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Hameed M, Yeung J, Boone D, Mallett S, Halligan S. Meta-research: How many diagnostic or prognostic models published in radiological journals are evaluated externally? Eur Radiol 2024; 34:2524-2533. [PMID: 37696974 PMCID: PMC10957714 DOI: 10.1007/s00330-023-10168-3] [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: 12/03/2022] [Revised: 07/03/2023] [Accepted: 07/11/2023] [Indexed: 09/13/2023]
Abstract
OBJECTIVES Prognostic and diagnostic models must work in their intended clinical setting, proven via "external evaluation", preferably by authors uninvolved with model development. By systematic review, we determined the proportion of models published in high-impact radiological journals that are evaluated subsequently. METHODS We hand-searched three radiological journals for multivariable diagnostic/prognostic models 2013-2015 inclusive, developed using regression. We assessed completeness of data presentation to allow subsequent external evaluation. We then searched literature to August 2022 to identify external evaluations of these index models. RESULTS We identified 98 index studies (73 prognostic; 25 diagnostic) describing 145 models. Only 15 (15%) index studies presented an evaluation (two external). No model was updated. Only 20 (20%) studies presented a model equation. Just 7 (15%) studies developing Cox models presented a risk table, and just 4 (9%) presented the baseline hazard. Two (4%) studies developing non-Cox models presented the intercept. Just 20 (20%) articles presented a Kaplan-Meier curve of the final model. The 98 index studies attracted 4224 citations (including 559 self-citations), median 28 per study. We identified just six (6%) subsequent external evaluations of an index model, five of which were external evaluations by researchers uninvolved with model development, and from a different institution. CONCLUSIONS Very few prognostic or diagnostic models published in radiological literature are evaluated externally, suggesting wasted research effort and resources. Authors' published models should present data sufficient to allow external evaluation by others. To achieve clinical utility, researchers should concentrate on model evaluation and updating rather than continual redevelopment. CLINICAL RELEVANCE STATEMENT The large majority of prognostic and diagnostic models published in high-impact radiological journals are never evaluated. It would be more efficient for researchers to evaluate existing models rather than practice continual redevelopment. KEY POINTS • Systematic review of highly cited radiological literature identified few diagnostic or prognostic models that were evaluated subsequently by researchers uninvolved with the original model. • Published radiological models frequently omit important information necessary for others to perform an external evaluation: Only 20% of studies presented a model equation or nomogram. • A large proportion of research citing published models focuses on redevelopment and ignores evaluation and updating, which would be a more efficient use of research resources.
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Affiliation(s)
- Maira Hameed
- Centre for Medical Imaging, University College London UCL, Charles Bell House, 43-45 Foley Street, London, W1W 7TS, UK
| | - Jason Yeung
- Centre for Medical Imaging, University College London UCL, Charles Bell House, 43-45 Foley Street, London, W1W 7TS, UK
| | - Darren Boone
- Centre for Medical Imaging, University College London UCL, Charles Bell House, 43-45 Foley Street, London, W1W 7TS, UK
| | - Sue Mallett
- Centre for Medical Imaging, University College London UCL, Charles Bell House, 43-45 Foley Street, London, W1W 7TS, UK
| | - Steve Halligan
- Centre for Medical Imaging, University College London UCL, Charles Bell House, 43-45 Foley Street, London, W1W 7TS, UK.
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Mukherjee S, Papadopoulos D, Chari N, Ellis D, Charitopoulos K, Charitopoulos I, Bishara S. High-grade prostate cancer demonstrates preferential growth in the cranio-caudal axis and provides discrimination of disease grade in an MRI parametric model. Br J Radiol 2024; 97:574-582. [PMID: 38276882 PMCID: PMC11027337 DOI: 10.1093/bjr/tqad066] [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: 07/12/2023] [Revised: 10/06/2023] [Accepted: 10/16/2023] [Indexed: 01/27/2024] Open
Abstract
OBJECTIVES To determine if multiparametric MRI prostate cancer (PC) lesion dimensions in different axes could distinguish between PC, grade group (GG) >2, and GG >3 on targeted transperineal biopsy and create and validate a predictive model on a separate cohort. METHODS The maximum transverse, anterio-posterior, and cranio-caudal lesion dimensions were assessed against the presence of any cancer, GG >2, and GG >3 on biopsy by binary logistic regression. The optimum multivariate models were evaluated on a separate cohort. RESULTS One hundred and ninety-three lesions from 148 patients were evaluated. Increased lesion volume, Prostate Specific Antigen (PSA), Prostate Imaging Reporting and Data System score, and decreased Apparent Diffusion Coefficient (ADC) were associated with increased GG (P < .001). The ratio of cranio-caudal to anterior-posterior lesion dimension increased from 1.20 (95% CI, 1.14-1.25) for GG ≤ 3 to 1.43 (95% CI, 1.28-1.57) for GG > 3 (P = .0022). The cranio-caudal dimension of the lesion was the strongest predictor of GG >3 (P = .000, area under the receiver operator characteristic curve [AUC] = 0.81). The best multivariate models had an AUC of 0.84 for cancer, 0.88 for GG > 2, and 0.89 for GG > 3. These models were evaluated on a separate cohort of 40 patients with 61 lesions. They demonstrated an AUC, sensitivity, and specificity of 0.82, 82.3%, and 55.5%, respectively, for the detection of cancer. For GG > 2, the models achieved an AUC of 0.84, sensitivity of 91.7%, and specificity of 69.4%. Additionally, for GG > 3, the models showed an AUC of 0.92, sensitivity of 88.9%, and specificity of 98.1%. CONCLUSIONS Cranio-caudal lesion dimension when used in conjunction with other parameters can create a model superior to the Prostate Imaging Reporting and Data Systems score in predicting cancer. ADVANCES IN KNOWLEDGE Higher-grade PC has a propensity to grow in the cranio-caudal direction, and this could be factored into MRI-based predictive models of prostate biopsy grade.
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Affiliation(s)
- Subhabrata Mukherjee
- Department of Urology, West Middlesex Hospital, Chelsea and Westminster NHS Trust, Twickenham Road, London, TW7 6AF, United Kingdom
| | - Dimitrios Papadopoulos
- Department of Urology, West Middlesex Hospital, Chelsea and Westminster NHS Trust, Twickenham Road, London, TW7 6AF, United Kingdom
| | - Natasha Chari
- Department of Urology, West Middlesex Hospital, Chelsea and Westminster NHS Trust, Twickenham Road, London, TW7 6AF, United Kingdom
| | - David Ellis
- Department of Urology, West Middlesex Hospital, Chelsea and Westminster NHS Trust, Twickenham Road, London, TW7 6AF, United Kingdom
| | - Konstantinos Charitopoulos
- Department of Urology, West Middlesex Hospital, Chelsea and Westminster NHS Trust, Twickenham Road, London, TW7 6AF, United Kingdom
| | - Ivo Charitopoulos
- Department of Urology, West Middlesex Hospital, Chelsea and Westminster NHS Trust, Twickenham Road, London, TW7 6AF, United Kingdom
| | - Samuel Bishara
- Department of Urology, West Middlesex Hospital, Chelsea and Westminster NHS Trust, Twickenham Road, London, TW7 6AF, United Kingdom
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Ching JCF, Lam S, Lam CCH, Lui AOY, Kwong JCK, Lo AYH, Chan JWH, Cai J, Leung WS, Lee SWY. Integrating CT-based radiomic model with clinical features improves long-term prognostication in high-risk prostate cancer. Front Oncol 2023; 13:1060687. [PMID: 37205204 PMCID: PMC10186349 DOI: 10.3389/fonc.2023.1060687] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Accepted: 04/10/2023] [Indexed: 05/21/2023] Open
Abstract
Objective High-risk prostate cancer (PCa) is often treated by prostate-only radiotherapy (PORT) owing to its favourable toxicity profile compared to whole-pelvic radiotherapy. Unfortunately, more than 50% patients still developed disease progression following PORT. Conventional clinical factors may be unable to identify at-risk subgroups in the era of precision medicine. In this study, we aimed to investigate the prognostic value of pre-treatment planning computed tomography (pCT)-based radiomic features and clinical attributes to predict 5-year progression-free survival (PFS) in high-risk PCa patients following PORT. Materials and methods A total of 176 biopsy-confirmed PCa patients who were treated at the Hong Kong Princess Margaret Hospital were retrospectively screened for eligibility. Clinical data and pCT of one hundred eligible high-risk PCa patients were analysed. Radiomic features were extracted from the gross-tumour-volume (GTV) with and without applying Laplacian-of-Gaussian (LoG) filter. The entire patient cohort was temporally stratified into a training and an independent validation cohort in a ratio of 3:1. Radiomics (R), clinical (C) and radiomic-clinical (RC) combined models were developed by Ridge regression through 5-fold cross-validation with 100 iterations on the training cohort. A model score was calculated for each model based on the included features. Model classification performance on 5-year PFS was evaluated in the independent validation cohort by average area-under-curve (AUC) of receiver-operating-characteristics (ROC) curve and precision-recall curve (PRC). Delong's test was used for model comparison. Results The RC combined model which contains 6 predictive features (tumour flatness, root-mean-square on fine LoG-filtered image, prostate-specific antigen serum concentration, Gleason score, Roach score and GTV volume) was the best-performing model (AUC = 0.797, 95%CI = 0.768-0.826), which significantly outperformed the R-model (AUC = 0.795, 95%CI = 0.774-0.816) and C-model (AUC = 0.625, 95%CI = 0.585-0.665) in the independent validation cohort. Besides, only the RC model score significantly classified patients in both cohorts into progression and progression-free groups regarding their 5-year PFS (p< 0.05). Conclusion Combining pCT-based radiomic and clinical attributes provided superior prognostication value regarding 5-year PFS in high-risk PCa patients following PORT. A large multi-centre study will potentially aid clinicians in implementing personalised treatment for this vulnerable subgroup in the future.
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Affiliation(s)
- Jerry C. F. Ching
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Saikit Lam
- Department of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
- Research Institute for Smart Aging, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Cody C. H. Lam
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Angie O. Y. Lui
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Joanne C. K. Kwong
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Anson Y. H. Lo
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Jason W. H. Chan
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - W. S. Leung
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
- *Correspondence: Shara W. Y. Lee, ; W. S. Leung,
| | - Shara W. Y. Lee
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
- *Correspondence: Shara W. Y. Lee, ; W. S. Leung,
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Banno T, Nakamura K, Kaneda Y, Ozaki A, Kouchi Y, Ohira T, Shimmura H. Detection rate and variables associated with incidental prostate cancer by holmium laser enucleation of the prostate. Int J Urol 2022; 29:860-865. [PMID: 35584916 DOI: 10.1111/iju.14917] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Accepted: 04/17/2022] [Indexed: 01/08/2023]
Abstract
OBJECTIVES Holmium laser enucleation of the prostate is well-established and effective for bladder outlet obstruction due to benign prostatic hyperplasia. The objective of this study was to examine the detection rate of incidental prostate cancer by holmium laser enucleation of the prostate and variables associated with them. METHODS A total of 612 patients were enrolled. We retrospectively examined the detection rate of incidental prostate cancer and perioperative variables associated with them. RESULTS Forty-nine of 612 patients were diagnosed with incidental prostate cancer. Univariate logistic regression analysis showed that higher prostate-specific antigen density (odds ratio 3.34, 95% confidence interval 1.02-10.94, P = 0.05), higher prostate-specific antigen density of the transition zone (odds ratio 2.28, 95% confidence interval 1.02-5.09, P = 0.04), and findings of the prostate cancer on magnetic resonance imaging (peripheral zone: odds ratio 4.71, 95% confidence interval 1.70-13.1, P = 0.003; transition zone: odds ratio 3.46, 95% confidence interval 1.74-6.86, P < 0.001; peripheral and transition zones: odds ratio 6.00, 95% confidence interval 1.51-23.8, P = 0.01) were significantly associated with incidental prostate cancer. Multivariate logistic regression analysis showed that findings of the prostate cancer on magnetic resonance imaging (peripheral zone: odds ratio 4.36, 95% confidence interval 1.49-12.8, P = 0.001; transition zone: odds ratio 3.54, 95% confidence interval 1.75-7.16, P < 0.001; peripheral and transition zones: odds ratio 6.14, 95% confidence interval 1.53-24.5, P = 0.01) was an independent risk factor for incidental prostate cancer. CONCLUSION The detection rate of incidental prostate cancer was 8.0%, and findings of the prostate cancer on magnetic resonance imaging were an independent predictive factor for incidental prostate cancer.
