1
|
Aghakhanyan G, Filidei T, Febi M, Fanni SC, Marciano A, Francischello R, Caputo FP, Tumminello L, Cioni D, Neri E, Volterrani D. Advancing Pediatric Sarcomas through Radiomics: A Systematic Review and Prospective Assessment Using Radiomics Quality Score (RQS) and Methodological Radiomics Score (METRICS). Diagnostics (Basel) 2024; 14:832. [PMID: 38667477 PMCID: PMC11049622 DOI: 10.3390/diagnostics14080832] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2024] [Revised: 04/12/2024] [Accepted: 04/15/2024] [Indexed: 04/28/2024] Open
Abstract
Pediatric sarcomas, rare malignancies of mesenchymal origin, pose diagnostic and therapeutic challenges. In this review, we explore the role of radiomics in reshaping our understanding of pediatric sarcomas, emphasizing methodological considerations and applications such as diagnostics and predictive modeling. A systematic review conducted up to November 2023 identified 72 papers on radiomics analysis in pediatric sarcoma from PubMed/MEDLINE, Web of Knowledge, and Scopus. Following inclusion and exclusion criteria, 10 reports were included in this review. The studies, predominantly retrospective, focus on Ewing sarcoma and osteosarcoma, utilizing diverse imaging modalities, including CT, MRI, PET/CT, and PET/MRI. Manual segmentation is common, with a median of 35 features extracted. Radiomics Quality Score (RQS) and Methodological Radiomics Score (METRICS) assessments reveal a consistent emphasis on non-radiomic features, validation criteria, and improved methodological rigor in recent publications. Diagnostic applications dominate, with innovative studies exploring prognostic and treatment response aspects. Challenges include feature heterogeneity and sample size variations. The evolving landscape underscores the need for standardized methodologies. Despite challenges, the diagnostic and predictive potential of radiomics in pediatric oncology is evident, paving the way for precision medicine advancements.
Collapse
Affiliation(s)
- Gayane Aghakhanyan
- Department of Translational Research and of New Surgical and Medical Technology, University of Pisa, 56126 Pisa, Italy
| | - Tommaso Filidei
- Department of Translational Research and of New Surgical and Medical Technology, University of Pisa, 56126 Pisa, Italy
| | - Maria Febi
- Department of Translational Research and of New Surgical and Medical Technology, Academic Radiology, University of Pisa, 56126 Pisa, Italy (D.C.)
| | - Salvatore C. Fanni
- Department of Translational Research and of New Surgical and Medical Technology, Academic Radiology, University of Pisa, 56126 Pisa, Italy (D.C.)
| | - Andrea Marciano
- Department of Translational Research and of New Surgical and Medical Technology, University of Pisa, 56126 Pisa, Italy
| | - Roberto Francischello
- Department of Translational Research and of New Surgical and Medical Technology, Academic Radiology, University of Pisa, 56126 Pisa, Italy (D.C.)
| | - Francesca Pia Caputo
- Department of Translational Research and of New Surgical and Medical Technology, Academic Radiology, University of Pisa, 56126 Pisa, Italy (D.C.)
| | - Lorenzo Tumminello
- Department of Translational Research and of New Surgical and Medical Technology, Academic Radiology, University of Pisa, 56126 Pisa, Italy (D.C.)
| | - Dania Cioni
- Department of Translational Research and of New Surgical and Medical Technology, Academic Radiology, University of Pisa, 56126 Pisa, Italy (D.C.)
| | - Emanuele Neri
- Department of Translational Research and of New Surgical and Medical Technology, Academic Radiology, University of Pisa, 56126 Pisa, Italy (D.C.)
