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Espedal H, Fasmer KE, Berg HF, Lyngstad JM, Schilling T, Krakstad C, Haldorsen IS. MRI radiomics captures early treatment response in patient-derived organoid endometrial cancer mouse models. Front Oncol 2024; 14:1334541. [PMID: 38774411 PMCID: PMC11106402 DOI: 10.3389/fonc.2024.1334541] [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: 11/07/2023] [Accepted: 04/23/2024] [Indexed: 05/24/2024] Open
Abstract
Background Radiomics can capture microscale information in medical images beyond what is visible to the naked human eye. Using a clinically relevant mouse model for endometrial cancer, the objective of this study was to develop and validate a radiomic signature (RS) predicting response to standard chemotherapy. Methods Mice orthotopically implanted with a patient-derived grade 3 endometrioid endometrial cancer organoid model (O-PDX) were allocated to chemotherapy (combined paclitaxel/carboplatin, n=11) or saline/control (n=13). During tumor progression, the mice underwent weekly T2-weighted (T2w) magnetic resonance imaging (MRI). Segmentation of primary tumor volume (vMRI) allowed extraction of radiomic features from whole-volume tumor masks. A radiomic model for predicting treatment response was derived employing least absolute shrinkage and selection operator (LASSO) statistics at endpoint images in the orthotopic O-PDX (RS_O), and subsequently applied on the earlier study timepoints (RS_O at baseline, and week 1-3). For external validation, the radiomic model was tested in a separate T2w-MRI dataset on segmented whole-volume subcutaneous tumors (RS_S) from the same O-PDX model, imaged at three timepoints (baseline, day 3 and day 10/endpoint) after start of chemotherapy (n=8 tumors) or saline/control (n=8 tumors). Results The RS_O yielded rapidly increasing area under the receiver operating characteristic (ROC) curves (AUCs) for predicting treatment response from baseline until endpoint; AUC=0.38 (baseline); 0.80 (week 1), 0.85 (week 2), 0.96 (week 3) and 1.0 (endpoint). In comparison, vMRI yielded AUCs of 0.37 (baseline); 0.69 (w1); 0.83 (week 2); 0.92 (week 3) and 0.97 (endpoint). When tested in the external validation dataset, RS_S yielded high accuracy for predicting treatment response at day10/endpoint (AUC=0.85) and tended to yield higher AUC than vMRI (AUC=0.78, p=0.18). Neither RS_S nor vMRI predicted response at day 3 in the external validation set (AUC=0.56 for both). Conclusions We have developed and validated a radiomic signature that was able to capture chemotherapeutic treatment response both in an O-PDX and in a subcutaneous endometrial cancer mouse model. This study supports the promising role of preclinical imaging including radiomic tumor profiling to assess early treatment response in endometrial cancer models.
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Affiliation(s)
- Heidi Espedal
- Department of Clinical Medicine, University of Bergen, Bergen, Norway
- Mohn Medical Imaging and Visualization Centre, Department of Radiology, Haukeland University Hospital, Bergen, Norway
- Western Australia National Imaging Facility, Centre for Microscopy, Characterization and Analysis, University of Western Australia, Perth, WA, Australia
| | - Kristine E. Fasmer
- Department of Clinical Medicine, University of Bergen, Bergen, Norway
- Mohn Medical Imaging and Visualization Centre, Department of Radiology, Haukeland University Hospital, Bergen, Norway
| | - Hege F. Berg
- Centre for Cancer Biomarkers, Department of Clinical Science, University of Bergen, Bergen, Norway
- Department of Gynecology and Obstetrics, Haukeland University Hospital, Bergen, Norway
| | - Jenny M. Lyngstad
- Department of Clinical Medicine, University of Bergen, Bergen, Norway
- Mohn Medical Imaging and Visualization Centre, Department of Radiology, Haukeland University Hospital, Bergen, Norway
| | - Tomke Schilling
- Department of Clinical Medicine, University of Bergen, Bergen, Norway
- Mohn Medical Imaging and Visualization Centre, Department of Radiology, Haukeland University Hospital, Bergen, Norway
| | - Camilla Krakstad
- Centre for Cancer Biomarkers, Department of Clinical Science, University of Bergen, Bergen, Norway
- Department of Gynecology and Obstetrics, Haukeland University Hospital, Bergen, Norway
| | - Ingfrid S. Haldorsen
- Department of Clinical Medicine, University of Bergen, Bergen, Norway
- Mohn Medical Imaging and Visualization Centre, Department of Radiology, Haukeland University Hospital, Bergen, Norway
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Lu W, Wu G, Miao X, Ma J, Wang Y, Xu H, Shentu D, Xue S, Xia Q, Wang Y, Wang L. The radiomics nomogram predicts the prognosis of pancreatic cancer patients with hepatic metastasis after chemoimmunotherapy. Cancer Immunol Immunother 2024; 73:87. [PMID: 38554161 PMCID: PMC10981596 DOI: 10.1007/s00262-024-03644-2] [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: 11/28/2023] [Accepted: 01/29/2024] [Indexed: 04/01/2024]
Abstract
