown promising results in the evaluation of vertebral fractures, the need for manual segmentation of fractured vertebrae limited the routine clinical implementation of radiomics. Therefore, automated segmentation of fractured vertebrae is needed for successful clinical use of radiomics. In this study, we aimed to develop and validate an automated algorithm for segmentation of fractured vertebral bodies on CT, and to evaluate the applicability of the algorithm in a radiomics prediction model to differentiate benign and malignant fractures. A convolutional neural network was trained to perform automated segmentation of fractured vertebral bodies using 341 vertebrae with benign or malignant fractures from 158 patients, and was validated on independent test sets (internal test, 86 vertebrae [59 patients]; external test, 102 vertebrae [59 patients]). Then, a radiomics model predicting fracture malignancy on CT was constructed, and the prediction performance was compared between automated and human expert segmentations. The algorithm achieved good agreement with human expert segmentation at testing (Dice similarity coefficient, 0.93-0.94; cross-sectional area error, 2.66-2.97%; average surface distance, 0.40-0.54 mm). The radiomics model demonstrated good performance in the training set (AUC, 0.93). In the test sets, automated and human expert segmentations showed comparable prediction performances (AUC, internal test, 0.80 vs 0.87, p = 0.044; external test, 0.83 vs 0.80, p = 0.37). In summary, we developed and validated an automated segmentation algorithm that showed comparable performance to human expert segmentation in a CT radiomics model to predict fracture malignancy, which may enable more practical clinical utilization of radiomics.
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20
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Simple delineations cannot substitute full 3d tumor delineations for MR-based radiomics prediction of locoregional control in oropharyngeal cancer. Eur J Radiol 2022; 148:110167. [DOI: 10.1016/j.ejrad.2022.110167] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2021] [Revised: 12/20/2021] [Accepted: 01/15/2022] [Indexed: 11/20/2022]
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21
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Pfaehler E, Zhovannik I, Wei L, Boellaard R, Dekker A, Monshouwer R, El Naqa I, Bussink J, Gillies R, Wee L, Traverso A. A systematic review and quality of reporting checklist for repeatability and reproducibility of radiomic features. Phys Imaging Radiat Oncol 2021; 20:69-75. [PMID: 34816024 PMCID: PMC8591412 DOI: 10.1016/j.phro.2021.10.007] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 10/28/2021] [Accepted: 10/29/2021] [Indexed: 12/12/2022] Open
Abstract
Main factors impacting feature stability: Image acquisition, reconstruction, tumor segmentation, and interpolation. Textural features are less robust than morphological or statistical features. A checklist is provided including items that should be reported in a radiomic study.
Purpose Although quantitative image biomarkers (radiomics) show promising value for cancer diagnosis, prognosis, and treatment assessment, these biomarkers still lack reproducibility. In this systematic review, we aimed to assess the progress in radiomics reproducibility and repeatability in the recent years. Methods and materials Four hundred fifty-one abstracts were retrieved according to the original PubMed search pattern with the publication dates ranging from 2017/05/01 to 2020/12/01. Each abstract including the keywords was independently screened by four observers. Forty-two full-text articles were selected for further analysis. Patient population data, radiomic feature classes, feature extraction software, image preprocessing, and reproducibility results were extracted from each article. To support the community with a standardized reporting strategy, we propose a specific reporting checklist to evaluate the feasibility to reproduce each study. Results Many studies continue to under-report essential reproducibility information: all but one clinical and all but two phantom studies missed to report at least one important item reporting image acquisition. The studies included in this review indicate that all radiomic features are sensitive to image acquisition, reconstruction, tumor segmentation, and interpolation. However, the amount of sensitivity is feature dependent, for instance, textural features were, in general, less robust than statistical features. Conclusions Radiomics repeatability, reproducibility, and reporting quality can substantially be improved regarding feature extraction software and settings, image preprocessing and acquisition, cutoff values for stable feature selection. Our proposed radiomics reporting checklist can serve to simplify and improve the reporting and, eventually, guarantee the possibility to fully replicate and validate radiomic studies.
