1
|
Soldatelli MD, Namdar K, Tabori U, Hawkins C, Yeom K, Khalvati F, Ertl-Wagner BB, Wagner MW. Identification of Multiclass Pediatric Low-Grade Neuroepithelial Tumor Molecular Subtype with ADC MR Imaging and Machine Learning. AJNR Am J Neuroradiol 2024; 45:753-760. [PMID: 38604736 DOI: 10.3174/ajnr.a8199] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Accepted: 01/16/2024] [Indexed: 04/13/2024]
Abstract
BACKGROUND AND PURPOSE Molecular biomarker identification increasingly influences the treatment planning of pediatric low-grade neuroepithelial tumors (PLGNTs). We aimed to develop and validate a radiomics-based ADC signature predictive of the molecular status of PLGNTs. MATERIALS AND METHODS In this retrospective bi-institutional study, we searched the PACS for baseline brain MRIs from children with PLGNTs. Semiautomated tumor segmentation on ADC maps was performed using the semiautomated level tracing effect tool with 3D Slicer. Clinical variables, including age, sex, and tumor location, were collected from chart review. The molecular status of tumors was derived from biopsy. Multiclass random forests were used to predict the molecular status and fine-tuned using a grid search on the validation sets. Models were evaluated using independent and unseen test sets based on the combined data, and the area under the receiver operating characteristic curve (AUC) was calculated for the prediction of 3 classes: KIAA1549-BRAF fusion, BRAF V600E mutation, and non-BRAF cohorts. Experiments were repeated 100 times using different random data splits and model initializations to ensure reproducible results. RESULTS Two hundred ninety-nine children from the first institution and 23 children from the second institution were included (53.6% male; mean, age 8.01 years; 51.8% supratentorial; 52.2% with KIAA1549-BRAF fusion). For the 3-class prediction using radiomics features only, the average test AUC was 0.74 (95% CI, 0.73-0.75), and using clinical features only, the average test AUC was 0.67 (95% CI, 0.66-0.68). The combination of both radiomics and clinical features improved the AUC to 0.77 (95% CI, 0.75-0.77). The diagnostic performance of the per-class test AUC was higher in identifying KIAA1549-BRAF fusion tumors among the other subgroups (AUC = 0.81 for the combined radiomics and clinical features versus 0.75 and 0.74 for BRAF V600E mutation and non-BRAF, respectively). CONCLUSIONS ADC values of tumor segmentations have differentiative signals that can be used for training machine learning classifiers for molecular biomarker identification of PLGNTs. ADC-based pretherapeutic differentiation of the BRAF status of PLGNTs has the potential to avoid invasive tumor biopsy and enable earlier initiation of targeted therapy.
Collapse
Affiliation(s)
- Matheus D Soldatelli
- From the Department Diagnostic Imaging (M.D.S., B.B.E.-W., M.W.W.), Division of Neuroradiology, The Hospital for Sick Children, Toronto, Ontario, Canada
- Department of Medical Imaging (M.D.S., K.N., F.K., B.B.E.-W., M.W.W.), University of Toronto, Toronto, Ontario, Canada
- Institute of Medical Science (M.D.S., K.N., U.T., F.K., B.B.E.-W.), University of Toronto, Toronto, Ontario, Canada
| | - Khashayar Namdar
- Department of Medical Imaging (M.D.S., K.N., F.K., B.B.E.-W., M.W.W.), University of Toronto, Toronto, Ontario, Canada
- Institute of Medical Science (M.D.S., K.N., U.T., F.K., B.B.E.-W.), University of Toronto, Toronto, Ontario, Canada
- Vector Institute (K.N., F.K.), Toronto, Ontario, Canada
| | - Uri Tabori
- Institute of Medical Science (M.D.S., K.N., U.T., F.K., B.B.E.-W.), University of Toronto, Toronto, Ontario, Canada
- The Arthur and Sonia Labatt Brain Tumour Research Centre (U.T., C.H.), The Hospital for Sick Children, Toronto, Ontario, Canada
- Program in Genetics and Genome Biology (U.T.) The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Cynthia Hawkins