OBJECTIVE To construct a prognostic model based on MR features and clinical data to evaluate the progression free survival (PFS), overall survival (OS) and objective response rate (ORR) of pancreatic cancer patients with hepatic metastases who received chemoimmunotherapy. METHODS 105 pancreatic cancer patients with hepatic metastases who received chemoimmunotherapy were assigned to the training set (n = 52), validation set (n = 22), and testing set (n = 31). Multi-lesion volume of interest were delineated, multi-sequence radiomics features were extracted, and the radiomics models for predicting PFS, OS and ORR were constructed, respectively. Clinical variables were extracted, and the clinical models for predicting PFS, OS and ORR were constructed, respectively. The nomogram was jointly constructed by radiomics model and clinical model. RESULT The ORR exhibits no significant correlation with either PFS or OS. The area under the curve (AUC) of nomogram for predicting 6-month PFS reached 0.847 (0.737-0.957), 0.786 (0.566-1.000) and 0.864 (0.735-0.994) in the training set, validation set and testing set, respectively. The AUC of nomogram for predicting 1-year OS reached 0.770 (0.635-0.906), 0.743 (0.479-1.000) and 0.818 (0.630-1.000), respectively. The AUC of nomogram for predicting ORR reached 0.914 (0.828-1.00), 0.938 (0.840-1.00) and 0.846 (0.689-1.00), respectively. CONCLUSION The prognostic models based on MR imaging features and clinical data are effective in predicting the PFS, OS and ORR of chemoimmunotherapy in pancreatic cancer patients with hepatic metastasis, and can be used to evaluate the prognosis of patients.
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Affiliation(s)
- Wenxin Lu
- Department of Oncology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, China
| | - Guangyu Wu
- Department of Radiology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, China
| | - Xianyuan Miao
- Department of Oncology, Ning Bo Hangzhou Bay Hospital, Ningbo, 315336, China
| | - Jingyu Ma
- Department of Oncology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, China
| | - Yanling Wang
- Department of Oncology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, China
| | - Haiyan Xu
- Department of Oncology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, China
| | - Daiyuan Shentu
- Department of Oncology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, China
| | - Shengbai Xue
- Department of Oncology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, China
| | - Qing Xia
- Department of Oncology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, China
| | - Yu Wang
- Department of Oncology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, China.
| | - Liwei Wang
- Department of Oncology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, China.
- State Key Laboratory of Systems Medicine for Cancer of Shanghai Cancer Institute, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, China.
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Deng XY, Cao PW, Nan SM, Pan YP, Yu C, Pan T, Dai G. Differentiation Between Phyllodes Tumors and Fibroadenomas of Breast Using Mammography-based Machine Learning Methods: A Preliminary Study. Clin Breast Cancer 2023; 23:729-736. [PMID: 37481337 DOI: 10.1016/j.clbc.2023.07.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Revised: 07/03/2023] [Accepted: 07/04/2023] [Indexed: 07/24/2023]
Abstract
OBJECTIVE To investigate the diagnostic performance of a mammography-based radiomics model for distinguishing phyllodes tumors (PTs) from fibroadenomas (FAs) of the breast. MATERIALS AND METHODS A total of 156 patients were retrospectively included (75 with PTs, 81 with FAs) and divided into training and validation groups at a ratio of 7:3. Radiomics features were extracted from craniocaudal and mediolateral oblique images. The least absolute shrinkage and selection operator (LASSO) algorithm and principal component analysis (PCA) were performed to select features. Three machine learning classifiers, including logistic regression (LR), K-nearest neighbor classifier (KNN) and support vector machine (SVM), were implemented in the radiomics model, imaging model and combined model. Receiver operating characteristic curves, area under the curve (AUC), sensitivity and specificity were computed. RESULTS Among 1084 features, the LASSO algorithm selected 17 features, and PCA further selected 6 features. Three machine learning classifiers yielded the same AUC of 0.935 in the validation group for the radiomics model. In the imaging model, KNN yielded the highest accuracy rate of 89.4% and AUC of 0.947 in the validation set. For the combined model, the SVM classifier reached the highest AUC of 0.918 with an accuracy rate of 86.2%, sensitivity of 83.9%, and specificity of 89.4% in the training group. In the validation group, LR yielded the highest AUC of 0.973. The combined model had a relatively higher AUC than the radiomics model or imaging model, especially in the validation group. CONCLUSIONS Mammography-based radiomics features demonstrate good diagnostic performance for discriminating PTs from FAs.