| | - Duccio Volterrani
- Department of Translational Research and of New Surgical and Medical Technology, University of Pisa, 56126 Pisa, Italy
- Regional Center of Nuclear Medicine, University Hospital of Pisa, 56126 Pisa, Italy
| |
Collapse
|
2
|
Al Turkestani N, Cai L, Cevidanes L, Bianchi J, Zhang W, Gurgel M, Gillot M, Baquero B, Soroushmehr R. Osteoarthritis Diagnosis Integrating Whole Joint Radiomics and Clinical Features for Robust Learning Models Using Biological Privileged Information. MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2023 WORKSHOPS : ISIC 2023, CARE-AI 2023, MEDAGI 2023, DECAF 2023, HELD IN CONJUNCTION WITH MICCAI 2023, VANCOUVER, BC, CANADA, OCTOBER 8-12, 2023, PROCEEDINGS 2023; 14394:193-204. [PMID: 38533395 PMCID: PMC10964798 DOI: 10.1007/978-3-031-47425-5_18] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/28/2024]
Abstract
This paper proposes a machine learning model using privileged information (LUPI) and normalized mutual information feature selection method (NMIFS) to build a robust and accurate framework to diagnose patients with Temporomandibular Joint Osteoarthritis (TMJ OA). To build such a model, we employ clinical, quantitative imaging and additional biological markers as privileged information. We show that clinical features play a leading role in the TMJ OA diagnosis and quantitative imaging features, extracted from cone-beam computerized tomography (CBCT) scans, improve the model performance. As the proposed LUPI model employs biological data in the training phase (which boosted the model performance), this data is unnecessary for the testing stage, indicating the model can be widely used even when only clinical and imaging data are collected. The model was validated using 5-fold stratified cross-validation with hyperparameter tuning to avoid the bias of data splitting. Our method achieved an AUC, specificity and precision of 0.81, 0.79 and 0.77, respectively.
Collapse
Affiliation(s)
- Najla Al Turkestani
- Department of Orthodontics and Pediatric Dentistry, University of Michigan, 1011 North University Avenue, Ann Arbor, MI 48109, USA
- Department of Restorative and Aesthetic Dentistry, Faculty of Dentistry, King Abdulaziz University, Jeddah 22252, Saudi Arabia
| | - Lingrui Cai
- Department of Computational Medicine and Bioinformatics, University of Michigan, 100 Washtenaw Avenue, Ann Arbor, MI 48109, USA
| | - Lucia Cevidanes
- Department of Orthodontics and Pediatric Dentistry, University of Michigan, 1011 North University Avenue, Ann Arbor, MI 48109, USA
| | - Jonas Bianchi
- Department of Orthodontics, University of the Pacific, Arthur A. Dugoni School of Dentistry, 155 5th Street, San Francisco, CA 94103, USA
| | - Winston Zhang
- Department of Computational Medicine and Bioinformatics, University of Michigan, 100 Washtenaw Avenue, Ann Arbor, MI 48109, USA
| | - Marcela Gurgel
- Department of Orthodontics and Pediatric Dentistry, University of Michigan, 1011 North University Avenue, Ann Arbor, MI 48109, USA
| | - Maxime Gillot
- Department of Orthodontics and Pediatric Dentistry, University of Michigan, 1011 North University Avenue, Ann Arbor, MI 48109, USA
| | - Baptiste Baquero
- Department of Orthodontics and Pediatric Dentistry, University of Michigan, 1011 North University Avenue, Ann Arbor, MI 48109, USA
| | - Reza Soroushmehr
- Department of Computational Medicine and Bioinformatics, University of Michigan, 100 Washtenaw Avenue, Ann Arbor, MI 48109, USA
| |
Collapse
|
3
|
Baidya Kayal E, Ganguly S, Sasi A, Sharma S, DS D, Saini M, Rangarajan K, Kandasamy D, Bakhshi S, Mehndiratta A. A proposed methodology for detecting the malignant potential of pulmonary nodules in sarcoma using computed tomographic imaging and artificial intelligence-based models. Front Oncol 2023; 13:1212526. [PMID: 37671060 PMCID: PMC10476362 DOI: 10.3389/fonc.2023.1212526] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Accepted: 07/31/2023] [Indexed: 09/07/2023] Open
Abstract
The presence of lung metastases in patients with primary malignancies is an important criterion for treatment management and prognostication. Computed tomography (CT) of the chest is the preferred method to detect lung metastasis. However, CT has limited efficacy in differentiating metastatic nodules from benign nodules (e.g., granulomas due to tuberculosis) especially at early stages (<5 mm). There is also a significant subjectivity associated in making this distinction, leading to frequent CT follow-ups and additional radiation exposure along with financial and emotional burden to the patients and family. Even 18F-fluoro-deoxyglucose positron emission technology-computed tomography (18F-FDG PET-CT) is not always confirmatory for this clinical problem. While pathological biopsy is the gold standard to demonstrate malignancy, invasive sampling of small lung nodules is often not clinically feasible. Currently, there is no non-invasive imaging technique that can reliably characterize lung metastases. The lung is one of the favored sites of metastasis in sarcomas. Hence, patients with sarcomas, especially from tuberculosis prevalent developing countries, can provide an ideal platform to develop a model to differentiate lung metastases from benign nodules. To overcome the lack of optimal specificity of CT scan in detecting pulmonary metastasis, a novel artificial intelligence (AI)-based protocol is proposed utilizing a combination of radiological and clinical biomarkers to identify lung nodules and characterize it as benign or metastasis. This protocol includes a retrospective cohort of nearly 2,000-2,250 sample nodules (from at least 450 patients) for training and testing and an ambispective cohort of nearly 500 nodules (from 100 patients; 50 patients each from the retrospective and prospective cohort) for validation. Ground-truth annotation of lung nodules will be performed using an in-house-built segmentation tool. Ground-truth labeling of lung nodules (metastatic/benign) will be performed based on histopathological results or baseline and/or follow-up radiological findings along with clinical outcome of the patient. Optimal methods for data handling and statistical analysis are included to develop a robust protocol for early detection and classification of pulmonary metastasis at baseline and at follow-up and identification of associated potential clinical and radiological markers.
