1
|
Hodneland E, Andersen E, Wagner-Larsen KS, Dybvik JA, Lura N, Fasmer KE, Halle MK, Krakstad C, Haldorsen I. Impact of MRI radiomic feature normalization for prognostic modelling in uterine endometrial and cervical cancers. Sci Rep 2024; 14:16826. [PMID: 39039099 PMCID: PMC11263557 DOI: 10.1038/s41598-024-66659-w] [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: 02/02/2024] [Accepted: 07/03/2024] [Indexed: 07/24/2024] Open
Abstract
Widespread clinical use of MRI radiomic tumor profiling for prognostication and treatment planning in cancers faces major obstacles due to limitations in standardization of radiomic features. The purpose of the current work was to assess the impact of different MRI scanning- and normalization protocols for the statistical analyses of tumor radiomic data in two patient cohorts with uterine endometrial-(EC) (n = 136) and cervical (CC) (n = 132) cancer. 1.5 T and 3 T, T1-weighted MRI 2 min post-contrast injection, T2-weighted turbo spin echo imaging, and diffusion-weighted imaging were acquired. Radiomic features were extracted from within manually segmented tumors in 3D and normalized either using z-score normalization or a linear regression model (LRM) accounting for linear dependencies with MRI acquisition parameters. Patients were clustered into two groups based on radiomic profile. Impact of MRI scanning parameters on cluster composition and prognostication were analyzed using Kruskal-Wallis tests, Kaplan-Meier plots, log-rank test, random survival forests and LASSO Cox regression with time-dependent area under curve (tdAUC) (α = 0.05). A large proportion of the radiomic features was statistically associated with MRI scanning protocol in both cohorts (EC: 162/385 [42%]; CC: 180/292 [62%]). A substantial number of EC (49/136 [36%]) and CC (50/132 [38%]) patients changed cluster when clustering was performed after z-score-versus LRM normalization. Prognostic modeling based on cluster groups yielded similar outputs for the two normalization methods in the EC/CC cohorts (log-rank test; z-score: p = 0.02/0.33; LRM: p = 0.01/0.45). Mean tdAUC for prognostic modeling of disease-specific survival (DSS) by the radiomic features in EC/CC was similar for the two normalization methods (random survival forests; z-score: mean tdAUC = 0.77/0.78; LRM: mean tdAUC = 0.80/0.75; LASSO Cox; z-score: mean tdAUC = 0.64/0.76; LRM: mean tdAUC = 0.76/0.75). Severe biases in tumor radiomics data due to MRI scanning parameters exist. Z-score normalization does not eliminate these biases, whereas LRM normalization effectively does. Still, radiomic cluster groups after z-score- and LRM normalization were similarly associated with DSS in EC and CC patients.
Collapse
Affiliation(s)
- Erlend Hodneland
- MMIV Mohn Medical Imaging and Visualization Centre, Department of Radiology, Haukeland University Hospital, Jonas Lies Vei 65, 5021, Bergen, Norway.
- Department of Mathematics, University of Bergen, Bergen, Norway.
| | - Erling Andersen
- Department of Clinical Engineering, Haukeland University Hospital, Bergen, Norway
| | - Kari S Wagner-Larsen
- MMIV Mohn Medical Imaging and Visualization Centre, Department of Radiology, Haukeland University Hospital, Jonas Lies Vei 65, 5021, Bergen, Norway
- Section for Radiology, Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Julie A Dybvik
- MMIV Mohn Medical Imaging and Visualization Centre, Department of Radiology, Haukeland University Hospital, Jonas Lies Vei 65, 5021, Bergen, Norway
- Section for Radiology, Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Njål Lura
- MMIV Mohn Medical Imaging and Visualization Centre, Department of Radiology, Haukeland University Hospital, Jonas Lies Vei 65, 5021, Bergen, Norway
- Section for Radiology, Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Kristine E Fasmer
- MMIV Mohn Medical Imaging and Visualization Centre, Department of Radiology, Haukeland University Hospital, Jonas Lies Vei 65, 5021, Bergen, Norway
| | - Mari K Halle
- Department of Obstetrics and Gynecology, Haukeland University Hospital, Bergen, Norway
- Department of Clinical Science, Centre for Cancer Biomarkers, University of Bergen, Bergen, Norway
| | - Camilla Krakstad
- Department of Obstetrics and Gynecology, Haukeland University Hospital, Bergen, Norway
- Department of Clinical Science, Centre for Cancer Biomarkers, University of Bergen, Bergen, Norway
| | - Ingfrid Haldorsen
- MMIV Mohn Medical Imaging and Visualization Centre, Department of Radiology, Haukeland University Hospital, Jonas Lies Vei 65, 5021, Bergen, Norway
- Section for Radiology, Department of Clinical Medicine, University of Bergen, Bergen, Norway
| |
Collapse
|
2
|
Lin JY, Ye JY, Chen JG, Lin ST, Lin S, Cai SQ. Prediction of Receptor Status in Radiomics: Recent Advances in Breast Cancer Research. Acad Radiol 2024; 31:3004-3014. [PMID: 38151383 DOI: 10.1016/j.acra.2023.12.012] [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: 08/16/2023] [Revised: 12/05/2023] [Accepted: 12/05/2023] [Indexed: 12/29/2023]
Abstract
Breast cancer is a multifactorial heterogeneous disease and the leading cause of cancer-related deaths in women; its diagnosis and treatment require clinical sensitivity and a comprehensive disciplinary research approach. The expression of different receptors on tumor cells not only provides the basis for molecular typing of breast cancer but also has a decisive role in the diagnosis, treatment, and prognosis of breast cancer. To date, immunohistochemistry (IHC), which uses invasive histological sampling, has been extensively used in clinical practice to analyze the status of receptors and to make an accurate diagnosis of breast cancer. As an invasive assay, IHC can provide important biological information on tumors at a single point in time, but cannot predict future changes (due to treatment or tumor mutations) without additional invasive procedures. These issues highlight the need to develop a non-invasive method for predicting receptor status. The emerging field of radiomics may offer a non-invasive approach to identification of receptor status without requiring biopsy. In this paper, we present a review of the latest research results in radiomics for predicting the status of breast cancer receptors, with potential important clinical applications.
Collapse
Affiliation(s)
- Jun-Yuan Lin
- Department of Radiology, the Second Affiliated Hospital of Fujian Medical University, No. 34 North Zhongshan Road, Quanzhou, 362000, Fujian Province, China (J.Y.L., S.Q.C.)
| | - Jia-Yi Ye
- Department of Radiology, the Second Affiliated Hospital of Fujian Medical University, No. 34 North Zhongshan Road, Quanzhou, 362000, Fujian Province, China (J.Y.L., S.Q.C.)
| | - Jin-Guo Chen
- Department of Radiology, the Second Affiliated Hospital of Fujian Medical University, No. 34 North Zhongshan Road, Quanzhou, 362000, Fujian Province, China (J.Y.L., S.Q.C.)
| | - Shu-Ting Lin
- Department of Radiology, the Second Affiliated Hospital of Fujian Medical University, No. 34 North Zhongshan Road, Quanzhou, 362000, Fujian Province, China (J.Y.L., S.Q.C.)
| | - Shu Lin
- Center of Neurological and Metabolic Research, the Second Affiliated Hospital of Fujian Medical University, No. 34 North Zhongshan Road, Quanzhou, 362000, Fujian Province, China (J.Y.Y., J.G.C., S.T.L., S.L.); Group of Neuroendocrinology, Garvan Institute of Medical Research, 384 Victoria St, Sydney, Australia (S.L.)
| | - Si-Qing Cai
- Department of Radiology, the Second Affiliated Hospital of Fujian Medical University, No. 34 North Zhongshan Road, Quanzhou, 362000, Fujian Province, China (J.Y.L., S.Q.C.).
| |
Collapse
|
3
|
Castaldo R, Brancato V, Cavaliere C, Pecchia L, Illiano E, Costantini E, Ragozzino A, Salvatore M, Nicolai E, Franzese M. Risk score model to automatically detect prostate cancer patients by integrating diagnostic parameters. Front Oncol 2024; 14:1323247. [PMID: 38873254 PMCID: PMC11171723 DOI: 10.3389/fonc.2024.1323247] [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: 10/17/2023] [Accepted: 05/01/2024] [Indexed: 06/15/2024] Open
Abstract
Introduction Prostate cancer (PCa) is one of the prevailing forms of cancer among men. At present, multiparametric MRI is the imaging method for localizing tumors and staging cancer. Radiomics plays a key role and hold potential for PCa detection, reducing the need for unnecessary biopsies, characterizing tumor aggression, and overseeing PCa recurrence post-treatment. Methods Furthermore, the integration of radiomics data with clinical and histopathological data can further enhance the understanding and management of PCa and decrease unnecessary transfers to specialized care for expensive and intrusive biopsies. Therefore, the aim of this study is to develop a risk model score to automatically detect PCa patients by integrating non-invasive diagnostic parameters (radiomics and Prostate-Specific Antigen levels) along with patient's age. Results The proposed approach was evaluated using a dataset of 189 PCa patients who underwent bi-parametric MRI from two centers. Elastic-Net Regularized Generalized Linear Model achieved 91% AUC to automatically detect PCa patients. The model risk score was also used to assess doubt cases of PCa at biopsy and then compared to bi-parametric PI-RADS v2. Discussion This study explored the relative utility of a well-developed risk model by combining radiomics, Prostate-Specific Antigen levels and age for objective and accurate PCa risk stratification and supporting the process of making clinical decisions during follow up.