- The Arthur and Sonia Labatt Brain Tumour Research Centre (U.T., C.H.), The Hospital for Sick Children, Toronto, Ontario, Canada
- Department of Laboratory Medicine and Pathobiology (C.H.), University of Toronto, Toronto, Ontario, Canada
- Division of Pathology (C.H.), The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Kristen Yeom
- Department of Radiology (K.Y.), Lucile Packard Children's Hospital, Stanford University School of Medicine, Stanford, California
| | - Farzad Khalvati
- Department of Medical Imaging (M.D.S., K.N., F.K., B.B.E.-W., M.W.W.), University of Toronto, Toronto, Ontario, Canada
- Institute of Medical Science (M.D.S., K.N., U.T., F.K., B.B.E.-W.), University of Toronto, Toronto, Ontario, Canada
- Vector Institute (K.N., F.K.), Toronto, Ontario, Canada
- Department of Computer Science (F.K.), University of Toronto, Toronto, Ontario, Canada
| | - Birgit B Ertl-Wagner
- From the Department Diagnostic Imaging (M.D.S., B.B.E.-W., M.W.W.), Division of Neuroradiology, The Hospital for Sick Children, Toronto, Ontario, Canada
- Department of Medical Imaging (M.D.S., K.N., F.K., B.B.E.-W., M.W.W.), University of Toronto, Toronto, Ontario, Canada
- Institute of Medical Science (M.D.S., K.N., U.T., F.K., B.B.E.-W.), University of Toronto, Toronto, Ontario, Canada
| | - Matthias W Wagner
- From the Department Diagnostic Imaging (M.D.S., B.B.E.-W., M.W.W.), Division of Neuroradiology, The Hospital for Sick Children, Toronto, Ontario, Canada
- Department of Medical Imaging (M.D.S., K.N., F.K., B.B.E.-W., M.W.W.), University of Toronto, Toronto, Ontario, Canada
- Department of Diagnostic and Interventional Neuroradiology (M.W.W.), University Hospital Augsburg, Augsburg, Germany
| |
Collapse
|
2
|
Stevens JB, Riley BA, Je J, Gao Y, Wang C, Mowery YM, Brizel DM, Yin FF, Liu JG, Lafata KJ. Radiomics on spatial-temporal manifolds via Fokker-Planck dynamics. Med Phys 2024; 51:3334-3347. [PMID: 38190505 DOI: 10.1002/mp.16905] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Revised: 11/17/2023] [Accepted: 12/04/2023] [Indexed: 01/10/2024] Open
Abstract
BACKGROUND Delta radiomics is a high-throughput computational technique used to describe quantitative changes in serial, time-series imaging by considering the relative change in radiomic features of images extracted at two distinct time points. Recent work has demonstrated a lack of prognostic signal of radiomic features extracted using this technique. We hypothesize that this lack of signal is due to the fundamental assumptions made when extracting features via delta radiomics, and that other methods should be investigated. PURPOSE The purpose of this work was to show a proof-of-concept of a new radiomics paradigm for sparse, time-series imaging data, where features are extracted from a spatial-temporal manifold modeling the time evolution between images, and to assess the prognostic value on patients with oropharyngeal cancer (OPC). METHODS To accomplish this, we developed an algorithm to mathematically describe the relationship between two images acquired at timet = 0 $t = 0$ andt > 0 $t > 0$ . These images serve as boundary conditions of a partial differential equation describing the transition from one image to the other. To solve this equation, we propagate the position and momentum of each voxel according to Fokker-Planck dynamics (i.e., a technique common in statistical mechanics). This transformation is driven by an underlying potential force uniquely determined by the equilibrium image. The solution generates a spatial-temporal manifold (3 spatial dimensions + time) from which we define dynamic radiomic features. First, our approach was numerically verified by stochastically sampling dynamic Gaussian processes of monotonically decreasing noise. The transformation from high to low noise was compared between our Fokker-Planck estimation and