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Affiliation(s)
- Xue-Ying Deng
- Department of Radiology, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China.
| | - Pei-Wei Cao
- Department of Radiology, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Shuai-Ming Nan
- Department of Radiology, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Yue-Peng Pan
- Department of Radiology, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Chang Yu
- Department of Pathology, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Ting Pan
- Department of Radiology, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Gang Dai
- Department of Radiology, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
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Gabryś HS, Gote-Schniering J, Brunner M, Bogowicz M, Blüthgen C, Frauenfelder T, Guckenberger M, Maurer B, Tanadini-Lang S. Transferability of radiomic signatures from experimental to human interstitial lung disease. Front Med (Lausanne) 2022; 9:988927. [DOI: 10.3389/fmed.2022.988927] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Accepted: 10/31/2022] [Indexed: 11/18/2022] Open
Abstract
BackgroundInterstitial lung disease (ILD) defines a group of parenchymal lung disorders, characterized by fibrosis as their common final pathophysiological stage. To improve diagnosis and treatment of ILD, there is a need for repetitive non-invasive characterization of lung tissue by quantitative parameters. In this study, we investigated whether CT image patterns found in mice with bleomycin induced lung fibrosis can be translated as prognostic factors to human patients diagnosed with ILD.MethodsBleomycin was used to induce lung fibrosis in mice (n_control = 36, n_experimental = 55). The patient cohort consisted of 98 systemic sclerosis (SSc) patients (n_ILD = 65). Radiomic features (n_histogram = 17, n_texture = 137) were extracted from microCT (mice) and HRCT (patients) images. Predictive performance of the models was evaluated with the area under the receiver-operating characteristic curve (AUC). First, predictive performance of individual features was examined and compared between murine and patient data sets. Second, multivariate models predicting ILD were trained on murine data and tested on patient data. Additionally, the models were reoptimized on patient data to reduce the influence of the domain shift on the performance scores.ResultsPredictive power of individual features in terms of AUC was highly correlated between mice and patients (r = 0.86). A model based only on mean image intensity in the lung scored AUC = 0.921 ± 0.048 in mice and AUC = 0.774 (CI95% 0.677-0.859) in patients. The best radiomic model based on three radiomic features scored AUC = 0.994 ± 0.013 in mice and validated with AUC = 0.832 (CI95% 0.745-0.907) in patients. However, reoptimization of the model weights in the patient cohort allowed to increase the model’s performance to AUC = 0.912 ± 0.058.ConclusionRadiomic signatures of experimental ILD derived from microCT scans translated to HRCT of humans with SSc-ILD. We showed that the experimental model of BLM-induced ILD is a promising system to test radiomic models for later application and validation in human cohorts.