Collapse
Affiliation(s)
- Rossana Castaldo
- Bioinformatics and Biostatistics Lab, IRCCS SYNLAB SDN, Naples, Italy
| | | | - Carlo Cavaliere
- Bioinformatics and Biostatistics Lab, IRCCS SYNLAB SDN, Naples, Italy
| | - Leandro Pecchia
- School of Engineering, University of Warwick, Coventry, United Kingdom
- Università Campus Bio-Medico Roma, Roma, Italy
- Campus Bio-Medico, Fondazione Policlinico Universitario, Roma, Italy
| | - Ester Illiano
- Adrology and Urogynecological Clinic, Santa Maria Terni Hospital, University of Perugia, Terni, Italy
| | - Elisabetta Costantini
- Adrology and Urogynecological Clinic, Santa Maria Terni Hospital, University of Perugia, Terni, Italy
| | - Alfonso Ragozzino
- Bioinformatics and Biostatistics Lab, IRCCS SYNLAB SDN, Naples, Italy
| | - Marco Salvatore
- Bioinformatics and Biostatistics Lab, IRCCS SYNLAB SDN, Naples, Italy
| | - Emanuele Nicolai
- Bioinformatics and Biostatistics Lab, IRCCS SYNLAB SDN, Naples, Italy
| | - Monica Franzese
- Bioinformatics and Biostatistics Lab, IRCCS SYNLAB SDN, Naples, Italy
| |
Collapse
|
4
|
Cai L, Sidey-Gibbons C, Nees J, Riedel F, Schaefgen B, Togawa R, Killinger K, Heil J, Pfob A, Golatta M. Ultrasound Radiomics Features to Identify Patients With Triple-Negative Breast Cancer: A Retrospective, Single-Center Study. JOURNAL OF ULTRASOUND IN MEDICINE : OFFICIAL JOURNAL OF THE AMERICAN INSTITUTE OF ULTRASOUND IN MEDICINE 2024; 43:467-478. [PMID: 38069582 DOI: 10.1002/jum.16377] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Accepted: 11/04/2023] [Indexed: 02/08/2024]
Abstract
OBJECTIVES Patients with triple-negative breast cancer (TNBC) exhibit a fast tumor growth rate and poor survival outcomes. In this study, we aimed to develop and compare intelligent algorithms using ultrasound radiomics features in addition to clinical variables to identify patients with TNBC prior to histopathologic diagnosis. METHODS We used single-center, retrospective data of patients who underwent ultrasound before histopathologic verification and subsequent neoadjuvant systemic treatment (NAST). We developed a logistic regression with an elastic net penalty algorithm using pretreatment ultrasound radiomics features in addition to patient and tumor variables to identify patients with TNBC. Findings were compared to the histopathologic evaluation of the biopsy specimen. The main outcome measure was the area under the curve (AUC). RESULTS We included 1161 patients, 813 in the development set and 348 in the validation set. Median age was 50.1 years and 24.4% (283 of 1161) had TNBC. The integrative model using radiomics and clinical information showed significantly better performance in identifying TNBC compared to the radiomics model (AUC: 0.71, 95% confidence interval [CI]: 0.65-0.76 versus 0.64, 95% CI: 0.57-0.71, P = .004). The five most important variables were cN status, shape surface volume ratio (SA:V), gray level co-occurrence matrix (GLCM) correlation, gray level dependence matrix (GLDM) dependence nonuniformity normalized, and age. Patients with TNBC were more often categorized as BI-RADS 4 than BI-RADS 5 compared to non-TNBC patients (P = .002). CONCLUSION A machine learning algorithm showed promising potential to identify patients with TNBC using ultrasound radiomics features and clinical information prior to histopathologic evaluation.
Collapse
Affiliation(s)
- Lie Cai
- Department of Obstetrics and Gynecology, Heidelberg University Hospital, Heidelberg, Germany
| | - Chris Sidey-Gibbons
- MD Anderson Center for INSPiRED Cancer Care (Integrated Systems for Patient-Reported Data), The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
- Department of Symptom Research, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Juliane Nees
- Department of Obstetrics and Gynecology, Heidelberg University Hospital, Heidelberg, Germany
| | - Fabian Riedel
- Department of Obstetrics and Gynecology, Heidelberg University Hospital, Heidelberg, Germany
| | - Benedikt Schaefgen
- Department of Obstetrics and Gynecology, Heidelberg University Hospital, Heidelberg, Germany
| | - Riku Togawa
- Department of Obstetrics and Gynecology, Heidelberg University Hospital, Heidelberg, Germany
| | - Kristina Killinger
- Department of Obstetrics and Gynecology, Heidelberg University Hospital, Heidelberg, Germany
| | - Joerg Heil
- Department of Obstetrics and Gynecology, Heidelberg University Hospital, Heidelberg, Germany
| | - André Pfob
- Department of Obstetrics and Gynecology, Heidelberg University Hospital, Heidelberg, Germany
- MD Anderson Center for INSPiRED Cancer Care (Integrated Systems for Patient-Reported Data), The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
- National Center for Tumor Diseases (NCT) and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Michael Golatta
- Department of Obstetrics and Gynecology, Heidelberg University Hospital, Heidelberg, Germany
| |
Collapse
|
5
|
Demircioğlu A. The effect of feature normalization methods in radiomics. Insights Imaging 2024; 15:2. [PMID: 38185786 PMCID: PMC10772134 DOI: 10.1186/s13244-023-01575-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Accepted: 11/25/2023] [Indexed: 01/09/2024] Open
Abstract
OBJECTIVES In radiomics, different feature normalization methods, such as z-Score or Min-Max, are currently utilized, but their specific impact on the model is unclear. We aimed to measure their effect on the predictive performance and the feature selection. METHODS We employed fifteen publicly available radiomics datasets to compare seven normalization methods. Using four feature selection and classifier methods, we used cross-validation to measure the area under the curve (AUC) of the resulting models, the agreement of selected features, and the model calibration. In addition, we assessed whether normalization before cross-validation introduces bias. RESULTS On average, the difference between the normalization methods was relatively small, with a gain of at most + 0.012 in AUC when comparing the z-Score (mean AUC: 0.707 ± 0.102) to no normalization (mean AUC: 0.719 ± 0.107). However, on some datasets, the difference reached + 0.051. The z-Score performed best, while the tanh transformation showed the worst performance and even decreased the overall predictive performance. While quantile transformation performed, on average, slightly worse than the z-Score, it outperformed all other methods on one out of three datasets. The agreement between the features selected by different normalization methods was only mild, reaching at most 62%. Applying the normalization before cross-validation did not introduce significant bias. CONCLUSION The choice of the feature normalization method influenced the predictive performance but depended strongly on the dataset. It strongly impacted the set of selected features. CRITICAL RELEVANCE STATEMENT Feature normalization plays a crucial role in the preprocessing and influences the predictive performance and the selected features, complicating feature interpretation. KEY POINTS • The impact of feature normalization methods on radiomic models was measured. • Normalization methods performed similarly on average, but differed more strongly on some datasets. • Different methods led to different sets of selected features, impeding feature interpretation. • Model calibration was not largely affected by the normalization method.
Collapse
Affiliation(s)
- Aydin Demircioğlu
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstrasse 55, 45147, Essen, Germany.
| |
Collapse
|
6
|
Zhang L, Zhou XX, Liu L, Liu AY, Zhao WJ, Zhang HX, Zhu YM, Kuai ZX. Comparison of Dynamic Contrast-Enhanced MRI and Non-Mono-Exponential Model-Based Diffusion-Weighted Imaging for the Prediction of Prognostic Biomarkers and Molecular Subtypes of Breast Cancer Based on Radiomics. J Magn Reson Imaging 2023; 58:1590-1602. [PMID: 36661350 DOI: 10.1002/jmri.28611] [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: 11/29/2022] [Revised: 01/10/2023] [Accepted: 01/10/2023] [Indexed: 01/21/2023] Open
Abstract
BACKGROUND Dynamic contrast-enhanced (DCE) MRI and non-mono-exponential model-based diffusion-weighted imaging (NME-DWI) that does not require contrast agent can both characterize breast cancer. However, which technique is superior remains unclear. PURPOSE To compare the performances of DCE-MRI, NME-DWI and their combination as multiparametric MRI (MP-MRI) in the prediction of breast cancer prognostic biomarkers and molecular subtypes based on radiomics. STUDY TYPE Prospective. POPULATION A total of 477 female patients with 483 breast cancers (5-fold cross-validation: training/validation, 80%/20%). FIELD STRENGTH/SEQUENCE A 3.0 T/DCE-MRI (6 dynamic frames) and NME-DWI (13 b values). ASSESSMENT After data preprocessing, high-throughput features were extracted from each tumor volume of interest, and optimal features were selected using recursive feature elimination method. To identify ER+ vs. ER-, PR+ vs. PR-, HER2+ vs. HER2-, Ki-67+ vs. Ki-67-, luminal A/B vs. nonluminal A/B, and triple negative (TN) vs. non-TN, the following models were implemented: random forest, adaptive boosting, support vector machine, linear discriminant analysis, and logistic regression. STATISTICAL TESTS Student's t, chi-square, and Fisher's exact tests were applied on clinical characteristics to confirm whether significant differences exist between different statuses (±) of prognostic biomarkers or molecular subtypes. The model performances were compared between the DCE-MRI, NME-DWI, and MP-MRI datasets using the area under the receiver-operating characteristic curve (AUC) and the DeLong test. P < 0.05 was considered significant. RESULTS With few exceptions, no significant differences (P = 0.062-0.984) were observed in the AUCs of models for six classification tasks between the DCE-MRI (AUC = 0.62-0.87) and NME-DWI (AUC = 0.62-0.91) datasets, while the model performances on the two imaging datasets were significantly poorer than on the MP-MRI dataset (AUC = 0.68-0.93). Additionally, the random forest and adaptive boosting models (AUC = 0.62-0.93) outperformed other three models (AUC = 0.62-0.90). DATA CONCLUSION NME-DWI was comparable with DCE-MRI in predictive performance and could be used as an alternative technique. Besides, MP-MRI demonstrated significantly higher AUCs than either DCE-MRI or NME-DWI. EVIDENCE LEVEL 2. TECHNICAL EFFICACY Stage 2.
Collapse
Affiliation(s)
- Lan Zhang
- Imaging Center, Harbin Medical University Cancer Hospital, Harbin, China
| | - Xin-Xiang Zhou
- Imaging Center, Harbin Medical University Cancer Hospital, Harbin, China
| | - Lu Liu
- Imaging Center, Harbin Medical University Cancer Hospital, Harbin, China
| | - Ao-Yu Liu
- Imaging Center, Harbin Medical University Cancer Hospital, Harbin, China
| | - Wen-Juan Zhao
- Imaging Center, Harbin Medical University Cancer Hospital, Harbin, China
| | - Hong-Xia Zhang
- Imaging Center, Harbin Medical University Cancer Hospital, Harbin, China
| | - Yue-Min Zhu
- CREATIS, CNRS UMR 5220-INSERM U1206-University Lyon 1-INSA Lyon-University Jean Monnet Saint-Etienne, Lyon, France
| | - Zi-Xiang Kuai
- Imaging Center, Harbin Medical University Cancer Hospital, Harbin, China
| |
Collapse
|
7
|
Demircioğlu A. Deep Features from Pretrained Networks Do Not Outperform Hand-Crafted Features in Radiomics. Diagnostics (Basel) 2023; 13:3266. [PMID: 37892087 PMCID: PMC10606594 DOI: 10.3390/diagnostics13203266] [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: 09/14/2023] [Revised: 10/16/2023] [Accepted: 10/19/2023] [Indexed: 10/29/2023] Open
Abstract
In radiomics, utilizing features extracted from pretrained deep networks could result in models with a higher predictive performance than those relying on hand-crafted features. This study compared the predictive performance of models trained with either deep features, hand-crafted features, or a combination of these features in terms of the area under the receiver-operating characteristic curve (AUC) and other metrics. We trained models on ten radiological datasets using five feature selection methods and three classifiers. Our results indicate that models based on deep features did not show an improved AUC compared to those utilizing hand-crafted features (deep: AUC 0.775, hand-crafted: AUC 0.789; p = 0.28). Including morphological features alongside deep features led to overall improvements in prediction performance for all models (+0.02 gain in AUC; p < 0.001); however, the best model did not benefit from this (+0.003 gain in AUC; p = 0.57). Using all hand-crafted features in addition to the deep features resulted in a further overall improvement (+0.034 in AUC; p < 0.001), but only a minor improvement could be observed for the best model (deep: AUC 0.798, hand-crafted: AUC 0.789; p = 0.92). Furthermore, our results show that models based on deep features extracted from networks pretrained on medical data have no advantage in predictive performance over models relying on features extracted from networks pretrained on ImageNet data. Our study contributes a benchmarking analysis of models trained on hand-crafted and deep features from pretrained networks across multiple datasets. It also provides a comprehensive understanding of their applicability and limitations in radiomics. Our study shows, in conclusion, that models based on features extracted from pretrained deep networks do not outperform models trained on hand-crafted ones.
