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Lu H, Parra NA, Qi J, Gage K, Li Q, Fan S, Feuerlein S, Pow-Sang J, Gillies R, Choi JW, Balagurunathan Y. Repeatability of Quantitative Imaging Features in Prostate Magnetic Resonance Imaging. Front Oncol 2020; 10:551. [PMID: 32457827 PMCID: PMC7221156 DOI: 10.3389/fonc.2020.00551] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2019] [Accepted: 03/27/2020] [Indexed: 01/31/2023] Open
Abstract
Background: Multiparametric magnetic resonance imaging (mpMRI) has emerged as a non-invasive modality to diagnose and monitor prostate cancer. Quantitative metrics on the regions of abnormality have shown to be useful descriptors to discriminate clinically significant cancers. In this study, we evaluate the reproducibility of quantitative imaging features using repeated mpMRI on the same patients. Methods: We retrospectively obtained the deidentified records of patients, who underwent two mpMRI scans within 2 weeks of the first baseline scan. The patient records were obtained as deidentified data (including imaging), obtained through the TCIA (The Cancer Imaging Archive) repository and analyzed in our institution with an institutional review board-approved Health Insurance Portability and Accountability Act-compliant retrospective study protocol. Indicated biopsied regions were used as a marker for our study radiologists to delineate the regions of interest. We extracted 307 quantitative features in each mpMRI modality [T2-weighted MR sequence image (T2w) and apparent diffusion coefficient (ADC) with b values of 0 and 1,400 mm/s2] across the two sequential scans. Concordance correlation coefficients (CCCs) were computed on the features extracted from sequential scans. Redundant features were removed by computing the coefficient of determination (R 2) among them and replaced with a feature that had the highest dynamic range within intercorrelated groups. Results: We have assessed the reproducibility of quantitative imaging features among sequential scans and found that habitat region characterization improves repeatability in ADC maps. There were 19 T2w features and two ADC features in radiologist drawn regions (native raw image), compared to 18 T2w and 15 ADC features in habitat regions (sphere), which were reproducible (CCC ≥0.65) and non-redundant (R 2 ≥ 0.99). We also found that z-transformation of the images prior to feature extraction reduced the number of reproducible features with no detrimental effect. Conclusion: We have shown that there are quantitative imaging features that are reproducible across sequential prostate mpMRI acquisition at a preset level of filters. We also found that a habitat approach improves feature repeatability in ADC. A validated set of reproducible image features in mpMRI will allow us to develop clinically useful disease risk stratification, enabling the possibility of using imaging as a surrogate to invasive biopsies.
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Affiliation(s)
- Hong Lu
- Department of Radiology, Tianjin Medical and Cancer Hospital, Tianjin, China
- Departments of Cancer Physiology, H. Lee Moffitt Cancer Center, Tampa, FL, United States
| | - Nestor A. Parra
- Departments of Cancer Physiology, H. Lee Moffitt Cancer Center, Tampa, FL, United States
| | - Jin Qi
- Departments of Cancer Physiology, H. Lee Moffitt Cancer Center, Tampa, FL, United States
| | - Kenneth Gage
- Departments of Diagnostic Imaging, H. Lee Moffitt Cancer Center, Tampa, FL, United States
| | - Qian Li
- Department of Radiology, Tianjin Medical and Cancer Hospital, Tianjin, China
| | - Shuxuan Fan
- Department of Radiology, Tianjin Medical and Cancer Hospital, Tianjin, China
- Departments of Cancer Physiology, H. Lee Moffitt Cancer Center, Tampa, FL, United States
| | - Sebastian Feuerlein
- Departments of Diagnostic Imaging, H. Lee Moffitt Cancer Center, Tampa, FL, United States
| | - Julio Pow-Sang
- Departments of Genitourinary Oncology, H. Lee Moffitt Cancer Center, Tampa, FL, United States
| | - Robert Gillies
- Departments of Cancer Physiology, H. Lee Moffitt Cancer Center, Tampa, FL, United States
- Departments of Diagnostic Imaging, H. Lee Moffitt Cancer Center, Tampa, FL, United States
| | - Jung W. Choi
- Departments of Diagnostic Imaging, H. Lee Moffitt Cancer Center, Tampa, FL, United States
| | - Yoganand Balagurunathan
- Departments of Diagnostic Imaging, H. Lee Moffitt Cancer Center, Tampa, FL, United States
- Departments of Genitourinary Oncology, H. Lee Moffitt Cancer Center, Tampa, FL, United States
- Departments of Bioinformatics & Biostatistics, H. Lee Moffitt Cancer Center, Tampa, FL, United States
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Zhang W, Mao N, Wang Y, Xie H, Duan S, Zhang X, Wang B. A Radiomics nomogram for predicting bone metastasis in newly diagnosed prostate cancer patients. Eur J Radiol 2020; 128:109020. [PMID: 32371181 DOI: 10.1016/j.ejrad.2020.109020] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2019] [Revised: 02/25/2020] [Accepted: 04/13/2020] [Indexed: 11/24/2022]
Abstract
PURPOSE To establish and validate a radiomics nomogram for predicting bone metastasis (BM) in patients with newly diagnosed prostate cancer (PCa). METHOD One-hundred and sixteen patients (training cohort: n = 81; validation cohort: n = 35) who underwent prostate MR imaging and confirmed by pathology with newly diagnosed PCa from January 2014 to January 2019 were enrolled. Radiomic features were extracted from diffusion-weighted, axial T2-weighted fat suppression, and dynamic contrast-enhanced T1-weighted MRI of each patient. Dimension reduction, feature selection, and radiomics feature construction were performed using the least absolute shrinkage and selection operator (LASSO) regression. Combined with independent clinical risk factors, a multivariate logistic regression model was used to establish a radiomics nomogram. Nomogram calibration and discrimination were evaluated in training cohort and verified in the validation cohort. Finally, the clinical usefulness of the nomogram was estimated through decision curve analysis (DCA). RESULTS Radiomics signature consisting of 12 selected features was significantly correlated with bone status (P < 0.001 for both training and validation sets). The radiomics nomogram combined a radiomics signature from multiparametric MR images with independent clinic risk factors. The model showed good discrimination and calibration in the training cohort (AUC 0.93, 95% CI, 0.86 to 0.99) and the validation cohort (AUC 0.92, 95% CI, 0.84 to 0.99). DCA also demonstrated the clinical use of the radiomics model. CONCLUSION The radiomics nomogram, which incorporates the multiparametric MRI-based radiomics signature and clinical risk factors, can be conveniently used to promote individualized prediction of BM in patients with newly diagnosed PCa.
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Affiliation(s)
- Wenjie Zhang
- School of Clinical Medicine, Binzhou Medical University, Yantai, Shandong, 264000, PR China
| | - Ning Mao
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, 264000, PR China
| | - Yongsheng Wang
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, 264000, PR China
| | - Haizhu Xie
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, 264000, PR China
| | | | - Xuexi Zhang
- GE Healthcare, China, Shanghai, 200000, PR China
| | - Bin Wang
- School of Clinical Medicine, Binzhou Medical University, Yantai, Shandong, 264000, PR China.
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Sklinda K, Mruk B, Walecki J. Active Surveillance of Prostate Cancer Using Multiparametric Magnetic Resonance Imaging: A Review of the Current Role and Future Perspectives. Med Sci Monit 2020; 26:e920252. [PMID: 32279066 PMCID: PMC7172004 DOI: 10.12659/msm.920252] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023] Open
Abstract
Clinically, active surveillance involves continuous monitoring of patients who may be at risk for disease. Patients with low-grade and early-stage prostate cancer may benefit from active surveillance, rather than undergoing surgical and medical treatments that are associated with side effects. In these cases, the role of active surveillance is to ensure that there is no progression of the disease. However, active surveillance may be associated with a risk of under-diagnosis. Previously, the assignment of risk categories and patient monitoring were based on digital rectal examination, transrectal prostate biopsy, and monitoring of serum levels of prostate-specific antigen (PSA). Multiparametric magnetic resonance imaging (MRI) of the prostate gland has an estimated negative predictive value of 95% for the detection of prostate cancer, which makes this an effective imaging method for targeting biopsies and for monitoring patients over time. Also, multiparametric MRI-guided biopsy at the initial stage of the risk stratification for patients who are newly diagnosed with prostate cancer may reduce the number of underdiagnosed patients, improve long-term patient prognosis, and reduce the number of patients who are overtreated, which may reduce healthcare costs and reduce treatment morbidity. For these reasons, multiparametric MRI has become an accepted monitoring tool in patients who are enrolled in active surveillance programs. This review aims to present the current status of the use of multiparametric MRI in active surveillance of prostate cancer and to discuss future perspectives, supported by recent literature.