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Eresen A, Sun C, Zhou K, Shangguan J, Wang B, Pan L, Hu S, Ma Q, Yang J, Zhang Z, Yaghmai V. Early Differentiation of Irreversible Electroporation Ablation Regions With Radiomics Features of Conventional MRI. Acad Radiol 2022; 29:1378-1386. [PMID: 34933803 PMCID: PMC10029937 DOI: 10.1016/j.acra.2021.11.020] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 11/19/2021] [Accepted: 11/22/2021] [Indexed: 01/05/2023]
Abstract
RATIONALE AND OBJECTIVES Irreversible electroporation (IRE) is a promising non-thermal ablation technique for the treatment of patients with hepatocellular carcinoma. Early differentiation of the IRE zone from surrounding reversibly electroporated (RE) penumbra is vital for the evaluation of treatment response. In this study, an advanced statistical learning framework was developed by evaluating standard MRI data to differentiate IRE ablation zones, and to correlate with histological tumor biomarkers. MATERIALS AND METHODS Fourteen rabbits with VX2 liver tumors were scanned following IRE ablation and forty-six features were extracted from T1w and T2w MRI. Following identification of key imaging variables through two-step feature analysis, multivariable classification and regression models were generated for differentiation of IRE ablation zones, and correlation with histological markers reflecting viable tumor cells, microvessel density, and apoptosis rate. The performance of the multivariable models was assessed by measuring accuracy, receiver operating characteristics curve analysis, and Spearman correlation coefficients. RESULTS The classifiers integrating four radiomics features of T1w, T2w, and T1w+T2w MRI data distinguished IRE from RE zones with an accuracy of 97%, 80%, and 97%, respectively. Also, pixelwise classification models of T1w, T2w, and T1w+T2w MRI labeled each voxel with an accuracy of 82.8%, 66.5%, and 82.9%, respectively. Regression models obtained a strong correlation with behavior of viable tumor cells (0.62 ≤ r2 ≤ 0.85, p < 0.01), apoptosis (0.40 ≤ r2 ≤ 0.82, p < 0.01), and microvessel density (0.48 ≤ r2 ≤ 0.58, p < 0.01). CONCLUSION MRI radiomics features provide descriptive power for early differentiation of IRE and RE zones while observing strong correlations among multivariable MRI regression models and histological tumor biomarkers.
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Affiliation(s)
- Aydin Eresen
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois; Department of Radiological Sciences, University of California Irvine, Irvine, California
| | - Chong Sun
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois; Department of Orthopedics, Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Kang Zhou
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois; Department of Radiology, Peking Union Medical College Hospital, Beijing, China
| | - Junjie Shangguan
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
| | - Bin Wang
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois; Department of General Surgery, Nanfang Hospital, Southern Medical University, Guangdong Provincial Engineering Technology Research Center of Minimally Invasive Surgery, Guangzhou, China
| | - Liang Pan
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois; Department of Radiology, Third Affiliated Hospital of Suzhou University, Changzhou, Jiangsu, China
| | - Su Hu
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois; Department of Radiology, First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
| | - Quanhong Ma
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois; Department of Radiological Sciences, University of California Irvine, Irvine, California
| | - Jia Yang
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
| | - Zhuoli Zhang
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois; Department of Radiological Sciences, University of California Irvine, Irvine, California; Robert H. Lurie Comprehensive Cancer Center of Northwestern University, Chicago, Illinois; Chao Family Comprehensive Cancer Center, University of California Irvine, Irvine, California
| | - Vahid Yaghmai
- Department of Radiological Sciences, University of California Irvine, Irvine, California; Chao Family Comprehensive Cancer Center, University of California Irvine, Irvine, California.
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Dercle L, McGale J, Sun S, Marabelle A, Yeh R, Deutsch E, Mokrane FZ, Farwell M, Ammari S, Schoder H, Zhao B, Schwartz LH. Artificial intelligence and radiomics: fundamentals, applications, and challenges in immunotherapy. J Immunother Cancer 2022; 10:jitc-2022-005292. [PMID: 36180071 PMCID: PMC9528623 DOI: 10.1136/jitc-2022-005292] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/01/2022] [Indexed: 11/04/2022] Open
Abstract
Immunotherapy offers the potential for durable clinical benefit but calls into question the association between tumor size and outcome that currently forms the basis for imaging-guided treatment. Artificial intelligence (AI) and radiomics allow for discovery of novel patterns in medical images that can increase radiology’s role in management of patients with cancer, although methodological issues in the literature limit its clinical application. Using keywords related to immunotherapy and radiomics, we performed a literature review of MEDLINE, CENTRAL, and Embase from database inception through February 2022. We removed all duplicates, non-English language reports, abstracts, reviews, editorials, perspectives, case reports, book chapters, and non-relevant studies. From the remaining articles, the following information was extracted: publication information, sample size, primary tumor site, imaging modality, primary and secondary study objectives, data collection strategy (retrospective vs prospective, single center vs multicenter), radiomic signature validation strategy, signature performance, and metrics for calculation of a Radiomics Quality Score (RQS). We identified 351 studies, of which 87 were unique reports relevant to our research question. The median (IQR) of cohort sizes was 101 (57–180). Primary stated goals for radiomics model development were prognostication (n=29, 33.3%), treatment response prediction (n=24, 27.6%), and characterization of tumor phenotype (n=14, 16.1%) or immune environment (n=13, 14.9%). Most studies were retrospective (n=75, 86.2%) and recruited patients from a single center (n=57, 65.5%). For studies with available information on model testing, most (n=54, 65.9%) used a validation set or better. Performance metrics were generally highest for radiomics signatures predicting treatment response or tumor phenotype, as opposed to immune environment and overall prognosis. Out of a possible maximum of 36 points, the median (IQR) of RQS was 12 (10–16). While a rapidly increasing number of promising results offer proof of concept that AI and radiomics could drive precision medicine approaches for a wide range of indications, standardizing the data collection as well as optimizing the methodological quality and rigor are necessary before these results can be translated into clinical practice.