simulated ground-truth. To demonstrate feasibility and clinical impact, we applied our approach to 18F-FDG-PET images to estimate early metabolic response of patients (n = 57) undergoing definitive (chemo)radiation for OPC. Images were acquired pre-treatment and 2-weeks intra-treatment (after 20 Gy). Dynamic radiomic features capturing changes in texture and morphology were then extracted. Patients were partitioned into two groups based on similar dynamic radiomic feature expression via k-means clustering and compared by Kaplan-Meier analyses with log-rank tests (p < 0.05). These results were compared to conventional delta radiomics to test the added value of our approach. RESULTS Numerical results confirmed our technique can recover image noise characteristics given sparse input data as boundary conditions. Our technique was able to model tumor shrinkage and metabolic response. While no delta radiomics features proved prognostic, Kaplan-Meier analyses identified nine significant dynamic radiomic features. The most significant feature was Gray-Level-Size-Zone-Matrix gray-level variance (p = 0.011), which demonstrated prognostic improvement over its corresponding delta radiomic feature (p = 0.722). CONCLUSIONS We developed, verified, and demonstrated the prognostic value of a novel, physics-based radiomics approach over conventional delta radiomics via data assimilation of quantitative imaging and differential equations.
Collapse
Affiliation(s)
- Jack B Stevens
- Medical Physics Graduate Program, Duke University, Durham, North Carolina, USA
| | - Breylon A Riley
- Medical Physics Graduate Program, Duke University, Durham, North Carolina, USA
| | - Jihyeon Je
- Department of Electrical and Computer Engineering, Duke University Pratt School of Engineering, Durham, North Carolina, USA
| | - Yuan Gao
- Department of Mathematics, Purdue University, West Lafayette, Indiana, USA
| | - Chunhao Wang
- Medical Physics Graduate Program, Duke University, Durham, North Carolina, USA
- Department of Radiation Oncology, Duke University School of Medicine, Durham, North Carolina, USA
| | - Yvonne M Mowery
- Department of Radiation Oncology, Duke University School of Medicine, Durham, North Carolina, USA
- Department of Head and Neck Surgery & Communication Sciences, Duke University School of Medicine, Durham, North Carolina, USA
- Department of Radiation Oncology, UPMC Hillman Cancer Center/University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - David M Brizel
- Department of Radiation Oncology, Duke University School of Medicine, Durham, North Carolina, USA
- Department of Head and Neck Surgery & Communication Sciences, Duke University School of Medicine, Durham, North Carolina, USA
| | - Fang-Fang Yin
- Medical Physics Graduate Program, Duke University, Durham, North Carolina, USA
- Department of Radiation Oncology, Duke University School of Medicine, Durham, North Carolina, USA
| | - Jian-Guo Liu
- Department of Mathematics, Duke University, Durham, North Carolina, USA
- Department of Physics, Duke University, Durham, North Carolina, USA
| | - Kyle J Lafata
- Medical Physics Graduate Program, Duke University, Durham, North Carolina, USA
- Department of Electrical and Computer Engineering, Duke University Pratt School of Engineering, Durham, North Carolina, USA
- Department of Radiation Oncology, Duke University School of Medicine, Durham, North Carolina, USA
- Department of Radiology, Duke University School of Medicine, Durham, North Carolina, USA
- Department of Pathology, Duke University School of Medicine, Durham, North Carolina, USA
| |
Collapse
|
3
|
Li W, Zhou Q, Liu W, Xu C, Tang ZR, Dong S, Wang H, Li W, Zhang K, Li R, Zhang W, Hu Z, Shibin S, Liu Q, Kuang S, Yin C. A Machine Learning-Based Predictive Model for Predicting Lymph Node Metastasis in Patients With Ewing's Sarcoma. Front Med (Lausanne) 2022; 9:832108. [PMID: 35463005 PMCID: PMC9020377 DOI: 10.3389/fmed.2022.832108] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Accepted: 02/24/2022] [Indexed: 11/13/2022] Open