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Affiliation(s)
- Taro Banno
- Department of Urology, Jyoban Hospital of Tokiwa Foundation, Iwaki, Japan
| | - Kazutaka Nakamura
- Department of Urology, Jyoban Hospital of Tokiwa Foundation, Iwaki, Japan
| | - Yudai Kaneda
- School of Medicine, Hokkaido University, Sapporo, Japan
| | - Akihiko Ozaki
- Department of Breast Surgery, Jyoban Hospital of Tokiwa Foundation, Iwaki, Japan
| | - Yukiko Kouchi
- Department of Urology, Jyoban Hospital of Tokiwa Foundation, Iwaki, Japan
| | | | - Hiroaki Shimmura
- Department of Urology, Jyoban Hospital of Tokiwa Foundation, Iwaki, Japan
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Souza SAS, Reis LO, Alves AFF, Silva LC, Medeiros MCK, Andrade DL, Billis A, Amaro JL, Martins DL, Trindade AP, Miranda JRA, Pina DR. Multiple analyses suggests texture features can indicate the presence of tumor in the prostate tissue. Phys Eng Sci Med 2022; 45:525-535. [PMID: 35325377 DOI: 10.1007/s13246-022-01118-2] [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: 06/03/2021] [Accepted: 03/09/2022] [Indexed: 10/18/2022]
Abstract
Several studies have demonstrated statistical and texture analysis abilities to differentiate cancerous from healthy tissue in magnetic resonance imaging. This study developed a method based on texture analysis and machine learning to differentiate prostate findings. Forty-eight male patients with PI-RADS classification and subsequent radical prostatectomy histopathological analysis were used as gold standard. Experienced radiologists delimited the regions of interest in magnetic resonance images. Six different groups of images were used to perform multiple analyses (seven analyses variations). Those analyses were outlined by specialists in urology as those of most significant importance for the classification. Forty texture features were extracted from each image and processed with Random Forest, Support Vector Machine, K-Nearest Neighbors, and Naive Bayes. Those seven analyses variation results were described in terms of area under the ROC curve (AUC), accuracy, F-score, precision and sensitivity. The highest AUC (93.7%) and accuracy (88.8%) were obtained when differentiating the group with both MRI and histopathology positive findings against the group with both negative MRI and histopathology. When differentiating the group with both MRI and histopathology positive findings versus the peripheral image zone group the AUC value was 86.6%. When differentiating the group with negative MRI/positive histopathology versus the group with both negative MRI and histopathology the AUC value was 80.7%. The evaluation of statistical and texture analysis promoted very suggestive indications for future work in prostate cancer suspicious regions. The method is fast for both region of interest selection and classification with machine learning and the result brings original contributions in the classification of different groups of patients. This tool is low-cost, and can be used to assist diagnostic decisions.
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Affiliation(s)
- Sérgio Augusto Santana Souza
- São Paulo State University Júlio de Mesquita Filho, R. Prof. Dr. Antônio Celso Wagner Zanin, 250 - Distrito de Rubião Junior, Botucatu, SP, CEP: 18618-689, Brazil
| | - Leonardo Oliveira Reis
- Department of Urology, UroScience, State University of Campinas, Unicamp and Pontifical Catholic University of Campinas, PUC-Campinas, Av. John Boyd Dunlop-Jardim Ipaussurama, Campinas, SP, CEP: 13034-685, Brazil
| | - Allan Felipe Fattori Alves
- Botucatu Medical School, Clinics Hospital, Medical Physics and Radioprotection Nucleus, Av. Prof. Mário Rubens Guimarães Montenegro, s/n - UNESP - Campus de Botucatu, Botucatu, SP, CEP: 18618687, Brazil
| | - Letícia Cotinguiba Silva
- São Paulo State University Júlio de Mesquita Filho, R. Prof. Dr. Antônio Celso Wagner Zanin, 250 - Distrito de Rubião Junior, Botucatu, SP, CEP: 18618-689, Brazil
| | | | - Danilo Leite Andrade
- Department of Urology, UroScience, State University of Campinas, Unicamp and Pontifical Catholic University of Campinas, PUC-Campinas, Av. John Boyd Dunlop-Jardim Ipaussurama, Campinas, SP, CEP: 13034-685, Brazil
| | - Athanase Billis
- Department of Anatomic Pathology and Urology, School of Medical Sciences, State University of Campinas (Unicamp), Campinas, Brazil
| | - João Luiz Amaro
- Department of Urology, Botucatu Medical School, São Paulo State University (UNESP), Botucatu, SP, Brazil
| | | | - André Petean Trindade
- Botucatu Medical School, São Paulo State University Júlio de Mesquita Filho, Av. Prof. Mário Rubens Guimarães Montenegro, s/n - UNESP - Campus de Botucatu, Botucatu, SP, CEP:18618687, Brazil
| | - José Ricardo Arruda Miranda
- Institute of Bioscience, São Paulo State University Júlio de Mesquita Filho, R. Prof. Dr. Antônio Celso Wagner Zanin, 250 - Distrito de Rubião Junior, Botucatu, SP, CEP: 8618-689, Brazil
| | - Diana Rodrigues Pina
- Botucatu Medical School, São Paulo State University Júlio de Mesquita Filho, Av. Prof. Mário Rubens Guimarães Montenegro, s/n - UNESP - Campus de Botucatu, Botucatu, SP, CEP:18618687, Brazil.
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Gibbons M, Starobinets O, Simko JP, Kurhanewicz J, Carroll PR, Noworolski SM. Identification of prostate cancer using multiparametric MR imaging characteristics of prostate tissues referenced to whole mount histopathology. Magn Reson Imaging 2022; 85:251-261. [PMID: 34666162 PMCID: PMC9931199 DOI: 10.1016/j.mri.2021.10.008] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Revised: 10/05/2021] [Accepted: 10/12/2021] [Indexed: 12/24/2022]
Abstract
In this study, the objective was to characterize the MR signatures of the various benign prostate tissues and to differentiate them from cancer. Data was from seventy prostate cancer patients who underwent multiparametric MRI (mpMRI) and subsequent prostatectomy. The scans included T2-weighted imaging (T2W), diffusion weighted imaging, dynamic contrast-enhanced MRI (DCE MRI), and MR spectroscopic imaging. Histopathology tissue information was translated to MRI images. The mpMRI parameters were characterized separately per zone and by tissue type. The tissues were ordered according to trends in tissue parameter means. The peripheral zone tissue order was cystic atrophy, high grade prostatic intraepithelial neoplasia (HGPIN), normal, atrophy, inflammation, and cancer. Decreasing values for tissue order were exhibited by ADC (1.8 10-3 mm2/s to 1.2 10-3 mm2/s) and T2W intensity (3447 to 2576). Increasing values occurred for DCE MRI peak (143% to 157%), DCE MRI slope (101%/min to 169%/min), fractional anisotropy (FA) (0.16 to 0.19), choline (7.2 to 12.2), and choline / citrate (0.3 to 0.9). The transition zone tissue order was cystic atrophy, mixed benign prostatic hyperplasia (BPH), normal, atrophy, inflammation, stroma, anterior fibromuscular stroma, and cancer. Decreasing values occurred for ADC (1.6 10-3 mm2/s to 1.1 10-3 mm2/s) and T2W intensity (2863 to 2001). Increasing values occurred for DCE MRI peak (143% to 150%), DCE MRI slope (101%/min to 137%/min), FA (0.18 to 0.25), choline (7.9 to 11.7), and choline / citrate (0.3 to 0.7). Logistic regression was used to create parameter model fits to differentiate cancer from benign prostate tissues. The fits achieved AUCs ≥0.91. This study quantified the mpMRI characteristics of benign prostate tissues and demonstrated the capability of mpMRI to discriminate among benign as well as cancer tissues, potentially aiding future discrimination of cancer from benign confounders.
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Affiliation(s)
- Matthew Gibbons
- Deparment of Radiology and Biomedical Imaging, University of California, 185 Berry Street, San Francisco, CA, USA.
| | - Olga Starobinets
- Deparment of Radiology and Biomedical Imaging, University of California, San Francisco, 185 Berry Street, San Francisco, CA, USA
| | - Jeffry P. Simko
- Department of Urology, University of California, San Francisco, 550 16th Street, San Francisco, CA, USA,Department of Pathology, University of California, San Francisco, 1825 4th Street, San Francisco, CA, USA
| | - John Kurhanewicz
- Deparment of Radiology and Biomedical Imaging, University of California, 185 Berry Street, San Francisco, CA, USA; Department of Urology, University of California, 550 16th Street, San Francisco, CA, USA.
| | - Peter R Carroll
- Department of Urology, University of California, 550 16th Street, San Francisco, CA, USA.
| | - Susan M Noworolski
- Deparment of Radiology and Biomedical Imaging, University of California, 185 Berry Street, San Francisco, CA, USA.
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Dynamic contract-enhanced CT-based radiomics for differentiation of pancreatobiliary-type and intestinal-type periampullary carcinomas. Clin Radiol 2021; 77:e75-e83. [PMID: 34753589 DOI: 10.1016/j.crad.2021.09.010] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Accepted: 09/09/2021] [Indexed: 11/21/2022]
Abstract
AIM To investigate whether computed tomography (CT) radiomics can differentiate pancreatobiliary-type from intestinal-type periampullary carcinomas. MATERIALS AND METHODS CT radiomics of 96 patients (54 pancreatobiliary type and 42 intestinal type) with surgically confirmed periampullary carcinoma were assessed retrospectively. Volumes of interest (VOIs) were delineated manually. Radiomic features were extracted from preoperative CT images. A single-phase model and combined-phase model were constructed. Five-fold cross-validation and five machine-learning algorithms were utilised for model construction. The diagnostic performance of the models was evaluated by receiver operating characteristic (ROC) curves, and indicators included area under the curve (AUC), accuracy, sensitivity, specificity, and precision. ROC curves were compared using DeLong's test. RESULTS A total of 788 features were extracted on each phase. After feature selection using least absolute shrinkage and selection operator (LASSO) algorithm, the number of selected optimal feature was 18 (plain scan), nine (arterial phase), two (venous phase), 23 (delayed phase), 15 (three enhanced phases), and 29 (all phases), respectively. For the single-phase model, the delayed-phase model using the logistic regression (LR) algorithm showed the best prediction performance with AUC, accuracy, sensitivity, specificity, and precision of 0.89, 0.83, 0.80, 0.88, and 0.93, respectively. Two combined-phase models showed better results than the single-phase models. The model of all phases using the LR algorithm showed the best prediction performance with AUC, accuracy, sensitivity, specificity, and precision of 0.96, 0.88, 0.90, 0.93, and 0.92, respectively. CONCLUSION Radiomic models based on preoperative CT images can differentiate pancreatobiliary-type from intestinal-type periampullary carcinomas, in particular, the model of all phases using the LR algorithm.
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Syer T, Mehta P, Antonelli M, Mallett S, Atkinson D, Ourselin S, Punwani S. Artificial Intelligence Compared to Radiologists for the Initial Diagnosis of Prostate Cancer on Magnetic Resonance Imaging: A Systematic Review and Recommendations for Future Studies. Cancers (Basel) 2021; 13:3318. [PMID: 34282762 PMCID: PMC8268820 DOI: 10.3390/cancers13133318] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Revised: 06/24/2021] [Accepted: 06/30/2021] [Indexed: 11/16/2022] Open
Abstract
Computer-aided diagnosis (CAD) of prostate cancer on multiparametric magnetic resonance imaging (mpMRI), using artificial intelligence (AI), may reduce missed cancers and unnecessary biopsies, increase inter-observer agreement between radiologists, and alleviate pressures caused by rising case incidence and a shortage of specialist radiologists to read prostate mpMRI. However, well-designed evaluation studies are required to prove efficacy above current clinical practice. A systematic search of the MEDLINE, EMBASE, and arXiv electronic databases was conducted for studies that compared CAD for prostate cancer detection or classification on MRI against radiologist interpretation and a histopathological reference standard, in treatment-naïve men with a clinical suspicion of prostate cancer. Twenty-seven studies were included in the final analysis. Due to substantial heterogeneities in the included studies, a narrative synthesis is presented. Several studies reported superior diagnostic accuracy for CAD over radiologist interpretation on small, internal patient datasets, though this was not observed in the few studies that performed evaluation using external patient data. Our review found insufficient evidence to suggest the clinical deployment of artificial intelligence algorithms at present. Further work is needed to develop and enforce methodological standards, promote access to large diverse datasets, and conduct prospective evaluations before clinical adoption can be considered.
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Affiliation(s)
- Tom Syer
- Centre for Medical Imaging, Division of Medicine, Bloomsbury Campus, University College London, London WC1E 6DH, UK; (T.S.); (S.M.); (D.A.)
| | - Pritesh Mehta
- Department of Medical Physics and Biomedical Engineering, Faculty of Engineering Sciences, Bloomsbury Campus, University College London, London WC1E 6DH, UK;
| | - Michela Antonelli
- School of Biomedical Engineering & Imaging Sciences, Faculty of Life Sciences and Medicine, St Thomas’ Campus, King’s College London, London SE1 7EH, UK; (M.A.); (S.O.)
| | - Sue Mallett
- Centre for Medical Imaging, Division of Medicine, Bloomsbury Campus, University College London, London WC1E 6DH, UK; (T.S.); (S.M.); (D.A.)
| | - David Atkinson
- Centre for Medical Imaging, Division of Medicine, Bloomsbury Campus, University College London, London WC1E 6DH, UK; (T.S.); (S.M.); (D.A.)
| | - Sébastien Ourselin
- School of Biomedical Engineering & Imaging Sciences, Faculty of Life Sciences and Medicine, St Thomas’ Campus, King’s College London, London SE1 7EH, UK; (M.A.); (S.O.)
| | - Shonit Punwani
- Centre for Medical Imaging, Division of Medicine, Bloomsbury Campus, University College London, London WC1E 6DH, UK; (T.S.); (S.M.); (D.A.)