Collapse
Affiliation(s)
- Esha Baidya Kayal
- Centre for Biomedical Engineering, Indian Institute of Technology Delhi, New Delhi, India
| | - Shuvadeep Ganguly
- Medical Oncology, Dr. B.R.Ambedkar Institute Rotary Cancer Hospital, All India Institute of Medical Sciences, New Delhi, Delhi, India
| | - Archana Sasi
- Medical Oncology, Dr. B.R.Ambedkar Institute Rotary Cancer Hospital, All India Institute of Medical Sciences, New Delhi, Delhi, India
| | - Swetambri Sharma
- Medical Oncology, Dr. B.R.Ambedkar Institute Rotary Cancer Hospital, All India Institute of Medical Sciences, New Delhi, Delhi, India
| | - Dheeksha DS
- Department of Radiodiagnosis, All India Institute of Medical Sciences, New Delhi, Delhi, India
| | - Manish Saini
- Department of Radiodiagnosis, All India Institute of Medical Sciences, New Delhi, Delhi, India
| | - Krithika Rangarajan
- Radiodiagnosis, Dr. B.R.Ambedkar Institute Rotary Cancer Hospital, All India Institute of Medical Sciences, New Delhi, Delhi, India
| | | | - Sameer Bakhshi
- Medical Oncology, Dr. B.R.Ambedkar Institute Rotary Cancer Hospital, All India Institute of Medical Sciences, New Delhi, Delhi, India
| | - Amit Mehndiratta
- Centre for Biomedical Engineering, Indian Institute of Technology Delhi, New Delhi, India
- Department of Biomedical Engineering, All India Institute of Medical Sciences, New Delhi, Delhi, India
| |
Collapse
|
4
|
Cai Z, Xu J, Sun X, Zhang R, Xie L, Wang J, Tang X, Yang R, Guo W. How to confront the high prevalence of pulmonary micro nodules (PMNs) in osteosarcoma patients? INTERNATIONAL ORTHOPAEDICS 2022; 46:2425-2436. [PMID: 35941258 DOI: 10.1007/s00264-022-05534-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/05/2022] [Accepted: 07/25/2022] [Indexed: 10/15/2022]
Abstract
PURPOSE Pulmonary metastasis was a negative factor of osteosarcoma prognosis. However, there is no universal criteria to confirm pulmonary metastasis at pulmonary micro nodule (PMN, Dmax ≤ 5 mm) stage other than pathology. We aimed to identify prevalence of PMNs, determine prognosis of osteosarcoma with PMNs, and analyze risk factors related to PMN progression. METHODS We retrospectively reviewed 425 consecutive osteosarcoma patients. According to dynamic change in size and number of PMNs, patients were divided into PMN progression and non-progression group. Demographic data, initial laboratory data, radiological features, and oncological evaluations were analyzed. Cox regression was used to identify risk factors for PMN progression. Overall survival rate was measured and analyzed with Kaplan-Meier method. Differences with p < 0.05 were considered significant. RESULTS PMNs were found in 74% (315/425) osteosarcoma patients, half of whom (157/315) suffering PMN progression. Overall survival rate was 70.2%, while survival rates for PMN progression group and non-progression group were 53.40% and 87.40%, respectively. Clinical risk factors for PMN progression in certain patients included blood vessel invasion, extrapulmonary metastases, low tumour cell necrosis rate, and large tumour size. Radiologic risk factors included greatest diameter, distance to pleura, CT value, solid components, and smooth border. CONCLUSION PMN is quite common in osteosarcoma patients. PMN progression is related to both certain clinical and radiological factors, which could assist surgeons to determine its possibility to progress at an early stage.