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Affiliation(s)
- Elisabeth Pfaehler
- Department of Nuclear Medicine and Molecular Imaging, Medical Imaging Center, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Ivan Zhovannik
- Department of Radiation Oncology, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The Netherlands.,Department of Radiation Oncology (MAASTRO), GROW School for Oncology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Lise Wei
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA
| | - Ronald Boellaard
- Department of Nuclear Medicine and Molecular Imaging, Medical Imaging Center, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.,Department of Radiology & Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands
| | - Andre Dekker
- Department of Radiation Oncology (MAASTRO), GROW School for Oncology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - René Monshouwer
- Department of Radiation Oncology, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Issam El Naqa
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA
| | - Jan Bussink
- Department of Radiation Oncology, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Robert Gillies
- Department of Radiology, Moffitt Cancer Center, Tampa, FL, USA
| | - Leonard Wee
- Department of Radiation Oncology (MAASTRO), GROW School for Oncology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Alberto Traverso
- Department of Radiation Oncology (MAASTRO), GROW School for Oncology, Maastricht University Medical Centre+, Maastricht, The Netherlands
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22
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Chen C, Qin Y, Chen H, Zhu D, Gao F, Zhou X. A meta-analysis of the diagnostic performance of machine learning-based MRI in the prediction of axillary lymph node metastasis in breast cancer patients. Insights Imaging 2021; 12:156. [PMID: 34731343 PMCID: PMC8566689 DOI: 10.1186/s13244-021-01034-1] [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: 04/09/2021] [Accepted: 06/02/2021] [Indexed: 02/08/2023] Open
Abstract
Background Despite that machine learning (ML)-based MRI has been evaluated for diagnosis of axillary lymph node metastasis (ALNM) in breast cancer patients, diagnostic values they showed have been variable. In this study, we aimed to assess the use of ML to classify ALNM on MRI and to identify potential covariates that might influence the diagnostic performance of ML. Methods A systematic research of PubMed, Embase, Web of Science, and the Cochrane Library was conducted until 27 December 2020 to collect the included articles. Subgroup analysis was also performed. Findings Fourteen studies assessing a total of 2247 breast cancer patients were included in the analysis. The overall AUC for ML in the validation set was 0.80 (95% confidence interval [CI] 0.76–0.83) with a negative predictive value of 0.83. The pooled sensitivity and specificity were 0.79 (95% CI 0.74–0.84) and 0.77 (95% CI 0.73–0.81), respectively. In the subgroup analysis of the validation set, T1-weighted contrast-enhanced (T1CE) imaging with ML yielded a higher sensitivity (0.80 vs. 0.67 vs. 0.76) than the T2-weighted fat-suppressed (T2-FS) imaging and diffusion-weighted imaging (DWI). Support vector machines (SVMs) had a higher specificity than linear regression (LR) and linear discriminant analysis (LDA) (0.79 vs. 0.78 vs. 0.75), whereas LDA showed a higher sensitivity than LR and SVM (0.83 vs. 0.70 vs. 0.77). Interpretation MRI sequences and algorithms were the main factors that affect the diagnostic performance of ML. Although its results were encouraging with the pooled sensitivity of around 0.80, it meant that 1 in 5 women that would go with undetected metastases, which may have a detrimental effect on the overall survival for 20% of patients with positive SLN status. Despite that a high NPV of 0.83 meant that ML could potentially benefit those with negative SLN, it might also translate to 1 in 5 tests being false negative. We would like to suggest that ML may not be yet usable in clinical routine especially when patient survival is used as a primary measurement of its outcome. Supplementary Information The online version contains supplementary material available at 10.1186/s13244-021-01034-1.
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Affiliation(s)
- Chen Chen
- Department of Radiology, West China Hospital, Sichuan University, 37 Guoxue Road, Chengdu, 610041, Sichuan, People's Republic of China
| | - Yuhui Qin
- Department of Radiology, West China Hospital, Sichuan University, 37 Guoxue Road, Chengdu, 610041, Sichuan, People's Republic of China
| | - Haotian Chen
- Department of Radiology, West China Hospital, Sichuan University, 37 Guoxue Road, Chengdu, 610041, Sichuan, People's Republic of China
| | - Dongyong Zhu
- Department of Radiology, West China Hospital, Sichuan University, 37 Guoxue Road, Chengdu, 610041, Sichuan, People's Republic of China
| | - Fabao Gao
- Department of Radiology, West China Hospital, Sichuan University, 37 Guoxue Road, Chengdu, 610041, Sichuan, People's Republic of China.
