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Wei R, Lu S, Lai S, Liang F, Zhang W, Jiang X, Zhen X, Yang R. A subregion-based RadioFusionOmics model discriminates between grade 4 astrocytoma and glioblastoma on multisequence MRI. J Cancer Res Clin Oncol 2024; 150:73. [PMID: 38305926 PMCID: PMC10837235 DOI: 10.1007/s00432-023-05603-3] [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: 09/17/2023] [Accepted: 12/26/2023] [Indexed: 02/03/2024]
Abstract
PURPOSE To explore a subregion-based RadioFusionOmics (RFO) model for discrimination between adult-type grade 4 astrocytoma and glioblastoma according to the 2021 WHO CNS5 classification. METHODS 329 patients (40 grade 4 astrocytomas and 289 glioblastomas) with histologic diagnosis was retrospectively collected from our local institution and The Cancer Imaging Archive (TCIA). The volumes of interests (VOIs) were obtained from four multiparametric MRI sequences (T1WI, T1WI + C, T2WI, T2-FLAIR) using (1) manual segmentation of the non-enhanced tumor (nET), enhanced tumor (ET), and peritumoral edema (pTE), and (2) K-means clustering of four habitats (H1: high T1WI + C, high T2-FLAIR; (2) H2: high T1WI + C, low T2-FLAIR; (3) H3: low T1WI + C, high T2-FLAIR; and (4) H4: low T1WI + C, low T2-FLAIR). The optimal VOI and best MRI sequence combination were determined. The performance of the RFO model was evaluated using the area under the precision-recall curve (AUPRC) and the best signatures were identified. RESULTS The two best VOIs were manual VOI3 (putative peritumoral edema) and clustering H34 (low T1WI + C, high T2-FLAIR (H3) combined with low T1WI + C and low T2-FLAIR (H4)). Features fused from four MRI sequences ([Formula: see text]) outperformed those from either a single sequence or other sequence combinations. The RFO model that was trained using fused features [Formula: see text] achieved the AUPRC of 0.972 (VOI3) and 0.976 (H34) in the primary cohort (p = 0.905), and 0.971 (VOI3) and 0.974 (H34) in the testing cohort (p = 0.402). CONCLUSION The performance of subregions defined by clustering was comparable to that of subregions that were manually defined. Fusion of features from the edematous subregions of multiple MRI sequences by the RFO model resulted in differentiation between grade 4 astrocytoma and glioblastoma.
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Affiliation(s)
- Ruili Wei
- Department of Radiology, the Second Affiliated Hospital, School of Medicine, South China University of Technology, GuangZhou, China
| | - Songlin Lu
- School of Biomedical Engineering, Southern Medical University, GuangZhou, China
| | - Shengsheng Lai
- School of Medical Equipment, Guangdong Food and Drug Vocational College, Guangzhou, China
| | - Fangrong Liang
- Department of Radiology, the Second Affiliated Hospital, School of Medicine, South China University of Technology, GuangZhou, China
| | - Wanli Zhang
- Department of Radiology, the Second Affiliated Hospital, School of Medicine, South China University of Technology, GuangZhou, China
| | - Xinqing Jiang
- Department of Radiology, the Second Affiliated Hospital, School of Medicine, South China University of Technology, GuangZhou, China
| | - Xin Zhen
- School of Biomedical Engineering, Southern Medical University, GuangZhou, China.
| | - Ruimeng Yang
- Department of Radiology, the Second Affiliated Hospital, School of Medicine, South China University of Technology, GuangZhou, China.