Collapse
Affiliation(s)
- Zhenyu Cai
- Musculoskeletal Tumor Center, Peking University People's Hospital, Beijing, 100044, China
| | - Jie Xu
- Musculoskeletal Tumor Center, Peking University People's Hospital, Beijing, 100044, China
| | - Xin Sun
- Musculoskeletal Tumor Center, Peking University People's Hospital, Beijing, 100044, China
| | - Ranxin Zhang
- Musculoskeletal Tumor Center, Peking University People's Hospital, Beijing, 100044, China
| | - Lu Xie
- Musculoskeletal Tumor Center, Peking University People's Hospital, Beijing, 100044, China
| | - Jichuan Wang
- Musculoskeletal Tumor Center, Peking University People's Hospital, Beijing, 100044, China
| | - Xiaodong Tang
- Musculoskeletal Tumor Center, Peking University People's Hospital, Beijing, 100044, China
| | - Rongli Yang
- Musculoskeletal Tumor Center, Peking University People's Hospital, Beijing, 100044, China
| | - Wei Guo
- Musculoskeletal Tumor Center, Peking University People's Hospital, Beijing, 100044, China.
| |
Collapse
|
5
|
Zhong J, Hu Y, Zhang G, Xing Y, Ding D, Ge X, Pan Z, Yang Q, Yin Q, Zhang H, Zhang H, Yao W. An updated systematic review of radiomics in osteosarcoma: utilizing CLAIM to adapt the increasing trend of deep learning application in radiomics. Insights Imaging 2022; 13:138. [PMID: 35986808 PMCID: PMC9392674 DOI: 10.1186/s13244-022-01277-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Accepted: 07/24/2022] [Indexed: 11/10/2022] Open
Abstract
Abstract
Objective
To update the systematic review of radiomics in osteosarcoma.
Methods
PubMed, Embase, Web of Science, China National Knowledge Infrastructure, and Wanfang Data were searched to identify articles on osteosarcoma radiomics until May 15, 2022. The studies were assessed by Radiomics Quality Score (RQS), Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement, Checklist for Artificial Intelligence in Medical Imaging (CLAIM), and modified Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. The evidence supporting radiomics application for osteosarcoma was rated according to meta-analysis results.
Results
Twenty-nine articles were included. The average of the ideal percentage of RQS, the TRIPOD adherence rate and the CLAIM adherence rate were 29.2%, 59.2%, and 63.7%, respectively. RQS identified a radiomics-specific issue of phantom study. TRIPOD addressed deficiency in blindness of assessment. CLAIM and TRIPOD both pointed out shortness in missing data handling and sample size or power calculation. CLAIM identified extra disadvantages in data de-identification and failure analysis. External validation and open science were emphasized by all the above three tools. The risk of bias and applicability concerns were mainly related to the index test. The meta-analysis of radiomics predicting neoadjuvant chemotherapy response by MRI presented a diagnostic odds ratio (95% confidence interval) of 28.83 (10.27–80.95) on testing datasets and was rated as weak evidence.
Conclusions
The quality of osteosarcoma radiomics studies is insufficient. More investigation is needed before using radiomics to optimize osteosarcoma treatment. CLAIM is recommended to guide the design and reporting of radiomics research.
Collapse
|
6
|
Liang TI, Lee EY. Pediatric Pulmonary Nodules: Imaging Guidelines and Recommendations. Radiol Clin North Am 2021; 60:55-67. [PMID: 34836566 DOI: 10.1016/j.rcl.2021.08.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
Incidental pulmonary nodules are not infrequently identified on computed tomography imaging in the pediatric population and can be a challenge in suggesting appropriate follow-up recommendations. An evidence-based and practical imaging approach for diagnosis and appropriate directed management is essential for optimal patient care. This article provides an up-to-date review of the pediatric pulmonary nodule literature and suggests a practical algorithm to manage pulmonary nodules in the pediatric population.
Collapse
Affiliation(s)
- Teresa I Liang
- Department of Radiology & Diagnostic Imaging, Stollery Children's Hospital and University of Alberta, 8440 112 Street NW, Edmonton, AB T6G 2B7, Canada.
| | - Edward Y Lee
- Department of Radiology, Boston Children's Hospital, Harvard Medical School, 330 Longwood Avenue, Boston, MA 02115, USA
| |
Collapse
|
7
|
Saifuddin A, Baig MS, Dalal P, Strauss SJ. The diagnosis of pulmonary metastases on chest computed tomography in primary bone sarcoma and musculoskeletal soft tissue sarcoma. Br J Radiol 2021; 94:20210088. [PMID: 33989031 DOI: 10.1259/bjr.20210088] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
The lungs are the commonest site of metastasis for primary high-grade bone and soft tissue sarcoma, but current guidelines on the management of pulmonary nodules do not specifically cater for this group of patients. The current article reviews the literature from the past 20 years that has reported the CT features of pulmonary metastases in the setting of known primary bone and soft tissue sarcoma, with emphasis on osteosarcoma, chondrosarcoma, and trunk and extremity soft tissue sarcoma, the aim being to aid radiologists who report chest CT of musculoskeletal sarcoma patients in deciding which lesions should be considered metastatic, which lesions are indeterminate and require follow-up, and which lesions are of no concern.