| | - Xiaoyue Zhou
- Siemens Healthineers Ltd., Shanghai, People's Republic of China
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23
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Wan T, Wu C, Meng M, Liu T, Li C, Ma J, Qin Z. Radiomic Features on Multiparametric MRI for Preoperative Evaluation of Pituitary Macroadenomas Consistency: Preliminary Findings. J Magn Reson Imaging 2021; 55:1491-1503. [PMID: 34549842 DOI: 10.1002/jmri.27930] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Revised: 09/05/2021] [Accepted: 09/10/2021] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND Preoperative assessment of the consistency of pituitary macroadenomas (PMA) might be needed for surgical planning. PURPOSE To investigate the diagnostic performance of radiomics models based on multiparametric magnetic resonance imaging (mpMRI) for preoperatively evaluating the tumor consistency of PMA. STUDY TYPE Retrospective. POPULATION One hundred and fifty-six PMA patients (soft consistency, N = 104 vs. hard consistency, N = 52), divided into training (N = 108) and test (N = 48) cohorts. The tumor consistency was determined on surgical findings. FIELD STRENGTH/SEQUENCE T1-weighted imaging (T1WI), contrast-enhanced T1WI (T1CE), and T2-weighted imaging (T2WI) using spin-echo sequences with a 3.0-T scanner. ASSESSMENT An automated three-dimensional (3D) segmentation was performed to generate the volume of interest (VOI) on T2WI, then T1WI/T1CE were coregistered to T2WI. A total of 388 radiomic features were extracted on each VOI of mpMRI. The top-discriminative features were identified using the minimum-redundancy maximum-relevance method and 0.632+ bootstrapping. The radiomics models based on each sequence and their combinations were established via the random forest (RF) and support vector machine (SVM), and independently evaluated for their ability in distinguishing PMA consistency. STATISTICAL TESTS Mann-Whitney U-test and Chi-square test were used for comparison analysis. The area under the receiver operating characteristic curve (AUC), accuracy (ACC), sensitivity (SEN), specificity (SPE), and relative standard deviation (RSD) were calculated to evaluate each model's performance. ACC with P-value<0.05 was considered statistically significant. RESULTS Eleven mpMRI-based features exhibited statistically significant differences between soft and hard PMA in the training cohort. The radiomics model built on combined T1WI/T1CE/T2WI demonstrated the best performance among all the radiomics models with an AUC of 0.90 (95% confidence interval [CI]: 0.87-0.92), ACC of 0.87 (CI: 0.84-0.89), SEN of 0.83 (CI: 0.81-0.85), and SPE of 0.87 (CI: 0.85-0.99) in the test cohort. DATA CONCLUSION Radiomic features based on mpMRI have good performance in the presurgical evaluation of PMA consistency. LEVEL OF EVIDENCE 3 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Tao Wan
- School of Biological Science and Medical Engineering, Beihang University, Beijing, China.,Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, China
| | - Chunxue Wu
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Ming Meng
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Tao Liu
- School of Biological Science and Medical Engineering, Beihang University, Beijing, China.,Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, China
| | - Chuzhong Li
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Jun Ma
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Zengchang Qin
- School of Automation Science and Electrical Engineering, Beihang University, Beijing, China
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Li S, Deng YQ, Zhu ZL, Hua HL, Tao ZZ. A Comprehensive Review on Radiomics and Deep Learning for Nasopharyngeal Carcinoma Imaging. Diagnostics (Basel) 2021; 11:1523. [PMID: 34573865 PMCID: PMC8465998 DOI: 10.3390/diagnostics11091523] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Revised: 08/10/2021] [Accepted: 08/19/2021] [Indexed: 12/23/2022] Open
Abstract
Nasopharyngeal carcinoma (NPC) is one of the most common malignant tumours of the head and neck, and improving the efficiency of its diagnosis and treatment strategies is an important goal. With the development of the combination of artificial intelligence (AI) technology and medical imaging in recent years, an increasing number of studies have been conducted on image analysis of NPC using AI tools, especially radiomics and artificial neural network methods. In this review, we present a comprehensive overview of NPC imaging research based on radiomics and deep learning. These studies depict a promising prospect for the diagnosis and treatment of NPC. The deficiencies of the current studies and the potential of radiomics and deep learning for NPC imaging are discussed. We conclude that future research should establish a large-scale labelled dataset of NPC images and that studies focused on screening for NPC using AI are necessary.