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Wu MY, Han QJ, Ai Z, Liang YY, Yan HW, Xie Q, Xiang ZM. Assessment of chemotherapy resistance changes in human colorectal cancer xenografts in rats based on MRI histogram features. Front Oncol 2024; 14:1301649. [PMID: 38357206 PMCID: PMC10864667 DOI: 10.3389/fonc.2024.1301649] [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: 09/27/2023] [Accepted: 01/11/2024] [Indexed: 02/16/2024] Open
Abstract
Purpose We investigated the value of magnetic resonance imaging (MRI) histogram features, a non-invasive method, in assessing the changes in chemoresistance of colorectal cancer xenografts in rats. Methods A total of 50 tumor-bearing mice with colorectal cancer were randomly divided into two groups: control group and 5-fluorouracil (5-FU) group. The MRI histogram characteristics and the expression levels of p53 protein and MRP1 were obtained at 24 h, 48 h, 72 h, 120 h, and 168 h after treatment. Results Sixty highly repeatable MRI histogram features were obtained. There were 16 MRI histogram parameters and MRP1 resistance protein differences between groups. At 24 h after treatment, the MRI histogram texture parameters of T2-weighted imaging (T2WI) images (10%, 90%, median, energy, and RootMeanSquared) and D images (10% and Range) were positively correlated with MRP1 (r = 0.925, p = 0.005). At 48 h after treatment, histogram texture parameters of apparent diffusion coefficient (ADC) images (Energy) were positively correlated with the presence of MRP1 resistance protein (r = 0.900, p = 0.037). There was no statistically significant difference between MRI histogram features and p53 protein expression level. Conclusions MRI histogram texture parameters based on T2WI, D, and ADC maps can help to predict the change of 5-FU resistance in colorectal cancer in the early stage and provide important reference significance for clinical treatment.
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Affiliation(s)
- Min-Yi Wu
- Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, China
| | - Qi-Jia Han
- Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, China
| | - Zhu Ai
- Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, China
| | - Yu-Ying Liang
- Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, China
| | - Hao-Wen Yan
- Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, China
| | - Qi Xie
- Department of Radiology, Guangzhou First People’s Hospital/Department of Medical Imaging, Nansha Hospital, Guangzhou, Guangzhou, China
| | - Zhi-Ming Xiang
- Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, China
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Feng S, Yin J. Dynamic contrast-enhanced magnetic resonance imaging radiomics analysis based on intratumoral subregions for predicting luminal and nonluminal breast cancer. Quant Imaging Med Surg 2023; 13:6735-6749. [PMID: 37869317 PMCID: PMC10585575 DOI: 10.21037/qims-22-1073] [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: 10/06/2022] [Accepted: 08/14/2023] [Indexed: 10/24/2023]
Abstract
Background Breast cancer is a heterogeneous disease with different morphological and biological characteristics. The molecular subtypes of breast cancer are closely related to the treatment and prognosis of patients. In order to predict the luminal type of breast cancer in a noninvasive manner, our study developed and validated a radiomics nomogram combining clinical factors with a radiomics score based on the features of the intratumoral subregion to distinguish between luminal and nonluminal breast cancer. Methods From January 2018 to January 2020, 153 women with clinically and pathologically diagnosed breast cancer with an average age of 50.08 years were retrospectively analyzed. Using a semiautomatic segmentation method, the whole tumor was divided into 3 subregions on the basis of the time required for the contrast agent to reach its peak; 540 features were extracted from 3 subregions and the whole tumor region. Subsequently, 2 machine learning classifiers were developed. The least absolute shrinkage and selection operator method was used for feature selection and radiomics score (Rad-score) construction. Moreover, multivariable logistic regression analysis was applied to select independent factors from the Rad-score and clinical factors to establish a prediction model in the form of a nomogram. The performance of the nomogram was evaluated through calibration, discrimination, and clinical usefulness. Results The prediction performance of texture features from the rapid subregion was the best in the 3 intratumoral subregions, and the area under the receiver operating characteristic curve (AUC) values in the training and validation cohort were 0.805 (95% CI: 0.719-0.892) and 0.737 (95% CI: 0.581-0.893), respectively. The Rad-score, consisting of 5 features from the rapid subregion, was associated with the luminal type of breast cancer (P=0.001 and P=0.035 in the training and validation cohorts, respectively). The predictors included in the personalized prediction nomogram included Rad-score, human epidermal growth factor receptor 2 (HER2) status, and tumor histological grade. The nomogram showed good discrimination, with an area under the receiver operating characteristic curve in the training and validation cohorts of 0.830 (95% CI: 0.746-0.896) and 0.879 (95% CI: 0.748-0.957), respectively. The calibration curve of the 2 cohorts and decision curve analysis demonstrated that the nomogram had good calibration and clinical usefulness. Conclusions We proposed a nomogram model that combined clinical factors and Rad-score, which showed good performance in predicting the luminal type of breast cancer.