Collapse
Affiliation(s)
- Asif Saifuddin
- Royal National Orthopaedic Hospital, Brockley Hill, HA7 4LP, Stanmore, UK
| | - Mirza Shaheer Baig
- Guy's and St Thomas' NHS Foundation Trust, Westminster Bridge Rd, SE1 7EH, London, UK
| | - Paras Dalal
- Royal Brompton and Harefield NHS Foundation Trust, Britten St, SW3 6NJ, London, UK
| | - Sandra J Strauss
- UCL Cancer Institute, 72 Huntley St, WC1E 6DD, London, UK.,University College London Hospitals NHS Trust, 235 Euston Rd, NW1 2BU, London, UK
| |
Collapse
|
8
|
CT Features of Benign Intrapulmonary Lymph Nodes in Pediatric Patients With Known Extrapulmonary Solid Malignancy. AJR Am J Roentgenol 2021; 216:1357-1362. [PMID: 33729884 DOI: 10.2214/ajr.20.23363] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
OBJECTIVE. The purpose of our study was to determine the CT features of benign intrapulmonary lymph nodes in pediatric patients with known extrapulmonary solid malignancy. MATERIALS AND METHODS. A retrospective review of surgical pathology archives was performed to identify consecutive chest CT studies of pediatric patients (≤ 18 years) with extrapulmonary solid malignancy and histologically confirmed benign intrapulmonary lymph nodes between January 1, 2004, and March 15, 2020. CT features of intrapulmonary lymph nodes-including size, shape, margin, type, associated calcification or fat, and location-were independently evaluated by two pediatric radiologist reviewers. The CT features of benign intrapulmonary lymph nodes in pediatric patients were analyzed using summary statistics. Interobserver agreement was measured with the kappa coefficient. RESULTS. There were 36 pathology-confirmed benign intrapulmonary lymph nodes in 27 pediatric patients (18 boys and nine girls; mean age, 12 years; age range, 1-18.2 years). Twenty-three (63.9%) of the benign intrapulmonary lymph nodes were biopsied from the right lung and 13 (36.1%) from the left lung (p = .03). The mean size, determined from CT studies, of benign intrapulmonary lymph nodes was 3.6 mm (SD, 1.4 mm; range, 1.3-7.8 mm). Triangular shape (25/36, 69.4%) was the most common shape of the benign intrapulmonary lymph nodes. Less commonly seen shapes of benign intrapulmonary lymph nodes were oval (6/36, 16.7%), round (3/36, 8.3%), and trapezoidal (2/36, 5.6%). All benign intrapulmonary lymph nodes were smoothly marginated and solid without associated calcification or fat. Of the 36 benign intrapulmonary lymph nodes, 15 (41.7%) were pleura-based; 11 (30.6%), perifissural; and 10 (27.8%), parenchymal. The kappa value for interobserver agreement between the two reviewers was 0.917 (95% CI, 0.825-1.000; standard error, 0.047), which corresponds to near-perfect agreement. CONCLUSION. In pediatric patients with known extrapulmonary solid malignancy, benign intrapulmonary lymph nodes are subcentimeter (mean size, 3.6 mm), smoothly marginated, and solid without containing calcification or fat on CT. In particular, triangular shape was the most commonly encountered shape of a benign intrapulmonary lymph node.