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Affiliation(s)
- Song Li
- Department of Otolaryngology-Head and Neck Surgery, Renmin Hospital of Wuhan University, 238 Jie-Fang Road, Wuhan 430060, China; (S.L.); (Y.-Q.D.); (H.-L.H.)
| | - Yu-Qin Deng
- Department of Otolaryngology-Head and Neck Surgery, Renmin Hospital of Wuhan University, 238 Jie-Fang Road, Wuhan 430060, China; (S.L.); (Y.-Q.D.); (H.-L.H.)
| | - Zhi-Ling Zhu
- Department of Otolaryngology-Head and Neck Surgery, Tongji Hospital Affiliated to Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China;
| | - Hong-Li Hua
- Department of Otolaryngology-Head and Neck Surgery, Renmin Hospital of Wuhan University, 238 Jie-Fang Road, Wuhan 430060, China; (S.L.); (Y.-Q.D.); (H.-L.H.)
| | - Ze-Zhang Tao
- Department of Otolaryngology-Head and Neck Surgery, Renmin Hospital of Wuhan University, 238 Jie-Fang Road, Wuhan 430060, China; (S.L.); (Y.-Q.D.); (H.-L.H.)
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25
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Paudyal R, Deasy JO, Shukla-Dave A. Editorial for "Differences in Radiomics Signatures Between Patients with Early and Advanced T-Stage Nasopharyngeal Carcinoma Facilitate Prognostication". J Magn Reson Imaging 2021; 56:221-222. [PMID: 34370347 DOI: 10.1002/jmri.27882] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Accepted: 07/14/2021] [Indexed: 11/07/2022] Open
Affiliation(s)
- Ramesh Paudyal
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Joseph O Deasy
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Amita Shukla-Dave
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA.,Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
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Assessment of MRI-Based Radiomics in Preoperative T Staging of Rectal Cancer: Comparison between Minimum and Maximum Delineation Methods. BIOMED RESEARCH INTERNATIONAL 2021; 2021:5566885. [PMID: 34337027 PMCID: PMC8289571 DOI: 10.1155/2021/5566885] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/06/2021] [Revised: 05/24/2021] [Accepted: 07/02/2021] [Indexed: 12/24/2022]
Abstract
The manual delineation of the lesion is mainly used as a conventional segmentation method, but it is subjective and has poor stability and repeatability. The purpose of this study is to validate the effect of a radiomics model based on MRI derived from two delineation methods in the preoperative T staging of patients with rectal cancer (RC). A total of 454 consecutive patients with pathologically confirmed RC who underwent preoperative MRI between January 2018 and December 2019 were retrospectively analyzed. RC patients were grouped according to whether the muscularis propria was penetrated. Two radiologists segmented lesions, respectively, by minimum delineation (Method 1) and maximum delineation (Method 2), after which radiomics features were extracted. Inter- and intraclass correlation coefficient (ICC) of all features was evaluated. After feature reduction, the support vector machine (SVM) was trained to build a prediction model. The diagnostic performances of models were determined by receiver operating characteristic (ROC) curves. Then, the areas under the curve (AUCs) were compared by the DeLong test. Decision curve analysis (DCA) was performed to evaluate clinical benefit. Finally, 317 patients were assessed, including 152 cases in the training set and 165 cases in the validation set. Moreover, 1288/1409 (91.4%) features of Method 1 and 1273/1409 (90.3%) features of Method 2 had good robustness (P < 0.05). The AUCs of Model 1 and Model 2 were 0.808 and 0.903 in the validation set, respectively (P = 0.035). DCA showed that the maximum delineation yielded more net benefit. MRI-based radiomics models derived from two segmentation methods demonstrated good performance in the preoperative T staging of RC. The minimum delineation had better stability in feature selection, while the maximum delineation method was more clinically beneficial.