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A CNN-Based Autoencoder and Machine Learning Model for Identifying Betel-Quid Chewers Using Functional MRI Features. Brain Sci 2021; 11:brainsci11060809. [PMID: 34207169 PMCID: PMC8234239 DOI: 10.3390/brainsci11060809] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Revised: 06/11/2021] [Accepted: 06/16/2021] [Indexed: 11/17/2022] Open
Abstract
Betel quid (BQ) is one of the most commonly used psychoactive substances in some parts of Asia and the Pacific. Although some studies have shown brain function alterations in BQ chewers, it is virtually impossible for radiologists’ to visually distinguish MRI maps of BQ chewers from others. In this study, we aimed to construct autoencoder and machine-learning models to discover brain alterations in BQ chewers based on the features of resting-state functional magnetic resonance imaging. Resting-state functional magnetic resonance imaging (rs-fMRI) was obtained from 16 BQ chewers, 15 tobacco- and alcohol-user controls (TA), and 17 healthy controls (HC). We used an autoencoder and machine learning model to identify BQ chewers among the three groups. A convolutional neural network (CNN)-based autoencoder model and supervised machine learning algorithm logistic regression (LR) were used to discriminate BQ chewers from TA and HC. Classifying the brain MRIs of HC, TA controls, and BQ chewers by conducting leave-one-out-cross-validation (LOOCV) resulted in the highest accuracy of 83%, which was attained by LR with two rs-fMRI feature sets. In our research, we constructed an autoencoder and machine-learning model that was able to identify BQ chewers from among TA controls and HC, which were based on data from rs-fMRI, and this might provide a helpful approach for tracking BQ chewers in the future.
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Sunoqrot MRS, Nketiah GA, Selnæs KM, Bathen TF, Elschot M. Automated reference tissue normalization of T2-weighted MR images of the prostate using object recognition. MAGNETIC RESONANCE MATERIALS IN PHYSICS BIOLOGY AND MEDICINE 2020; 34:309-321. [PMID: 32737628 PMCID: PMC8018925 DOI: 10.1007/s10334-020-00871-3] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/08/2020] [Revised: 07/02/2020] [Accepted: 07/21/2020] [Indexed: 01/17/2023]
Abstract
Objectives To develop and evaluate an automated method for prostate T2-weighted (T2W) image normalization using dual-reference (fat and muscle) tissue. Materials and methods Transverse T2W images from the publicly available PROMISE12 (N = 80) and PROSTATEx (N = 202) challenge datasets, and an in-house collected dataset (N = 60) were used. Aggregate channel features object detectors were trained to detect reference fat and muscle tissue regions, which were processed and utilized to normalize the 3D images by linear scaling. Mean prostate pseudo T2 values after normalization were compared to literature values. Inter-patient histogram intersections of voxel intensities in the prostate were compared between our approach, the original images, and other commonly used normalization methods. Healthy vs. malignant tissue classification performance was compared before and after normalization. Results The prostate pseudo T2 values of the three tested datasets (mean ± standard deviation = 78.49 ± 9.42, 79.69 ± 6.34 and 79.29 ± 6.30 ms) corresponded well to T2 values from literature (80 ± 34 ms). Our normalization approach resulted in significantly higher (p < 0.001) inter-patient histogram intersections (median = 0.746) than the original images (median = 0.417) and most other normalization methods. Healthy vs. malignant classification also improved significantly (p < 0.001) in peripheral (AUC 0.826 vs. 0.769) and transition (AUC 0.743 vs. 0.678) zones. Conclusion An automated dual-reference tissue normalization of T2W images could help improve the quantitative assessment of prostate cancer. Electronic supplementary material The online version of this article (10.1007/s10334-020-00871-3) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Mohammed R S Sunoqrot
- Department of Circulation and Medical Imaging, NTNU, Norwegian University of Science and Technology, 7030, Trondheim, Norway.
| | - Gabriel A Nketiah
- Department of Circulation and Medical Imaging, NTNU, Norwegian University of Science and Technology, 7030, Trondheim, Norway.,Department of Radiology and Nuclear Medicine, St. Olavs Hospital, Trondheim University Hospital, 7030, Trondheim, Norway
| | - Kirsten M Selnæs
- Department of Circulation and Medical Imaging, NTNU, Norwegian University of Science and Technology, 7030, Trondheim, Norway.,Department of Radiology and Nuclear Medicine, St. Olavs Hospital, Trondheim University Hospital, 7030, Trondheim, Norway
| | - Tone F Bathen
- Department of Circulation and Medical Imaging, NTNU, Norwegian University of Science and Technology, 7030, Trondheim, Norway.,Department of Radiology and Nuclear Medicine, St. Olavs Hospital, Trondheim University Hospital, 7030, Trondheim, Norway
| | - Mattijs Elschot
- Department of Circulation and Medical Imaging, NTNU, Norwegian University of Science and Technology, 7030, Trondheim, Norway.,Department of Radiology and Nuclear Medicine, St. Olavs Hospital, Trondheim University Hospital, 7030, Trondheim, Norway
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11
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Cuocolo R, Cipullo MB, Stanzione A, Romeo V, Green R, Cantoni V, Ponsiglione A, Ugga L, Imbriaco M. Machine learning for the identification of clinically significant prostate cancer on MRI: a meta-analysis. Eur Radiol 2020; 30:6877-6887. [PMID: 32607629 DOI: 10.1007/s00330-020-07027-w] [Citation(s) in RCA: 54] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2020] [Revised: 05/08/2020] [Accepted: 06/10/2020] [Indexed: 12/13/2022]
Abstract
OBJECTIVES The aim of this study was to systematically review the literature and perform a meta-analysis of machine learning (ML) diagnostic accuracy studies focused on clinically significant prostate cancer (csPCa) identification on MRI. METHODS Multiple medical databases were systematically searched for studies on ML applications in csPCa identification up to July 31, 2019. Two reviewers screened all papers independently for eligibility. The area under the receiver operating characteristic curves (AUC) was pooled to quantify predictive accuracy. A random-effects model estimated overall effect size while statistical heterogeneity was assessed with the I2 value. A funnel plot was used to investigate publication bias. Subgroup analyses were performed based on reference standard (biopsy or radical prostatectomy) and ML type (deep and non-deep). RESULTS After the final revision, 12 studies were included in the analysis. Statistical heterogeneity was high both in overall and in subgroup analyses. The overall pooled AUC for ML in csPCa identification was 0.86, with 0.81-0.91 95% confidence intervals (95%CI). The biopsy subgroup (n = 9) had a pooled AUC of 0.85 (95%CI = 0.79-0.91) while the radical prostatectomy one (n = 3) of 0.88 (95%CI = 0.76-0.99). Deep learning ML (n = 4) had a 0.78 AUC (95%CI = 0.69-0.86) while the remaining 8 had AUC = 0.90 (95%CI = 0.85-0.94). CONCLUSIONS ML pipelines using prostate MRI to identify csPCa showed good accuracy and should be further investigated, possibly with better standardisation in design and reporting of results. KEY POINTS • Overall pooled AUC was 0.86 with 0.81-0.91 95% confidence intervals. • In the reference standard subgroup analysis, algorithm accuracy was similar with pooled AUCs of 0.85 (0.79-0.91 95% confidence intervals) and 0.88 (0.76-0.99 95% confidence intervals) for studies employing biopsies and radical prostatectomy, respectively. • Deep learning pipelines performed worse (AUC = 0.78, 0.69-0.86 95% confidence intervals) than other approaches (AUC = 0.90, 0.85-0.94 95% confidence intervals).
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Affiliation(s)
- Renato Cuocolo
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via Pansini 5, 80131, Naples, Italy
| | - Maria Brunella Cipullo
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via Pansini 5, 80131, Naples, Italy
| | - Arnaldo Stanzione
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via Pansini 5, 80131, Naples, Italy.
| | - Valeria Romeo
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via Pansini 5, 80131, Naples, Italy
| | - Roberta Green
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via Pansini 5, 80131, Naples, Italy
| | - Valeria Cantoni
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via Pansini 5, 80131, Naples, Italy
| | - Andrea Ponsiglione
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via Pansini 5, 80131, Naples, Italy
| | - Lorenzo Ugga
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via Pansini 5, 80131, Naples, Italy
| | - Massimo Imbriaco
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via Pansini 5, 80131, Naples, Italy
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12
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Automated Classification of Significant Prostate Cancer on MRI: A Systematic Review on the Performance of Machine Learning Applications. Cancers (Basel) 2020; 12:cancers12061606. [PMID: 32560558 PMCID: PMC7352160 DOI: 10.3390/cancers12061606] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2020] [Revised: 06/13/2020] [Accepted: 06/14/2020] [Indexed: 11/16/2022] Open
Abstract
Significant prostate carcinoma (sPCa) classification based on MRI using radiomics or deep learning approaches has gained much interest, due to the potential application in assisting in clinical decision-making. OBJECTIVE To systematically review the literature (i) to determine which algorithms are most frequently used for sPCa classification, (ii) to investigate whether there exists a relation between the performance and the method or the MRI sequences used, (iii) to assess what study design factors affect the performance on sPCa classification, and (iv) to research whether performance had been evaluated in a clinical setting Methods: The databases Embase and Ovid MEDLINE were searched for studies describing machine learning or deep learning classification methods discriminating between significant and nonsignificant PCa on multiparametric MRI that performed a valid validation procedure. Quality was assessed by the modified radiomics quality score. We computed the median area under the receiver operating curve (AUC) from overall methods and the interquartile range. RESULTS From 2846 potentially relevant publications, 27 were included. The most frequent algorithms used in the literature for PCa classification are logistic regression (22%) and convolutional neural networks (CNNs) (22%). The median AUC was 0.79 (interquartile range: 0.77-0.87). No significant effect of number of included patients, image sequences, or reference standard on the reported performance was found. Three studies described an external validation and none of the papers described a validation in a prospective clinical trial. CONCLUSIONS To unlock the promising potential of machine and deep learning approaches, validation studies and clinical prospective studies should be performed with an established protocol to assess the added value in decision-making.
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13
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Byun J, Park KJ, Kim MH, Kim JK. Direct Comparison of PI-RADS Version 2 and 2.1 in Transition Zone Lesions for Detection of Prostate Cancer: Preliminary Experience. J Magn Reson Imaging 2020; 52:577-586. [PMID: 32045072 DOI: 10.1002/jmri.27080] [Citation(s) in RCA: 45] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2019] [Revised: 01/18/2020] [Accepted: 01/21/2020] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND There appears to be less agreement in the identification of cancers in the transition zone (TZ), which is not as reliable as those in peripheral zone when using the Prostate Imaging Reporting and Data System (PI-RADS) version 2 (v2). In response to such shortcomings, the updated version 2.1 was introduced, which incorporated diffusion-weighted imaging (DWI) into category 2 and clarified lexicons. PURPOSE To compare the diagnostic performance for the detection of clinically significant TZ prostate cancers (csPCa) and interreader agreement between PI-RADS v2.1 and v2. STUDY TYPE Retrospective study. POPULATION In all, 142 patients, 201 TZ lesions. FIELD STRENGTH/SEQUENCE 3.0T; T2 -weighted image and DWI. ASSESSMENT Lesions were scored by three independent readers using PI-RADS v2 and v2.1. STATISTICAL TESTS The sensitivity and specificity at category ≥3 were compared between v2 and v2.1 using the generalized estimating equation model. Detection rates for csPCa of upgraded and downgraded lesions in the use of PI-RADS v2.1 from v2 were assessed. Interreader agreement was assessed using κ statistics. RESULTS PI-RADS v2.1 showed a higher sensitivity and specificity (94.5% and 60.9%) than v2 (91.8% and 56.3%) for category ≥3 lesions in the detection of csPCa, although not significantly. Of eight upgraded lesions from category 2 to 3 (2 + 1) with an incorporated DWI, 50% (4/8) were csPCa. This was significantly higher than category 2 lesions (4.4%; P = 0.003). No csPCa was detected among the 22.8% (46/201) downgraded lesions. There was a moderate interreader agreement for scores ≥3 (κ = 0.565) in v2.1, which was slightly higher than that for v2 (κ = 0.534), although not significantly. DATA CONCLUSION PI-RADS v2.1 provides moderate and comparable interreader agreement at category ≥3 than v2 in the TZ lesions. Upgraded lesions from category 2 to 3 demonstrated a higher detection rate of csPCa than category 2 lesions in v2.1. LEVEL OF EVIDENCE 4 TECHNICAL EFFICACY STAGE: 2 J. Magn. Reson. Imaging 2020;52:577-586.