Collapse
|
9
|
Averill LW. CORR Insights®: How Are Indeterminate Pulmonary Nodules at Diagnosis Associated with Survival in Patients with High-Grade Osteosarcoma? Clin Orthop Relat Res 2021; 479:309-311. [PMID: 33332885 PMCID: PMC7899492 DOI: 10.1097/corr.0000000000001529] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Accepted: 09/18/2020] [Indexed: 01/31/2023]
Affiliation(s)
- Lauren W Averill
- L. W. Averill, Department of Medical Imaging, Nemours Children's Health System, Alfred I. duPont Hospital for Children, Wilmington, DE, USA
| |
Collapse
|
10
|
Lennartz S, Mager A, Große Hokamp N, Schäfer S, Zopfs D, Maintz D, Reinhardt HC, Thomas RK, Caldeira L, Persigehl T. Texture analysis of iodine maps and conventional images for k-nearest neighbor classification of benign and metastatic lung nodules. Cancer Imaging 2021; 21:17. [PMID: 33499939 PMCID: PMC7836145 DOI: 10.1186/s40644-020-00374-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2020] [Accepted: 12/18/2020] [Indexed: 11/28/2022] Open
Abstract
BACKGROUND The purpose of this study was to analyze if the use of texture analysis on spectral detector CT (SDCT)-derived iodine maps (IM) in addition to conventional images (CI) improves lung nodule differentiation, when being applied to a k-nearest neighbor (KNN) classifier. METHODS 183 cancer patients who underwent contrast-enhanced, venous phase SDCT of the chest were included: 85 patients with 146 benign lung nodules (BLN) confirmed by either prior/follow-up CT or histopathology and 98 patients with 425 lung metastases (LM) verified by histopathology, 18F-FDG-PET-CT or unequivocal change during treatment. Semi-automatic 3D segmentation of BLN/LM was performed, and volumetric HU attenuation and iodine concentration were acquired. For conventional images and iodine maps, average, standard deviation, entropy, kurtosis, mean of the positive pixels (MPP), skewness, uniformity and uniformity of the positive pixels (UPP) within the volumes of interests were calculated. All acquired parameters were transferred to a KNN classifier. RESULTS Differentiation between BLN and LM was most accurate, when using all CI-derived features combined with the most significant IM-derived feature, entropy (Accuracy:0.87; F1/Dice:0.92). However, differentiation accuracy based on the 4 most powerful CI-derived features performed only slightly inferior (Accuracy:0.84; F1/Dice:0.89, p=0.125). Mono-parametric lung nodule differentiation based on either feature alone (i.e. attenuation or iodine concentration) was poor (AUC=0.65, 0.58, respectively). CONCLUSIONS First-order texture feature analysis of contrast-enhanced staging SDCT scans of the chest yield accurate differentiation between benign and metastatic lung nodules. In our study cohort, the most powerful iodine map-derived feature slightly, yet insignificantly increased classification accuracy compared to classification based on conventional image features only.
Collapse
Affiliation(s)
- Simon Lennartz
- Department of Diagnostic and Interventional Radiology, University of Cologne, Faculty of Medicine and University Hospital Cologne, Kerpener Straße 62, 50937, Cologne, Germany
- Else Kröner Forschungskolleg Clonal Evolution in Cancer, University Hospital Cologne, Weyertal 115b, 50931, Cologne, Germany
- Department of Radiology, Massachusetts General Hospital, 55 Fruit St, White 270, Boston, MA, 02114, USA
| | - Alina Mager
- Department of Diagnostic and Interventional Radiology, University of Cologne, Faculty of Medicine and University Hospital Cologne, Kerpener Straße 62, 50937, Cologne, Germany
| | - Nils Große Hokamp
- Department of Diagnostic and Interventional Radiology, University of Cologne, Faculty of Medicine and University Hospital Cologne, Kerpener Straße 62, 50937, Cologne, Germany
| | | | - David Zopfs
- Department of Diagnostic and Interventional Radiology, University of Cologne, Faculty of Medicine and University Hospital Cologne, Kerpener Straße 62, 50937, Cologne, Germany
| | - David Maintz
- Department of Diagnostic and Interventional Radiology, University of Cologne, Faculty of Medicine and University Hospital Cologne, Kerpener Straße 62, 50937, Cologne, Germany
| | - Hans Christian Reinhardt
- Clinic I of Internal Medicine, University Hospital Cologne, 50931, Cologne, Germany
- Department of Hematology and Stem Cell Transplantation, University Hospital Essen, University Duisburg-Essen, German Cancer Consortium (DKTK partner site Essen), Essen, Germany
| | - Roman K Thomas
- Department of Translational Genomics, Center of Integrated Oncology Cologne-Bonn, Medical Faculty, University of Cologne, 50931, Cologne, Germany
| | - Liliana Caldeira
- Department of Diagnostic and Interventional Radiology, University of Cologne, Faculty of Medicine and University Hospital Cologne, Kerpener Straße 62, 50937, Cologne, Germany
| | - Thorsten Persigehl
- Department of Diagnostic and Interventional Radiology, University of Cologne, Faculty of Medicine and University Hospital Cologne, Kerpener Straße 62, 50937, Cologne, Germany.