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Affiliation(s)
- Katarzyna Sklinda
- Department of Radiology, Centre of Postgraduate Medical Education, Warsaw, Poland
| | - Bartosz Mruk
- Department of Radiology, Centre of Postgraduate Medical Education, Warsaw, Poland
| | - Jerzy Walecki
- Department of Radiology, Centre of Postgraduate Medical Education, Warsaw, Poland
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Her EJ, Haworth A, Rowshanfarzad P, Ebert MA. Progress towards Patient-Specific, Spatially-Continuous Radiobiological Dose Prescription and Planning in Prostate Cancer IMRT: An Overview. Cancers (Basel) 2020; 12:E854. [PMID: 32244821 PMCID: PMC7226478 DOI: 10.3390/cancers12040854] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2020] [Revised: 03/12/2020] [Accepted: 03/27/2020] [Indexed: 01/30/2023] Open
Abstract
Advances in imaging have enabled the identification of prostate cancer foci with an initial application to focal dose escalation, with subvolumes created with image intensity thresholds. Through quantitative imaging techniques, correlations between image parameters and tumour characteristics have been identified. Mathematical functions are typically used to relate image parameters to prescription dose to improve the clinical relevance of the resulting dose distribution. However, these relationships have remained speculative or invalidated. In contrast, the use of radiobiological models during treatment planning optimisation, termed biological optimisation, has the advantage of directly considering the biological effect of the resulting dose distribution. This has led to an increased interest in the accurate derivation of radiobiological parameters from quantitative imaging to inform the models. This article reviews the progress in treatment planning using image-informed tumour biology, from focal dose escalation to the current trend of individualised biological treatment planning using image-derived radiobiological parameters, with the focus on prostate intensity-modulated radiotherapy (IMRT).
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Affiliation(s)
- Emily Jungmin Her
- Department of Physics, University of Western Australia, Crawley, WA 6009, Australia
| | - Annette Haworth
- Institute of Medical Physics, University of Sydney, Camperdown, NSW 2050, Australia
| | - Pejman Rowshanfarzad
- Department of Physics, University of Western Australia, Crawley, WA 6009, Australia
| | - Martin A. Ebert
- Department of Physics, University of Western Australia, Crawley, WA 6009, Australia
- Department of Radiation Oncology, Sir Charles Gairdner Hospital, Nedlands, WA 6009, Australia
- 5D Clinics, Claremont, WA 6010, Australia
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Giganti F, Pecoraro M, Fierro D, Campa R, Del Giudice F, Punwani S, Kirkham A, Allen C, Emberton M, Catalano C, Moore CM, Panebianco V. DWI and PRECISE criteria in men on active surveillance for prostate cancer: A multicentre preliminary experience of different ADC calculations. Magn Reson Imaging 2020; 67:50-58. [PMID: 31899283 DOI: 10.1016/j.mri.2019.12.007] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2019] [Revised: 12/11/2019] [Accepted: 12/27/2019] [Indexed: 01/21/2023]
Abstract
PURPOSE The PRECISE score estimates the likelihood of radiological progression in patients on active surveillance (AS) for prostate cancer (PCa) with serial multiparametric magnetic resonance imaging (mpMRI). A PRECISE score of 1 or 2 denotes radiological regression, PRECISE 3 indicates stability and PRECISE 4 or 5 implies progression. We evaluated the inter-reader reproducibility of different apparent diffusion coefficient (ADC) calculations and their relationship to the PRECISE score. MATERIAL AND METHODS Baseline and follow-up scans (on the same MR systems) of 30 patients with visible lesions from two different institutions (University College London and Sapienza University of Rome) were analysed by two radiologists (one from each site). The PRECISE score was initially assessed in consensus. At least six weeks later, to reduce the likelihood of being influenced by the consensus PRECISE reading, each radiologist independently calculated ADC for the following: lesion, non-cancerous tissue and urine in the bladder. Normalised ADC ratios were calculated with respect to normal prostatic tissue (npADC) and urine. Spearman's correlation (ρ), intraclass correlation coefficients (ICC), differences in ADC and ROC curves were computed. RESULTS Interobserver reproducibility was very good (ρ > 0.8; ICC > 0.90). Lesion ADC (0.91 vs 0.73 × 10-3 mm2/s; p=0.025) and npADC ratio (0.68 vs 0.53; p=0.012) at follow-up mpMRI were different between patients with radiological regression or stability vs progression. Cut-offs of 0.77 × 10-3 mm2/s (lesion ADC) and 0.59 (npADC ratio) could differentiate the two groups (area under the curve: 0.74 and 0.77, respectively). CONCLUSION The ADC, npADC ratio and the PRECISE score should be recorded for MRI-based AS.
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Affiliation(s)
- Francesco Giganti
- Department of Radiology, University College London Hospital NHS Foundation Trust, London, UK; Division of Surgery & Interventional Science, University College London, London, UK; Department of Radiological Sciences, Oncology and Pathology, Sapienza University of Rome, Rome, Italy.
| | - Martina Pecoraro
- Department of Radiological Sciences, Oncology and Pathology, Sapienza University of Rome, Rome, Italy
| | - Davide Fierro
- Department of Radiological Sciences, Oncology and Pathology, Sapienza University of Rome, Rome, Italy
| | - Riccardo Campa
- Department of Radiological Sciences, Oncology and Pathology, Sapienza University of Rome, Rome, Italy
| | | | - Shonit Punwani
- Department of Radiology, University College London Hospital NHS Foundation Trust, London, UK; Centre for Medical Imaging, University College London, London, UK
| | - Alex Kirkham
- Department of Radiology, University College London Hospital NHS Foundation Trust, London, UK
| | - Clare Allen
- Department of Radiology, University College London Hospital NHS Foundation Trust, London, UK
| | - Mark Emberton
- Division of Surgery & Interventional Science, University College London, London, UK; Department of Urology, University College London Hospital NHS Foundation Trust, London, UK
| | - Carlo Catalano
- Department of Radiological Sciences, Oncology and Pathology, Sapienza University of Rome, Rome, Italy
| | - Caroline M Moore
- Division of Surgery & Interventional Science, University College London, London, UK; Department of Urology, University College London Hospital NHS Foundation Trust, London, UK
| | - Valeria Panebianco
- Department of Radiological Sciences, Oncology and Pathology, Sapienza University of Rome, Rome, Italy
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Teoh JYC, Chiong E, Ng CF. Can artificial intelligence optimize case selection for hemi-gland ablation? BJU Int 2020; 125:333-334. [PMID: 32067364 DOI: 10.1111/bju.14993] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Affiliation(s)
- Jeremy Yuen-Chun Teoh
- Department of Surgery, S. H. Ho Urology Centre, Chinese University of Hong Kong, Hong Kong, China
| | - Edmund Chiong
- Department of Urology, National University Hospital, National University Health System, Singapore
| | - Chi-Fai Ng
- Department of Surgery, S. H. Ho Urology Centre, Chinese University of Hong Kong, Hong Kong, China
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Hamm CA, Beetz NL, Savic LJ, Penzkofer T. [Artificial intelligence and radiomics in MRI-based prostate diagnostics]. Radiologe 2020; 60:48-55. [PMID: 31802148 DOI: 10.1007/s00117-019-00613-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
CLINICAL/METHODICAL ISSUE In view of the diagnostic complexity and the large number of examinations, modern radiology is challenged to identify clinically significant prostate cancer (PCa) with high sensitivity and specificity. Meanwhile overdiagnosis and overtreatment of clinically nonsignificant carcinomas need to be avoided. STANDARD RADIOLOGICAL METHODS Increasingly, international guidelines recommend multiparametric magnetic resonance imaging (mpMRI) as first-line investigation in patients with suspected PCa. METHODICAL INNOVATIONS Image interpretation according to the PI-RADS criteria is limited by interobserver variability. Thus, rapid developments in the field of automated image analysis tools, including radiomics and artificial intelligence (AI; machine learning, deep learning), give hope for further improvement in patient care. PERFORMANCE AI focuses on the automated detection and classification of PCa, but it also attempts to stratify tumor aggressiveness according to the Gleason score. Recent studies present good to very good results in radiomics or AI-supported mpMRI diagnosis. Nevertheless, these systems are not widely used in clinical practice. ACHIEVEMENTS AND PRACTICAL RECOMMENDATIONS In order to apply these innovative technologies, a growing awareness for the need of structured data acquisition, development of robust systems and an increased acceptance of AI as diagnostic support are needed. If AI overcomes these obstacles, it may play a key role in the quantitative and reproducible image-based diagnosis of ever-increasing prostate MRI examination volumes.
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Affiliation(s)
- Charlie Alexander Hamm
- Institute of Radiology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität, and Berlin Institute of Health, Augustenburger Platz 1, 13353, Berlin, Deutschland
| | - Nick Lasse Beetz
- Institute of Radiology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität, and Berlin Institute of Health, Augustenburger Platz 1, 13353, Berlin, Deutschland
| | - Lynn Jeanette Savic
- Institute of Radiology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität, and Berlin Institute of Health, Augustenburger Platz 1, 13353, Berlin, Deutschland
| | - Tobias Penzkofer
- Institute of Radiology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität, and Berlin Institute of Health, Augustenburger Platz 1, 13353, Berlin, Deutschland. .,Berlin Institute of Health, 10178, Berlin, Deutschland.