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27
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Spadarella G, Calareso G, Garanzini E, Ugga L, Cuocolo A, Cuocolo R. MRI based radiomics in nasopharyngeal cancer: Systematic review and perspectives using radiomic quality score (RQS) assessment. Eur J Radiol 2021; 140:109744. [PMID: 33962253 DOI: 10.1016/j.ejrad.2021.109744] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Revised: 04/23/2021] [Accepted: 04/27/2021] [Indexed: 12/12/2022]
Abstract
BACKGROUND MRI based radiomics has the potential to better define tumor biology compared to qualitative MRI assessment and support decisions in patients affected by nasopharyngeal carcinoma. Aim of this review was to systematically evaluate the methodological quality of studies using MRI- radiomics for nasopharyngeal cancer patient evaluation. METHODS A systematic search was performed in PUBMED, WEB OF SCIENCE and SCOPUS using "MRI, magnetic resonance imaging, radiomic, texture analysis, nasopharyngeal carcinoma, nasopharyngeal cancer" in all possible combinations. The methodological quality of study included ( = 24) was evaluated according to the RQS (Radiomic quality score). Subgroup, for journal type (imaging/clinical) and biomarker (prognostic/predictive), and correlation, between RQS and journal Impact Factor, analyses were performed. Mann-Whitney U test and Spearman's correlation were performed. P value < .05 were defined as statistically significant. RESULTS Overall, no studies reported a phantom study or a test re-test for assessing stability in image, biological correlation or open science data. Only 8% of them included external validation. Almost half of articles (45 %) performed multivariable analysis with non-radiomics features. Only 1 study was prospective (4%). The mean RQS was 7.5 ± 5.4. No significant differences were detected between articles published in clinical/imaging journal and between studies with a predictive or prognostic biomarker. No significant correlation was found between total RQS and Impact Factor of the year of publication (p always > 0.05). CONCLUSIONS Radiomic articles in nasopharyngeal cancer are mostly of low methodological quality. The greatest limitations are the lack of external validation, biological correlates, prospective design and open science.
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Affiliation(s)
- Gaia Spadarella
- Department of Translational Medical Sciences, University of Naples "Federico II", Naples, Italy.
| | - Giuseppina Calareso
- Department of Radiology, Fondazione IRCCS, Istituto Nazionale Dei Tumori, Milan, Italy
| | - Enrico Garanzini
- Department of Radiology, Fondazione IRCCS, Istituto Nazionale Dei Tumori, Milan, Italy
| | - Lorenzo Ugga
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples, Italy
| | - Alberto Cuocolo
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples, Italy
| | - Renato Cuocolo
- Department of Clinical Medicine and Surgery, University of Naples "Federico II", Naples, Italy; Laboratory of Augmented Reality for Health Monitoring (ARHeMLab), Department of Electrical Engineering and Information Technology, University of Naples "Federico II", Naples, Italy
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Veres G, Vas NF, Lyngby Lassen M, Béresová M, K. Krizsan A, Forgács A, Berényi E, Balkay L. Effect of grey-level discretization on texture feature on different weighted MRI images of diverse disease groups. PLoS One 2021; 16:e0253419. [PMID: 34143830 PMCID: PMC8213143 DOI: 10.1371/journal.pone.0253419] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Accepted: 06/04/2021] [Indexed: 12/12/2022] Open
Abstract
PURPOSE Many studies of MRI radiomics do not include the discretization method used for the analyses, which might indicate that the discretization methods used are considered irrelevant. Our goals were to compare three frequently used discretization methods (lesion relative resampling (LRR), lesion absolute resampling (LAR) and absolute resampling (AR)) applied to the same data set, along with two different lesion segmentation approaches. METHODS We analyzed the effects of altering bin widths or bin numbers for the three different sampling methods using 40 texture indices (TIs). The impact was evaluated on brain MRI studies obtained for 71 patients divided into three different disease groups: multiple sclerosis (MS, N = 22), ischemic stroke (IS, N = 22), cancer patients (N = 27). Two different MRI acquisition protocols were considered for all patients, a T2- and a post-contrast 3D T1-weighted MRI sequence. Elliptical and manually drawn VOIs were employed for both imaging series. Three different types of gray-level discretization methods were used: LRR, LAR and AR. Hypothesis tests were done among all diseased and control areas to compare the TI values in these areas. We also did correlation analyses between TI values and lesion volumes. RESULTS In general, no significant differences were reported in the results when employing the AR and LAR discretization methods. It was found that employing 38 TIs introduced variation in the results when the number of bin parameters was altered, suggesting that both the degree and direction of monotonicity between each TI value and binning parameters were characteristic for each TI. Furthermore, while TIs were changing with altering binning values, no changes correlated to neither disease nor the MRI sequence. We found that most indices correlated weakly with the volume, while the correlation coefficients were independent of both diseases analyzed and MR contrast. Several cooccurrence-matrix based texture parameters show a definite higher correlation when employing the LRR discretization method However, with the best correlations obtained for the manually drawn VOI. Hypothesis tests among all disease and control areas (co-lateral hemisphere) revealed that the AR or LAR discretization techniques provide more suitable texture features than LRR. In addition, the manually drawn segmentation gave fewer significantly different TIs than the ellipsoid segmentations. In addition, the amount of TIs with significant differences was increasing with increasing the number of bins, or decreasing bin widths. CONCLUSION Our findings indicate that the AR discretization method may offer the best texture analysis in MR image assessments. Employing too many bins or too large bin widths might reduce the selection of TIs that can be used for differential diagnosis. In general, more statistically different TIs were observed for elliptical segmentations when compared to the manually drawn VOIs. In the texture analysis of MR studies, studies and publications should report on all important parameters and methods related to data collection, corrections, normalization, discretization, and segmentation.