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Affiliation(s)
- Shuqian Feng
- Department of Nuclear Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jiandong Yin
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
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Bagher-Ebadian H, Brown SL, Ghassemi MM, Nagaraja TN, Movsas B, Ewing JR, Chetty IJ. Radiomics characterization of tissues in an animal brain tumor model imaged using dynamic contrast enhanced (DCE) MRI. Sci Rep 2023; 13:10693. [PMID: 37394559 DOI: 10.1038/s41598-023-37723-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 06/27/2023] [Indexed: 07/04/2023] Open
Abstract
Here, we investigate radiomics-based characterization of tumor vascular and microenvironmental properties in an orthotopic rat brain tumor model measured using dynamic-contrast-enhanced (DCE) MRI. Thirty-two immune compromised-RNU rats implanted with human U-251N cancer cells were imaged using DCE-MRI (7Tesla, Dual-Gradient-Echo). The aim was to perform pharmacokinetic analysis using a nested model (NM) selection technique to classify brain regions according to vasculature properties considered as the source of truth. A two-dimensional convolutional-based radiomics analysis was performed on the raw-DCE-MRI of the rat brains to generate dynamic radiomics maps. The raw-DCE-MRI and respective radiomics maps were used to build 28 unsupervised Kohonen self-organizing-maps (K-SOMs). A Silhouette-Coefficient (SC), k-fold Nested-Cross-Validation (k-fold-NCV), and feature engineering analyses were performed on the K-SOMs' feature spaces to quantify the distinction power of radiomics features compared to raw-DCE-MRI for classification of different Nested Models. Results showed that eight radiomics features outperformed respective raw-DCE-MRI in prediction of the three nested models. The average percent difference in SCs between radiomics features and raw-DCE-MRI was: 29.875% ± 12.922%, p < 0.001. This work establishes an important first step toward spatiotemporal characterization of brain regions using radiomics signatures, which is fundamental toward staging of tumors and evaluation of tumor response to different treatments.
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Affiliation(s)
- Hassan Bagher-Ebadian
- Department of Radiation Oncology, Henry Ford Health, Detroit, MI, 48202, USA.
- Department of Radiology, Michigan State University, East Lansing, MI, 48824, USA.
- Department of Osteopathic Medicine, Michigan State University, East Lansing, MI, 48824, USA.
- Department of Physics, Oakland University, Rochester, MI, 48309, USA.
| | - Stephen L Brown
- Department of Radiation Oncology, Henry Ford Health, Detroit, MI, 48202, USA
- Department of Radiology, Michigan State University, East Lansing, MI, 48824, USA
- Department of Radiation Oncology, Wayne State University, Detroit, MI, 48202, USA
| | - Mohammad M Ghassemi
- Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, 48824, USA
| | - Tavarekere N Nagaraja
- Department of Radiology, Michigan State University, East Lansing, MI, 48824, USA
- Department of Neurosurgery, Henry Ford Health, Detroit, MI, 48202, USA
| | - Benjamin Movsas
- Department of Radiation Oncology, Henry Ford Health, Detroit, MI, 48202, USA
- Department of Radiology, Michigan State University, East Lansing, MI, 48824, USA
- Department of Radiation Oncology, Wayne State University, Detroit, MI, 48202, USA
| | - James R Ewing
- Department of Radiology, Michigan State University, East Lansing, MI, 48824, USA
- Department of Physics, Oakland University, Rochester, MI, 48309, USA
- Department of Neurosurgery, Henry Ford Health, Detroit, MI, 48202, USA
- Department of Neurology, Henry Ford Health, Detroit, MI, 48202, USA
- Department of Neurology, Wayne State University, Detroit, MI, 48202, USA
| | - Indrin J Chetty
- Department of Radiation Oncology, Henry Ford Health, Detroit, MI, 48202, USA
- Department of Physics, Oakland University, Rochester, MI, 48309, USA
- Department of Radiation Oncology, Wayne State University, Detroit, MI, 48202, USA
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Sherminie LPG, Jayatilake ML. Fractal Dimension Analysis of Pixel Dynamic Contrast Enhanced-Magnetic Resonance Imaging Pharmacokinetic Parameters for Discrimination of Benign and Malignant Breast Lesions. JCO Clin Cancer Inform 2023; 7:e2200101. [PMID: 36745858 DOI: 10.1200/cci.22.00101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023] Open