Collapse
Affiliation(s)
- Aydin Demircioğlu
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstraße 55, 45147 Essen, Germany
| |
Collapse
|
8
|
Sjöholm T, Tarai S, Malmberg F, Strand R, Korenyushkin A, Enblad G, Ahlström H, Kullberg J. A whole-body diffusion MRI normal atlas: development, evaluation and initial use. Cancer Imaging 2023; 23:87. [PMID: 37710346 PMCID: PMC10503210 DOI: 10.1186/s40644-023-00603-5] [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: 03/09/2023] [Accepted: 08/28/2023] [Indexed: 09/16/2023] Open
Abstract
BACKGROUND Statistical atlases can provide population-based descriptions of healthy volunteers and/or patients and can be used for region- and voxel-based analysis. This work aims to develop whole-body diffusion atlases of healthy volunteers scanned at 1.5T and 3T. Further aims include evaluating the atlases by establishing whole-body Apparent Diffusion Coefficient (ADC) values of healthy tissues and including healthy tissue deviations in an automated tumour segmentation task. METHODS Multi-station whole-body Diffusion Weighted Imaging (DWI) and water-fat Magnetic Resonance Imaging (MRI) of healthy volunteers (n = 45) were acquired at 1.5T (n = 38) and/or 3T (n = 29), with test-retest imaging for five subjects per scanner. Using deformable image registration, whole-body MRI data was registered and composed into normal atlases. Healthy tissue ADCmean was manually measured for ten tissues, with test-retest percentage Repeatability Coefficient (%RC), and effect of age, sex and scanner assessed. Voxel-wise whole-body analyses using the normal atlases were studied with ADC correlation analyses and an automated tumour segmentation task. For the latter, lymphoma patient MRI scans (n = 40) with and without information about healthy tissue deviations were entered into a 3D U-Net architecture. RESULTS Sex- and Body Mass Index (BMI)-stratified whole-body high b-value DWI and ADC normal atlases were created at 1.5T and 3T. %RC of healthy tissue ADCmean varied depending on tissue assessed (4-48% at 1.5T, 6-70% at 3T). Scanner differences in ADCmean were visualised in Bland-Altman analyses of dually scanned subjects. Sex differences were measurable for liver, muscle and bone at 1.5T, and muscle at 3T. Volume of Interest (VOI)-based multiple linear regression, and voxel-based correlations in normal atlas space, showed that age and ADC were negatively associated for liver and bone at 1.5T, and positively associated with brain tissue at 1.5T and 3T. Adding voxel-wise information about healthy tissue deviations in an automated tumour segmentation task gave numerical improvements in the segmentation metrics Dice score, sensitivity and precision. CONCLUSIONS Whole-body DWI and ADC normal atlases were created at 1.5T and 3T, and applied in whole-body voxel-wise analyses.
Collapse
Affiliation(s)
- Therese Sjöholm
- Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
| | - Sambit Tarai
- Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
| | - Filip Malmberg
- Department of Information Technology, Uppsala University, Uppsala, Sweden
| | - Robin Strand
- Department of Information Technology, Uppsala University, Uppsala, Sweden
| | | | - Gunilla Enblad
- Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | - Håkan Ahlström
- Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
- Antaros Medical AB, Mölndal, Sweden
| | - Joel Kullberg
- Department of Surgical Sciences, Uppsala University, Uppsala, Sweden.
- Antaros Medical AB, Mölndal, Sweden.
| |
Collapse
|
9
|
Furtney I, Bradley R, Kabuka MR. Patient Graph Deep Learning to Predict Breast Cancer Molecular Subtype. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:3117-3127. [PMID: 37379184 PMCID: PMC10623656 DOI: 10.1109/tcbb.2023.3290394] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/30/2023]
Abstract
Breast cancer is a heterogeneous disease consisting of a diverse set of genomic mutations and clinical characteristics. The molecular subtypes of breast cancer are closely tied to prognosis and therapeutic treatment options. We investigate using deep graph learning on a collection of patient factors from multiple diagnostic disciplines to better represent breast cancer patient information and predict molecular subtype. Our method models breast cancer patient data into a multi-relational directed graph with extracted feature embeddings to directly represent patient information and diagnostic test results. We develop a radiographic image feature extraction pipeline to produce vector representation of breast cancer tumors in DCE-MRI and an autoencoder-based genomic variant embedding method to map variant assay results to a low-dimensional latent space. We leverage related-domain transfer learning to train and evaluate a Relational Graph Convolutional Network to predict the probabilities of molecular subtypes for individual breast cancer patient graphs. Our work found that utilizing information from multiple multimodal diagnostic disciplines improved the model's prediction results and produced more distinct learned feature representations for breast cancer patients. This research demonstrates the capabilities of graph neural networks and deep learning feature representation to perform multimodal data fusion and representation in the breast cancer domain.
Collapse
|
10
|
Champendal M, Marmy L, Malamateniou C, Sá Dos Reis C. Artificial intelligence to support person-centred care in breast imaging - A scoping review. J Med Imaging Radiat Sci 2023; 54:511-544. [PMID: 37183076 DOI: 10.1016/j.jmir.2023.04.001] [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: 02/28/2023] [Revised: 04/05/2023] [Accepted: 04/11/2023] [Indexed: 05/16/2023]
Abstract
AIM To overview Artificial Intelligence (AI) developments and applications in breast imaging (BI) focused on providing person-centred care in diagnosis and treatment for breast pathologies. METHODS The scoping review was conducted in accordance with the Joanna Briggs Institute methodology. The search was conducted on MEDLINE, Embase, CINAHL, Web of science, IEEE explore and arxiv during July 2022 and included only studies published after 2016, in French and English. Combination of keywords and Medical Subject Headings terms (MeSH) related to breast imaging and AI were used. No keywords or MeSH terms related to patients, or the person-centred care (PCC) concept were included. Three independent reviewers screened all abstracts and titles, and all eligible full-text publications during a second stage. RESULTS 3417 results were identified by the search and 106 studies were included for meeting all criteria. Six themes relating to the AI-enabled PCC in BI were identified: individualised risk prediction/growth and prediction/false negative reduction (44.3%), treatment assessment (32.1%), tumour type prediction (11.3%), unnecessary biopsies reduction (5.7%), patients' preferences (2.8%) and other issues (3.8%). The main BI modalities explored in the included studies were magnetic resonance imaging (MRI) (31.1%), mammography (27.4%) and ultrasound (23.6%). The studies were predominantly retrospective, and some variations (age range, data source, race, medical imaging) were present in the datasets used. CONCLUSIONS The AI tools for person-centred care are mainly designed for risk and cancer prediction and disease management to identify the most suitable treatment. However, further studies are needed for image acquisition optimisation for different patient groups, improvement and customisation of patient experience and for communicating to patients the options and pathways of disease management.
Collapse
Affiliation(s)
- Mélanie Champendal
- School of Health Sciences HESAV, HES-SO; University of Applied Sciences Western Switzerland: Lausanne, CH.
| | - Laurent Marmy
- School of Health Sciences HESAV, HES-SO; University of Applied Sciences Western Switzerland: Lausanne, CH.
| | - Christina Malamateniou
- School of Health Sciences HESAV, HES-SO; University of Applied Sciences Western Switzerland: Lausanne, CH; Department of Radiography, Division of Midwifery and Radiography, School of Health Sciences, University of London, London, UK.
| | - Cláudia Sá Dos Reis
- School of Health Sciences HESAV, HES-SO; University of Applied Sciences Western Switzerland: Lausanne, CH.
| |
Collapse
|
11
|
Siviengphanom S, Gandomkar Z, Lewis SJ, Brennan PC. Global Radiomic Features from Mammography for Predicting Difficult-To-Interpret Normal Cases. J Digit Imaging 2023; 36:1541-1552. [PMID: 37253894 PMCID: PMC10406750 DOI: 10.1007/s10278-023-00836-7] [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: 11/28/2022] [Revised: 04/05/2023] [Accepted: 04/13/2023] [Indexed: 06/01/2023] Open
Abstract
This work aimed to investigate whether global radiomic features (GRFs) from mammograms can predict difficult-to-interpret normal cases (NCs). Assessments from 537 readers interpreting 239 normal mammograms were used to categorise cases as 120 difficult-to-interpret and 119 easy-to-interpret based on cases having the highest and lowest difficulty scores, respectively. Using lattice- and squared-based approaches, 34 handcrafted GRFs per image were extracted and normalised. Three classifiers were constructed: (i) CC and (ii) MLO using the GRFs from corresponding craniocaudal and mediolateral oblique images only, based on the random forest technique for distinguishing difficult- from easy-to-interpret NCs, and (iii) CC + MLO using the median predictive scores from both CC and MLO models. Useful GRFs for the CC and MLO models were recognised using a scree test. The CC and MLO models were trained and validated using the leave-one-out-cross-validation. The models' performances were assessed by the AUC and compared using the DeLong test. A Kruskal-Wallis test was used to examine if the 34 GRFs differed between difficult- and easy-to-interpret NCs and if difficulty level based on the traditional breast density (BD) categories differed among 115 low-BD and 124 high-BD NCs. The CC + MLO model achieved higher performance (0.71 AUC) than the individual CC and MLO model alone (0.66 each), but statistically non-significant difference was found (all p > 0.05). Six GRFs were identified to be valuable in describing difficult-to-interpret NCs. Twenty features, when compared between difficult- and easy-to-interpret NCs, differed significantly (p < 0.05). No statistically significant difference was observed in difficulty between low- and high-BD NCs (p = 0.709). GRF mammographic analysis can predict difficult-to-interpret NCs.