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Li M, Chen T, Zhao W, Wei C, Li X, Duan S, Ji L, Lu Z, Shen J. Radiomics prediction model for the improved diagnosis of clinically significant prostate cancer on biparametric MRI. Quant Imaging Med Surg 2020; 10:368-379. [PMID: 32190563 DOI: 10.21037/qims.2019.12.06] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Background To evaluate the potential of clinical-based model, a biparametric MRI-based radiomics model and a clinical-radiomics combined model for predicting clinically significant prostate cancer (PCa). Methods In total, 381 patients with clinically suspicious PCa were included in this retrospective study; of those, 199 patients did not have PCa upon biopsy, while 182 patients had PCa. All patients underwent 3.0-T MRI examinations with the same acquisition parameters, and clinical risk factors associated with PCa (age, prostate volume, serum PSA, etc.) were collected. We randomly stratified the training and test sets using a 6:4 ratio. The radiomic features included gradient-based histogram features, grey-level co-occurrence matrix (GLCM), run-length matrix (RLM), and grey-level size zone matrix (GLSZM). Three models were developed using multivariate logistic regression analysis to predict clinically significant PCa: a clinical model, a radiomics model and a clinical-radiomics combined model. The diagnostic performance and clinical net benefit of each model were compared via receiver operating characteristic (ROC) curve analysis and decision curves, respectively. Results Both the radiomics model (AUC: 0.98) and the clinical-radiomics combined model (AUC: 0.98) achieved greater predictive efficacy than the clinical model (AUC: 0.79). The decision curve analysis also showed that the radiomics model and combined model had higher net benefits than the clinical model. Conclusions Compared with the evaluation of clinical risk factors associated with PCa only, the radiomics-based machine learning model can improve the predictive accuracy for clinically significant PCa, in terms of both diagnostic performance and clinical net benefit.
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Affiliation(s)
- Mengjuan Li
- Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou 215000, China
| | - Tong Chen
- Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou 215000, China
| | - Wenlu Zhao
- Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou 215000, China
| | - Chaogang Wei
- Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou 215000, China
| | - Xiaobo Li
- GE Healthcare Life Science, Shanghai 200000, China
| | | | - Libiao Ji
- Department of Radiology, The Affiliated Changshu Hospital of Soochow University, Suzhou 215501, China
| | - Zhihua Lu
- Department of Radiology, The Affiliated Changshu Hospital of Soochow University, Suzhou 215501, China
| | - Junkang Shen
- Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou 215000, China.,Institute of Radiation Oncology Therapeutics of Soochow University, Suzhou 215000, China
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An investigation of machine learning methods in delta-radiomics feature analysis. PLoS One 2019; 14:e0226348. [PMID: 31834910 PMCID: PMC6910670 DOI: 10.1371/journal.pone.0226348] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2019] [Accepted: 11/25/2019] [Indexed: 01/08/2023] Open
Abstract
Purpose This study aimed to investigate the effectiveness of using delta-radiomics to predict overall survival (OS) for patients with recurrent malignant gliomas treated by concurrent stereotactic radiosurgery and bevacizumab, and to investigate the effectiveness of machine learning methods for delta-radiomics feature selection and building classification models. Methods The pre-treatment, one-week post-treatment, and two-month post-treatment T1 and T2 fluid-attenuated inversion recovery (FLAIR) MRI were acquired. 61 radiomic features (intensity histogram-based, morphological, and texture features) were extracted from the gross tumor volume in each image. Delta-radiomics were calculated between the pre-treatment and post-treatment features. Univariate Cox regression and 3 multivariate machine learning methods (L1-regularized logistic regression [L1-LR], random forest [RF] or neural networks [NN]) were used to select a reduced number of features, and 7 machine learning methods (L1-LR, L2-LR, RF, NN, kernel support vector machine [KSVM], linear support vector machine [LSVM], or naïve bayes [NB]) was used to build classification models for predicting OS. The performances of the total 21 model combinations built based on single-time-point radiomics (pre-treatment, one-week post-treatment, and two-month post-treatment) and delta-radiomics were evaluated by the area under the receiver operating characteristic curve (AUC). Results For a small cohort of 12 patients, delta-radiomics resulted in significantly higher AUC than pre-treatment radiomics (p-value<0.01). One-week/two-month delta-features resulted in significantly higher AUC (p-value<0.01) than the one-week/two-month post-treatment features, respectively. 18/21 model combinations were with higher AUC from one-week delta-features than two-month delta-features. With one-week delta-features, RF feature selector + KSVM classifier and RF feature selector + NN classifier showed the highest AUC of 0.889. Conclusions The results indicated that delta-features could potentially provide better treatment assessment than single-time-point features. The treatment assessment is substantially affected by the time point for computing the delta-features and the combination of machine learning methods for feature selection and classification.
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Checcucci E, Autorino R, Cacciamani GE, Amparore D, De Cillis S, Piana A, Piazzolla P, Vezzetti E, Fiori C, Veneziano D, Tewari A, Dasgupta P, Hung A, Gill I, Porpiglia F. Artificial intelligence and neural networks in urology: current clinical applications. MINERVA UROL NEFROL 2019; 72:49-57. [PMID: 31833725 DOI: 10.23736/s0393-2249.19.03613-0] [Citation(s) in RCA: 84] [Impact Index Per Article: 16.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
INTRODUCTION As we enter the era of "big data," an increasing amount of complex health-care data will become available. These data are often redundant, "noisy," and characterized by wide variability. In order to offer a precise and transversal view of a clinical scenario the artificial intelligence (AI) with machine learning (ML) algorithms and Artificial neuron networks (ANNs) process were adopted, with a promising wide diffusion in the near future. The present work aims to provide a comprehensive and critical overview of the current and potential applications of AI and ANNs in urology. EVIDENCE ACQUISITION A non-systematic review of the literature was performed by screening Medline, PubMed, the Cochrane Database, and Embase to detect pertinent studies regarding the application of AI and ANN in Urology. EVIDENCE SYNTHESIS The main application of AI in urology is the field of genitourinary cancers. Focusing on prostate cancer, AI was applied for the prediction of prostate biopsy results. For bladder cancer, the prediction of recurrence-free probability and diagnostic evaluation were analysed with ML algorithms. For kidney and testis cancer, anecdotal experiences were reported for staging and prediction of diseases recurrence. More recently, AI has been applied in non-oncological diseases like stones and functional urology. CONCLUSIONS AI technologies are growing their role in health care; but, up to now, their "real-life" implementation remains limited. However, in the near future, the potential of AI-driven era could change the clinical practice in Urology, improving overall patient outcomes.
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Affiliation(s)
- Enrico Checcucci
- Department of Urology, San Luigi Gonzaga Hospital, University of Turin, Orbassano, Turin, Italy -
| | | | | | - Daniele Amparore
- Department of Urology, San Luigi Gonzaga Hospital, University of Turin, Orbassano, Turin, Italy
| | - Sabrina De Cillis
- Department of Urology, San Luigi Gonzaga Hospital, University of Turin, Orbassano, Turin, Italy
| | - Alberto Piana
- Department of Urology, San Luigi Gonzaga Hospital, University of Turin, Orbassano, Turin, Italy
| | - Pietro Piazzolla
- Department of Management and Production Engineer, Politechnic University of Turin, Turin, Italy
| | - Enrico Vezzetti
- Department of Management and Production Engineer, Politechnic University of Turin, Turin, Italy
| | - Cristian Fiori
- Department of Urology, San Luigi Gonzaga Hospital, University of Turin, Orbassano, Turin, Italy
| | - Domenico Veneziano
- Department of Urology and Renal Transplantation, Bianchi-Melacrino-Morelli Hospital, Reggio Calabria, Italy
| | - Ash Tewari
- Icahn School of Medicine of Mount Sinai, New York, NY, USA
| | | | - Andrew Hung
- USC Institute of Urology, University of Southern California, Los Angeles, CA, USA
| | - Inderbir Gill
- USC Institute of Urology, University of Southern California, Los Angeles, CA, USA
| | - Francesco Porpiglia
- Department of Urology, San Luigi Gonzaga Hospital, University of Turin, Orbassano, Turin, Italy
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Merisaari H, Taimen P, Shiradkar R, Ettala O, Pesola M, Saunavaara J, Boström PJ, Madabhushi A, Aronen HJ, Jambor I. Repeatability of radiomics and machine learning for DWI: Short-term repeatability study of 112 patients with prostate cancer. Magn Reson Med 2019; 83:2293-2309. [PMID: 31703155 DOI: 10.1002/mrm.28058] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2019] [Revised: 10/03/2019] [Accepted: 10/09/2019] [Indexed: 02/06/2023]
Abstract
PURPOSE To evaluate repeatability of prostate DWI-derived radiomics and machine learning methods for prostate cancer (PCa) characterization. METHODS A total of 112 patients with diagnosed PCa underwent 2 prostate MRI examinations (Scan1 and Scan2) performed on the same day. DWI was performed using 12 b-values (0-2000 s/mm2 ), post-processed using kurtosis function, and PCa areas were annotated using whole mount prostatectomy sections. A total of 1694 radiomic features including Sobel, Kirch, Gradient, Zernike Moments, Gabor, Haralick, CoLIAGe, Haar wavelet coefficients, 3D analogue to Laws features, 2D contours, and corner detectors were calculated. Radiomics and 4 feature pruning methods (area under the receiver operator characteristic curve, maximum relevance minimum redundancy, Spearman's ρ, Wilcoxon rank-sum) were evaluated in terms of Scan1-Scan2 repeatability using intraclass correlation coefficient (ICC)(3,1). Classification performance for clinically significant and insignificant PCa with Gleason grade groups 1 versus >1 was evaluated by area under the receiver operator characteristic curve in unseen random 30% data split. RESULTS The ICC(3,1) values for conventional radiomics and feature pruning methods were in the range of 0.28-0.90. The machine learning classifications varied between Scan1 and Scan2 with % of same class labels between Scan1 and Scan2 in the range of 61-81%. Surface-to-volume ratio and corner detector-based features were among the most represented features with high repeatability, ICC(3,1) >0.75, consistently high ranking using all 4 feature pruning methods, and classification performance with area under the receiver operator characteristic curve >0.70. CONCLUSION Surface-to-volume ratio and corner detectors for prostate DWI led to good classification of unseen data and performed similarly in Scan1 and Scan2 in contrast to multiple conventional radiomic features.