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Affiliation(s)
- Laurent Dercle
- Radiology, NewYork-Presbyterian/Columbia University Medical Center, New York, New York, USA
| | - Jeremy McGale
- Radiology, NewYork-Presbyterian/Columbia University Medical Center, New York, New York, USA
| | - Shawn Sun
- Radiology, NewYork-Presbyterian/Columbia University Medical Center, New York, New York, USA
| | - Aurelien Marabelle
- Therapeutic Innovation and Early Trials, Gustave Roussy, Villejuif, Île-de-France, France
| | - Randy Yeh
- Molecular Imaging and Therapy Service, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Eric Deutsch
- Radiation Oncology, Gustave Roussy, Villejuif, Île-de-France, France
| | | | - Michael Farwell
- Division of Nuclear Medicine and Molecular Imaging, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Samy Ammari
- Radiation Oncology, Gustave Roussy, Villejuif, Île-de-France, France.,Radiology, Institut de Cancérologie Paris Nord, Sarcelles, France
| | - Heiko Schoder
- Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Binsheng Zhao
- Radiology, NewYork-Presbyterian/Columbia University Medical Center, New York, New York, USA
| | - Lawrence H Schwartz
- Radiology, NewYork-Presbyterian/Columbia University Medical Center, New York, New York, USA
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Li Q, Yu J, Zhang H, Meng Y, Liu YF, Jiang H, Zhu M, Li N, Zhou J, Liu F, Fang X, Li J, Feng X, Lu J, Shao C, Bian Y. Prediction of Tumor-Infiltrating CD20 + B-Cells in Patients with Pancreatic Ductal Adenocarcinoma Using a Multilayer Perceptron Network Classifier Based on Non-contrast MRI. Acad Radiol 2022; 29:e167-e177. [PMID: 34922828 DOI: 10.1016/j.acra.2021.11.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Revised: 11/13/2021] [Accepted: 11/17/2021] [Indexed: 11/01/2022]
Abstract
RATIONALE AND OBJECTIVES Conventional chemotherapy has limited benefit in pancreatic ductal adenocarcinoma (PDAC), necessitating identification of novel therapeutic targets. Radiomics may enable non-invasive prediction of CD20 expression, a hypothesized therapeutic target in PDAC. To develop a machine learning classifier based on noncontrast magnetic resonance imaging for predicting CD20 expression in PDAC. MATERIALS AND METHODS Retrospective study was conducted on preoperative noncontrast magnetic resonance imaging of 156 patients with pathologically confirmed PDAC from January 2017 to April 2018. For each patient, 1409 radiomics features were selected using minimum absolute contraction and selective operator logistic regression algorithms. CD20 expression was quantified using immunohistochemistry. A multilayer perceptron network classifier was developed using the training and validation set. RESULTS A log-rank test showed that the CD20-high group (22.37 months, 95% CI: 19.10-25.65) had significantly longer survival than the CD20-low group (14.9 months, 95% CI: 10.96-18.84). The predictive model showed good differentiation in training (area under the curve [AUC], 0.79) and validation (AUC, 0.79) sets. Sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were 73.21%, 75.47%, 0.74, 0.76, and 0.73, respectively, for the training set and 69.23%, 80.95%, 0.74, 0.82, and 0.68, respectively, for the validation set. CONCLUSION Multilayer perceptron classifier based on noncontrast magnetic resonance imaging scanning can predict the level of CD20 expression in PDAC patients.