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Affiliation(s)
- Gergő Veres
- Division of Radiology and Imaging Science, Department of Medical Imaging, Faculty of Medicine, University of Debrecen, Debrecen, Hungary
| | - Norman Félix Vas
- Division of Nuclear Medicine and Translational Imaging, Department of Medical Imaging, Faculty of Medicine, University of Debrecen, Debrecen, Hungary
| | - Martin Lyngby Lassen
- Cedars-Sinai Medical Center, AIM Group, Los Angeles, CA, United States of America
- Department of Clinical Physiology, Nuclear Medicine and PET and Cluster for Molecular Imaging, University of Copenhagen, Copenhagen, Denmark
| | - Monika Béresová
- Division of Radiology and Imaging Science, Department of Medical Imaging, Faculty of Medicine, University of Debrecen, Debrecen, Hungary
| | | | | | - Ervin Berényi
- Division of Radiology and Imaging Science, Department of Medical Imaging, Faculty of Medicine, University of Debrecen, Debrecen, Hungary
| | - László Balkay
- Division of Nuclear Medicine and Translational Imaging, Department of Medical Imaging, Faculty of Medicine, University of Debrecen, Debrecen, Hungary
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Iantsen A, Ferreira M, Lucia F, Jaouen V, Reinhold C, Bonaffini P, Alfieri J, Rovira R, Masson I, Robin P, Mervoyer A, Rousseau C, Kridelka F, Decuypere M, Lovinfosse P, Pradier O, Hustinx R, Schick U, Visvikis D, Hatt M. Convolutional neural networks for PET functional volume fully automatic segmentation: development and validation in a multi-center setting. Eur J Nucl Med Mol Imaging 2021; 48:3444-3456. [PMID: 33772335 PMCID: PMC8440243 DOI: 10.1007/s00259-021-05244-z] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Accepted: 02/07/2021] [Indexed: 11/12/2022]
Abstract
Purpose In this work, we addressed fully automatic determination of tumor functional uptake from positron emission tomography (PET) images without relying on other image modalities or additional prior constraints, in the context of multicenter images with heterogeneous characteristics. Methods In cervical cancer, an additional challenge is the location of the tumor uptake near or even stuck to the bladder. PET datasets of 232 patients from five institutions were exploited. To avoid unreliable manual delineations, the ground truth was generated with a semi-automated approach: a volume containing the tumor and excluding the bladder was first manually determined, then a well-validated, semi-automated approach relying on the Fuzzy locally Adaptive Bayesian (FLAB) algorithm was applied to generate the ground truth. Our model built on the U-Net architecture incorporates residual blocks with concurrent spatial squeeze and excitation modules, as well as learnable non-linear downsampling and upsampling blocks. Experiments relied on cross-validation (four institutions for training and validation, and the fifth for testing). Results The model achieved good Dice similarity coefficient (DSC) with little variability across institutions (0.80 ± 0.03), with higher recall (0.90 ± 0.05) than precision (0.75 ± 0.05) and improved results over the standard U-Net (DSC 0.77 ± 0.05, recall 0.87 ± 0.02, precision 0.74 ± 0.08). Both vastly outperformed a fixed threshold at 40% of SUVmax (DSC 0.33 ± 0.15, recall 0.52 ± 0.17, precision 0.30 ± 0.16). In all cases, the model could determine the tumor uptake without including the bladder. Neither shape priors nor anatomical information was required to achieve efficient training. Conclusion The proposed method could facilitate the deployment of a fully automated radiomics pipeline in such a challenging multicenter context. Supplementary Information The online version contains supplementary material available at 10.1007/s00259-021-05244-z.