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Affiliation(s)
- Harri Merisaari
- Department of Diagnostic Radiology, University of Turku, Turku, Finland.,Department of Future Technologies, University of Turku, Turku, Finland.,Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio
| | - Pekka Taimen
- Institute of Biomedicine, University of Turku, Turku, Finland.,Department of Pathology, Turku University Hospital, Turku, Finland
| | - Rakesh Shiradkar
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio
| | - Otto Ettala
- Department of Urology, University of Turku and Turku University hospital, Turku, Finland
| | - Marko Pesola
- Department of Diagnostic Radiology, University of Turku, Turku, Finland
| | - Jani Saunavaara
- Medical Imaging Centre of Southwest Finland, Turku University Hospital, Turku, Finland
| | - Peter J Boström
- Department of Urology, University of Turku and Turku University hospital, Turku, Finland
| | - Anant Madabhushi
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio
| | - Hannu J Aronen
- Department of Diagnostic Radiology, University of Turku, Turku, Finland.,Medical Imaging Centre of Southwest Finland, Turku University Hospital, Turku, Finland
| | - Ivan Jambor
- Department of Diagnostic Radiology, University of Turku, Turku, Finland.,Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, New York
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Hausmann D, Zoellner FG, Kubik-Huch RA. Editorial for "Qualitative and Quantitative Reporting of a Unique Biparametric MRI: Towards Biparametric MRI-Based Nomograms for Prediction of Prostate Biopsy Outcome in Men With a Clinical Suspicion of Prostate Cancer (IMPROD and MULTI-IMPROD Trials)". J Magn Reson Imaging 2019; 51:1568-1569. [PMID: 31675130 DOI: 10.1002/jmri.26980] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2019] [Revised: 10/10/2019] [Accepted: 10/11/2019] [Indexed: 11/09/2022] Open
Abstract
LEVEL OF EVIDENCE 5 Technical Efficacy Stage: 5 J. Magn. Reson. Imaging 2020;51:1568-1569.
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Affiliation(s)
- Daniel Hausmann
- Department of Radiology, Kantonsspital Baden, Baden, Switzerland.,Department of Clinical Radiology and Nuclear Medicine, University Medical Center Mannheim, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Frank Gerrit Zoellner
- Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Germany
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Stanzione A, Cuocolo R, Cocozza S, Romeo V, Persico F, Fusco F, Longo N, Brunetti A, Imbriaco M. Detection of Extraprostatic Extension of Cancer on Biparametric MRI Combining Texture Analysis and Machine Learning: Preliminary Results. Acad Radiol 2019; 26:1338-1344. [PMID: 30655050 DOI: 10.1016/j.acra.2018.12.025] [Citation(s) in RCA: 47] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2018] [Revised: 12/18/2018] [Accepted: 12/28/2018] [Indexed: 02/08/2023]
Abstract
RATIONALE AND OBJECTIVES Extraprostatic extension of disease (EPE) has a major role in risk stratification of prostate cancer patients. Currently, pretreatment local staging is performed with MRI, while the gold standard is represented by histopathological analysis after radical prostatectomy. Texture analysis (TA) is a quantitative postprocessing method for data extraction, while machine learning (ML) employs artificial intelligence algorithms for data classification. Purpose of this study was to assess whether ML algorithms could predict histopathological EPE using TA features extracted from unenhanced MR images. MATERIALS AND METHODS Index lesions from biparametric MRI examinations of 39 patients with prostate cancer who underwent radical prostatectomy were manually segmented on both T2-weighted images and ADC maps for TA data extraction. Combinations of different feature selection methods and ML classifiers were tested, and their performance was compared to a baseline accuracy reference. RESULTS The classifier showing the best performance was the Bayesian Network, using the dataset obtained by the Subset Evaluator feature selection method. It showed a percentage of correctly classified instances of 82%, an area under the curve of 0.88, a weighted true positive rate of 0.82 and a weighted true negative rate of 0.80. CONCLUSION A combined ML and TA approach appears as a feasible tool to predict histopathological EPE on biparametric MR images.
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64
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Chen J, Remulla D, Nguyen JH, Dua A, Liu Y, Dasgupta P, Hung AJ. Current status of artificial intelligence applications in urology and their potential to influence clinical practice. BJU Int 2019; 124:567-577. [PMID: 31219658 DOI: 10.1111/bju.14852] [Citation(s) in RCA: 65] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
OBJECTIVE To investigate the applications of artificial intelligence (AI) in diagnosis, treatment and outcome predictionin urologic diseases and evaluate its advantages over traditional models and methods. MATERIALS AND METHODS A literature search was performed after PROSPERO registration (CRD42018103701) and in compliance with Preferred Reported Items for Systematic Reviews and Meta-Analyses (PRISMA) methods. Articles between 1994 and 2018 using the search terms "urology", "artificial intelligence", "machine learning" were included and categorized by the application of AI in urology. Review articles, editorial comments, articles with no full-text access, and nonurologic studies were excluded. RESULTS Initial search yielded 231 articles, but after excluding duplicates and following full-text review and examination of article references, only 111 articles were included in the final analysis. AI applications in urology include: utilizing radiomic imaging or ultrasonic echo data to improve or automate cancer detection or outcome prediction, utilizing digitized tissue specimen images to automate detection of cancer on pathology slides, and combining patient clinical data, biomarkers, or gene expression to assist disease diagnosis or outcome prediction. Some studies employed AI to plan brachytherapy and radiation treatments while others used video based or robotic automated performance metrics to objectively evaluate surgical skill. Compared to conventional statistical analysis, 71.8% of studies concluded that AI is superior in diagnosis and outcome prediction. CONCLUSION AI has been widely adopted in urology. Compared to conventional statistics AI approaches are more accurate in prediction and more explorative for analyzing large data cohorts. With an increasing library of patient data accessible to clinicians, AI may help facilitate evidence-based and individualized patient care.
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Affiliation(s)
- Jian Chen
- Catherine & Joseph Aresty Department of Urology, Center for Robotic Simulation & Education, University of Southern California Institute of Urology, Los Angeles, CA, USA
| | - Daphne Remulla
- Catherine & Joseph Aresty Department of Urology, Center for Robotic Simulation & Education, University of Southern California Institute of Urology, Los Angeles, CA, USA
| | - Jessica H Nguyen
- Catherine & Joseph Aresty Department of Urology, Center for Robotic Simulation & Education, University of Southern California Institute of Urology, Los Angeles, CA, USA
| | - Aastha Dua
- Computer Science Department, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA
| | - Yan Liu
- Computer Science Department, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA
| | - Prokar Dasgupta
- Division of Transplantation Immunology and Mucosal Biology, Faculty of Life Sciences and Medicine, Kings College London, London, UK
| | - Andrew J Hung
- Catherine & Joseph Aresty Department of Urology, Center for Robotic Simulation & Education, University of Southern California Institute of Urology, Los Angeles, CA, USA
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65
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Local recurrence of soft tissue sarcoma: a radiomic analysis. Radiol Oncol 2019; 53:300-306. [PMID: 31553702 PMCID: PMC6765164 DOI: 10.2478/raon-2019-0041] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2019] [Accepted: 07/25/2019] [Indexed: 12/13/2022] Open
Abstract
Background To perform a radiomics analysis in local recurrence (LR) surveillance of limb soft tissue sarcoma (STS) Patients and methods This is a sub-study of a prospective multicenter study with Institutional Review Board approval supported by ESSR (European Society of Musculoskeletal Radiology). radiomics analysis was done on fast spin echo axial T1w, T2w fat saturated and post-contrast T1w (T1wGd) 1.5T MRI images of consecutively recruited patients between March 2016 and September 2018. Results N = 11 adult patients (6 men and 5 women; mean age 57.8 ± 17.8) underwent MRI to exclude STS LR: a total of 33 follow-up events were evaluated. A total of 198 data-sets per patients of both pathological and normal tissue were analyzed. Four radiomics features were significantly correlated to tumor size (p < 0.02) and four radiomics features were correlated with grading (p < 0.05). ROC analysis showed an AUC between 0.71 (95%CI: 0.55-0.87) for T1w and 0.96 (95%CI: 0.87-1.00) for post-contrast T1w. Conclusions radiomics features allow to differentiate normal tissue from pathological tissue in MRI surveillance of local recurrence of STS. radiomics in STS evaluation is useful not only for detection purposes but also for lesion characterization.