Abstract
Objective In order to provide reference for clinicians and bring convenience to clinical work, we seeked to develop and validate a risk prediction model for lymph node metastasis (LNM) of Ewing’s sarcoma (ES) based on machine learning (ML) algorithms. Methods Clinicopathological data of 923 ES patients from the Surveillance, Epidemiology, and End Results (SEER) database and 51 ES patients from multi-center external validation set were retrospectively collected. We applied ML algorithms to establish a risk prediction model. Model performance was checked using 10-fold cross-validation in the training set and receiver operating characteristic (ROC) curve analysis in external validation set. After determining the best model, a web-based calculator was made to promote the clinical application. Results LNM was confirmed or unable to evaluate in 13.86% (135 out of 974) ES patients. In multivariate logistic regression, race, T stage, M stage and lung metastases were independent predictors for LNM in ES. Six prediction models were established using random forest (RF), naive Bayes classifier (NBC), decision tree (DT), xgboost (XGB), gradient boosting machine (GBM), logistic regression (LR). In 10-fold cross-validation, the average area under curve (AUC) ranked from 0.705 to 0.764. In ROC curve analysis, AUC ranged from 0.612 to 0.727. The performance of the RF model ranked best. Accordingly, a web-based calculator was developed (https://share.streamlit.io/liuwencai2/es_lnm/main/es_lnm.py). Conclusion With the help of clinicopathological data, clinicians can better identify LNM in ES patients. Risk prediction models established in this study performed well, especially the RF model.
Collapse
Affiliation(s)
- Wenle Li
- Department of Orthopedics, Xianyang Central Hospital, Xianyang, China.,Clinical Medical Research Center, Xianyang Central Hospital, Xianyang, China
| | - Qian Zhou
- Department of Respiratory and Critical Care Medicine, The First People's Hospital of Chongqing Liang Jiang New Area, Chongqing, China
| | - Wencai Liu
- Department of Orthopaedic Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Chan Xu
- Department of Respiratory and Critical Care Medicine, The First People's Hospital of Chongqing Liang Jiang New Area, Chongqing, China.,Department of Dermatology, Xianyang Central Hospital, Xianyang, China
| | - Zhi-Ri Tang
- School of Physics and Technology, Wuhan University, Wuhan, China
| | - Shengtao Dong
- Department of Spine Surgery, Second Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Haosheng Wang
- Department of Orthopaedics, The Second Hospital of Jilin University, Changchun, China
| | - Wanying Li
- Clinical Medical Research Center, Xianyang Central Hospital, Xianyang, China
| | - Kai Zhang
- Department of Orthopedics, Xianyang Central Hospital, Xianyang, China.,Clinical Medical Research Center, Xianyang Central Hospital, Xianyang, China
| | - Rong Li
- The First Clinical Medical College, Shaanxi University of Traditional Chinese Medicine, Xianyang, China
| | - Wenshi Zhang
- The First Clinical Medical College, Shaanxi University of Traditional Chinese Medicine, Xianyang, China
| | - Zhaohui Hu
- Department of Spinal Surgery, Liuzhou People's Hospital, Liuzhou, China
| | - Su Shibin
- Department of Business Management, Xiamen Bank, Xiamen, China
| | - Qiang Liu
- Clinical Medical Research Center, Xianyang Central Hospital, Xianyang, China
| | - Sirui Kuang
- Faculty of Medicine, Macau University of Science and Technology, Macau, China
| | - Chengliang Yin
- Faculty of Medicine, Macau University of Science and Technology, Macau, China
| |
Collapse
|
4
|