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Affiliation(s)
- Jieun Byun
- Department of Radiology, Hallym University College of Medicine, Kangnam Sacred Heart Hospital, Seoul, Republic of Korea
| | - Kye Jin Park
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Mi-Hyun Kim
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Jeong Kon Kim
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Seoul, Republic of Korea
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14
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Panda A, Obmann VC, Lo WC, Margevicius S, Jiang Y, Schluchter M, Patel IJ, Nakamoto D, Badve C, Griswold MA, Jaeger I, Ponsky LE, Gulani V. MR Fingerprinting and ADC Mapping for Characterization of Lesions in the Transition Zone of the Prostate Gland. Radiology 2019; 292:685-694. [PMID: 31335285 PMCID: PMC6716564 DOI: 10.1148/radiol.2019181705] [Citation(s) in RCA: 52] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2018] [Revised: 05/11/2019] [Accepted: 06/13/2019] [Indexed: 11/11/2022]
Abstract
BackgroundPreliminary studies have shown that MR fingerprinting-based relaxometry combined with apparent diffusion coefficient (ADC) mapping can be used to differentiate normal peripheral zone from prostate cancer and prostatitis. The utility of relaxometry and ADC mapping for the transition zone (TZ) is unknown.PurposeTo evaluate the utility of MR fingerprinting combined with ADC mapping for characterizing TZ lesions.Materials and MethodsTZ lesions that were suspicious for cancer in men who underwent MRI with T2-weighted imaging and ADC mapping (b values, 50-1400 sec/mm2), MR fingerprinting with steady-state free precession, and targeted biopsy (60 in-gantry and 15 cognitive targeting) between September 2014 and August 2018 in a single university hospital were retrospectively analyzed. Two radiologists blinded to Prostate Imaging Reporting and Data System (PI-RADS) scores and pathologic diagnosis drew regions of interest on cancer-suspicious lesions and contralateral visually normal TZs (NTZs) on MR fingerprinting and ADC maps. Linear mixed models compared two-reader means of T1, T2, and ADC. Generalized estimating equations logistic regression analysis was used to evaluate both MR fingerprinting and ADC in differentiating NTZ, cancers and noncancers, clinically significant (Gleason score ≥ 7) cancers from clinically insignificant lesions (noncancers and Gleason 6 cancers), and characterizing PI-RADS version 2 category 3 lesions.ResultsIn 67 men (mean age, 66 years ± 8 [standard deviation]) with 75 lesions, targeted biopsy revealed 37 cancers (six PI-RADS category 3 cancers and 31 PI-RADS category 4 or 5 cancers) and 38 noncancers (31 PI-RADS category 3 lesions and seven PI-RADS category 4 or 5 lesions). The T1, T2, and ADC of NTZ (1800 msec ± 150, 65 msec ± 22, and [1.13 ± 0.19] × 10-3 mm2/sec, respectively) were higher than those in cancers (1450 msec ± 110, 36 msec ± 11, and [0.57 ± 0.13] × 10-3 mm2/sec, respectively; P < .001 for all). The T1, T2, and ADC in cancers were lower than those in noncancers (1620 msec ± 120, 47 msec ± 16, and [0.82 ± 0.13] × 10-3 mm2/sec, respectively; P = .001 for T1 and ADC and P = .03 for T2). The area under the receiver operating characteristic curve (AUC) for T1 plus ADC was 0.94 for separation. T1 and ADC in clinically significant cancers (1440 msec ± 140 and [0.58 ± 0.14] × 10-3 mm2/sec, respectively) were lower than those in clinically insignificant lesions (1580 msec ± 120 and [0.75 ± 0.17] × 10-3 mm2/sec, respectively; P = .001 for all). The AUC for T1 plus ADC was 0.81 for separation. Within PI-RADS category 3 lesions, T1 and ADC of cancers (1430 msec ± 220 and [0.60 ± 0.17] × 10-3 mm2/sec, respectively) were lower than those of noncancers (1630 msec ± 120 and [0.81 ± 0.13] × 10-3 mm2/sec, respectively; P = .006 for T1 and P = .004 for ADC). The AUC for T1 was 0.79 for differentiating category 3 lesions.ConclusionMR fingerprinting-based relaxometry combined with apparent diffusion coefficient mapping may improve transition zone lesion characterization.© RSNA, 2019Online supplemental material is available for this article.
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Affiliation(s)
- Ananya Panda
- From the Department of Radiology, Mayo Clinic, Rochester, Minn (A.P.); Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland (V.C.O.); Departments of Biomedical Engineering (W.C.L., M.A.G.), Epidemiology and Biostatistics (S.M., M.S.), and Radiology (Y.J., C.B., M.A.G., V.G.), Case Western Reserve University, Cleveland, Ohio; Department of Radiology, University of Michigan, UH B1 G503, 1500 E. Medical Center Drive, SPC 5030, Ann Arbor, MI 48109-5030 (Y.J., V.G.); Department of Radiology, Mayo Clinic, Phoenix, Az (I.J.P.); Departments of Radiology (I.J.P., D.N., C.B., M.A.G.) and Urology (I.J., L.E.P.), University Hospitals Cleveland Medical Center, Cleveland, Ohio
| | - Verena C. Obmann
- From the Department of Radiology, Mayo Clinic, Rochester, Minn (A.P.); Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland (V.C.O.); Departments of Biomedical Engineering (W.C.L., M.A.G.), Epidemiology and Biostatistics (S.M., M.S.), and Radiology (Y.J., C.B., M.A.G., V.G.), Case Western Reserve University, Cleveland, Ohio; Department of Radiology, University of Michigan, UH B1 G503, 1500 E. Medical Center Drive, SPC 5030, Ann Arbor, MI 48109-5030 (Y.J., V.G.); Department of Radiology, Mayo Clinic, Phoenix, Az (I.J.P.); Departments of Radiology (I.J.P., D.N., C.B., M.A.G.) and Urology (I.J., L.E.P.), University Hospitals Cleveland Medical Center, Cleveland, Ohio
| | - Wei-Ching Lo
- From the Department of Radiology, Mayo Clinic, Rochester, Minn (A.P.); Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland (V.C.O.); Departments of Biomedical Engineering (W.C.L., M.A.G.), Epidemiology and Biostatistics (S.M., M.S.), and Radiology (Y.J., C.B., M.A.G., V.G.), Case Western Reserve University, Cleveland, Ohio; Department of Radiology, University of Michigan, UH B1 G503, 1500 E. Medical Center Drive, SPC 5030, Ann Arbor, MI 48109-5030 (Y.J., V.G.); Department of Radiology, Mayo Clinic, Phoenix, Az (I.J.P.); Departments of Radiology (I.J.P., D.N., C.B., M.A.G.) and Urology (I.J., L.E.P.), University Hospitals Cleveland Medical Center, Cleveland, Ohio
| | - Seunghee Margevicius
- From the Department of Radiology, Mayo Clinic, Rochester, Minn (A.P.); Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland (V.C.O.); Departments of Biomedical Engineering (W.C.L., M.A.G.), Epidemiology and Biostatistics (S.M., M.S.), and Radiology (Y.J., C.B., M.A.G., V.G.), Case Western Reserve University, Cleveland, Ohio; Department of Radiology, University of Michigan, UH B1 G503, 1500 E. Medical Center Drive, SPC 5030, Ann Arbor, MI 48109-5030 (Y.J., V.G.); Department of Radiology, Mayo Clinic, Phoenix, Az (I.J.P.); Departments of Radiology (I.J.P., D.N., C.B., M.A.G.) and Urology (I.J., L.E.P.), University Hospitals Cleveland Medical Center, Cleveland, Ohio
| | - Yun Jiang
- From the Department of Radiology, Mayo Clinic, Rochester, Minn (A.P.); Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland (V.C.O.); Departments of Biomedical Engineering (W.C.L., M.A.G.), Epidemiology and Biostatistics (S.M., M.S.), and Radiology (Y.J., C.B., M.A.G., V.G.), Case Western Reserve University, Cleveland, Ohio; Department of Radiology, University of Michigan, UH B1 G503, 1500 E. Medical Center Drive, SPC 5030, Ann Arbor, MI 48109-5030 (Y.J., V.G.); Department of Radiology, Mayo Clinic, Phoenix, Az (I.J.P.); Departments of Radiology (I.J.P., D.N., C.B., M.A.G.) and Urology (I.J., L.E.P.), University Hospitals Cleveland Medical Center, Cleveland, Ohio
| | - Mark Schluchter
- From the Department of Radiology, Mayo Clinic, Rochester, Minn (A.P.); Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland (V.C.O.); Departments of Biomedical Engineering (W.C.L., M.A.G.), Epidemiology and Biostatistics (S.M., M.S.), and Radiology (Y.J., C.B., M.A.G., V.G.), Case Western Reserve University, Cleveland, Ohio; Department of Radiology, University of Michigan, UH B1 G503, 1500 E. Medical Center Drive, SPC 5030, Ann Arbor, MI 48109-5030 (Y.J., V.G.); Department of Radiology, Mayo Clinic, Phoenix, Az (I.J.P.); Departments of Radiology (I.J.P., D.N., C.B., M.A.G.) and Urology (I.J., L.E.P.), University Hospitals Cleveland Medical Center, Cleveland, Ohio
| | - Indravadan J. Patel
- From the Department of Radiology, Mayo Clinic, Rochester, Minn (A.P.); Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland (V.C.O.); Departments of Biomedical Engineering (W.C.L., M.A.G.), Epidemiology and Biostatistics (S.M., M.S.), and Radiology (Y.J., C.B., M.A.G., V.G.), Case Western Reserve University, Cleveland, Ohio; Department of Radiology, University of Michigan, UH B1 G503, 1500 E. Medical Center Drive, SPC 5030, Ann Arbor, MI 48109-5030 (Y.J., V.G.); Department of Radiology, Mayo Clinic, Phoenix, Az (I.J.P.); Departments of Radiology (I.J.P., D.N., C.B., M.A.G.) and Urology (I.J., L.E.P.), University Hospitals Cleveland Medical Center, Cleveland, Ohio
| | - Dean Nakamoto
- From the Department of Radiology, Mayo Clinic, Rochester, Minn (A.P.); Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland (V.C.O.); Departments of Biomedical Engineering (W.C.L., M.A.G.), Epidemiology and Biostatistics (S.M., M.S.), and Radiology (Y.J., C.B., M.A.G., V.G.), Case Western Reserve University, Cleveland, Ohio; Department of Radiology, University of Michigan, UH B1 G503, 1500 E. Medical Center Drive, SPC 5030, Ann Arbor, MI 48109-5030 (Y.J., V.G.); Department of Radiology, Mayo Clinic, Phoenix, Az (I.J.P.); Departments of Radiology (I.J.P., D.N., C.B., M.A.G.) and Urology (I.J., L.E.P.), University Hospitals Cleveland Medical Center, Cleveland, Ohio
| | - Chaitra Badve
- From the Department of Radiology, Mayo Clinic, Rochester, Minn (A.P.); Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland (V.C.O.); Departments of Biomedical Engineering (W.C.L., M.A.G.), Epidemiology and Biostatistics (S.M., M.S.), and Radiology (Y.J., C.B., M.A.G., V.G.), Case Western Reserve University, Cleveland, Ohio; Department of Radiology, University of Michigan, UH B1 G503, 1500 E. Medical Center Drive, SPC 5030, Ann Arbor, MI 48109-5030 (Y.J., V.G.); Department of Radiology, Mayo Clinic, Phoenix, Az (I.J.P.); Departments of Radiology (I.J.P., D.N., C.B., M.A.G.) and Urology (I.J., L.E.P.), University Hospitals Cleveland Medical Center, Cleveland, Ohio
| | - Mark A. Griswold
- From the Department of Radiology, Mayo Clinic, Rochester, Minn (A.P.); Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland (V.C.O.); Departments of Biomedical Engineering (W.C.L., M.A.G.), Epidemiology and Biostatistics (S.M., M.S.), and Radiology (Y.J., C.B., M.A.G., V.G.), Case Western Reserve University, Cleveland, Ohio; Department of Radiology, University of Michigan, UH B1 G503, 1500 E. Medical Center Drive, SPC 5030, Ann Arbor, MI 48109-5030 (Y.J., V.G.); Department of Radiology, Mayo Clinic, Phoenix, Az (I.J.P.); Departments of Radiology (I.J.P., D.N., C.B., M.A.G.) and Urology (I.J., L.E.P.), University Hospitals Cleveland Medical Center, Cleveland, Ohio
| | - Irina Jaeger
- From the Department of Radiology, Mayo Clinic, Rochester, Minn (A.P.); Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland (V.C.O.); Departments of Biomedical Engineering (W.C.L., M.A.G.), Epidemiology and Biostatistics (S.M., M.S.), and Radiology (Y.J., C.B., M.A.G., V.G.), Case Western Reserve University, Cleveland, Ohio; Department of Radiology, University of Michigan, UH B1 G503, 1500 E. Medical Center Drive, SPC 5030, Ann Arbor, MI 48109-5030 (Y.J., V.G.); Department of Radiology, Mayo Clinic, Phoenix, Az (I.J.P.); Departments of Radiology (I.J.P., D.N., C.B., M.A.G.) and Urology (I.J., L.E.P.), University Hospitals Cleveland Medical Center, Cleveland, Ohio
| | - Lee E. Ponsky
- From the Department of Radiology, Mayo Clinic, Rochester, Minn (A.P.); Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland (V.C.O.); Departments of Biomedical Engineering (W.C.L., M.A.G.), Epidemiology and Biostatistics (S.M., M.S.), and Radiology (Y.J., C.B., M.A.G., V.G.), Case Western Reserve University, Cleveland, Ohio; Department of Radiology, University of Michigan, UH B1 G503, 1500 E. Medical Center Drive, SPC 5030, Ann Arbor, MI 48109-5030 (Y.J., V.G.); Department of Radiology, Mayo Clinic, Phoenix, Az (I.J.P.); Departments of Radiology (I.J.P., D.N., C.B., M.A.G.) and Urology (I.J., L.E.P.), University Hospitals Cleveland Medical Center, Cleveland, Ohio