Collapse
Affiliation(s)
- Somphone Siviengphanom
- Medical Image Optimisation and Perception Group, Discipline of Medical Imaging Science, Sydney School of Health Sciences, Faculty of Medicine and Health, the University of Sydney, Sydney, NSW, 2006, Australia.
| | - Ziba Gandomkar
- Medical Image Optimisation and Perception Group, Discipline of Medical Imaging Science, Sydney School of Health Sciences, Faculty of Medicine and Health, the University of Sydney, Sydney, NSW, 2006, Australia
| | - Sarah J Lewis
- Medical Image Optimisation and Perception Group, Discipline of Medical Imaging Science, Sydney School of Health Sciences, Faculty of Medicine and Health, the University of Sydney, Sydney, NSW, 2006, Australia
| | - Patrick C Brennan
- Medical Image Optimisation and Perception Group, Discipline of Medical Imaging Science, Sydney School of Health Sciences, Faculty of Medicine and Health, the University of Sydney, Sydney, NSW, 2006, Australia
| |
Collapse
|
12
|
Zhou J, Xie T, Shan H, Cheng G. HLA-DQA1 expression is associated with prognosis and predictable with radiomics in breast cancer. Radiat Oncol 2023; 18:117. [PMID: 37434241 DOI: 10.1186/s13014-023-02314-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Accepted: 07/05/2023] [Indexed: 07/13/2023] Open
Abstract
BACKGROUND High HLA-DQA1 expression is associated with a better prognosis in many cancers. However, the association between HLA-DQA1 expression and prognosis of breast cancer and the noninvasive assessment of HLA-DQA1 expression are still unclear. This study aimed to reveal the association and investigate the potential of radiomics to predict HLA-DQA1 expression in breast cancer. METHODS In this retrospective study, transcriptome sequencing data, medical imaging data, clinical and follow-up data were downloaded from the TCIA ( https://www.cancerimagingarchive.net/ ) and TCGA ( https://portal.gdc.cancer.gov/ ) databases. The clinical characteristic differences between the high HLA-DQA1 expression group (HHD group) and the low HLA-DQA1 expression group were explored. Gene set enrichment analysis, Kaplan‒Meier survival analysis and Cox regression were performed. Then, 107 dynamic contrast-enhanced magnetic resonance imaging features were extracted, including size, shape and texture. Using recursive feature elimination and gradient boosting machine, a radiomics model was established to predict HLA-DQA1 expression. Receiver operating characteristic (ROC) curves, precision-recall curves, calibration curves, and decision curves were used for model evaluation. RESULTS The HHD group had better survival outcomes. The differentially expressed genes in the HHD group were significantly enriched in oxidative phosphorylation (OXPHOS) and estrogen response early and late signalling pathways. The radiomic score (RS) output from the model was associated with HLA-DQA1 expression. The area under the ROC curves (95% CI), accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the radiomic model were 0.866 (0.775-0.956), 0.825, 0.939, 0.7, 0.775, and 0.913 in the training set and 0.780 (0.629-0.931), 0.659, 0.81, 0.5, 0.63, and 0.714 in the validation set, respectively, showing a good prediction effect. CONCLUSIONS High HLA-DQA1 expression is associated with a better prognosis in breast cancer. Quantitative radiomics as a noninvasive imaging biomarker has potential value for predicting HLA-DQA1 expression.
Collapse
Affiliation(s)
- JingYu Zhou
- Department of Radiology, Peking University Shenzhen Hospital, LianHua Road, Shenzhen, 518000, Guangdong, China
| | - TingTing Xie
- Department of Radiology, Peking University Shenzhen Hospital, LianHua Road, Shenzhen, 518000, Guangdong, China
| | - HuiMing Shan
- Department of Radiology, Peking University Shenzhen Hospital, LianHua Road, Shenzhen, 518000, Guangdong, China
| | - GuanXun Cheng
- Department of Radiology, Peking University Shenzhen Hospital, LianHua Road, Shenzhen, 518000, Guangdong, China.
| |
Collapse
|
13
|
Guo J, Hu J, Zheng Y, Zhao S, Ma J. Artificial intelligence: opportunities and challenges in the clinical applications of triple-negative breast cancer. Br J Cancer 2023; 128:2141-2149. [PMID: 36871044 PMCID: PMC10241896 DOI: 10.1038/s41416-023-02215-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 02/08/2023] [Accepted: 02/21/2023] [Indexed: 03/06/2023] Open
Abstract
Triple-negative breast cancer (TNBC) accounts for 15-20% of all invasive breast cancer subtypes. Owing to its clinical characteristics, such as the lack of effective therapeutic targets, high invasiveness, and high recurrence rate, TNBC is difficult to treat and has a poor prognosis. Currently, with the accumulation of large amounts of medical data and the development of computing technology, artificial intelligence (AI), particularly machine learning, has been applied to various aspects of TNBC research, including early screening, diagnosis, identification of molecular subtypes, personalised treatment, and prediction of prognosis and treatment response. In this review, we discussed the general principles of artificial intelligence, summarised its main applications in the diagnosis and treatment of TNBC, and provided new ideas and theoretical basis for the clinical diagnosis and treatment of TNBC.
Collapse
Affiliation(s)
- Jiamin Guo
- Department of Medical Oncology, West China Hospital, Sichuan University, 610041, Chengdu, Sichuan Province, P. R. China
| | - Junjie Hu
- Machine Intelligence Laboratory, College of Computer Science, Sichuan University, 610065, Chengdu, Sichuan Province, P. R. China
| | - Yichen Zheng
- Department of Medical Oncology, West China Hospital, Sichuan University, 610041, Chengdu, Sichuan Province, P. R. China
| | - Shuang Zhao
- Department of Radiology, West China Hospital of Sichuan University, 610041, Chengdu, Sichuan Province, P. R. China.
| | - Ji Ma
- Department of Medical Oncology, West China Hospital, Sichuan University, 610041, Chengdu, Sichuan Province, P. R. China.
| |
Collapse
|
14
|
A radiomics feature-based machine learning models to detect brainstem infarction (RMEBI) may enable early diagnosis in non-contrast enhanced CT. Eur Radiol 2023; 33:1004-1014. [PMID: 36169689 DOI: 10.1007/s00330-022-09130-6] [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: 05/30/2022] [Revised: 07/21/2022] [Accepted: 08/24/2022] [Indexed: 02/03/2023]
Abstract
OBJECTIVES Magnetic resonance imaging has high sensitivity in detecting early brainstem infarction (EBI). However, MRI is not practical for all patients who present with possible stroke and would lead to delayed treatment. The detection rate of EBI on non-contrast computed tomography (NCCT) is currently very low. Thus, we aimed to develop and validate the radiomics feature-based machine learning models to detect EBI (RMEBIs) on NCCT. METHODS In this retrospective observational study, 355 participants from a multicentre multimodal database established by Huashan Hospital were randomly divided into two data sets: a training cohort (70%) and an internal validation cohort (30%). Fifty-seven participants from the Second Affiliated Hospital of Xuzhou Medical University were included as the external validation cohort. Brainstems were segmented by a radiologist committee on NCCT and 1781 radiomics features were automatically computed. After selecting the relevant features, 7 machine learning models were assessed in the training cohort to predict early brainstem infarction. Accuracy, sensitivity, specificity, positive predictive value, negative predictive value, F1-score, and the area under the receiver operating characteristic curve (AUC) were used to evaluate the performance of the prediction models. RESULTS The multilayer perceptron (MLP) RMEBI showed the best performance (AUC: 0.99 [95% CI: 0.96-1.00]) in the internal validation cohort. The AUC value in external validation cohort was 0.91 (95% CI: 0.82-0.98). CONCLUSIONS RMEBIs have the potential in routine clinical practice to enable accurate computer-assisted diagnoses of early brainstem infarction in patients with NCCT, which may have important clinical value in reducing therapeutic decision-making time. KEY POINTS • RMEBIs have the potential to enable accurate diagnoses of early brainstem infarction in patients with NCCT. • RMEBIs are suitable for various multidetector CT scanners. • The patient treatment decision-making time is shortened.
Collapse
|
15
|
An integrative non-invasive malignant brain tumors classification and Ki-67 labeling index prediction pipeline with radiomics approach. Eur J Radiol 2023; 158:110639. [PMID: 36463703 DOI: 10.1016/j.ejrad.2022.110639] [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: 06/10/2022] [Revised: 10/05/2022] [Accepted: 11/25/2022] [Indexed: 11/29/2022]
Abstract
BACKGROUND The histological sub-classes of brain tumors and the Ki-67 labeling index (LI) of tumor cells are major factors in the diagnosis, prognosis, and treatment management of patients. Many existing studies primarily focused on the classification of two classes of brain tumors and the Ki-67LI of gliomas. This study aimed to develop a preoperative non-invasive radiomics pipeline based on multiparametric-MRI to classify-three types of brain tumors, glioblastoma (GBM), metastasis (MET) and primary central nervous system lymphoma (PCNSL), and to predict their corresponding Ki-67LI. METHODS In this retrospective study, 153 patients with malignant brain tumors were involved. The radiomics features were extracted from three types of MRI (T1-weighted imaging (T1WI), fluid-attenuated inversion recovery (FLAIR), and contrast-enhanced T1-weighted imaging (CE-T1WI)) with three masks (tumor core, edema, and whole tumor masks) and selected by a combination of Pearson correlation coefficient (CORR), LASSO, and Max-Relevance and Min-Redundancy (mRMR) filters. The performance of six classifiers was compared and the top three performing classifiers were used to construct the ensemble learning model (ELM). The proposed ELM was evaluated in the training dataset (108 patients) by 5-fold cross-validation and in the test dataset (45 patients) by hold-out. The accuracy (ACC), sensitivity (SEN), specificity (SPE), F1-Score, and the area under the receiver operating characteristic curve (AUC) indicators evaluated the performance of the models. RESULTS The best feature sets and ELM with the optimal performance were selected to construct the tri-categorized brain tumor aided diagnosis model (training dataset AUC: 0.96 (95% CI: 0.93, 0.99); test dataset AUC: 0.93) and Ki-67LI prediction model (training dataset AUC: 0.96 (95% CI: 0.94, 0.98); test dataset AUC: 0.91). The CE-T1WI was the best single modality for all classifiers. Meanwhile, the whole tumor was the most vital mask for the tumor classification and the tumor core was the most vital mask for the Ki-67LI prediction. CONCLUSION The developed radiomics models led to the precise preoperative classification of GBM, MET, and PCNSL and the prediction of Ki-67LI, which could be utilized in clinical practice for the treatment planning for brain tumors.
Collapse
|
16
|
A Framework of Analysis to Facilitate the Harmonization of Multicenter Radiomic Features in Prostate Cancer. J Clin Med 2022; 12:jcm12010140. [PMID: 36614941 PMCID: PMC9821561 DOI: 10.3390/jcm12010140] [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: 11/08/2022] [Revised: 12/16/2022] [Accepted: 12/21/2022] [Indexed: 12/28/2022] Open
Abstract
Pooling radiomic features coming from different centers in a statistical framework is challenging due to the variability in scanner models, acquisition protocols, and reconstruction settings. To remove technical variability, commonly called batch effects, different statistical harmonization strategies have been widely used in genomics but less considered in radiomics. The aim of this work was to develop a framework of analysis to facilitate the harmonization of multicenter radiomic features extracted from prostate T2-weighted magnetic resonance imaging (MRI) and to improve the power of radiomics for prostate cancer (PCa) management in order to develop robust non-invasive biomarkers translating into clinical practice. To remove technical variability and correct for batch effects, we investigated four different statistical methods (ComBat, SVA, Arsynseq, and mixed effect). The proposed approaches were evaluated using a dataset of 210 prostate cancer (PCa) patients from two centers. The impacts of the different statistical approaches were evaluated by principal component analysis and classification methods (LogitBoost, random forest, K-nearest neighbors, and decision tree). The ComBat method outperformed all other methods by achieving 70% accuracy and 78% AUC with the random forest method to automatically classify patients affected by PCa. The proposed statistical framework enabled us to define and develop a standardized pipeline of analysis to harmonize multicenter T2W radiomic features, yielding great promise to support PCa clinical practice.