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66
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Cuocolo R, Cipullo MB, Stanzione A, Ugga L, Romeo V, Radice L, Brunetti A, Imbriaco M. Machine learning applications in prostate cancer magnetic resonance imaging. Eur Radiol Exp 2019; 3:35. [PMID: 31392526 PMCID: PMC6686027 DOI: 10.1186/s41747-019-0109-2] [Citation(s) in RCA: 84] [Impact Index Per Article: 16.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2019] [Accepted: 07/02/2019] [Indexed: 12/17/2022] Open
Abstract
With this review, we aimed to provide a synopsis of recently proposed applications of machine learning (ML) in radiology focusing on prostate magnetic resonance imaging (MRI). After defining the difference between ML and classical rule-based algorithms and the distinction among supervised, unsupervised and reinforcement learning, we explain the characteristic of deep learning (DL), a particular new type of ML, including its structure mimicking human neural networks and its 'black box' nature. Differences in the pipeline for applying ML and DL to prostate MRI are highlighted. The following potential clinical applications in different settings are outlined, many of them based only on MRI-unenhanced sequences: gland segmentation; assessment of lesion aggressiveness to distinguish between clinically significant and indolent cancers, allowing for active surveillance; cancer detection/diagnosis and localisation (transition versus peripheral zone, use of prostate imaging reporting and data system (PI-RADS) version 2), reading reproducibility, differentiation of cancers from prostatitis benign hyperplasia; local staging and pre-treatment assessment (detection of extraprostatic disease extension, planning of radiation therapy); and prediction of biochemical recurrence. Results are promising, but clinical applicability still requires more robust validation across scanner vendors, field strengths and institutions.
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Affiliation(s)
- Renato Cuocolo
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via S. Pansini, 5, 80131, Naples, Italy
| | - Maria Brunella Cipullo
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via S. Pansini, 5, 80131, Naples, Italy
| | - Arnaldo Stanzione
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via S. Pansini, 5, 80131, Naples, Italy.
| | - Lorenzo Ugga
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via S. Pansini, 5, 80131, Naples, Italy
| | - Valeria Romeo
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via S. Pansini, 5, 80131, Naples, Italy
| | - Leonardo Radice
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via S. Pansini, 5, 80131, Naples, Italy
| | - Arturo Brunetti
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via S. Pansini, 5, 80131, Naples, Italy
| | - Massimo Imbriaco
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via S. Pansini, 5, 80131, Naples, Italy
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67
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Korn RL, Rahmanuddin S, Borazanci E. Use of Precision Imaging in the Evaluation of Pancreas Cancer. Cancer Treat Res 2019; 178:209-236. [PMID: 31209847 DOI: 10.1007/978-3-030-16391-4_8] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Pancreas cancer is an aggressive and fatal disease that will become one of the leading causes of cancer mortality by 2030. An all-out effort is underway to better understand the basic biologic mechanisms of this disease ranging from early development to metastatic disease. In order to change the course of this disease, diagnostic radiology imaging may play a vital role in providing a precise, noninvasive method for early diagnosis and assessment of treatment response. Recent progress in combining medical imaging, advanced image analysis and artificial intelligence, termed radiomics, can offer an innovate approach in detecting the earliest changes of tumor development as well as a rapid method for the detection of response. In this chapter, we introduce the principles of radiomics and demonstrate how it can provide additional information into tumor biology, early detection, and response assessments advancing the goals of precision imaging to deliver the right treatment to the right person at the right time.
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Affiliation(s)
- Ronald L Korn
- Virginia G Piper Cancer Center at HonorHealth, Scottsdale, AZ, USA. .,Translational Genomics Research Institute, An Affiliate of City of Hope, Phoenix, AZ, USA. .,Imaging Endpoints Core Lab, Scottsdale, AZ, USA.
| | | | - Erkut Borazanci
- Virginia G Piper Cancer Center at HonorHealth, Scottsdale, AZ, USA.,Translational Genomics Research Institute, An Affiliate of City of Hope, Phoenix, AZ, USA
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68
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Osman SOS, Leijenaar RTH, Cole AJ, Lyons CA, Hounsell AR, Prise KM, O'Sullivan JM, Lambin P, McGarry CK, Jain S. Computed Tomography-based Radiomics for Risk Stratification in Prostate Cancer. Int J Radiat Oncol Biol Phys 2019; 105:448-456. [PMID: 31254658 DOI: 10.1016/j.ijrobp.2019.06.2504] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2019] [Revised: 05/14/2019] [Accepted: 06/14/2019] [Indexed: 01/29/2023]
Abstract
PURPOSE To explore the role of Computed tomography (CT)-based radiomics features in prostate cancer risk stratification. METHODS AND MATERIALS The study population consisted of 506 patients with prostate cancer collected from a clinically annotated database. After applying exclusion criteria, 342 patients were included in the final analysis. CT-based radiomics features were extracted from planning CT scans for prostate gland-only structure, and machine learning was used to train models for Gleason score (GS) and risk group (RG) classifications. Repeated cross-validation was used. The discriminatory performance of the developed models was assessed using receiver operating characteristic area under the curve (AUC) analysis. RESULTS Classifiers using CT-based radiomics features distinguished between GS ≤ 6 versus GS ≥ 7 with AUC = 0.90 and GS 7(3 + 4) versus GS 7(4 + 3) with AUC = 0.98. Developed classifiers also showed excellent performance in distinguishing low versus high RG (AUC = 0.96) and low versus intermediate RG (AUC = 1.00), but poorer performance was observed for GS 7 versus GS > 7 (AUC = 0.69). An overall modest performance was observed for validation on holdout data sets with the highest AUC of 0.75 for classifiers of low versus high RG and an AUC of 0.70 for GS 7 versus GS > 7. CONCLUSIONS Our results show that radiomics features from routinely acquired planning CT scans could provide insights into prostate cancer aggressiveness in a noninvasive manner. Assessing models on training data sets, the classifiers were especially accurate in discerning high-risk from low-risk patients and in classifying GS 7 versus GS > 7 and GS 7(3 + 4) versus G7(4 + 3); however, classifiers were less adept at distinguishing high RG versus intermediate RG. External validation and prospective studies are warranted to verify the presented findings. These findings could potentially guide targeted radiation therapy strategies in radical intent radiation therapy for prostate cancer.
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Affiliation(s)
- Sarah O S Osman
- Centre of Cancer Research and Cell Biology, Queen's University Belfast, Belfast, United Kingdom; Radiotherapy Physics, Northern Ireland Cancer Centre, Belfast Health and Social Care Trust, Belfast, United Kingdom.