Song R, Cui Y, Ren J, Zhang J, Yang Z, Li D, Li Z, Yang X. CT-based radiomics analysis in the prediction of response to neoadjuvant chemotherapy in locally advanced gastric cancer: A dual-center study. Radiother Oncol 2022; 171:155-163. [DOI: 10.1016/j.radonc.2022.04.023] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Revised: 03/26/2022] [Accepted: 04/21/2022] [Indexed: 12/24/2022]
|
5
|
Guiot J, Vaidyanathan A, Deprez L, Zerka F, Danthine D, Frix AN, Lambin P, Bottari F, Tsoutzidis N, Miraglio B, Walsh S, Vos W, Hustinx R, Ferreira M, Lovinfosse P, Leijenaar RTH. A review in radiomics: Making personalized medicine a reality via routine imaging. Med Res Rev 2021; 42:426-440. [PMID: 34309893 DOI: 10.1002/med.21846] [Citation(s) in RCA: 86] [Impact Index Per Article: 28.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Revised: 07/05/2021] [Accepted: 07/07/2021] [Indexed: 12/14/2022]
Abstract
Radiomics is the quantitative analysis of standard-of-care medical imaging; the information obtained can be applied within clinical decision support systems to create diagnostic, prognostic, and/or predictive models. Radiomics analysis can be performed by extracting hand-crafted radiomics features or via deep learning algorithms. Radiomics has evolved tremendously in the last decade, becoming a bridge between imaging and precision medicine. Radiomics exploits sophisticated image analysis tools coupled with statistical elaboration to extract the wealth of information hidden inside medical images, such as computed tomography (CT), magnetic resonance (MR), and/or Positron emission tomography (PET) scans, routinely performed in the everyday clinical practice. Many efforts have been devoted in recent years to the standardization and validation of radiomics approaches, to demonstrate their usefulness and robustness beyond any reasonable doubts. However, the booming of publications and commercial applications of radiomics approaches warrant caution and proper understanding of all the factors involved to avoid "scientific pollution" and overly enthusiastic claims by researchers and clinicians alike. For these reasons the present review aims to be a guidebook of sorts, describing the process of radiomics, its pitfalls, challenges, and opportunities, along with its ability to improve clinical decision-making, from oncology and respiratory medicine to pharmacological and genotyping studies.
Collapse
Affiliation(s)
- Julien Guiot
- Department of Pneumology, University Hospital of Liège, Liège, Belgium
| | - Akshayaa Vaidyanathan
- Radiomics (Oncoradiomics SA), Liège, Belgium.,The D-Lab, Department of Precision Medicine, Department of Nuclear Medicine, GROW-School for Oncology, Maastricht University, Maastricht, The Netherlands
| | - Louis Deprez
- Department of Radiology, University Hospital of Liège, Liège, Belgium
| | - Fadila Zerka
- Radiomics (Oncoradiomics SA), Liège, Belgium.,The D-Lab, Department of Precision Medicine, Department of Nuclear Medicine, GROW-School for Oncology, Maastricht University, Maastricht, The Netherlands
| | - Denis Danthine
- Department of Radiology, University Hospital of Liège, Liège, Belgium
| | - Anne-Noelle Frix
- Department of Pneumology, University Hospital of Liège, Liège, Belgium
| | - Philippe Lambin
- The D-Lab, Department of Precision Medicine, Department of Nuclear Medicine, GROW-School for Oncology, Maastricht University, Maastricht, The Netherlands
| | | | | | | | - Sean Walsh
- Radiomics (Oncoradiomics SA), Liège, Belgium
| | - Wim Vos
- Radiomics (Oncoradiomics SA), Liège, Belgium
| | - Roland Hustinx
- Department of Nuclear Medicine and Oncological Imaging, University Hospital of Liege, Liege, Belgium.,GIGA-CRC in vivo imaging, University of Liège, Liège, Belgium
| | - Marta Ferreira
- GIGA-CRC in vivo imaging, University of Liège, Liège, Belgium
| | - Pierre Lovinfosse
- Department of Nuclear Medicine and Oncological Imaging, University Hospital of Liege, Liege, Belgium
| | | |
Collapse
|