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Affiliation(s)
- Qi Li
- Department of Radiology, Changhai Hospital, Naval Medical University, 168 Changhai Road, Shanghai 200433, China
| | - Jieyu Yu
- Department of Radiology, Changhai Hospital, Naval Medical University, 168 Changhai Road, Shanghai 200433, China
| | - Hao Zhang
- Department of Radiology, Changhai Hospital, Naval Medical University, 168 Changhai Road, Shanghai 200433, China
| | - Yinghao Meng
- Department of Radiology, Changhai Hospital, Naval Medical University, 168 Changhai Road, Shanghai 200433, China
| | - Yan Fang Liu
- Department of Pathology, Changhai Hospital, Naval Medical University, Shanghai, China
| | - Hui Jiang
- Department of Pathology, Changhai Hospital, Naval Medical University, Shanghai, China
| | - Mengmeng Zhu
- Department of Radiology, Changhai Hospital, Naval Medical University, 168 Changhai Road, Shanghai 200433, China
| | - Na Li
- Department of Radiology, Changhai Hospital, Naval Medical University, 168 Changhai Road, Shanghai 200433, China
| | - Jian Zhou
- Department of Radiology, Changhai Hospital, Naval Medical University, 168 Changhai Road, Shanghai 200433, China
| | - Fang Liu
- Department of Radiology, Changhai Hospital, Naval Medical University, 168 Changhai Road, Shanghai 200433, China
| | - Xu Fang
- Department of Radiology, Changhai Hospital, Naval Medical University, 168 Changhai Road, Shanghai 200433, China
| | - Jing Li
- Department of Radiology, Changhai Hospital, Naval Medical University, 168 Changhai Road, Shanghai 200433, China
| | - Xiaochen Feng
- Department of Radiology, Changhai Hospital, Naval Medical University, 168 Changhai Road, Shanghai 200433, China
| | - Jianping Lu
- Department of Radiology, Changhai Hospital, Naval Medical University, 168 Changhai Road, Shanghai 200433, China
| | - Chengwei Shao
- Department of Radiology, Changhai Hospital, Naval Medical University, 168 Changhai Road, Shanghai 200433, China
| | - Yun Bian
- Department of Radiology, Changhai Hospital, Naval Medical University, 168 Changhai Road, Shanghai 200433, China.
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Eresen A, Zhou K, Sun C, Shangguan J, Wang B, Pan L, Hu S, Pang Y, Zhang Z, Tran RMN, Bhatia AP, Nouizi F, Abi-Jaoudeh N, Yaghmai V, Zhang Z. Early assessment of irreversible electroporation ablation outcomes by analyzing MRI texture: preclinical study in an animal model of liver tumor. Am J Transl Res 2022; 14:5541-5551. [PMID: 36105031 PMCID: PMC9452330] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Accepted: 06/22/2022] [Indexed: 01/05/2023]
Abstract
OBJECTIVES Accurate differentiation of temporary vs. permanent changes occurring following irreversible electroporation (IRE) holds immense importance for the early assessment of ablative treatment outcomes. Here, we investigated the benefits of advanced statistical learning models for an immediate evaluation of therapeutic outcomes by interpreting quantitative characteristics captured with conventional MRI. METHODS The preclinical study integrated twenty-six rabbits with anatomical and perfusion MRI data acquired with a 3T clinical MRI scanner. T1w and T2w MRI data were quantitatively analyzed, and forty-six quantitative features were computed with four feature extraction methods. The candidate key features were determined by graph clustering following the filtering-based feature selection technique, RELIEFF algorithm. Kernel-based support vector machines (SVM) and random forest (RF) classifiers interpreting quantitative features of T1w, T2w, and combination (T1w+T2w) MRI were developed for replicating the underlying characteristics of the tissues to distinguish IRE ablation regions for immediate assessment of treatment response. Accuracy, sensitivity, specificity, and area under the receiver operating characteristics curve were used to evaluate classification performance. RESULTS Following the analysis of quantitative variables, three features were integrated to develop a SVM classification model, while five features were utilized for generating RF classifiers. SVM classifiers demonstrated detection accuracy of 91.06%, 96.15%, and 98.04% for individual and combination MRI data, respectively. Besides, RF classifiers obtained slightly lower accuracy compared to SVM which were 95.06%, 89.40%, and 94.38% respectively. CONCLUSIONS Quantitative models integrating structural characteristics of conventional T1w and T2w MRI data with statistical learning techniques identified IRE ablation regions allowing early assessment of treatment status.