Collapse
|
17
|
Fan Y, Pan X, Yang F, Liu S, Wang Z, Sun J, Chen J. Preoperative Computed Tomography Radiomics Analysis for Predicting Receptors Status and Ki-67 Levels in Breast Cancer. Am J Clin Oncol 2022; 45:526-533. [PMID: 36413682 PMCID: PMC9698095 DOI: 10.1097/coc.0000000000000951] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
BACKGROUND To assess the prediction performance of preoperative chest computed tomography (CT) based radiomics features for estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor-2 (HER2+), and Ki-67 status of breast cancer. MATERIALS AND METHODS This study enrolled 108 breast cancer patients who received preoperative chest CT examinations in our institution from July 2018 to January 2020. Radiomics features were separately extracted from nonenhanced, arterial, and portal-venous phases CT images. The least absolute shrinkage and selection operator logistic regression was used for feature selection. Then the radiomics signatures for each phase and a combined model of 3 phases were built. Finally, the receiver operating characteristic curves and calibration curves were used to confirm the performance of the radiomics signatures and combined model. In addition, the decision curves were performed to estimate the clinical usefulness of the combined model. RESULTS The 20 most predictive features were finally selected to build radiomics signatures for each phase. The combined model achieved the overall best performance than using either of the nonenhanced, arterial and portal-venous phases alone, achieving an area under the receiver operating characteristic curve of 0.870 for ER+ versus ER-, 0.797 for PR+ versus PR-, 0.881 for HER2+ versus HER2-, and 0.726 for Ki-67. The decision curve demonstrated that the CT-based radiomics features were clinically useful. CONCLUSION This study indicated preopreative chest CT radiomics analysis might be able to assess ER, PR, HER2+, and Ki-67 status of breast cancer. The findings need further to be verified in future larger studies.
Collapse
Affiliation(s)
- Yuan Fan
- General Surgery Department, Qujing City First People’s Hospital, Qujing Yunnan
| | | | | | - Siyun Liu
- GE Healthcare life science, Shanghai, People’s Republic of China
| | - Zhu Wang
- Laboratory of Molecular Diagnosis of Cancer, Cancer Center
| | | | - Jie Chen
- Department of Breast Surgery, West China Hospital of Sichuan University, Chengdu Sichuan
| |
Collapse
|
18
|
Chitalia R, Pati S, Bhalerao M, Thakur SP, Jahani N, Belenky V, McDonald ES, Gibbs J, Newitt DC, Hylton NM, Kontos D, Bakas S. Expert tumor annotations and radiomics for locally advanced breast cancer in DCE-MRI for ACRIN 6657/I-SPY1. Sci Data 2022; 9:440. [PMID: 35871247 PMCID: PMC9308769 DOI: 10.1038/s41597-022-01555-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2021] [Accepted: 06/29/2022] [Indexed: 11/30/2022] Open
Abstract
Breast cancer is one of the most pervasive forms of cancer and its inherent intra- and inter-tumor heterogeneity contributes towards its poor prognosis. Multiple studies have reported results from either private institutional data or publicly available datasets. However, current public datasets are limited in terms of having consistency in: a) data quality, b) quality of expert annotation of pathology, and c) availability of baseline results from computational algorithms. To address these limitations, here we propose the enhancement of the I-SPY1 data collection, with uniformly curated data, tumor annotations, and quantitative imaging features. Specifically, the proposed dataset includes a) uniformly processed scans that are harmonized to match intensity and spatial characteristics, facilitating immediate use in computational studies, b) computationally-generated and manually-revised expert annotations of tumor regions, as well as c) a comprehensive set of quantitative imaging (also known as radiomic) features corresponding to the tumor regions. This collection describes our contribution towards repeatable, reproducible, and comparative quantitative studies leading to new predictive, prognostic, and diagnostic assessments.
Collapse
Affiliation(s)
- Rhea Chitalia
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Sarthak Pati
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Megh Bhalerao
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Siddhesh Pravin Thakur
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Nariman Jahani
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Vivian Belenky
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Elizabeth S McDonald
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Jessica Gibbs
- University of California San Francisco (UCSF), San Francisco, CA, 94115, USA
| | - David C Newitt
- University of California San Francisco (UCSF), San Francisco, CA, 94115, USA
| | - Nola M Hylton
- University of California San Francisco (UCSF), San Francisco, CA, 94115, USA
| | - Despina Kontos
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Spyridon Bakas
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
| |
Collapse
|
19
|
Radiogenomic analysis reveals tumor heterogeneity of triple-negative breast cancer. Cell Rep Med 2022; 3:100694. [PMID: 35858585 PMCID: PMC9381418 DOI: 10.1016/j.xcrm.2022.100694] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Revised: 06/07/2022] [Accepted: 06/23/2022] [Indexed: 11/04/2022]
Abstract
Triple-negative breast cancer (TNBC) is a subset of breast cancer with an adverse prognosis and significant tumor heterogeneity. Here, we extract quantitative radiomic features from contrast-enhanced magnetic resonance images to construct a breast cancer radiomic dataset (n = 860) and a TNBC radiogenomic dataset (n = 202). We develop and validate radiomic signatures that can fairly differentiate TNBC from other breast cancer subtypes and distinguish molecular subtypes within TNBC. A radiomic feature that captures peritumoral heterogeneity is determined to be a prognostic factor for recurrence-free survival (p = 0.01) and overall survival (p = 0.004) in TNBC. Combined with the established matching TNBC transcriptomic and metabolomic data, we demonstrate that peritumoral heterogeneity is associated with immune suppression and upregulated fatty acid synthesis in tumor samples. Collectively, this multi-omic dataset serves as a useful public resource to promote precise subtyping of TNBC and helps to understand the biological significance of radiomics. A radiomic signature identifies TNBC from other subtypes of breast cancer Radiomic features are predictive of TNBC molecular subtypes A radiomic feature reflecting peritumoral heterogeneity indicates TNBC prognosis Peritumoral heterogeneity correlates with metabolic and immune abnormalities in TNBC
Collapse
|
20
|
Darvish L, Bahreyni-Toossi MT, Roozbeh N, Azimian H. The role of radiogenomics in the diagnosis of breast cancer: a systematic review. EGYPTIAN JOURNAL OF MEDICAL HUMAN GENETICS 2022. [DOI: 10.1186/s43042-022-00310-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Abstract
Background
One of the most common cancers diagnosed worldwide is breast cancer (BC), which is the leading cause of cancer death among women. The radiogenomics method is more accurate for managing and inhibiting this disease, which takes individual diagnosis on genes, environments, and lifestyles of each person. The present study aims to highlight the current state-of-the-art, the current role and limitations, and future directions of radiogenomics in breast cancer.
Method
This systematic review article was searched from databases such as Embase, PubMed, Web of Science, Google Scholar, Scopus, and Cochrane Library without any date or language limitations of databases. Searches were performed using Boolean OR and AND operators between the main terms and keywords of particular topic of the subject under investigation. All retrospective, prospective, cohort, and pilot studies were included, which were provided with more details about the topic. Articles such as letter to the editor, review, and short communications were excluded because of lack of information, discussions, or use of radiogenomics method on other cancers. For quality assessment of articles, STROBE checklist was used.
Result
For the systematic review, 18 articles were approved after assessing the full text of selected articles. In this review, 3614 patients with BC of selected articles were evaluated, and all radiogenomics were associated with more power in classification, differential diagnosis, and prognosis of BC. Among the various modalities to predict genomic indicators and molecular subtypes, DCE-MRI has the higher performance and finally the highest amount of AUC value (0.956) belonged to PI3K gene.
Conclusion
This review shows that radiogenomics can help with the diagnosis and treatment of breast cancer in patients. It has shown that recognizing and specifying radiogenomic phenotypes in the genomic signatures can be helpful in treatment and diagnosis of disease. The molecular methods used in these articles are limited to miRNAs expression, gene expression, Ki67 proliferation index, next-generation RNA sequencing, whole RNA sequencing, and molecular histopathology that can be completed in future studies by other methods such as exosomal miRNAs, specific proteins expression, DNA repair capacity, and other biomarkers that have prognostic and predictive value for cancer treatment response. Studies with control group and large sample size for evaluation of radiogenomics in diagnosis and treatment recommended.
Collapse
|
21
|
Xu A, Chu X, Zhang S, Zheng J, Shi D, Lv S, Li F, Weng X. Prediction Breast Molecular Typing of Invasive Ductal Carcinoma Based on Dynamic Contrast Enhancement Magnetic Resonance Imaging Radiomics Characteristics: A Feasibility Study. Front Oncol 2022; 12:799232. [PMID: 35664741 PMCID: PMC9160981 DOI: 10.3389/fonc.2022.799232] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Accepted: 04/14/2022] [Indexed: 11/13/2022] Open
Abstract
ObjectiveTo investigate the feasibility of radiomics in predicting molecular subtype of breast invasive ductal carcinoma (IDC) based on dynamic contrast enhancement magnetic resonance imaging (DCE-MRI).MethodsA total of 303 cases with pathologically confirmed IDC from January 2018 to March 2021 were enrolled in this study, including 223 cases from Fudan University Shanghai Cancer Center (training/test set) and 80 cases from Shaoxing Central Hospital (validation set). All the cases were classified as HR+/Luminal, HER2-enriched, and TNBC according to immunohistochemistry. DCE-MRI original images were treated by semi-automated segmentation to initially extract original and wavelet-transformed radiomic features. The extended logistic regression with least absolute shrinkage and selection operator (LASSO) penalty was applied to identify the optimal radiomic features, which were then used to establish predictive models combined with significant clinical risk factors. Receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis were adopted to evaluate the effectiveness and clinical benefit of the models established.ResultsOf the 223 cases from Fudan University Shanghai Cancer Center, HR+/Luminal cancers were diagnosed in 116 cases (52.02%), HER2-enriched in 71 cases (31.84%), and TNBC in 36 cases (16.14%). Based on the training set, 788 radiomic features were extracted in total and 8 optimal features were further identified, including 2 first-order features, 1 gray-level run length matrix (GLRLM), 4 gray-level co-occurrence matrices (GLCM), and 1 3D shape feature. Three multi-class classification models were constructed by extended logistic regression: clinical model (age, menopause, tumor location, Ki-67, histological grade, and lymph node metastasis), radiomic model, and combined model. The macro-average areas under the ROC curve (macro-AUC) for the three models were 0.71, 0.81, and 0.84 in the training set, 0.73, 0.81, and 0.84 in the test set, and 0.76, 0.82, and 0.83 in the validation set, respectively.ConclusionThe DCE-MRI-based radiomic features are significant biomarkers for distinguishing molecular subtypes of breast cancer noninvasively. Notably, the classification performance could be improved with the fusion analysis of multi-modal features.