| | - Vikas Gulani
- From the Department of Radiology, Mayo Clinic, Rochester, Minn (A.P.); Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland (V.C.O.); Departments of Biomedical Engineering (W.C.L., M.A.G.), Epidemiology and Biostatistics (S.M., M.S.), and Radiology (Y.J., C.B., M.A.G., V.G.), Case Western Reserve University, Cleveland, Ohio; Department of Radiology, University of Michigan, UH B1 G503, 1500 E. Medical Center Drive, SPC 5030, Ann Arbor, MI 48109-5030 (Y.J., V.G.); Department of Radiology, Mayo Clinic, Phoenix, Az (I.J.P.); Departments of Radiology (I.J.P., D.N., C.B., M.A.G.) and Urology (I.J., L.E.P.), University Hospitals Cleveland Medical Center, Cleveland, Ohio
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15
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Dikaios N, Giganti F, Sidhu HS, Johnston EW, Appayya MB, Simmons L, Freeman A, Ahmed HU, Atkinson D, Punwani S. Multi-parametric MRI zone-specific diagnostic model performance compared with experienced radiologists for detection of prostate cancer. Eur Radiol 2019; 29:4150-4159. [PMID: 30456585 PMCID: PMC6610264 DOI: 10.1007/s00330-018-5799-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2018] [Revised: 09/04/2018] [Accepted: 09/24/2018] [Indexed: 02/06/2023]
Abstract
OBJECTIVES Compare the performance of zone-specific multi-parametric-MRI (mp-MRI) diagnostic models in prostate cancer detection with experienced radiologists. METHODS A single-centre, IRB approved, prospective STARD compliant 3 T MRI test dataset of 203 patients was generated to test validity and generalisability of previously reported 1.5 T mp-MRI diagnostic models. All patients included within the test dataset underwent 3 T mp-MRI, comprising T2, diffusion-weighted and dynamic contrast-enhanced imaging followed by transperineal template ± targeted index lesion biopsy. Separate diagnostic models (transition zone (TZ) and peripheral zone (PZ)) were applied to respective zones. Sensitivity/specificity and the area under the receiver operating characteristic curve (ROC-AUC) were calculated for the two zone-specific models. Two radiologists (A and B) independently Likert scored test 3 T mp-MRI dataset, allowing ROC analysis for each radiologist for each prostate zone. RESULTS Diagnostic models applied to the test dataset demonstrated a ROC-AUC = 0.74 (95% CI 0.67-0.81) in the PZ and 0.68 (95% CI 0.61-0.75) in the TZ. Radiologist A/B had a ROC-AUC = 0.78/0.74 in the PZ and 0.69/0.69 in the TZ. Radiologists A and B each scored 51 patients in the PZ and 41 and 45 patients respectively in the TZ as Likert 3. The PZ model demonstrated a ROC-AUC = 0.65/0.67 for the patients Likert scored as indeterminate by radiologist A/B respectively, whereas the TZ model demonstrated a ROC-AUC = 0.74/0.69. CONCLUSION Zone-specific mp-MRI diagnostic models demonstrate generalisability between 1.5 and 3 T mp-MRI protocols and show similar classification performance to experienced radiologists for prostate cancer detection. Results also indicate the ability of diagnostic models to classify cases with an indeterminate radiologist score. KEY POINTS • MRI diagnostic models had similar performance to experienced radiologists for classification of prostate cancer. • MRI diagnostic models may help radiologists classify tumour in patients with indeterminate Likert 3 scores.
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Affiliation(s)
- Nikolaos Dikaios
- Centre for Medical Imaging, University College London, 2nd floor, Charles Bell House, 43-45 Foley Street, London, W1W 7TS, UK
- Centre for Vision, Speech and Signal Processing, University of Surrey, 388 Stag Hill, Guildford, GU2 7XH, UK
| | - Francesco Giganti
- Departments of Radiology, University College London Hospital, 235 Euston Road, London, NW1 2BU, UK
- Division of Surgery & Interventional Science, University College London, London, UK
| | - Harbir S Sidhu
- Centre for Medical Imaging, University College London, 2nd floor, Charles Bell House, 43-45 Foley Street, London, W1W 7TS, UK
| | - Edward W Johnston
- Centre for Medical Imaging, University College London, 2nd floor, Charles Bell House, 43-45 Foley Street, London, W1W 7TS, UK
| | - Mrishta B Appayya
- Centre for Medical Imaging, University College London, 2nd floor, Charles Bell House, 43-45 Foley Street, London, W1W 7TS, UK
| | - Lucy Simmons
- Research Department of Urology, Division of Surgery and Interventional Science, University College London, London, NW1 2PG, UK
| | - Alex Freeman
- Department of Histopathology, University College London Hospital, London, NW1 2PG, UK
| | - Hashim U Ahmed
- Department of Surgery and Cancer, Imperial College London, London, UK
| | - David Atkinson
- Centre for Medical Imaging, University College London, 2nd floor, Charles Bell House, 43-45 Foley Street, London, W1W 7TS, UK
- Departments of Radiology, University College London Hospital, 235 Euston Road, London, NW1 2BU, UK
| | - Shonit Punwani
- Centre for Medical Imaging, University College London, 2nd floor, Charles Bell House, 43-45 Foley Street, London, W1W 7TS, UK.
- Departments of Radiology, University College London Hospital, 235 Euston Road, London, NW1 2BU, UK.
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16
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Hameed M, Ganeshan B, Shur J, Mukherjee S, Afaq A, Batura D. The clinical utility of prostate cancer heterogeneity using texture analysis of multiparametric MRI. Int Urol Nephrol 2019; 51:817-824. [PMID: 30929224 DOI: 10.1007/s11255-019-02134-0] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2019] [Accepted: 03/21/2019] [Indexed: 11/28/2022]
Abstract
PURPOSE To determine if multiparametric MRI (mpMRI) derived filtration-histogram based texture analysis (TA) can differentiate between different Gleason scores (GS) and the D'Amico risk in prostate cancer. METHODS We retrospectively studied patients whose pre-operative 1.5T mpMRI had shown a visible tumour and who subsequently underwent radical prostatectomy (RP). Guided by tumour location from the histopathology report, we drew a region of interest around the dominant visible lesion on a single axial slice on the T2, Apparent Diffusion Coefficient (ADC) map and early arterial phase post-contrast T1 image. We then performed TA with a filtration-histogram software (TexRAD -Feedback Medical Ltd, Cambridge, UK). We correlated GS and D'Amico risk with texture using the Spearman's rank correlation test. RESULTS We had 26 RP patients with an MR-visible tumour. Mean of positive pixels (MPP) on ADC showed a significant negative correlation with GS at coarse texture scales. MPP showed a significant negative correlation with GS without filtration and with medium filtration. MRI contrast texture without filtration showed a significant, negative correlation with D'Amico score. MR T2 texture showed a significant, negative correlation with the D'Amico risk, particularly at textures without filtration, medium texture scales and coarse texture scales. CONCLUSION ADC map mpMRI TA correlated negatively with GS, and T2 and post-contrast images with the D'Amico risk score. These associations may allow for better assessment of disease prognosis and a non-invasive method of follow-up for patients on surveillance. Further, identifying clinically significant prostate cancer is essential to reduce harm from over-diagnosis and over-treatment.
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Affiliation(s)
- Maira Hameed
- Department of Radiology, Imperial College Healthcare NHS Trust, South Wharf Road, London, UK
| | - Balaji Ganeshan
- Institute of Nuclear Medicine, University College London Hospitals NHS Foundation Trust, Euston Road, London, UK
| | - Joshua Shur
- Joint Department of Medical Imaging, University Health Network, Toronto, Canada
| | - Subhabrata Mukherjee
- Department of Urology, Dartford and Gravesham NHS Trust, Darenth Wood Road, Dartford, UK
| | - Asim Afaq
- Institute of Nuclear Medicine, University College London Hospitals NHS Foundation Trust, Euston Road, London, UK
| | - Deepak Batura
- Department of Urology, London North West University Healthcare NHS Trust, Watford Road, London, UK.
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17
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Sun Y, Reynolds HM, Parameswaran B, Wraith D, Finnegan ME, Williams S, Haworth A. Multiparametric MRI and radiomics in prostate cancer: a review. AUSTRALASIAN PHYSICAL & ENGINEERING SCIENCES IN MEDICINE 2019; 42:3-25. [PMID: 30762223 DOI: 10.1007/s13246-019-00730-z] [Citation(s) in RCA: 65] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/19/2018] [Accepted: 01/22/2019] [Indexed: 12/30/2022]
Abstract
Multiparametric MRI (mpMRI) is an imaging modality that combines anatomical MR imaging with one or more functional MRI sequences. It has become a versatile tool for detecting and characterising prostate cancer (PCa). The traditional role of mpMRI was confined to PCa staging, but due to the advanced imaging techniques, its role has expanded to various stages in clinical practises including tumour detection, disease monitor during active surveillance and sequential imaging for patient follow-up. Meanwhile, with the growing speed of data generation and the increasing volume of imaging data, it is highly demanded to apply computerised methods to process mpMRI data and extract useful information. Hence quantitative analysis for imaging data using radiomics has become an emerging paradigm. The application of radiomics approaches in prostate cancer has not only enabled automatic localisation of the disease but also provided a non-invasive solution to assess tumour biology (e.g. aggressiveness and the presence of hypoxia). This article reviews mpMRI and its expanding role in PCa detection, staging and patient management. Following that, an overview of prostate radiomics will be provided, with a special focus on its current applications as well as its future directions.
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Affiliation(s)
- Yu Sun
- University of Sydney, Sydney, Australia. .,Peter MacCallum Cancer Centre, Melbourne, Australia.
| | | | | | - Darren Wraith
- Queensland University of Technology, Brisbane, Australia
| | - Mary E Finnegan
- Imperial College Healthcare NHS Trust, London, UK.,Imperial College London, London, UK
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18
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Chen VCH, Lin TY, Yeh DC, Chai JW, Weng JC. Predicting chemo-brain in breast cancer survivors using multiple MRI features and machine-learning. Magn Reson Med 2018; 81:3304-3313. [PMID: 30417933 DOI: 10.1002/mrm.27607] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2018] [Accepted: 10/22/2018] [Indexed: 12/15/2022]
Abstract
PURPOSE Breast cancer (BC) is the most common cancer in women worldwide. There exist various advanced chemotherapy drugs for BC; however, chemotherapy drugs may result in brain damage during treatment. When a patient's brain is changed in response to chemo drugs, it is termed chemo-brain. In this study, we aimed to construct machine-learning models to detect the subtle alternations of the brain in postchemotherapy BC patients. METHODS Nineteen BC patients undergoing chemotherapy and 20 healthy controls (HCs) were recruited for this study. Both groups underwent resting-state functional MRI and generalized q-sampling imaging (GQI). RESULTS Logistic regression (LR) with GQI indices in standardized voxel-wise analysis, LR with mean regional homogeneity in regional summation analysis, decision tree classifier (CART) with generalized fractional anisotropy in voxel-wise analysis, and XGBoost (XGB) with normalized quantitative anisotropy had formidable performances in classifying subjects into a chemo-brain group or an HC group. Classifying the brain MRIs of HC and postchemotherapy patients by conducting leave-one-out cross-validation resulted in the highest accuracy of 84%, which was attained by LR, CART, and XGB with multiple feature sets. CONCLUSIONS In our study, we constructed the machine-learning models that were able to identify chemo-brains from normal brains. We are hopeful that these results will be helpful in clinically tracking chemo-brains in the future.