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Affiliation(s)
- Aydin Eresen
- Department of Radiology, Feinberg School of Medicine, Northwestern UniversityChicago, IL, USA,Department of Radiological Sciences, University of California IrvineIrvine, CA, USA
| | - Kang Zhou
- Department of Radiology, Feinberg School of Medicine, Northwestern UniversityChicago, IL, USA,Department of Radiology, Peking Union Medical College HospitalBeijing 100000, China
| | - Chong Sun
- Department of Radiology, Feinberg School of Medicine, Northwestern UniversityChicago, IL, USA,Department of Orthopedics, Affiliated Hospital of Qingdao UniversityQingdao 266000, Shandong, China
| | - Junjie Shangguan
- Department of Radiology, Feinberg School of Medicine, Northwestern UniversityChicago, IL, USA
| | - Bin Wang
- Department of Radiology, Feinberg School of Medicine, Northwestern UniversityChicago, IL, USA,Department of General Surgery, Nanfang Hospital, Southern Medical UniversityGuangzhou 510000, Guangdong, China
| | - Liang Pan
- Department of Radiology, Feinberg School of Medicine, Northwestern UniversityChicago, IL, USA,Department of Radiology, Third Affiliated Hospital of Suzhou UniversityChangzhou 213000, Jiangsu, China
| | - Su Hu
- Department of Radiology, Feinberg School of Medicine, Northwestern UniversityChicago, IL, USA,Department of Radiology, First Affiliated Hospital of Soochow UniversitySuzhou 215000, Jiangsu, China
| | - Yongsheng Pang
- Department of Radiological Sciences, University of California IrvineIrvine, CA, USA
| | - Zigeng Zhang
- Department of Radiological Sciences, University of California IrvineIrvine, CA, USA
| | | | - Ajeet Pal Bhatia
- Department of Radiological Sciences, University of California IrvineIrvine, CA, USA
| | - Farouk Nouizi
- Department of Radiological Sciences, University of California IrvineIrvine, CA, USA,Chao Family Comprehensive Cancer Center, University of California IrvineIrvine, CA, USA
| | - Nadine Abi-Jaoudeh
- Department of Radiological Sciences, University of California IrvineIrvine, CA, USA,Chao Family Comprehensive Cancer Center, University of California IrvineIrvine, CA, USA
| | - Vahid Yaghmai
- Department of Radiological Sciences, University of California IrvineIrvine, CA, USA,Chao Family Comprehensive Cancer Center, University of California IrvineIrvine, CA, USA
| | - Zhuoli Zhang
- Department of Radiology, Feinberg School of Medicine, Northwestern UniversityChicago, IL, USA,Department of Radiological Sciences, University of California IrvineIrvine, CA, USA,Chao Family Comprehensive Cancer Center, University of California IrvineIrvine, CA, USA,Robert H. Lurie Comprehensive Cancer Center of Northwestern UniversityChicago, IL, USA,Department of Biomedical Engineering, University of California IrvineIrvine, CA, USA,Department of Pathology and Laboratory Medicine, University of California IrvineIrvine, CA, USA
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9
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Sun R, Henry T, Laville A, Carré A, Hamaoui A, Bockel S, Chaffai I, Levy A, Chargari C, Robert C, Deutsch E. Imaging approaches and radiomics: toward a new era of ultraprecision radioimmunotherapy? J Immunother Cancer 2022; 10:jitc-2022-004848. [PMID: 35793875 PMCID: PMC9260846 DOI: 10.1136/jitc-2022-004848] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/21/2022] [Indexed: 11/17/2022] Open
Abstract
Strong rationale and a growing number of preclinical and clinical studies support combining radiotherapy and immunotherapy to improve patient outcomes. However, several critical questions remain, such as the identification of patients who will benefit from immunotherapy and the identification of the best modalities of treatment to optimize patient response. Imaging biomarkers and radiomics have recently emerged as promising tools for the non-invasive assessment of the whole disease of the patient, allowing comprehensive analysis of the tumor microenvironment, the spatial heterogeneity of the disease and its temporal changes. This review presents the potential applications of medical imaging and the challenges to address, in order to help clinicians choose the optimal modalities of both radiotherapy and immunotherapy, to predict patient’s outcomes and to assess response to these promising combinations.