Collapse
Affiliation(s)
- Aqiao Xu
- Department of Radiology, The Central Hospital Affiliated to Shaoxing University (Shaoxing Central Hospital), Shaoxing, China
- *Correspondence: Aqiao Xu, ; Xiaobo Weng,
| | - Xiufeng Chu
- Department of Surgical, The Central Hospital Affiliated to Shaoxing University (Shaoxing Central Hospital), Shaoxing, China
| | - Shengjian Zhang
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Jing Zheng
- Department of Radiology, The Central Hospital Affiliated to Shaoxing University (Shaoxing Central Hospital), Shaoxing, China
| | - Dabao Shi
- Department of Radiology, The Central Hospital Affiliated to Shaoxing University (Shaoxing Central Hospital), Shaoxing, China
| | - Shasha Lv
- Department of Radiology, The Central Hospital Affiliated to Shaoxing University (Shaoxing Central Hospital), Shaoxing, China
| | - Feng Li
- Department of Research Collaboration, Research & Development Center (R&D), Beijing Deepwise & League of Doctor of Philosophy (PHD) Technology Co., Ltd, Beijing, China
| | - Xiaobo Weng
- Department of Radiology, The Central Hospital Affiliated to Shaoxing University (Shaoxing Central Hospital), Shaoxing, China
- *Correspondence: Aqiao Xu, ; Xiaobo Weng,
| |
Collapse
|
22
|
A Complex Radiomic Signature in Luminal Breast Cancer from a Weighted Statistical Framework: A Pilot Study. Diagnostics (Basel) 2022; 12:diagnostics12020499. [PMID: 35204589 PMCID: PMC8871349 DOI: 10.3390/diagnostics12020499] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 02/08/2022] [Accepted: 02/12/2022] [Indexed: 01/27/2023] Open
Abstract
Radiomics is rapidly advancing in precision diagnostics and cancer treatment. However, there are several challenges that need to be addressed before translation to clinical use. This study presents an ad-hoc weighted statistical framework to explore radiomic biomarkers for a better characterization of the radiogenomic phenotypes in breast cancer. Thirty-six female patients with breast cancer were enrolled in this study. Radiomic features were extracted from MRI and PET imaging techniques for malignant and healthy lesions in each patient. To reduce within-subject bias, the ratio of radiomic features extracted from both lesions was calculated for each patient. Radiomic features were further normalized, comparing the z-score, quantile, and whitening normalization methods to reduce between-subjects bias. After feature reduction by Spearman’s correlation, a methodological approach based on a principal component analysis (PCA) was applied. The results were compared and validated on twenty-seven patients to investigate the tumor grade, Ki-67 index, and molecular cancer subtypes using classification methods (LogitBoost, random forest, and linear discriminant analysis). The classification techniques achieved high area-under-the-curve values with one PC that was calculated by normalizing the radiomic features via the quantile method. This pilot study helped us to establish a robust framework of analysis to generate a combined radiomic signature, which may lead to more precise breast cancer prognosis.
Collapse
|
23
|
Davey MG, Davey MS, Boland MR, Ryan ÉJ, Lowery AJ, Kerin MJ. Radiomic differentiation of breast cancer molecular subtypes using pre-operative breast imaging - A systematic review and meta-analysis. Eur J Radiol 2021; 144:109996. [PMID: 34624649 DOI: 10.1016/j.ejrad.2021.109996] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Revised: 09/17/2021] [Accepted: 09/30/2021] [Indexed: 01/12/2023]
Abstract
INTRODUCTION Breast cancer has four distinct molecular subtypes which are discriminated using gene expression profiling following biopsy. Radiogenomics is an emerging field which utilises diagnostic imaging to reveal genomic properties of disease. We aimed to perform a systematic review of the current literature to evaluate the value radiomics in differentiating breast cancers into their molecular subtypes using diagnostic imaging. METHODS A systematic review was performed as per PRISMA guidelines. Studies assessing radiomictumour analysis in differentiatingbreast cancer molecular subtypeswere included. Quality was assessed using the radiomics quality score (RQS). Diagnostic sensitivity and specificity of radiomic analyses were included for meta-analysis; Study specific sensitivity and specificity were retrieved and summary ROC analysis were performed to compile pooled sensitivities and specificities. RESULTS Forty-one studies were included. Overall, there were 10,090 female patients (mean age of 47.6 ± 11.7 years, range: 21-93) and molecular subtypewas reported in 7,693 of cases, with Luminal A (LABC), Luminal B (LBBC), Human Epidermal Growth Factor Receptor-2 overexpressing (HER2+), and Triple Negative (TNBC) breast cancers representing 51.3%, 19.9%, 12.3% and 16.3% of tumour respectively. Seven studies provided radiomic analysis to determine molecular subtypes using mammography to differentiateTNBCvs.others (sensitivity: 0.82,specificity:0.79). Thirty-five studies reported on radiomic analysis of magnetic resonance imaging (MRI); LABC versus others(sensitivity:0.78,specificity:0.83),HER2+versusothers(sensitivity:0.87,specificity:0.88), andLBBCversusTNBC (sensitivity: 0.79,specificity:0.88) respectively. CONCLUSION Radiomic tumour assessment of contemporary breast imaging provide a novel option in determining breast cancer molecular subtypes. However, amelioration of such techniques are required and genetic expression assessment will remain the gold standard.
Collapse
Affiliation(s)
- Matthew G Davey
- The Lambe Institute for Translational Research, National University of Ireland, Galway H91 YR91, Ireland.
| | - Martin S Davey
- The Lambe Institute for Translational Research, National University of Ireland, Galway H91 YR91, Ireland
| | - Michael R Boland
- The Lambe Institute for Translational Research, National University of Ireland, Galway H91 YR91, Ireland
| | - Éanna J Ryan
- The Lambe Institute for Translational Research, National University of Ireland, Galway H91 YR91, Ireland
| | - Aoife J Lowery
- The Lambe Institute for Translational Research, National University of Ireland, Galway H91 YR91, Ireland
| | - Michael J Kerin
- The Lambe Institute for Translational Research, National University of Ireland, Galway H91 YR91, Ireland
| |
Collapse
|
24
|
Baselice S, Castaldo R, Giannatiempo R, Casaretta G, Franzese M, Salvatore M, Mirabelli P. Impact of Breast Tumor Onset on Blood Count, Carcinoembryonic Antigen, Cancer Antigen 15-3 and Lymphoid Subpopulations Supported by Automatic Classification Approach: A Pilot Study. Cancer Control 2021; 28:10732748211048612. [PMID: 34620015 PMCID: PMC8504274 DOI: 10.1177/10732748211048612] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Abstract
Background Recent observations showed that systemic immune changes are detectable in case of breast cancer (BC). In this preliminary study, we investigated routinely measured peripheral blood (PB) parameters for malignant BC cases in comparison to benign breast conditions. Complete blood count, circulating lymphoid subpopulation, and serological carcinoembryonic antigen (CEA) and cancer antigen 15-3 (CA15-3) levels were considered. Methods A total of 127 female patients affected by malignant (n = 77, mean age = 63 years, min = 36, max = 90) BC at diagnosis (naïve patients) or benign breast conditions (n = 50, mean age = 33 years, min = 18, max = 60) were included in this study. For each patient, complete blood count and lymphoid subpopulations (T-helper, T-cytotoxic, B-, NK-, and NKT-cells) analysis on PB samples were performed. Hormonal receptor status, Ki-67 expression, and serological CEA and CA15-3 levels were assessed in the case of patients with malignant BC via statistical analysis. Results Women with malignant BC disclosed increased circulating T-helper lymphocytes and CD4/CD8 ratio in PB when compared to those affected by benign breast conditions (2.345 vs 1.894, P < .05 Wilcoxon rank-sum test). In the case of malignant BC patients, additive logistic regression method was able to identify malignant BC cases with increased CA15-3 levels (CA15-3 >25 UI/mL) via the hematocrit and neutrophils/lymphocytes ratio values. Moreover, in the case of women with aggressive malignant BC featured by high levels of Ki-67 proliferation marker, an increasing number of correlations were found among blood count parameters and lymphocytes subpopulations by performing a Spearman’s correlation analysis. Conclusions This preliminary study confirms the ability of malignant BC to determine systemic modifications. The stratification of malignant BC cases according to the Ki-67 proliferation marker highlighted increasing detectable alterations in the periphery of women with aggressive BC. The advent of novel and more sensitive biomarkers, as well as deep immunophenotyping technologies, will provide additional insights for describing the relationship between tumor onset and peripheral alterations.
Collapse
|
25
|
La Greca Saint-Esteven A, Vuong D, Tschanz F, van Timmeren JE, Dal Bello R, Waller V, Pruschy M, Guckenberger M, Tanadini-Lang S. Systematic Review on the Association of Radiomics with Tumor Biological Endpoints. Cancers (Basel) 2021; 13:cancers13123015. [PMID: 34208595 PMCID: PMC8234501 DOI: 10.3390/cancers13123015] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Revised: 06/10/2021] [Accepted: 06/11/2021] [Indexed: 12/23/2022] Open
Abstract
Radiomics supposes an alternative non-invasive tumor characterization tool, which has experienced increased interest with the advent of more powerful computers and more sophisticated machine learning algorithms. Nonetheless, the incorporation of radiomics in cancer clinical-decision support systems still necessitates a thorough analysis of its relationship with tumor biology. Herein, we present a systematic review focusing on the clinical evidence of radiomics as a surrogate method for tumor molecular profile characterization. An extensive literature review was conducted in PubMed, including papers on radiomics and a selected set of clinically relevant and commonly used tumor molecular markers. We summarized our findings based on different cancer entities, additionally evaluating the effect of different modalities for the prediction of biomarkers at each tumor site. Results suggest the existence of an association between the studied biomarkers and radiomics from different modalities and different tumor sites, even though a larger number of multi-center studies are required to further validate the reported outcomes.
Collapse
Affiliation(s)
- Agustina La Greca Saint-Esteven
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, 8091 Zurich, Switzerland; (D.V.); (J.E.v.T.); (R.D.B.); (M.G.); (S.T.-L.)