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Affiliation(s)
- Vincent Chin-Hung Chen
- School of Medicine, Chang Gung University, Taoyuan, Taiwan.,Department of Psychiatry, Chang Gung Memorial Hospital, Chiayi, Taiwan
| | - Tung-Yeh Lin
- Department of Medical Imaging and Radiological Sciences, Chang Gung University, Taoyuan, Taiwan
| | - Dah-Cherng Yeh
- Breast Medical Center, Cheng Ching Hospital Chung Kang Branch, Taichung, Taiwan
| | - Jyh-Wen Chai
- Department of Radiology, Taichung Veterans General Hospital, Taichung, Taiwan.,College of Medicine, China Medical University, Taichung, Taiwan
| | - Jun-Cheng Weng
- Department of Psychiatry, Chang Gung Memorial Hospital, Chiayi, Taiwan.,Department of Medical Imaging and Radiological Sciences, Chang Gung University, Taoyuan, Taiwan
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19
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Dinh AH, Melodelima C, Souchon R, Moldovan PC, Bratan F, Pagnoux G, Mège-Lechevallier F, Ruffion A, Crouzet S, Colombel M, Rouvière O. Characterization of Prostate Cancer with Gleason Score of at Least 7 by Using Quantitative Multiparametric MR Imaging: Validation of a Computer-aided Diagnosis System in Patients Referred for Prostate Biopsy. Radiology 2018; 287:525-533. [DOI: 10.1148/radiol.2017171265] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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20
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Ertas G. Detection of high GS risk group prostate tumors by diffusion tensor imaging and logistic regression modelling. Magn Reson Imaging 2018; 50:125-133. [PMID: 29649574 DOI: 10.1016/j.mri.2018.04.003] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2017] [Revised: 04/05/2018] [Accepted: 04/06/2018] [Indexed: 11/19/2022]
Abstract
PURPOSE To assess the value of joint evaluation of diffusion tensor imaging (DTI) measures by using logistic regression modelling to detect high GS risk group prostate tumors. MATERIALS AND METHODS Fifty tumors imaged using DTI on a 3 T MRI device were analyzed. Regions of interests focusing on the center of tumor foci and noncancerous tissue on the maps of mean diffusivity (MD) and fractional anisotropy (FA) were used to extract the minimum, the maximum and the mean measures. Measure ratio was computed by dividing tumor measure by noncancerous tissue measure. Logistic regression models were fitted for all possible pair combinations of the measures using 5-fold cross validation. RESULTS Systematic differences are present for all MD measures and also for all FA measures in distinguishing the high risk tumors [GS ≥ 7(4 + 3)] from the low risk tumors [GS ≤ 7(3 + 4)] (P < 0.05). Smaller value for MD measures and larger value for FA measures indicate the high risk. The models enrolling the measures achieve good fits and good classification performances (R2adj = 0.55-0.60, AUC = 0.88-0.91), however the models using the measure ratios perform better (R2adj = 0.59-0.75, AUC = 0.88-0.95). The model that employs the ratios of minimum MD and maximum FA accomplishes the highest sensitivity, specificity and accuracy (Se = 77.8%, Sp = 96.9% and Acc = 90.0%). CONCLUSION Joint evaluation of MD and FA diffusion tensor imaging measures is valuable to detect high GS risk group peripheral zone prostate tumors. However, use of the ratios of the measures improves the accuracy of the detections substantially. Logistic regression modelling provides a favorable solution for the joint evaluations easily adoptable in clinical practice.
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Affiliation(s)
- Gokhan Ertas
- Department of Biomedical Engineering, Yeditepe University, Istanbul, Turkey.
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21
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Starobinets O, Simko JP, Kuchinsky K, Kornak J, Carroll PR, Greene KL, Kurhanewicz J, Noworolski SM. Characterization and stratification of prostate lesions based on comprehensive multiparametric MRI using detailed whole-mount histopathology as a reference standard. NMR IN BIOMEDICINE 2017; 30:10.1002/nbm.3796. [PMID: 28961382 PMCID: PMC9592076 DOI: 10.1002/nbm.3796] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/06/2017] [Revised: 07/25/2017] [Accepted: 07/26/2017] [Indexed: 06/07/2023]
Abstract
The purpose of this study was to characterize prostate cancer (PCa) based on multiparametric MR (mpMR) measures derived from MRI, diffusion, spectroscopy, and dynamic contrast-enhanced (DCE) MRI, and to validate mpMRI in detecting PCa and predicting PCa aggressiveness by correlating mpMRI findings with whole-mount histopathology. Seventy-eight men with untreated PCa received 3 T mpMR scans prior to radical prostatectomy. Cancerous regions were outlined, graded, and cancer amount estimated on whole-mount histology. Regions of interest were manually drawn on T2 -weighted images based on histopathology. Logistic regression was used to identify optimal combinations of parameters for the peripheral zone and transition zone to separate: (i) benign from malignant tissues; (ii) Gleason score (GS) ≤3 + 3 disease from ≥GS3 + 4; and (iii) ≤ GS3 + 4 from ≥GS4 + 3 cancers. The performance of the models was assessed using repeated fourfold cross-validation. Additionally, the performance of the logistic regression models created under the assumption that one or more modality has not been acquired was evaluated. Logistic regression models yielded areas under the curve (AUCs) of 1.0 and 0.99 when separating benign from malignant tissues in the peripheral zone and the transition zone, respectively. Within the peripheral zone, combining choline, maximal enhancement slope, apparent diffusion coefficient (ADC), and citrate measures for separating ≤GS3 + 3 from ≥GS3 + 4 PCa yielded AUC = 0.84. Combining creatine, choline, and washout slope yielded AUC = 0.81 for discriminating ≤GS3 + 4 from ≥GS4 + 3 disease. Within the transition zone, combining washout slope, ADC, and creatine yielded AUC = 0.93 for discriminating ≤GS3 + 3 and ≥GS3 + 4 cancers. When separating ≤GS3 + 4 from ≥GS4 + 3 PCa, combining choline and washout slope yielded AUC = 0.92. MpMRI provides excellent separation between benign tissues and PCa, and across PCa tissues of different aggressiveness. The final models prominently feature spectroscopy and DCE-derived metrics, underlining their value within a comprehensive mpMRI examination.
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Affiliation(s)
- Olga Starobinets
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, USA
- Graduate Group in Bioengineering, University of California, San Francisco and Berkeley, USA
| | - Jeffry P Simko
- Department of Pathology, University of California, San Francisco, USA
- Department of Urology, University of California, San Francisco, USA
| | - Kyle Kuchinsky
- Department of Pathology, University of California, San Francisco, USA
| | - John Kornak
- Department of Epidemiology and Biostatistics, University of California, San Francisco, USA
| | - Peter R Carroll
- Department of Urology, University of California, San Francisco, USA
| | - Kirsten L Greene
- Department of Urology, University of California, San Francisco, USA
| | - John Kurhanewicz
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, USA
- Graduate Group in Bioengineering, University of California, San Francisco and Berkeley, USA
| | - Susan M Noworolski
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, USA
- Graduate Group in Bioengineering, University of California, San Francisco and Berkeley, USA
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22
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Multiparametric magnetic resonance imaging for transition zone prostate cancer: essential findings, limitations, and future directions. Abdom Radiol (NY) 2017; 42:2732-2744. [PMID: 28702787 DOI: 10.1007/s00261-017-1184-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
OBJECTIVE Review the multiparametric MRI (mpMRI) findings of transition zone (TZ) prostate cancer (PCa) using T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and dynamic contrast-enhanced (DCE) MRI and to integrate mpMRI findings with clinical history, laboratory values, and histopathology. CONCLUSION TZ prostate tumors are challenging to detect clinically and at MRI. mpMRI using the combination of sequences has the potential to improve accuracy of TZ cancer detection and staging.
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23
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Sidhu HS, Benigno S, Ganeshan B, Dikaios N, Johnston EW, Allen C, Kirkham A, Groves AM, Ahmed HU, Emberton M, Taylor SA, Halligan S, Punwani S. "Textural analysis of multiparametric MRI detects transition zone prostate cancer". Eur Radiol 2017; 27:2348-2358. [PMID: 27620864 PMCID: PMC5408048 DOI: 10.1007/s00330-016-4579-9] [Citation(s) in RCA: 67] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2016] [Revised: 08/10/2016] [Accepted: 08/22/2016] [Indexed: 01/03/2023]
Abstract
OBJECTIVES To evaluate multiparametric-MRI (mpMRI) derived histogram textural-analysis parameters for detection of transition zone (TZ) prostatic tumour. METHODS Sixty-seven consecutive men with suspected prostate cancer underwent 1.5T mpMRI prior to template-mapping-biopsy (TPM). Twenty-six men had 'significant' TZ tumour. Two radiologists in consensus matched TPM to the single axial slice best depicting tumour, or largest TZ diameter for those with benign histology, to define single-slice whole TZ-regions-of-interest (ROIs). Textural-parameter differences between single-slice whole TZ-ROI containing significant tumour versus benign/insignificant tumour were analysed using Mann Whitney U test. Diagnostic accuracy was assessed by receiver operating characteristic area under curve (ROC-AUC) analysis cross-validated with leave-one-out (LOO) analysis. RESULTS ADC kurtosis was significantly lower (p < 0.001) in TZ containing significant tumour with ROC-AUC 0.80 (LOO-AUC 0.78); the difference became non-significant following exclusion of significant tumour from single-slice whole TZ-ROI (p = 0.23). T1-entropy was significantly lower (p = 0.004) in TZ containing significant tumour with ROC-AUC 0.70 (LOO-AUC 0.66) and was unaffected by excluding significant tumour from TZ-ROI (p = 0.004). Combining these parameters yielded ROC-AUC 0.86 (LOO-AUC 0.83). CONCLUSION Textural features of the whole prostate TZ can discriminate significant prostatic cancer through reduced kurtosis of the ADC-histogram where significant tumour is included in TZ-ROI and reduced T1 entropy independent of tumour inclusion. KEY POINTS • MR textural features of prostate transition zone may discriminate significant prostatic cancer. • Transition zone (TZ) containing significant tumour demonstrates a less peaked ADC histogram. • TZ containing significant tumour reveals higher post-contrast T1-weighted homogeneity. • The utility of MR texture analysis in prostate cancer merits further investigation.
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Affiliation(s)
- Harbir S Sidhu
- Centre for Medical Imaging, University College London, 3rd Floor East, 250 Euston Road, London, NW1 2BU, UK
- University College London Hospitals NHS Foundation Trust, 235 Euston Road, London, NW1 2BU, UK
| | - Salvatore Benigno
- Centre for Medical Imaging, University College London, 3rd Floor East, 250 Euston Road, London, NW1 2BU, UK
| | - Balaji Ganeshan
- Institute of Nuclear Medicine, University College London, University College Hospital, 235 Euston Road, London, NW1 2BU, UK
| | - Nikos Dikaios
- Centre for Medical Imaging, University College London, 3rd Floor East, 250 Euston Road, London, NW1 2BU, UK
| | - Edward W Johnston
- Centre for Medical Imaging, University College London, 3rd Floor East, 250 Euston Road, London, NW1 2BU, UK
- University College London Hospitals NHS Foundation Trust, 235 Euston Road, London, NW1 2BU, UK
| | - Clare Allen
- University College London Hospitals NHS Foundation Trust, 235 Euston Road, London, NW1 2BU, UK
| | - Alex Kirkham
- University College London Hospitals NHS Foundation Trust, 235 Euston Road, London, NW1 2BU, UK
| | - Ashley M Groves
- University College London Hospitals NHS Foundation Trust, 235 Euston Road, London, NW1 2BU, UK
- Institute of Nuclear Medicine, University College London, University College Hospital, 235 Euston Road, London, NW1 2BU, UK
| | - Hashim U Ahmed
- University College London Hospitals NHS Foundation Trust, 235 Euston Road, London, NW1 2BU, UK
- Research Department of Urology, University College London, 3rd Floor, Charles Bell House 67 Riding House Street, London, W1P 7NN, UK
| | - Mark Emberton
- University College London Hospitals NHS Foundation Trust, 235 Euston Road, London, NW1 2BU, UK
- Research Department of Urology, University College London, 3rd Floor, Charles Bell House 67 Riding House Street, London, W1P 7NN, UK
| | - Stuart A Taylor
- Centre for Medical Imaging, University College London, 3rd Floor East, 250 Euston Road, London, NW1 2BU, UK
- University College London Hospitals NHS Foundation Trust, 235 Euston Road, London, NW1 2BU, UK
| | - Steve Halligan
- Centre for Medical Imaging, University College London, 3rd Floor East, 250 Euston Road, London, NW1 2BU, UK
- University College London Hospitals NHS Foundation Trust, 235 Euston Road, London, NW1 2BU, UK
| | - Shonit Punwani
- Centre for Medical Imaging, University College London, 3rd Floor East, 250 Euston Road, London, NW1 2BU, UK.
- University College London Hospitals NHS Foundation Trust, 235 Euston Road, London, NW1 2BU, UK.
- Centre for Medical Imaging, University College London and University College London Hospitals NIHR Biomedical Research Centre, 250 Euston Road, London, NW1 2BU, UK.