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Affiliation(s)
- Roger Sun
- Department of Radiation Oncology, Gustave Roussy, Villejuif, France.,Radiothérapie Moléculaire et Innovation Thérapeutique, Université Paris-Saclay, Institut Gustave Roussy, Inserm, Villejuif, France
| | - Théophraste Henry
- Radiothérapie Moléculaire et Innovation Thérapeutique, Université Paris-Saclay, Institut Gustave Roussy, Inserm, Villejuif, France.,Department of Nuclear Medicine, Gustave Roussy, Villejuif, France
| | - Adrien Laville
- Radiothérapie Moléculaire et Innovation Thérapeutique, Université Paris-Saclay, Institut Gustave Roussy, Inserm, Villejuif, France
| | - Alexandre Carré
- Radiothérapie Moléculaire et Innovation Thérapeutique, Université Paris-Saclay, Institut Gustave Roussy, Inserm, Villejuif, France
| | - Anthony Hamaoui
- Radiothérapie Moléculaire et Innovation Thérapeutique, Université Paris-Saclay, Institut Gustave Roussy, Inserm, Villejuif, France
| | - Sophie Bockel
- Department of Radiation Oncology, Gustave Roussy, Villejuif, France.,Radiothérapie Moléculaire et Innovation Thérapeutique, Université Paris-Saclay, Institut Gustave Roussy, Inserm, Villejuif, France
| | - Ines Chaffai
- Radiothérapie Moléculaire et Innovation Thérapeutique, Université Paris-Saclay, Institut Gustave Roussy, Inserm, Villejuif, France
| | - Antonin Levy
- Department of Radiation Oncology, Gustave Roussy, Villejuif, France.,Radiothérapie Moléculaire et Innovation Thérapeutique, Université Paris-Saclay, Institut Gustave Roussy, Inserm, Villejuif, France
| | - Cyrus Chargari
- Radiothérapie Moléculaire et Innovation Thérapeutique, Université Paris-Saclay, Institut Gustave Roussy, Inserm, Villejuif, France.,Department of Radiation Oncology, Brachytherapy Unit, Gustave Roussy, Villejuif, France
| | - Charlotte Robert
- Department of Radiation Oncology, Gustave Roussy, Villejuif, France.,Radiothérapie Moléculaire et Innovation Thérapeutique, Université Paris-Saclay, Institut Gustave Roussy, Inserm, Villejuif, France
| | - Eric Deutsch
- Department of Radiation Oncology, Gustave Roussy, Villejuif, France .,Radiothérapie Moléculaire et Innovation Thérapeutique, Université Paris-Saclay, Institut Gustave Roussy, Inserm, Villejuif, France.,INSERM U1030, Gustave Roussy, Villejuif, France
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10
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Xue C, Yuan J, Lo GG, Chang ATY, Poon DMC, Wong OL, Zhou Y, Chu WCW. Radiomics feature reliability assessed by intraclass correlation coefficient: a systematic review. Quant Imaging Med Surg 2021; 11:4431-4460. [PMID: 34603997 DOI: 10.21037/qims-21-86] [Citation(s) in RCA: 63] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Accepted: 05/17/2021] [Indexed: 12/13/2022]
Abstract
Radiomics research is rapidly growing in recent years, but more concerns on radiomics reliability are also raised. This review attempts to update and overview the current status of radiomics reliability research in the ever expanding medical literature from the perspective of a single reliability metric of intraclass correlation coefficient (ICC). To conduct this systematic review, Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed. After literature search and selection, a total of 481 radiomics studies using CT, PET, or MRI, covering a wide range of subject and disease types, were included for review. In these highly heterogeneous studies, feature reliability to image segmentation was much more investigated than reliability to other factors, such as image acquisition, reconstruction, post-processing, and feature quantification. The reported ICCs also suggested high radiomics feature reliability to image segmentation. Image acquisition was found to introduce much more feature variability than image segmentation, in particular for MRI, based on the reported ICC values. Image post-processing and feature quantification yielded different levels of radiomics reliability and might be used to mitigate image acquisition-induced variability. Some common flaws and pitfalls in ICC use were identified, and suggestions on better ICC use were given. Due to the extremely high study heterogeneities and possible risks of bias, the degree of radiomics feature reliability that has been achieved could not yet be safely synthesized or derived in this review. More future researches on radiomics reliability are warranted.
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Affiliation(s)
- Cindy Xue
- Medical Physics and Research Department, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong, China.,Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China
| | - Jing Yuan
- Medical Physics and Research Department, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong, China
| | - Gladys G Lo
- Department of Diagnostic & Interventional Radiology, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong, China
| | - Amy T Y Chang
- Comprehensive Oncology Centre, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong, China
| | - Darren M C Poon
- Comprehensive Oncology Centre, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong, China
| | - Oi Lei Wong
- Medical Physics and Research Department, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong, China
| | - Yihang Zhou
- Medical Physics and Research Department, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong, China
| | - Winnie C W Chu
- Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China
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