| | - Ralph T H Leijenaar
- The D-Lab: Decision Support for Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, the Netherlands
| | - Aidan J Cole
- Centre of Cancer Research and Cell Biology, Queen's University Belfast, Belfast, United Kingdom; Clinical Oncology, Northern Ireland Cancer Centre, Belfast Health and Social Care Trust, Belfast, United Kingdom
| | - Ciara A Lyons
- Centre of Cancer Research and Cell Biology, Queen's University Belfast, Belfast, United Kingdom; Clinical Oncology, Northern Ireland Cancer Centre, Belfast Health and Social Care Trust, Belfast, United Kingdom
| | - Alan R Hounsell
- Centre of Cancer Research and Cell Biology, Queen's University Belfast, Belfast, United Kingdom; Radiotherapy Physics, Northern Ireland Cancer Centre, Belfast Health and Social Care Trust, Belfast, United Kingdom
| | - Kevin M Prise
- Centre of Cancer Research and Cell Biology, Queen's University Belfast, Belfast, United Kingdom
| | - Joe M O'Sullivan
- Centre of Cancer Research and Cell Biology, Queen's University Belfast, Belfast, United Kingdom; Clinical Oncology, Northern Ireland Cancer Centre, Belfast Health and Social Care Trust, Belfast, United Kingdom
| | - Philippe Lambin
- The D-Lab: Decision Support for Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, the Netherlands
| | - Conor K McGarry
- Centre of Cancer Research and Cell Biology, Queen's University Belfast, Belfast, United Kingdom; Radiotherapy Physics, Northern Ireland Cancer Centre, Belfast Health and Social Care Trust, Belfast, United Kingdom
| | - Suneil Jain
- Centre of Cancer Research and Cell Biology, Queen's University Belfast, Belfast, United Kingdom; Clinical Oncology, Northern Ireland Cancer Centre, Belfast Health and Social Care Trust, Belfast, United Kingdom
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69
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Radiomics Signatures of Computed Tomography Imaging for Predicting Risk Categorization and Clinical Stage of Thymomas. BIOMED RESEARCH INTERNATIONAL 2019; 2019:3616852. [PMID: 31275968 PMCID: PMC6558631 DOI: 10.1155/2019/3616852] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/11/2018] [Revised: 03/20/2019] [Accepted: 05/12/2019] [Indexed: 12/12/2022]
Abstract
Purpose The aim of this study is to develop and compare performance of radiomics signatures using texture features extracted from noncontrast enhanced CT (NECT) and contrast enhanced CT (CECT) images for preoperative predicting risk categorization and clinical stage of thymomas. Materials and Methods Between January 2010 and October 2018, 199 patients with surgical resection and histopathologically confirmed thymoma were enrolled in this retrospective study. We extracted 841 radiomics features separately from volume of interest (VOI) in NECT and CECT images. The features with poor reproducibility and highly redundancy were removed. Then a least absolute shrinkage and selection operator method (LASSO) logistic regression model with 10-fold cross validation was used for further feature selection and radiomics signatures build. The predictive performances of radiomics signatures were assessed by receiver operating characteristic (ROC) analysis. The areas under the receiver operating characteristic curve (AUC) between radiomics signatures were compared by using Delong test. Result In differentiating high risk thymomas from low risk thymomas, the AUC, sensitivity, and specificity were 0.801(95% CI 0.740–0.863), 0.752 and 0.767 for radiomics signature based on NECT images, and 0.827 (95% CI 0.771 -0.884), 0.798, and 0.722 for radiomics signature based on CECT images. But there was no significant difference (p=0.365) between them. In differentiating advanced stage thymomas from early stage thymomas, the AUC, sensitivity, and specificity were 0.829 (95%CI 0.757-0.900), 0.712, and 0.806 for radiomics signature based on NECT images and 0.860 (95%CI 0.803-0.917), 0.699, and 0.889 for radiomics signature based on CECT images. There was no significant difference (p=0.069) between them. The accuracy was 0.819 for radiomics signature based on NECT images, 0.869 for radiomics signature based on CECT images, and 0.779 for radiologists. Both radiomics signatures had a better performance than radiologists. But there was significant difference (p = 0.025) only between CECT radiomics signature and radiologists. Conclusion Radiomics signatures based on texture analysis from NECT and CECT images could be utilized as noninvasive biomarkers for differentiating high risk thymomas from low risk thymomas and advanced stage thymomas from early stage thymoma. As a quantitative method, radiomics signature can provide complementary diagnostic information and help to plan personalized treatment for patients with thymomas.
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Daniel M, Kuess P, Andrzejewski P, Nyholm T, Helbich T, Polanec S, Dragschitz F, Goldner G, Georg D, Baltzer P. Impact of androgen deprivation therapy on apparent diffusion coefficient and T2w MRI for histogram and texture analysis with respect to focal radiotherapy of prostate cancer. Strahlenther Onkol 2019; 195:402-411. [PMID: 30478670 PMCID: PMC6488548 DOI: 10.1007/s00066-018-1402-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2018] [Accepted: 11/12/2018] [Indexed: 12/20/2022]
Abstract
PURPOSE Accurate prostate cancer (PCa) detection is essential for planning focal external beam radiotherapy (EBRT). While biparametric MRI (bpMRI) including T2-weighted (T2w) and diffusion-weighted images (DWI) is an accurate tool to localize PCa, its value is less clear in the case of additional androgen deprivation therapy (ADT). The aim of this study was to investigate the value of a textural feature (TF) approach on bpMRI analysis in prostate cancer patients with and without neoadjuvant ADT with respect to future dose-painting applications. METHODS 28 PCa patients (54-80 years) with (n = 14) and without (n = 14) ADT who underwent bpMRI with T2w and DWI were analyzed retrospectively. Lesions, central gland (CG), and peripheral zone (PZ) were delineated by an experienced urogenital radiologist based on localized pre-therapeutic histopathology. Histogram parameters and 20 Haralick TF were calculated. Regional differences (i. e., tumor vs. PZ, tumor vs. CG) were analyzed for all imaging parameters. Receiver-operating characteristic (ROC) analysis was performed to measure diagnostic performance to distinguish PCa from benign prostate tissue and to identify the features with best discriminative power in both patient groups. RESULTS The obtained sensitivities were equivalent or superior when utilizing the TF in the no-ADT group, while specificity was higher for the histogram parameters. However, in the ADT group, TF outperformed the conventional histogram parameters in both specificity and sensitivity. Rule-in and rule-out criteria for ADT patients could exclusively be defined with the aid of TF. CONCLUSIONS The TF approach has the potential for quantitative image-assisted boost volume delineation in PCa patients even if they are undergoing neoadjuvant ADT.
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Affiliation(s)
- M Daniel
- Christian Doppler Laboratory for Medical Radiation Research for Radiation Oncology, Medical University of Vienna, Vienna, Austria.
- Department of Radiotherapy, Comprehensive Cancer Center, Medical University of Vienna/Vienna General Hospital, Vienna, Austria.
| | - P Kuess
- Christian Doppler Laboratory for Medical Radiation Research for Radiation Oncology, Medical University of Vienna, Vienna, Austria
- Department of Radiotherapy, Comprehensive Cancer Center, Medical University of Vienna/Vienna General Hospital, Vienna, Austria
| | - P Andrzejewski
- Christian Doppler Laboratory for Medical Radiation Research for Radiation Oncology, Medical University of Vienna, Vienna, Austria
- Medical Physics, EBG MedAustron GmbH, Wiener Neustadt, Austria
| | - T Nyholm
- Department of Radiation Sciences, Umeå University, Umeå, Sweden
| | - T Helbich
- Christian Doppler Laboratory for Medical Radiation Research for Radiation Oncology, Medical University of Vienna, Vienna, Austria
| | - S Polanec
- Christian Doppler Laboratory for Medical Radiation Research for Radiation Oncology, Medical University of Vienna, Vienna, Austria
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna/Vienna General Hospital, Vienna, Austria
| | - F Dragschitz
- Department of Radiotherapy, Comprehensive Cancer Center, Medical University of Vienna/Vienna General Hospital, Vienna, Austria
| | - G Goldner
- Department of Radiotherapy, Comprehensive Cancer Center, Medical University of Vienna/Vienna General Hospital, Vienna, Austria
| | - D Georg
- Christian Doppler Laboratory for Medical Radiation Research for Radiation Oncology, Medical University of Vienna, Vienna, Austria
- Department of Radiotherapy, Comprehensive Cancer Center, Medical University of Vienna/Vienna General Hospital, Vienna, Austria
| | - P Baltzer
- Christian Doppler Laboratory for Medical Radiation Research for Radiation Oncology, Medical University of Vienna, Vienna, Austria
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna/Vienna General Hospital, Vienna, Austria
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Chirra P, Leo P, Yim M, Bloch BN, Rastinehad AR, Purysko A, Rosen M, Madabhushi A, Viswanath SE. Multisite evaluation of radiomic feature reproducibility and discriminability for identifying peripheral zone prostate tumors on MRI. J Med Imaging (Bellingham) 2019; 6:024502. [PMID: 31259199 PMCID: PMC6566001 DOI: 10.1117/1.jmi.6.2.024502] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2018] [Accepted: 05/15/2019] [Indexed: 12/18/2022] Open
Abstract
Recent advances in the field of radiomics have enabled the development of a number of prognostic and predictive imaging-based tools for a variety of diseases. However, wider clinical adoption of these tools is contingent on their generalizability across multiple sites and scanners. This may be particularly relevant in the context of radiomic features derived from T1- or T2-weighted magnetic resonance images (MRIs), where signal intensity values are known to lack tissue-specific meaning and vary based on differing acquisition protocols between institutions. We present the first empirical study of benchmarking five different radiomic feature families in terms of both reproducibility and discriminability in a multisite setting, specifically, for identifying prostate tumors in the peripheral zone on MRI. Our cohort comprised 147 patient T2-weighted MRI datasets from four different sites, all of which are first preprocessed to correct for acquisition-related artifacts such as bias field, differing voxel resolutions, and intensity drift (nonstandardness). About 406 three-dimensional voxel-wise radiomic features from five different families (gray, Haralick, gradient, Laws, and Gabor) were evaluated in a cross-site setting to determine (a) how reproducible they are within a relatively homogeneous nontumor tissue region and (b) how well they could discriminate tumor regions from nontumor regions. Our results demonstrate that a majority of the popular Haralick features are reproducible in over 99% of all cross-site comparisons, as well as achieve excellent cross-site discriminability (classification accuracy of ≈ 0.8 ). By contrast, a majority of Laws features are highly variable across sites (reproducible in < 75 % of all cross-site comparisons) as well as resulting in low cross-site classifier accuracies ( < 0.6 ), likely due to a large number of noisy filter responses that can be extracted. These trends suggest that only a subset of radiomic features and associated parameters may be both reproducible and discriminable enough for use within machine learning classifier schemes.