| |
Collapse
|
11
|
Zhong J, Hu Y, Si L, Jia G, Xing Y, Zhang H, Yao W. A systematic review of radiomics in osteosarcoma: utilizing radiomics quality score as a tool promoting clinical translation. Eur Radiol 2020; 31:1526-1535. [PMID: 32876837 DOI: 10.1007/s00330-020-07221-w] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2020] [Revised: 07/12/2020] [Accepted: 08/21/2020] [Indexed: 12/11/2022]
Abstract
OBJECTIVES To assess the methodological quality and risk of bias in radiomics studies investigating diagnosis, therapy response, and survival of patients with osteosarcoma. METHODS In this systematic review, literatures on radiomics in osteosarcoma were included and assessed for methodological quality through the radiomics quality score (RQS). The risk of bias and concern of application was assessed using the Quality Assessment of Diagnostic Accuracy Studies tool. A meta-analysis of studies focusing on predicting osteosarcoma response to neoadjuvant chemotherapy was performed. RESULTS Twelve radiomics studies exploring osteosarcoma were identified, and five were included in meta-analysis. The RQS reached an average of 20.4% (6.92 of 36) with good inter-rater agreement (ICC 0.95, 95% CI 0.85-0.99). Four studies validated results with an internal dataset, none of which used external dataset; one study was prospectively designed, and another one shared part of the dataset. The risk of bias and concern of application were mainly related to index test aspect. The meta-analysis showed a diagnostic odds ratio of 43.68 (95%CI 13.5-141.31) for predicting response to neoadjuvant chemotherapy with high heterogeneity and low methodological quality. CONCLUSIONS The overall scientific quality of included studies is insufficient; however, radiomics remains a promising technology for predicting treatment response, which might guide therapeutic decision-making and related to prognosis. Improvements in study design, validation, and open science needs to be made to demonstrate the generalizability of findings and to achieve clinical applications. Widespread application of RQS, pre-trained RQS scoring procedure, and modification of RQS in response to clinical needs are necessary. KEY POINTS • Limited radiomics studies were established in osteosarcoma with mean RQS of 20.4%, commonly due to unvalidated results, retrospective study design, and absence of open science. • Meta-analysis of radiomics studies predicting osteosarcoma response to neoadjuvant chemotherapy showed high diagnostic odds ratio 43.68, while high heterogeneity and low methodological quality were the main concerns. • A previously trained data extraction instrument allowed reaching moderate inter-rater agreement in RQS applications, while RQS still needs improvement to become a wide adaptive tool in reviews of radiomics studies, in routine self-check before manuscript submitting and in study design.
Collapse
Affiliation(s)
- Jingyu Zhong
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, No. 1111 Xianxia Road, Changning District, Shanghai, 200050, China
| | - Yangfan Hu
- Department of Radiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, No. 600 Yishan Road, Xuhui District, Shanghai, 200233, China
| | - Liping Si
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, No. 1111 Xianxia Road, Changning District, Shanghai, 200050, China
| | - Geng Jia
- Department of Radiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, No. 600 Yishan Road, Xuhui District, Shanghai, 200233, China
| | - Yue Xing
- Department of Radiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, No. 600 Yishan Road, Xuhui District, Shanghai, 200233, China
| | - Huan Zhang
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197 Ruijin 2nd Road, Huangpu District, Shanghai, 200025, China.
| | - Weiwu Yao
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, No. 1111 Xianxia Road, Changning District, Shanghai, 200050, China.