- Correspondence:
| | - Diem Vuong
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, 8091 Zurich, Switzerland; (D.V.); (J.E.v.T.); (R.D.B.); (M.G.); (S.T.-L.)
| | - Fabienne Tschanz
- Laboratory of Applied Radiobiology, Department of Radiation Oncology, University of Zurich, 8091 Zurich, Switzerland; (F.T.); (V.W.); (M.P.)
| | - Janita E. van Timmeren
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, 8091 Zurich, Switzerland; (D.V.); (J.E.v.T.); (R.D.B.); (M.G.); (S.T.-L.)
| | - Riccardo Dal Bello
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, 8091 Zurich, Switzerland; (D.V.); (J.E.v.T.); (R.D.B.); (M.G.); (S.T.-L.)
| | - Verena Waller
- Laboratory of Applied Radiobiology, Department of Radiation Oncology, University of Zurich, 8091 Zurich, Switzerland; (F.T.); (V.W.); (M.P.)
| | - Martin Pruschy
- Laboratory of Applied Radiobiology, Department of Radiation Oncology, University of Zurich, 8091 Zurich, Switzerland; (F.T.); (V.W.); (M.P.)
| | - Matthias Guckenberger
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, 8091 Zurich, Switzerland; (D.V.); (J.E.v.T.); (R.D.B.); (M.G.); (S.T.-L.)
| | - Stephanie Tanadini-Lang
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, 8091 Zurich, Switzerland; (D.V.); (J.E.v.T.); (R.D.B.); (M.G.); (S.T.-L.)
| |
Collapse
|
26
|
Priya S, Liu Y, Ward C, Le NH, Soni N, Pillenahalli Maheshwarappa R, Monga V, Zhang H, Sonka M, Bathla G. Radiomic Based Machine Learning Performance for a Three Class Problem in Neuro-Oncology: Time to Test the Waters? Cancers (Basel) 2021; 13:2568. [PMID: 34073840 PMCID: PMC8197204 DOI: 10.3390/cancers13112568] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Revised: 04/28/2021] [Accepted: 05/04/2021] [Indexed: 01/06/2023] Open
Abstract
Prior radiomics studies have focused on two-class brain tumor classification, which limits generalizability. The performance of radiomics in differentiating the three most common malignant brain tumors (glioblastoma (GBM), primary central nervous system lymphoma (PCNSL), and metastatic disease) is assessed; factors affecting the model performance and usefulness of a single sequence versus multiparametric MRI (MP-MRI) remain largely unaddressed. This retrospective study included 253 patients (120 metastatic (lung and brain), 40 PCNSL, and 93 GBM). Radiomic features were extracted for whole a tumor mask (enhancing plus necrotic) and an edema mask (first pipeline), as well as for separate enhancing and necrotic and edema masks (second pipeline). Model performance was evaluated using MP-MRI, individual sequences, and the T1 contrast enhanced (T1-CE) sequence without the edema mask across 45 model/feature selection combinations. The second pipeline showed significantly high performance across all combinations (Brier score: 0.311-0.325). GBRM fit using the full feature set from the T1-CE sequence was the best model. The majority of the top models were built using a full feature set and inbuilt feature selection. No significant difference was seen between the top-performing models for MP-MRI (AUC 0.910) and T1-CE sequence with (AUC 0.908) and without edema masks (AUC 0.894). T1-CE is the single best sequence with comparable performance to that of multiparametric MRI (MP-MRI). Model performance varies based on tumor subregion and the combination of model/feature selection methods.
Collapse
Affiliation(s)
- Sarv Priya
- Department of Radiology, University of Iowa Hospitals and Clinics, Iowa City, IA 52242, USA; (N.S.); (R.P.M.); (G.B.)
| | - Yanan Liu
- College of Engineering, University of Iowa, Iowa City, IA 52242, USA; (Y.L.); (N.H.L.); (H.Z.); (M.S.)
| | - Caitlin Ward
- Department of Biostatistics, University of Iowa, Iowa City, IA 52242, USA;
| | - Nam H. Le
- College of Engineering, University of Iowa, Iowa City, IA 52242, USA; (Y.L.); (N.H.L.); (H.Z.); (M.S.)
| | - Neetu Soni
- Department of Radiology, University of Iowa Hospitals and Clinics, Iowa City, IA 52242, USA; (N.S.); (R.P.M.); (G.B.)
| | | | - Varun Monga
- Department of Medicine, University of Iowa Hospitals and Clinics, Iowa City, IA 52242, USA;
| | - Honghai Zhang
- College of Engineering, University of Iowa, Iowa City, IA 52242, USA; (Y.L.); (N.H.L.); (H.Z.); (M.S.)
| | - Milan Sonka
- College of Engineering, University of Iowa, Iowa City, IA 52242, USA; (Y.L.); (N.H.L.); (H.Z.); (M.S.)
| | - Girish Bathla
- Department of Radiology, University of Iowa Hospitals and Clinics, Iowa City, IA 52242, USA; (N.S.); (R.P.M.); (G.B.)
| |
Collapse
|
27
|
Radiomics-Based Differentiation between Glioblastoma, CNS Lymphoma, and Brain Metastases: Comparing Performance across MRI Sequences and Machine Learning Models. Cancers (Basel) 2021. [DOI: 10.3390/cancers13092261] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023] Open
Abstract
Prior radiomics studies have focused on two-class brain tumor classification, which limits generalizability. The performance of radiomics in differentiating the three most common malignant brain tumors (glioblastoma (GBM), primary central nervous system lymphoma (PCNSL), and metastatic disease) is assessed; factors affecting the model performance and usefulness of a single sequence versus multiparametric MRI (MP-MRI) remain largely unaddressed. This retrospective study included 253 patients (120 metastatic (lung and brain), 40 PCNSL, and 93 GBM). Radiomic features were extracted for whole a tumor mask (enhancing plus necrotic) and an edema mask (first pipeline), as well as for separate enhancing and necrotic and edema masks (second pipeline). Model performance was evaluated using MP-MRI, individual sequences, and the T1 contrast enhanced (T1-CE) sequence without the edema mask across 45 model/feature selection combinations. The second pipeline showed significantly high performance across all combinations (Brier score: 0.311–0.325). GBRM fit using the full feature set from the T1-CE sequence was the best model. The majority of the top models were built using a full feature set and inbuilt feature selection. No significant difference was seen between the top-performing models for MP-MRI (AUC 0.910) and T1-CE sequence with (AUC 0.908) and without edema masks (AUC 0.894). T1-CE is the single best sequence with comparable performance to that of multiparametric MRI (MP-MRI). Model performance varies based on tumor subregion and the combination of model/feature selection methods.
Collapse
|
28
|
Castaldo R, Cavaliere C, Soricelli A, Salvatore M, Pecchia L, Franzese M. Radiomic and Genomic Machine Learning Method Performance for Prostate Cancer Diagnosis: Systematic Literature Review. J Med Internet Res 2021; 23:e22394. [PMID: 33792552 PMCID: PMC8050752 DOI: 10.2196/22394] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Revised: 11/26/2020] [Accepted: 01/17/2021] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Machine learning algorithms have been drawing attention at the joining of pathology and radiology in prostate cancer research. However, due to their algorithmic learning complexity and the variability of their architecture, there is an ongoing need to analyze their performance. OBJECTIVE This study assesses the source of heterogeneity and the performance of machine learning applied to radiomic, genomic, and clinical biomarkers for the diagnosis of prostate cancer. One research focus of this study was on clearly identifying problems and issues related to the implementation of machine learning in clinical studies. METHODS Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) protocol, 816 titles were identified from the PubMed, Scopus, and OvidSP databases. Studies that used machine learning to detect prostate cancer and provided performance measures were included in our analysis. The quality of the eligible studies was assessed using the QUADAS-2 (quality assessment of diagnostic accuracy studies-version 2) tool. The hierarchical multivariate model was applied to the pooled data in a meta-analysis. To investigate the heterogeneity among studies, I2 statistics were performed along with visual evaluation of coupled forest plots. Due to the internal heterogeneity among machine learning algorithms, subgroup analysis was carried out to investigate the diagnostic capability of machine learning systems in clinical practice. RESULTS In the final analysis, 37 studies were included, of which 29 entered the meta-analysis pooling. The analysis of machine learning methods to detect prostate cancer reveals the limited usage of the methods and the lack of standards that hinder the implementation of machine learning in clinical applications. CONCLUSIONS The performance of machine learning for diagnosis of prostate cancer was considered satisfactory for several studies investigating the multiparametric magnetic resonance imaging and urine biomarkers; however, given the limitations indicated in our study, further studies are warranted to extend the potential use of machine learning to clinical settings. Recommendations on the use of machine learning techniques were also provided to help researchers to design robust studies to facilitate evidence generation from the use of radiomic and genomic biomarkers.
Collapse
|
29
|
MuSA: a graphical user interface for multi-OMICs data integration in radiogenomic studies. Sci Rep 2021; 11:1550. [PMID: 33452365 PMCID: PMC7811020 DOI: 10.1038/s41598-021-81200-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2020] [Accepted: 01/04/2021] [Indexed: 12/27/2022] Open
Abstract
Analysis of large-scale omics data along with biomedical images has gaining a huge interest in predicting phenotypic conditions towards personalized medicine. Multiple layers of investigations such as genomics, transcriptomics and proteomics, have led to high dimensionality and heterogeneity of data. Multi-omics data integration can provide meaningful contribution to early diagnosis and an accurate estimate of prognosis and treatment in cancer. Some multi-layer data structures have been developed to integrate multi-omics biological information, but none of these has been developed and evaluated to include radiomic data. We proposed to use MultiAssayExperiment (MAE) as an integrated data structure to combine multi-omics data facilitating the exploration of heterogeneous data. We improved the usability of the MAE, developing a Multi-omics Statistical Approaches (MuSA) tool that uses a Shiny graphical user interface, able to simplify the management and the analysis of radiogenomic datasets. The capabilities of MuSA were shown using public breast cancer datasets from TCGA-TCIA databases. MuSA architecture is modular and can be divided in Pre-processing and Downstream analysis. The pre-processing section allows data filtering and normalization. The downstream analysis section contains modules for data science such as correlation, clustering (i.e., heatmap) and feature selection methods. The results are dynamically shown in MuSA. MuSA tool provides an easy-to-use way to create, manage and analyze radiogenomic data. The application is specifically designed to guide no-programmer researchers through different computational steps. Integration analysis is implemented in a modular structure, making MuSA an easily expansible open-source software.