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24
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Iyama Y, Nakaura T, Katahira K, Iyama A, Nagayama Y, Oda S, Utsunomiya D, Yamashita Y. Development and validation of a logistic regression model to distinguish transition zone cancers from benign prostatic hyperplasia on multi-parametric prostate MRI. Eur Radiol 2017; 27:3600-3608. [PMID: 28289941 DOI: 10.1007/s00330-017-4775-2] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2017] [Accepted: 02/13/2017] [Indexed: 11/25/2022]
Abstract
PURPOSE To develop a prediction model to distinguish between transition zone (TZ) cancers and benign prostatic hyperplasia (BPH) on multi-parametric prostate magnetic resonance imaging (mp-MRI). MATERIALS AND METHODS This retrospective study enrolled 60 patients with either BPH or TZ cancer, who had undergone 3 T-MRI. We generated ten parameters for T2-weighted images (T2WI), diffusion-weighted images (DWI) and dynamic MRI. Using a t-test and multivariate logistic regression (LR) analysis to evaluate the parameters' accuracy, we developed LR models. We calculated the area under the receiver operating characteristic curve (ROC) of LR models by a leave-one-out cross-validation procedure, and the LR model's performance was compared with radiologists' performance with their opinion and with the Prostate Imaging Reporting and Data System (Pi-RADS v2) score. RESULTS Multivariate LR analysis showed that only standardized T2WI signal and mean apparent diffusion coefficient (ADC) maintained their independent values (P < 0.001). The validation analysis showed that the AUC of the final LR model was comparable to that of board-certified radiologists, and superior to that of Pi-RADS scores. CONCLUSION A standardized T2WI and mean ADC were independent factors for distinguishing between BPH and TZ cancer. The performance of the LR model was comparable to that of experienced radiologists. KEY POINTS • It is difficult to diagnose transition zone (TZ) cancer. • We performed quantitative image analysis in multi-parametric MRI. • Standardized-T2WI and mean-ADC were independent factors for diagnosing TZ cancer. • We developed logistic-regression analysis to diagnose TZ cancer accurately. • The performance of the logistic-regression analysis was higher than PIRADSv2.
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Affiliation(s)
- Yuji Iyama
- Department of Diagnostic Radiology, Kumamoto Chuo Hospital, Tainoshima 1-5-1, Kumamoto, Kumamoto, 862-0965, Japan. .,Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, Honjo 1-1-1, Kumamoto, Kumamoto, 860-8556, Japan.
| | - Takeshi Nakaura
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, Honjo 1-1-1, Kumamoto, Kumamoto, 860-8556, Japan
| | - Kazuhiro Katahira
- Department of Diagnostic Radiology, Kumamoto Chuo Hospital, Tainoshima 1-5-1, Kumamoto, Kumamoto, 862-0965, Japan
| | - Ayumi Iyama
- Department of Diagnostic Radiology, National Hospital Organization Kumamoto Medical Center, Ninomaru 1-5, Kumamoto, Kumamoto, 860-0008, Japan
| | - Yasunori Nagayama
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, Honjo 1-1-1, Kumamoto, Kumamoto, 860-8556, Japan
| | - Seitaro Oda
- Department of Diagnostic Radiology, Kumamoto Chuo Hospital, Tainoshima 1-5-1, Kumamoto, Kumamoto, 862-0965, Japan
| | - Daisuke Utsunomiya
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, Honjo 1-1-1, Kumamoto, Kumamoto, 860-8556, Japan
| | - Yasuyuki Yamashita
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, Honjo 1-1-1, Kumamoto, Kumamoto, 860-8556, Japan
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25
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Dikaios N, Atkinson D, Tudisca C, Purpura P, Forster M, Ahmed H, Beale T, Emberton M, Punwani S. A comparison of Bayesian and non-linear regression methods for robust estimation of pharmacokinetics in DCE-MRI and how it affects cancer diagnosis. Comput Med Imaging Graph 2017; 56:1-10. [PMID: 28192761 DOI: 10.1016/j.compmedimag.2017.01.003] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2016] [Revised: 11/16/2016] [Accepted: 01/26/2017] [Indexed: 11/23/2022]
Abstract
The aim of this work is to compare Bayesian Inference for nonlinear models with commonly used traditional non-linear regression (NR) algorithms for estimating tracer kinetics in Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI). The algorithms are compared in terms of accuracy, and reproducibility under different initialization settings. Further it is investigated how a more robust estimation of tracer kinetics affects cancer diagnosis. The derived tracer kinetics from the Bayesian algorithm were validated against traditional NR algorithms (i.e. Levenberg-Marquardt, simplex) in terms of accuracy on a digital DCE phantom and in terms of goodness-of-fit (Kolmogorov-Smirnov test) on ROI-based concentration time courses from two different patient cohorts. The first cohort consisted of 76 men, 20 of whom had significant peripheral zone prostate cancer (any cancer-core-length (CCL) with Gleason>3+3 or any-grade with CCL>=4mm) following transperineal template prostate mapping biopsy. The second cohort consisted of 9 healthy volunteers and 24 patients with head and neck squamous cell carcinoma. The diagnostic ability of the derived tracer kinetics was assessed with receiver operating characteristic area under curve (ROC AUC) analysis. The Bayesian algorithm accurately recovered the ground-truth tracer kinetics for the digital DCE phantom consistently improving the Structural Similarity Index (SSIM) across the 50 different initializations compared to NR. For optimized initialization, Bayesian did not improve significantly the fitting accuracy on both patient cohorts, and it only significantly improved the ve ROC AUC on the HN population from ROC AUC=0.56 for the simplex to ROC AUC=0.76. For both cohorts, the values and the diagnostic ability of tracer kinetic parameters estimated with the Bayesian algorithm weren't affected by their initialization. To conclude, the Bayesian algorithm led to a more accurate and reproducible quantification of tracer kinetic parameters in DCE-MRI, improving their ROC-AUC and decreasing their dependence on initialization settings.
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Affiliation(s)
- Nikolaos Dikaios
- Centre for Vision, Speech and Signal Processing, University of Surrey, United Kingdom.
| | - David Atkinson
- Centre for Medical Imaging, University College London, United Kingdom
| | - Chiara Tudisca
- Centre for Medical Imaging, University College London, United Kingdom
| | - Pierpaolo Purpura
- Centre for Medical Imaging, University College London, United Kingdom
| | - Martin Forster
- Department of Head and Neck Oncology, University College London Hospital, United Kingdom; Cancer Institute, University College London, United Kingdom
| | - Hashim Ahmed
- Department of Urology, University College London, London NW1 2 PG, United Kingdom
| | - Timothy Beale
- Department of Head and Neck Oncology, University College London Hospital, United Kingdom
| | - Mark Emberton
- Department of Urology, University College London, London NW1 2 PG, United Kingdom
| | - Shonit Punwani
- Centre for Medical Imaging, University College London, United Kingdom; Department of Head and Neck Oncology, University College London Hospital, United Kingdom
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Ginsburg SB, Algohary A, Pahwa S, Gulani V, Ponsky L, Aronen HJ, Boström PJ, Böhm M, Haynes AM, Brenner P, Delprado W, Thompson J, Pulbrock M, Taimen P, Villani R, Stricker P, Rastinehad AR, Jambor I, Madabhushi A. Radiomic features for prostate cancer detection on MRI differ between the transition and peripheral zones: Preliminary findings from a multi-institutional study. J Magn Reson Imaging 2016; 46:184-193. [PMID: 27990722 DOI: 10.1002/jmri.25562] [Citation(s) in RCA: 95] [Impact Index Per Article: 11.9] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2016] [Accepted: 11/03/2016] [Indexed: 01/07/2023] Open
Abstract
PURPOSE To evaluate in a multi-institutional study whether radiomic features useful for prostate cancer (PCa) detection from 3 Tesla (T) multi-parametric MRI (mpMRI) in the transition zone (TZ) differ from those in the peripheral zone (PZ). MATERIALS AND METHODS 3T mpMRI, including T2-weighted (T2w), apparent diffusion coefficient (ADC) maps, and dynamic contrast-enhanced MRI (DCE-MRI), were retrospectively obtained from 80 patients at three institutions. This study was approved by the institutional review board of each participating institution. First-order statistical, co-occurrence, and wavelet features were extracted from T2w MRI and ADC maps, and contrast kinetic features were extracted from DCE-MRI. Feature selection was performed to identify 10 features for PCa detection in the TZ and PZ, respectively. Two logistic regression classifiers used these features to detect PCa and were evaluated by area under the receiver-operating characteristic curve (AUC). Classifier performance was compared with a zone-ignorant classifier. RESULTS Radiomic features that were identified as useful for PCa detection differed between TZ and PZ. When classification was performed on a per-voxel basis, a PZ-specific classifier detected PZ tumors on an independent test set with significantly higher accuracy (AUC = 0.61-0.71) than a zone-ignorant classifier trained to detect cancer throughout the entire prostate (P < 0.05). When classifiers were evaluated on MRI data from multiple institutions, statistically similar AUC values (P > 0.14) were obtained for all institutions. CONCLUSION A zone-aware classifier significantly improves the accuracy of cancer detection in the PZ. LEVEL OF EVIDENCE 3 Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2017;46:184-193.
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Affiliation(s)
- Shoshana B Ginsburg
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
| | - Ahmad Algohary
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
| | - Shivani Pahwa
- Department of Radiology, Case Western Reserve University, Cleveland, Ohio, USA
| | - Vikas Gulani
- Department of Radiology, Case Western Reserve University, Cleveland, Ohio, USA
| | - Lee Ponsky
- Department of Urology, Case Western Reserve University, Cleveland, Ohio, USA
| | - Hannu J Aronen
- Department of Diagnostic Radiology, University of Turku, Turku, Finland
| | - Peter J Boström
- Department of Urology, Turku University Hospital, Turku, Finland
| | - Maret Böhm
- Garvan Institute of Medical Research, Sydney, Australia
| | | | - Phillip Brenner
- Department of Urology, St. Vincent's Hospital, Sydney, Australia
| | | | | | | | - Pekka Taimen
- Department of Pathology, University of Turku and Turku University Hospital, Turku, Finland
| | - Robert Villani
- Department of Radiology, Hofstra North Shore-LIJ, New Hyde Park, New York, USA
| | - Phillip Stricker
- Department of Urology, St. Vincent's Hospital, Sydney, Australia
| | - Ardeshir R Rastinehad
- Department of Radiology, Icahn School of Medicine at Mount Sinai, Manhattan, New York, USA
| | - Ivan Jambor
- Department of Diagnostic Radiology, University of Turku, Turku, Finland
| | - Anant Madabhushi
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
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Johnston E, Punwani S. Can We Improve the Reproducibility of Quantitative Multiparametric Prostate MR Imaging Metrics? Radiology 2016; 281:652-653. [PMID: 27755936 DOI: 10.1148/radiol.2016161197] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Edward Johnston
- UCL Centre for Medical Imaging, 5th Floor, Wolfson House, 4 Stephenson Way, London NW1 2HE
| | - Shonit Punwani
- UCL Centre for Medical Imaging, 5th Floor, Wolfson House, 4 Stephenson Way, London NW1 2HE
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How are we going to train a generation of radiologists (and urologists) to read prostate MRI? Curr Opin Urol 2016; 25:522-35. [PMID: 26375060 DOI: 10.1097/mou.0000000000000217] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
PURPOSE OF REVIEW Multiparametric MRI has gained tremendous importance in the daily practice for patients at risk or diagnosed with prostate cancer. Interpretation of multiparametric-MRI is a complex task, supposedly restricted to experienced radiologists. The purpose of this review is to analyze fundamentals of multiparametric-MRI interpretation and to describe how multiparametric-MRI training could be organized. RECENT FINDINGS Recently, professional guidelines have been published to provide technical and interpretation frameworks and harmonize multiparametric-MRI practice, but the question of physicians training in prostate multiparametric-MRI reading is still pending. What kind of education, practice, and training makes a radiologist able to reliably interpret a prostate multiparametric-MRI? How can findings be reported to be easily understood? How much experience is needed? How can we train urologists and other physicians to review the examinations they request? Is double-reading necessary? SUMMARY An institutional-based competency certification process for prostate multiparametric-MRI interpretation may encourage nonspecialized radiologists to qualify for prostate imaging in a standardized and reproducible way, exactly as urologists need it.
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Differentiation of prostate cancer lesions with high and with low Gleason score by diffusion-weighted MRI. Eur Radiol 2016; 27:1547-1555. [DOI: 10.1007/s00330-016-4449-5] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2016] [Revised: 05/17/2016] [Accepted: 05/23/2016] [Indexed: 11/27/2022]
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Salehi HS, Li H, Merkulov A, Kumavor PD, Vavadi H, Sanders M, Kueck A, Brewer MA, Zhu Q. Coregistered photoacoustic and ultrasound imaging and classification of ovarian cancer: ex vivo and in vivo studies. JOURNAL OF BIOMEDICAL OPTICS 2016; 21:46006. [PMID: 27086690 PMCID: PMC4833884 DOI: 10.1117/1.jbo.21.4.046006] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/05/2015] [Accepted: 03/24/2016] [Indexed: 05/20/2023]
Abstract
Most ovarian cancers are diagnosed at advanced stages due to the lack of efficacious screening techniques. Photoacoustic tomography (PAT) has a potential to image tumor angiogenesis and detect early neovascular changes of the ovary. We have developed a coregistered PAT and ultrasound (US) prototype system for real-time assessment of ovarian masses. Features extracted from PAT and US angular beams, envelopes, and images were input to a logistic classifier and a support vector machine (SVM) classifier to diagnose ovaries as benign or malignant. A total of 25 excised ovaries of 15 patients were studied and the logistic and SVM classifiers achieved sensitivities of 70.4 and 87.7%, and specificities of 95.6 and 97.9%, respectively. Furthermore, the ovaries of two patients were noninvasively imaged using the PAT/US system before surgical excision. By using five significant features and the logistic classifier, 12 out of 14 images (86% sensitivity) from a malignant ovarian mass and all 17 images (100% specificity) from a benign mass were accurately classified; the SVM correctly classified 10 out of 14 malignant images (71% sensitivity) and all 17 benign images (100% specificity). These initial results demonstrate the clinical potential of the PAT/US technique for ovarian cancer diagnosis.