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Affiliation(s)
- Prathyush Chirra
- Case Western Reserve University, Department of Biomedical Engineering, Cleveland, Ohio, United States
| | - Patrick Leo
- Case Western Reserve University, Department of Biomedical Engineering, Cleveland, Ohio, United States
| | - Michael Yim
- Northeast Ohio Medical University, College of Medicine, Rootstown, Ohio, United States
| | - B. Nicolas Bloch
- Boston University School of Medicine, Department of Radiology, Boston, Massachusetts, United States
| | - Ardeshir R. Rastinehad
- Icahn School of Medicine at Mount Sinai, Department of Urology, New York, New York, United States
| | - Andrei Purysko
- Cleveland Clinic, Department of Radiology, Cleveland, Ohio, United States
| | - Mark Rosen
- Hospital of the University of Pennsylvania, Department of Radiology, Philadelphia, Pennsylvania, United States
| | - Anant Madabhushi
- Case Western Reserve University, Department of Biomedical Engineering, Cleveland, Ohio, United States
- Louis Stokes Cleveland Veterans Administration Medical Center, Cleveland, Ohio, United States
| | - Satish E. Viswanath
- Case Western Reserve University, Department of Biomedical Engineering, Cleveland, Ohio, United States
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Min X, Li M, Dong D, Feng Z, Zhang P, Ke Z, You H, Han F, Ma H, Tian J, Wang L. Multi-parametric MRI-based radiomics signature for discriminating between clinically significant and insignificant prostate cancer: Cross-validation of a machine learning method. Eur J Radiol 2019; 115:16-21. [PMID: 31084754 DOI: 10.1016/j.ejrad.2019.03.010] [Citation(s) in RCA: 74] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2018] [Revised: 03/13/2019] [Accepted: 03/14/2019] [Indexed: 02/06/2023]
Abstract
PURPOSE To evaluate the performance of a multi-parametric MRI (mp-MRI)-based radiomics signature for discriminating between clinically significant prostate cancer (csPCa) and insignificant PCa (ciPCa). MATERIALS AND METHODS Two hundred and eighty patients with pathology-proven PCa were enrolled and were randomly divided into training and test cohorts. Eight hundred and nineteen radiomics features were extracted from mp-MRI for each patient. The minority group in the training cohort was balanced via the synthetic minority over-sampling technique (SMOTE) method. We used minimum-redundancy maximum-relevance (mRMR) selection and the LASSO algorithm for feature selection and radiomics signature building. The classification performance of the radiomics signature for csPCa and ciPCa was evaluated by receiver operating characteristic curve analysis in the training and test cohorts. RESULTS Nine features were selected for the radiomics signature building. Significant differences in the radiomics signature existed between the csPCa and ciPCa groups in both the training and test cohorts (p < 0.01 for both). The AUC, sensitivity and specificity of the radiomics signature were 0.872 (95% CI: 0.823-0.921), 0.883, and 0.753, respectively, in the training cohort, and 0.823 (95% CI: 0.669-0.976), 0.841, and 0.727, respectively, in the test cohort. CONCLUSION Mp-MRI-based radiomics signature have the potential to noninvasively discriminate between csPCa and ciPCa.
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Affiliation(s)
- Xiangde Min
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, PR China
| | - Min Li
- Sino-Dutch Biomedical and Information Engineering School, Northeastern University, Shenyang, Liaoning Province, PR China
| | - Di Dong
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, PR China; University of Chinese Academy of Sciences, Beijing, PR China
| | - Zhaoyan Feng
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, PR China
| | - Peipei Zhang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, PR China
| | - Zan Ke
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, PR China
| | - Huijuan You
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, PR China
| | - Fangfang Han
- Sino-Dutch Biomedical and Information Engineering School, Northeastern University, Shenyang, Liaoning Province, PR China
| | - He Ma
- Sino-Dutch Biomedical and Information Engineering School, Northeastern University, Shenyang, Liaoning Province, PR China.
| | - Jie Tian
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, PR China; University of Chinese Academy of Sciences, Beijing, PR China; Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine, Beihang University, Beijing, PR China.
| | - Liang Wang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, PR China.
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Liu Z, Wang S, Dong D, Wei J, Fang C, Zhou X, Sun K, Li L, Li B, Wang M, Tian J. The Applications of Radiomics in Precision Diagnosis and Treatment of Oncology: Opportunities and Challenges. Theranostics 2019; 9:1303-1322. [PMID: 30867832 PMCID: PMC6401507 DOI: 10.7150/thno.30309] [Citation(s) in RCA: 489] [Impact Index Per Article: 97.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2018] [Accepted: 01/10/2019] [Indexed: 12/14/2022] Open
Abstract
Medical imaging can assess the tumor and its environment in their entirety, which makes it suitable for monitoring the temporal and spatial characteristics of the tumor. Progress in computational methods, especially in artificial intelligence for medical image process and analysis, has converted these images into quantitative and minable data associated with clinical events in oncology management. This concept was first described as radiomics in 2012. Since then, computer scientists, radiologists, and oncologists have gravitated towards this new tool and exploited advanced methodologies to mine the information behind medical images. On the basis of a great quantity of radiographic images and novel computational technologies, researchers developed and validated radiomic models that may improve the accuracy of diagnoses and therapy response assessments. Here, we review the recent methodological developments in radiomics, including data acquisition, tumor segmentation, feature extraction, and modelling, as well as the rapidly developing deep learning technology. Moreover, we outline the main applications of radiomics in diagnosis, treatment planning and evaluations in the field of oncology with the aim of developing quantitative and personalized medicine. Finally, we discuss the challenges in the field of radiomics and the scope and clinical applicability of these methods.
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Affiliation(s)
- Zhenyu Liu
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, 100190, China
- University of Chinese Academy of Sciences, Beijing, 100080, China
| | - Shuo Wang
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, 100190, China
- University of Chinese Academy of Sciences, Beijing, 100080, China
| | - Di Dong
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, 100190, China
- University of Chinese Academy of Sciences, Beijing, 100080, China
| | - Jingwei Wei
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, 100190, China
- University of Chinese Academy of Sciences, Beijing, 100080, China
| | - Cheng Fang
- Department of Hepatobiliary Surgery, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, 646000, China
| | - Xuezhi Zhou
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, 100190, China
- Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, 710126, China
| | - Kai Sun
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, 100190, China
- Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, 710126, China
| | - Longfei Li
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, 100190, China
- Collaborative Innovation Center for Internet Healthcare, Zhengzhou University, Zhengzhou, Henan, 450052, China
| | - Bo Li
- Department of Hepatobiliary Surgery, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, 646000, China
| | - Meiyun Wang
- Department of Radiology, Henan Provincial People's Hospital & the People's Hospital of Zhengzhou University, Zhengzhou, Henan, 450003, China
| | - Jie Tian
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, 100190, China
- Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, 710126, China
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University, Beijing, 100191, China
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74
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Objective risk stratification of prostate cancer using machine learning and radiomics applied to multiparametric magnetic resonance images. Sci Rep 2019; 9:1570. [PMID: 30733585 PMCID: PMC6367324 DOI: 10.1038/s41598-018-38381-x] [Citation(s) in RCA: 52] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2018] [Accepted: 12/27/2018] [Indexed: 12/24/2022] Open
Abstract
Multiparametric magnetic resonance imaging (mpMRI) has become increasingly important for the clinical assessment of prostate cancer (PCa), but its interpretation is generally variable due to its relatively subjective nature. Radiomics and classification methods have shown potential for improving the accuracy and objectivity of mpMRI-based PCa assessment. However, these studies are limited to a small number of classification methods, evaluation using the AUC score only, and a non-rigorous assessment of all possible combinations of radiomics and classification methods. This paper presents a systematic and rigorous framework comprised of classification, cross-validation and statistical analyses that was developed to identify the best performing classifier for PCa risk stratification based on mpMRI-derived radiomic features derived from a sizeable cohort. This classifier performed well in an independent validation set, including performing better than PI-RADS v2 in some aspects, indicating the value of objectively interpreting mpMRI images using radiomics and classification methods for PCa risk assessment.