| |
Collapse
|
12
|
Lin P, Yang PF, Chen S, Shao YY, Xu L, Wu Y, Teng W, Zhou XZ, Li BH, Luo C, Xu LM, Huang M, Niu TY, Ye ZM. A Delta-radiomics model for preoperative evaluation of Neoadjuvant chemotherapy response in high-grade osteosarcoma. Cancer Imaging 2020; 20:7. [PMID: 31937372 PMCID: PMC6958668 DOI: 10.1186/s40644-019-0283-8] [Citation(s) in RCA: 60] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2019] [Accepted: 12/29/2019] [Indexed: 12/12/2022] Open
Abstract
Background The difficulty of assessment of neoadjuvant chemotherapeutic response preoperatively may hinder personalized-medicine strategies that depend on the results from pathological examination. Methods A total of 191 patients with high-grade osteosarcoma (HOS) were enrolled retrospectively from November 2013 to November 2017 and received neoadjuvant chemotherapy (NCT). A cutoff time of November 2016 was used to divide the training set and validation set. All patients underwent diagnostic CTs before and after chemotherapy. By quantifying the tumor regions on the CT images before and after NCT, 540 delta-radiomic features were calculated. The interclass correlation coefficients for segmentations of inter/intra-observers and feature pair-wise correlation coefficients (Pearson) were used for robust feature selection. A delta-radiomics signature was constructed using the lasso algorithm based on the training set. Radiomics signatures built from single-phase CT were constructed for comparison purpose. A radiomics nomogram was then developed from the multivariate logistic regression model by combining independent clinical factors and the delta-radiomics signature. The prediction performance was assessed using area under the ROC curve (AUC), calibration curves and decision curve analysis (DCA). Results The delta-radiomics signature showed higher AUC than single-CT based radiomics signatures in both training and validation cohorts. The delta-radiomics signature, consisting of 8 selected features, showed significant differences between the pathologic good response (pGR) (necrosis fraction ≥90%) group and the non-pGR (necrosis fraction < 90%) group (P < 0.0001, in both training and validation sets). The delta-radiomics nomogram, which consisted of the delta-radiomics signature and new pulmonary metastasis during chemotherapy showed good calibration and great discrimination capacity with AUC 0.871 (95% CI, 0.804 to 0.923) in the training cohort, and 0.843 (95% CI, 0.718 to 0.927) in the validation cohort. The DCA confirmed the clinical utility of the radiomics model. Conclusion The delta-radiomics nomogram incorporating the radiomics signature and clinical factors in this study could be used for individualized pathologic response evaluation after chemotherapy preoperatively and help tailor appropriate chemotherapy and further treatment plans.
Collapse
Affiliation(s)
- Peng Lin
- Musculoskeletal Tumor Center, Department of Orthopaedics, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang, 310009, Hangzhou, China.,Institute of Orthopaedics Research, No.88 Jiefang Road, Hangzhou City, Zhejiang Province, 310009, China
| | - Peng-Fei Yang
- Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Institute of Translational Medicine, Zhejiang University, Zhejiang, Hangzhou, China.,College of Biomedical Engineering &Instrument Science, Zhejiang University, Zhejiang, Hangzhou, China
| | - Shi Chen
- Musculoskeletal Tumor Center, Department of Orthopaedics, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang, 310009, Hangzhou, China.,Department of Orthopaedics, Ninghai First Hospital, Ningbo, Zhejiang, 315600, China
| | - You-You Shao
- Department of Pediatrics, Children's Hospital, Zhejiang University School of Medicine, Zhejiang, 310052, Hangzhou, China
| | - Lei Xu
- Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Institute of Translational Medicine, Zhejiang University, Zhejiang, Hangzhou, China
| | - Yan Wu
- Musculoskeletal Tumor Center, Department of Orthopaedics, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang, 310009, Hangzhou, China.,Institute of Orthopaedics Research, No.88 Jiefang Road, Hangzhou City, Zhejiang Province, 310009, China
| | - Wangsiyuan Teng
- Musculoskeletal Tumor Center, Department of Orthopaedics, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang, 310009, Hangzhou, China.,Institute of Orthopaedics Research, No.88 Jiefang Road, Hangzhou City, Zhejiang Province, 310009, China
| | - Xing-Zhi Zhou
- Musculoskeletal Tumor Center, Department of Orthopaedics, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang, 310009, Hangzhou, China.,Institute of Orthopaedics Research, No.88 Jiefang Road, Hangzhou City, Zhejiang Province, 310009, China
| | - Bing-Hao Li
- Musculoskeletal Tumor Center, Department of Orthopaedics, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang, 310009, Hangzhou, China.,Institute of Orthopaedics Research, No.88 Jiefang Road, Hangzhou City, Zhejiang Province, 310009, China
| | - Chen Luo
- Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Institute of Translational Medicine, Zhejiang University, Zhejiang, Hangzhou, China
| | - Lei-Ming Xu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang, 310009, Hangzhou, China
| | - Mi Huang
- Department of Radiation Oncology, Duke University Medical Center, Durham, North Carolina, 27708, USA
| | - Tian-Ye Niu
- Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Institute of Translational Medicine, Zhejiang University, Zhejiang, Hangzhou, China. .,Nuclear & Radiological Engineering and Medical Physics Programs, Woodruff School of Mechanical Engineering, Georgia Institute of Technology, 770 State Street, Boggs 385, Atlanta, GA, 30332-0745, USA.
| | - Zhao-Ming Ye
- Musculoskeletal Tumor Center, Department of Orthopaedics, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang, 310009, Hangzhou, China. .,Institute of Orthopaedics Research, No.88 Jiefang Road, Hangzhou City, Zhejiang Province, 310009, China.
| |
Collapse
|