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Affiliation(s)
- Andrei Iantsen
- LaTIM, INSERM, UMR 1101, University Brest, Brest, France.
| | - Marta Ferreira
- GIGA-CRC in vivo Imaging, University of Liège, Liège, Belgium
| | - Francois Lucia
- LaTIM, INSERM, UMR 1101, University Brest, Brest, France
| | - Vincent Jaouen
- LaTIM, INSERM, UMR 1101, University Brest, Brest, France
| | - Caroline Reinhold
- Department of Radiology, McGill University Health Centre (MUHC), Montreal, Canada
| | - Pietro Bonaffini
- Department of Radiology, McGill University Health Centre (MUHC), Montreal, Canada
| | - Joanne Alfieri
- Department of Radiation Oncology, McGill University Health Centre (MUHC), Montreal, Canada
| | - Ramon Rovira
- Gynecology Oncology and Laparoscopy Department, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain
| | - Ingrid Masson
- Department of Radiation Oncology, Institut de Cancérologie de l'Ouest (ICO), Nantes, France
| | - Philippe Robin
- Nuclear Medicine Department, University Hospital, Brest, France
| | - Augustin Mervoyer
- Department of Radiation Oncology, Institut de Cancérologie de l'Ouest (ICO), Nantes, France
| | - Caroline Rousseau
- Nuclear Medicine Department, Institut de Cancérologie de l'Ouest (ICO), Nantes, France
| | - Frédéric Kridelka
- Division of Oncological Gynecology, University Hospital of Liège, Liège, Belgium
| | - Marjolein Decuypere
- Division of Oncological Gynecology, University Hospital of Liège, Liège, Belgium
| | - Pierre Lovinfosse
- Division of Nuclear Medicine and Oncological Imaging, University Hospital of Liège, Liège, Belgium
| | | | - Roland Hustinx
- GIGA-CRC in vivo Imaging, University of Liège, Liège, Belgium
| | - Ulrike Schick
- LaTIM, INSERM, UMR 1101, University Brest, Brest, France
| | | | - Mathieu Hatt
- LaTIM, INSERM, UMR 1101, University Brest, Brest, France
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Brancato V, Aiello M, Basso L, Monti S, Palumbo L, Di Costanzo G, Salvatore M, Ragozzino A, Cavaliere C. Evaluation of a multiparametric MRI radiomic-based approach for stratification of equivocal PI-RADS 3 and upgraded PI-RADS 4 prostatic lesions. Sci Rep 2021; 11:643. [PMID: 33436929 PMCID: PMC7804929 DOI: 10.1038/s41598-020-80749-5] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Accepted: 12/24/2020] [Indexed: 12/11/2022] Open
Abstract
Despite the key-role of the Prostate Imaging and Reporting and Data System (PI-RADS) in the diagnosis and characterization of prostate cancer (PCa), this system remains to be affected by several limitations, primarily associated with the interpretation of equivocal PI-RADS 3 lesions and with the debated role of Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI), which is only used to upgrade peripheral PI-RADS category 3 lesions to PI-RADS category 4 if enhancement is focal. We aimed at investigating the usefulness of radiomics for detection of PCa lesions (Gleason Score ≥ 6) in PI-RADS 3 lesions and in peripheral PI-RADS 3 upgraded to PI-RADS 4 lesions (upPI-RADS 4). Multiparametric MRI (mpMRI) data of patients who underwent prostatic mpMRI between April 2013 and September 2018 were retrospectively evaluated. Biopsy results were used as gold standard. PI-RADS 3 and PI-RADS 4 lesions were re-scored according to the PI-RADS v2.1 before and after DCE-MRI evaluation. Radiomic features were extracted from T2-weighted MRI (T2), Apparent diffusion Coefficient (ADC) map and DCE-MRI subtracted images using PyRadiomics. Feature selection was performed using Wilcoxon-ranksum test and Minimum Redundancy Maximum Relevance (mRMR). Predictive models were constructed for PCa detection in PI-RADS 3 and upPI-RADS 4 lesions using at each step an imbalance-adjusted bootstrap resampling (IABR) on 1000 samples. 41 PI-RADS 3 and 32 upPI-RADS 4 lesions were analyzed. Among 293 radiomic features, the top selected features derived from T2 and ADC. For PI-RADS 3 stratification, second order model showed higher performances (Area Under the Receiver Operating Characteristic Curve-AUC- = 80%), while for upPI-RADS 4 stratification, first order model showed higher performances respect to superior order models (AUC = 89%). Our results support the significant role of T2 and ADC radiomic features for PCa detection in lesions scored as PI-RADS 3 and upPI-RADS 4. Radiomics models showed high diagnostic efficacy in classify PI-RADS 3 and upPI-RADS 4 lesions, outperforming PI-RADS v2.1 performance.