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75
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Fedorov A, Schwier M, Clunie D, Herz C, Pieper S, Kikinis R, Tempany C, Fennessy F. An annotated test-retest collection of prostate multiparametric MRI. Sci Data 2018; 5:180281. [PMID: 30512014 PMCID: PMC6278692 DOI: 10.1038/sdata.2018.281] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2018] [Accepted: 10/26/2018] [Indexed: 12/13/2022] Open
Abstract
Multiparametric Magnetic Resonance Imaging (mpMRI) is widely used for characterizing prostate cancer. Standard of care use of mpMRI in clinic relies on visual interpretation of the images by an expert. mpMRI is also increasingly used as a quantitative imaging biomarker of the disease. Little is known about repeatability of such quantitative measurements, and no test-retest datasets have been available publicly to support investigation of the technical characteristics of the MRI-based quantification in the prostate. Here we present an mpMRI dataset consisting of baseline and repeat prostate MRI exams for 15 subjects, manually annotated to define regions corresponding to lesions and anatomical structures, and accompanied by region-based measurements. This dataset aims to support further investigation of the repeatability of mpMRI-derived quantitative prostate measurements, study of the robustness and reliability of the automated analysis approaches, and to support development and validation of new image analysis techniques. The manuscript can also serve as an example of the use of DICOM for standardized encoding of the image annotation and quantification results.
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Affiliation(s)
- Andriy Fedorov
- Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Michael Schwier
- Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | | | - Christian Herz
- Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | | | - Ron Kikinis
- Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Fraunhofer MEVIS, Bremen, Germany
- Mathematics/Computer Science Faculty, University of Bremen, Bremen, Germany
| | - Clare Tempany
- Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Fiona Fennessy
- Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
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Liu H, Zhang C, Wang L, Luo R, Li J, Zheng H, Yin Q, Zhang Z, Duan S, Li X, Wang D. MRI radiomics analysis for predicting preoperative synchronous distant metastasis in patients with rectal cancer. Eur Radiol 2018; 29:4418-4426. [PMID: 30413955 DOI: 10.1007/s00330-018-5802-7] [Citation(s) in RCA: 50] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2018] [Revised: 09/12/2018] [Accepted: 09/25/2018] [Indexed: 12/13/2022]
Abstract
OBJECTIVES To investigate the value of MRI radiomics based on T2-weighted (T2W) images in predicting preoperative synchronous distant metastasis (SDM) in patients with rectal cancer. METHODS This retrospective study enrolled 177 patients with histopathology-confirmed rectal adenocarcinoma (123 patients in the training cohort and 54 in the validation cohort). A total of 385 radiomics features were extracted from pretreatment T2W images. Five steps, including univariate statistical tests and a random forest algorithm, were performed to select the best preforming features for predicting SDM. Multivariate logistic regression analysis was conducted to build the clinical and clinical-radiomics combined models in the training cohort. The predictive performance was validated by receiver operating characteristics curve (ROC) analysis and clinical utility implementing a nomogram and decision curve analysis. RESULTS Fifty-nine patients (33.3%) were confirmed to have SDM. Six radiomics features and four clinical characteristics were selected for predicting SDM. The clinical-radiomics combined model performed better than the clinical model in both the training and validation datasets. A threshold of 0.44 yielded an area under the ROC (AUC) value of 0.827 (95% confidence interval (CI), 0.6963-0.9580), a sensitivity of 72.2%, a specificity of 94.4%, and an accuracy of 87.0% in the validation cohort for the combined model. A clinical-radiomics nomogram and decision curve analysis confirmed the clinical utility of the combined model. CONCLUSIONS Our proposed clinical-radiomics combined model could be utilized as a noninvasive biomarker for identifying patients at high risk of SDM, which could aid in tailoring treatment strategies. KEY POINTS • T2WI-based radiomics analysis helps predict synchronous distant metastasis (SDM) of rectal cancer. • The clinical-radiomics combined model could be utilized as a noninvasive biomarker for predicting SDM. • Personalized treatment can be carried out with greater confidence based on the risk stratification for SDM in rectal cancer.
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Affiliation(s)
- Huanhuan Liu
- Department of Radiology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, No. 1665 Kongjiang Road, Shanghai, 200092, China
| | - Caiyuan Zhang
- Department of Radiology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, No. 1665 Kongjiang Road, Shanghai, 200092, China
| | - Lijun Wang
- Department of Radiology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, No. 1665 Kongjiang Road, Shanghai, 200092, China
| | - Ran Luo
- Department of Radiology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, No. 1665 Kongjiang Road, Shanghai, 200092, China
| | - Jinning Li
- Department of Radiology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, No. 1665 Kongjiang Road, Shanghai, 200092, China
| | - Hui Zheng
- Department of Radiology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, No. 1665 Kongjiang Road, Shanghai, 200092, China
| | - Qiufeng Yin
- Department of Radiology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, No. 1665 Kongjiang Road, Shanghai, 200092, China
| | - Zhongyang Zhang
- Department of Radiology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, No. 1665 Kongjiang Road, Shanghai, 200092, China
| | - Shaofeng Duan
- GE Healthcare, Pudong New Town, No.1, Huatuo Road, Shanghai, 210000, China
| | - Xin Li
- GE Healthcare, Pudong New Town, No.1, Huatuo Road, Shanghai, 210000, China
| | - Dengbin Wang
- Department of Radiology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, No. 1665 Kongjiang Road, Shanghai, 200092, China.
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Zamboglou C, Eiber M, Fassbender TR, Eder M, Kirste S, Bock M, Schilling O, Reichel K, van der Heide UA, Grosu AL. Multimodal imaging for radiation therapy planning in patients with primary prostate cancer. Phys Imaging Radiat Oncol 2018; 8:8-16. [PMID: 33458410 PMCID: PMC7807571 DOI: 10.1016/j.phro.2018.10.001] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2018] [Revised: 10/22/2018] [Accepted: 10/23/2018] [Indexed: 12/22/2022] Open
Abstract
Implementation of advanced imaging techniques like multiparametric magnetic resonance imaging (mpMRI) or Positron Emission Tomography (PET) in radiation therapy (RT) planning of patients with primary prostate cancer demands several preconditions: accurate staging of the extraprostatic and intraprostatic tumor mass, robust delineation of the intraprostatic gross tumor volume (GTV) and a reproducible characterization of the prostate cancer's biological properties. In the current review we searched for the currently available imaging techniques and we discussed their ability to fulfill these preconditions. We found that current pretreatment imaging was mainly performed with mpMRI and/or Prostate-specific membrane antigen PET imaging. Both techniques offered an accurate detection of the extraprostatic and intraprostatic tumor burden and had a major impact on RT concepts. However, some studies postulated that mpMRI and PSMA PET had complementary information for intraprostatic GTV detection. Moreover, interobserver differences for intraprostatic tumor delineation based on mpMRI were observed. It is currently unclear whether PET based GTV delineation underlies also interobserver heterogeneity. Further research is warranted to answer whether multimodal imaging is able to visualize biological processes related to prostate cancer pathophysiology and radiation resistance.
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Affiliation(s)
- Constantinos Zamboglou
- Department of Radiation Oncology, Medical Center – University of Freiburg, Faculty of Medicine, University of Freiburg, Germany
- German Cancer Consortium (DKTK), Partner Site Freiburg, Germany
- Berta-Ottenstein-Programme, Faculty of Medicine, University of Freiburg, Germany
| | - Matthias Eiber
- Department of Nuclear Medicine, Klinikum rechts der Isar, Technical University Munich, Germany
- German Cancer Consortium (DKTK), Partner Site Freiburg, Germany
| | - Thomas R. Fassbender
- Department of Nuclear Medicine, Medical Center – University of Freiburg, Faculty of Medicine, University of Freiburg, Germany
| | - Matthias Eder
- Department of Nuclear Medicine, Medical Center – University of Freiburg, Faculty of Medicine, University of Freiburg, Germany
| | - Simon Kirste
- Department of Radiation Oncology, Medical Center – University of Freiburg, Faculty of Medicine, University of Freiburg, Germany
- German Cancer Consortium (DKTK), Partner Site Freiburg, Germany
| | - Michael Bock
- Division of Medical Physics, Department of Radiology, Medical Center – University of Freiburg, Faculty of Medicine, University of Freiburg, Germany
- German Cancer Consortium (DKTK), Partner Site Freiburg, Germany
| | - Oliver Schilling
- Institute of Surgical Pathology, Medical Center – University of Freiburg, Faculty of Medicine, University of Freiburg, Germany
- German Cancer Consortium (DKTK), Partner Site Freiburg, Germany
| | - Kathrin Reichel
- Department of Urology, Medical Center – University of Freiburg, Faculty of Medicine, University of Freiburg, Germany
| | - Uulke A. van der Heide
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Anca L. Grosu
- Department of Radiation Oncology, Medical Center – University of Freiburg, Faculty of Medicine, University of Freiburg, Germany
- German Cancer Consortium (DKTK), Partner Site Freiburg, Germany
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