Collapse
|
30
|
LncRNA HAND2-AS1 suppressed the growth of triple negative breast cancer via reducing secretion of MSCs derived exosomal miR-106a-5p. Aging (Albany NY) 2020; 13:424-436. [PMID: 33290256 PMCID: PMC7835037 DOI: 10.18632/aging.202148] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Accepted: 07/21/2020] [Indexed: 12/18/2022]
Abstract
Background: Triple-negative breast cancer (TNBC) is a special type of breast cancer, its tumor cell metastasis rate is much higher than other types, and at the same time has a high rate of postoperative recurrence, which significantly threatens the health of women. Thus, it is urgent to explore a new treatment for TNBC. Results: MiR-106a-5p was up-regulated in TNBC tissues and cells, and was positively correlated with the tumor grade, which indicated poor prognosis in TNBC patients. Mesenchymal stem cells (MSCs) can transport miR-106a-5p into TNBC cells via exosomes. Functional analysis showed exo-miR-106a-5p secreted by MSCs promoted tumor progression in TNBC cells. Furthermore, lncRNA HAND2-AS1 inhibited miR-106a-5p levels, and HAND2-AS1 was decreased in TNBC tissues and cells. Besides, overexpression of HAND2-AS1 reduced the secretion of exo-miR-106a-5p secretion from MSCs, thus suppressed TNBC development. Conclusion: Our study revealed that HAND2-AS1 inhibited the growth of TNBC, which were mediated by the inhibitory effects of MSC-derived exosomal miR-106a-5p.
Collapse
|
31
|
Saint Martin MJ, Orlhac F, Akl P, Khalid F, Nioche C, Buvat I, Malhaire C, Frouin F. A radiomics pipeline dedicated to Breast MRI: validation on a multi-scanner phantom study. MAGNETIC RESONANCE MATERIALS IN PHYSICS BIOLOGY AND MEDICINE 2020; 34:355-366. [PMID: 33180226 DOI: 10.1007/s10334-020-00892-y] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/16/2020] [Revised: 09/27/2020] [Accepted: 10/23/2020] [Indexed: 12/14/2022]
Abstract
OBJECTIVE Quantitative analysis in MRI is challenging due to variabilities in intensity distributions across patients, acquisitions and scanners and suffers from bias field inhomogeneity. Radiomic studies are impacted by these effects that affect radiomic feature values. This paper describes a dedicated pipeline to increase reproducibility in breast MRI radiomic studies. MATERIALS AND METHODS T1, T2, and T1-DCE MR images of two breast phantoms were acquired using two scanners and three dual breast coils. Images were retrospectively corrected for bias field inhomogeneity and further normalised using Z score or histogram matching. Extracted radiomic features were harmonised between coils by the ComBat method. The whole pipeline was assessed qualitatively and quantitatively using statistical comparisons on two series of radiomic feature values computed in the gel mimicking the normal breast tissue or in dense lesions. RESULTS Intra and inter-acquisition variabilities were strongly reduced by the standardisation pipeline. Harmonisation by ComBat lowered the percentage of radiomic features significantly different between the three coils from 87% after bias field correction and MR normalisation to 3% in the gel, while preserving or improving performance of lesion classification in the phantoms. DISCUSSION A dedicated standardisation pipeline was developed to reduce variabilities in breast MRI, which paves the way for robust multi-scanner radiomic studies but needs to be assessed on patient data.
Collapse
Affiliation(s)
- Marie-Judith Saint Martin
- Inserm, Institut Curie,Université Paris-Saclay, Laboratoire d'Imagerie Translationnelle en Oncologie (LITO), Centre de Recherche de l'Institut Curie, Bât 101B rue Henri Becquerel, 91401, Orsay, France.
| | - Fanny Orlhac
- Inserm, Institut Curie,Université Paris-Saclay, Laboratoire d'Imagerie Translationnelle en Oncologie (LITO), Centre de Recherche de l'Institut Curie, Bât 101B rue Henri Becquerel, 91401, Orsay, France
| | - Pia Akl
- Inserm, Institut Curie,Université Paris-Saclay, Laboratoire d'Imagerie Translationnelle en Oncologie (LITO), Centre de Recherche de l'Institut Curie, Bât 101B rue Henri Becquerel, 91401, Orsay, France
- HCL, Radiologie du Groupement Hospitalier Est, Hôpital Femme Mère Enfant, Unité Fonctionnelle: Imagerie de la Femme, 3 Quai des Célestins, 69002, Lyon, France
- Institut Curie, Service de Radiodiagnostic, 26 rue d'Ulm, 75005, Paris, France
| | - Fahad Khalid
- Inserm, Institut Curie,Université Paris-Saclay, Laboratoire d'Imagerie Translationnelle en Oncologie (LITO), Centre de Recherche de l'Institut Curie, Bât 101B rue Henri Becquerel, 91401, Orsay, France
| | - Christophe Nioche
- Inserm, Institut Curie,Université Paris-Saclay, Laboratoire d'Imagerie Translationnelle en Oncologie (LITO), Centre de Recherche de l'Institut Curie, Bât 101B rue Henri Becquerel, 91401, Orsay, France
| | - Irène Buvat
- Inserm, Institut Curie,Université Paris-Saclay, Laboratoire d'Imagerie Translationnelle en Oncologie (LITO), Centre de Recherche de l'Institut Curie, Bât 101B rue Henri Becquerel, 91401, Orsay, France
| | - Caroline Malhaire
- Inserm, Institut Curie,Université Paris-Saclay, Laboratoire d'Imagerie Translationnelle en Oncologie (LITO), Centre de Recherche de l'Institut Curie, Bât 101B rue Henri Becquerel, 91401, Orsay, France
- Institut Curie, Service de Radiodiagnostic, 26 rue d'Ulm, 75005, Paris, France
| | - Frédérique Frouin
- Inserm, Institut Curie,Université Paris-Saclay, Laboratoire d'Imagerie Translationnelle en Oncologie (LITO), Centre de Recherche de l'Institut Curie, Bât 101B rue Henri Becquerel, 91401, Orsay, France
| |
Collapse
|
32
|
Wang Y, Wang Y, Guo C, Xie X, Liang S, Zhang R, Pang W, Huang L. Cancer genotypes prediction and associations analysis from imaging phenotypes: a survey on radiogenomics. Biomark Med 2020; 14:1151-1164. [PMID: 32969248 DOI: 10.2217/bmm-2020-0248] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
In this paper, we present a survey on the progress of radiogenomics research, which predicts cancer genotypes from imaging phenotypes and investigates the associations between them. First, we present an overview of the popular technology modalities for obtaining diagnostic medical images. Second, we summarize recently used methodologies for radiogenomics analysis, including statistical analysis, radiomics and deep learning. And then, we give a survey on the recent research based on several types of cancers. Finally, we discuss these studies and propose possible future research directions. In conclusion, we have identified strong correlations between cancer genotypes and imaging phenotypes. In addition, with the rapid growth of medical data, deep learning models show great application potential for radiogenomics.
Collapse
Affiliation(s)
- Yao Wang
- Key Laboratory of Symbol Computation & Knowledge Engineering, Ministry of Education, College of Computer Science & Technology, Jilin University, Changchun, 130012, PR China
| | - Yan Wang
- Key Laboratory of Symbol Computation & Knowledge Engineering, Ministry of Education, College of Computer Science & Technology, Jilin University, Changchun, 130012, PR China.,School of Artificial Intelligence, Jilin University, Changchun 130012, PR China
| | - Chunjie Guo
- Department of Radiology, The First Hospital of Jilin University, Changchun 130012, PR China
| | - Xuping Xie
- Key Laboratory of Symbol Computation & Knowledge Engineering, Ministry of Education, College of Computer Science & Technology, Jilin University, Changchun, 130012, PR China
| | - Sen Liang
- State Key Lab of CAD & CG, Zhejiang University, Hangzhou 310058, PR China
| | - Ruochi Zhang
- School of Artificial Intelligence, Jilin University, Changchun 130012, PR China
| | - Wei Pang
- School of Mathematical & Computer Sciences, Heriot-Watt University, Edinburgh, EH14 4AS, UK
| | - Lan Huang
- Key Laboratory of Symbol Computation & Knowledge Engineering, Ministry of Education, College of Computer Science & Technology, Jilin University, Changchun, 130012, PR China.,Zhuhai Laboratory of Key Laboratory of Symbolic Computation & Knowledge Engineering of Ministry of Education, Department of Computer Science & Technology, Zhuhai College of Jilin University, Zhuhai 519041, China
| |
Collapse
|
33
|
Pane K, Mirabelli P, Coppola L, Illiano E, Salvatore M, Franzese M. New Roadmaps for Non-muscle-invasive Bladder Cancer With Unfavorable Prognosis. Front Chem 2020; 8:600. [PMID: 32850635 PMCID: PMC7413024 DOI: 10.3389/fchem.2020.00600] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2020] [Accepted: 06/09/2020] [Indexed: 12/13/2022] Open
Abstract
About 70% of bladder cancers (BCs) are diagnosed as non-muscle-invasive BCs (NMIBCs), while the remaining are muscle-invasive BCs (MIBCs). The European Association of Urology (EAU) guidelines stratify NMIBCs into low, intermediate, and high risk for treatment options. Low-risk NMIBCs undergo only the transurethral resection of the bladder (TURB), whereas for intermediate-risk and high-risk NMIBCs, the transurethral resection of the bladder (TURB) with or without Bacillus Calmette-Guérin (BCG) immune or chemotherapy is the standard treatment. A minority of NMIBCs show unfavorable prognosis. High-risk NMIBCs have a high rate of disease recurrence and/or progression to muscle-invasive tumor and BCG treatment failure. The heterogeneous nature of NMIBCs poses challenges for clinical decision-making. In 2020, the EAU made some changes to NMIBCs BCG failure definitions and treatment options, highlighting the need for reliable molecular markers for improving the predictive accuracy of currently available risk tables. Nowadays, next-generation sequencing (NGS) has revolutionized the study of cancer biology, providing diagnostic, prognostic, and therapy response biomarkers in support of precision medicine. Integration of NGS with other cutting-edge technologies might help to decipher also bladder tumor surrounding aspects such as immune system, stromal component, microbiome, and urobiome; altogether, this might impact the clinical outcomes of NMBICs especially in the BCG responsiveness. This review focuses on NMIBCs with unfavorable prognoses, providing molecular prognostic factors from tumor immune and stromal cells, and the perspective of urobiome and microbiome profiling on therapy response. We provide information on the cornerstone of immunotherapy and new promising bladder-preserving treatments and ongoing clinical trials for BCG–unresponsive NMIBCs.
Collapse
Affiliation(s)
| | | | | | - Ester Illiano
- Andrological and Urogynecological Clinic, Santa Maria Terni Hospital, University of Perugia, Terni, Italy
| | | | | |
Collapse
|
34
|
Evangelista L, Fanti S. What Is the Role of Imaging in Cancers? Cancers (Basel) 2020; 12:cancers12061494. [PMID: 32521685 PMCID: PMC7352968 DOI: 10.3390/cancers12061494] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Accepted: 06/04/2020] [Indexed: 11/16/2022] Open
Affiliation(s)
- Laura Evangelista
- Nuclear Medicine Unit, Department of Medicine (DIMED), University of Padua, 35128 Padua, Italy
- Correspondence: ; Tel.: +39-0498211310; Fax: +39-0498213008
| | - Stefano Fanti
- Department of Nuclear Medicine, Sant’Orsola-Malpighi Hospital, University of Bologna, 40138 Bologna, Italy;
| |
Collapse
|