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Affiliation(s)
| | | | | | - Serena Monti
- Institute of Biostructures and Bioimaging, National Research Council, Naples, Italy
| | - Luigi Palumbo
- Department of Radiology, S. Maria Delle Grazie Hospital, Pozzuoli, Italy
| | | | | | - Alfonso Ragozzino
- Department of Radiology, S. Maria Delle Grazie Hospital, Pozzuoli, Italy
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Ge YX, Xu WB, Wang Z, Zhang JQ, Zhou XY, Duan SF, Hu SD, Fei BJ. Prognostic value of CT radiomics in evaluating lymphovascular invasion in rectal cancer: Diagnostic performance based on different volumes of interest. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2021; 29:663-674. [PMID: 34024807 DOI: 10.3233/xst-210877] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
OBJECTIVES This study aims to evaluate diagnostic performance of radiomic analysis using computed tomography (CT) to identify lymphovascular invasion (LVI) in patients diagnosed with rectal cancer and assess diagnostic performance of different lesion segmentations. METHODS The study is applied to 169 pre-treatment CT images and the clinical features of patients with rectal cancer. Radiomic features are extracted from two different volumes of interest (VOIs) namely, gross tumor volume and peri-tumor tissue volume. The maximum relevance and the minimum redundancy, and the least absolute shrinkage selection operator based logistic regression analyses are performed to select the optimal feature subset on the training cohort. Then, Rad and Rad-clinical combined models for LVI prediction are built and compared. Finally, the models are externally validated. RESULTS Eighty-three patients had positive LVI on pathology, while 86 had negative LVI. An optimal multi-mode radiology nomogram for LVI estimation is established. The area under the receiver operating characteristic curves of the Rad and Rad-clinical combined model in the peri-tumor VOI group are significantly higher than those in the tumor VOI group (Rad: peri-tumor vs. tumor: 0.85 vs. 0.68; Rad-clinical: peri-tumor vs. tumor: 0.90 vs 0.82) in the validation cohort. Decision curve analysis shows that the peri-tumor-based Rad-clinical combined model has the best performance in identifying LVI than other models. CONCLUSIONS CT radiomics model based on peri-tumor volumes improves prediction performance of LVI in rectal cancer compared with the model based on tumor volumes.
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Affiliation(s)
- Yu-Xi Ge
- Department of Radiology, Affiliated Hospital of Jiangnan University, Wuxi, Jiangsu, China
| | - Wen-Bo Xu
- Wuxi Research Institute, Fudan University, Wuxi, Jiangsu, China
| | - Zi Wang
- Department of Radiology, Affiliated Hospital of Jiangnan University, Wuxi, Jiangsu, China
| | - Jun-Qin Zhang
- Department of radiology, The First People's Hospital of Yuhang District, Hangzhou, Zhejiang Province, China
| | - Xin-Yi Zhou
- Department of Pathology, Affiliated Hospital of Jiangnan University, 200 Huihe Road, Wuxi, Jiangsu, China
| | | | - Shu-Dong Hu
- Department of Radiology, Affiliated Hospital of Jiangnan University, Wuxi, Jiangsu, China
| | - Bo-Jian Fei
- Department of Gastrointestinal Surgery, Affiliated Hospital of Jiangnan University, Wuxi, Jiangsu, China
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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: 560] [Impact Index Per Article: 140.0] [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.
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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.
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