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Affiliation(s)
- Hassan S. Salehi
- University of Connecticut, Department of Electrical and Computer Engineering, Storrs, Connecticut 06269, United States
| | - Hai Li
- University of Connecticut, Department of Electrical and Computer Engineering, Storrs, Connecticut 06269, United States
| | - Alex Merkulov
- University of Connecticut Health Center, Division of Radiology, Farmington, Connecticut 06030, United States
| | - Patrick D. Kumavor
- University of Connecticut, Department of Biomedical Engineering, Storrs, Connecticut 06269, United States
| | - Hamed Vavadi
- University of Connecticut, Department of Biomedical Engineering, Storrs, Connecticut 06269, United States
| | - Melinda Sanders
- University of Connecticut Health Center, Department of Pathology, Farmington, Connecticut 06030, United States
| | - Angela Kueck
- University of Connecticut Health Center, Division of Gynecologic Oncology, Farmington, Connecticut 06030, United States
| | - Molly A. Brewer
- University of Connecticut Health Center, Division of Gynecologic Oncology, Farmington, Connecticut 06030, United States
| | - Quing Zhu
- University of Connecticut, Department of Electrical and Computer Engineering, Storrs, Connecticut 06269, United States
- University of Connecticut, Department of Biomedical Engineering, Storrs, Connecticut 06269, United States
- Address all correspondence to: Quing Zhu, E-mail:
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Multiparametric Prostate Magnetic Resonance Imaging at 3 T: Failure of Magnetic Resonance Spectroscopy to Provide Added Value. J Comput Assist Tomogr 2015; 39:674-80. [PMID: 25938212 DOI: 10.1097/rct.0000000000000261] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
PURPOSE To assess the effect of proton magnetic resonance spectroscopy imaging (MRSI) on the accuracy of multiparametric magnetic resonance imaging (mpMRI) at 3 T for prostate cancer detection. MATERIALS AND METHODS Thirty-four patients with prostate cancer were included in this retrospective study. All patients underwent preoperative mpMRI on a 3-T scanner before radical prostatectomy. Magnetic resonance imaging evaluation was based on the prostate imaging-reporting and data system classification system. The accuracy of mpMRI with and without MRSI was determined using receiver operating characteristic analysis, with histology as the reference standard. RESULTS Multiparametric MRI including MRSI had a sensitivity of 57.0% and a specificity of 89.2% for sextant-based cancer detection. Multiparametric MRI without MRSI had a sensitivity of 58.1% and a specificity of 87.4%. There was no significant difference regarding the accuracy of mpMRI with and without MRSI (P = 0.48). CONCLUSION The addition of MRSI does not improve the accuracy of 3 T mpMRI for sextant localization of prostate cancer.
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Latifoltojar A, Dikaios N, Ridout A, Moore C, Illing R, Kirkham A, Taylor S, Halligan S, Atkinson D, Allen C, Emberton M, Punwani S. Evolution of multi-parametric MRI quantitative parameters following transrectal ultrasound-guided biopsy of the prostate. Prostate Cancer Prostatic Dis 2015; 18:343-51. [PMID: 26195470 PMCID: PMC4763162 DOI: 10.1038/pcan.2015.33] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2015] [Revised: 05/03/2015] [Accepted: 05/31/2015] [Indexed: 11/08/2022]
Abstract
BACKGROUND To determine the evolution of prostatic multi-parametric magnetic resonance imaging (mp-MRI) signal following transrectal ultrasound (TRUS)-guided biopsy. METHODS Local ethical permission and informed written consent was obtained from all the participants (n=14, aged 43-69, mean 64 years). Patients with a clinical suspicion of prostate cancer (PSA range 2.2-11.7, mean 6.2) and a negative (PIRAD 1-2/5) pre-biopsy mp-MRI (pre-contrast T1, T2, diffusion-weighted and dynamic-contrast-enhanced MRI) who underwent 10-core TRUS-guided biopsy were recruited for additional mp-MRI examinations performed at 1, 2 and 6 months post biopsy. We quantified mp-MRI peripheral zone (PZ) and transition zone (TZ) normalized T2 signal intensity (nT2-SI); T1 relaxation time (T10); diffusion-weighted MRI, apparent diffusion coefficient (ADC); dynamic contrast-enhanced MRI, maximum enhancement (ME); slope of enhancement (SoE) and area-under-the-contrast-enhancement-curve at 120 s (AUC120). Significant changes in mp-MRI parameters were identified by analysis of variance with Dunnett's post testing. RESULTS Diffuse signal changes were observed post-biopsy throughout the PZ. No significant signal change occurred following biopsy within the TZ. Left and right PZ mean nT2-SI (left PZ: 5.73, 5.16, 4.90 and 5.12; right PZ: 5.80, 5.10, 4.84 and 5.05 at pre-biopsy, 1, 2 and 6 months post biopsy, respectively) and mean T10 (left PZ: 1.02, 0.67, 0.78, 0.85; right PZ: 1.29, 0.64, 0.78, 0.87 at pre-biopsy, 1, 2 and 6 months post biopsy, respectively) were reduced significantly (P<0.05) from pre-biopsy values for up to 6 months post biopsy. Significant changes (P<0.05) of PZ-ME and AUC120 were observed at 1 month but resolved by 2 months post biopsy. PZ ADC did not change significantly following biopsy (P=0.23-1.0). There was no significant change of any TZ mp-MRI parameter at any time point following biopsy (P=0.1-1.0). CONCLUSIONS Significant PZ (but not TZ) T2 signal changes persist up to 6 months post biopsy, whereas PZ and TZ ADC is not significantly altered as early as 1 month post biopsy. Caution must be exercised when interpreting T1- and T2-weighted imaging early post biopsy, whereas ADC images are more likely to maintain clinical efficacy.
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Affiliation(s)
- A Latifoltojar
- Centre for Medical Imaging, University College London, London, UK
| | - N Dikaios
- Centre for Medical Imaging, University College London, London, UK
| | - A Ridout
- Department of Urology, University College London Hospital, London, UK
| | - C Moore
- Department of Urology, University College London Hospital, London, UK
| | - R Illing
- Department of Radiology, University College London Hospital, London, UK
| | - A Kirkham
- Department of Radiology, University College London Hospital, London, UK
| | - S Taylor
- Centre for Medical Imaging, University College London, London, UK
- Department of Radiology, University College London Hospital, London, UK
| | - S Halligan
- Centre for Medical Imaging, University College London, London, UK
- Department of Radiology, University College London Hospital, London, UK
| | - D Atkinson
- Centre for Medical Imaging, University College London, London, UK
| | - C Allen
- Department of Radiology, University College London Hospital, London, UK
| | - M Emberton
- Department of Urology, University College London Hospital, London, UK
| | - S Punwani
- Centre for Medical Imaging, University College London, London, UK
- Department of Radiology, University College London Hospital, London, UK
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Cobben DCP, de Boer HCJ, Tijssen RH, Rutten EGGM, van Vulpen M, Peerlings J, Troost EGC, Hoffmann AL, van Lier ALHMW. Emerging Role of MRI for Radiation Treatment Planning in Lung Cancer. Technol Cancer Res Treat 2015; 15:NP47-NP60. [PMID: 26589726 DOI: 10.1177/1533034615615249] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2015] [Accepted: 10/01/2015] [Indexed: 12/25/2022] Open
Abstract
Magnetic resonance imaging (MRI) provides excellent soft-tissue contrast and allows for specific scanning sequences to optimize differentiation between various tissue types and properties. Moreover, it offers the potential for real-time motion imaging. This makes magnetic resonance imaging an ideal candidate imaging modality for radiation treatment planning in lung cancer. Although the number of clinical research protocols for the application of magnetic resonance imaging for lung cancer treatment is increasing (www.clinicaltrials.gov) and the magnetic resonance imaging sequences are becoming faster, there are still some technical challenges. This review describes the opportunities and challenges of magnetic resonance imaging for radiation treatment planning in lung cancer.
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Affiliation(s)
- David C P Cobben
- Department of Radiation Oncology, University Medical Center, Utrecht, the Netherlands
| | - Hans C J de Boer
- Department of Radiation Oncology, University Medical Center, Utrecht, the Netherlands
| | - Rob H Tijssen
- Department of Radiation Oncology, University Medical Center, Utrecht, the Netherlands
| | - Emma G G M Rutten
- Department of Radiation Oncology, University Medical Center, Utrecht, the Netherlands
| | - Marco van Vulpen
- Department of Radiation Oncology, University Medical Center, Utrecht, the Netherlands
| | - Jurgen Peerlings
- Department of Radiation Oncology, MAASTRO Clinic, GROW-School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, the Netherlands
| | - Esther G C Troost
- Department of Radiation Oncology, MAASTRO Clinic, GROW-School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, the Netherlands.,Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany.,OncoRay, National Center for Radiation Research in Oncology, Dresden, Germany.,Department of Radiation Oncology, Medical Faculty and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Aswin L Hoffmann
- Department of Radiation Oncology, MAASTRO Clinic, GROW-School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, the Netherlands.,Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany.,OncoRay, National Center for Radiation Research in Oncology, Dresden, Germany.,Department of Radiation Oncology, Medical Faculty and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
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Bhatnagar G, Dikaios N, Prezzi D, Vega R, Halligan S, Taylor SA. Changes in dynamic contrast-enhanced pharmacokinetic and diffusion-weighted imaging parameters reflect response to anti-TNF therapy in Crohn's disease. Br J Radiol 2015; 88:20150547. [PMID: 26402217 DOI: 10.1259/bjr.20150547] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
OBJECTIVE To investigate the effect of tumour necrosis factor (TNF)-α antagonists on MRI dynamic contrast-enhanced (DCE) and diffusion-weighted imaging (DWI) parameters in Crohn's disease (CD). METHODS 42 patients with CD (median age 24 years; 22 females) commencing anti-TNF-α therapy with baseline and follow-up (median 51 weeks) 1.5-T MR enterography (MRE) were retrospectively identified. MRE included DCE (n = 20) and/or multi-b-value DWI (n = 17). Slope of enhancement (SoE), maximum enhancement (ME), area under the time-intensity curve (AUC), Ktrans (transfer constant), ve (fractional volume of the extravascular-extracellular space), apparent diffusion coefficient (ADC) and ADCfast/slow were derived from the most inflamed bowel segments. A physician global assessment of disease activity (remission, mild, moderate and severe) at the time of MRE was assigned, and the cohort was divided into responders and non-responders. Data were compared using Mann-Whitney U test and analysis of variance. RESULTS Follow-up Ktrans, ME, SoE, AUC and ADCME changed significantly in clinical responders but not in non-responders, baseline {[median [interquartile range (IQR)]: 0.42 (0.38), 1.24 (0.52), 0.18 (0.17), 17.68 (4.70) and 1.56 mm(2) s(-1) (0.39 mm(2) s(-1)) vs follow-up [median (IQR): 0.15 (0.22), 0.50 (0.54), 0.07 (0.1), 14.73 (2.06) and 2.14 mm(2) s(-1) (0.62 mm(2) s(-1)), for responders, respectively, p = 0.006 to p = 0.037}. SoE was higher and ME and AUC lower for patients in remission than for those with severe activity [mean (standard deviation): 0.55 (0.46), 0.49 (0.28), 14.32 (1.32)] vs [0.32 (0.37), 2.21 (2.43) and 23.05 (13.66), respectively p = 0.017 to 0.033]. ADC was significantly higher for patients in remission [2.34 mm(2) s(-1) (0.67 mm(2) s(-1))] than for those with moderate [1.59 mm(2) s(-1) (0.26 mm(2) s(-1))] (p = 0.005) and severe disease [1.63 mm(2) s(-1) (0.21 mm(2) s(-1))] (p = 0.038). CONCLUSION DCE and DWI parameters change significantly in responders to TNF-α antagonists and are significantly different according to clinically defined disease activity status. ADVANCES IN KNOWLEDGE DCE and DWI parameters change significantly in responders to TNF-α antagonists in CD, suggesting an effect on bowel wall vascularity.
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Affiliation(s)
| | - Nikolaos Dikaios
- 1 Centre for Medical Imaging, University College London, London, UK
| | - Davide Prezzi
- 1 Centre for Medical Imaging, University College London, London, UK.,2 Department of Cancer Imaging, King's College London, UCL CMI, London, UK
| | - Roser Vega
- 3 Gastroenterology Department, University College London Hospitals, UCLH, London, UK
| | - Steve Halligan
- 1 Centre for Medical Imaging, University College London, London, UK
| | - Stuart A Taylor
- 1 Centre for Medical Imaging, University College London, London, UK
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