Abstract
PURPOSE Breast cancer is the most frequent cancer in women worldwide. However, its diagnosis mostly depends on visual examination of radiologic images, leading to an overdiagnosis with substantial costs. Therefore, a quantitative approach such as dynamic contrast enhanced (DCE)-magnetic resonance imaging (MRI) through pharmacokinetic (PK) modeling is required for reliable analysis. As PK parameters lack information on parameter heterogeneity, texture-based analysis is required to quantify PK parameter heterogeneity. Therefore, this study focused on determining the usefulness of fractal dimension (FD) as a potential imaging biomarker of tumor heterogeneity for discriminating benign and malignant breast lesions. METHODS Parametric maps for PK parameters, extravasation rate of contrast agent from blood plasma to extravascular extracellular space (Ktrans) and volume fraction of extravascular extracellular space (ve), were generated for the regions of interest (ROIs) under the standard model using 18 lesions. Then, tumor ROI and pixel DCE-MRI time-course data were analyzed to extract pixel values of Ktrans and ve. For each ROI, FD values of Ktrans and ve were computed using the blanket method. RESULTS The FD values of Ktrans for benign and malignant lesions varied from 2.96 to 3.49 and from 2.37 to 3.16, respectively, whereas FD values of ve for benign and malignant lesions varied from 3.01 to 5.15 and 2.42 to 3.44, respectively. There were significant differences in FD values derived from Ktrans parametric maps (P = .0053) and ve parametric maps (P = .0271) between benign and malignant lesions according to the statistical analysis. CONCLUSION Incorporating texture heterogeneity changes in breast lesions captured by FD with quantitative DCE-MRI parameters generated under the standard model is a potential marker for prediction of malignant lesions.
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Affiliation(s)
- Lahanda Purage G Sherminie
- Department of Nuclear Science, Faculty of Science, University of Colombo, Colombo, Sri Lanka.,Department of Radiography/Radiotherapy, Faculty of Allied Health Sciences, University of Peradeniya, Peradeniya, Sri Lanka
| | - Mohan L Jayatilake
- Department of Radiography/Radiotherapy, Faculty of Allied Health Sciences, University of Peradeniya, Peradeniya, Sri Lanka
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Altabella L, Benetti G, Camera L, Cardano G, Montemezzi S, Cavedon C. Machine learning for multi-parametric breast MRI: radiomics-based approaches for lesion classification. Phys Med Biol 2022; 67. [DOI: 10.1088/1361-6560/ac7d8f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 06/30/2022] [Indexed: 11/11/2022]
Abstract
Abstract
In the artificial intelligence era, machine learning (ML) techniques have gained more and more importance in the advanced analysis of medical images in several fields of modern medicine. Radiomics extracts a huge number of medical imaging features revealing key components of tumor phenotype that can be linked to genomic pathways. The multi-dimensional nature of radiomics requires highly accurate and reliable machine-learning methods to create predictive models for classification or therapy response assessment.
Multi-parametric breast magnetic resonance imaging (MRI) is routinely used for dense breast imaging as well for screening in high-risk patients and has shown its potential to improve clinical diagnosis of breast cancer. For this reason, the application of ML techniques to breast MRI, in particular to multi-parametric imaging, is rapidly expanding and enhancing both diagnostic and prognostic power. In this review we will focus on the recent literature related to the use of ML in multi-parametric breast MRI for tumor classification and differentiation of molecular subtypes. Indeed, at present, different models and approaches have been employed for this task, requiring a detailed description of the advantages and drawbacks of each technique and a general overview of their performances.
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